Extension of optical lithography by mask-litho integration with computational lithography

NASA Astrophysics Data System (ADS)

Takigawa, T.; Gronlund, K.; Wiley, J.

2010-05-01

Wafer lithography process windows can be enlarged by using source mask co-optimization (SMO). Recently, SMO including freeform wafer scanner illumination sources has been developed. Freeform sources are generated by a programmable illumination system using a micro-mirror array or by custom Diffractive Optical Elements (DOE). The combination of freeform sources and complex masks generated by SMO show increased wafer lithography process window and reduced MEEF. Full-chip mask optimization using source optimized by SMO can generate complex masks with small variable feature size sub-resolution assist features (SRAF). These complex masks create challenges for accurate mask pattern writing and low false-defect inspection. The accuracy of the small variable-sized mask SRAF patterns is degraded by short range mask process proximity effects. To address the accuracy needed for these complex masks, we developed a highly accurate mask process correction (MPC) capability. It is also difficult to achieve low false-defect inspections of complex masks with conventional mask defect inspection systems. A printability check system, Mask Lithography Manufacturability Check (M-LMC), is developed and integrated with 199-nm high NA inspection system, NPI. M-LMC successfully identifies printable defects from all of the masses of raw defect images collected during the inspection of a complex mask. Long range mask CD uniformity errors are compensated by scanner dose control. A mask CD uniformity error map obtained by mask metrology system is used as input data to the scanner. Using this method, wafer CD uniformity is improved. As reviewed above, mask-litho integration technology with computational lithography is becoming increasingly important.

Working set selection using functional gain for LS-SVM.

PubMed

Bo, Liefeng; Jiao, Licheng; Wang, Ling

2007-09-01

The efficiency of sequential minimal optimization (SMO) depends strongly on the working set selection. This letter shows how the improvement of SMO in each iteration, named the functional gain (FG), is used to select the working set for least squares support vector machine (LS-SVM). We prove the convergence of the proposed method and give some theoretical support for its performance. Empirical comparisons demonstrate that our method is superior to the maximum violating pair (MVP) working set selection.

Sterol Methyl Oxidases Affect Embryo Development via Auxin-Associated Mechanisms.

PubMed

Zhang, Xia; Sun, Shuangli; Nie, Xiang; Boutté, Yohann; Grison, Magali; Li, Panpan; Kuang, Susu; Men, Shuzhen

2016-05-01

Sterols are essential molecules for multiple biological processes, including embryogenesis, cell elongation, and endocytosis. The plant sterol biosynthetic pathway is unique in the involvement of two distinct sterol 4α-methyl oxidase (SMO) families, SMO1 and SMO2, which contain three and two isoforms, respectively, and are involved in sequential removal of the two methyl groups at C-4. In this study, we characterized the biological functions of members of the SMO2 gene family. SMO2-1 was strongly expressed in most tissues during Arabidopsis (Arabidopsis thaliana) development, whereas SMO2-2 showed a more specific expression pattern. Although single smo2 mutants displayed no obvious phenotype, the smo2-1 smo2-2 double mutant was embryonic lethal, and the smo2-1 smo2-2/+ mutant was dwarf, whereas the smo2-1/+ smo2-2 mutant exhibited a moderate phenotype. The phenotypes of the smo2 mutants resembled those of auxin-defective mutants. Indeed, the expression of DR5rev:GFP, an auxin-responsive reporter, was reduced and abnormal in smo2-1 smo2-2 embryos. Furthermore, the expression and subcellular localization of the PIN1 auxin efflux facilitator also were altered. Consistent with these observations, either the exogenous application of auxin or endogenous auxin overproduction (YUCCA9 overexpression) partially rescued the smo2-1 smo2-2 embryonic lethality. Surprisingly, the dwarf phenotype of smo2-1 smo2-2/+ was completely rescued by YUCCA9 overexpression. Gas chromatography-mass spectrometry analysis revealed a substantial accumulation of 4α-methylsterols, substrates of SMO2, in smo2 heterozygous double mutants. Together, our data suggest that SMO2s are important for correct sterol composition and function partially through effects on auxin accumulation, auxin response, and PIN1 expression to regulate Arabidopsis embryogenesis and postembryonic development. © 2016 American Society of Plant Biologists. All Rights Reserved.

Sterol Methyl Oxidases Affect Embryo Development via Auxin-Associated Mechanisms1

PubMed Central

Zhang, Xia; Sun, Shuangli; Nie, Xiang; Boutté, Yohann; Grison, Magali; Li, Panpan; Kuang, Susu

2016-01-01

Sterols are essential molecules for multiple biological processes, including embryogenesis, cell elongation, and endocytosis. The plant sterol biosynthetic pathway is unique in the involvement of two distinct sterol 4α-methyl oxidase (SMO) families, SMO1 and SMO2, which contain three and two isoforms, respectively, and are involved in sequential removal of the two methyl groups at C-4. In this study, we characterized the biological functions of members of the SMO2 gene family. SMO2-1 was strongly expressed in most tissues during Arabidopsis (Arabidopsis thaliana) development, whereas SMO2-2 showed a more specific expression pattern. Although single smo2 mutants displayed no obvious phenotype, the smo2-1 smo2-2 double mutant was embryonic lethal, and the smo2-1 smo2-2/+ mutant was dwarf, whereas the smo2-1/+ smo2-2 mutant exhibited a moderate phenotype. The phenotypes of the smo2 mutants resembled those of auxin-defective mutants. Indeed, the expression of DR5rev:GFP, an auxin-responsive reporter, was reduced and abnormal in smo2-1 smo2-2 embryos. Furthermore, the expression and subcellular localization of the PIN1 auxin efflux facilitator also were altered. Consistent with these observations, either the exogenous application of auxin or endogenous auxin overproduction (YUCCA9 overexpression) partially rescued the smo2-1 smo2-2 embryonic lethality. Surprisingly, the dwarf phenotype of smo2-1 smo2-2/+ was completely rescued by YUCCA9 overexpression. Gas chromatography-mass spectrometry analysis revealed a substantial accumulation of 4α-methylsterols, substrates of SMO2, in smo2 heterozygous double mutants. Together, our data suggest that SMO2s are important for correct sterol composition and function partially through effects on auxin accumulation, auxin response, and PIN1 expression to regulate Arabidopsis embryogenesis and postembryonic development. PMID:27006488

MicroRNA-1271 inhibits proliferation and promotes apoptosis of multiple myeloma cells through inhibiting smoothened-mediated Hedgehog signaling pathway.

PubMed

Xu, Zhengwei; Huang, Chen; Hao, Dingjun

2017-02-01

MicroRNAs (miRNAs) have emerged as important regulators in multiple myeloma (MM). miR-1271 is a tumor suppressor in many cancer types. However, the biological role of miR-1271 in MM remains unclear. In the present study, we elucidated the biological role of miR-1271 in MM. Results showed that miR-1271 was significantly decreased in primary MM cells from MM patients and MM cell lines. Overexpression of miR-1271 inhibited proliferation and promoted apoptosis of MM cells. Conversely, suppression of miR-1271 showed the opposite effect. Bioinformatics algorithm analysis predicted that smoothened (SMO), the activator of Hedgehog (HH) signaling pathway, was a direct target of miR-1271 that was experimentally verified by a dual-luciferase reporter assay. Furthermore, overexpression of miR-1271 inhibited SMO expression and HH signaling pathway. Conversely, the restoration of SMO expression markedly abolished the effect of miR-1271 overexpression on cell proliferation, apoptosis and HH signaling pathway in MM cells. Taken together, the present study suggests that miR-1271 functions as a tumor suppressor that inhibits proliferation and promotes apoptosis of MM cells through inhibiting SMO-mediated HH signaling pathway. This finding implies that miR-1271 is a potential therapeutic target for the treatment of MM.

The Smo/Smo model: hedgehog-induced medulloblastoma with 90% incidence and leptomeningeal spread.

PubMed

Hatton, Beryl A; Villavicencio, Elisabeth H; Tsuchiya, Karen D; Pritchard, Joel I; Ditzler, Sally; Pullar, Barbara; Hansen, Stacey; Knoblaugh, Sue E; Lee, Donghoon; Eberhart, Charles G; Hallahan, Andrew R; Olson, James M

2008-03-15

Toward the goal of generating a mouse medulloblastoma model with increased tumor incidence, we developed a homozygous version of our ND2:SmoA1 model. Medulloblastomas form in 94% of homozygous Smo/Smo mice by 2 months of age. Tumor formation is, thus, predictable by age, before the symptomatic appearance of larger lesions. This high incidence and early onset of tumors is ideal for preclinical studies because mice can be enrolled before symptom onset and with a greater latency period before late-stage disease. Smo/Smo tumors also display leptomeningeal dissemination of neoplastic cells to the brain and spine, which occurs in many human cases. Despite an extended proliferation of granule neuron precursors (GNP) in the postnatal external granular layer (EGL), the internal granular layer formed normally in Smo/Smo mice and tumor formation occurred only in localized foci on the superficial surface of the molecular layer. Thus, tumor formation is not simply the result of over proliferation of GNPs within the EGL. Moreover, Smo/Smo medulloblastomas were transplantable and serially passaged in vivo, demonstrating the aggressiveness of tumor cells and their transformation beyond a hyperplastic state. The Smo/Smo model is the first mouse medulloblastoma model to show leptomeningeal spread. The adherence to human pathology, high incidence, and early onset of tumors thus make Smo/Smo mice an efficient model for preclinical studies.

Heterologous expression and characterization of mouse spermine oxidase.

PubMed

Cervelli, Manuela; Polticelli, Fabio; Federico, Rodolfo; Mariottini, Paolo

2003-02-14

Polyamine oxidases are key enzymes responsible of the polyamine interconversion metabolism in animal cells. Recently, a novel enzyme belonging to this class of enzymes has been characterized for its capability to oxidize preferentially spermine and designated as spermine oxidase. This is a flavin adenine dinucleotide-containing enzyme, and it has been expressed both in vitro and in vivo systems. The primary structure of mouse spermine oxidase (mSMO) was deduced from a cDNA clone (Image Clone 264769) recovered by a data base search utilizing the human counterpart of polyamine oxidases, PAOh1. The open reading frame predicts a 555-amino acid protein with a calculated M(r) of 61,852.30, which shows a 95.1% identity with PAOh1. To understand the biochemical properties of mSMO and its structure/function relationship, the mSMO cDNA has been subcloned and expressed in secreted and secreted-tagged forms into Escherichia coli BL21 DE3 cells. The recombinant enzyme shows an optimal pH value of 8.0 and is able to oxidize rapidly spermine to spermidine and 3-aminopropanal and fails to act upon spermidine and N(1)-acetylpolyamines. The purified recombinant-tagged form enzyme (M(r) approximately 68,000) has K(m) and k(cat) values of 90 microm and 4.5 s(-1), respectively, using spermine as substrate at pH 8.0. Molecular modeling of mSMO protein based on maize polyamine oxidase three-dimensional structure suggests that the general features of maize polyamine oxidase active site are conserved in mSMO.

Robust In-Flight Sensor Fault Diagnostics for Aircraft Engine Based on Sliding Mode Observers

PubMed Central

Chang, Xiaodong; Huang, Jinquan; Lu, Feng

2017-01-01

For a sensor fault diagnostic system of aircraft engines, the health performance degradation is an inevitable interference that cannot be neglected. To address this issue, this paper investigates an integrated on-line sensor fault diagnostic scheme for a commercial aircraft engine based on a sliding mode observer (SMO). In this approach, one sliding mode observer is designed for engine health performance tracking, and another for sensor fault reconstruction. Both observers are employed in in-flight applications. The results of the former SMO are analyzed for post-flight updating the baseline model of the latter. This idea is practical and feasible since the updating process does not require the algorithm to be regulated or redesigned, so that ground-based intervention is avoided, and the update process is implemented in an economical and efficient way. With this setup, the robustness of the proposed scheme to the health degradation is much enhanced and the latter SMO is able to fulfill sensor fault reconstruction over the course of the engine life. The proposed sensor fault diagnostic system is applied to a nonlinear simulation of a commercial aircraft engine, and its effectiveness is evaluated in several fault scenarios. PMID:28398255

Robust In-Flight Sensor Fault Diagnostics for Aircraft Engine Based on Sliding Mode Observers.

PubMed

Chang, Xiaodong; Huang, Jinquan; Lu, Feng

2017-04-11

For a sensor fault diagnostic system of aircraft engines, the health performance degradation is an inevitable interference that cannot be neglected. To address this issue, this paper investigates an integrated on-line sensor fault diagnostic scheme for a commercial aircraft engine based on a sliding mode observer (SMO). In this approach, one sliding mode observer is designed for engine health performance tracking, and another for sensor fault reconstruction. Both observers are employed in in-flight applications. The results of the former SMO are analyzed for post-flight updating the baseline model of the latter. This idea is practical and feasible since the updating process does not require the algorithm to be regulated or redesigned, so that ground-based intervention is avoided, and the update process is implemented in an economical and efficient way. With this setup, the robustness of the proposed scheme to the health degradation is much enhanced and the latter SMO is able to fulfill sensor fault reconstruction over the course of the engine life. The proposed sensor fault diagnostic system is applied to a nonlinear simulation of a commercial aircraft engine, and its effectiveness is evaluated in several fault scenarios.

Ground-based FTIR retrievals of SF6 on Reunion Island

NASA Astrophysics Data System (ADS)

Zhou, Minqiang; Langerock, Bavo; Vigouroux, Corinne; Wang, Pucai; Hermans, Christian; Stiller, Gabriele; Walker, Kaley A.; Dutton, Geoff; Mahieu, Emmanuel; De Mazière, Martine

2018-02-01

SF6 total columns were successfully retrieved from FTIR (Fourier transform infrared) measurements (Saint Denis and Maïdo) on Reunion Island (21° S, 55° E) between 2004 and 2016 using the SFIT4 algorithm: the retrieval strategy and the error budget were presented. The FTIR SF6 retrieval has independent information in only one individual layer, covering the whole of the troposphere and the lower stratosphere. The trend in SF6 was analysed based on the FTIR-retrieved dry-air column-averaged mole fractions (XSF6) on Reunion Island, the in situ measurements at America Samoa (SMO) and the collocated satellite measurements (Michelson Interferometer for Passive Atmospheric Sounding, MIPAS, and Atmospheric Chemistry Experiment Fourier Transform Spectrometer, ACE-FTS) in the southern tropics. The SF6 annual growth rate from FTIR retrievals is 0.265 ± 0.013 pptv year-1 for 2004-2016, which is slightly weaker than that from the SMO in situ measurements (0.285 ± 0.002 pptv year-1) for the same time period. The SF6 trend in the troposphere from MIPAS and ACE-FTS observations is also close to the ones from the FTIR retrievals and the SMO in situ measurements.

Max-margin weight learning for medical knowledge network.

PubMed

Jiang, Jingchi; Xie, Jing; Zhao, Chao; Su, Jia; Guan, Yi; Yu, Qiubin

2018-03-01

The application of medical knowledge strongly affects the performance of intelligent diagnosis, and method of learning the weights of medical knowledge plays a substantial role in probabilistic graphical models (PGMs). The purpose of this study is to investigate a discriminative weight-learning method based on a medical knowledge network (MKN). We propose a training model called the maximum margin medical knowledge network (M 3 KN), which is strictly derived for calculating the weight of medical knowledge. Using the definition of a reasonable margin, the weight learning can be transformed into a margin optimization problem. To solve the optimization problem, we adopt a sequential minimal optimization (SMO) algorithm and the clique property of a Markov network. Ultimately, M 3 KN not only incorporates the inference ability of PGMs but also deals with high-dimensional logic knowledge. The experimental results indicate that M 3 KN obtains a higher F-measure score than the maximum likelihood learning algorithm of MKN for both Chinese Electronic Medical Records (CEMRs) and Blood Examination Records (BERs). Furthermore, the proposed approach is obviously superior to some classical machine learning algorithms for medical diagnosis. To adequately manifest the importance of domain knowledge, we numerically verify that the diagnostic accuracy of M 3 KN is gradually improved as the number of learned CEMRs increase, which contain important medical knowledge. Our experimental results show that the proposed method performs reliably for learning the weights of medical knowledge. M 3 KN outperforms other existing methods by achieving an F-measure of 0.731 for CEMRs and 0.4538 for BERs. This further illustrates that M 3 KN can facilitate the investigations of intelligent healthcare. Copyright © 2018 Elsevier B.V. All rights reserved.

Spermine oxidase (SMO) activity in breast tumor tissues and biochemical analysis of the anticancer spermine analogues BENSpm and CPENSpm.

PubMed

Cervelli, Manuela; Bellavia, Gabriella; Fratini, Emiliano; Amendola, Roberto; Polticelli, Fabio; Barba, Marco; Federico, Rodolfo; Signore, Fabrizio; Gucciardo, Giacomo; Grillo, Rosalba; Woster, Patrick M; Casero, Robert A; Mariottini, Paolo

2010-10-14

Polyamine metabolism has a critical role in cell death and proliferation representing a potential target for intervention in breast cancer (BC). This study investigates the expression of spermine oxidase (SMO) and its prognostic significance in BC. Biochemical analysis of Spm analogues BENSpm and CPENSpm, utilized in anticancer therapy, was also carried out to test their property in silico and in vitro on the recombinant SMO enzyme. BC tissue samples were analyzed for SMO transcript level and SMO activity. Student's t test was applied to evaluate the significance of the differences in value observed in T and NT samples. The structure modeling analysis of BENSpm and CPENSpm complexes formed with the SMO enzyme and their inhibitory activity, assayed by in vitro experiments, were examined. Both the expression level of SMO mRNA and SMO enzyme activity were significantly lower in BC samples compared to NT samples. The modeling of BENSpm and CPENSpm complexes formed with SMO and their inhibition properties showed that both were good inhibitors. This study shows that underexpression of SMO is a negative marker in BC. The SMO induction is a remarkable chemotherapeutical target. The BENSpm and CPENSpm are efficient SMO inhibitors. The inhibition properties shown by these analogues could explain their poor positive outcomes in Phases I and II of clinical trials.

Spermine oxidase (SMO) activity in breast tumor tissues and biochemical analysis of the anticancer spermine analogues BENSpm and CPENSpm

PubMed Central

2010-01-01

Background Polyamine metabolism has a critical role in cell death and proliferation representing a potential target for intervention in breast cancer (BC). This study investigates the expression of spermine oxidase (SMO) and its prognostic significance in BC. Biochemical analysis of Spm analogues BENSpm and CPENSpm, utilized in anticancer therapy, was also carried out to test their property in silico and in vitro on the recombinant SMO enzyme. Methods BC tissue samples were analyzed for SMO transcript level and SMO activity. Student's t test was applied to evaluate the significance of the differences in value observed in T and NT samples. The structure modeling analysis of BENSpm and CPENSpm complexes formed with the SMO enzyme and their inhibitory activity, assayed by in vitro experiments, were examined. Results Both the expression level of SMO mRNA and SMO enzyme activity were significantly lower in BC samples compared to NT samples. The modeling of BENSpm and CPENSpm complexes formed with SMO and their inhibition properties showed that both were good inhibitors. Conclusions This study shows that underexpression of SMO is a negative marker in BC. The SMO induction is a remarkable chemotherapeutical target. The BENSpm and CPENSpm are efficient SMO inhibitors. The inhibition properties shown by these analogues could explain their poor positive outcomes in Phases I and II of clinical trials. PMID:20946629

CFS-SMO based classification of breast density using multiple texture models.

PubMed

Sharma, Vipul; Singh, Sukhwinder

2014-06-01

It is highly acknowledged in the medical profession that density of breast tissue is a major cause for the growth of breast cancer. Increased breast density was found to be linked with an increased risk of breast cancer growth, as high density makes it difficult for radiologists to see an abnormality which leads to false negative results. Therefore, there is need for the development of highly efficient techniques for breast tissue classification based on density. This paper presents a hybrid scheme for classification of fatty and dense mammograms using correlation-based feature selection (CFS) and sequential minimal optimization (SMO). In this work, texture analysis is done on a region of interest selected from the mammogram. Various texture models have been used to quantify the texture of parenchymal patterns of breast. To reduce the dimensionality and to identify the features which differentiate between breast tissue densities, CFS is used. Finally, classification is performed using SMO. The performance is evaluated using 322 images of mini-MIAS database. Highest accuracy of 96.46% is obtained for two-class problem (fatty and dense) using proposed approach. Performance of selected features by CFS is also evaluated by Naïve Bayes, Multilayer Perceptron, RBF Network, J48 and kNN classifier. The proposed CFS-SMO method outperforms all other classifiers giving a sensitivity of 100%. This makes it suitable to be taken as a second opinion in classifying breast tissue density.

METABOLISM OF N-ALKYLATED SPERMINE ANALOGUES BY POLYAMINE AND SPERMINE OXIDASES

PubMed Central

Häkkinen, Merja R.; Hyvönen, Mervi T.; Auriola, Seppo; Casero, Robert A.; Vepsäläinen, Jouko; Khomutov, Alex R.; Alhonen, Leena; Keinänen, Tuomo A.

2010-01-01

SUMMARY N-alkylated polyamine analogues have potential as anticancer and antiparasitic drugs. However, their metabolism in the host has remained incompletely defined thus potentially limiting their utility. Here, we have studied the degradation of three different spermine analogues N,N′-bis-(3-ethylaminopropyl)butane-1,4-diamine (DESPM), N-(3-benzyl-aminopropyl)-N'-(3-ethylaminopropyl)butane-1,4-diamine (BnEtSPM) and N,N′-bis-(3-benzylaminopropyl)butane-1,4-diamine (DBSPM) and related mono-alkylated derivatives as substrates of recombinant human polyamine oxidase (APAO) and spermine oxidase (SMO). APAO and SMO metabolized DESPM to EtSPD (Km(APAO)=10μM, kcat(APAO)=1.1s−1 and Km(SMO)=28μM, kcat(SMO)=0.8s−1, respectively), metabolized BnEtSPM to EtSPD (Km(APAO)=0.9 μM, kcat(APAO)=1.1s−1 and Km(SMO)=51μM, kcat(SMO)=0.4s−1, respectively), and metabolized DBSPM to BnSPD (Km(APAO)=5.4μM, kcat(APAO)= 2.0s−1 and Km(SMO)=33μM, kcat(SMO)=0.3s−1, respectively). Interestingly, mono-alkylated spermine derivatives were metabolized by APAO and SMO to SPD (EtSPM Km(APAO)=16μM, kcat(APAO)=1.5s−1; Km(SMO)=25μM, kcat(SMO) =8.2s−1; BnSPM Km(APAO)=6.0μM, kcat(APAO)=2.8s−1; Km(SMO)=19μM, kcat(SMO)=0.8s−1, respectively). Surprisingly, E t S P D ( Km(APAO)=37μM, kcat(APAO)=0.1s−1; Km(SMO)=48μM, kcat(SMO)=0.05s−1) and BnSPD (Km(APAO)=2.5μM, kcat(APAO)=3.5s−1; Km(SMO)=60μM, kcat(SMO)=0.54s−1) were metabolized to SPD by both the oxidases. Furthermore, we studied the degradation of DESPM, BnEtSPM or DBSPM in the DU145 prostate carcinoma cell line. The same major metabolites EtSPD and/or BnSPD were detected both in the culture medium and intracellularly after 48 hours of culture. Moreover, EtSPM and BnSPM were detected from cell samples. Present data shows that inducible SMO parallel with APAO could play an important role in polyamine based drug action, i.e. degradation of parent drug and its metabolites, having significant impact on efficiency of these drugs, and hence for the development of novel N-alkylated polyamine analogues. PMID:20012116

Electronic structure of SmO and SmO- via slow photoelectron velocity-map imaging spectroscopy and spin-orbit CASPT2 calculations

NASA Astrophysics Data System (ADS)

Weichman, Marissa L.; Vlaisavljevich, Bess; DeVine, Jessalyn A.; Shuman, Nicholas S.; Ard, Shaun G.; Shiozaki, Toru; Neumark, Daniel M.; Viggiano, Albert A.

2017-12-01

The chemi-ionization reaction of atomic samarium, Sm + O → SmO+ + e-, has been investigated by the Air Force Research Laboratory as a means to modify local electron density in the ionosphere for reduction of scintillation of high-frequency radio waves. Neutral SmO is a likely unwanted byproduct. The spectroscopy of SmO is of great interest to aid in interpretation of optical emission spectra recorded following atmospheric releases of Sm as part of the Metal Oxide Space Cloud (MOSC) observations. Here, we report a joint experimental and theoretical study of SmO using slow photoelectron velocity-map imaging spectroscopy of cryogenically cooled SmO- anions (cryo-SEVI) and high-level spin-orbit complete active space calculations with corrections from second order perturbation theory (CASPT2). With cryo-SEVI, we measure the electron affinity of SmO to be 1.0581(11) eV and report electronic and vibrational structure of low-lying electronic states of SmO in good agreement with theory and prior experimental work. We also obtain spectra of higher-lying excited states of SmO for direct comparison to the MOSC results.

SmoXYB1C1Z of Mycobacterium sp. Strain NBB4: a Soluble Methane Monooxygenase (sMMO)-Like Enzyme, Active on C2 to C4 Alkanes and Alkenes

PubMed Central

Martin, Kiri E.; Ozsvar, Jazmin

2014-01-01

Monooxygenase (MO) enzymes initiate the aerobic oxidation of alkanes and alkenes in bacteria. A cluster of MO genes (smoXYB1C1Z) of thus-far-unknown function was found previously in the genomes of two Mycobacterium strains (NBB3 and NBB4) which grow on hydrocarbons. The predicted Smo enzymes have only moderate amino acid identity (30 to 60%) to their closest homologs, the soluble methane and butane MOs (sMMO and sBMO), and the smo gene cluster has a different organization from those of sMMO and sBMO. The smoXYB1C1Z genes of NBB4 were cloned into pMycoFos to make pSmo, which was transformed into Mycobacterium smegmatis mc2-155. Cells of mc2-155(pSmo) metabolized C2 to C4 alkanes, alkenes, and chlorinated hydrocarbons. The activities of mc2-155(pSmo) cells were 0.94, 0.57, 0.12, and 0.04 nmol/min/mg of protein with ethene, ethane, propane, and butane as substrates, respectively. The mc2-155(pSmo) cells made epoxides from ethene, propene, and 1-butene, confirming that Smo was an oxygenase. Epoxides were not produced from larger alkenes (1-octene and styrene). Vinyl chloride and 1,2-dichloroethane were biodegraded by cells expressing Smo, with production of inorganic chloride. This study shows that Smo is a functional oxygenase which is active against small hydrocarbons. M. smegmatis mc2-155(pSmo) provides a new model for studying sMMO-like monooxygenases. PMID:25015887

Plant sterol biosynthesis: identification of two distinct families of sterol 4alpha-methyl oxidases.

PubMed Central

Darnet, Sylvain; Rahier, Alain

2004-01-01

In plants, the conversion of cycloartenol into functional phytosterols requires the removal of the two methyl groups at C-4 by an enzymic complex including a sterol 4alpha-methyl oxidase (SMO). We report the cloning of candidate genes for SMOs in Arabidopsis thaliana, belonging to two distinct families termed SMO1 and SMO2 and containing three and two isoforms respectively. SMO1 and SMO2 shared low sequence identity with each other and were orthologous to the ERG25 gene from Saccharomyces cerevisiae which encodes the SMO. The plant SMO amino acid sequences possess all the three histidine-rich motifs (HX3H, HX2HH and HX2HH), characteristic of the small family of membrane-bound non-haem iron oxygenases that are involved in lipid oxidation. To elucidate the precise functions of SMO1 and SMO2 gene families, we have reduced their expression by using a VIGS (virus-induced gene silencing) approach in Nicotiana benthamiana. SMO1 and SMO2 cDNA fragments were inserted into a viral vector and N. benthamiana inoculated with the viral transcripts. After silencing with SMO1, a substantial accumulation of 4,4-dimethyl-9beta,19-cyclopropylsterols (i.e. 24-methylenecycloartanol) was obtained, whereas qualitative and quantitative levels of 4alpha-methylsterols were not affected. In the case of silencing with SMO2, a large accumulation of 4alpha-methyl-Delta7-sterols (i.e. 24-ethylidenelophenol and 24-ethyllophenol) was found, with no change in the levels of 4,4-dimethylsterols. These clear and distinct biochemical phenotypes demonstrate that, in contrast with animals and fungi, in photosynthetic eukaryotes, these two novel families of cDNAs are coding two distinct types of C-4-methylsterol oxidases controlling the level of 4,4-dimethylsterol and 4alpha-methylsterol precursors respectively. PMID:14653780

Structural basis for Smoothened receptor modulation and chemoresistance to anti-cancer drugs

PubMed Central

Wang, Chong; Wu, Huixian; Evron, Tama; Vardy, Eyal; Han, Gye Won; Huang, Xi-Ping; Hufeisen, Sandy J.; Mangano, Thomas J.; Urban, Dan J.; Katritch, Vsevolod; Cherezov, Vadim; Caron, Marc G.; Roth, Bryan L.; Stevens, Raymond C.

2014-01-01

The Smoothened receptor (SMO) mediates signal transduction in the hedgehog pathway, which is implicated in normal development and carcinogenesis. SMO antagonists can suppress the growth of some tumors; however, mutations at SMO have been found to abolish their anti-tumor effects, a phenomenon known as chemoresistance. Here we report three crystal structures of human SMO bound to the antagonists SANT1 and Anta XV, and the agonist, SAG1.5, at 2.6–2.8Å resolution. The long and narrow cavity in the transmembrane domain of SMO harbors multiple ligand binding sites, where SANT1 binds at a deeper site as compared with other ligands. Distinct interactions at D4736.55 elucidated the structural basis for the differential effects of chemoresistance mutations on SMO antagonists. The agonist SAG1.5 induces a conformational rearrangement of the binding pocket residues, which could contribute to SMO activation. Collectively, these studies reveal the structural basis for the modulation of SMO by small molecules. PMID:25008467

PI(4)P Promotes Phosphorylation and Conformational Change of Smoothened through Interaction with Its C-terminal Tail

PubMed Central

Zhang, Jie; Li, Xiang-An; Evers, B. Mark; Zhu, Haining; Jia, Jianhang

2016-01-01

In Hedgehog (Hh) signaling, binding of Hh to the Patched-Interference Hh (Ptc-Ihog) receptor complex relieves Ptc inhibition on Smoothened (Smo). A longstanding question is how Ptc inhibits Smo and how such inhibition is relieved by Hh stimulation. In this study, we found that Hh elevates production of phosphatidylinositol 4-phosphate (PI(4)P). Increased levels of PI(4)P promote, whereas decreased levels of PI(4)P inhibit, Hh signaling activity. We further found that PI(4)P directly binds Smo through an arginine motif, which then triggers Smo phosphorylation and activation. Moreover, we identified the pleckstrin homology (PH) domain of G protein-coupled receptor kinase 2 (Gprk2) as an essential component for enriching PI(4)P and facilitating Smo activation. PI(4)P also binds mouse Smo (mSmo) and promotes its phosphorylation and ciliary accumulation. Finally, Hh treatment increases the interaction between Smo and PI(4)P but decreases the interaction between Ptc and PI(4)P, indicating that, in addition to promoting PI(4)P production, Hh regulates the pool of PI(4)P associated with Ptc and Smo. PMID:26863604

Antineoplastic Efficacy of Novel Polyamine Analogues in Human Breast Cancer

DTIC Science & Technology

2006-06-01

Davidson, N.E., and Casero, R.A.. Spermine oxidase SMO(PAOh1), not N1-acetylpolyamine oxidase (PAO) is the primary source of cytotoxic H2O2 in...human spermine oxidase SMO(PAOh1). SMO(PAOh1) uses unacetylated spermine as substrate and is inducible by specific polyamine analogs [15,16]. These...technique to find the identical clone termed spermine oxidase (SMO) [16]. The function of SMO(PAOh1) as a spermine oxidase has been confirmed [15,67,68

Pharmacodynamic interactions of a solid formulation of sodium oxybate and ethanol in healthy volunteers

PubMed Central

Pross, Nathalie; Patat, Alain; Vivet, Philippe; Bidaut, Michelle; Fauchoux, Nicolas

2015-01-01

Aim The pharmacologic effects of sodium oxybate (SO) have a number of similarities with those of alcohol. This study evaluated the pharmacodynamic interaction of SMO.IR (a solid immediate release formulation of SO) and alcohol (0.7 (males) or 0.57 (females) g kg–1 alcohol using 40% vodka). Methods In a randomized, double-blind, double-dummy, crossover trial, 24 healthy volunteers received randomly a) 2.25 g SMO.IR and placebo alcohol preparation, b) 2.25 g f SMO.IR and alcohol, c) 2.25 g SMO.IR matching placebo and alcohol and d) 2.25 g of SMO.IR matching placebo and placebo alcohol preparation. Objective and subjective cognitive parameters, adverse events and vital signs were assessed before, 15 and 165 min after treatment administration. Results Alcohol produced the expected cognitive impairment and the expected subjective sedation rapidly after intake (from 15 min). The objective effects of SMO.IR were much less pronounced than those of alcohol. The reverse was observed for subjective complaints, which were related to lesser stimulation and greater sedation. Nevertheless, 165 min after administration this sedation feeling was less with SMO.IR than with alcohol. There was a significant interaction between SMO.IR and alcohol at 15 min (i.e. increase in alertness and stimulation and decrease in sedation). In addition, an isolated mild decrease in digit vigilance accuracy occurred at 165 min post-dose after the combination. The co-administration of SMO.IR and alcohol was safe and well-tolerated. Conclusion SMO.IR and alcohol have distinct adverse effect profiles. The objective effects of SMO.IR are much less marked than those of alcohol. No deleterious interaction was observed. PMID:25782469

Discovery of novel hedgehog antagonists from cell-based screening: Isosteric modification of p38 bisamides as potent inhibitors of SMO.

PubMed

Yang, Bin; Hird, Alexander W; Russell, Daniel John; Fauber, Benjamin P; Dakin, Les A; Zheng, Xiaolan; Su, Qibin; Godin, Robert; Brassil, Patrick; Devereaux, Erik; Janetka, James W

2012-07-15

Cell-based subset screening of compounds using a Gli transcription factor reporter cell assay and shh stimulated cell differentiation assay identified a series of bisamide compounds as hedgehog pathway inhibitors with good potency. Using a ligand-based optimization strategy, heteroaryl groups were utilized as conformationally restricted amide isosteres replacing one of the amides which significantly increased their potency against SMO and the hedgehog pathway while decreasing activity against p38α kinase. We report herein the identification of advanced lead compounds such as imidazole 11c and 11f encompassing good p38α selectivity, low nanomolar potency in both cell assays, excellent physiochemical properties and in vivo pharmacokinetics. Copyright © 2012 Elsevier Ltd. All rights reserved.

Dielectric tunability of vertically aligned ferroelectric-metal oxide nanocomposite films controlled by out-of-plane misfit strain

NASA Astrophysics Data System (ADS)

Wu, Huaping; Ma, Xuefu; Zhang, Zheng; Zhu, Jun; Wang, Jie; Chai, Guozhong

2016-04-01

A nonlinear thermodynamic model based on the vertically aligned nanocomposite (VAN) thin films of ferroelectric-metal oxide system has been developed to investigate the physical properties of the epitaxial Ba0.6Sr0.4TiO3 (BST) films containing vertical Sm2O3 (SmO) nanopillar arrays on the SrTiO3 substrate. The phase diagrams of out-of-plane lattice mismatch vs. volume fraction of SmO are calculated by minimizing the total free energy. It is found that the phase transformation and dielectric response of BST-SmO VAN systems are extremely dependent on the in-plane misfit strain, the out-of-plane lattice mismatch, the volume fraction of SmO phase, and the external electric field applied to the nanocomposite films at room temperature. In particular, the BST-SmO VAN systems exhibit higher dielectric properties than pure BST films. Giant dielectric response and maximum tunability are obtained near the lattice mismatch where the phase transition occurs. Under the in-plane misfit strain of umf=0.3 % and the out-of-plane lattice mismatch of u3=0.002 , the dielectric tunability can be dramatically enhanced to 90% with the increase of SmO volume fraction, which is well consistent with previous experimental results. This work represents an approach to further understand the dependence of physical properties on the lattice mismatch (in-plane and out-of-plane) and volume fraction, and to manipulate or optimize functionalities in the nanocomposite oxide thin films.

Hydrogen Gas Sensors Based on Semiconductor Oxide Nanostructures

PubMed Central

Gu, Haoshuang; Wang, Zhao; Hu, Yongming

2012-01-01

Recently, the hydrogen gas sensing properties of semiconductor oxide (SMO) nanostructures have been widely investigated. In this article, we provide a comprehensive review of the research progress in the last five years concerning hydrogen gas sensors based on SMO thin film and one-dimensional (1D) nanostructures. The hydrogen sensing mechanism of SMO nanostructures and some critical issues are discussed. Doping, noble metal-decoration, heterojunctions and size reduction have been investigated and proved to be effective methods for improving the sensing performance of SMO thin films and 1D nanostructures. The effect on the hydrogen response of SMO thin films and 1D nanostructures of grain boundary and crystal orientation, as well as the sensor architecture, including electrode size and nanojunctions have also been studied. Finally, we also discuss some challenges for the future applications of SMO nanostructured hydrogen sensors. PMID:22778599

Constitutive Activation of Smoothened in the Renal Collecting Ducts Leads to Renal Hypoplasia, Hydronephrosis, and Hydroureter.

PubMed

Gupta, Deepak Prasad; Hwang, Jae-Won; Cho, Eui-Sic; Kim, Won; Song, Chang Ho; Chai, Ok Hee

2017-01-01

Sonic Hedgehog (Shh) signaling plays a major role in and is essential for regulation, patterning, and proliferation during renal development. Smoothened (Smo) plays a pivot role in transducing the Shh-glioma-associated oncogene Kruppel family member. However, the cellular and molecular mechanism underlying the role of sustained Smo activation in postnatal kidney development is still not clearly understood. Using a conditional knockin mouse model that expresses a constitutively activated form of Smo (SmoM2) upon Homeobox-B7-mediated recombination (Hoxb7-Cre), the effects of Shh signaling were determined in postnatal kidney development. SmoM2;Hoxb7-Cre mutant mice showed growth retardation with a reduction of body weight. Constitutive activation of Smo in the renal collecting ducts caused renal hypoplasia, hydronephrosis, and hydroureter. The parenchymal area and glomerular numbers were reduced, but the glomerular density was increased in SmoM2;Hoxb7-Cre mutant mice. The expression of Patched 1, the receptor of Shh and a downstream target gene of the Shh signaling pathway, was highly restricted and it was upregulated in the inner medullary collecting ducts of the kidney. The proliferative cells in the mesenchyme and collecting ducts were decreased in SmoM2;Hoxb7-Cre mutant mice. This study showed for the first time that sustained Smo inhibits postnatal kidney development by suppressing the proliferation of the mesenchyme and medullary collecting ducts in mice. © 2017 S. Karger AG, Basel.

DYRK1B as therapeutic target in Hedgehog/GLI-dependent cancer cells with Smoothened inhibitor resistance

PubMed Central

Gruber, Wolfgang; Hutzinger, Martin; Elmer, Dominik Patrick; Parigger, Thomas; Sternberg, Christina; Cegielkowski, Lukasz; Zaja, Mirko; Leban, Johann; Michel, Susanne; Hamm, Svetlana; Vitt, Daniel; Aberger, Fritz

2016-01-01

A wide range of human malignancies displays aberrant activation of Hedgehog (HH)/GLI signaling, including cancers of the skin, brain, gastrointestinal tract and hematopoietic system. Targeting oncogenic HH/GLI signaling with small molecule inhibitors of the essential pathway effector Smoothened (SMO) has shown remarkable therapeutic effects in patients with advanced and metastatic basal cell carcinoma. However, acquired and de novo resistance to SMO inhibitors poses severe limitations to the use of SMO antagonists and urgently calls for the identification of novel targets and compounds. Here we report on the identification of the Dual-Specificity-Tyrosine-Phosphorylation-Regulated Kinase 1B (DYRK1B) as critical positive regulator of HH/GLI signaling downstream of SMO. Genetic and chemical inhibition of DYRK1B in human and mouse cancer cells resulted in marked repression of HH signaling and GLI1 expression, respectively. Importantly, DYRK1B inhibition profoundly impaired GLI1 expression in both SMO-inhibitor sensitive and resistant settings. We further introduce a novel small molecule DYRK1B inhibitor, DYRKi, with suitable pharmacologic properties to impair SMO-dependent and SMO-independent oncogenic GLI activity. The results support the use of DYRK1B antagonists for the treatment of HH/GLI-associated cancers where SMO inhibitors fail to demonstrate therapeutic efficacy. PMID:26784250

Inhibition of polyamine and spermine oxidases by polyamine analogues.

PubMed

Bianchi, Marzia; Polticelli, Fabio; Ascenzi, Paolo; Botta, Maurizio; Federico, Rodolfo; Mariottini, Paolo; Cona, Alessandra

2006-03-01

Polyamine oxidase (PAO) and spermine oxidase (SMO) are involved in the catabolism of polyamines--basic regulators of cell growth and proliferation. The discovery of selective inhibitors of PAO and SMO represents an important tool in studying the involvement of these enzymes in polyamine homeostasis and a starting point for the development of novel antineoplastic drugs. Here, a comparative study on murine PAO (mPAO) and SMO (mSMO) inhibition by the polyamine analogues 1,8-diaminooctane, 1,12-diaminododecane, N-prenylagmatine (G3), guazatine and N,N1-bis(2,3-butadienyl)-1,4-butanediamine (MDL72527) is reported. Interestingly, 1,12-Diaminododecane and G3 behave as specific inhibitors of mPAO, values of K(i) for mPAO inhibition being lower than those for mSMO inactivation by several orders of magnitude. The analysis of molecular models of mPAO and mSMO indicates a significant reduction of the hydrophobic pocket located in maize PAO (MPAO) at the wider catalytic tunnel opening. This observation provides a rationale to explain the lower affinity displayed by G3, guazatine and MDL72527 for mPAO and mSMO as compared to MPAO. The different behaviour displayed by 1,12-diaminododecane towards mPAO and mSMO reveals the occurrence of basic differences in the ligand binding mode of the two enzymes, the first enzyme interacting mainly with substrate secondary amino groups and the second one with substrate primary amino groups. Thus, the data reported here provide the basis for the development of novel and selective inhibitors able to discriminate between mammalian SMO and PAO activities.

The intrahepatic signalling niche of hedgehog is defined by primary cilia positive cells during chronic liver injury.

PubMed

Grzelak, Candice Alexandra; Martelotto, Luciano Gastón; Sigglekow, Nicholas David; Patkunanathan, Bramilla; Ajami, Katerina; Calabro, Sarah Ruth; Dwyer, Benjamin James; Tirnitz-Parker, Janina Elke Eleonore; Watkins, D Neil; Warner, Fiona Jane; Shackel, Nicholas Adam; McCaughan, Geoffrey William

2014-01-01

In vertebrates, canonical Hedgehog (Hh) pathway activation requires Smoothened (SMO) translocation to the primary cilium (Pc), followed by a GLI-mediated transcriptional response. In addition, a similar gene regulation occurs in response to growth factors/cytokines, although independently of SMO signalling. The Hh pathway plays a critical role in liver fibrosis/regeneration, however, the mechanism of activation in chronic liver injury is poorly understood. This study aimed to characterise Hh pathway activation upon thioacetamide (TAA)-induced chronic liver injury in vivo by defining Hh-responsive cells, namely cells harbouring Pc and Pc-localised SMO. C57BL/6 mice (wild-type or Ptc1(+/-)) were TAA-treated. Liver injury and Hh ligand/pathway mRNA and protein expression were assessed in vivo. SMO/GLI manipulation and SMO-dependent/independent activation of GLI-mediated transcriptional response in Pc-positive (Pc(+)) cells were studied in vitro. In vivo, Hh activation was progressively induced following TAA. At the epithelial-mesenchymal interface, injured hepatocytes produced Hh ligands. Progenitors, myofibroblasts, leukocytes and hepatocytes were GLI2(+). Pc(+) cells increased following TAA, but only EpCAM(+)/GLI2(+) progenitors were Pc(+)/SMO(+). In vitro, SMO knockdown/hGli3-R overexpression reduced proliferation/viability in Pc(+) progenitors, whilst increased proliferation occurred with hGli1 overexpression. HGF induced GLI transcriptional activity independently of Pc/SMO. Ptc1(+/-) mice exhibited increased progenitor, myofibroblast and fibrosis responses. In chronic liver injury, Pc(+) progenitors receive Hh ligand signals and process it through Pc/SMO-dependent activation of GLI-mediated transcriptional response. Pc/SMO-independent GLI activation likely occurs in Pc(-)/GLI2(+) cells. Increased fibrosis in Hh gain-of-function mice likely occurs by primary progenitor expansion/proliferation and secondary fibrotic myofibroblast expansion, in close contact with progenitors. Copyright © 2013 European Association for the Study of the Liver. All rights reserved.

Best Matching Protein Conformations and Docking Programs for a Virtual Screening Campaign Against SMO Receptor.

PubMed

Amendola, Giorgio; Di Maio, Danilo; La Pietra, Valeria; Cosconati, Sandro

2016-09-01

SMO receptor is one of the main components of the Hedgehog biochemical pathway. In the last decades compelling body of evidence demonstrated that this receptor is a pertinent target for the treatment of various types of solid tumors. Recently, the X-ray determination of the three-dimensional structure of SMO in complex with different antagonists opened up the way for the structure-based design of new antagonists for this receptor that could possibly overcome the limitations connected with the induction of acquired tumor resistance. Herein, taking advantage of three different docking software (namely Glide, PLANTS, and Vina) and of the available SMO structures we set up a retrospective virtual screening (VS) protocol. A database, made up by known SMO antagonists and compounds with no alleged activity against the receptor was created and screened against the different SMO structures. To evaluate the performance of the ranking in VS calculations different statistical metrics (EF, AUAC and BEDROC) were employed allowing to identify the best performing VS docking protocol. Results of these studies will serve as a platform for the application of structure-based VS against the pharmaceutically relevant SMO receptor. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Induction of human spermine oxidase SMO(PAOh1) is regulated at the levels of new mRNA synthesis, mRNA stabilization and newly synthesized protein

PubMed Central

2004-01-01

The oxidation of polyamines induced by antitumour polyamine analogues has been associated with tumour response to specific agents. The human spermine oxidase, SMO(PAOh1), is one enzyme that may play a direct role in the cellular response to the antitumour polyamine analogues. In the present study, the induction of SMO(PAOh1) enzyme activity by CPENSpm [N1-ethyl-N11-(cyclopropyl)methyl-4,8,diazaundecane] is demonstrated to be a result of newly synthesized mRNA and protein. Inhibition of new RNA synthesis by actinomycin D inhibits both the appearance of SMO(PAOh1) mRNA and enzyme activity. Similarly, inhibition of newly synthesized protein with cycloheximide prevents analogue-induced enzyme activity. Half-life determinations indicate that stabilization of SMO(PAOh1) protein does not play a significant role in analogue-induced activity. However, half-life experiments using actinomycin D indicate that CPENSpm treatment not only increases mRNA expression, but also leads to a significant increase in mRNA half-life (17.1 and 8.8 h for CPENSpm-treated cells and control respectively). Using reporter constructs encompassing the SMO(PAOh1) promoter region, a 30–90% increase in transcription is observed after exposure to CPENSpm. The present results are consistent with the hypothesis that analogue-induced expression of SMO(PAOh1) is a result of increased transcription and stabilization of SMO(PAOh1) mRNA, leading to increased protein production and enzyme activity. These data indicate that the major level of control of SMO(PAOh1) expression in response to polyamine analogues exposure is at the level of mRNA. PMID:15496143

Spermine oxidase, a polyamine catabolic enzyme that links Helicobacter pylori CagA and gastric cancer risk

PubMed Central

Chaturvedi, Rupesh; de Sablet, Thibaut; Peek, Richard M.; Wilson, Keith T.

2012-01-01

We have recently reported that Helicobacter pylori strains expressing the virulence factor cytotoxin-associated gene A (CagA) stimulate increased levels of spermine oxidase (SMO) in gastric epithelial cells, while cagA– strains did not. SMO catabolizes the polyamine spermine and produces H2O2 that results in both apoptosis and DNA damage. Exogenous overexpression of CagA confirmed these findings, and knockdown or inhibition of SMO blocked CagA-mediated apoptosis and DNA damage. The strong association of SMO, apoptosis, and DNA damage was also demonstrated in humans infected with cagA+, but not cagA– strains. In infected gerbils and mice, DNA damage was CagA-dependent and only present in epithelial cells that expressed SMO. We also discovered SMOhigh gastric epithelial cells from infected animals with dysplasia that are resistant to apoptosis despite high levels of DNA damage. Inhibition of polyamine synthesis or SMO could abrogate the development of this cell population that may represent precursors for neoplastic transformation. PMID:22555547

Subcellular Localization of Patched and Smoothened, the Receptors for Sonic Hedgehog Signaling, in the Hippocampal Neuron

PubMed Central

Petralia, Ronald S.; Schwartz, Catherine M.; Wang, Ya-Xian; Mattson, Mark P.; Yao, Pamela J.

2011-01-01

Cumulative evidence suggests that, aside from patterning the embryonic neural tube, Sonic hedgehog (Shh) signaling plays important roles in the mature nervous system. In this study, we investigate the expression and localization of the Shh signaling receptors, Patched (Ptch) and Smoothened (Smo), in the hippocampal neurons of young and mature rats. Reverse transcriptase-polymerase chain reaction and immunoblotting analyses show that the expression of Ptch and Smo remains at a moderate level in young postnatal and adult brains. By using immunofluorescence light microscopy and immunoelectron microscopy, we examine the spatial distribution of Ptch and Smo within the hippocampal neurons. In young developing neurons, Ptch and Smo are present in the processes and are clustered at their growth cones. In mature neurons, Ptch and Smo are concentrated in dendrites, spines, and postsynaptic sites. Synaptic Ptch and Smo often co-exist with unusual structures—synaptic spinules and autophagosomes. Our results reveal the anatomical organization of the Shh receptors within both the young and the mature hippocampal neurons. PMID:21618238

CoSMoS Unravels Mysteries of Transcription Initiation

PubMed Central

Gourse, Richard L.; Landick, Robert

2013-01-01

Using a fluorescence method called colocalization single-molecule spectroscopy (CoSMoS), Friedman and Gelles dissect the kinetics of transcription initiation at a bacterial promoter. Ultimately, CoSMoS could greatly aid the study of the effects of DNA sequence and transcription factors on both prokaryotic and eukaryotic promoters. PMID:22341438

Changes in ankle joint motion after Supramalleolar osteotomy: a cadaveric model.

PubMed

Kim, Hak Jun; Yeo, Eui Dong; Rhyu, Im Joo; Lee, Soon-Hyuck; Lee, Yeon Soo; Lee, Young Koo

2017-09-09

Malalignment of the ankle joint has been found after trauma, by neurological disorders, genetic predisposition and other unidentified factors, and results in asymmetrical joint loading. For a medial open wedge supramalleolar osteotomy(SMO), there are some debates as to whether concurrent fibular osteotomy should be performed. We assessed the changes in motion of ankle joint and plantar pressure after supramalleolar osteotomy without fibular osteotomy. Ten lower leg specimens below the knee were prepared from fresh-frozen human cadavers. They were harvested from five males (10 ankles)whose average age was 70 years. We assessed the motion of ankle joint as well as plantar pressure for SS(supra-syndesmotic) SMO and IS(intra-syndesmotic) SMO. After the osteotomy, each specimen was subjected to axial compression from 20 N preload to 350 N representing half-body weight. For the measurement of the motion of ankle joint, the changes in gap and point, angles in ankle joint were measured. The plantar pressure were also recorded using TekScan sensors. The changes in the various gap, point, and angles movements on SS-SMO and IS-SMO showed no statistically significant differences between the two groups. Regarding the shift of plantar center of force (COF) were noted in the anterolateral direction, but not statistically significant. SS-SMO and IS-SMO with intact fibula showed similar biomechanical effect on the ankle joint. We propose that IS-SMO should be considered carefully for the treatment of osteoarthrosis when fibular osteotomy is not performed because lateral cortex fracture was less likely using the intrasyndesmosis plane because of soft tissue support.

Subcellular localization of Patched and Smoothened, the receptors for Sonic hedgehog signaling, in the hippocampal neuron.

PubMed

Petralia, Ronald S; Schwartz, Catherine M; Wang, Ya-Xian; Mattson, Mark P; Yao, Pamela J

2011-12-15

Cumulative evidence suggests that, aside from patterning the embryonic neural tube, Sonic hedgehog (Shh) signaling plays important roles in the mature nervous system. In this study, we investigate the expression and localization of the Shh signaling receptors, Patched (Ptch) and Smoothened (Smo), in the hippocampal neurons of young and mature rats. Reverse transcriptase-polymerase chain reaction and immunoblotting analyses show that the expression of Ptch and Smo remains at a moderate level in young postnatal and adult brains. By using immunofluorescence light microscopy and immunoelectron microscopy, we examine the spatial distribution of Ptch and Smo within the hippocampal neurons. In young developing neurons, Ptch and Smo are present in the processes and are clustered at their growth cones. In mature neurons, Ptch and Smo are concentrated in dendrites, spines, and postsynaptic sites. Synaptic Ptch and Smo often co-exist with unusual structures-synaptic spinules and autophagosomes. Our results reveal the anatomical organization of the Shh receptors within both the young and the mature hippocampal neurons. Copyright © 2011 Wiley-Liss, Inc.

Silybum marianum oil attenuates oxidative stress and ameliorates mitochondrial dysfunction in mice treated with D-galactose

PubMed Central

Zhu, Shu Yun; Dong, Ying; Tu, Jie; Zhou, Yue; Zhou, Xing Hua; Xu, Bin

2014-01-01

Background: Silybum marianum has been used as herbal medicine for the treatment of liver disease, liver cirrhosis, and to prevent liver cancer in Europe and Asia since ancient times. Silybum marianum oil (SMO), a by-product of silymarin production, is rich in essential fatty acids, phospholipids, sterols, and vitamin E. However, it has not been very good development and use. Objective: In the present study, we used olive oil as a control to investigate the antioxidant and anti-aging effect of SMO in D-galactose (D-gal)-induced aging mice. Materials and Methods: D-gal was injected intraperitoneally (500 mg/kg body weight daily) for 7 weeks while SMO was simultaneously administered orally. The triglycerides (TRIG) and cholesterol (CHOL) levels were estimated in the serum. Superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), total antioxidant capacity (T-AOC), monoamine oxidase (MAO), malondialdehyde (MDA), caspase-3, and Bcl-2 were determined in the liver and brain. The activities of Na+-K+-adenosine triphosphatase (ATPase), Ca2+-Mg2+-ATPase, membrane potential (ΔΨm), and membrane fluidity of the liver mitochondrial were estimated. Results: SMO decreased levels of TRIG and CHOL in aging mice. SMO administration elevated the activities of SOD, GSH-Px, and T-AOC, which are suppressed by aging. The levels of MAO and MDA in the liver and brain were reduced by SMO administration in aging mice. Enzyme linked immunosorbent assay showed that SMO significantly decreased the concentration of caspase-3 and improved the activity of Bcl-2 in the liver and brain of aging mice. Furthermore, SMO significantly attenuated the D-gal induced liver mitochondrial dysfunction by improving the activities of Na+-K+-ATPase, Ca2+-Mg2+-ATPase, membrane potential (ΔΨm), and membrane fluidity. Conclusion: These results indicate that SMO effectively attenuated oxidative damage and improved apoptosis related factors as well as liver mitochondrial dysfunction in aging mice. PMID:24914315

Probing mammalian spermine oxidase enzyme-substrate complex through molecular modeling, site-directed mutagenesis and biochemical characterization.

PubMed

Tavladoraki, Paraskevi; Cervelli, Manuela; Antonangeli, Fabrizio; Minervini, Giovanni; Stano, Pasquale; Federico, Rodolfo; Mariottini, Paolo; Polticelli, Fabio

2011-04-01

Spermine oxidase (SMO) and acetylpolyamine oxidase (APAO) are FAD-dependent enzymes that are involved in the highly regulated pathways of polyamine biosynthesis and degradation. Polyamine content is strictly related to cell growth, and dysfunctions in polyamine metabolism have been linked with cancer. Specific inhibitors of SMO and APAO would allow analyzing the precise role of these enzymes in polyamine metabolism and related pathologies. However, none of the available polyamine oxidase inhibitors displays the desired characteristics of selective affinity and specificity. In addition, repeated efforts to obtain structural details at the atomic level on these two enzymes have all failed. In the present study, in an effort to better understand structure-function relationships, SMO enzyme-substrate complex has been probed through a combination of molecular modeling, site-directed mutagenesis and biochemical studies. Results obtained indicate that SMO binds spermine in a similar conformation as that observed in the yeast polyamine oxidase FMS1-spermine complex and demonstrate a major role for residues His82 and Lys367 in substrate binding and catalysis. In addition, the SMO enzyme-substrate complex highlights the presence of an active site pocket with highly polar characteristics, which may explain the different substrate specificity of SMO with respect to APAO and provide the basis for the design of specific inhibitors for SMO and APAO.

CoSMoS unravels mysteries of transcription initiation.

PubMed

Gourse, Richard L; Landick, Robert

2012-02-17

Using a fluorescence method called colocalization single-molecule spectroscopy (CoSMoS), Friedman and Gelles dissect the kinetics of transcription initiation at a bacterial promoter. Ultimately, CoSMoS could greatly aid the study of the effects of DNA sequence and transcription factors on both prokaryotic and eukaryotic promoters. Copyright © 2012 Elsevier Inc. All rights reserved.

Spermine oxidase is a regulator of macrophage host response to Helicobacter pylori: enhancement of antimicrobial nitric oxide generation by depletion of spermine.

PubMed

Chaturvedi, Rupesh; Asim, Mohammad; Barry, Daniel P; Frye, Jeanetta W; Casero, Robert A; Wilson, Keith T

2014-03-01

The gastric pathogen Helicobacter pylori causes peptic ulcer disease and gastric cancer. We have reported that in H. pylori-activated macrophages, nitric oxide (NO) derived from inducible NO synthase (iNOS) can kill the bacterium, iNOS protein expression is dependent on uptake of its substrate L-arginine (L-Arg), the polyamine spermine can inhibit iNOS translation by inhibiting L-Arg uptake, and inhibition of polyamine synthesis enhances NO-mediated bacterial killing. Because spermine oxidase (SMO), which back-converts spermine to spermidine, is induced in macrophages by H. pylori, we determined its role in iNOS-dependent host defense. SMO shRNA knockdown in RAW 264.7 murine macrophages resulted in a marked decrease in H. pylori-stimulated iNOS protein, but not mRNA expression, and a 90% reduction in NO levels; NO production was also inhibited in primary murine peritoneal macrophages with SMO knockdown. There was an increase in spermine levels after H. pylori stimulation that rapidly decreased, while SMO knockdown caused a greater increase in spermine that was sustained. With SMO knockdown, L-Arg uptake and killing of H. pylori by macrophages was prevented. The overexpression of SMO by transfection of an expression plasmid prevented the H. pylori-stimulated increase in spermine levels, and led to increased L-Arg uptake, iNOS protein expression and NO production, and H. pylori killing. In two human monocytic cell lines, U937 and THP-1, overexpression of SMO caused a significant enhancement of NO production with H. pylori stimulation. By depleting spermine, SMO can abrogate the inhibitory effect of polyamines on innate immune responses to H. pylori by enhancing antimicrobial NO production.

Nuclear localization of human spermine oxidase isoforms – possible implications in drug response and disease etiology

PubMed Central

Murray-Stewart, Tracy; Wang, Yanlin; Goodwin, Andrew; Hacker, Amy; Meeker, Alan; Casero, Robert A.

2013-01-01

The recent discovery of the direct oxidation of spermine via spermine oxidase (SMO) as a mechanism through which specific antitumor polyamine analogues exert their cytotoxic effects has fueled interest in the study of the polyamine catabolic pathway. A major byproduct of spermine oxidation is H2O2, a source of toxic reactive oxygen species. Recent targeted small interfering RNA studies have confirmed that SMO-produced reactive oxygen species are directly responsible for oxidative stress capable of inducing apoptosis and potentially mutagenic DNA damage. In the present study, we describe a second catalytically active splice variant protein of the human spermine oxidase gene, designated SMO5, which exhibits substrate specificities and affinities comparable to those of the originally identified human spermine oxidase-1, SMO/PAOh1, and, as such, is an additional source of H2O2. Importantly, overexpression of either of these SMO isoforms in NCI-H157 human non-small cell lung carcinoma cells resulted in significant localization of SMO protein in the nucleus, as determined by confocal microscopy. Furthermore, cell lines overexpressing either SMO/PAOh1 or SMO5 demonstrated increased spermine oxidation in the nucleus, with accompanying alterations in individual nuclear polyamine concentrations. This increased oxidation of spermine in the nucleus therefore increases the production of highly reactive H2O2 in close proximity to DNA, as well as decreases nuclear spermine levels, thus altering the protective roles of spermine in free radical scavenging and DNA shielding, and resulting in an overall increased potential for oxidative DNA damage in these cells. The results of these studies therefore have considerable significance both with respect to targeting polyamine oxidation as an antineoplastic strategy, and in regard to the potential role of spermine oxidase in inflammation-induced carcinogenesis. PMID:18422650

Multivariate Time Series Forecasting of Crude Palm Oil Price Using Machine Learning Techniques

NASA Astrophysics Data System (ADS)

Kanchymalay, Kasturi; Salim, N.; Sukprasert, Anupong; Krishnan, Ramesh; Raba'ah Hashim, Ummi

2017-08-01

The aim of this paper was to study the correlation between crude palm oil (CPO) price, selected vegetable oil prices (such as soybean oil, coconut oil, and olive oil, rapeseed oil and sunflower oil), crude oil and the monthly exchange rate. Comparative analysis was then performed on CPO price forecasting results using the machine learning techniques. Monthly CPO prices, selected vegetable oil prices, crude oil prices and monthly exchange rate data from January 1987 to February 2017 were utilized. Preliminary analysis showed a positive and high correlation between the CPO price and soy bean oil price and also between CPO price and crude oil price. Experiments were conducted using multi-layer perception, support vector regression and Holt Winter exponential smoothing techniques. The results were assessed by using criteria of root mean square error (RMSE), means absolute error (MAE), means absolute percentage error (MAPE) and Direction of accuracy (DA). Among these three techniques, support vector regression(SVR) with Sequential minimal optimization (SMO) algorithm showed relatively better results compared to multi-layer perceptron and Holt Winters exponential smoothing method.

A Synthesis Model of Sustainable Market Orientation: Conceptualization, Measurement, and Influence on Academic Accreditation--A Case Study of Egyptian-Accredited Faculties

ERIC Educational Resources Information Center

Abou-Warda, Sherein H.

2014-01-01

Higher education institutions are increasingly concerned about accreditation. Although sustainable market orientation (SMO) bears on academic accreditation, to date, no study has developed a valid scale of SMO or assessed its influence on accreditation. The purpose of this paper is to construct and validate an SMO scale that was developed in…

Application of resist-profile-aware source optimization in 28 nm full chip optical proximity correction

NASA Astrophysics Data System (ADS)

Zhu, Jun; Zhang, David Wei; Kuo, Chinte; Wang, Qing; Wei, Fang; Zhang, Chenming; Chen, Han; He, Daquan; Hsu, Stephen D.

2017-07-01

As technology node shrinks, aggressive design rules for contact and other back end of line (BEOL) layers continue to drive the need for more effective full chip patterning optimization. Resist top loss is one of the major challenges for 28 nm and below technology nodes, which can lead to post-etch hotspots that are difficult to predict and eventually degrade the process window significantly. To tackle this problem, we used an advanced programmable illuminator (FlexRay) and Tachyon SMO (Source Mask Optimization) platform to make resistaware source optimization possible, and it is proved to greatly improve the imaging contrast, enhance focus and exposure latitude, and minimize resist top loss thus improving the yield.

Joint optimization of source, mask, and pupil in optical lithography

NASA Astrophysics Data System (ADS)

Li, Jia; Lam, Edmund Y.

2014-03-01

Mask topography effects need to be taken into consideration for more advanced resolution enhancement techniques in optical lithography. However, rigorous 3D mask model achieves high accuracy at a large computational cost. This work develops a combined source, mask and pupil optimization (SMPO) approach by taking advantage of the fact that pupil phase manipulation is capable of partially compensating for mask topography effects. We first design the pupil wavefront function by incorporating primary and secondary spherical aberration through the coefficients of the Zernike polynomials, and achieve optimal source-mask pair under the condition of aberrated pupil. Evaluations against conventional source mask optimization (SMO) without incorporating pupil aberrations show that SMPO provides improved performance in terms of pattern fidelity and process window sizes.

Single-Molecule Analysis of Pre-mRNA Splicing with Colocalization Single-Molecule Spectroscopy (CoSMoS).

PubMed

Braun, Joerg E; Serebrov, Victor

2017-01-01

Recent development of single-molecule techniques to study pre-mRNA splicing has provided insights into the dynamic nature of the spliceosome. Colocalization single-molecule spectroscopy (CoSMoS) allows following spliceosome assembly in real time at single-molecule resolution in the full complexity of cellular extracts. A detailed protocol of CoSMoS has been published previously (Anderson and Hoskins, Methods Mol Biol 1126:217-241, 2014). Here, we provide an update on the technical advances since the first CoSMoS studies including slide surface treatment, data processing, and representation. We describe various labeling strategies to generate RNA reporters with multiple dyes (or other moieties) at specific locations.

Methanol Adsorption and Reaction on Samaria Thin Films on Pt(111).

PubMed

Jhang, Jin-Hao; Schaefer, Andreas; Zielasek, Volkmar; Weaver, Jason F; Bäumer, Marcus

2015-09-17

We investigated the adsorption and reaction of methanol on continuous and discontinuous films of samarium oxide (SmO x ) grown on Pt(111) in ultrahigh vacuum. The methanol decomposition was studied by temperature programmed desorption (TPD) and infrared reflection absorption spectroscopy (IRRAS), while structural changes of the oxide surface were monitored by low-energy electron diffraction (LEED). Methanol dehydrogenates to adsorbed methoxy species on both the continuous and discontinuous SmO x films, eventually leading to the desorption of CO and H₂ which desorbs at temperatures in the range 400-600 K. Small quantities of CO₂ are also detected mainly on as-prepared Sm₂O₃ thin films, but the production of CO₂ is limited during repeated TPD runs. The discontinuous film exhibits the highest reactivity compared to the continuous film and the Pt(111) substrate. The reactivity of methanol on reduced and reoxidized films was also investigated, revealing how SmO x structures influence the chemical behavior. Over repeated TPD experiments, a SmO x structural/chemical equilibrium condition is found which can be approached either from oxidized or reduced films. We also observed hydrogen absence in TPD which indicates that hydrogen is stored either in SmO x films or as OH groups on the SmO x surfaces.

Dissipation of sulfamethoxazole in pasture soils as affected by soil and environmental factors.

PubMed

Srinivasan, Prakash; Sarmah, Ajit K

2014-05-01

The dissipation of sulfamethoxazole (SMO) antibiotic in three different soils was investigated through laboratory incubation studies. The experiments were conducted under different incubation conditions such as initial chemical concentration, soil depth, temperature, and with sterilisation. The results indicate that SMO dissipated rapidly in New Zealand pasture soils, and the 50% dissipation times (DT50) in Hamilton, Te Kowhai and Horotiu soils under non-sterile conditions were 9.24, 4.3 and 13.33 days respectively. During the incubation period for each sampling event the soil dehydrogenase activity (DHA) and the variation in microbial community were monitored thorough phospholipid fatty acid extraction analysis (PLFA). The DHA data correlated well with the dissipation rate constants of SMO antibiotic, an increase in the DHA activity resulted in faster antibiotic dissipation. The PLFA analysis was indicative of higher bacterial presence as compared to fungal community, highlighting the type of microbial community responsible for dissipation. The results indicate that with increasing soil depth, SMO dissipation in soil was slower (except for Horotiu) while with increase in temperature the antibiotic loss was faster, and was noticeable in all the soils. Both the degree of biological activity and the temperature of the soil influenced overall SMO dissipation. SMO is not likely to persist more than 5-6 months in all three soils suggesting that natural biodegradation may be sufficient for the removal of these contaminants from the soil. Its dissipation in sterile soils indicated abiotic factors such as strong sorption onto soil components to play a role in the dissipation of SMO. Copyright © 2014 Elsevier B.V. All rights reserved.

Digging a hole under Hedgehog: downstream inhibition as an emerging anticancer strategy.

PubMed

Di Magno, Laura; Coni, Sonia; Di Marcotullio, Lucia; Canettieri, Gianluca

2015-08-01

Hedgehog signaling is a key regulator of development and stem cell fate and its aberrant activation is a leading cause of a number of tumors. Activating germline or somatic mutations of genes encoding Hh pathway components are found in Basal Cell Carcinoma (BCC) and Medulloblastoma (MB). Ligand-dependent Hedgehog hyperactivation, due to autocrine or paracrine mechanisms, is also observed in a large number of malignancies of the breast, colon, skin, bladder, pancreas and other tissues. The key tumorigenic role of Hedgehog has prompted effort aimed at identifying inhibitors of this signaling. To date, only the antagonists of the membrane transducer Smo have been approved for therapy or are under clinical trials in patients with BCC and MB linked to Ptch or Smo mutations. Despite the good initial response, patients treated with Smo antagonists have eventually developed resistance due to the occurrence of compensating mechanisms. Furthermore, Smo antagonists are not effective in tumors where the Hedgehog hyperactivation is due to mutations of pathway components downstream of Smo, or in case of non-canonical, Smo-independent activation of the Gli transcription factors. For all these reasons, the research of Hh inhibitors acting downstream of Smo is becoming an area of intensive investigation. In this review we illustrate the progresses made in the identification of effective Hedgehog inhibitors and their application in cancer, with a special emphasis on the newly identified downstream inhibitors. We describe in detail the Gli inhibitors and illustrate their mode of action and applications in experimental and/or clinical settings. Copyright © 2015 Elsevier B.V. All rights reserved.

CoSMoMVPA: Multi-Modal Multivariate Pattern Analysis of Neuroimaging Data in Matlab/GNU Octave.

PubMed

Oosterhof, Nikolaas N; Connolly, Andrew C; Haxby, James V

2016-01-01

Recent years have seen an increase in the popularity of multivariate pattern (MVP) analysis of functional magnetic resonance (fMRI) data, and, to a much lesser extent, magneto- and electro-encephalography (M/EEG) data. We present CoSMoMVPA, a lightweight MVPA (MVP analysis) toolbox implemented in the intersection of the Matlab and GNU Octave languages, that treats both fMRI and M/EEG data as first-class citizens. CoSMoMVPA supports all state-of-the-art MVP analysis techniques, including searchlight analyses, classification, correlations, representational similarity analysis, and the time generalization method. These can be used to address both data-driven and hypothesis-driven questions about neural organization and representations, both within and across: space, time, frequency bands, neuroimaging modalities, individuals, and species. It uses a uniform data representation of fMRI data in the volume or on the surface, and of M/EEG data at the sensor and source level. Through various external toolboxes, it directly supports reading and writing a variety of fMRI and M/EEG neuroimaging formats, and, where applicable, can convert between them. As a result, it can be integrated readily in existing pipelines and used with existing preprocessed datasets. CoSMoMVPA overloads the traditional volumetric searchlight concept to support neighborhoods for M/EEG and surface-based fMRI data, which supports localization of multivariate effects of interest across space, time, and frequency dimensions. CoSMoMVPA also provides a generalized approach to multiple comparison correction across these dimensions using Threshold-Free Cluster Enhancement with state-of-the-art clustering and permutation techniques. CoSMoMVPA is highly modular and uses abstractions to provide a uniform interface for a variety of MVP measures. Typical analyses require a few lines of code, making it accessible to beginner users. At the same time, expert programmers can easily extend its functionality. CoSMoMVPA comes with extensive documentation, including a variety of runnable demonstration scripts and analysis exercises (with example data and solutions). It uses best software engineering practices including version control, distributed development, an automated test suite, and continuous integration testing. It can be used with the proprietary Matlab and the free GNU Octave software, and it complies with open source distribution platforms such as NeuroDebian. CoSMoMVPA is Free/Open Source Software under the permissive MIT license. Website: http://cosmomvpa.org Source code: https://github.com/CoSMoMVPA/CoSMoMVPA.

CoSMoMVPA: Multi-Modal Multivariate Pattern Analysis of Neuroimaging Data in Matlab/GNU Octave

PubMed Central

Oosterhof, Nikolaas N.; Connolly, Andrew C.; Haxby, James V.

2016-01-01

Recent years have seen an increase in the popularity of multivariate pattern (MVP) analysis of functional magnetic resonance (fMRI) data, and, to a much lesser extent, magneto- and electro-encephalography (M/EEG) data. We present CoSMoMVPA, a lightweight MVPA (MVP analysis) toolbox implemented in the intersection of the Matlab and GNU Octave languages, that treats both fMRI and M/EEG data as first-class citizens. CoSMoMVPA supports all state-of-the-art MVP analysis techniques, including searchlight analyses, classification, correlations, representational similarity analysis, and the time generalization method. These can be used to address both data-driven and hypothesis-driven questions about neural organization and representations, both within and across: space, time, frequency bands, neuroimaging modalities, individuals, and species. It uses a uniform data representation of fMRI data in the volume or on the surface, and of M/EEG data at the sensor and source level. Through various external toolboxes, it directly supports reading and writing a variety of fMRI and M/EEG neuroimaging formats, and, where applicable, can convert between them. As a result, it can be integrated readily in existing pipelines and used with existing preprocessed datasets. CoSMoMVPA overloads the traditional volumetric searchlight concept to support neighborhoods for M/EEG and surface-based fMRI data, which supports localization of multivariate effects of interest across space, time, and frequency dimensions. CoSMoMVPA also provides a generalized approach to multiple comparison correction across these dimensions using Threshold-Free Cluster Enhancement with state-of-the-art clustering and permutation techniques. CoSMoMVPA is highly modular and uses abstractions to provide a uniform interface for a variety of MVP measures. Typical analyses require a few lines of code, making it accessible to beginner users. At the same time, expert programmers can easily extend its functionality. CoSMoMVPA comes with extensive documentation, including a variety of runnable demonstration scripts and analysis exercises (with example data and solutions). It uses best software engineering practices including version control, distributed development, an automated test suite, and continuous integration testing. It can be used with the proprietary Matlab and the free GNU Octave software, and it complies with open source distribution platforms such as NeuroDebian. CoSMoMVPA is Free/Open Source Software under the permissive MIT license. Website: http://cosmomvpa.org Source code: https://github.com/CoSMoMVPA/CoSMoMVPA PMID:27499741

A Human Activity Recognition System Based on Dynamic Clustering of Skeleton Data.

PubMed

Manzi, Alessandro; Dario, Paolo; Cavallo, Filippo

2017-05-11

Human activity recognition is an important area in computer vision, with its wide range of applications including ambient assisted living. In this paper, an activity recognition system based on skeleton data extracted from a depth camera is presented. The system makes use of machine learning techniques to classify the actions that are described with a set of a few basic postures. The training phase creates several models related to the number of clustered postures by means of a multiclass Support Vector Machine (SVM), trained with Sequential Minimal Optimization (SMO). The classification phase adopts the X-means algorithm to find the optimal number of clusters dynamically. The contribution of the paper is twofold. The first aim is to perform activity recognition employing features based on a small number of informative postures, extracted independently from each activity instance; secondly, it aims to assess the minimum number of frames needed for an adequate classification. The system is evaluated on two publicly available datasets, the Cornell Activity Dataset (CAD-60) and the Telecommunication Systems Team (TST) Fall detection dataset. The number of clusters needed to model each instance ranges from two to four elements. The proposed approach reaches excellent performances using only about 4 s of input data (~100 frames) and outperforms the state of the art when it uses approximately 500 frames on the CAD-60 dataset. The results are promising for the test in real context.

Combining malaria control with house electrification: adherence to recommended behaviours for proper deployment of solar-powered mosquito trapping systems, Rusinga Island, western Kenya.

PubMed

Oria, Prisca A; Alaii, Jane; Ayugi, Margaret; Takken, Willem; Leeuwis, Cees

2015-08-01

To investigate community adherence to recommended behaviours for proper deployment of solar-powered mosquito trapping systems (SMoTS) after 3- to 10-week use. Solar-powered mosquito trapping system, which also provided power for room lighting and charging mobile phones, were installed in houses in Rusinga Island, western Kenya. We used a structured checklist for observations and a semi-structured questionnaire for interviews in 24 homesteads. We also analysed the subject of 224 community calls to the project team for technical maintenance of SMoTS. Most respondents cared for SMoTS by fencing, emptying and cleaning the trap. Our observations revealed that most traps were fenced, clean and in good working condition. A significantly higher proportion of community calls was lighting-related. Lighting was the main reason respondents liked SMoTS because it reduced or eliminated expenditure on kerosene. However, some respondents observed they no longer heard sounds of mosquitoes inside their houses. All respondents reportedly slept under insecticide-treated nets (ITNs) before receiving SMoTS. After receiving SMoTS, most respondents reportedly continued to use ITNs citing that the project advised them to do so. Some beach residents stopped using ITNs because they no longer heard mosquitoes or due to heat discomfort caused by lights. Electricity-related incentives played a greater role in encouraging adherence to recommended behaviours for proper deployment of SMoTS than the potential health benefits in the early stages of the intervention. Although energy-related financial incentives may play a role, they are insufficient to ensure adherence to health advice, even in the short term. Ongoing community engagement and research monitors and addresses adherence to recommended behaviours including continuation of current malaria control strategies. © 2015 John Wiley & Sons Ltd.

High-Energy Radiation from a Model of Quasars, Active Galactic Nuclei, and the Galactic Center with Magnetic Monopoles

NASA Astrophysics Data System (ADS)

Peng, Qiu-he; Chou, Chih-kang

2001-04-01

The fact that magnetic monopoles may catalyze nucleon decay (the Rubakov-Callan [RC] effect) as predicated by the grand unified theory of particle physics is invoked as the energy source of quasars and active galactic nuclei. Recent study of this model revealed that the radius of the supermassive object (SMO) located at the Galactic center is much larger than its Schwarzschild radius. We propose that this SMOs could be the source of high-energy gamma-ray radiation, although the emitted radiation may be mainly concentrated in the infrared. The surface temperature of the SMO at the Galactic center is taken as 121 K, inferred from the observed maximum of the flux spectrum of Sgr A* at the near infrared (1×1013 Hz); the radius of the SMO is about 8.1×1015 cm or 1.1×104RS (RS is the Schwarzschild radius). The mass of the SMO is derived from the observed total luminosity of Sgr A* (1×1037 ergs s-1) as 2.5×106 Msolar. Strong gamma-ray radiation with energy higher than 0.5 MeV may be emitted from the SMO. The flux of positrons emitted from the SMO is estimated to be 6.5×1042e+ s-1. The content parameter of magnetic monopoles ξ≡[(Nm/NB)/1.9×10- 25](<σβ>/10-27) also may be deduced from observations to be 230. Taking the cross section of the RC effect as 1×10-27 cm2, the strength of the radial magnetic field at the surface of the SMO is estimated to be 20-100 G. Our model also can predict the production of extreme ultra-high-energy cosmic rays.

Molecular Characterization of Bovine SMO Gene and Effects of Its Genetic Variations on Body Size Traits in Qinchuan Cattle (Bos taurus).

PubMed

Zhang, Ya-Ran; Gui, Lin-Sheng; Li, Yao-Kun; Jiang, Bi-Jie; Wang, Hong-Cheng; Zhang, Ying-Ying; Zan, Lin-Sen

2015-07-27

Smoothened (Smo)-mediated Hedgehog (Hh) signaling pathway governs the patterning, morphogenesis and growth of many different regions within animal body plans. This study evaluated the effects of genetic variations of the bovine SMO gene on economically important body size traits in Chinese Qinchuan cattle. Altogether, eight single nucleotide polymorphisms (SNPs: 1-8) were identified and genotyped via direct sequencing covering most of the coding region and 3'UTR of the bovine SMO gene. Both the p.698Ser.>Ser. synonymous mutation resulted from SNP1 and the p.700Ser.>Pro. non-synonymous mutation caused by SNP2 mapped to the intracellular C-terminal tail of bovine Smo protein; the other six SNPs were non-coding variants located in the 3'UTR. The linkage disequilibrium was analyzed, and five haplotypes were discovered in 520 Qinchuan cattle. Association analyses showed that SNP2, SNP3/5, SNP4 and SNP6/7 were significantly associated with some body size traits (p < 0.05) except SNP1/8 (p > 0.05). Meanwhile, cattle with wild-type combined haplotype Hap1/Hap1 had significantly (p < 0.05) greater body length than those with Hap2/Hap2. Our results indicate that variations in the SMO gene could affect body size traits of Qinchuan cattle, and the wild-type haplotype Hap1 together with the wild-type alleles of these detected SNPs in the SMO gene could be used to breed cattle with superior body size traits. Therefore, our results could be helpful for marker-assisted selection in beef cattle breeding programs.

Molecular Characterization of Bovine SMO Gene and Effects of Its Genetic Variations on Body Size Traits in Qinchuan Cattle (Bos taurus)

PubMed Central

Zhang, Ya-Ran; Gui, Lin-Sheng; Li, Yao-Kun; Jiang, Bi-Jie; Wang, Hong-Cheng; Zhang, Ying-Ying; Zan, Lin-Sen

2015-01-01

Smoothened (Smo)-mediated Hedgehog (Hh) signaling pathway governs the patterning, morphogenesis and growth of many different regions within animal body plans. This study evaluated the effects of genetic variations of the bovine SMO gene on economically important body size traits in Chinese Qinchuan cattle. Altogether, eight single nucleotide polymorphisms (SNPs: 1–8) were identified and genotyped via direct sequencing covering most of the coding region and 3ʹUTR of the bovine SMO gene. Both the p.698Ser.>Ser. synonymous mutation resulted from SNP1 and the p.700Ser.>Pro. non-synonymous mutation caused by SNP2 mapped to the intracellular C-terminal tail of bovine Smo protein; the other six SNPs were non-coding variants located in the 3ʹUTR. The linkage disequilibrium was analyzed, and five haplotypes were discovered in 520 Qinchuan cattle. Association analyses showed that SNP2, SNP3/5, SNP4 and SNP6/7 were significantly associated with some body size traits (p < 0.05) except SNP1/8 (p > 0.05). Meanwhile, cattle with wild-type combined haplotype Hap1/Hap1 had significantly (p < 0.05) greater body length than those with Hap2/Hap2. Our results indicate that variations in the SMO gene could affect body size traits of Qinchuan cattle, and the wild-type haplotype Hap1 together with the wild-type alleles of these detected SNPs in the SMO gene could be used to breed cattle with superior body size traits. Therefore, our results could be helpful for marker-assisted selection in beef cattle breeding programs. PMID:26225956

Hedgehog signaling contributes to basic fibroblast growth factor-regulated fibroblast migration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhu, Zhong Xin; Sun, Cong Cong; Wenzhou People's Hospital, Wenzhou, Zhejiang

Fibroblast migration is a central process in skin wound healing, which requires the coordination of several types of growth factors. bFGF, a well-known fibroblast growth factor (FGF), is able to accelerate fibroblast migration; however, the underlying mechanism of bFGF regulation fibroblast migration remains unclear. Through the RNA-seq analysis, we had identified that the hedgehog (Hh) canonical pathway genes including Smoothened (Smo) and Gli1, were regulated by bFGF. Further analysis revealed that activation of the Hh pathway via up-regulation of Smo promoted fibroblast migration, invasion, and skin wound healing, but which significantly reduced by GANT61, a selective antagonist of Gli1/Gli2. Westernmore » blot analyses and siRNA transfection assays demonstrated that Smo acted upstream of phosphoinositide 3-kinase (PI3K)-c-Jun N-terminal kinase (JNK)-β-catenin to promote cell migration. Moreover, RNA-seq and qRT-PCR analyses revealed that Hh pathway genes including Smo and Gli1 were under control of β-catenin, suggesting that β-catenin turn feedback activates Hh signaling. Taken together, our analyses identified a new bFGF-regulating mechanism by which Hh signaling regulates human fibroblast migration, and the data presented here opens a new avenue for the wound healing therapy. - Highlights: • bFGF regulates Hedgehog (Hh) signaling in fibroblasts. • The Smo and Gli two master regulators of Hh signaling positively regulate fibroblast migration. • Smo facilitates β-catenin nuclear translocation via activation PI3K/JNK/GSK3β. • β-catenin positively regulates fibroblast cell migration and the expression of Hh signaling genes including Smo and Gli.« less

GENERAL VIEW, LOOKING SOUTHEAST, OF STANDARDIZING MAGNETIC OBSERVATORY (SMO) WHICH ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

GENERAL VIEW, LOOKING SOUTHEAST, OF STANDARDIZING MAGNETIC OBSERVATORY (SMO) WHICH IS TO THE RIGHT. THE BUILDING TO THE LEFT IS 'STATION 'A'', ALSO A NON-MAGNETIC STRUCTURE, ONCE USED FOR COMPARISONS OF MAGNETIC INSTRUMENTS WITH THE SMO. THE BUILDING IN THE CENTER CONTAINED A SEARCH-LIGHT USED IN CONJUNCTION WITH MEASUREMENTS OF THE EARTH'S ATMOSPHERE. - Carnegie Institution of Washington, Department of Terrestrial Magnetism, Standardizing Magnetic Observatory, 5241 Broad Branch Drive Northwest, Washington, District of Columbia, DC

Dynamics of a bistable Miura-origami structure

NASA Astrophysics Data System (ADS)

Fang, Hongbin; Li, Suyi; Ji, Huimin; Wang, K. W.

2017-05-01

Origami-inspired structures and materials have shown extraordinary properties and performances originating from the intricate geometries of folding. However, current state of the art studies have mostly focused on static and quasistatic characteristics. This research performs a comprehensive experimental and analytical study on the dynamics of origami folding through investigating a stacked Miura-Ori (SMO) structure with intrinsic bistability. We fabricate and experimentally investigated a bistable SMO prototype with rigid facets and flexible crease lines. Under harmonic base excitation, the SMO exhibits both intrawell and interwell oscillations. Spectrum analyses reveal that the dominant nonlinearities of SMO are quadratic and cubic, which generate rich dynamics including subharmonic and chaotic oscillations. The identified nonlinearities indicate that a third-order polynomial can be employed to approximate the measured force-displacement relationship. Such an approximation is validated via numerical study by qualitatively reproducing the phenomena observed in the experiments. The dynamic characteristics of the bistable SMO resemble those of a Helmholtz-Duffing oscillator (HDO); this suggests the possibility of applying the established tools and insights of HDO to predict origami dynamics. We also show that the bistability of SMO can be programmed within a large design space via tailoring the crease stiffness and initial stress-free configurations. The results of this research offer a wealth of fundamental insights into the dynamics of origami folding, and provide a solid foundation for developing foldable and deployable structures and materials with embedded dynamic functionalities.

Reduced Smoothened level rescues Aβ-induced memory deficits and neuronal inflammation in animal models of Alzheimer's disease.

PubMed

Ma, Weiwei; Wu, Mengnan; Zhou, Siyan; Tao, Ye; Xie, Zuolei; Zhong, Yi

2018-05-20

Emerging evidence suggests that neuro-inflammation begins early and drives the pathogenesis of Alzheimer's disease (AD), and anti-inflammatory therapies are under clinical development. However, several anti-inflammatory compounds failed to improve memory in clinical trials, indicating that reducing inflammation alone might not be enough. On the other hand, neuro-inflammation is implicated in a number of mental disorders which share the same therapeutic targets. Based on these observations, we screened a batch of genes related with mental disorder and neuro-inflammation in a classical olfactory conditioning in an amyloid beta (Aβ) overexpression fly model. A Smoothened (SMO) mutant was identified as a genetic modifier of Aβ toxicity in 3-min memory and downregulation of SMO rescued Aβ-induced 3-min and 1-h memory deficiency. Also, Aβ activated innate inflammatory response in fly by increasing the expression of antimicrobial peptides, which were alleviated by downregulating SMO. Furthermore, pharmaceutical administration of a SMO antagonist LDE rescued Aβ-induced upregulation of SMO in astrocytes of mouse hippocampus, improved memory in Morris water maze (MWM), and reduced expression of astrocyte secreting pro-inflammatory factors IL-1β, TNFα and the microglia marker IBA-1 in an APP/PS1 transgenic mouse model. Our study suggests that SMO is an important conserved modulator of Aβ toxicity in both fly and mouse models of AD. Copyright © 2018. Published by Elsevier Ltd.

Patient with Gorlin syndrome and metastatic basal cell carcinoma refractory to smoothened inhibitors.

PubMed

Zhu, Gefei A; Li, Angela S; Chang, Anne Lynn S

2014-08-01

Basal cell carcinomas (BCCs) in patients with Gorlin syndrome have been reported to be extremely sensitive to Smoothened (SMO) inhibitors, a novel targeted therapy against the Hedgehog pathway, because of characteristic mutations in these patients. A few cases of disease refractory to oral therapy with SMO inhibitors have been reported in patients with Gorlin syndrome and nonmetastatic BCCs, but refractory disease in distantly metastatic tumors has not been documented in this high-risk group. A man with Gorlin syndrome and innumerable cutaneous BCCs presented with biopsy-proven BCC in his lungs. After SMO inhibitor therapy, almost all of his cutaneous tumors shrank, but his lung metastases did not. These lung metastases remained refractory to treatment despite institution of a second SMO inhibitor. We report a case of Gorlin syndrome in a patient with metastatic BCC refractory to SMO inhibitors. Furthermore, clinical responses in this patient's cutaneous tumors did not parallel the responses in the distant site. However, serial imaging after diagnosis of metastatic disease can be critical to monitor for response to therapy.

Magnetic and electronic properties of SrMnO3 thin films

NASA Astrophysics Data System (ADS)

Mandal, Arup Kumar; Panchal, Gyanendra; Choudhary, R. J.; Phase, D. M.

2018-05-01

Single phase hexagonal bulk SrMnO3 (SMO) was prepared by solid state route and it was used for depositing thin films by pulsed laser deposition (PLD) technique on single crystalline (100) oriented SrTiO3 (STO) substrate. X-ray diffraction shows that the thin film is deposited in cubic SrMnO3 phase. From X-ray absorption at the Mn L edge we observed the mixed valency of Mn (Mn3+& Mn4+) due to strain induced by the lattice mismatching between SMO and STO. Due to this mixed valency of Mn ion in SMO film, the ferromagnetic nature is observed at lower temperature because of double exchange. After post annealing with very low oxygen partial pressure, magnetic and electronic property of SMO films are effectively modified.

Near-Infrared Spectroscopic Measurement of the Effect of Leg Dominance on Muscle Oxygen Saturation During Cycling

NASA Technical Reports Server (NTRS)

Ellerby, Gwenn E. C.; Lee, Stuart M. C.; Paunescu, Lelia Adelina; Pereira, Chelsea; Smith, Charles P.; Soller, Babs R.

2011-01-01

The effect of leg dominance on the symmetry of the biomechanics during cycling remains uncertain -- asymmetries have been observed in kinematics and kinetics, while symmetries were found in muscle activation. No studies have yet investigated the symmetry of muscle metabolism during cycling. Near-infrared spectroscopy (NIRS) provides a non-invasive method to investigate the metabolic responses of specific muscles during cycling. PURPOSE: To determine whether there was an effect of leg dominance on thigh muscle oxygen saturation (SmO2) during incrementally loaded submaximal cycling using NIRS. METHODS: Eight right leg dominant, untrained subjects (5 men, 3 women; 31+/-2 yrs; 168.6+/-1.0 cm; 67.2+/-1.8 kg, mean +/- SE) volunteered to participate. Spectra were collected bilaterally from the vastus lateralis (VL) during supine rest and cycling. SmO2 was calculated using previously published methods. Subjects pedaled at 65 rpm while resistance to pedaling was increased in 0.5 kp increments from 0.5 kp every 3 min until the subject reached 80% of age-predicted maximal heart rate. SmO2 was averaged over 3 min for each completed stage. A two-way ANOVA was performed to test for leg differences. A priori contrasts were used to compare work levels to rest. RESULTS: VL SmO2 was not different between the dominant and non-dominant legs at rest and during exercise (p=0.57). How SmO2 changed with workload was also not different between legs (p=0.32). SmO2 at 0.5 kp (60.3+/-4.0, p=0.12) and 1.0 kp (59.5+/-4.0, p=0.10) was not different from rest (69.1+/-4.0). SmO2 at 1.5 kp (55.4 4.0, p=0.02), 2.0 kp (55.7+/-5.0, p=0.04), and 2.5 kp (43.4+/-7.9, p=0.01) was significantly lower than rest. CONCLUSION: VL SmO2 during cycling is not different between dominant and non-dominant legs and decreases with moderate workload in untrained cyclists. Assuming blood flow is directed equally to both legs, similar levels of oxygen extraction (as indicated by SmO2) suggests the metabolic load of cycling is not different between legs. This is in agreement with a recent study demonstrating symmetrical increase of muscle activation of the VL during cycling. Leg dominance did not influence VL SmO2 during submaximal cycling, but may have an effect at higher loads or during other forms of exercise, such as walking and running.

Antineoplastic Efficacy of Novel Polyamine Analogues in Human Breast Cancer

DTIC Science & Technology

2005-06-01

Davidson, N.E., and Casero, R.A.. Spermine oxidase SMO(PAOh1), not N1-acetylpolyamine oxidase (PAO) is the primary source of cytotoxic H2O2 in polyamine... spermine oxidase (PAOh1/SMO) mRNA and activity by a polyamine analogue in human breast cancer cell lines. The fourth Era of Hope meeting for the...SMO/PAOh1 Spermine Oxidase DFMO α-difluoromethylornithine BENSpm N1, N11-bis(ethyl)norspermine CHEMSpm N1-(cycloheptylmethyl)-N11-ethyl- 4,8

Sequence Based Prediction of Antioxidant Proteins Using a Classifier Selection Strategy

PubMed Central

Zhang, Lina; Zhang, Chengjin; Gao, Rui; Yang, Runtao; Song, Qing

2016-01-01

Antioxidant proteins perform significant functions in maintaining oxidation/antioxidation balance and have potential therapies for some diseases. Accurate identification of antioxidant proteins could contribute to revealing physiological processes of oxidation/antioxidation balance and developing novel antioxidation-based drugs. In this study, an ensemble method is presented to predict antioxidant proteins with hybrid features, incorporating SSI (Secondary Structure Information), PSSM (Position Specific Scoring Matrix), RSA (Relative Solvent Accessibility), and CTD (Composition, Transition, Distribution). The prediction results of the ensemble predictor are determined by an average of prediction results of multiple base classifiers. Based on a classifier selection strategy, we obtain an optimal ensemble classifier composed of RF (Random Forest), SMO (Sequential Minimal Optimization), NNA (Nearest Neighbor Algorithm), and J48 with an accuracy of 0.925. A Relief combined with IFS (Incremental Feature Selection) method is adopted to obtain optimal features from hybrid features. With the optimal features, the ensemble method achieves improved performance with a sensitivity of 0.95, a specificity of 0.93, an accuracy of 0.94, and an MCC (Matthew’s Correlation Coefficient) of 0.880, far better than the existing method. To evaluate the prediction performance objectively, the proposed method is compared with existing methods on the same independent testing dataset. Encouragingly, our method performs better than previous studies. In addition, our method achieves more balanced performance with a sensitivity of 0.878 and a specificity of 0.860. These results suggest that the proposed ensemble method can be a potential candidate for antioxidant protein prediction. For public access, we develop a user-friendly web server for antioxidant protein identification that is freely accessible at http://antioxidant.weka.cc. PMID:27662651

Acyl-CoA-Binding Protein ACBP1 Modulates Sterol Synthesis during Embryogenesis1[OPEN

PubMed Central

Hsiao, An-Shan; Xue, Yan

2017-01-01

Fatty acids (FAs) and sterols are primary metabolites that exert interrelated functions as structural and signaling lipids. Despite their common syntheses from acetyl-coenzyme A, homeostatic cross talk remains enigmatic. Six Arabidopsis (Arabidopsis thaliana) acyl-coenzyme A-binding proteins (ACBPs) are involved in FA metabolism. ACBP1 interacts with PHOSPHOLIPASE Dα1 and regulates phospholipid composition. Here, its specific role in the negative modulation of sterol synthesis during embryogenesis is reported. ACBP1, likely in a liganded state, interacts with STEROL C4-METHYL OXIDASE1-1 (SMO1-1), a rate-limiting enzyme in the sterol pathway. Proembryo abortion in the double mutant indicated that the ACBP1-SMO1-1 interaction is synthetic lethal, corroborating with their strong promoter activities in developing ovules. Gas chromatography-mass spectrometry revealed quantitative and compositional changes in FAs and sterols upon overexpression or mutation of ACBP1 and/or SMO1-1. Aberrant levels of these metabolites may account for the downstream defect in lipid signaling. GLABRA2 (GL2), encoding a phospholipid/sterol-binding homeodomain transcription factor, was up-regulated in developing seeds of acbp1, smo1-1, and ACBP1+/−smo1-1 in comparison with the wild type. Consistent with the corresponding transcriptional alteration of GL2 targets, high-oil, low-mucilage phenotypes of gl2 were phenocopied in ACBP1+/−smo1-1. Thus, ACBP1 appears to modulate the metabolism of two important lipid classes (FAs and sterols) influencing cellular signaling. PMID:28500265

Validation of a New NIRS Method for Measuring Muscle Oxygenation During Rhythmic Handgrip Exercise

NASA Technical Reports Server (NTRS)

Hagan, R. Donald; Soller, Babs R.; Soyemi, Olusola; Landry, Michelle; Shear, Michael; Wu, Jacqueline

2006-01-01

Near infrared spectroscopy (NIRS) is commonly used to measure muscle oxygenation during exercise and recovery. Current NIRS algorithms do not account for variation in water content and optical pathlength during exercise. The current effort attempts to validate a newly developed NIRS algorithm during rhythmic handgrip exercise and recovery. Six female subjects, aver age 28 +/- 6 yrs, participated in the study. A venous catheter was placed in the retrograde direction in the antecubital space. A NIRS sensor with 30 mm source-detector separation was placed on the flexor digitorum profundus. Subjects performed two 5-min bouts of rhythmic handgrip exercise (2 s contraction/1 s relaxation) at 15% and 30% of maximal voluntary contraction. Venous blood was sampled before each bout, during the last minute of exercise, and after 5 minutes of recovery. Venous oxygen saturation (SvO2) was measured with a I-stat CG-4+ cartridge. Spectra were collected between 700-900 nm. A modified Beer's Law formula was used to calculate the absolute concentration of oxyhemoglobin (HbO2), deoxyhemoglobin (Hb) and water, as well as effective pathlength for each spectrum. Muscle oxygen saturation (SmO2) was calculated from the HbO2 and Hb results. The correlation between SvO2 and SmO2 was determined. Optical pathlength and water varied significantly during each exercise bout, with pathlength increasing approximately 20% and water increasing about 2%. R2 between blood and muscle SO2 was found to be 0.74, the figure shows the relationship over SvO2 values between 22% and 82%. The NIRS measurement was, on average, 6% lower than the blood measurement. It was concluded that pathlength changes during exercise because muscle contraction causes variation in optical scattering. Water concentration also changes, but only slightly. A new NIRS algorithm which accounts for exercise-induced variation in water and pathlength provided an accurate assessment of muscle oxygen saturation before, during and after exercise.

Validation of Procedures for Monitoring Crewmember Immune Function SDBI-1900, SMO-015 - Integrated Immune

NASA Technical Reports Server (NTRS)

Crucian, Brian; Stowe, Raymond; Mehta, Satish; Uchakin, Peter; Nehlsen-Cannarella, Sandra; Morukov, Boris; Pierson, Duane; Sams, Clarence

2007-01-01

There is ample evidence to suggest that space flight leads to immune system dysregulation. This may be a result of microgravity, confinement, physiological stress, radiation, environment or other mission-associated factors. The clinical risk from prolonged immune dysregulation during space flight are not yet determined, but may include increased incidence of infection, allergy, hypersensitivity, hematological malignancy or altered wound healing. Each of the clinical events resulting from immune dysfunction has the potential to impact mission critical objectives during exploration-class missions. To date, precious little in-flight immune data has been generated to assess this phenomenon. The majority of recent flight immune studies have been post-flight assessments, which may not accurately reflect the in-flight condition. There are no procedures currently in place to monitor immune function or its effect on crew health. The objective of this Supplemental Medical Objective (SMO) is to develop and validate an immune monitoring strategy consistent with operational flight requirements and constraints. This SMO will assess the clinical risks resulting from the adverse effects of space flight on the human immune system and will validate a flight-compatible immune monitoring strategy. Characterization of the clinical risk and the development of a monitoring strategy are necessary prerequisite activities prior to validating countermeasures. This study will determine, to the best level allowed by current technology, the in-flight status of crewmembers immune system. Pre-flight, in-flight and post-flight assessments of immune status, immune function, viral reactivation and physiological stress will be performed. The in-flight samples will allow a distinction between legitimate in-flight alterations and the physiological stresses of landing and readaptation which are believed to alter landing day assessments. The overall status of the immune system during flight (activation, deficiency, dysregulation) and the response of the immune system to specific latent virus reactivation (known to occur during space flight) will be thoroughly assessed. Following completion of the SMO the data will be evaluated to determine the optimal set of assays for routine monitoring of crewmember immune system function, should the clinical risk warrant such monitoring.

Computer-Aided Diagnosis of Anterior Segment Eye Abnormalities using Visible Wavelength Image Analysis Based Machine Learning.

PubMed

S V, Mahesh Kumar; R, Gunasundari

2018-06-02

Eye disease is a major health problem among the elderly people. Cataract and corneal arcus are the major abnormalities that exist in the anterior segment eye region of aged people. Hence, computer-aided diagnosis of anterior segment eye abnormalities will be helpful for mass screening and grading in ophthalmology. In this paper, we propose a multiclass computer-aided diagnosis (CAD) system using visible wavelength (VW) eye images to diagnose anterior segment eye abnormalities. In the proposed method, the input VW eye images are pre-processed for specular reflection removal and the iris circle region is segmented using a circular Hough Transform (CHT)-based approach. The first-order statistical features and wavelet-based features are extracted from the segmented iris circle and used for classification. The Support Vector Machine (SVM) by Sequential Minimal Optimization (SMO) algorithm was used for the classification. In experiments, we used 228 VW eye images that belong to three different classes of anterior segment eye abnormalities. The proposed method achieved a predictive accuracy of 96.96% with 97% sensitivity and 99% specificity. The experimental results show that the proposed method has significant potential for use in clinical applications.

The efficacy of novel anatomical sites for the assessment of muscle oxygenation during central hypovolemia.

PubMed

Sprick, Justin D; Soller, Babs R; Rickards, Caroline A

2016-11-01

Muscle tissue oxygenation (SmO 2 ) can track central blood volume loss associated with hemorrhage. Traditional peripheral measurement sites (e.g., forearm) may not be practical due to excessive movement or injury (e.g., amputation). The aim of this study was to evaluate the efficacy of three novel anatomical sites for the assessment of SmO 2 under progressive central hypovolemia. 10 male volunteers were exposed to stepwise prone lower body negative pressure to decrease central blood volume, while SmO 2 was assessed at four sites-the traditional site of the flexor carpi ulnaris (ARM), and three novel sites not previously investigated during lower body negative pressure, the deltoid, latissimus dorsi, and trapezius. SmO 2 at the novel sites was compared to the ARM sensor and to stroke volume responses. A reduction in SmO 2 was detected by the ARM sensor at the first level of lower body negative pressure (-15 mmHg; P = 0.007), and at -30 (the deltoid), -45 (latissimus dorsi), and -60 mmHg lower body negative pressure (trapezius) at the novel sites (P ≤ 0.04). SmO 2 responses at all novel sites were correlated with responses at the ARM (R ≥ 0.89), and tracked the reduction in stroke volume (R ≥ 0.87); the latissimus dorsi site exhibited the strongest linear correlations (R ≥ 0.96). Of the novel sensor sites, the latissimus dorsi exhibited the strongest linear associations with SmO 2 at the ARM, and with reductions in central blood volume. These findings have important implications for detection of hemorrhage in austere environments (e.g., combat) when use of a peripheral sensor may not be ideal, and may facilitate incorporation of these sensors into uniforms. © 2016 by the Society for Experimental Biology and Medicine.

Infrared spectra and density functional calculations for SMO2 molecules (M = Cr, Mo, W).

PubMed

Wang, Xuefeng; Andrews, Lester

2009-08-06

Infrared absorptions of the matrix isolated SMO2 (M = Cr, Mo, W) molecules were observed following laser-ablated metal atom reactions with SO2 during condensation in solid argon and neon. The symmetric and antisymmetric M-O stretching mode assignments were confirmed by appropriate S18O2 and S(16,18)O2 isotopic shifts. The much weaker Cr-S stretching mode was identified through its 34S shift. Density functional (B3LYP and BPW91) calculations were performed to obtain molecular structures and to reproduce the infrared spectra. Computed pyramidal structures for the SMO2 molecules are very similar to those for the analogous trioxides and this functional group in [MO2S(bdt)]2- complexes. Additional weaker absorptions are assigned to the (SO2)(SMO2) adducts, which are stabilized by a four-membered ring.

Sensorless control of ship propulsion interior permanent magnet synchronous motor based on a new sliding mode observer.

PubMed

Ren, Jun-Jie; Liu, Yan-Cheng; Wang, Ning; Liu, Si-Yuan

2015-01-01

This paper proposes a sensorless speed control strategy for ship propulsion interior permanent magnet synchronous motor (IPMSM) based on a new sliding-mode observer (SMO). In the SMO the low-pass filter and the method of arc-tangent calculation of extended electromotive force (EMF) or phase-locked loop (PLL) technique are not used. The calculation of the rotor speed is deduced from the Lyapunov function stability analysis. In order to reduce system chattering, sigmoid functions with switching gains being adaptively updated by fuzzy logic systems are innovatively incorporated into the SMO. Finally, simulation results for a 4.088 MW ship propulsion IPMSM and experimental results from a 7.5 kW IPMSM drive are provided to verify the effectiveness of the proposed SMO method. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Structure of the human smoothened receptor 7TM bound to an antitumor agent

PubMed Central

Wang, Chong; Wu, Huixian; Katritch, Vsevolod; Han, Gye Won; Huang, Xi-Ping; Liu, Wei; Siu, Fai Yiu; Roth, Bryan L.; Cherezov, Vadim; Stevens, Raymond C.

2013-01-01

The smoothened (SMO) receptor, a key signal transducer in the Hedgehog (Hh) signaling pathway is both responsible for the maintenance of normal embryonic development and implicated in carcinogenesis. The SMO receptor is classified as a class Frizzled (class F) G protein-coupled receptor (GPCR), although the canonical Hh signaling pathway involves the transcription factor Gli and the sequence similarity with class A GPCRs is less than 10%. Here we report the crystal structure at 2.5 Å resolution of the transmembrane domain of the human SMO receptor bound to the small molecule antagonist LY2940680. Although the SMO receptor shares the seven transmembrane helical (7TM) fold, most conserved motifs for class A GPCRs are absent, and the structure reveals an unusually complex arrangement of long extracellular loops stabilized by four disulfide bonds. The ligand binds at the extracellular end of the 7TM bundle and forms extensive contacts with the loops. PMID:23636324

Acyl-CoA-Binding Protein ACBP1 Modulates Sterol Synthesis during Embryogenesis.

PubMed

Lung, Shiu-Cheung; Liao, Pan; Yeung, Edward C; Hsiao, An-Shan; Xue, Yan; Chye, Mee-Len

2017-07-01

Fatty acids (FAs) and sterols are primary metabolites that exert interrelated functions as structural and signaling lipids. Despite their common syntheses from acetyl-coenzyme A, homeostatic cross talk remains enigmatic. Six Arabidopsis ( Arabidopsis thaliana ) acyl-coenzyme A-binding proteins (ACBPs) are involved in FA metabolism. ACBP1 interacts with PHOSPHOLIPASE Dα1 and regulates phospholipid composition. Here, its specific role in the negative modulation of sterol synthesis during embryogenesis is reported. ACBP1, likely in a liganded state, interacts with STEROL C4-METHYL OXIDASE1-1 (SMO1-1), a rate-limiting enzyme in the sterol pathway. Proembryo abortion in the double mutant indicated that the ACBP1-SMO1-1 interaction is synthetic lethal, corroborating with their strong promoter activities in developing ovules. Gas chromatography-mass spectrometry revealed quantitative and compositional changes in FAs and sterols upon overexpression or mutation of ACBP1 and/or SMO1-1 Aberrant levels of these metabolites may account for the downstream defect in lipid signaling. GLABRA2 ( GL2 ), encoding a phospholipid/sterol-binding homeodomain transcription factor, was up-regulated in developing seeds of acbp1 , smo1-1 , and ACBP1 +/- smo1-1 in comparison with the wild type. Consistent with the corresponding transcriptional alteration of GL2 targets, high-oil, low-mucilage phenotypes of gl2 were phenocopied in ACBP1 +/- smo1-1 Thus, ACBP1 appears to modulate the metabolism of two important lipid classes (FAs and sterols) influencing cellular signaling. © 2017 American Society of Plant Biologists. All Rights Reserved.

Effects of bovine SMO gene polymorphisms on the body measurement and meat quality traits of Qinchuan cattle.

PubMed

Zhang, Y R; Li, Y K; Fu, C Z; Wang, J L; Wang, H B; Zan, L S

2014-10-07

Beef cattle breeding programs focus on improving important economic traits, including growth rates, and meat quantity and quality. Molecular marker-assisted selection based on genetic variation represents a potential method for breeding genetically improved livestock with better economic traits. Smoothened (SMO) protein is a signal transducer that contributes to the regulation of both osteogenesis and adipogenesis through the hedgehog pathway. In this study, we detected polymorphisms in the bovine SMO gene of Qinchuan cattle, and we analyzed their associations with body measurement traits (BMTs) and meat quality traits (MQTs). Using DNA sequencing and polymerase chain reaction-restriction fragment length polymorphism, 3 novel single nucleotide polymorphisms were identified in the SMO gene of 562 cattle: 1 G > C mutation on exon 9 (G21234C) and 2 C > T mutations on exon 11 (C22424T and C22481T). Association analysis showed that polymorphisms on both the G21234C and C22424T loci significantly affected certain BMTs and MQTs (P < 0.05 or P < 0.01), whereas those on the C22481T locus did not (P > 0.05). Therefore, the SMO gene could be used as a candidate gene to alter BMTs and MQTs in Qinchuan cattle or for marker-assisted selection to breed cattle with superior BMTs and MQTs.

Regaining motor control in musician's dystonia by restoring sensorimotor organisation

PubMed Central

Rosenkranz, Karin; Butler, Katherine; Williamon, Aaron; Rothwell, John C.

2010-01-01

Professional musicians are an excellent human model of long term effects of skilled motor training on the structure and function of the motor system. However, such effects are accompanied by an increased risk of developing motor abnormalities, in particular musician's dystonia. Previously we found that there was an expanded spatial integration of proprioceptive input into the hand area of motor cortex (sensorimotor organisation, SMO) in healthy musicians as tested with a transcranial magnetic stimulation (TMS) paradigm. In musician's dystonia, this expansion was even larger, resulting in a complete lack of somatotopic organisation. We hypothesised that the disordered motor control in musician's dystonia is a consequence of the disordered SMO. In the present paper we test this idea by giving pianists with musician's dystonia 15 min experience of a modified proprioceptive training task. This restored SMO towards that seen in healthy pianists. Crucially, motor control of the affected task improved significantly and objectively as measured with a MIDI piano, and the amount of behavioural improvement was significantly correlated to the degree of sensorimotor re-organisation. In healthy pianists and non-musicians, the SMO and motor performance remained essentially unchanged. These findings suggest a link between the differentiation of SMO in the hand motor cortex and the degree of motor control of intensively practiced tasks in highly skilled individuals. PMID:19923295

Sorption of selected veterinary antibiotics onto dairy farming soils of contrasting nature.

PubMed

Srinivasan, Prakash; Sarmah, Ajit K; Manley-Harris, Merilyn

2014-02-15

The sorption potential for three sulfonamides (SAs), sulfamethoxazole (SMO), sulfachloropyridazine (SCP) and sulfamethazine (SM) and a macrolide, tylosin tartrate (TT) was assessed on six New Zealand dairy farming soils of contrasting physico-chemical properties. Kinetics studies showed that the sorption was rapid in the first few hours of the contact time (0-2h for SA and 0-4h for TT) and thereafter apparent equilibrium was achieved. Batch sorption isotherm data revealed that the degree of isotherm linearity (N) for SCP and SM varied between 0.50 and 1.08 in the six soils. Isotherms of both TT and SMO were mostly non-linear with the degree of non-linearity for TT (N=0.38-0.71) being greater than for SMO (0.42-0.75) in all soils except Manawatu (TT) and Te Kowhai (SMO) where a linear pattern was observed. Concentration-dependent effective distribution coefficient (Kd(eff)) values for the SMO, SCP and SM antibiotics in the soils ranged from 0.85 to 16.35 L kg(-1), while that for TT was 1.6 to 1,042 L kg(-1). The sorption affinity for all soils followed an order: TT>SCP>SM>SMO. Remarkable high sorption for tylosin in Matawhero soil as compared to other soils was attributed to the presence of oxygen containing acidic polar functional groups as evident in the FT-IR spectra of the soil. Furthermore, it was hypothesised that sorption of TT onto soils was mostly driven by metal oxide-surface mediated transformations whereas for sulfonamides it was primarily due to hydrophobic interactions. Copyright © 2013 Elsevier B.V. All rights reserved.

The α Subunit of the G Protein G13 Regulates Activity of One or More Gli Transcription Factors Independently of Smoothened*

PubMed Central

Douglas, Andrew E.; Heim, Jennifer A.; Shen, Feng; Almada, Luciana L.; Riobo, Natalia A.; Fernández-Zapico, Martin E.; Manning, David R.

2011-01-01

Smoothened (Smo) is a seven-transmembrane (7-TM) receptor that is essential to most actions of the Hedgehog family of morphogens. We found previously that Smo couples to members of the Gi family of heterotrimeric G proteins, which in some cases are integral although alone insufficient in the activation of Gli transcription factors through Hedgehog signaling. In response to a report that the G12/13 family is relevant to Hedgehog signaling as well, we re-evaluated the coupling of Smo to one member of this family, G13, and investigated the capacity of this and other G proteins to activate one or more of forms of Gli. We found no evidence that Smo couples directly to G13. We found nonetheless that Gα13 and to some extent Gαq and Gα12 are able to effect activation of Gli(s). This capacity is realized in some cells, e.g. C3H10T1/2, MC3T3, and pancreatic cancer cells, but not all cells. The mechanism employed is distinct from that achieved through canonical Hedgehog signaling, as the activation does not involve autocrine signaling or in any other way require active Smo and does not necessarily involve enhanced transcription of Gli1. The activation by Gα13 can be replicated through a Gq/G12/13-coupled receptor, CCKA, and is attenuated by inhibitors of p38 mitogen-activated protein kinase and Tec tyrosine kinases. We posit that G proteins, and perhaps G13 in particular, provide access to Gli that is independent of Smo and that they thus establish a basis for control of at least some forms of Gli-mediated transcription apart from Hedgehogs. PMID:21757753

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ge, Xin; Lyu, Pengwei; Gu, Yuanting

Sonic hesgehog (Shh) signaling has been reported to play an essential role in cancer progression. The mechanism of Shh involved in breast cancer carcinogenesis remains unclear. The present study sought to explore whether Shh signaling could regulate the glycolytic metabolism in breast cancers. Overexpression of the smoothed (Smo) and Gli-1 was found in human primary breast cancers. The expressions of Shh and Gli-1 correlated significantly with tumor size and tumor stage. In vitro, human recombinant Shh (rShh) triggered Smo and Gli-1 expression, promoted glucose utilization and lactate production, and accelerated cell proliferation in MCF-7 and MDA-MB-231 cells. Notably, rShh did notmore » alter 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 3 (PFKFB3) expression but augmented PFKFB3 phosphorylation on ser{sup 461}, along with elevated fructose-2,6-bisphosphate (F2,6BP) generation by MCF-7 and MDA-MB-231 cells. This effect could be dampened by Smo siRNA but not by Gli-1 siRNA. In addition, our data showed the upregulated expressions of MAPK by rShh and elevatory PFKFB3 phosphorylation by p38/MAPK activated kinase (MK2). In conclusion, our study characterized a novel role of Shh in promoting glycolysis and proliferation of breast cancer cells via PFKFB3 phosphorylation, which was mediated by Smo and p38/MK2. - Highlights: • Overexpression of Smo and Gli-1 was found in human primary breast cancers. • Shh promoted glucose utilization, lactate production, and cell proliferation. • Shh did not alter PFKFB3 expression but augmented PFKFB3 phosphorylation on ser461. • Shh acts on PFKFB3 phosphorylation via Smo and p38 MAPK/MK2.« less

Synthesis of bilayer MoS{sub 2} nanosheets by a facile hydrothermal method and their methyl orange adsorption capacity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ye, Lijuan; Xu, Haiyan; Zhang, Dingke

2014-07-01

Highlights: • Hexagonal phase of MoS{sub 2} nanosheets was synthesized by a facile hydrothermal method. • FE-SEM and TEM images show the sheets-like morphology of MoS{sub 2}. • Bilayer MoS{sub 2} can be grown under the optimized mole ratio of 2:1 of S:Mo at 180 °C for 50 h. • The MoS{sub 2} nanosheets possess high methyl orange adsorption capacity due to the large surface area. - Abstract: Molybdenum disulfide (MoS{sub 2}) nanosheets have received significant attention recently due to the potential applications for exciting physics and technology. Here we show that MoS{sub 2} nanosheets can be prepared by amore » facile hydrothermal method. The study of the properties of the MoS{sub 2} nanosheets prepared at different conditions suggests that the mole ratio of precursors and hydrothermal time significantly influences the purity, crystalline quality and thermal stability of MoS{sub 2}. X-ray diffraction, Raman spectra and transmission electron microscopy results indicate that bilayer MoS{sub 2} can be grown under an optimized mole ratio of 2:1 of S:Mo at 180 °C for 50 h. Moreover, such ultrathin nanosheets exhibit a prominent photoluminescence and possess high methyl orange adsorption capacity due to the large surface area, which can be potentially used in photodevice and photochemical catalyst.« less

Optimization of a methamphetamine conjugate vaccine for antibody production in mice.

PubMed

Stevens, Misty W; Gunnell, Melinda G; Tawney, Rachel; Owens, S Michael

2016-06-01

There are still no approved medications for treating patients who abuse methamphetamine. Active vaccines for treating abuse of nicotine and cocaine are in clinical studies, but have not proven effective seemingly due to inadequate anti-drug antibody production. The current studies aimed to optimize the composition, adjuvant and route of administration of a methamphetamine conjugate vaccine, ICKLH-SMO9, in mice with the goal of generating significantly higher antibody levels. A range of hapten epitope densities were compared, as were the adjuvants Alhydrogel and a new Toll-like receptor 4 (TLR4) agonist called GLA-SE. While methamphetamine hapten density did not strongly affect the antibody response, the adjuvant did. Glucopyranosyl lipid A in a stable oil-in-water emulsion (GLA-SE) produced much higher levels of antibody in response to immunization compared with Alhydrogel; immunization with GLA-SE also produced antibodies with higher affinities for methamphetamine. GLA-SE has been used in human studies of vaccines for influenza among others and like some other clinical TLR4 agonists, it is safe and elicits a strong immune response. GLA-SE adjuvanted vaccines are typically administered by intramuscular injection and this also proved effective in these mouse studies. Clinical studies of the ICKLH-SMO9 methamphetamine vaccine adjuvanted with GLA-SE have the potential for demonstrating efficacy by generating much higher levels of antibody than substance abuse vaccines that have unsuccessfully used aluminum-based adjuvants. Copyright © 2016 Elsevier B.V. All rights reserved.

Decoding the phosphorylation code in Hedgehog signal transduction

PubMed Central

Chen, Yongbin; Jiang, Jin

2013-01-01

Hedgehog (Hh) signaling plays pivotal roles in embryonic development and adult tissue homeostasis, and its deregulation leads to numerous human disorders including cancer. Binding of Hh to Patched (Ptc), a twelve-transmembrane protein, alleviates its inhibition of Smoothened (Smo), a seven-transmembrane protein related to G-protein-coupled receptors (GPCRs), leading to Smo phosphorylation and activation. Smo acts through intracellular signaling complexes to convert the latent transcription factor Cubitus interruptus (Ci)/Gli from a truncated repressor to a full-length activator, leading to derepression/activation of Hh target genes. Increasing evidence suggests that phosphorylation participates in almost every step in the signal relay from Smo to Ci/Gli, and that differential phosphorylation of several key pathway components may be crucial for translating the Hh morphogen gradient into graded pathway activities. In this review, we focus on the multifaceted roles that phosphorylation plays in Hh signal transduction, and discuss the conservation and difference between Drosophila and mammalian Hh signaling mechanisms. PMID:23337587

Sonic hedgehog signaling regulates actin cytoskeleton via Tiam1-Rac1 cascade during spine formation.

PubMed

Sasaki, Nobunari; Kurisu, Junko; Kengaku, Mineko

2010-12-01

The sonic hedgehog (Shh) pathway has essential roles in several processes during development of the vertebrate central nervous system (CNS). Here, we report that Shh regulates dendritic spine formation in hippocampal pyramidal neurons via a novel pathway that directly regulates the actin cytoskeleton. Shh signaling molecules Patched (Ptc) and Smoothened (Smo) are expressed in several types of postmitotic neurons, including cerebellar Purkinje cells and hippocampal pyramidal neurons. Knockdown of Smo induces dendritic spine formation in cultured hippocampal neurons independently of Gli-mediated transcriptional activity. Smo interacts with Tiam1, a guanine nucleotide exchange factor for Rac1, via its cytoplasmic C-terminal region. Inhibition of Tiam1 or Rac1 activity suppresses spine induction by Smo knockdown. Shh induces remodeling of the actin cytoskeleton independently of transcriptional activation in mouse embryonic fibroblasts. These findings demonstrate a novel Shh pathway that regulates the actin cytoskeleton via Tiam1-Rac1 activation. Copyright © 2010 Elsevier Inc. All rights reserved.

Peptide amphiphile nanofiber hydrogel delivery of sonic hedgehog protein to the cavernous nerve to promote regeneration and prevent erectile dysfunction.

PubMed

Choe, Shawn; Bond, Christopher W; Harrington, Daniel A; Stupp, Samuel I; McVary, Kevin T; Podlasek, Carol A

2017-01-01

Erectile dysfunction (ED) has high impact on quality of life in prostatectomy, diabetic and aging patients. An underlying mechanism is cavernous nerve (CN) injury, which causes ED in up to 80% of prostatectomy patients. We examine how sonic hedgehog (SHH) treatment with innovative peptide amphiphile nanofiber hydrogels (PA), promotes CN regeneration after injury. SHH and its receptors patched (PTCH1) and smoothened (SMO) are localized in PG neurons and glia. SMO undergoes anterograde transport to signal to downstream targets. With crush injury, PG neurons degenerate and undergo apoptosis. SHH protein decreases, SMO localization changes to the neuronal cell surface, and anterograde transport stops. With SHH treatment SHH is taken up at the injury site and undergoes retrograde transport to PG neurons, allowing SMO transport to occur, and neurons remain intact. SHH treatment prevents neuronal degeneration, maintains neuronal, glial and downstream target signaling, and is significant as a regenerative therapy. Published by Elsevier Inc.

Cholesterol activates the G-protein coupled receptor Smoothened to promote Hedgehog signaling

PubMed Central

Luchetti, Giovanni; Sircar, Ria; Kong, Jennifer H; Nachtergaele, Sigrid; Sagner, Andreas; Byrne, Eamon FX; Covey, Douglas F; Siebold, Christian; Rohatgi, Rajat

2016-01-01

Cholesterol is necessary for the function of many G-protein coupled receptors (GPCRs). We find that cholesterol is not just necessary but also sufficient to activate signaling by the Hedgehog (Hh) pathway, a prominent cell-cell communication system in development. Cholesterol influences Hh signaling by directly activating Smoothened (SMO), an orphan GPCR that transmits the Hh signal across the membrane in all animals. Unlike many GPCRs, which are regulated by cholesterol through their heptahelical transmembrane domains, SMO is activated by cholesterol through its extracellular cysteine-rich domain (CRD). Residues shown to mediate cholesterol binding to the CRD in a recent structural analysis also dictate SMO activation, both in response to cholesterol and to native Hh ligands. Our results show that cholesterol can initiate signaling from the cell surface by engaging the extracellular domain of a GPCR and suggest that SMO activity may be regulated by local changes in cholesterol abundance or accessibility. DOI: http://dx.doi.org/10.7554/eLife.20304.001 PMID:27705744

Muscle Oxygen Saturation Improves Diagnostic Association Between Initial Vital Signs and Major Hemorrhage: A Prospective Observational Study.

PubMed

Reisner, Andrew T; Edla, Shwetha; Liu, Jianbo; Rubin, John T; Thorsen, Jill E; Kittell, Erin; Smith, Jason B; Yeh, Daniel D; Reifman, Jaques

2016-03-01

During initial assessment of trauma patients, vital signs do not identify all patients with life-threatening hemorrhage. We hypothesized that a novel vital sign, muscle oxygen saturation (SmO2 ), could provide independent diagnostic information beyond routine vital signs for identification of hemorrhaging patients who require packed red blood cell (RBC) transfusion. This was an observational study of adult trauma patients treated at a Level I trauma center. Study staff placed the CareGuide 1100 tissue oximeter (Reflectance Medical Inc., Westborough, MA), and we analyzed average values of SmO2 , systolic blood pressure (sBP), pulse pressure (PP), and heart rate (HR) during 10 minutes of early emergency department evaluation. We excluded subjects without a full set of vital signs during the observation interval. The study outcome was hemorrhagic injury and RBC transfusion ≥ 3 units in 24 hours (24-hr RBC ≥ 3). To test the hypothesis that SmO2 added independent information beyond routine vital signs, we developed one logistic regression model with HR, sBP, and PP and one with SmO2 in addition to HR, sBP, and PP and compared their areas under receiver operating characteristic curves (ROC AUCs) using DeLong's test. We enrolled 487 subjects; 23 received 24-hr RBC ≥ 3. Compared to the model without SmO2 , the regression model with SmO2 had a significantly increased ROC AUC for the prediction of ≥ 3 units of 24-hr RBC volume, 0.85 (95% confidence interval [CI], 0.75-0.91) versus 0.77 (95% CI, 0.66-0.86; p < 0.05 per DeLong's test). Results were similar for ROC AUCs predicting patients (n = 11) receiving 24-hr RBC ≥ 9. SmO2 significantly improved the diagnostic association between initial vital signs and hemorrhagic injury with blood transfusion. This parameter may enhance the early identification of patients who require blood products for life-threatening hemorrhage. © 2016 The Authors. Academic Emergency Medicine published by Wiley Periodicals, Inc. on behalf of Society for Academic Emergency Medicine.

Application of Deep Learning Architectures for Accurate and Rapid Detection of Internal Mechanical Damage of Blueberry Using Hyperspectral Transmittance Data.

PubMed

Wang, Zhaodi; Hu, Menghan; Zhai, Guangtao

2018-04-07

Deep learning has become a widely used powerful tool in many research fields, although not much so yet in agriculture technologies. In this work, two deep convolutional neural networks (CNN), viz. Residual Network (ResNet) and its improved version named ResNeXt, are used to detect internal mechanical damage of blueberries using hyperspectral transmittance data. The original structure and size of hypercubes are adapted for the deep CNN training. To ensure that the models are applicable to hypercube, we adjust the number of filters in the convolutional layers. Moreover, a total of 5 traditional machine learning algorithms, viz. Sequential Minimal Optimization (SMO), Linear Regression (LR), Random Forest (RF), Bagging and Multilayer Perceptron (MLP), are performed as the comparison experiments. In terms of model assessment, k-fold cross validation is used to indicate that the model performance does not vary with the different combination of dataset. In real-world application, selling damaged berries will lead to greater interest loss than discarding the sound ones. Thus, precision, recall, and F1-score are also used as the evaluation indicators alongside accuracy to quantify the false positive rate. The first three indicators are seldom used by investigators in the agricultural engineering domain. Furthermore, ROC curves and Precision-Recall curves are plotted to visualize the performance of classifiers. The fine-tuned ResNet/ResNeXt achieve average accuracy and F1-score of 0.8844/0.8784 and 0.8952/0.8905, respectively. Classifiers SMO/ LR/RF/Bagging/MLP obtain average accuracy and F1-score of 0.8082/0.7606/0.7314/0.7113/0.7827 and 0.8268/0.7796/0.7529/0.7339/0.7971, respectively. Two deep learning models achieve better classification performance than the traditional machine learning methods. Classification for each testing sample only takes 5.2 ms and 6.5 ms respectively for ResNet and ResNeXt, indicating that the deep learning framework has great potential for online fruit sorting. The results of this study demonstrate the potential of deep CNN application on analyzing the internal mechanical damage of fruit.

Application of Deep Learning Architectures for Accurate and Rapid Detection of Internal Mechanical Damage of Blueberry Using Hyperspectral Transmittance Data

PubMed Central

Hu, Menghan; Zhai, Guangtao

2018-01-01

Deep learning has become a widely used powerful tool in many research fields, although not much so yet in agriculture technologies. In this work, two deep convolutional neural networks (CNN), viz. Residual Network (ResNet) and its improved version named ResNeXt, are used to detect internal mechanical damage of blueberries using hyperspectral transmittance data. The original structure and size of hypercubes are adapted for the deep CNN training. To ensure that the models are applicable to hypercube, we adjust the number of filters in the convolutional layers. Moreover, a total of 5 traditional machine learning algorithms, viz. Sequential Minimal Optimization (SMO), Linear Regression (LR), Random Forest (RF), Bagging and Multilayer Perceptron (MLP), are performed as the comparison experiments. In terms of model assessment, k-fold cross validation is used to indicate that the model performance does not vary with the different combination of dataset. In real-world application, selling damaged berries will lead to greater interest loss than discarding the sound ones. Thus, precision, recall, and F1-score are also used as the evaluation indicators alongside accuracy to quantify the false positive rate. The first three indicators are seldom used by investigators in the agricultural engineering domain. Furthermore, ROC curves and Precision-Recall curves are plotted to visualize the performance of classifiers. The fine-tuned ResNet/ResNeXt achieve average accuracy and F1-score of 0.8844/0.8784 and 0.8952/0.8905, respectively. Classifiers SMO/ LR/RF/Bagging/MLP obtain average accuracy and F1-score of 0.8082/0.7606/0.7314/0.7113/0.7827 and 0.8268/0.7796/0.7529/0.7339/0.7971, respectively. Two deep learning models achieve better classification performance than the traditional machine learning methods. Classification for each testing sample only takes 5.2 ms and 6.5 ms respectively for ResNet and ResNeXt, indicating that the deep learning framework has great potential for online fruit sorting. The results of this study demonstrate the potential of deep CNN application on analyzing the internal mechanical damage of fruit. PMID:29642454

Spontaneous sulfur dioxide activation by Group V metal (V, Nb, Ta) atoms in excess argon at cryogenic temperatures.

PubMed

Liu, Xing; Wang, Xuefeng; Wang, Qiang; Andrews, Lester

2013-06-28

Reactions of laser-ablated V, Nb and Ta atoms with SO2 in excess argon during condensation gave new absorptions in the M=O stretching region, which were assigned to metal sulfide oxides SMO2 and anions SMO2(-) (M = V, Nb, Ta). The metal oxide complex OV(η(2)-SO) was also identified through the V=O and the characteristic side-on coordinated S-O stretching modes. The assignments of major vibrational modes were confirmed by appropriate S(18)O2 and (34)SO2 isotopic shifts, and density functional frequency calculations. DFT calculations were employed to study the behavior of reactions of Group V bare metal atoms with SO2, and a representative profile was derived which not only showed the preferred coordinating fashion of metal atoms but also tracked the path of S-O bond activation. The η(2)-O,O' bridge coordinated complexes are preferred with energy decreases of ca. 50 kcal mol(-1) for all three metals, which facilitate the activation of two S-O bonds in succession and finally direct the reaction to the most stable molecules SMO2 (M = V, Nb, Ta) along the potential energy surface (PES). Finally the SMO2 molecules capture electrons to give anions SMO2(-) with about 3.6 eV electron affinities based on DFT calculations.

The Hedgehog processing pathway is required for NSCLC growth and survival

PubMed Central

Rodriguez-Blanco, Jezabel; Schilling, Neal S.; Tokhunts, Robert; Giambelli, Camilla; Long, Jun; Liang Fei, Dennis; Singh, Samer; Black, Kendall E.; Wang, Zhiqiang; Galimberti, Fabrizio; Bejarano, Pablo A.; Elliot, Sharon; Glassberg, Marilyn K.; Nguyen, Dao M.; Lockwood, William W.; Lam, Wan L.; Dmitrovsky, Ethan; Capobianco, Anthony J.; Robbins, David J.

2013-01-01

Considerable interest has been generated from the results of recent clinical trials using SMOOTHENED (SMO) antagonists to inhibit the growth of HEDGEHOG (HH) signaling dependent tumors. This interest is tempered by the discovery of SMO mutations mediating resistance, underscoring the rationale for developing therapeutic strategies that interrupt HH signaling at levels distinct from those inhibiting SMO function. Here, we demonstrate that HH dependent non-small cell lung carcinoma (NSCLC) growth is sensitive to blockade of the HH pathway upstream of SMO, at the level of HH ligand processing. Individually, the use of different lentivirally delivered shRNA constructs targeting two functionally distinct HH-processing proteins, SKINNY HEDGEHOG (SKN) or DISPATCHED-1 (DISP-1), in NSCLC cell lines produced similar decreases in cell proliferation and increased cell death. Further, providing either an exogenous source of processed HH or a SMO agonist reverses these effects. The attenuation of HH processing, by knocking down either of these gene products, also abrogated tumor growth in mouse xenografts. Finally, we extended these findings to primary clinical specimens, showing that SKN is frequently over-expressed in NSCLC and that higher DISP-1 expression is associated with an unfavorable clinical outcome. Our results show a critical role for HH processing in HH-dependent tumors, identifies two potential druggable targets in the HH pathway, and suggest that similar therapeutic strategies could be explored to treat patients harboring HH ligand dependent cancers. PMID:22733134

Long range personalized cancer treatment strategies incorporating evolutionary dynamics.

PubMed

Yeang, Chen-Hsiang; Beckman, Robert A

2016-10-22

Current cancer precision medicine strategies match therapies to static consensus molecular properties of an individual's cancer, thus determining the next therapeutic maneuver. These strategies typically maintain a constant treatment while the cancer is not worsening. However, cancers feature complicated sub-clonal structure and dynamic evolution. We have recently shown, in a comprehensive simulation of two non-cross resistant therapies across a broad parameter space representing realistic tumors, that substantial improvement in cure rates and median survival can be obtained utilizing dynamic precision medicine strategies. These dynamic strategies explicitly consider intratumoral heterogeneity and evolutionary dynamics, including predicted future drug resistance states, and reevaluate optimal therapy every 45 days. However, the optimization is performed in single 45 day steps ("single-step optimization"). Herein we evaluate analogous strategies that think multiple therapeutic maneuvers ahead, considering potential outcomes at 5 steps ahead ("multi-step optimization") or 40 steps ahead ("adaptive long term optimization (ALTO)") when recommending the optimal therapy in each 45 day block, in simulations involving both 2 and 3 non-cross resistant therapies. We also evaluate an ALTO approach for situations where simultaneous combination therapy is not feasible ("Adaptive long term optimization: serial monotherapy only (ALTO-SMO)"). Simulations utilize populations of 764,000 and 1,700,000 virtual patients for 2 and 3 drug cases, respectively. Each virtual patient represents a unique clinical presentation including sizes of major and minor tumor subclones, growth rates, evolution rates, and drug sensitivities. While multi-step optimization and ALTO provide no significant average survival benefit, cure rates are significantly increased by ALTO. Furthermore, in the subset of individual virtual patients demonstrating clinically significant difference in outcome between approaches, by far the majority show an advantage of multi-step or ALTO over single-step optimization. ALTO-SMO delivers cure rates superior or equal to those of single- or multi-step optimization, in 2 and 3 drug cases respectively. In selected virtual patients incurable by dynamic precision medicine using single-step optimization, analogous strategies that "think ahead" can deliver long-term survival and cure without any disadvantage for non-responders. When therapies require dose reduction in combination (due to toxicity), optimal strategies feature complex patterns involving rapidly interleaved pulses of combinations and high dose monotherapy. This article was reviewed by Wendy Cornell, Marek Kimmel, and Andrzej Swierniak. Wendy Cornell and Andrzej Swierniak are external reviewers (not members of the Biology Direct editorial board). Andrzej Swierniak was nominated by Marek Kimmel.

A reduced-order nonlinear sliding mode observer for vehicle slip angle and tyre forces

NASA Astrophysics Data System (ADS)

Chen, Yuhang; Ji, Yunfeng; Guo, Konghui

2014-12-01

In this paper, a reduced-order sliding mode observer (RO-SMO) is developed for vehicle state estimation. Several improvements are achieved in this paper. First, the reference model accuracy is improved by considering vehicle load transfers and using a precise nonlinear tyre model 'UniTire'. Second, without the reference model accuracy degraded, the computing burden of the state observer is decreased by a reduced-order approach. Third, nonlinear system damping is integrated into the SMO to speed convergence and reduce chattering. The proposed RO-SMO is evaluated through simulation and experiments based on an in-wheel motor electric vehicle. The results show that the proposed observer accurately predicts the vehicle states.

CoSMoS: Conserved Sequence Motif Search in the proteome

PubMed Central

Liu, Xiao I; Korde, Neeraj; Jakob, Ursula; Leichert, Lars I

2006-01-01

Background With the ever-increasing number of gene sequences in the public databases, generating and analyzing multiple sequence alignments becomes increasingly time consuming. Nevertheless it is a task performed on a regular basis by researchers in many labs. Results We have now created a database called CoSMoS to find the occurrences and at the same time evaluate the significance of sequence motifs and amino acids encoded in the whole genome of the model organism Escherichia coli K12. We provide a precomputed set of multiple sequence alignments for each individual E. coli protein with all of its homologues in the RefSeq database. The alignments themselves, information about the occurrence of sequence motifs together with information on the conservation of each of the more than 1.3 million amino acids encoded in the E. coli genome can be accessed via the web interface of CoSMoS. Conclusion CoSMoS is a valuable tool to identify highly conserved sequence motifs, to find regions suitable for mutational studies in functional analyses and to predict important structural features in E. coli proteins. PMID:16433915

GLI activation by atypical protein kinase C ι/λ regulates the growth of basal cell carcinomas.

PubMed

Atwood, Scott X; Li, Mischa; Lee, Alex; Tang, Jean Y; Oro, Anthony E

2013-02-28

Growth of basal cell carcinomas (BCCs) requires high levels of hedgehog (HH) signalling through the transcription factor GLI. Although inhibitors of membrane protein smoothened (SMO) effectively suppress HH signalling, early tumour resistance illustrates the need for additional downstream targets for therapy. Here we identify atypical protein kinase C ι/λ (aPKC-ι/λ) as a novel GLI regulator in mammals. aPKC-ι/λ and its polarity signalling partners co-localize at the centrosome and form a complex with missing-in-metastasis (MIM), a scaffolding protein that potentiates HH signalling. Genetic or pharmacological loss of aPKC-ι/λ function blocks HH signalling and proliferation of BCC cells. Prkci is a HH target gene that forms a positive feedback loop with GLI and exists at increased levels in BCCs. Genome-wide transcriptional profiling shows that aPKC-ι/λ and SMO control the expression of similar genes in tumour cells. aPKC-ι/λ functions downstream of SMO to phosphorylate and activate GLI1, resulting in maximal DNA binding and transcriptional activation. Activated aPKC-ι/λ is upregulated in SMO-inhibitor-resistant tumours and targeting aPKC-ι/λ suppresses signalling and growth of resistant BCC cell lines. These results demonstrate that aPKC-ι/λ is critical for HH-dependent processes and implicates aPKC-ι/λ as a new, tumour-selective therapeutic target for the treatment of SMO-inhibitor-resistant cancers.

CoSMoS and TwinPaW: initial report on two new German twin studies.

PubMed

Spinath, Frank M; Wolf, Heike

2006-12-01

After briefly recapitulating two earlier German twin studies (BiLSAT and GOSAT), we present two new German twin studies with a longitudinal perspective: CoSMoS and TwinPaW. The twin study on Cognitive ability, Self-reported Motivation and School performance (CoSMoS) aims to investigate predictors and influences of school performance in a genetically sensitive design, beginning with children in late elementary school. The Twin study on Personality And Wellbeing (TwinPaW) focuses on adult personality and its relation to physical health as well as health-related behavior in an adult sample of twins. Both studies are characterized by an effort to recruit new large twin samples through a novel recruitment procedure aimed at reducing self-selective sampling. In two German federal states, contact information on persons born on the same day and with the same name was retrieved from record sections. From the resulting pool of more than 36,000 addresses we contacted approximately 2000 parents of twins aged 9 and 10 for CoSMoS, as well as 2000 adult twin pairs for TwinPaW by telephone and mail. Personal contact by telephone proved to be more efficient with agreement rates of 63% in the children sample and 65% in the adult sample. In this article we briefly describe the rationale and the study aims of CoSMoS and TwinPaW as well as the characteristics of the sample we have recruited so far.

Stenotrophomonas maltophilia responds to exogenous AHL signals through the LuxR solo SmoR (Smlt1839).

PubMed

Martínez, Paula; Huedo, Pol; Martinez-Servat, Sònia; Planell, Raquel; Ferrer-Navarro, Mario; Daura, Xavier; Yero, Daniel; Gibert, Isidre

2015-01-01

Quorum Sensing (QS) mediated by Acyl Homoserine Lactone (AHL) molecules are probably the most widespread and studied among Gram-negative bacteria. Canonical AHL systems are composed by a synthase (LuxI family) and a regulator element (LuxR family), whose genes are usually adjacent in the genome. However, incomplete AHL-QS machinery lacking the synthase LuxI is frequently observed in Proteobacteria, and the regulator element is then referred as LuxR solo. It has been shown that certain LuxR solos participate in interspecific communication by detecting signals produced by different organisms. In the case of Stenotrophomonas maltophilia, a preliminary genome sequence analysis revealed numerous putative luxR genes, none of them associated to a luxI gene. From these, the hypothetical LuxR solo Smlt1839, here designated SmoR, presents a conserved AHL binding domain and a helix-turn-helix DNA binding motif. Its genomic organization-adjacent to hchA gene-indicate that SmoR belongs to the new family "LuxR regulator chaperone HchA-associated." AHL-binding assays revealed that SmoR binds to AHLs in-vitro, at least to oxo-C8-homoserine lactone, and it regulates operon transcription, likely by recognizing a conserved palindromic regulatory box in the hchA upstream region. Supplementation with concentrated supernatants from Pseudomonas aeruginosa, which contain significant amounts of AHLs, promoted swarming motility in S. maltophilia. Contrarily, no swarming stimulation was observed when the P. aeruginosa supernatant was treated with the lactonase AiiA from Bacillus subtilis, confirming that AHL contributes to enhance the swarming ability of S. maltophilia. Finally, mutation of smoR resulted in a swarming alteration and an apparent insensitivity to the exogenous AHLs provided by P. aeruginosa. In conclusion, our results demonstrate that S. maltophilia senses AHLs produced by neighboring bacteria through the LuxR solo SmoR, regulating population behaviors such as swarming motility.

Impact of topographic mask models on scanner matching solutions

NASA Astrophysics Data System (ADS)

Tyminski, Jacek K.; Pomplun, Jan; Renwick, Stephen P.

2014-03-01

Of keen interest to the IC industry are advanced computational lithography applications such as Optical Proximity Correction of IC layouts (OPC), scanner matching by optical proximity effect matching (OPEM), and Source Optimization (SO) and Source-Mask Optimization (SMO) used as advanced reticle enhancement techniques. The success of these tasks is strongly dependent on the integrity of the lithographic simulators used in computational lithography (CL) optimizers. Lithographic mask models used by these simulators are key drivers impacting the accuracy of the image predications, and as a consequence, determine the validity of these CL solutions. Much of the CL work involves Kirchhoff mask models, a.k.a. thin masks approximation, simplifying the treatment of the mask near-field images. On the other hand, imaging models for hyper-NA scanner require that the interactions of the illumination fields with the mask topography be rigorously accounted for, by numerically solving Maxwell's Equations. The simulators used to predict the image formation in the hyper-NA scanners must rigorously treat the masks topography and its interaction with the scanner illuminators. Such imaging models come at a high computational cost and pose challenging accuracy vs. compute time tradeoffs. Additional complication comes from the fact that the performance metrics used in computational lithography tasks show highly non-linear response to the optimization parameters. Finally, the number of patterns used for tasks such as OPC, OPEM, SO, or SMO range from tens to hundreds. These requirements determine the complexity and the workload of the lithography optimization tasks. The tools to build rigorous imaging optimizers based on first-principles governing imaging in scanners are available, but the quantifiable benefits they might provide are not very well understood. To quantify the performance of OPE matching solutions, we have compared the results of various imaging optimization trials obtained with Kirchhoff mask models to those obtained with rigorous models involving solutions of Maxwell's Equations. In both sets of trials, we used sets of large numbers of patterns, with specifications representative of CL tasks commonly encountered in hyper-NA imaging. In this report we present OPEM solutions based on various mask models and discuss the models' impact on hyper- NA scanner matching accuracy. We draw conclusions on the accuracy of results obtained with thin mask models vs. the topographic OPEM solutions. We present various examples representative of the scanner image matching for patterns representative of the current generation of IC designs.

Prediction of carbonate rock type from NMR responses using data mining techniques

NASA Astrophysics Data System (ADS)

Gonçalves, Eduardo Corrêa; da Silva, Pablo Nascimento; Silveira, Carla Semiramis; Carneiro, Giovanna; Domingues, Ana Beatriz; Moss, Adam; Pritchard, Tim; Plastino, Alexandre; Azeredo, Rodrigo Bagueira de Vasconcellos

2017-05-01

Recent studies have indicated that the accurate identification of carbonate rock types in a reservoir can be employed as a preliminary step to enhance the effectiveness of petrophysical property modeling. Furthermore, rock typing activity has been shown to be of key importance in several steps of formation evaluation, such as the study of sedimentary series, reservoir zonation and well-to-well correlation. In this paper, a methodology based exclusively on the analysis of 1H-NMR (Nuclear Magnetic Resonance) relaxation responses - using data mining algorithms - is evaluated to perform the automatic classification of carbonate samples according to their rock type. We analyze the effectiveness of six different classification algorithms (k-NN, Naïve Bayes, C4.5, Random Forest, SMO and Multilayer Perceptron) and two data preprocessing strategies (discretization and feature selection). The dataset used in this evaluation is formed by 78 1H-NMR T2 distributions of fully brine-saturated rock samples from six different rock type classes. The experiments reveal that the combination of preprocessing strategies with classification algorithms is able to achieve a prediction accuracy of 97.4%.

Amine oxidase-based biosensors for spermine and spermidine determination.

PubMed

Boffi, Alberto; Favero, Gabriele; Federico, Rodolfo; Macone, Alberto; Antiochia, Riccarda; Tortolini, Cristina; Sanzó, Gabriella; Mazzei, Franco

2015-02-01

The present work describes the development and optimization of electrochemical biosensors for specific determination of the biogenic polyamine spermine (Spm) and spermidine (Spmd) whose assessment represents a novel important analytical tool in food analysis and human diagnostics. These biosensors have been prepared using novel engineered enzymes: polyamine oxidase (PAO) endowed with selectivity towards Spm and Spmd and spermine oxidase (SMO) characterized by strict specificity towards Spm. The current design entails biosensors in which the enzymes were entrapped in poly(vinyl alcohol) bearing styrylpyridinium groups (PVA-SbQ), a photocrosslinkable gel, onto an electrode surface. Screen-printed electrodes (SPEs) were used as electrochemical transducers for enzymatically produced hydrogen peroxide, operating at different potential vs Ag/AgCl according to the material of the working electrode (WE): +700 mV for graphite (GP) or -100 mV for Prussian blue (PB)-modified SPE, respectively. Biosensor performances were evaluated by means of flow injection amperometric (FIA) measurements. The modified electrodes showed good sensitivity, long-term stability and reproducibility. Under optimal conditions, the PAO biosensor showed a linear range 0.003-0.3 mM for Spm and 0.01-0.4 mM for Spmd, while with the SMO biosensor, a linear range of 0.004-0.5 mM for Spm has been obtained. The main kinetic parameters apparent Michaelis constant (K M), turnover number (K cat) and steady-state current (I max) were determined. The proposed device was then applied to the determination of biogenic amines in blood samples. The results obtained were in good agreement with those obtained with the GC-MS reference method.

Sperm whale assessment in the Western Ionian Sea using acoustic data from deep sea observatories

NASA Astrophysics Data System (ADS)

Caruso, Francesco; Bellia, Giorgio; Beranzoli, Laura; De Domenico, Emilio; Larosa, Giuseppina; Marinaro, Giuditta; Papale, Elena; Pavan, Gianni; Pellegrino, Carmelo; Pulvirenti, Sara; Riccobene, Giorgio; Scandura, Danila; Sciacca, Virginia; Viola, Salvatore

2015-04-01

The Italian National Institute of Nuclear Physics (INFN) operates two deep sea infrastructures: Capo Passero, Western Ionian Sea 3,600 meters of depth, and Catania Wester Ionian Sea 2,100 m depth. At the two sites, several research observatories have been run: OnDE, NEMO-SN1, SMO, KM3NeT-Italia most of them jointly operated between INFN and INGV. In all these observatories, passive acoustic sensors (hydrophones) have been installed. Passive Acoustics Monitoring (PAM) is nowadays the main tool of the bioacoustics to study marine mammals. In particular, receiving the sounds emitted by cetaceans from a multi-hydrophones array installed in a cabled seafloor observatory, a research about the ecological dynamics of the species may be performed. Data acquired with the hydrophones installed aboard the OnDE, SMO and KM3NeT-Italia observatories will be reported. Thanks to acquired data, the acoustic presence of the sperm whales was assessed and studied for several years (2005:2013). An "ad hoc" algorithm was also developed to allow the automatic identification of the "clicks" emitted by the sperm whales and measure the size of detected animals. According to the results obtained, the sperm whale population in the area is well-distributed in size, sex and sexual maturity. Although specimens more than 14 meters of length (old males) seem to be absent.

Regaining motor control in musician's dystonia by restoring sensorimotor organization.

PubMed

Rosenkranz, Karin; Butler, Katherine; Williamon, Aaron; Rothwell, John C

2009-11-18

Professional musicians are an excellent model of long-term motor learning effects on structure and function of the sensorimotor system. However, intensive motor skill training has been associated with task-specific deficiency in hand motor control, which has a higher prevalence among musicians (musician's dystonia) than in the general population. Using a transcranial magnetic stimulation paradigm, we previously found an expanded spatial integration of proprioceptive input into the hand motor cortex [sensorimotor organization (SMO)] in healthy musicians. In musician's dystonia, however, this expansion was even larger. Whereas motor skills of musicians are likely to be supported by a spatially expanded SMO, we hypothesized that in musician's dystonia this might have developed too far and now disrupts rather than assists task-specific motor control. If so, motor control should be regained by reversing the excessive reorganization in musician's dystonia. Here, we test this hypothesis and show that a 15 min intervention with proprioceptive input (proprioceptive training) restored SMO in pianists with musician's dystonia to the pattern seen in healthy pianists. Crucially, task-specific motor control improved significantly and objectively as measured with a MIDI (musical instrument digital interface) piano, and the amount of behavioral improvement was significantly correlated to the degree of sensorimotor reorganization. In healthy pianists and nonmusicians, the SMO and motor performance remained essentially unchanged. These findings suggest that the differentiation of SMO in the hand motor cortex and the degree of motor control of intensively practiced tasks are significantly linked and finely balanced. Proprioceptive training restored this balance in musician's dystonia to the behaviorally beneficial level of healthy musicians.

Suppression of hedgehog signaling regulates hepatic stellate cell activation and collagen secretion.

PubMed

Li, Tao; Leng, Xi-Sheng; Zhu, Ji-Ye; Wang, Gang

2015-01-01

Hepatic stellate cells (HSCs) play an important role in liver fibrosis. This study investigates the expression of hedgehog in HSC and the role of hedgehog signaling on activation and collagen secretion of HSC. Liver ex vivo perfusion with collagenase IV and density gradient centrifugation were used to isolate HSC. Expression of hedgehog signaling components Ihh, Smo, Ptc, Gli2 and Gli3 in HSC were detected by RT-PCR. Hedgehog siRNA vectors targeting Ihh, Smo and Gli2 were constructed and transfected into HSC respectively. Suppression of hedgehog signaling were detected by SYBR Green fluorescence quantitative RT-PCR. Effects of hedgehog signaling inhibition on HSC activation and collagen I secretion were analyzed. Hedgehog signaling components Ihh, Smo, Ptc, Gli2 and Gli3 were expressed in HSC. siRNA vectors targeting Ihh, Smo and Gli2 were successfully constructed and decreased target gene expression. Suppression of hedgehog signaling significantly decreased the expression of α-SMA in HSC (P<0.01). Collagen type I secretion of HSC were also significantly decreased (P<0.01). In summary, HSC activation and collagen secretion can be regulated by hedgehog signaling. Hedgehog may play a role in the pathogenesis of liver fibrosis.

Bariatric surgery outcomes in black patients with super morbid obesity: a 1-year postoperative review.

PubMed

Onyewu, Samuel C; Ogundimu, Ololade O; Ortega, Gezzer; Bauer, Edward S; Emenari, Chijindu C; Molyneaux, Neh D; Layne, Sylvonne A; Changoor, Navin R; Tapscott, Denia; Tran, Daniel D; Fullum, Terrence M

2017-01-01

Super morbid obesity (body mass index [BMI] > 50 kg/m 2 ) is associated with significant comorbidities and is disparagingly prevalent among the black population. There is paucity of data regarding bariatric surgery outcomes among super morbid obese (SMO) blacks. Our aim is to evaluate the reduction in weight and resolution of comorbidities after bariatric surgery among SMO black patients at an urban academic institution. A retrospective review of SMO black patients who underwent bariatric surgery from August 2008 to June 2013 at Howard University Hospital. Outcomes of interest include weight loss, improvement or resolution of hypertension, type 2 diabetes, and hyperlipidemia at 12 months. Eighty-seven patients met our inclusion criteria. Mean preoperative weight and BMI were 347.2 lbs and 56.8 kg/m 2 , respectively. At 12 months, mean weight and BMI were 245.3 lbs and 40.1 kg/m 2 , respectively. There was also significant improvement or resolution of hypertension, type 2 diabetes, and hyperlipidemia. Bariatric surgery may result in significant weight loss and improvement or resolution of comorbidities in SMO black patients. Copyright © 2016. Published by Elsevier Inc.

Medical students as teachers at CoSMO, Columbia University's student-run clinic: a pilot study and literature review.

PubMed

Hamso, Magni; Ramsdell, Amanda; Balmer, Dorene; Boquin, Cyrus

2012-01-01

Although medical students are expected to teach as soon as they begin residency, medical schools have just recently begun adding teacher training to their curricula. Student-run clinics (SRCs) may provide opportunities in clinical teaching before residency. The aim of this pilot study was to examine students' experiences in clinical teaching at Columbia Student Medical Outreach (CoSMO), Columbia University's SRC, during the 2009-2010 school year. A mixed-methods approach was used. Data included closed and open-ended surveys (n = 34), combined interviews with preclinical and clinical student pairs (n = 5), individual interviews (n = 10), and focus groups (n = 3). The transcripts were analyzed using the principles of grounded theory. Many students had their first clinical teaching experience while volunteering at CoSMO. Clinical students' ability to teach affected the quality of the learning experience for their preclinical peers. Preclinical students who asked questions and engaged in patient care challenged their clinical peers to balance teaching with patient care. Clinical students began to see themselves as teachers while volunteering at CoSMO. The practical experiences in clinical teaching that students have at SRCs can supplement classroom-based trainings. Medical schools might revisit their SRCs as places for exposure to clinical teaching.

A novel methodology for litho-to-etch pattern fidelity correction for SADP process

NASA Astrophysics Data System (ADS)

Chen, Shr-Jia; Chang, Yu-Cheng; Lin, Arthur; Chang, Yi-Shiang; Lin, Chia-Chi; Lai, Jun-Cheng

2017-03-01

For 2x nm node semiconductor devices and beyond, more aggressive resolution enhancement techniques (RETs) such as source-mask co-optimization (SMO), litho-etch-litho-etch (LELE) and self-aligned double patterning (SADP) are utilized for the low k1 factor lithography processes. In the SADP process, the pattern fidelity is extremely critical since a slight photoresist (PR) top-loss or profile roughness may impact the later core trim process, due to its sensitivity to environment. During the subsequent sidewall formation and core removal processes, the core trim profile weakness may worsen and induces serious defects that affect the final electrical performance. To predict PR top-loss, a rigorous lithography simulation can provide a reference to modify mask layouts; but it takes a much longer run time and is not capable of full-field mask data preparation. In this paper, we first brought out an algorithm which utilizes multi-intensity levels from conventional aerial image simulation to assess the physical profile through lithography to core trim etching steps. Subsequently, a novel correction method was utilized to improve the post-etch pattern fidelity without the litho. process window suffering. The results not only matched PR top-loss in rigorous lithography simulation, but also agreed with post-etch wafer data. Furthermore, this methodology can also be incorporated with OPC and post-OPC verification to improve core trim profile and final pattern fidelity at an early stage.

A Counter-Social Movement Approach to Deconstructing Daesh

DTIC Science & Technology

alternative, the US military could view Daesh as a transnational social movement organization (SMO), and by doing so, planners could develop a more effective... social movement theory (SMT) to determine lines of effort (LOE) against which US military forces could best apply resources to counteract the SMO. This... study is divided into four sections. The first section constitutes an overview of SMT as a form of contentious politics. The second section presents a

Crevice Corrosion Behavior of 45 Molybdenum-Containing Stainless Steels in Seawater.

DTIC Science & Technology

1981-12-01

Armco, Avesta Jernverks, Cabot, Carpenter Technology, Crucible, Eastern, Firth-Brown, Huntington, Jessup, Langley Alloys, and Uddeholm. 16...Department of Energy, Report ANL/OTEC-BCM-022. 7. Wallen, B., and M. Liljas, " Avesta 254 SMO - A New, High Molybdenum Stainless Steel," presented at NKM8...1977).; 11. Wallen, B., " Avesta 254 SMO - A Stainless Steel for Seawater Service," presented at the Advanced Stainless Steels for Turbine Condensors

JWST-MIRI spectrometer main optics design and main results

NASA Astrophysics Data System (ADS)

Navarro, Ramón; Schoenmaker, Ton; Kroes, Gabby; Oudenhuysen, Ad; Jager, Rieks; Venema, Lars

2017-11-01

MIRI ('Mid InfraRed Instrument') is the combined imager and integral field spectrometer for the 5-29 micron wavelength range under development for the James Webb Space Telescope JWST. The flight acceptance tests of the Spectrometer Main Optics flight models (SMO), part of the MIRI spectrometer, are completed in the summer of 2008 and the system is delivered to the MIRI-JWST consortium. The two SMO arms contain 14 mirrors and form the MIRI optical system together with 12 selectable gratings on grating wheels. The entire system operates at a temperature of 7 Kelvin and is designed on the basis of a 'no adjustments' philosophy. This means that the optical alignment precision depends strongly on the design, tolerance analysis and detailed knowledge of the manufacturing process. Because in principle no corrections are needed after assembly, continuous tracking of the alignment performance during the design and manufacturing phases is important. The flight hardware is inspected with respect to performance parameters like alignment and image quality. The stability of these parameters is investigated after exposure to various vibration levels and successive cryogenic cool downs. This paper describes the philosophy behind the acceptance tests, the chosen test strategy and reports the results of these tests. In addition the paper covers the design of the optical test setup, focusing on the simulation of the optical interfaces of the SMO. Also the relation to the SMO qualification and verification program is addressed.

Efficacy of Asparaginase Erwinia chrysanthemi With and Without Temozolomide Against Glioma Cells and Intracranial Mouse Medulloblastoma.

PubMed

Sanghez, Valentina; Chen, Mengqing; Li, Shan; Chou, Tsui-Fen; Iacovino, Michelina; Lin, Henry J; Lasky, Joseph L; Panosyan, Eduard H

2018-05-01

Anti-metabolites are less-myelosuppressive than DNA-damaging anticancer drugs and may be useful against brain tumors. We evaluated the asparagine/glutamine-deaminating agent Erwinaze with/without temozolomide against brain tumor cells and mouse medulloblastomas. Erwinaze treatment of cell lines and neurospheres led to dose-dependent reductions of cells (reversible by L-glutamine), with half maximal inhibitory concentrations (IC 50 s) of 0.12->10 IU/ml. Erwinaze at <1 IU/ml reduced temozolomide IC 50 s by 3.6- to 13-fold (300-1,200 μM to 40-330 μM). Seven-week-old SMO/SMO mice treated with Erwinaze (regardless of temozolomide treatment) had better survival 11 weeks post-therapy, compared to those not treated with Erwinaze (81.25% vs. 46.15, p=0.08). Temozolomide-treated mice developed 10% weight loss, impairing survival. All 16 mice treated with temozolomide (regardless of Erwinaze treatment) succumbed by 40-weeks of age, whereas 5/8 animals treated with Erwinaze alone and 2/6 controls survived (p=0.035). Erwinaze enhances cytotoxicity of temozolomide in vitro, and improves survival in SMO/SMO mice, likely by reducing cerebrospinal fluid glutamine. Temozolomide-associated toxicity prevented demonstration of any potential combinatorial advantage with Erwinaze in vivo. Copyright© 2018, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

A New Sample Transect through the Sierra Madre Occidental Silicic Large Igneous Province in Southern Chihuahua State, Mexico: First Stratigraphic, Petrologic, and Geochemical Results

NASA Astrophysics Data System (ADS)

Andrews, G. D.; Davila Harris, P.; Brown, S. R.; Anderson, L.; Moreno, N.

2014-12-01

We completed a field sampling transect across the northern Sierra Madre Occidental silicic large igneous province (SMO) in December 2013. Here we present the first stratigraphic, petrological, and geochemical data from the transect between Hidalgo del Parral and Guadalupe y Calvo, Chihuahua, Mexico. This is the first new transect across the SMO in 25 years and the only one between existing NE - SW transects at Chihuahua - Hermosillo and Durango - Mazatlan. The 245 km-long transect along Mexican Highway 24 crosses the boundary between the extended (Basin and Range) and non-extended (Sierra Madre Occidental plateau) parts of the SMO, and allows sampling of previously undescribed Oligocene (?) - early Miocene (?) rhyolitic ignimbrites and lavas, and occasional post-rhyolite, Miocene (?) SCORBA basaltic andesite lavas. 54 samples of rhyolitic ignimbrites (40) and lavas (7), and basaltic andesite lavas (7) were sampled along the transect, including 8 canyon sections with more than one unit. The ignimbrites are overwhelming rhyodacitic (plagioclase and hornblende or biotite phyric) or rhyolitic (quartz (+/- sanidine) in additon to plagioclase and hornblende or biotite phyric) and sparsely to highly phyric. Preliminary petrographic (phenocryst abundances) and geochemical (major and trace element) will be presented and compared to existing data from elsewhere in the SMO. Future work will include U-Pb zircon dating and whole rock and in-zircon radiogenic isotopes analyses.

Validating high-resolution California coastal flood modeling with Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR)

NASA Astrophysics Data System (ADS)

O'Neill, A.

2015-12-01

The Coastal Storm Modeling System (CoSMoS) is a numerical modeling scheme used to predict coastal flooding due to sea level rise and storms influenced by climate change, currently in use in central California and in development for Southern California (Pt. Conception to the Mexican border). Using a framework of circulation, wave, analytical, and Bayesian models at different geographic scales, high-resolution results are translated as relevant hazards projections at the local scale that include flooding, wave heights, coastal erosion, shoreline change, and cliff failures. Ready access to accurate, high-resolution coastal flooding data is critical for further validation and refinement of CoSMoS and improved coastal hazard projections. High-resolution Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) provides an exceptional data source as appropriately-timed flights during extreme tides or storms provide a geographically-extensive method for determining areas of inundation and flooding extent along expanses of complex and varying coastline. Landward flood extents are numerically identified via edge-detection in imagery from single flights, and can also be ascertained via change detection using additional flights and imagery collected during average wave/tide conditions. The extracted flooding positions are compared against CoSMoS results for similar tide, water level, and storm-intensity conditions, allowing for robust testing and validation of CoSMoS and providing essential feedback for supporting regional and local model improvement.

Ecosystem service trade-offs and their influencing factors: A case study in the Loess Plateau of China.

PubMed

Feng, Qiang; Zhao, Wenwu; Fu, Bojie; Ding, Jingyi; Wang, Shuai

2017-12-31

Soil erosion control (SEC), carbon sequestration (CAS), and soil moisture (SMO) strongly interact in the semi-arid Loess Plateau. Since SMO has supportive effects on SEC and CAS, it can be considered as ecosystem service (ES), and there is an immediate need to coordinate the relationships among these ecosystem services (ESs) to promote the sustainability of vegetation recovery. In this study, we quantified the ESs, ES trade-offs, and the environmental factors in 151 sample plots in the Ansai watershed, and we used a redundancy analysis (RDA) to clarify the effects of environmental factors on these ESs and their trade-offs. The results were as follows: (1) the general trend in the SEC of vegetation types was Robinia pseudoacacia (CH)>native grass (NG)>small arbor (ST)>Hippophae rhamnoides (SJ)>artificial grass (AG)>Caragana korshinskii (NT)>apple orchard (GY)>crop (CP); the CAS trend was CH>SJ>NT>AG>CP>ST>GY>NG; and the SMO trend was CP>NG>GY>AG>SJ>ST>CH>NT. (2) For SEC-SMO trade-offs, the influence of vegetation type, altitude, silt and sand composition was dominant. The arrangement of NG, AG, and SJ could decrease the extent of the trade-offs. (3) For CAS-SMO trade-offs, vegetation coverage and types were the dominant factors, but the effects were not complex. The extent of these trade-offs was lowest for NT, and that for SJ was the second lowest. (4) Considering the relationships among the three ESs, SJ was the most appropriate afforestation plant. Combing the vegetation types, slope position, slope gradient, and soil properties could regulate these ES relationships. The dominant factors influencing ES trade-offs varied among the different soil layers, so we must consider the corresponding influencing factors to regulate ESs. Moreover, manual management measures were also important for coordinating the ES relationships. Our research provides a better understanding of the mechanisms influencing the relationships among ESs. Copyright © 2017 Elsevier B.V. All rights reserved.

Validating the WHO maternal near miss tool: comparing high- and low-resource settings.

PubMed

Witteveen, Tom; Bezstarosti, Hans; de Koning, Ilona; Nelissen, Ellen; Bloemenkamp, Kitty W; van Roosmalen, Jos; van den Akker, Thomas

2017-06-19

WHO proposed the WHO Maternal Near Miss (MNM) tool, classifying women according to several (potentially) life-threatening conditions, to monitor and improve quality of obstetric care. The objective of this study is to analyse merged data of one high- and two low-resource settings where this tool was applied and test whether the tool may be suitable for comparing severe maternal outcome (SMO) between these settings. Using three cohort studies that included SMO cases, during two-year time frames in the Netherlands, Tanzania and Malawi we reassessed all SMO cases (as defined by the original studies) with the WHO MNM tool (five disease-, four intervention- and seven organ dysfunction-based criteria). Main outcome measures were prevalence of MNM criteria and case fatality rates (CFR). A total of 3172 women were studied; 2538 (80.0%) from the Netherlands, 248 (7.8%) from Tanzania and 386 (12.2%) from Malawi. Total SMO detection was 2767 (87.2%) for disease-based criteria, 2504 (78.9%) for intervention-based criteria and 1211 (38.2%) for organ dysfunction-based criteria. Including every woman who received ≥1 unit of blood in low-resource settings as life-threatening, as defined by organ dysfunction criteria, led to more equally distributed populations. In one third of all Dutch and Malawian maternal death cases, organ dysfunction criteria could not be identified from medical records. Applying solely organ dysfunction-based criteria may lead to underreporting of SMO. Therefore, a tool based on defining MNM only upon establishing organ failure is of limited use for comparing settings with varying resources. In low-resource settings, lowering the threshold of transfused units of blood leads to a higher detection rate of MNM. We recommend refined disease-based criteria, accompanied by a limited set of intervention- and organ dysfunction-based criteria to set a measure of severity.

RET selection on state-of-the-art NAND flash

NASA Astrophysics Data System (ADS)

Lafferty, Neal V.; He, Yuan; Pei, Jinhua; Shao, Feng; Liu, QingWei; Shi, Xuelong

2015-03-01

We present results generated using a new gauge-based Resolution Enhancement Technique (RET) Selection flow during the technology set up phase of a 3x-node NAND Flash product. As a testcase, we consider a challenging critical level for this ash product. The RET solutions include inverse lithography technology (ILT) optimized masks with sub-resolution assist features (SRAF) and companion illumination sources developed using a new pixel based Source Mask Optimization (SMO) tool that uses measurement gauges as a primary input. The flow includes verification objectives which allow tolerancing of particular measurement gauges based on lithographic criteria. Relative importance for particular gauges may also be set, to aid in down-selection from several candidate sources. The end result is a sensitive, objective score of RET performance. Using these custom-defined importance metrics, decisions on the final RET style can be made in an objective way.

Role of Polyamine Oxidase (PAOh1/SMO) in Human Breast Cancer

DTIC Science & Technology

2007-04-01

polyamine spermidine , 3-aminopropanal, and the reactive oxygen species, H2O2. Previous work by our research group has shown that SMO can be induced by...spermine/ spermidine N1-acetyltransferase (SSAT). Treatment of MCF-10a cells with CSE resulted in a rapid 3-fold induction of SSAT mRNA at the one hour time...and spermine/ spermidine N1 acetyl- transferase (SSAT, open bars) in MCF- 10a human breast cancer cells by cigarette smoke extract (CSE) at the

Molecular evolution of the polyamine oxidase gene family in Metazoa

PubMed Central

2012-01-01

Background Polyamine oxidase enzymes catalyze the oxidation of polyamines and acetylpolyamines. Since polyamines are basic regulators of cell growth and proliferation, their homeostasis is crucial for cell life. Members of the polyamine oxidase gene family have been identified in a wide variety of animals, including vertebrates, arthropodes, nematodes, placozoa, as well as in plants and fungi. Polyamine oxidases (PAOs) from yeast can oxidize spermine, N1-acetylspermine, and N1-acetylspermidine, however, in vertebrates two different enzymes, namely spermine oxidase (SMO) and acetylpolyamine oxidase (APAO), specifically catalyze the oxidation of spermine, and N1-acetylspermine/N1-acetylspermidine, respectively. Little is known about the molecular evolutionary history of these enzymes. However, since the yeast PAO is able to catalyze the oxidation of both acetylated and non acetylated polyamines, and in vertebrates these functions are addressed by two specialized polyamine oxidase subfamilies (APAO and SMO), it can be hypothesized an ancestral reference for the former enzyme from which the latter would have been derived. Results We analysed 36 SMO, 26 APAO, and 14 PAO homologue protein sequences from 54 taxa including various vertebrates and invertebrates. The analysis of the full-length sequences and the principal domains of vertebrate and invertebrate PAOs yielded consensus primary protein sequences for vertebrate SMOs and APAOs, and invertebrate PAOs. This analysis, coupled to molecular modeling techniques, also unveiled sequence regions that confer specific structural and functional properties, including substrate specificity, by the different PAO subfamilies. Molecular phylogenetic trees revealed a basal position of all the invertebrates PAO enzymes relative to vertebrate SMOs and APAOs. PAOs from insects constitute a monophyletic clade. Two PAO variants sampled in the amphioxus are basal to the dichotomy between two well supported monophyletic clades including, respectively, all the SMOs and APAOs from vertebrates. The two vertebrate monophyletic clades clustered strictly mirroring the organismal phylogeny of fishes, amphibians, reptiles, birds, and mammals. Evidences from comparative genomic analysis, structural evolution and functional divergence in a phylogenetic framework across Metazoa suggested an evolutionary scenario where the ancestor PAO coding sequence, present in invertebrates as an orthologous gene, has been duplicated in the vertebrate branch to originate the paralogous SMO and APAO genes. A further genome evolution event concerns the SMO gene of placental, but not marsupial and monotremate, mammals which increased its functional variation following an alternative splicing (AS) mechanism. Conclusions In this study the explicit integration in a phylogenomic framework of phylogenetic tree construction, structure prediction, and biochemical function data/prediction, allowed inferring the molecular evolutionary history of the PAO gene family and to disambiguate paralogous genes related by duplication event (SMO and APAO) and orthologous genes related by speciation events (PAOs, SMOs/APAOs). Further, while in vertebrates experimental data corroborate SMO and APAO molecular function predictions, in invertebrates the finding of a supported phylogenetic clusters of insect PAOs and the co-occurrence of two PAO variants in the amphioxus urgently claim the need for future structure-function studies. PMID:22716069

Molecular evolution of the polyamine oxidase gene family in Metazoa.

PubMed

Polticelli, Fabio; Salvi, Daniele; Mariottini, Paolo; Amendola, Roberto; Cervelli, Manuela

2012-06-20

Polyamine oxidase enzymes catalyze the oxidation of polyamines and acetylpolyamines. Since polyamines are basic regulators of cell growth and proliferation, their homeostasis is crucial for cell life. Members of the polyamine oxidase gene family have been identified in a wide variety of animals, including vertebrates, arthropodes, nematodes, placozoa, as well as in plants and fungi. Polyamine oxidases (PAOs) from yeast can oxidize spermine, N1-acetylspermine, and N1-acetylspermidine, however, in vertebrates two different enzymes, namely spermine oxidase (SMO) and acetylpolyamine oxidase (APAO), specifically catalyze the oxidation of spermine, and N1-acetylspermine/N1-acetylspermidine, respectively. Little is known about the molecular evolutionary history of these enzymes. However, since the yeast PAO is able to catalyze the oxidation of both acetylated and non acetylated polyamines, and in vertebrates these functions are addressed by two specialized polyamine oxidase subfamilies (APAO and SMO), it can be hypothesized an ancestral reference for the former enzyme from which the latter would have been derived. We analysed 36 SMO, 26 APAO, and 14 PAO homologue protein sequences from 54 taxa including various vertebrates and invertebrates. The analysis of the full-length sequences and the principal domains of vertebrate and invertebrate PAOs yielded consensus primary protein sequences for vertebrate SMOs and APAOs, and invertebrate PAOs. This analysis, coupled to molecular modeling techniques, also unveiled sequence regions that confer specific structural and functional properties, including substrate specificity, by the different PAO subfamilies. Molecular phylogenetic trees revealed a basal position of all the invertebrates PAO enzymes relative to vertebrate SMOs and APAOs. PAOs from insects constitute a monophyletic clade. Two PAO variants sampled in the amphioxus are basal to the dichotomy between two well supported monophyletic clades including, respectively, all the SMOs and APAOs from vertebrates. The two vertebrate monophyletic clades clustered strictly mirroring the organismal phylogeny of fishes, amphibians, reptiles, birds, and mammals. Evidences from comparative genomic analysis, structural evolution and functional divergence in a phylogenetic framework across Metazoa suggested an evolutionary scenario where the ancestor PAO coding sequence, present in invertebrates as an orthologous gene, has been duplicated in the vertebrate branch to originate the paralogous SMO and APAO genes. A further genome evolution event concerns the SMO gene of placental, but not marsupial and monotremate, mammals which increased its functional variation following an alternative splicing (AS) mechanism. In this study the explicit integration in a phylogenomic framework of phylogenetic tree construction, structure prediction, and biochemical function data/prediction, allowed inferring the molecular evolutionary history of the PAO gene family and to disambiguate paralogous genes related by duplication event (SMO and APAO) and orthologous genes related by speciation events (PAOs, SMOs/APAOs). Further, while in vertebrates experimental data corroborate SMO and APAO molecular function predictions, in invertebrates the finding of a supported phylogenetic clusters of insect PAOs and the co-occurrence of two PAO variants in the amphioxus urgently claim the need for future structure-function studies.

Second medical opinions: the views of oncology patients and their physicians.

PubMed

Philip, Jennifer; Gold, Michelle; Schwarz, Max; Komesaroff, Paul

2010-09-01

Second medical opinions (SMOs) are common in oncology practice, but the nature of these consultations has received relatively little attention. This study examines the views of patients with advanced cancer and their physicians of SMOs. Parallel, concurrent surveys were developed for patients and physicians. The first was distributed to outpatients with advanced cancer-attending specialist clinics in an Australian quaternary hospital. The second survey, developed on the basis of results of exploratory interviews with medical oncologists, was distributed to medical oncologists in Australia. Seventeen of fifty two (33%) patients had sought a SMO, most commonly prompted by concerns around communication with their first doctor, the extreme and desperate nature of their medical condition and the need for reassurance. Most (94%) patients found the SMO helpful, with satisfaction related to improved communication and reassurance. Patients were concerned that seeking a second medical opinion may affect their relationship with their primary doctor. Most physicians (82%) reported seeing between one and five SMO per month, with patients being motivated by the need for additional information and reassurance. Physicians regarded SMO patients as having greater information needs (84%), greater psychosocial needs (58%) and requiring more of the physician's time and energy (77%) than other patients. SMOs are common in cancer care with most patients motivated by the need for improved communication, additional information and reassurance. Physicians identify patients who seek SMOs as having additional psychosocial needs compared with other oncology patients.

Optimal configuration of power grid sources based on optimal particle swarm algorithm

NASA Astrophysics Data System (ADS)

Wen, Yuanhua

2018-04-01

In order to optimize the distribution problem of power grid sources, an optimized particle swarm optimization algorithm is proposed. First, the concept of multi-objective optimization and the Pareto solution set are enumerated. Then, the performance of the classical genetic algorithm, the classical particle swarm optimization algorithm and the improved particle swarm optimization algorithm are analyzed. The three algorithms are simulated respectively. Compared with the test results of each algorithm, the superiority of the algorithm in convergence and optimization performance is proved, which lays the foundation for subsequent micro-grid power optimization configuration solution.

Investigating gas sensing mechanism of graphene oxide (GO) thin films through cross-selectivity to various gases

NASA Astrophysics Data System (ADS)

Kumar, Shani; Dhingra, Vishal; Garg, Amit; Chowdhuri, Arijit

2016-05-01

Worldwide researchers are actively engaged in utilizing Graphene and its related materials in gas sensing applications. A high surface-to-volume ratio that offers scope of optimization leading to enhanced sensing performance besides lower sensor operating temperatures are some advantages that graphene based sensors possess over conventional semiconducting metal oxide (SMO) sensors. Conventional SMO based gas sensors are known to suffer from problems of cross-selectivity where selectivity is understood to be a gas sensor's ability to preferentially detect one particular gas without responding to or experiencing interference from other gases present in the ambient. In the current study gas sensing mechanism of Graphene oxide (GO) thin films is investigated by repeatedly exposing the sensing configuration to various gases and its cross-selectivity response to the same is examined. In the investigation typical gas sensing response characteristics of the sensor configuration are studied in both oxidizing as well as reducing environments. The gas sensing data is acquired by means of Keithley 6487 picoammeter which is interfaced with a customized Gas Sensing Test Rig (GSTR) that provides a controlled ambient to the sensors for measurement of reproducible characteristics. GSTR further provided the option of varying the operating temperature and gas concentration for the different sensor configurations under study. XRD studies indicate formation of GO with typical crystallite size of 4.2 nm. UV-Vis investigations reveal a typical band-gap of 4.42 (eV) which is in conformity with those reported in the available literature.1,2

Thyroid hormone regulates the expression of the sonic hedgehog signaling pathway in the embryonic and adult Mammalian brain.

PubMed

Desouza, Lynette A; Sathanoori, Malini; Kapoor, Richa; Rajadhyaksha, Neha; Gonzalez, Luis E; Kottmann, Andreas H; Tole, Shubha; Vaidya, Vidita A

2011-05-01

Thyroid hormone is important for development and plasticity in the immature and adult mammalian brain. Several thyroid hormone-responsive genes are regulated during specific developmental time windows, with relatively few influenced across the lifespan. We provide novel evidence that thyroid hormone regulates expression of the key developmental morphogen sonic hedgehog (Shh), and its coreceptors patched (Ptc) and smoothened (Smo), in the early embryonic and adult forebrain. Maternal hypo- and hyperthyroidism bidirectionally influenced Shh mRNA in embryonic forebrain signaling centers at stages before fetal thyroid hormone synthesis. Further, Smo and Ptc expression were significantly decreased in the forebrain of embryos derived from hypothyroid dams. Adult-onset thyroid hormone perturbations also regulated expression of the Shh pathway bidirectionally, with a significant induction of Shh, Ptc, and Smo after hyperthyroidism and a decline in Smo expression in the hypothyroid brain. Short-term T₃ administration resulted in a significant induction of cortical Shh mRNA expression and also enhanced reporter gene expression in Shh(+/LacZ) mice. Further, acute T₃ treatment of cortical neuronal cultures resulted in a rapid and significant increase in Shh mRNA, suggesting direct effects. Chromatin immunoprecipitation assays performed on adult neocortex indicated enhanced histone acetylation at the Shh promoter after acute T₃ administration, providing further support that Shh is a thyroid hormone-responsive gene. Our results indicate that maternal and adult-onset perturbations of euthyroid status cause robust and region-specific changes in the Shh pathway in the embryonic and adult forebrain, implicating Shh as a possible mechanistic link for specific neurodevelopmental effects of thyroid hormone.

Current twin studies in Germany: report on CoSMoS, SOEP, and ChronoS.

PubMed

Hahn, Elisabeth; Gottschling, Juliana; Spinath, Frank M

2013-02-01

This article summarizes the status of three recent German twin studies: CoSMoS, SOEP, and ChronoS. The German twin study on Cognitive Ability, Self-Reported Motivation, and School Achievement (CoSMoS) is a three-wave longitudinal study of monozygotic and dizygotic twins reared together, and aims to investigate predictors of and influences on school performance. In the first wave of the data collection in 2005, 408 pairs of twins aged between 7 and 11 as well as their parents participated in CoSMoS. The SOEP twin study is an extended twin study, which has combined data from monozygotic and dizygotic twins reared together with additional data from full sibling pairs, mother-child, and grandparent-child dyads who participated in the German Socio-Economic Panel (GSOEP) study. The SOEP twin project comprises about 350 twin and 950 non-twin pairs aged between 17 and 70. Data were collected between 2009 and 2010, with a focus on personality traits, wellbeing, education, employment, income, living situation, life-satisfaction, and several attitudes. The aim of the Chronotype twin study (ChronoS) was to examine genetic and environmental influences on chronotype (morningness and eveningness), coping strategies, and several aspects of the previous SOEP twin project in a sample of 301 twin pairs aged between 19 and 76 years, recruited in 2010 and 2011. Part of the ChronoS twin sample also participated in the earlier SOEP twin study, representing a second wave of assessments. We briefly describe the design and contents of these three studies as well as selected recent findings.

Projected 21st century coastal flooding in the Southern California Bight. Part 1: Development of the third generation CoSMoS model

USGS Publications Warehouse

O'Neill, Andrea; Erikson, Li; Barnard, Patrick; Limber, Patrick; Vitousek, Sean; Warrick, Jonathan; Foxgrover, Amy C.; Lovering, Jessica

2018-01-01

Due to the effects of climate change over the course of the next century, the combination of rising sea levels, severe storms, and coastal change will threaten the sustainability of coastal communities, development, and ecosystems as we know them today. To clearly identify coastal vulnerabilities and develop appropriate adaptation strategies due to projected increased levels of coastal flooding and erosion, coastal managers need local-scale hazards projections using the best available climate and coastal science. In collaboration with leading scientists world-wide, the USGS designed the Coastal Storm Modeling System (CoSMoS) to assess the coastal impacts of climate change for the California coast, including the combination of sea-level rise, storms, and coastal change. In this project, we directly address the needs of coastal resource managers in Southern California by integrating a vast range of global climate change projections in a thorough and comprehensive numerical modeling framework. In Part 1 of a two-part submission on CoSMoS, methods and the latest improvements are discussed, and an example of hazard projections is presented.

Metabolites in vertebrate Hedgehog signaling.

PubMed

Roberg-Larsen, Hanne; Strand, Martin Frank; Krauss, Stefan; Wilson, Steven Ray

2014-04-11

The Hedgehog (HH) signaling pathway is critical in embryonic development, stem cell biology, tissue homeostasis, chemoattraction and synapse formation. Irregular HH signaling is associated with a number of disease conditions including congenital disorders and cancer. In particular, deregulation of HH signaling has been linked to skin, brain, lung, colon and pancreatic cancers. Key mediators of the HH signaling pathway are the 12-pass membrane protein Patched (PTC), the 7-pass membrane protein Smoothened (SMO) and the GLI transcription factors. PTC shares homology with the RND family of small-molecule transporters and it has been proposed that it interferes with SMO through metabolites. Although a conclusive picture is lacking, substantial efforts are made to identify and understand natural metabolites/sterols, including cholesterol, vitamin D3, oxysterols and glucocorticoides, that may be affected by, or influence the HH signaling cascade at the level of PTC and SMO. In this review we will elaborate the role of metabolites in HH signaling with a focus on oxysterols, and discuss advancements in modern analytical approaches in the field. Copyright © 2014 Elsevier Inc. All rights reserved.

Primary Cilia Negatively Regulate Melanogenesis in Melanocytes and Pigmentation in a Human Skin Model.

PubMed

Choi, Hyunjung; Shin, Ji Hyun; Kim, Eun Sung; Park, So Jung; Bae, Il-Hong; Jo, Yoon Kyung; Jeong, In Young; Kim, Hyoung-June; Lee, Youngjin; Park, Hea Chul; Jeon, Hong Bae; Kim, Ki Woo; Lee, Tae Ryong; Cho, Dong-Hyung

2016-01-01

The primary cilium is an organelle protruding from the cell body that senses external stimuli including chemical, mechanical, light, osmotic, fluid flow, and gravitational signals. Skin is always exposed to the external environment and responds to external stimuli. Therefore, it is possible that primary cilia have an important role in skin. Ciliogenesis was reported to be involved in developmental processes in skin, such as keratinocyte differentiation and hair formation. However, the relation between skin pigmentation and primary cilia is largely unknown. Here, we observed that increased melanogenesis in melanocytes treated with a melanogenic inducer was inhibited by a ciliogenesis inducer, cytochalasin D, and serum-free culture. However, these inhibitory effects disappeared in GLI2 knockdown cells. In addition, activation of sonic hedgehog (SHH)-smoothened (Smo) signaling pathway by a Smo agonist, SAG inhibited melanin synthesis in melanocytes and pigmentation in a human skin model. On the contrary, an inhibitor of primary cilium formation, ciliobrevin A1, activated melanogenesis in melanocytes. These results suggest that skin pigmentation may be regulated partly by the induction of ciliogenesis through Smo-GLI2 signaling.

A general procedure to generate models for urban environmental-noise pollution using feature selection and machine learning methods.

PubMed

Torija, Antonio J; Ruiz, Diego P

2015-02-01

The prediction of environmental noise in urban environments requires the solution of a complex and non-linear problem, since there are complex relationships among the multitude of variables involved in the characterization and modelling of environmental noise and environmental-noise magnitudes. Moreover, the inclusion of the great spatial heterogeneity characteristic of urban environments seems to be essential in order to achieve an accurate environmental-noise prediction in cities. This problem is addressed in this paper, where a procedure based on feature-selection techniques and machine-learning regression methods is proposed and applied to this environmental problem. Three machine-learning regression methods, which are considered very robust in solving non-linear problems, are used to estimate the energy-equivalent sound-pressure level descriptor (LAeq). These three methods are: (i) multilayer perceptron (MLP), (ii) sequential minimal optimisation (SMO), and (iii) Gaussian processes for regression (GPR). In addition, because of the high number of input variables involved in environmental-noise modelling and estimation in urban environments, which make LAeq prediction models quite complex and costly in terms of time and resources for application to real situations, three different techniques are used to approach feature selection or data reduction. The feature-selection techniques used are: (i) correlation-based feature-subset selection (CFS), (ii) wrapper for feature-subset selection (WFS), and the data reduction technique is principal-component analysis (PCA). The subsequent analysis leads to a proposal of different schemes, depending on the needs regarding data collection and accuracy. The use of WFS as the feature-selection technique with the implementation of SMO or GPR as regression algorithm provides the best LAeq estimation (R(2)=0.94 and mean absolute error (MAE)=1.14-1.16 dB(A)). Copyright © 2014 Elsevier B.V. All rights reserved.

Program and Project Management Framework

NASA Technical Reports Server (NTRS)

Butler, Cassandra D.

2002-01-01

The primary objective of this project was to develop a framework and system architecture for integrating program and project management tools that may be applied consistently throughout Kennedy Space Center (KSC) to optimize planning, cost estimating, risk management, and project control. Project management methodology used in building interactive systems to accommodate the needs of the project managers is applied as a key component in assessing the usefulness and applicability of the framework and tools developed. Research for the project included investigation and analysis of industrial practices, KSC standards, policies, and techniques, Systems Management Office (SMO) personnel, and other documented experiences of project management experts. In addition, this project documents best practices derived from the literature as well as new or developing project management models, practices, and techniques.

Underwater acoustic positioning system for the SMO and KM3NeT - Italia projects

DOE Office of Scientific and Technical Information (OSTI.GOV)

Viola, S.; Barbagallo, G.; Cacopardo, G.

In the underwater neutrino telescopes, the positions of the Cherenkov light sensors and their movements must be known with an accuracy of few tens of centimetres. In this work, the activities of the SMO and KM3NeT-Italia teams for the development of an acoustic positioning system for KM3NeT-Italia project are presented. The KM3NeT-Italia project foresees the construction, within two years, of 8 towers in the view of the several km{sup 3}-scale neutrino telescope KM3NeT.

An efficient algorithm for function optimization: modified stem cells algorithm

NASA Astrophysics Data System (ADS)

Taherdangkoo, Mohammad; Paziresh, Mahsa; Yazdi, Mehran; Bagheri, Mohammad Hadi

2013-03-01

In this paper, we propose an optimization algorithm based on the intelligent behavior of stem cell swarms in reproduction and self-organization. Optimization algorithms, such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, Ant Colony Optimization (ACO) algorithm and Artificial Bee Colony (ABC) algorithm, can give solutions to linear and non-linear problems near to the optimum for many applications; however, in some case, they can suffer from becoming trapped in local optima. The Stem Cells Algorithm (SCA) is an optimization algorithm inspired by the natural behavior of stem cells in evolving themselves into new and improved cells. The SCA avoids the local optima problem successfully. In this paper, we have made small changes in the implementation of this algorithm to obtain improved performance over previous versions. Using a series of benchmark functions, we assess the performance of the proposed algorithm and compare it with that of the other aforementioned optimization algorithms. The obtained results prove the superiority of the Modified Stem Cells Algorithm (MSCA).

Several organic parameters on underlying hazardous constituents list can not be measured at the universal treatment standards

DOE Office of Scientific and Technical Information (OSTI.GOV)

Johnson, H.C.

1998-07-01

The Idaho National Engineering and Environmental Laboratory (INEEL) has several permitted treatment, storage and disposal facilities. The INEEL Sample Management Office (SMO) conducts all analysis subcontracting activities for Department of Energy Environmental Management programs at the INEEL. In this role, the INEEL SMO has had the opportunity to subcontract the analyses of various wastes (including ash from an interim status incinerator) requesting a target analyte list equivalent to the constituents listed in 40 Code of Federal Regulations. These analyses are required to ensure that treated wastes do not contain underlying hazardous constituents (UHC) at concentrations greater than the universal treatmentmore » standards (UTS) prior to land disposal. The INEEL SMO has conducted a good-faith effort by negotiating with several commercial laboratories to identify the lowest possible quantitation and detection limits that can be achieved for the organic UHC analytes. The results of this negotiating effort has been the discovery that no single laboratory (currently under subcontract with the INEEL SMO) can achieve a detection level that is within an order of magnitude of the UTS for all organic parameters on a clean sample matrix (e.g., sand). This does not mean that there is no laboratory that can achieve the order of magnitude requirements for all organic UHCs on a clean sample matrix. The negotiations held to date indicate that it is likely that no laboratory can achieve the order of magnitude requirements for a difficult sample matrix (e.g., an incinerator ash). The authors suggest that the regulation needs to be revised to address the disparity between what is achievable in the laboratory and the regulatory levels required by the UTS.« less

Development of the Coastal Storm Modeling System (CoSMoS) for predicting the impact of storms on high-energy, active-margin coasts

USGS Publications Warehouse

Barnard, Patrick; Maarten van Ormondt,; Erikson, Li H.; Jodi Eshleman,; Hapke, Cheryl J.; Peter Ruggiero,; Peter Adams,; Foxgrover, Amy C.

2014-01-01

The Coastal Storm Modeling System (CoSMoS) applies a predominantly deterministic framework to make detailed predictions (meter scale) of storm-induced coastal flooding, erosion, and cliff failures over large geographic scales (100s of kilometers). CoSMoS was developed for hindcast studies, operational applications (i.e., nowcasts and multiday forecasts), and future climate scenarios (i.e., sea-level rise + storms) to provide emergency responders and coastal planners with critical storm hazards information that may be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. The prototype system, developed for the California coast, uses the global WAVEWATCH III wave model, the TOPEX/Poseidon satellite altimetry-based global tide model, and atmospheric-forcing data from either the US National Weather Service (operational mode) or Global Climate Models (future climate mode), to determine regional wave and water-level boundary conditions. These physical processes are dynamically downscaled using a series of nested Delft3D-WAVE (SWAN) and Delft3D-FLOW (FLOW) models and linked at the coast to tightly spaced XBeach (eXtreme Beach) cross-shore profile models and a Bayesian probabilistic cliff failure model. Hindcast testing demonstrates that, despite uncertainties in preexisting beach morphology over the ~500 km alongshore extent of the pilot study area, CoSMoS effectively identifies discrete sections of the coast (100s of meters) that are vulnerable to coastal hazards under a range of current and future oceanographic forcing conditions, and is therefore an effective tool for operational and future climate scenario planning.

Vismodegib Exerts Targeted Efficacy Against Recurrent Sonic Hedgehog–Subgroup Medulloblastoma: Results From Phase II Pediatric Brain Tumor Consortium Studies PBTC-025B and PBTC-032

PubMed Central

Robinson, Giles W.; Orr, Brent A.; Wu, Gang; Gururangan, Sridharan; Lin, Tong; Qaddoumi, Ibrahim; Packer, Roger J.; Goldman, Stewart; Prados, Michael D.; Desjardins, Annick; Chintagumpala, Murali; Takebe, Naoko; Kaste, Sue C.; Rusch, Michael; Allen, Sariah J.; Onar-Thomas, Arzu; Stewart, Clinton F.; Fouladi, Maryam; Boyett, James M.; Gilbertson, Richard J.; Curran, Tom; Ellison, David W.; Gajjar, Amar

2015-01-01

Purpose Two phase II studies assessed the efficacy of vismodegib, a sonic hedgehog (SHH) pathway inhibitor that binds smoothened (SMO), in pediatric and adult recurrent medulloblastoma (MB). Patients and Methods Adult patients enrolled onto PBTC-025B and pediatric patients enrolled onto PBTC-032 were treated with vismodegib (150 to 300 mg/d). Protocol-defined response, which had to be sustained for 8 weeks, was confirmed by central neuroimaging review. Molecular tests to identify patterns of response and insensitivity were performed when tissue was available. Results A total of 31 patients were enrolled onto PBTC-025B, and 12 were enrolled onto PBTC-032. Three patients in PBTC-025B and one in PBTC-032, all with SHH-subgroup MB (SHH-MB), exhibited protocol-defined responses. Progression-free survival (PFS) was longer in those with SHH-MB than in those with non-SHH–MB, and prolonged disease stabilization occurred in 41% of patient cases of SHH-MB. Among those with SHH-MB, loss of heterozygosity of PTCH1 was associated with prolonged PFS, and diffuse staining of P53 was associated with reduced PFS. Whole-exome sequencing identified mutations in SHH genes downstream from SMO in four of four tissue samples from nonresponders and upstream of SMO in two of four patients with favorable responses. Conclusion Vismodegib exhibits activity against adult recurrent SHH-MB but not against recurrent non-SHH–MB. Inadequate accrual of pediatric patients precluded conclusions in this population. Molecular analyses support the hypothesis that SMO inhibitor activity depends on the genomic aberrations within the tumor. Such inhibitors should be advanced in SHH-MB studies; however, molecular and genomic work remains imperative to identify target populations that will truly benefit. PMID:26169613

Widespread GLI expression but limited canonical hedgehog signaling restricted to the ductular reaction in human chronic liver disease

PubMed Central

Tirnitz-Parker, Janina Elke Eleonore; Hamson, Elizabeth Jane; Warren, Alessandra; Maneck, Bharvi; Chen, Jinbiao; Patkunanathan, Bramilla; Boland, Jade; Cheng, Robert; Shackel, Nicholas Adam; Seth, Devanshi; Bowen, David Geoffrey; Martelotto, Luciano Gastón; Watkins, D. Neil; McCaughan, Geoffrey William

2017-01-01

Canonical Hedgehog (Hh) signaling in vertebrate cells occurs following Smoothened activation/translocation into the primary cilia (Pc), followed by a GLI transcriptional response. Nonetheless, GLI activation can occur independently of the canonical Hh pathway. Using a murine model of liver injury, we previously identified the importance of canonical Hh signaling within the Pc+ liver progenitor cell (LPC) population and noted that SMO-independent, GLI-mediated signals were important in multiple Pc-ve GLI2+ intrahepatic populations. This study extends these observations to human liver tissue, and analyses the effect of GLI inhibition on LPC viability/gene expression. Human donor and cirrhotic liver tissue specimens were evaluated for SHH, GLI2 and Pc expression using immunofluorescence and qRT-PCR. Changes to viability and gene expression in LPCs in vitro were assessed following GLI inhibition. Identification of Pc (as a marker of canonical Hh signaling) in human cirrhosis was predominantly confined to the ductular reaction and LPCs. In contrast, GLI2 was expressed in multiple cell populations including Pc-ve endothelium, hepatocytes, and leukocytes. HSCs/myofibroblasts (>99%) expressed GLI2, with only 1.92% displaying Pc. In vitro GLI signals maintained proliferation/viability within LPCs and GLI inhibition affected the expression of genes related to stemness, hepatocyte/biliary differentiation and Hh/Wnt signaling. At least two mechanisms of GLI signaling (Pc/SMO-dependent and Pc/SMO-independent) mediate chronic liver disease pathogenesis. This may have significant ramifications for the choice of Hh inhibitor (anti-SMO or anti-GLI) suitable for clinical trials. We also postulate GLI delivers a pro-survival signal to LPCs whilst maintaining stemness. PMID:28187190

Widespread GLI expression but limited canonical hedgehog signaling restricted to the ductular reaction in human chronic liver disease.

PubMed

Grzelak, Candice Alexandra; Sigglekow, Nicholas David; Tirnitz-Parker, Janina Elke Eleonore; Hamson, Elizabeth Jane; Warren, Alessandra; Maneck, Bharvi; Chen, Jinbiao; Patkunanathan, Bramilla; Boland, Jade; Cheng, Robert; Shackel, Nicholas Adam; Seth, Devanshi; Bowen, David Geoffrey; Martelotto, Luciano Gastón; Watkins, D Neil; McCaughan, Geoffrey William

2017-01-01

Canonical Hedgehog (Hh) signaling in vertebrate cells occurs following Smoothened activation/translocation into the primary cilia (Pc), followed by a GLI transcriptional response. Nonetheless, GLI activation can occur independently of the canonical Hh pathway. Using a murine model of liver injury, we previously identified the importance of canonical Hh signaling within the Pc+ liver progenitor cell (LPC) population and noted that SMO-independent, GLI-mediated signals were important in multiple Pc-ve GLI2+ intrahepatic populations. This study extends these observations to human liver tissue, and analyses the effect of GLI inhibition on LPC viability/gene expression. Human donor and cirrhotic liver tissue specimens were evaluated for SHH, GLI2 and Pc expression using immunofluorescence and qRT-PCR. Changes to viability and gene expression in LPCs in vitro were assessed following GLI inhibition. Identification of Pc (as a marker of canonical Hh signaling) in human cirrhosis was predominantly confined to the ductular reaction and LPCs. In contrast, GLI2 was expressed in multiple cell populations including Pc-ve endothelium, hepatocytes, and leukocytes. HSCs/myofibroblasts (>99%) expressed GLI2, with only 1.92% displaying Pc. In vitro GLI signals maintained proliferation/viability within LPCs and GLI inhibition affected the expression of genes related to stemness, hepatocyte/biliary differentiation and Hh/Wnt signaling. At least two mechanisms of GLI signaling (Pc/SMO-dependent and Pc/SMO-independent) mediate chronic liver disease pathogenesis. This may have significant ramifications for the choice of Hh inhibitor (anti-SMO or anti-GLI) suitable for clinical trials. We also postulate GLI delivers a pro-survival signal to LPCs whilst maintaining stemness.

Thyroid Hormone Regulates the Expression of the Sonic Hedgehog Signaling Pathway in the Embryonic and Adult Mammalian Brain

PubMed Central

Desouza, Lynette A.; Sathanoori, Malini; Kapoor, Richa; Rajadhyaksha, Neha; Gonzalez, Luis E.; Kottmann, Andreas H.; Tole, Shubha

2011-01-01

Thyroid hormone is important for development and plasticity in the immature and adult mammalian brain. Several thyroid hormone-responsive genes are regulated during specific developmental time windows, with relatively few influenced across the lifespan. We provide novel evidence that thyroid hormone regulates expression of the key developmental morphogen sonic hedgehog (Shh), and its coreceptors patched (Ptc) and smoothened (Smo), in the early embryonic and adult forebrain. Maternal hypo- and hyperthyroidism bidirectionally influenced Shh mRNA in embryonic forebrain signaling centers at stages before fetal thyroid hormone synthesis. Further, Smo and Ptc expression were significantly decreased in the forebrain of embryos derived from hypothyroid dams. Adult-onset thyroid hormone perturbations also regulated expression of the Shh pathway bidirectionally, with a significant induction of Shh, Ptc, and Smo after hyperthyroidism and a decline in Smo expression in the hypothyroid brain. Short-term T3 administration resulted in a significant induction of cortical Shh mRNA expression and also enhanced reporter gene expression in Shh+/LacZ mice. Further, acute T3 treatment of cortical neuronal cultures resulted in a rapid and significant increase in Shh mRNA, suggesting direct effects. Chromatin immunoprecipitation assays performed on adult neocortex indicated enhanced histone acetylation at the Shh promoter after acute T3 administration, providing further support that Shh is a thyroid hormone-responsive gene. Our results indicate that maternal and adult-onset perturbations of euthyroid status cause robust and region-specific changes in the Shh pathway in the embryonic and adult forebrain, implicating Shh as a possible mechanistic link for specific neurodevelopmental effects of thyroid hormone. PMID:21363934

Overactivation of hedgehog signaling alters development of the ovarian vasculature in mice.

PubMed

Ren, Yi; Cowan, Robert G; Migone, Fernando F; Quirk, Susan M

2012-06-01

The hedgehog (HH) signaling pathway is critical for ovarian function in Drosophila, but its role in the mammalian ovary has not been defined. Previously, expression of a dominant active allele of the HH signal transducer protein smoothened (SMO) in Amhr2(cre/+)SmoM2 mice caused anovulation in association with a lack of smooth muscle in the theca of developing follicles. The current study examined events during the first 2 wk of life in Amhr2(cre/+)SmoM2 mice to gain insight into the cause of anovulation. Expression of transcriptional targets of HH signaling, Gli1, Ptch1, and Hhip, which are used as measures of pathway activity, were elevated during the first several days of life in Amhr2(cre/+)SmoM2 mice compared to controls but were similar to controls in older mice. Microarray analysis showed that genes with increased expression in 2-day-old mutants compared to controls were enriched for the processes of vascular and tube development and steroidogenesis. The density of platelet endothelial cell adhesion molecule (PECAM)-labeled endothelial tubes was increased in the cortex of newborn ovaries of mutant mice. Costaining of preovulatory follicles for PECAM and smooth muscle actin showed that muscle-type vascular support cells are deficient in theca of mutant mice. Expression of genes for steroidogenic enzymes that are normally expressed in the fetal adrenal gland were elevated in newborn ovaries of mutant mice. In summary, overactivation of HH signaling during early life alters gene expression and vascular development and this is associated with the lifelong development of anovulatory follicles in which the thecal vasculature fails to mature appropriately.

Overactivation of Hedgehog Signaling Alters Development of the Ovarian Vasculature in Mice1

PubMed Central

Ren, Yi; Cowan, Robert G.; Migone, Fernando F.; Quirk, Susan M.

2012-01-01

ABSTRACT The hedgehog (HH) signaling pathway is critical for ovarian function in Drosophila, but its role in the mammalian ovary has not been defined. Previously, expression of a dominant active allele of the HH signal transducer protein smoothened (SMO) in Amhr2cre/+SmoM2 mice caused anovulation in association with a lack of smooth muscle in the theca of developing follicles. The current study examined events during the first 2 wk of life in Amhr2cre/+SmoM2 mice to gain insight into the cause of anovulation. Expression of transcriptional targets of HH signaling, Gli1, Ptch1, and Hhip, which are used as measures of pathway activity, were elevated during the first several days of life in Amhr2cre/+SmoM2 mice compared to controls but were similar to controls in older mice. Microarray analysis showed that genes with increased expression in 2-day-old mutants compared to controls were enriched for the processes of vascular and tube development and steroidogenesis. The density of platelet endothelial cell adhesion molecule (PECAM)-labeled endothelial tubes was increased in the cortex of newborn ovaries of mutant mice. Costaining of preovulatory follicles for PECAM and smooth muscle actin showed that muscle-type vascular support cells are deficient in theca of mutant mice. Expression of genes for steroidogenic enzymes that are normally expressed in the fetal adrenal gland were elevated in newborn ovaries of mutant mice. In summary, overactivation of HH signaling during early life alters gene expression and vascular development and this is associated with the lifelong development of anovulatory follicles in which the thecal vasculature fails to mature appropriately. PMID:22402963

Transcriptional profiles of SHH pathway genes in keratocystic odontogenic tumor and ameloblastoma.

PubMed

Gurgel, Clarissa Araújo Silva; Buim, Marcilei Eliza Cavichiolli; Carvalho, Kátia Cândido; Sales, Caroline Brandi Schlaepfer; Reis, Mitermayer Galvão; de Souza, Renata Oliveira; de Faro Valverde, Ludmila; de Azevedo, Roberto Almeida; Dos Santos, Jean Nunes; Soares, Fernando Augusto; Ramos, Eduardo Antônio Gonçalves

2014-09-01

Sonic hedgehog (SHH) pathway activation has been identified as a key factor in the development of many types of tumors, including odontogenic tumors. Our study examined the expression of genes in the SHH pathway to characterize their roles in the pathogenesis of keratocystic odontogenic tumors (KOT) and ameloblastomas (AB). We quantified the expression of SHH, SMO, PTCH1, SUFU, GLI1, CCND1, and BCL2 genes by qPCR in a total of 23 KOT, 11 AB, and three non-neoplastic oral mucosa (NNM). We also measured the expression of proteins related to this pathway (CCND1 and BCL2) by immunohistochemistry. We observed overexpression of SMO, PTCH1, GLI1, and CCND1 genes in both KOT (23/23) and AB (11/11). However, we did not detect expression of the SHH gene in 21/23 KOT and 10/11 AB tumors. Low levels of the SUFU gene were expressed in KOT (P = 0.0199) and AB (P = 0.0127) relative to the NNM. Recurrent KOT exhibited high levels of SMO (P = 0.035), PTCH1 (P = 0.048), CCND1 (P = 0.048), and BCL2 (P = 0.045) transcripts. Using immunolabeling of CCND1, we observed no statistical difference between primary and recurrent KOT (P = 0.8815), sporadic and NBCCS-KOT (P = 0.7688), and unicystic and solid AB (P = 0.7521). Overexpression of upstream (PTCH1 and SMO) and downstream (GLI1, CCND1 and BCL2) genes in the SHH pathway leads to the constitutive activation of this pathway in KOT and AB and may suggest a mechanism for the development of these types of tumors. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Beetroot-based gel supplementation improves handgrip strength, forearm muscle O2 saturation but not exercise tolerance and blood volume in jiu-jitsu athletes.

PubMed

de Oliveira, Gustavo Vieira; Nascimento, Luiz; Volino-Souza, Mônica; Mesquita, Jacilene; Alvares, Thiago

2018-03-22

The ergogenic effect of beetroot on the exercise performance of trained cyclists, runners, kayakers, and swimmers has been demonstrated. However, whether or not beetroot supplementation presents a beneficial effect on the exercise performance of jiu-jitsu athletes (JJA) remains inconclusive. Therefore, present study assessed the effect of beetroot-based gel (BG) supplementation on maximal voluntary contraction (MVC), exercise time until fatigue (ETF), muscle O2 saturation (SmO2), blood volume (tHb), and plasma nitrate and lactate in response to handgrip isotonic exercise (HIE) in JJA. In a randomized, crossover, double-blind design, 12 JJA performed three sets of HIE at 40% of the MVC until fatigue after 8 days (8th dose was offered 120 min previous exercise) of BG supplementation or a nitrate-depleted gel (PLA), and forearm SmO2 and tHb were continuously monitored by using near-infrared spectroscopy. Blood samples were taken before, immediately after exercise, and 20 min after exercise recovery in PLA and BG condition. MVC was evaluated at baseline and 20 min after HIE. There was a significant reduction in ∆MVC decline after HIE in BG condition. Forearm SmO2 during exercise recovery was significantly greater only after BG supplementation. No significant difference in ETF and tHb were observed between both BG and PLA in response to HIE. Plasma nitrate increased only after BG, whereas the exercise-induced increase in plasma lactate was significantly lower in BG when compared to PLA. In conclusion, BG supplementation may be a good nutritional strategy to improve forearm SmO2 and prevent force decline in response to exercise in JJA.

Sensorless sliding mode observer for a five-phase permanent magnet synchronous motor drive.

PubMed

Hosseyni, Anissa; Trabelsi, Ramzi; Mimouni, Med Faouzi; Iqbal, Atif; Alammari, Rashid

2015-09-01

This paper deals with the sensorless vector controlled five-phase permanent magnet synchronous motor (PMSM) drive based on a sliding mode observer (SMO). The observer is designed considering the back electromotive force (EMF) of five-phase permanent magnet synchronous motor. The SMO structure and design are illustrated. Stability of the proposed observer is demonstrated using Lyapunov stability criteria. The proposed strategy is asymptotically stable in the context of Lyapunov theory. Simulated results on a five-phase PMSM drive are displayed to validate the feasibility and the effectiveness of the proposed control strategy. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Cryptotermes brevis (Isoptera: Kalotermitidae) in the Azores: Lessons After 2 yr of Monitoring in the Archipelago

PubMed Central

Borges, Paulo A. V.; Guerreiro, Orlando; Ferreira, Maria T.; Borges, Annabella; Ferreira, Filomena; Bicudo, Nuno; Nunes, Lina; Marcos, Rita S.; Arroz, Ana M.; Scheffrahn, Rudolf H.; Myles, Timothy G.

2014-01-01

Abstract The dispersal flights of West Indian drywood termite, Cryptotermes brevis (Walker) (Isoptera: Kalotermitidae) were surveyed in the major cities of Azores. The sampling device used to estimate termite density consisted of a yellow adhesive trap (size 45 by 24 cm), placed with an artificial or natural light source in a dark attic environment. In addition, data from two other projects were used to improve the knowledge about the geographical distribution of the species. The level of infestation in the two main Azorean towns differed, with high levels in the houses of Angra do Heroísmo, whereas in Ponta Delgada, there are fewer houses with high levels of infestation. The infestation in Ponta Delgada shows a pattern of spreading from the center outward to the city’s periphery, whereas in Angra do Heroísmo, there was a pattern of spreading outward from several foci. The heavy infestation observed in Angra do Heroísmo and the clear increase of infestation levels observed from 2010 to 2011 is a reason for concern and calls for an urgent application of an Integrated Pest Management (IPM) control strategy. PMID:25368085

A semantic web ontology for small molecules and their biological targets.

PubMed

Choi, Jooyoung; Davis, Melissa J; Newman, Andrew F; Ragan, Mark A

2010-05-24

A wide range of data on sequences, structures, pathways, and networks of genes and gene products is available for hypothesis testing and discovery in biological and biomedical research. However, data describing the physical, chemical, and biological properties of small molecules have not been well-integrated with these resources. Semantically rich representations of chemical data, combined with Semantic Web technologies, have the potential to enable the integration of small molecule and biomolecular data resources, expanding the scope and power of biomedical and pharmacological research. We employed the Semantic Web technologies Resource Description Framework (RDF) and Web Ontology Language (OWL) to generate a Small Molecule Ontology (SMO) that represents concepts and provides unique identifiers for biologically relevant properties of small molecules and their interactions with biomolecules, such as proteins. We instanced SMO using data from three public data sources, i.e., DrugBank, PubChem and UniProt, and converted to RDF triples. Evaluation of SMO by use of predetermined competency questions implemented as SPARQL queries demonstrated that data from chemical and biomolecular data sources were effectively represented and that useful knowledge can be extracted. These results illustrate the potential of Semantic Web technologies in chemical, biological, and pharmacological research and in drug discovery.

A Survey of Distributed Optimization and Control Algorithms for Electric Power Systems

DOE PAGES

Molzahn, Daniel K.; Dorfler, Florian K.; Sandberg, Henrik; ...

2017-07-25

Historically, centrally computed algorithms have been the primary means of power system optimization and control. With increasing penetrations of distributed energy resources requiring optimization and control of power systems with many controllable devices, distributed algorithms have been the subject of significant research interest. Here, this paper surveys the literature of distributed algorithms with applications to optimization and control of power systems. In particular, this paper reviews distributed algorithms for offline solution of optimal power flow (OPF) problems as well as online algorithms for real-time solution of OPF, optimal frequency control, optimal voltage control, and optimal wide-area control problems.

A Survey of Distributed Optimization and Control Algorithms for Electric Power Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Molzahn, Daniel K.; Dorfler, Florian K.; Sandberg, Henrik

Historically, centrally computed algorithms have been the primary means of power system optimization and control. With increasing penetrations of distributed energy resources requiring optimization and control of power systems with many controllable devices, distributed algorithms have been the subject of significant research interest. Here, this paper surveys the literature of distributed algorithms with applications to optimization and control of power systems. In particular, this paper reviews distributed algorithms for offline solution of optimal power flow (OPF) problems as well as online algorithms for real-time solution of OPF, optimal frequency control, optimal voltage control, and optimal wide-area control problems.

Warehouse stocking optimization based on dynamic ant colony genetic algorithm

NASA Astrophysics Data System (ADS)

Xiao, Xiaoxu

2018-04-01

In view of the various orders of FAW (First Automotive Works) International Logistics Co., Ltd., the SLP method is used to optimize the layout of the warehousing units in the enterprise, thus the warehouse logistics is optimized and the external processing speed of the order is improved. In addition, the relevant intelligent algorithms for optimizing the stocking route problem are analyzed. The ant colony algorithm and genetic algorithm which have good applicability are emphatically studied. The parameters of ant colony algorithm are optimized by genetic algorithm, which improves the performance of ant colony algorithm. A typical path optimization problem model is taken as an example to prove the effectiveness of parameter optimization.

An improved marriage in honey bees optimization algorithm for single objective unconstrained optimization.

PubMed

Celik, Yuksel; Ulker, Erkan

2013-01-01

Marriage in honey bees optimization (MBO) is a metaheuristic optimization algorithm developed by inspiration of the mating and fertilization process of honey bees and is a kind of swarm intelligence optimizations. In this study we propose improved marriage in honey bees optimization (IMBO) by adding Levy flight algorithm for queen mating flight and neighboring for worker drone improving. The IMBO algorithm's performance and its success are tested on the well-known six unconstrained test functions and compared with other metaheuristic optimization algorithms.

Effects of annealing temperature on the H2-sensing properties of Pd-decorated WO3 nanorods

NASA Astrophysics Data System (ADS)

Lee, Sangmin; Lee, Woo Seok; Lee, Jae Kyung; Hyun, Soong Keun; Lee, Chongmu; Choi, Seungbok

2018-03-01

The temperature of the post-annealing treatment carried out after noble metal deposition onto semiconducting metal oxides (SMOs) must be carefully optimized to maximize the sensing performance of the metal-decorated SMO sensors. WO3 nanorods were synthesized by thermal evaporation of WO3 powders and decorated with Pd nanoparticles using a sol-gel method, followed by an annealing process. The effects of the annealing temperature on the hydrogen gas-sensing properties of the Pd-decorated WO3 nanorods were then examined; the optimal annealing temperature, leading to the highest response of the WO3 nanorod sensor to H2, was determined to be 600 °C. Post-annealing at 600 °C resulted in nanorods with the highest surface area-to-volume ratio, as well as in the optimal size and the largest number of deposited Pd nanoparticles, leading to the highest response and the shortest response/recovery times toward H2. The improved H2-sensing performance of the Pd-decorated WO3 nanorod sensor, compared to a sensor based on pristine WO3 nanorods, is attributed to the enhanced catalytic activity, increased surface area-to-volume ratio, and higher amounts of surface defects.

Multimodal optimization by using hybrid of artificial bee colony algorithm and BFGS algorithm

NASA Astrophysics Data System (ADS)

Anam, S.

2017-10-01

Optimization has become one of the important fields in Mathematics. Many problems in engineering and science can be formulated into optimization problems. They maybe have many local optima. The optimization problem with many local optima, known as multimodal optimization problem, is how to find the global solution. Several metaheuristic methods have been proposed to solve multimodal optimization problems such as Particle Swarm Optimization (PSO), Genetics Algorithm (GA), Artificial Bee Colony (ABC) algorithm, etc. The performance of the ABC algorithm is better than or similar to those of other population-based algorithms with the advantage of employing a fewer control parameters. The ABC algorithm also has the advantages of strong robustness, fast convergence and high flexibility. However, it has the disadvantages premature convergence in the later search period. The accuracy of the optimal value cannot meet the requirements sometimes. Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm is a good iterative method for finding a local optimum. Compared with other local optimization methods, the BFGS algorithm is better. Based on the advantages of the ABC algorithm and the BFGS algorithm, this paper proposes a hybrid of the artificial bee colony algorithm and the BFGS algorithm to solve the multimodal optimization problem. The first step is that the ABC algorithm is run to find a point. In the second step is that the point obtained by the first step is used as an initial point of BFGS algorithm. The results show that the hybrid method can overcome from the basic ABC algorithm problems for almost all test function. However, if the shape of function is flat, the proposed method cannot work well.

Interior search algorithm (ISA): a novel approach for global optimization.

PubMed

Gandomi, Amir H

2014-07-01

This paper presents the interior search algorithm (ISA) as a novel method for solving optimization tasks. The proposed ISA is inspired by interior design and decoration. The algorithm is different from other metaheuristic algorithms and provides new insight for global optimization. The proposed method is verified using some benchmark mathematical and engineering problems commonly used in the area of optimization. ISA results are further compared with well-known optimization algorithms. The results show that the ISA is efficiently capable of solving optimization problems. The proposed algorithm can outperform the other well-known algorithms. Further, the proposed algorithm is very simple and it only has one parameter to tune. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

An Improved Marriage in Honey Bees Optimization Algorithm for Single Objective Unconstrained Optimization

PubMed Central

Celik, Yuksel; Ulker, Erkan

2013-01-01

Marriage in honey bees optimization (MBO) is a metaheuristic optimization algorithm developed by inspiration of the mating and fertilization process of honey bees and is a kind of swarm intelligence optimizations. In this study we propose improved marriage in honey bees optimization (IMBO) by adding Levy flight algorithm for queen mating flight and neighboring for worker drone improving. The IMBO algorithm's performance and its success are tested on the well-known six unconstrained test functions and compared with other metaheuristic optimization algorithms. PMID:23935416

Performance comparison of genetic algorithms and particle swarm optimization for model integer programming bus timetabling problem

NASA Astrophysics Data System (ADS)

Wihartiko, F. D.; Wijayanti, H.; Virgantari, F.

2018-03-01

Genetic Algorithm (GA) is a common algorithm used to solve optimization problems with artificial intelligence approach. Similarly, the Particle Swarm Optimization (PSO) algorithm. Both algorithms have different advantages and disadvantages when applied to the case of optimization of the Model Integer Programming for Bus Timetabling Problem (MIPBTP), where in the case of MIPBTP will be found the optimal number of trips confronted with various constraints. The comparison results show that the PSO algorithm is superior in terms of complexity, accuracy, iteration and program simplicity in finding the optimal solution.

Optimization Of Feature Weight TheVoting Feature Intervals 5 Algorithm Using Partical Swarm Optimization Algorithm

NASA Astrophysics Data System (ADS)

Hayana Hasibuan, Eka; Mawengkang, Herman; Efendi, Syahril

2017-12-01

The use of Partical Swarm Optimization Algorithm in this research is to optimize the feature weights on the Voting Feature Interval 5 algorithm so that we can find the model of using PSO algorithm with VFI 5. Optimization of feature weight on Diabetes or Dyspesia data is considered important because it is very closely related to the livelihood of many people, so if there is any inaccuracy in determining the most dominant feature weight in the data will cause death. Increased accuracy by using PSO Algorithm ie fold 1 from 92.31% to 96.15% increase accuracy of 3.8%, accuracy of fold 2 on Algorithm VFI5 of 92.52% as well as generated on PSO Algorithm means accuracy fixed, then in fold 3 increase accuracy of 85.19% Increased to 96.29% Accuracy increased by 11%. The total accuracy of all three trials increased by 14%. In general the Partical Swarm Optimization algorithm has succeeded in increasing the accuracy to several fold, therefore it can be concluded the PSO algorithm is well used in optimizing the VFI5 Classification Algorithm.

Atomic-scale distortion of optically activated Sm dopants identified with site-selective X-ray absorption spectroscopy

NASA Astrophysics Data System (ADS)

Ishii, Masashi; Crowe, Iain F.; Halsall, Matthew P.; Hamilton, Bruce; Hu, Yongfeng; Sham, Tsun-Kong; Harako, Susumu; Zhao, Xin-Wei; Komuro, Shuji

2013-10-01

The local structure of luminescent Sm dopants was investigated using an X-ray absorption fine-structure technique with X-ray-excited optical luminescence. Because this technique evaluates X-ray absorption from luminescence, only optically active sites are analyzed. The Sm L3 near-edge spectrum contains split 5d states and a shake-up transition that are specific to luminescent Sm. Theoretical calculations using cluster models identified an atomic-scale distortion that can reproduce the split 5d states. The model with C4v local symmetry and compressive bond length of Sm-O of a six-fold oxygen (SmO6) cluster is most consistent with the experimental results.

Noncanonical Hedgehog Signaling

PubMed Central

Brennan, Donna; Chen, Xiaole; Cheng, Lan; Mahoney, My; Riobo, Natalia A.

2012-01-01

The notion of noncanonical hedgehog (Hh) signaling in mammals has started to receive support from numerous observations. By noncanonical, we refer to all those cellular and tissue responses to any of the Hh isoforms that are independent of transcriptional changes mediated by the Gli family of transcription factors. In this chapter, we discuss the most recent findings that suggest that Patched1 can regulate cell proliferation and apoptosis independently of Smoothened (Smo) and Gli and the reports that Smo modulates actin cytoskeleton-dependent processes such as fibroblast migration, endothelial cell tubulogenesis, axonal extension, and neurite formation by diverse mechanisms that exclude any involvement of Gli-dependent transcription. We also acknowledge the existence of less stronger evidence of noncanonical signaling in Drosophila. PMID:22391299

Semiconductor metal oxide compounds based gas sensors: A literature review

NASA Astrophysics Data System (ADS)

Patil, Sunil Jagannath; Patil, Arun Vithal; Dighavkar, Chandrakant Govindrao; Thakare, Kashinath Shravan; Borase, Ratan Yadav; Nandre, Sachin Jayaram; Deshpande, Nishad Gopal; Ahire, Rajendra Ramdas

2015-03-01

This paper gives a statistical view about important contributions and advances on semiconductor metal oxide (SMO) compounds based gas sensors developed to detect the air pollutants such as liquefied petroleum gas (LPG), H2S, NH3, CO2, acetone, ethanol, other volatile compounds and hazardous gases. Moreover, it is revealed that the alloy/composite made up of SMO gas sensors show better gas response than their counterpart single component gas sensors, i.e., they are found to enhance the 4S characteristics namely speed, sensitivity, selectivity and stability. Improvement of such types of sensors used for detection of various air pollutants, which are reported in last two decades, is highlighted herein.

Adaptive cockroach swarm algorithm

NASA Astrophysics Data System (ADS)

Obagbuwa, Ibidun C.; Abidoye, Ademola P.

2017-07-01

An adaptive cockroach swarm optimization (ACSO) algorithm is proposed in this paper to strengthen the existing cockroach swarm optimization (CSO) algorithm. The ruthless component of CSO algorithm is modified by the employment of blend crossover predator-prey evolution method which helps algorithm prevent any possible population collapse, maintain population diversity and create adaptive search in each iteration. The performance of the proposed algorithm on 16 global optimization benchmark function problems was evaluated and compared with the existing CSO, cuckoo search, differential evolution, particle swarm optimization and artificial bee colony algorithms.

A chaos wolf optimization algorithm with self-adaptive variable step-size

NASA Astrophysics Data System (ADS)

Zhu, Yong; Jiang, Wanlu; Kong, Xiangdong; Quan, Lingxiao; Zhang, Yongshun

2017-10-01

To explore the problem of parameter optimization for complex nonlinear function, a chaos wolf optimization algorithm (CWOA) with self-adaptive variable step-size was proposed. The algorithm was based on the swarm intelligence of wolf pack, which fully simulated the predation behavior and prey distribution way of wolves. It possessed three intelligent behaviors such as migration, summons and siege. And the competition rule as "winner-take-all" and the update mechanism as "survival of the fittest" were also the characteristics of the algorithm. Moreover, it combined the strategies of self-adaptive variable step-size search and chaos optimization. The CWOA was utilized in parameter optimization of twelve typical and complex nonlinear functions. And the obtained results were compared with many existing algorithms, including the classical genetic algorithm, the particle swarm optimization algorithm and the leader wolf pack search algorithm. The investigation results indicate that CWOA possess preferable optimization ability. There are advantages in optimization accuracy and convergence rate. Furthermore, it demonstrates high robustness and global searching ability.

New knowledge-based genetic algorithm for excavator boom structural optimization

NASA Astrophysics Data System (ADS)

Hua, Haiyan; Lin, Shuwen

2014-03-01

Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is proposed. A dual evolution mechanism combining knowledge evolution with genetic algorithm is established to extract, handle and utilize the shallow and deep implicit constraint knowledge to guide the optimal searching of genetic algorithm circularly. Based on this dual evolution mechanism, knowledge evolution and population evolution can be connected by knowledge influence operators to improve the configurability of knowledge and genetic operators. Then, the new knowledge-based selection operator, crossover operator and mutation operator are proposed to integrate the optimal process knowledge and domain culture to guide the excavator boom structural optimization. Eight kinds of testing algorithms, which include different genetic operators, are taken as examples to solve the structural optimization of a medium-sized excavator boom. By comparing the results of optimization, it is shown that the algorithm including all the new knowledge-based genetic operators can more remarkably improve the evolutionary rate and searching ability than other testing algorithms, which demonstrates the effectiveness of knowledge for guiding optimal searching. The proposed knowledge-based genetic algorithm by combining multi-level knowledge evolution with numerical optimization provides a new effective method for solving the complex engineering optimization problem.

Fireworks algorithm for mean-VaR/CVaR models

NASA Astrophysics Data System (ADS)

Zhang, Tingting; Liu, Zhifeng

2017-10-01

Intelligent algorithms have been widely applied to portfolio optimization problems. In this paper, we introduce a novel intelligent algorithm, named fireworks algorithm, to solve the mean-VaR/CVaR model for the first time. The results show that, compared with the classical genetic algorithm, fireworks algorithm not only improves the optimization accuracy and the optimization speed, but also makes the optimal solution more stable. We repeat our experiments at different confidence levels and different degrees of risk aversion, and the results are robust. It suggests that fireworks algorithm has more advantages than genetic algorithm in solving the portfolio optimization problem, and it is feasible and promising to apply it into this field.

A tabu search evalutionary algorithm for multiobjective optimization: Application to a bi-criterion aircraft structural reliability problem

NASA Astrophysics Data System (ADS)

Long, Kim Chenming

Real-world engineering optimization problems often require the consideration of multiple conflicting and noncommensurate objectives, subject to nonconvex constraint regions in a high-dimensional decision space. Further challenges occur for combinatorial multiobjective problems in which the decision variables are not continuous. Traditional multiobjective optimization methods of operations research, such as weighting and epsilon constraint methods, are ill-suited to solving these complex, multiobjective problems. This has given rise to the application of a wide range of metaheuristic optimization algorithms, such as evolutionary, particle swarm, simulated annealing, and ant colony methods, to multiobjective optimization. Several multiobjective evolutionary algorithms have been developed, including the strength Pareto evolutionary algorithm (SPEA) and the non-dominated sorting genetic algorithm (NSGA), for determining the Pareto-optimal set of non-dominated solutions. Although numerous researchers have developed a wide range of multiobjective optimization algorithms, there is a continuing need to construct computationally efficient algorithms with an improved ability to converge to globally non-dominated solutions along the Pareto-optimal front for complex, large-scale, multiobjective engineering optimization problems. This is particularly important when the multiple objective functions and constraints of the real-world system cannot be expressed in explicit mathematical representations. This research presents a novel metaheuristic evolutionary algorithm for complex multiobjective optimization problems, which combines the metaheuristic tabu search algorithm with the evolutionary algorithm (TSEA), as embodied in genetic algorithms. TSEA is successfully applied to bicriteria (i.e., structural reliability and retrofit cost) optimization of the aircraft tail structure fatigue life, which increases its reliability by prolonging fatigue life. A comparison for this application of the proposed algorithm, TSEA, with several state-of-the-art multiobjective optimization algorithms reveals that TSEA outperforms these algorithms by providing retrofit solutions with greater reliability for the same costs (i.e., closer to the Pareto-optimal front) after the algorithms are executed for the same number of generations. This research also demonstrates that TSEA competes with and, in some situations, outperforms state-of-the-art multiobjective optimization algorithms such as NSGA II and SPEA 2 when applied to classic bicriteria test problems in the technical literature and other complex, sizable real-world applications. The successful implementation of TSEA contributes to the safety of aeronautical structures by providing a systematic way to guide aircraft structural retrofitting efforts, as well as a potentially useful algorithm for a wide range of multiobjective optimization problems in engineering and other fields.

An Orthogonal Evolutionary Algorithm With Learning Automata for Multiobjective Optimization.

PubMed

Dai, Cai; Wang, Yuping; Ye, Miao; Xue, Xingsi; Liu, Hailin

2016-12-01

Research on multiobjective optimization problems becomes one of the hottest topics of intelligent computation. In order to improve the search efficiency of an evolutionary algorithm and maintain the diversity of solutions, in this paper, the learning automata (LA) is first used for quantization orthogonal crossover (QOX), and a new fitness function based on decomposition is proposed to achieve these two purposes. Based on these, an orthogonal evolutionary algorithm with LA for complex multiobjective optimization problems with continuous variables is proposed. The experimental results show that in continuous states, the proposed algorithm is able to achieve accurate Pareto-optimal sets and wide Pareto-optimal fronts efficiently. Moreover, the comparison with the several existing well-known algorithms: nondominated sorting genetic algorithm II, decomposition-based multiobjective evolutionary algorithm, decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes, multiobjective optimization by LA, and multiobjective immune algorithm with nondominated neighbor-based selection, on 15 multiobjective benchmark problems, shows that the proposed algorithm is able to find more accurate and evenly distributed Pareto-optimal fronts than the compared ones.

Honey Bees Inspired Optimization Method: The Bees Algorithm.

PubMed

Yuce, Baris; Packianather, Michael S; Mastrocinque, Ernesto; Pham, Duc Truong; Lambiase, Alfredo

2013-11-06

Optimization algorithms are search methods where the goal is to find an optimal solution to a problem, in order to satisfy one or more objective functions, possibly subject to a set of constraints. Studies of social animals and social insects have resulted in a number of computational models of swarm intelligence. Within these swarms their collective behavior is usually very complex. The collective behavior of a swarm of social organisms emerges from the behaviors of the individuals of that swarm. Researchers have developed computational optimization methods based on biology such as Genetic Algorithms, Particle Swarm Optimization, and Ant Colony. The aim of this paper is to describe an optimization algorithm called the Bees Algorithm, inspired from the natural foraging behavior of honey bees, to find the optimal solution. The algorithm performs both an exploitative neighborhood search combined with random explorative search. In this paper, after an explanation of the natural foraging behavior of honey bees, the basic Bees Algorithm and its improved versions are described and are implemented in order to optimize several benchmark functions, and the results are compared with those obtained with different optimization algorithms. The results show that the Bees Algorithm offering some advantage over other optimization methods according to the nature of the problem.

Teaching-learning-based Optimization Algorithm for Parameter Identification in the Design of IIR Filters

NASA Astrophysics Data System (ADS)

Singh, R.; Verma, H. K.

2013-12-01

This paper presents a teaching-learning-based optimization (TLBO) algorithm to solve parameter identification problems in the designing of digital infinite impulse response (IIR) filter. TLBO based filter modelling is applied to calculate the parameters of unknown plant in simulations. Unlike other heuristic search algorithms, TLBO algorithm is an algorithm-specific parameter-less algorithm. In this paper big bang-big crunch (BB-BC) optimization and PSO algorithms are also applied to filter design for comparison. Unknown filter parameters are considered as a vector to be optimized by these algorithms. MATLAB programming is used for implementation of proposed algorithms. Experimental results show that the TLBO is more accurate to estimate the filter parameters than the BB-BC optimization algorithm and has faster convergence rate when compared to PSO algorithm. TLBO is used where accuracy is more essential than the convergence speed.

Cryptotermes brevis (Isoptera: Kalotermitidae) in the Azores: lessons after 2 yr of monitoring in the Archipelago.

PubMed

Borges, Paulo A V; Guerreiro, Orlando; Ferreira, Maria T; Borges, Annabella; Ferreira, Filomena; Bicudo, Nuno; Nunes, Lina; Marcos, Rita S; Arroz, Ana M; Scheffrahn, Rudolf H; Myles, Timothy G

2014-01-01

The dispersal flights of West Indian drywood termite, Cryptotermes brevis (Walker) (Isoptera: Kalotermitidae) were surveyed in the major cities of Azores. The sampling device used to estimate termite density consisted of a yellow adhesive trap (size 45 by 24 cm), placed with an artificial or natural light source in a dark attic environment. In addition, data from two other projects were used to improve the knowledge about the geographical distribution of the species. The level of infestation in the two main Azorean towns differed, with high levels in the houses of Angra do Heroísmo, whereas in Ponta Delgada, there are fewer houses with high levels of infestation. The infestation in Ponta Delgada shows a pattern of spreading from the center outward to the city's periphery, whereas in Angra do Heroísmo, there was a pattern of spreading outward from several foci. The heavy infestation observed in Angra do Heroísmo and the clear increase of infestation levels observed from 2010 to 2011 is a reason for concern and calls for an urgent application of an Integrated Pest Management (IPM) control strategy. © The Author 2014. Published by Oxford University Press on behalf of the Entomological Society of America.

Protein-bound Polysaccharide-K Inhibits Hedgehog Signaling Through Down-regulation of MAML3 and RBPJ Transcription Under Hypoxia, Suppressing the Malignant Phenotype in Pancreatic Cancer.

PubMed

Yamasaki, Akio; Onishi, Hideya; Imaizumi, Akira; Kawamoto, Makoto; Fujimura, Akiko; Oyama, Yasuhiro; Katano, Mitsuo

2016-08-01

Hedgehog signaling is activated in pancreatic cancer and could be a therapeutic target. We previously demonstrated that recombination signal binding protein for immunoglobulin-kappa-J region (RBPJ) and mastermind-like 3 (MAML3) contribute to the hypoxia-induced up-regulation of Smoothened (SMO) transcription. We have also shown that protein-bound polysaccharide-K (PSK) could be effective for refractory pancreatic cancer that down-regulates SMO transcription under hypoxia. In this study, we evaluated whether the anticancer mechanism of PSK involves inhibiting RBPJ and MAML3 expression under hypoxia. PSK reduced SMO, MAML3 and RBPJ expression in pancreatic cancer cells under hypoxia. PSK also blocked RBPJ-induced invasiveness under hypoxia by inhibiting matrix metalloproteinase expression. Lastly, we showed that PSK attenuated RBPJ-induced proliferation both in vitro and in vivo. These results suggest that PSK suppresses Hedgehog signaling through down-regulation of MAML3 and RBPJ transcription under hypoxia, inhibiting the induction of a malignant phenotype in pancreatic cancer. Our results may lead to development of new treatments for refractory pancreatic cancer using PSK as a Hedgehog inhibitor. Copyright© 2016 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

Mutations in the human SC4MOL gene encoding a methyl sterol oxidase cause psoriasiform dermatitis, microcephaly, and developmental delay

PubMed Central

He, Miao; Kratz, Lisa E.; Michel, Joshua J.; Vallejo, Abbe N.; Ferris, Laura; Kelley, Richard I.; Hoover, Jacqueline J.; Jukic, Drazen; Gibson, K. Michael; Wolfe, Lynne A.; Ramachandran, Dhanya; Zwick, Michael E.; Vockley, Jerry

2011-01-01

Defects in cholesterol synthesis result in a wide variety of symptoms, from neonatal lethality to the relatively mild dysmorphic features and developmental delay found in individuals with Smith-Lemli-Opitz syndrome. We report here the identification of mutations in sterol-C4-methyl oxidase–like gene (SC4MOL) as the cause of an autosomal recessive syndrome in a human patient with psoriasiform dermatitis, arthralgias, congenital cataracts, microcephaly, and developmental delay. This gene encodes a sterol-C4-methyl oxidase (SMO), which catalyzes demethylation of C4-methylsterols in the cholesterol synthesis pathway. C4-Methylsterols are meiosis-activating sterols (MASs). They exist at high concentrations in the testis and ovary and play roles in meiosis activation. In this study, we found that an accumulation of MASs in the patient led to cell overproliferation in both skin and blood. SMO deficiency also substantially altered immunocyte phenotype and in vitro function. MASs serve as ligands for liver X receptors α and β (LXRα and LXRβ), which are important in regulating not only lipid transport in the epidermis, but also innate and adaptive immunity. Deficiency of SMO represents a biochemical defect in the cholesterol synthesis pathway, the clinical spectrum of which remains to be defined. PMID:21285510

Basal cell carcinoma pathogenesis and therapy involving hedgehog signaling and beyond.

PubMed

Bakshi, Anshika; Chaudhary, Sandeep C; Rana, Mehtab; Elmets, Craig A; Athar, Mohammad

2017-12-01

Basal cell carcinoma (BCC) of the skin is driven by aberrant hedgehog signaling. Thus blocking this signaling pathway by small molecules such as vismodegib inhibits tumor growth. Primary cilium in the epidermal cells plays an integral role in the processing of hedgehog signaling-related proteins. Recent genomic studies point to the involvement of additional genetic mutations that might be associated with the development of BCCs, suggesting significance of other signaling pathways, such as WNT, NOTCH, mTOR, and Hippo, aside from hedgehog in the pathogenesis of this human neoplasm. Some of these pathways could be regulated by noncoding microRNA. Altered microRNA expression profile is recognized with the progression of these lesions. Stopping treatment with Smoothened (SMO) inhibitors often leads to tumor reoccurrence in the patients with basal cell nevus syndrome, who develop 10-100 of BCCs. In addition, the initial effectiveness of these SMO inhibitors is impaired due to the onset of mutations in the drug-binding domain of SMO. These data point to a need to develop strategies to overcome tumor recurrence and resistance and to enhance efficacy by developing novel single agent-based or multiple agents-based combinatorial approaches. Immunotherapy and photodynamic therapy could be additional successful approaches particularly if developed in combination with chemotherapy for inoperable and metastatic BCCs. © 2017 Wiley Periodicals, Inc.

Basal cell carcinoma pathogenesis and therapy involving hedgehog signaling and beyond

PubMed Central

Bakshi, Anshika; Chaudhary, Sandeep C.; Rana, Mehtab; Elmets, Craig A.; Athar, Mohammad

2018-01-01

Basal cell carcinoma (BCC) of the skin is driven by aberrant hedgehog signaling. Thus blocking this signaling pathway by small molecules such as vismodegib inhibits tumor growth. Primary cilium in the epidermal cells plays an integral role in the processing of hedgehog signaling-related proteins. Recent genomic studies point to the involvement of additional genetic mutations that might be associated with the development of BCCs, suggesting significance of other signaling pathways, such as WNT, NOTCH, mTOR, and Hippo, aside from hedgehog in the pathogenesis of this human neoplasm. Some of these pathways could be regulated by noncoding microRNA. Altered microRNA expression profile is recognized with the progression of these lesions. Stopping treatment with Smoothened (SMO) inhibitors often leads to tumor reoccurrence in the patients with basal cell nevus syndrome, who develop 10–100 of BCCs. In addition, the initial effectiveness of these SMO inhibitors is impaired due to the onset of mutations in the drug-binding domain of SMO. These data point to a need to develop strategies to overcome tumor recurrence and resistance and to enhance efficacy by developing novel single agent-based or multiple agents-based combinatorial approaches. Immunotherapy and photodynamic therapy could be additional successful approaches particularly if developed in combination with chemotherapy for inoperable and metastatic BCCs. PMID:28574612

Queue and stack sorting algorithm optimization and performance analysis

NASA Astrophysics Data System (ADS)

Qian, Mingzhu; Wang, Xiaobao

2018-04-01

Sorting algorithm is one of the basic operation of a variety of software development, in data structures course specializes in all kinds of sort algorithm. The performance of the sorting algorithm is directly related to the efficiency of the software. A lot of excellent scientific research queue is constantly optimizing algorithm, algorithm efficiency better as far as possible, the author here further research queue combined with stacks of sorting algorithms, the algorithm is mainly used for alternating operation queue and stack storage properties, Thus avoiding the need for a large number of exchange or mobile operations in the traditional sort. Before the existing basis to continue research, improvement and optimization, the focus on the optimization of the time complexity of the proposed optimization and improvement, The experimental results show that the improved effectively, at the same time and the time complexity and space complexity of the algorithm, the stability study corresponding research. The improvement and optimization algorithm, improves the practicability.

Research on particle swarm optimization algorithm based on optimal movement probability

NASA Astrophysics Data System (ADS)

Ma, Jianhong; Zhang, Han; He, Baofeng

2017-01-01

The particle swarm optimization algorithm to improve the control precision, and has great application value training neural network and fuzzy system control fields etc.The traditional particle swarm algorithm is used for the training of feed forward neural networks,the search efficiency is low, and easy to fall into local convergence.An improved particle swarm optimization algorithm is proposed based on error back propagation gradient descent. Particle swarm optimization for Solving Least Squares Problems to meme group, the particles in the fitness ranking, optimization problem of the overall consideration, the error back propagation gradient descent training BP neural network, particle to update the velocity and position according to their individual optimal and global optimization, make the particles more to the social optimal learning and less to its optimal learning, it can avoid the particles fall into local optimum, by using gradient information can accelerate the PSO local search ability, improve the multi beam particle swarm depth zero less trajectory information search efficiency, the realization of improved particle swarm optimization algorithm. Simulation results show that the algorithm in the initial stage of rapid convergence to the global optimal solution can be near to the global optimal solution and keep close to the trend, the algorithm has faster convergence speed and search performance in the same running time, it can improve the convergence speed of the algorithm, especially the later search efficiency.

Trajectory optimization of spacecraft high-thrust orbit transfer using a modified evolutionary algorithm

NASA Astrophysics Data System (ADS)

Shirazi, Abolfazl

2016-10-01

This article introduces a new method to optimize finite-burn orbital manoeuvres based on a modified evolutionary algorithm. Optimization is carried out based on conversion of the orbital manoeuvre into a parameter optimization problem by assigning inverse tangential functions to the changes in direction angles of the thrust vector. The problem is analysed using boundary delimitation in a common optimization algorithm. A method is introduced to achieve acceptable values for optimization variables using nonlinear simulation, which results in an enlarged convergence domain. The presented algorithm benefits from high optimality and fast convergence time. A numerical example of a three-dimensional optimal orbital transfer is presented and the accuracy of the proposed algorithm is shown.

The oxidation of alkylaryl sulfides and benzo[b]thiophenes by Escherichia coli cells expressing wild-type and engineered styrene monooxygenase from Pseudomonas putida CA-3.

PubMed

Nikodinovic-Runic, Jasmina; Coulombel, Lydie; Francuski, Djordje; Sharma, Narain D; Boyd, Derek R; Ferrall, Rory Moore O; O'Connor, Kevin E

2013-06-01

Nine different sulfur-containing compounds were biotransformed to the corresponding sulfoxides by Escherichia coli Bl21(DE3) cells expressing styrene monooxygenase (SMO) from Pseudomonas putida CA-3. Thioanisole was consumed at 83.3 μmoles min(-1) g cell dry weight(-1) resulting mainly in the formation of R-thioanisole sulfoxide with an enantiomeric excess (ee) value of 45 %. The rate of 2-methyl-, 2-chloro- and 2-bromo-thioanisole consumption was 2-fold lower than that of thioanisole. Surprisingly, the 2-methylthioanisole sulfoxide product had the opposite (S) configuration to that of the other 2-substituted thioanisole derivatives and had a higher ee value (84 %). The rate of oxidation of 4-substituted thioanisoles was higher than the corresponding 2-substituted substrates but the ee values of the products were consistently lower (10-23 %). The rate of benzo[b]thiophene and 2-methylbenzo[b]thiophene sulfoxidation was approximately 10-fold lower than that of thioanisole. The ee value of the benzo[b]thiophene sulfoxide could not be determined as the product racemized rapidly. E. coli cells expressing an engineered SMO (SMOeng R3-11) oxidised 2-substituted thioanisoles between 1.8- and 2.8-fold faster compared to cells expressing the wild-type enzyme. SMOeng R3-11 oxidised benzo[b]thiophene and 2-methylbenzo[b]thiophene 10.1 and 5.6 times faster that the wild-type enzyme. The stereospecificity of the reaction catalysed by SMOeng was unchanged from that of the wild type. Using the X-ray crystal structure of the P. putida S12 SMO, it was evident that the entrance of substrates into the SMO active site is limited by the binding pocket bottleneck formed by the side chains of Val-211 and Asn-46 carboxyamide group.

A Modified Particle Swarm Optimization Technique for Finding Optimal Designs for Mixture Models

PubMed Central

Wong, Weng Kee; Chen, Ray-Bing; Huang, Chien-Chih; Wang, Weichung

2015-01-01

Particle Swarm Optimization (PSO) is a meta-heuristic algorithm that has been shown to be successful in solving a wide variety of real and complicated optimization problems in engineering and computer science. This paper introduces a projection based PSO technique, named ProjPSO, to efficiently find different types of optimal designs, or nearly optimal designs, for mixture models with and without constraints on the components, and also for related models, like the log contrast models. We also compare the modified PSO performance with Fedorov's algorithm, a popular algorithm used to generate optimal designs, Cocktail algorithm, and the recent algorithm proposed by [1]. PMID:26091237

Convex optimization problem prototyping for image reconstruction in computed tomography with the Chambolle-Pock algorithm

PubMed Central

Sidky, Emil Y.; Jørgensen, Jakob H.; Pan, Xiaochuan

2012-01-01

The primal-dual optimization algorithm developed in Chambolle and Pock (CP), 2011 is applied to various convex optimization problems of interest in computed tomography (CT) image reconstruction. This algorithm allows for rapid prototyping of optimization problems for the purpose of designing iterative image reconstruction algorithms for CT. The primal-dual algorithm is briefly summarized in the article, and its potential for prototyping is demonstrated by explicitly deriving CP algorithm instances for many optimization problems relevant to CT. An example application modeling breast CT with low-intensity X-ray illumination is presented. PMID:22538474

Discrimination of Picea chihuahuana Martinez populations on the basis of climatic, edaphic, dendrometric, genetic and population traits

PubMed Central

Dominguez-Guerrero, Iliana Karina; del Rocío Mariscal-Lucero, Samantha; Hernández-Díaz, José Ciro; Heinze, Berthold; Prieto-Ruiz, José Ángel

2017-01-01

Background Picea chihuahuana, which is endemic to Mexico, is currently listed as “Endangered” on the Red List. Chihuahua spruce is only found in the Sierra Madre Occidental (SMO), Mexico. About 42,600 individuals are distributed in forty populations. These populations are fragmented and can be classified into three geographically distinct clusters in the SMO. The total area covered by P. chihuahuana populations is less than 300 ha. A recent study suggested assisted migration as an alternative to the ex situ conservation of P. chihuahuana, taking into consideration the genetic structure and diversity of the populations and the predictions regarding the future climate of the habitat. However, detailed background information is required to enable development of plans for protecting and conserving species and for successful assisted migration. Thus, it is important to identify differences between populations in relation to environmental conditions. The genetic diversity of populations, which affect vigor, evolution and adaptability of the species, must also be considered. In this study, we examined 14 populations of P. chihuahuana, with the overall aim of discriminating the populations and form clusters of this species. Methods Each population was represented by one 50 × 50 m plot established in the center of its respective location. Climate, soil, dasometric, density variables and genetic and species diversities were assessed in these plots for further analyses. The putatively neutral and adaptive AFLP markers were used to calculate genetic diversity. Affinity Propagation (AP) clustering technique and k-means clustering algorithm were used to classify the populations in the optimal number of clusters. Later stepwise binomial logistic regression was applied to test for significant differences in variables of the southern and northern P. chihuahuana populations. Spearman’s correlation test was used to analyze the relationships among all variables studied. Results The binomial logistic regression analysis revealed that seven climate variables, the geographical longitude and sand proportion in the soil separated the southern from northern populations. The northern populations grow in more arid and continental conditions and on soils with lower sand proportion. The mean genetic diversity using all AFLP studied of P. chihuahuana was significantly correlated with the mean temperature in the warmest month, where warmer temperatures are associated to larger genetic diversity. Genetic diversity of P. chihuahuana calculated with putatively adaptive AFLP was not statistically significantly correlated with any environmental factor. Discussion Future reforestation programs should take into account that at least two different groups (the northern and southern cluster) of P. chihuahuana exist, as local adaptation takes place because of different environmental conditions. PMID:28626616

Production scheduling with ant colony optimization

NASA Astrophysics Data System (ADS)

Chernigovskiy, A. S.; Kapulin, D. V.; Noskova, E. E.; Yamskikh, T. N.; Tsarev, R. Yu

2017-10-01

The optimum solution of the production scheduling problem for manufacturing processes at an enterprise is crucial as it allows one to obtain the required amount of production within a specified time frame. Optimum production schedule can be found using a variety of optimization algorithms or scheduling algorithms. Ant colony optimization is one of well-known techniques to solve the global multi-objective optimization problem. In the article, the authors present a solution of the production scheduling problem by means of an ant colony optimization algorithm. A case study of the algorithm efficiency estimated against some others production scheduling algorithms is presented. Advantages of the ant colony optimization algorithm and its beneficial effect on the manufacturing process are provided.

Near-Infrared Spectroscopic Measurements of Calf Muscle during Walking at Simulated Reduced Gravity - Preliminary Results

NASA Technical Reports Server (NTRS)

Ellerby, Gwenn E. C.; Lee, Stuart M. C.; Stroud, Leah; Norcross, Jason; Gernhardt, Michael; Soller, Babs R.

2008-01-01

Consideration for lunar and planetary exploration space suit design can be enhanced by investigating the physiologic responses of individual muscles during locomotion in reduced gravity. Near-infrared spectroscopy (NIRS) provides a non-invasive method to study the physiology of individual muscles in ambulatory subjects during reduced gravity simulations. PURPOSE: To investigate calf muscle oxygen saturation (SmO2) and pH during reduced gravity walking at varying treadmill inclines and added mass conditions using NIRS. METHODS: Four male subjects aged 42.3 +/- 1.7 years (mean +/- SE) and weighing 77.9 +/- 2.4 kg walked at a moderate speed (3.2 +/- 0.2 km/h) on a treadmill at inclines of 0, 10, 20, and 30%. Unsuited subjects were attached to a partial gravity simulator which unloaded the subject to simulate body weight plus the additional weight of a space suit (121 kg) in lunar gravity (0.17G). Masses of 0, 11, 23, and 34 kg were added to the subject and then unloaded to maintain constant weight. Spectra were collected from the lateral gastrocnemius (LG), and SmO2 and pH were calculated using previously published methods (Yang et al. 2007 Optics Express ; Soller et al. 2008 J Appl Physiol). The effects of incline and added mass on SmO2 and pH were analyzed through repeated measures ANOVA. RESULTS: SmO2 and pH were both unchanged by added mass (p>0.05), so data from trials at the same incline were averaged. LG SmO2 decreased significantly with increasing incline (p=0.003) from 61.1 +/- 2.0% at 0% incline to 48.7 +/- 2.6% at 30% incline, while pH was unchanged by incline (p=0.12). CONCLUSION: Increasing the incline (and thus work performed) during walking causes the LG to extract more oxygen from the blood supply, presumably to support the increased metabolic cost of uphill walking. The lack of an effect of incline on pH may indicate that, while the intensity of exercise has increased, the LG has not reached a level of work above the anaerobic threshold. In these preliminary studies, 30% incline walking at reduced gravity may not require anaerobic LG activity due to the low exercise intensity (42.8 +/- 1.6% of VO(sub 2max)). It is also possible that at reduced gravity additional work is being done by muscle groups other than the calf.

Optimization of Straight Cylindrical Turning Using Artificial Bee Colony (ABC) Algorithm

NASA Astrophysics Data System (ADS)

Prasanth, Rajanampalli Seshasai Srinivasa; Hans Raj, Kandikonda

2017-04-01

Artificial bee colony (ABC) algorithm, that mimics the intelligent foraging behavior of honey bees, is increasingly gaining acceptance in the field of process optimization, as it is capable of handling nonlinearity, complexity and uncertainty. Straight cylindrical turning is a complex and nonlinear machining process which involves the selection of appropriate cutting parameters that affect the quality of the workpiece. This paper presents the estimation of optimal cutting parameters of the straight cylindrical turning process using the ABC algorithm. The ABC algorithm is first tested on four benchmark problems of numerical optimization and its performance is compared with genetic algorithm (GA) and ant colony optimization (ACO) algorithm. Results indicate that, the rate of convergence of ABC algorithm is better than GA and ACO. Then, the ABC algorithm is used to predict optimal cutting parameters such as cutting speed, feed rate, depth of cut and tool nose radius to achieve good surface finish. Results indicate that, the ABC algorithm estimated a comparable surface finish when compared with real coded genetic algorithm and differential evolution algorithm.

Firefly Algorithm, Lévy Flights and Global Optimization

NASA Astrophysics Data System (ADS)

Yang, Xin-She

Nature-inspired algorithms such as Particle Swarm Optimization and Firefly Algorithm are among the most powerful algorithms for optimization. In this paper, we intend to formulate a new metaheuristic algorithm by combining Lévy flights with the search strategy via the Firefly Algorithm. Numerical studies and results suggest that the proposed Lévy-flight firefly algorithm is superior to existing metaheuristic algorithms. Finally implications for further research and wider applications will be discussed.

A new hybrid meta-heuristic algorithm for optimal design of large-scale dome structures

NASA Astrophysics Data System (ADS)

Kaveh, A.; Ilchi Ghazaan, M.

2018-02-01

In this article a hybrid algorithm based on a vibrating particles system (VPS) algorithm, multi-design variable configuration (Multi-DVC) cascade optimization, and an upper bound strategy (UBS) is presented for global optimization of large-scale dome truss structures. The new algorithm is called MDVC-UVPS in which the VPS algorithm acts as the main engine of the algorithm. The VPS algorithm is one of the most recent multi-agent meta-heuristic algorithms mimicking the mechanisms of damped free vibration of single degree of freedom systems. In order to handle a large number of variables, cascade sizing optimization utilizing a series of DVCs is used. Moreover, the UBS is utilized to reduce the computational time. Various dome truss examples are studied to demonstrate the effectiveness and robustness of the proposed method, as compared to some existing structural optimization techniques. The results indicate that the MDVC-UVPS technique is a powerful search and optimization method for optimizing structural engineering problems.

Machining Parameters Optimization using Hybrid Firefly Algorithm and Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Farahlina Johari, Nur; Zain, Azlan Mohd; Haszlinna Mustaffa, Noorfa; Udin, Amirmudin

2017-09-01

Firefly Algorithm (FA) is a metaheuristic algorithm that is inspired by the flashing behavior of fireflies and the phenomenon of bioluminescent communication and the algorithm is used to optimize the machining parameters (feed rate, depth of cut, and spindle speed) in this research. The algorithm is hybridized with Particle Swarm Optimization (PSO) to discover better solution in exploring the search space. Objective function of previous research is used to optimize the machining parameters in turning operation. The optimal machining cutting parameters estimated by FA that lead to a minimum surface roughness are validated using ANOVA test.

N-terminus determines activity and specificity of styrene monooxygenase reductases.

PubMed

Heine, Thomas; Scholtissek, Anika; Westphal, Adrie H; van Berkel, Willem J H; Tischler, Dirk

2017-12-01

Styrene monooxygenases (SMOs) are two-enzyme systems that catalyze the enantioselective epoxidation of styrene to (S)-styrene oxide. The FADH 2 co-substrate of the epoxidase component (StyA) is supplied by an NADH-dependent flavin reductase (StyB). The genome of Rhodococcus opacus 1CP encodes two SMO systems. One system, which we define as E1-type, displays homology to the SMO from Pseudomonas taiwanensis VLB120. The other system, originally reported as a fused system (RoStyA2B), is defined as E2-type. Here we found that E1-type RoStyB is inhibited by FMN, while RoStyA2B is known to be active with FMN. To rationalize the observed specificity of RoStyB for FAD, we generated an artificial reductase, designated as RoStyBart, in which the first 22 amino acid residues of RoStyB were joined to the reductase part of RoStyA2B, while the oxygenase part (A2) was removed. RoStyBart mainly purified as apo-protein and mimicked RoStyB in being inhibited by FMN. Pre-incubation with FAD yielded a turnover number at 30°C of 133.9±3.5s -1 , one of the highest rates observed for StyB reductases. RoStyBart holo-enzyme switches to a ping-pong mechanism and fluorescence analysis indicated for unproductive binding of FMN to the second (co-substrate) binding site. In summary, it is shown for the first time that optimization of the N-termini of StyB reductases allows the evolution of their activity and specificity. Copyright © 2017 Elsevier B.V. All rights reserved.

The coral reefs optimization algorithm: a novel metaheuristic for efficiently solving optimization problems.

PubMed

Salcedo-Sanz, S; Del Ser, J; Landa-Torres, I; Gil-López, S; Portilla-Figueras, J A

2014-01-01

This paper presents a novel bioinspired algorithm to tackle complex optimization problems: the coral reefs optimization (CRO) algorithm. The CRO algorithm artificially simulates a coral reef, where different corals (namely, solutions to the optimization problem considered) grow and reproduce in coral colonies, fighting by choking out other corals for space in the reef. This fight for space, along with the specific characteristics of the corals' reproduction, produces a robust metaheuristic algorithm shown to be powerful for solving hard optimization problems. In this research the CRO algorithm is tested in several continuous and discrete benchmark problems, as well as in practical application scenarios (i.e., optimum mobile network deployment and off-shore wind farm design). The obtained results confirm the excellent performance of the proposed algorithm and open line of research for further application of the algorithm to real-world problems.

Guided particle swarm optimization method to solve general nonlinear optimization problems

NASA Astrophysics Data System (ADS)

Abdelhalim, Alyaa; Nakata, Kazuhide; El-Alem, Mahmoud; Eltawil, Amr

2018-04-01

The development of hybrid algorithms is becoming an important topic in the global optimization research area. This article proposes a new technique in hybridizing the particle swarm optimization (PSO) algorithm and the Nelder-Mead (NM) simplex search algorithm to solve general nonlinear unconstrained optimization problems. Unlike traditional hybrid methods, the proposed method hybridizes the NM algorithm inside the PSO to improve the velocities and positions of the particles iteratively. The new hybridization considers the PSO algorithm and NM algorithm as one heuristic, not in a sequential or hierarchical manner. The NM algorithm is applied to improve the initial random solution of the PSO algorithm and iteratively in every step to improve the overall performance of the method. The performance of the proposed method was tested over 20 optimization test functions with varying dimensions. Comprehensive comparisons with other methods in the literature indicate that the proposed solution method is promising and competitive.

The Coral Reefs Optimization Algorithm: A Novel Metaheuristic for Efficiently Solving Optimization Problems

PubMed Central

Salcedo-Sanz, S.; Del Ser, J.; Landa-Torres, I.; Gil-López, S.; Portilla-Figueras, J. A.

2014-01-01

This paper presents a novel bioinspired algorithm to tackle complex optimization problems: the coral reefs optimization (CRO) algorithm. The CRO algorithm artificially simulates a coral reef, where different corals (namely, solutions to the optimization problem considered) grow and reproduce in coral colonies, fighting by choking out other corals for space in the reef. This fight for space, along with the specific characteristics of the corals' reproduction, produces a robust metaheuristic algorithm shown to be powerful for solving hard optimization problems. In this research the CRO algorithm is tested in several continuous and discrete benchmark problems, as well as in practical application scenarios (i.e., optimum mobile network deployment and off-shore wind farm design). The obtained results confirm the excellent performance of the proposed algorithm and open line of research for further application of the algorithm to real-world problems. PMID:25147860

Identifying typical physical activity on smartphone with varying positions and orientations.

PubMed

Miao, Fen; He, Yi; Liu, Jinlei; Li, Ye; Ayoola, Idowu

2015-04-13

Traditional activity recognition solutions are not widely applicable due to a high cost and inconvenience to use with numerous sensors. This paper aims to automatically recognize physical activity with the help of the built-in sensors of the widespread smartphone without any limitation of firm attachment to the human body. By introducing a method to judge whether the phone is in a pocket, we investigated the data collected from six positions of seven subjects, chose five signals that are insensitive to orientation for activity classification. Decision trees (J48), Naive Bayes and Sequential minimal optimization (SMO) were employed to recognize five activities: static, walking, running, walking upstairs and walking downstairs. The experimental results based on 8,097 activity data demonstrated that the J48 classifier produced the best performance with an average recognition accuracy of 89.6% during the three classifiers, and thus would serve as the optimal online classifier. The utilization of the built-in sensors of the smartphone to recognize typical physical activities without any limitation of firm attachment is feasible.

A Comparative Study of Optimization Algorithms for Engineering Synthesis.

DTIC Science & Technology

1983-03-01

the ADS program demonstrates the flexibility a design engineer would have in selecting an optimization algorithm best suited to solve a particular...demonstrates the flexibility a design engineer would have in selecting an optimization algorithm best suited to solve a particular problem. 4 TABLE OF...algorithm to suit a particular problem. The ADS library of design optimization algorithms was . developed by Vanderplaats in response to the first

Particle Swarm Optimization Toolbox

NASA Technical Reports Server (NTRS)

Grant, Michael J.

2010-01-01

The Particle Swarm Optimization Toolbox is a library of evolutionary optimization tools developed in the MATLAB environment. The algorithms contained in the library include a genetic algorithm (GA), a single-objective particle swarm optimizer (SOPSO), and a multi-objective particle swarm optimizer (MOPSO). Development focused on both the SOPSO and MOPSO. A GA was included mainly for comparison purposes, and the particle swarm optimizers appeared to perform better for a wide variety of optimization problems. All algorithms are capable of performing unconstrained and constrained optimization. The particle swarm optimizers are capable of performing single and multi-objective optimization. The SOPSO and MOPSO algorithms are based on swarming theory and bird-flocking patterns to search the trade space for the optimal solution or optimal trade in competing objectives. The MOPSO generates Pareto fronts for objectives that are in competition. A GA, based on Darwin evolutionary theory, is also included in the library. The GA consists of individuals that form a population in the design space. The population mates to form offspring at new locations in the design space. These offspring contain traits from both of the parents. The algorithm is based on this combination of traits from parents to hopefully provide an improved solution than either of the original parents. As the algorithm progresses, individuals that hold these optimal traits will emerge as the optimal solutions. Due to the generic design of all optimization algorithms, each algorithm interfaces with a user-supplied objective function. This function serves as a "black-box" to the optimizers in which the only purpose of this function is to evaluate solutions provided by the optimizers. Hence, the user-supplied function can be numerical simulations, analytical functions, etc., since the specific detail of this function is of no concern to the optimizer. These algorithms were originally developed to support entry trajectory and guidance design for the Mars Science Laboratory mission but may be applied to any optimization problem.

Nonlinear dynamics optimization with particle swarm and genetic algorithms for SPEAR3 emittance upgrade

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, Xiaobiao; Safranek, James

2014-09-01

Nonlinear dynamics optimization is carried out for a low emittance upgrade lattice of SPEAR3 in order to improve its dynamic aperture and Touschek lifetime. Two multi-objective optimization algorithms, a genetic algorithm and a particle swarm algorithm, are used for this study. The performance of the two algorithms are compared. The result shows that the particle swarm algorithm converges significantly faster to similar or better solutions than the genetic algorithm and it does not require seeding of good solutions in the initial population. These advantages of the particle swarm algorithm may make it more suitable for many accelerator optimization applications.

A fast optimization algorithm for multicriteria intensity modulated proton therapy planning.

PubMed

Chen, Wei; Craft, David; Madden, Thomas M; Zhang, Kewu; Kooy, Hanne M; Herman, Gabor T

2010-09-01

To describe a fast projection algorithm for optimizing intensity modulated proton therapy (IMPT) plans and to describe and demonstrate the use of this algorithm in multicriteria IMPT planning. The authors develop a projection-based solver for a class of convex optimization problems and apply it to IMPT treatment planning. The speed of the solver permits its use in multicriteria optimization, where several optimizations are performed which span the space of possible treatment plans. The authors describe a plan database generation procedure which is customized to the requirements of the solver. The optimality precision of the solver can be specified by the user. The authors apply the algorithm to three clinical cases: A pancreas case, an esophagus case, and a tumor along the rib cage case. Detailed analysis of the pancreas case shows that the algorithm is orders of magnitude faster than industry-standard general purpose algorithms (MOSEK'S interior point optimizer, primal simplex optimizer, and dual simplex optimizer). Additionally, the projection solver has almost no memory overhead. The speed and guaranteed accuracy of the algorithm make it suitable for use in multicriteria treatment planning, which requires the computation of several diverse treatment plans. Additionally, given the low memory overhead of the algorithm, the method can be extended to include multiple geometric instances and proton range possibilities, for robust optimization.

Multidisciplinary Optimization of a Transport Aircraft Wing using Particle Swarm Optimization

NASA Technical Reports Server (NTRS)

Sobieszczanski-Sobieski, Jaroslaw; Venter, Gerhard

2002-01-01

The purpose of this paper is to demonstrate the application of particle swarm optimization to a realistic multidisciplinary optimization test problem. The paper's new contributions to multidisciplinary optimization is the application of a new algorithm for dealing with the unique challenges associated with multidisciplinary optimization problems, and recommendations as to the utility of the algorithm in future multidisciplinary optimization applications. The selected example is a bi-level optimization problem that demonstrates severe numerical noise and has a combination of continuous and truly discrete design variables. The use of traditional gradient-based optimization algorithms is thus not practical. The numerical results presented indicate that the particle swarm optimization algorithm is able to reliably find the optimum design for the problem presented here. The algorithm is capable of dealing with the unique challenges posed by multidisciplinary optimization as well as the numerical noise and truly discrete variables present in the current example problem.

Ckmeans.1d.dp: Optimal k-means Clustering in One Dimension by Dynamic Programming.

PubMed

Wang, Haizhou; Song, Mingzhou

2011-12-01

The heuristic k -means algorithm, widely used for cluster analysis, does not guarantee optimality. We developed a dynamic programming algorithm for optimal one-dimensional clustering. The algorithm is implemented as an R package called Ckmeans.1d.dp . We demonstrate its advantage in optimality and runtime over the standard iterative k -means algorithm.

Flight plan optimization

NASA Astrophysics Data System (ADS)

Dharmaseelan, Anoop; Adistambha, Keyne D.

2015-05-01

Fuel cost accounts for 40 percent of the operating cost of an airline. Fuel cost can be minimized by planning a flight on optimized routes. The routes can be optimized by searching best connections based on the cost function defined by the airline. The most common algorithm that used to optimize route search is Dijkstra's. Dijkstra's algorithm produces a static result and the time taken for the search is relatively long. This paper experiments a new algorithm to optimize route search which combines the principle of simulated annealing and genetic algorithm. The experimental results of route search, presented are shown to be computationally fast and accurate compared with timings from generic algorithm. The new algorithm is optimal for random routing feature that is highly sought by many regional operators.

Improving 130nm node patterning using inverse lithography techniques for an analog process

NASA Astrophysics Data System (ADS)

Duan, Can; Jessen, Scott; Ziger, David; Watanabe, Mizuki; Prins, Steve; Ho, Chi-Chien; Shu, Jing

2018-03-01

Developing a new lithographic process routinely involves usage of lithographic toolsets and much engineering time to perform data analysis. Process transfers between fabs occur quite often. One of the key assumptions made is that lithographic settings are equivalent from one fab to another and that the transfer is fluid. In some cases, that is far from the truth. Differences in tools can change the proximity effect seen in low k1 imaging processes. If you use model based optical proximity correction (MBOPC), then a model built in one fab will not work under the same conditions at another fab. This results in many wafers being patterned to try and match a baseline response. Even if matching is achieved, there is no guarantee that optimal lithographic responses are met. In this paper, we discuss the approach used to transfer and develop new lithographic processes and define MBOPC builds for the new lithographic process in Fab B which was transferred from a similar lithographic process in Fab A. By using PROLITHTM simulations to match OPC models for each level, minimal downtime in wafer processing was observed. Source Mask Optimization (SMO) was also used to optimize lithographic processes using novel inverse lithography techniques (ILT) to simultaneously optimize mask bias, depth of focus (DOF), exposure latitude (EL) and mask error enhancement factor (MEEF) for critical designs for each level.

Cuckoo Search Algorithm Based on Repeat-Cycle Asymptotic Self-Learning and Self-Evolving Disturbance for Function Optimization

PubMed Central

Wang, Jie-sheng; Li, Shu-xia; Song, Jiang-di

2015-01-01

In order to improve convergence velocity and optimization accuracy of the cuckoo search (CS) algorithm for solving the function optimization problems, a new improved cuckoo search algorithm based on the repeat-cycle asymptotic self-learning and self-evolving disturbance (RC-SSCS) is proposed. A disturbance operation is added into the algorithm by constructing a disturbance factor to make a more careful and thorough search near the bird's nests location. In order to select a reasonable repeat-cycled disturbance number, a further study on the choice of disturbance times is made. Finally, six typical test functions are adopted to carry out simulation experiments, meanwhile, compare algorithms of this paper with two typical swarm intelligence algorithms particle swarm optimization (PSO) algorithm and artificial bee colony (ABC) algorithm. The results show that the improved cuckoo search algorithm has better convergence velocity and optimization accuracy. PMID:26366164

Modified artificial bee colony algorithm for reactive power optimization

NASA Astrophysics Data System (ADS)

Sulaiman, Noorazliza; Mohamad-Saleh, Junita; Abro, Abdul Ghani

2015-05-01

Bio-inspired algorithms (BIAs) implemented to solve various optimization problems have shown promising results which are very important in this severely complex real-world. Artificial Bee Colony (ABC) algorithm, a kind of BIAs has demonstrated tremendous results as compared to other optimization algorithms. This paper presents a new modified ABC algorithm referred to as JA-ABC3 with the aim to enhance convergence speed and avoid premature convergence. The proposed algorithm has been simulated on ten commonly used benchmarks functions. Its performance has also been compared with other existing ABC variants. To justify its robust applicability, the proposed algorithm has been tested to solve Reactive Power Optimization problem. The results have shown that the proposed algorithm has superior performance to other existing ABC variants e.g. GABC, BABC1, BABC2, BsfABC dan IABC in terms of convergence speed. Furthermore, the proposed algorithm has also demonstrated excellence performance in solving Reactive Power Optimization problem.

A new optimized GA-RBF neural network algorithm.

PubMed

Jia, Weikuan; Zhao, Dean; Shen, Tian; Su, Chunyang; Hu, Chanli; Zhao, Yuyan

2014-01-01

When confronting the complex problems, radial basis function (RBF) neural network has the advantages of adaptive and self-learning ability, but it is difficult to determine the number of hidden layer neurons, and the weights learning ability from hidden layer to the output layer is low; these deficiencies easily lead to decreasing learning ability and recognition precision. Aiming at this problem, we propose a new optimized RBF neural network algorithm based on genetic algorithm (GA-RBF algorithm), which uses genetic algorithm to optimize the weights and structure of RBF neural network; it chooses new ways of hybrid encoding and optimizing simultaneously. Using the binary encoding encodes the number of the hidden layer's neurons and using real encoding encodes the connection weights. Hidden layer neurons number and connection weights are optimized simultaneously in the new algorithm. However, the connection weights optimization is not complete; we need to use least mean square (LMS) algorithm for further leaning, and finally get a new algorithm model. Using two UCI standard data sets to test the new algorithm, the results show that the new algorithm improves the operating efficiency in dealing with complex problems and also improves the recognition precision, which proves that the new algorithm is valid.

Metaheuristic Optimization and its Applications in Earth Sciences

NASA Astrophysics Data System (ADS)

Yang, Xin-She

2010-05-01

A common but challenging task in modelling geophysical and geological processes is to handle massive data and to minimize certain objectives. This can essentially be considered as an optimization problem, and thus many new efficient metaheuristic optimization algorithms can be used. In this paper, we will introduce some modern metaheuristic optimization algorithms such as genetic algorithms, harmony search, firefly algorithm, particle swarm optimization and simulated annealing. We will also discuss how these algorithms can be applied to various applications in earth sciences, including nonlinear least-squares, support vector machine, Kriging, inverse finite element analysis, and data-mining. We will present a few examples to show how different problems can be reformulated as optimization. Finally, we will make some recommendations for choosing various algorithms to suit various problems. References 1) D. H. Wolpert and W. G. Macready, No free lunch theorems for optimization, IEEE Trans. Evolutionary Computation, Vol. 1, 67-82 (1997). 2) X. S. Yang, Nature-Inspired Metaheuristic Algorithms, Luniver Press, (2008). 3) X. S. Yang, Mathematical Modelling for Earth Sciences, Dunedin Academic Press, (2008).

VDLLA: A virtual daddy-long legs optimization

NASA Astrophysics Data System (ADS)

Yaakub, Abdul Razak; Ghathwan, Khalil I.

2016-08-01

Swarm intelligence is a strong optimization algorithm based on a biological behavior of insects or animals. The success of any optimization algorithm is depending on the balance between exploration and exploitation. In this paper, we present a new swarm intelligence algorithm, which is based on daddy long legs spider (VDLLA) as a new optimization algorithm with virtual behavior. In VDLLA, each agent (spider) has nine positions which represent the legs of spider and each position represent one solution. The proposed VDLLA is tested on four standard functions using average fitness, Medium fitness and standard deviation. The results of proposed VDLLA have been compared against Particle Swarm Optimization (PSO), Differential Evolution (DE) and Bat Inspired Algorithm (BA). Additionally, the T-Test has been conducted to show the significant deference between our proposed and other algorithms. VDLLA showed very promising results on benchmark test functions for unconstrained optimization problems and also significantly improved the original swarm algorithms.

A Novel Particle Swarm Optimization Algorithm for Global Optimization

PubMed Central

Wang, Chun-Feng; Liu, Kui

2016-01-01

Particle Swarm Optimization (PSO) is a recently developed optimization method, which has attracted interest of researchers in various areas due to its simplicity and effectiveness, and many variants have been proposed. In this paper, a novel Particle Swarm Optimization algorithm is presented, in which the information of the best neighbor of each particle and the best particle of the entire population in the current iteration is considered. Meanwhile, to avoid premature, an abandoned mechanism is used. Furthermore, for improving the global convergence speed of our algorithm, a chaotic search is adopted in the best solution of the current iteration. To verify the performance of our algorithm, standard test functions have been employed. The experimental results show that the algorithm is much more robust and efficient than some existing Particle Swarm Optimization algorithms. PMID:26955387

Analysis of parameter estimation and optimization application of ant colony algorithm in vehicle routing problem

NASA Astrophysics Data System (ADS)

Xu, Quan-Li; Cao, Yu-Wei; Yang, Kun

2018-03-01

Ant Colony Optimization (ACO) is the most widely used artificial intelligence algorithm at present. This study introduced the principle and mathematical model of ACO algorithm in solving Vehicle Routing Problem (VRP), and designed a vehicle routing optimization model based on ACO, then the vehicle routing optimization simulation system was developed by using c ++ programming language, and the sensitivity analyses, estimations and improvements of the three key parameters of ACO were carried out. The results indicated that the ACO algorithm designed in this paper can efficiently solve rational planning and optimization of VRP, and the different values of the key parameters have significant influence on the performance and optimization effects of the algorithm, and the improved algorithm is not easy to locally converge prematurely and has good robustness.

Optimization of High-Dimensional Functions through Hypercube Evaluation

PubMed Central

Abiyev, Rahib H.; Tunay, Mustafa

2015-01-01

A novel learning algorithm for solving global numerical optimization problems is proposed. The proposed learning algorithm is intense stochastic search method which is based on evaluation and optimization of a hypercube and is called the hypercube optimization (HO) algorithm. The HO algorithm comprises the initialization and evaluation process, displacement-shrink process, and searching space process. The initialization and evaluation process initializes initial solution and evaluates the solutions in given hypercube. The displacement-shrink process determines displacement and evaluates objective functions using new points, and the search area process determines next hypercube using certain rules and evaluates the new solutions. The algorithms for these processes have been designed and presented in the paper. The designed HO algorithm is tested on specific benchmark functions. The simulations of HO algorithm have been performed for optimization of functions of 1000-, 5000-, or even 10000 dimensions. The comparative simulation results with other approaches demonstrate that the proposed algorithm is a potential candidate for optimization of both low and high dimensional functions. PMID:26339237

Towards enhancement of performance of K-means clustering using nature-inspired optimization algorithms.

PubMed

Fong, Simon; Deb, Suash; Yang, Xin-She; Zhuang, Yan

2014-01-01

Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time. It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism for the sake of enhancing its clustering quality by avoiding local optima and finding global optima, the new hybrids are anticipated to produce unprecedented performance. In this paper, we report the results of our evaluation experiments on the integration of nature-inspired optimization methods into K-means algorithms. In addition to the standard evaluation metrics in evaluating clustering quality, the extended K-means algorithms that are empowered by nature-inspired optimization methods are applied on image segmentation as a case study of application scenario.

Towards Enhancement of Performance of K-Means Clustering Using Nature-Inspired Optimization Algorithms

PubMed Central

Deb, Suash; Yang, Xin-She

2014-01-01

Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time. It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism for the sake of enhancing its clustering quality by avoiding local optima and finding global optima, the new hybrids are anticipated to produce unprecedented performance. In this paper, we report the results of our evaluation experiments on the integration of nature-inspired optimization methods into K-means algorithms. In addition to the standard evaluation metrics in evaluating clustering quality, the extended K-means algorithms that are empowered by nature-inspired optimization methods are applied on image segmentation as a case study of application scenario. PMID:25202730

Optimal Battery Sizing in Photovoltaic Based Distributed Generation Using Enhanced Opposition-Based Firefly Algorithm for Voltage Rise Mitigation

PubMed Central

Wong, Ling Ai; Shareef, Hussain; Mohamed, Azah; Ibrahim, Ahmad Asrul

2014-01-01

This paper presents the application of enhanced opposition-based firefly algorithm in obtaining the optimal battery energy storage systems (BESS) sizing in photovoltaic generation integrated radial distribution network in order to mitigate the voltage rise problem. Initially, the performance of the original firefly algorithm is enhanced by utilizing the opposition-based learning and introducing inertia weight. After evaluating the performance of the enhanced opposition-based firefly algorithm (EOFA) with fifteen benchmark functions, it is then adopted to determine the optimal size for BESS. Two optimization processes are conducted where the first optimization aims to obtain the optimal battery output power on hourly basis and the second optimization aims to obtain the optimal BESS capacity by considering the state of charge constraint of BESS. The effectiveness of the proposed method is validated by applying the algorithm to the 69-bus distribution system and by comparing the performance of EOFA with conventional firefly algorithm and gravitational search algorithm. Results show that EOFA has the best performance comparatively in terms of mitigating the voltage rise problem. PMID:25054184

Optimal battery sizing in photovoltaic based distributed generation using enhanced opposition-based firefly algorithm for voltage rise mitigation.

PubMed

Wong, Ling Ai; Shareef, Hussain; Mohamed, Azah; Ibrahim, Ahmad Asrul

2014-01-01

This paper presents the application of enhanced opposition-based firefly algorithm in obtaining the optimal battery energy storage systems (BESS) sizing in photovoltaic generation integrated radial distribution network in order to mitigate the voltage rise problem. Initially, the performance of the original firefly algorithm is enhanced by utilizing the opposition-based learning and introducing inertia weight. After evaluating the performance of the enhanced opposition-based firefly algorithm (EOFA) with fifteen benchmark functions, it is then adopted to determine the optimal size for BESS. Two optimization processes are conducted where the first optimization aims to obtain the optimal battery output power on hourly basis and the second optimization aims to obtain the optimal BESS capacity by considering the state of charge constraint of BESS. The effectiveness of the proposed method is validated by applying the algorithm to the 69-bus distribution system and by comparing the performance of EOFA with conventional firefly algorithm and gravitational search algorithm. Results show that EOFA has the best performance comparatively in terms of mitigating the voltage rise problem.

Multiple sequence alignment using multi-objective based bacterial foraging optimization algorithm.

PubMed

Rani, R Ranjani; Ramyachitra, D

2016-12-01

Multiple sequence alignment (MSA) is a widespread approach in computational biology and bioinformatics. MSA deals with how the sequences of nucleotides and amino acids are sequenced with possible alignment and minimum number of gaps between them, which directs to the functional, evolutionary and structural relationships among the sequences. Still the computation of MSA is a challenging task to provide an efficient accuracy and statistically significant results of alignments. In this work, the Bacterial Foraging Optimization Algorithm was employed to align the biological sequences which resulted in a non-dominated optimal solution. It employs Multi-objective, such as: Maximization of Similarity, Non-gap percentage, Conserved blocks and Minimization of gap penalty. BAliBASE 3.0 benchmark database was utilized to examine the proposed algorithm against other methods In this paper, two algorithms have been proposed: Hybrid Genetic Algorithm with Artificial Bee Colony (GA-ABC) and Bacterial Foraging Optimization Algorithm. It was found that Hybrid Genetic Algorithm with Artificial Bee Colony performed better than the existing optimization algorithms. But still the conserved blocks were not obtained using GA-ABC. Then BFO was used for the alignment and the conserved blocks were obtained. The proposed Multi-Objective Bacterial Foraging Optimization Algorithm (MO-BFO) was compared with widely used MSA methods Clustal Omega, Kalign, MUSCLE, MAFFT, Genetic Algorithm (GA), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Hybrid Genetic Algorithm with Artificial Bee Colony (GA-ABC). The final results show that the proposed MO-BFO algorithm yields better alignment than most widely used methods. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Use of the Hotelling observer to optimize image reconstruction in digital breast tomosynthesis

PubMed Central

Sánchez, Adrian A.; Sidky, Emil Y.; Pan, Xiaochuan

2015-01-01

Abstract. We propose an implementation of the Hotelling observer that can be applied to the optimization of linear image reconstruction algorithms in digital breast tomosynthesis. The method is based on considering information within a specific region of interest, and it is applied to the optimization of algorithms for detectability of microcalcifications. Several linear algorithms are considered: simple back-projection, filtered back-projection, back-projection filtration, and Λ-tomography. The optimized algorithms are then evaluated through the reconstruction of phantom data. The method appears robust across algorithms and parameters and leads to the generation of algorithm implementations which subjectively appear optimized for the task of interest. PMID:26702408

Portfolio optimization by using linear programing models based on genetic algorithm

NASA Astrophysics Data System (ADS)

Sukono; Hidayat, Y.; Lesmana, E.; Putra, A. S.; Napitupulu, H.; Supian, S.

2018-01-01

In this paper, we discussed the investment portfolio optimization using linear programming model based on genetic algorithms. It is assumed that the portfolio risk is measured by absolute standard deviation, and each investor has a risk tolerance on the investment portfolio. To complete the investment portfolio optimization problem, the issue is arranged into a linear programming model. Furthermore, determination of the optimum solution for linear programming is done by using a genetic algorithm. As a numerical illustration, we analyze some of the stocks traded on the capital market in Indonesia. Based on the analysis, it is shown that the portfolio optimization performed by genetic algorithm approach produces more optimal efficient portfolio, compared to the portfolio optimization performed by a linear programming algorithm approach. Therefore, genetic algorithms can be considered as an alternative on determining the investment portfolio optimization, particularly using linear programming models.

A Comprehensive Review of Swarm Optimization Algorithms

PubMed Central

2015-01-01

Many swarm optimization algorithms have been introduced since the early 60’s, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms. Selected algorithms are briefly explained and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantages and disadvantages are also discussed. A number of statistical tests are then carried out to determine the significant performances. The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered approaches. PMID:25992655

Severe maternal morbidity and near miss due to postpartum hemorrhage in a national multicenter surveillance study.

PubMed

Rocha Filho, Edilberto A; Costa, Maria L; Cecatti, Jose G; Parpinelli, Mary A; Haddad, Samira M; Pacagnella, Rodolfo C; Sousa, Maria H; Melo, Elias F; Surita, Fernanda G; Souza, Joao P

2015-02-01

To assess the occurrence of severe maternal complications owing to postpartum hemorrhage (PPH) and its associated factors. A secondary analysis of data from a multicenter cross-sectional prospective surveillance study included 9555 cases of severe maternal morbidity at 27 centers in Brazil between July 2009 and June 2010. Complications of PPH, conditions of severity management, and sociodemographic and obstetric characteristics were assessed. Factors independently associated with severe maternal outcome (SMO) were identified using multiple regression analysis. Overall, 1192 (12.5%) of the 9555 women experienced complications owing to PPH (981 had potentially life-threatening conditions, 181 maternal near miss, and 30 had died). The SMO ratio was 2.6 per 1000 live births among women with PPH and 8.5 per 1000 live births among women with other complications. Women with PPH had a higher risk of blood transfusion and return to the operating theater than did those with complications from other causes. Maternal age, length of pregnancy, previous uterine scar, and cesarean delivery were the main factors associated with an increased risk of SMO secondary to PPH. PPH frequently leads to severe maternal morbidity. A surveillance system can identify the main causes of morbidity and could help to improve care, especially among women identified as being at high risk of PPH. Copyright © 2014 International Federation of Gynecology and Obstetrics. Published by Elsevier Ireland Ltd. All rights reserved.

Privacy Preservation in Distributed Subgradient Optimization Algorithms.

PubMed

Lou, Youcheng; Yu, Lean; Wang, Shouyang; Yi, Peng

2017-07-31

In this paper, some privacy-preserving features for distributed subgradient optimization algorithms are considered. Most of the existing distributed algorithms focus mainly on the algorithm design and convergence analysis, but not the protection of agents' privacy. Privacy is becoming an increasingly important issue in applications involving sensitive information. In this paper, we first show that the distributed subgradient synchronous homogeneous-stepsize algorithm is not privacy preserving in the sense that the malicious agent can asymptotically discover other agents' subgradients by transmitting untrue estimates to its neighbors. Then a distributed subgradient asynchronous heterogeneous-stepsize projection algorithm is proposed and accordingly its convergence and optimality is established. In contrast to the synchronous homogeneous-stepsize algorithm, in the new algorithm agents make their optimization updates asynchronously with heterogeneous stepsizes. The introduced two mechanisms of projection operation and asynchronous heterogeneous-stepsize optimization can guarantee that agents' privacy can be effectively protected.

Particle swarm optimization: an alternative in marine propeller optimization?

NASA Astrophysics Data System (ADS)

Vesting, F.; Bensow, R. E.

2018-01-01

This article deals with improving and evaluating the performance of two evolutionary algorithm approaches for automated engineering design optimization. Here a marine propeller design with constraints on cavitation nuisance is the intended application. For this purpose, the particle swarm optimization (PSO) algorithm is adapted for multi-objective optimization and constraint handling for use in propeller design. Three PSO algorithms are developed and tested for the optimization of four commercial propeller designs for different ship types. The results are evaluated by interrogating the generation medians and the Pareto front development. The same propellers are also optimized utilizing the well established NSGA-II genetic algorithm to provide benchmark results. The authors' PSO algorithms deliver comparable results to NSGA-II, but converge earlier and enhance the solution in terms of constraints violation.

Segmentation of MRI Brain Images with an Improved Harmony Searching Algorithm.

PubMed

Yang, Zhang; Shufan, Ye; Li, Guo; Weifeng, Ding

2016-01-01

The harmony searching (HS) algorithm is a kind of optimization search algorithm currently applied in many practical problems. The HS algorithm constantly revises variables in the harmony database and the probability of different values that can be used to complete iteration convergence to achieve the optimal effect. Accordingly, this study proposed a modified algorithm to improve the efficiency of the algorithm. First, a rough set algorithm was employed to improve the convergence and accuracy of the HS algorithm. Then, the optimal value was obtained using the improved HS algorithm. The optimal value of convergence was employed as the initial value of the fuzzy clustering algorithm for segmenting magnetic resonance imaging (MRI) brain images. Experimental results showed that the improved HS algorithm attained better convergence and more accurate results than those of the original HS algorithm. In our study, the MRI image segmentation effect of the improved algorithm was superior to that of the original fuzzy clustering method.

Segmentation of MRI Brain Images with an Improved Harmony Searching Algorithm

PubMed Central

Yang, Zhang; Li, Guo; Weifeng, Ding

2016-01-01

The harmony searching (HS) algorithm is a kind of optimization search algorithm currently applied in many practical problems. The HS algorithm constantly revises variables in the harmony database and the probability of different values that can be used to complete iteration convergence to achieve the optimal effect. Accordingly, this study proposed a modified algorithm to improve the efficiency of the algorithm. First, a rough set algorithm was employed to improve the convergence and accuracy of the HS algorithm. Then, the optimal value was obtained using the improved HS algorithm. The optimal value of convergence was employed as the initial value of the fuzzy clustering algorithm for segmenting magnetic resonance imaging (MRI) brain images. Experimental results showed that the improved HS algorithm attained better convergence and more accurate results than those of the original HS algorithm. In our study, the MRI image segmentation effect of the improved algorithm was superior to that of the original fuzzy clustering method. PMID:27403428

Optimal design of low-density SNP arrays for genomic prediction: algorithm and applications

USDA-ARS?s Scientific Manuscript database

Low-density (LD) single nucleotide polymorphism (SNP) arrays provide a cost-effective solution for genomic prediction and selection, but algorithms and computational tools are needed for their optimal design. A multiple-objective, local optimization (MOLO) algorithm was developed for design of optim...

Skull removal in MR images using a modified artificial bee colony optimization algorithm.

PubMed

Taherdangkoo, Mohammad

2014-01-01

Removal of the skull from brain Magnetic Resonance (MR) images is an important preprocessing step required for other image analysis techniques such as brain tissue segmentation. In this paper, we propose a new algorithm based on the Artificial Bee Colony (ABC) optimization algorithm to remove the skull region from brain MR images. We modify the ABC algorithm using a different strategy for initializing the coordinates of scout bees and their direction of search. Moreover, we impose an additional constraint to the ABC algorithm to avoid the creation of discontinuous regions. We found that our algorithm successfully removed all bony skull from a sample of de-identified MR brain images acquired from different model scanners. The obtained results of the proposed algorithm compared with those of previously introduced well known optimization algorithms such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) demonstrate the superior results and computational performance of our algorithm, suggesting its potential for clinical applications.

Hybrid algorithms for fuzzy reverse supply chain network design.

PubMed

Che, Z H; Chiang, Tzu-An; Kuo, Y C; Cui, Zhihua

2014-01-01

In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA), Genetic Algorithm-Simulated Annealing (GA-SA), and Particle Swarm Optimization-Simulated Annealing (PSO-SA) for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods.

Hybrid Algorithms for Fuzzy Reverse Supply Chain Network Design

PubMed Central

Che, Z. H.; Chiang, Tzu-An; Kuo, Y. C.

2014-01-01

In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA), Genetic Algorithm-Simulated Annealing (GA-SA), and Particle Swarm Optimization-Simulated Annealing (PSO-SA) for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods. PMID:24892057

Hybrid cryptosystem RSA - CRT optimization and VMPC

NASA Astrophysics Data System (ADS)

Rahmadani, R.; Mawengkang, H.; Sutarman

2018-03-01

Hybrid cryptosystem combines symmetric algorithms and asymmetric algorithms. This combination utilizes speeds on encryption/decryption processes of symmetric algorithms and asymmetric algorithms to secure symmetric keys. In this paper we propose hybrid cryptosystem that combine symmetric algorithms VMPC and asymmetric algorithms RSA - CRT optimization. RSA - CRT optimization speeds up the decryption process by obtaining plaintext with dp and p key only, so there is no need to perform CRT processes. The VMPC algorithm is more efficient in software implementation and reduces known weaknesses in RC4 key generation. The results show hybrid cryptosystem RSA - CRT optimization and VMPC is faster than hybrid cryptosystem RSA - VMPC and hybrid cryptosystem RSA - CRT - VMPC. Keyword : Cryptography, RSA, RSA - CRT, VMPC, Hybrid Cryptosystem.

Evaluation of hybrid inverse planning and optimization (HIPO) algorithm for optimization in real-time, high-dose-rate (HDR) brachytherapy for prostate.

PubMed

Pokharel, Shyam; Rana, Suresh; Blikenstaff, Joseph; Sadeghi, Amir; Prestidge, Bradley

2013-07-08

The purpose of this study is to investigate the effectiveness of the HIPO planning and optimization algorithm for real-time prostate HDR brachytherapy. This study consists of 20 patients who underwent ultrasound-based real-time HDR brachytherapy of the prostate using the treatment planning system called Oncentra Prostate (SWIFT version 3.0). The treatment plans for all patients were optimized using inverse dose-volume histogram-based optimization followed by graphical optimization (GRO) in real time. The GRO is manual manipulation of isodose lines slice by slice. The quality of the plan heavily depends on planner expertise and experience. The data for all patients were retrieved later, and treatment plans were created and optimized using HIPO algorithm with the same set of dose constraints, number of catheters, and set of contours as in the real-time optimization algorithm. The HIPO algorithm is a hybrid because it combines both stochastic and deterministic algorithms. The stochastic algorithm, called simulated annealing, searches the optimal catheter distributions for a given set of dose objectives. The deterministic algorithm, called dose-volume histogram-based optimization (DVHO), optimizes three-dimensional dose distribution quickly by moving straight downhill once it is in the advantageous region of the search space given by the stochastic algorithm. The PTV receiving 100% of the prescription dose (V100) was 97.56% and 95.38% with GRO and HIPO, respectively. The mean dose (D(mean)) and minimum dose to 10% volume (D10) for the urethra, rectum, and bladder were all statistically lower with HIPO compared to GRO using the student pair t-test at 5% significance level. HIPO can provide treatment plans with comparable target coverage to that of GRO with a reduction in dose to the critical structures.

Improved hybrid optimization algorithm for 3D protein structure prediction.

PubMed

Zhou, Changjun; Hou, Caixia; Wei, Xiaopeng; Zhang, Qiang

2014-07-01

A new improved hybrid optimization algorithm - PGATS algorithm, which is based on toy off-lattice model, is presented for dealing with three-dimensional protein structure prediction problems. The algorithm combines the particle swarm optimization (PSO), genetic algorithm (GA), and tabu search (TS) algorithms. Otherwise, we also take some different improved strategies. The factor of stochastic disturbance is joined in the particle swarm optimization to improve the search ability; the operations of crossover and mutation that are in the genetic algorithm are changed to a kind of random liner method; at last tabu search algorithm is improved by appending a mutation operator. Through the combination of a variety of strategies and algorithms, the protein structure prediction (PSP) in a 3D off-lattice model is achieved. The PSP problem is an NP-hard problem, but the problem can be attributed to a global optimization problem of multi-extremum and multi-parameters. This is the theoretical principle of the hybrid optimization algorithm that is proposed in this paper. The algorithm combines local search and global search, which overcomes the shortcoming of a single algorithm, giving full play to the advantage of each algorithm. In the current universal standard sequences, Fibonacci sequences and real protein sequences are certified. Experiments show that the proposed new method outperforms single algorithms on the accuracy of calculating the protein sequence energy value, which is proved to be an effective way to predict the structure of proteins.

Optimal Golomb Ruler Sequences Generation for Optical WDM Systems: A Novel Parallel Hybrid Multi-objective Bat Algorithm

NASA Astrophysics Data System (ADS)

Bansal, Shonak; Singh, Arun Kumar; Gupta, Neena

2017-02-01

In real-life, multi-objective engineering design problems are very tough and time consuming optimization problems due to their high degree of nonlinearities, complexities and inhomogeneity. Nature-inspired based multi-objective optimization algorithms are now becoming popular for solving multi-objective engineering design problems. This paper proposes original multi-objective Bat algorithm (MOBA) and its extended form, namely, novel parallel hybrid multi-objective Bat algorithm (PHMOBA) to generate shortest length Golomb ruler called optimal Golomb ruler (OGR) sequences at a reasonable computation time. The OGRs found their application in optical wavelength division multiplexing (WDM) systems as channel-allocation algorithm to reduce the four-wave mixing (FWM) crosstalk. The performances of both the proposed algorithms to generate OGRs as optical WDM channel-allocation is compared with other existing classical computing and nature-inspired algorithms, including extended quadratic congruence (EQC), search algorithm (SA), genetic algorithms (GAs), biogeography based optimization (BBO) and big bang-big crunch (BB-BC) optimization algorithms. Simulations conclude that the proposed parallel hybrid multi-objective Bat algorithm works efficiently as compared to original multi-objective Bat algorithm and other existing algorithms to generate OGRs for optical WDM systems. The algorithm PHMOBA to generate OGRs, has higher convergence and success rate than original MOBA. The efficiency improvement of proposed PHMOBA to generate OGRs up to 20-marks, in terms of ruler length and total optical channel bandwidth (TBW) is 100 %, whereas for original MOBA is 85 %. Finally the implications for further research are also discussed.

Multiobjective optimization of temporal processes.

PubMed

Song, Zhe; Kusiak, Andrew

2010-06-01

This paper presents a dynamic predictive-optimization framework of a nonlinear temporal process. Data-mining (DM) and evolutionary strategy algorithms are integrated in the framework for solving the optimization model. DM algorithms learn dynamic equations from the process data. An evolutionary strategy algorithm is then applied to solve the optimization problem guided by the knowledge extracted by the DM algorithm. The concept presented in this paper is illustrated with the data from a power plant, where the goal is to maximize the boiler efficiency and minimize the limestone consumption. This multiobjective optimization problem can be either transformed into a single-objective optimization problem through preference aggregation approaches or into a Pareto-optimal optimization problem. The computational results have shown the effectiveness of the proposed optimization framework.

Training Recurrent Neural Networks With the Levenberg-Marquardt Algorithm for Optimal Control of a Grid-Connected Converter.

PubMed

Fu, Xingang; Li, Shuhui; Fairbank, Michael; Wunsch, Donald C; Alonso, Eduardo

2015-09-01

This paper investigates how to train a recurrent neural network (RNN) using the Levenberg-Marquardt (LM) algorithm as well as how to implement optimal control of a grid-connected converter (GCC) using an RNN. To successfully and efficiently train an RNN using the LM algorithm, a new forward accumulation through time (FATT) algorithm is proposed to calculate the Jacobian matrix required by the LM algorithm. This paper explores how to incorporate FATT into the LM algorithm. The results show that the combination of the LM and FATT algorithms trains RNNs better than the conventional backpropagation through time algorithm. This paper presents an analytical study on the optimal control of GCCs, including theoretically ideal optimal and suboptimal controllers. To overcome the inapplicability of the optimal GCC controller under practical conditions, a new RNN controller with an improved input structure is proposed to approximate the ideal optimal controller. The performance of an ideal optimal controller and a well-trained RNN controller was compared in close to real-life power converter switching environments, demonstrating that the proposed RNN controller can achieve close to ideal optimal control performance even under low sampling rate conditions. The excellent performance of the proposed RNN controller under challenging and distorted system conditions further indicates the feasibility of using an RNN to approximate optimal control in practical applications.

Chaos Quantum-Behaved Cat Swarm Optimization Algorithm and Its Application in the PV MPPT

PubMed Central

2017-01-01

Cat Swarm Optimization (CSO) algorithm was put forward in 2006. Despite a faster convergence speed compared with Particle Swarm Optimization (PSO) algorithm, the application of CSO is greatly limited by the drawback of “premature convergence,” that is, the possibility of trapping in local optimum when dealing with nonlinear optimization problem with a large number of local extreme values. In order to surmount the shortcomings of CSO, Chaos Quantum-behaved Cat Swarm Optimization (CQCSO) algorithm is proposed in this paper. Firstly, Quantum-behaved Cat Swarm Optimization (QCSO) algorithm improves the accuracy of the CSO algorithm, because it is easy to fall into the local optimum in the later stage. Chaos Quantum-behaved Cat Swarm Optimization (CQCSO) algorithm is proposed by introducing tent map for jumping out of local optimum in this paper. Secondly, CQCSO has been applied in the simulation of five different test functions, showing higher accuracy and less time consumption than CSO and QCSO. Finally, photovoltaic MPPT model and experimental platform are established and global maximum power point tracking control strategy is achieved by CQCSO algorithm, the effectiveness and efficiency of which have been verified by both simulation and experiment. PMID:29181020

Chaos Quantum-Behaved Cat Swarm Optimization Algorithm and Its Application in the PV MPPT.

PubMed

Nie, Xiaohua; Wang, Wei; Nie, Haoyao

2017-01-01

Cat Swarm Optimization (CSO) algorithm was put forward in 2006. Despite a faster convergence speed compared with Particle Swarm Optimization (PSO) algorithm, the application of CSO is greatly limited by the drawback of "premature convergence," that is, the possibility of trapping in local optimum when dealing with nonlinear optimization problem with a large number of local extreme values. In order to surmount the shortcomings of CSO, Chaos Quantum-behaved Cat Swarm Optimization (CQCSO) algorithm is proposed in this paper. Firstly, Quantum-behaved Cat Swarm Optimization (QCSO) algorithm improves the accuracy of the CSO algorithm, because it is easy to fall into the local optimum in the later stage. Chaos Quantum-behaved Cat Swarm Optimization (CQCSO) algorithm is proposed by introducing tent map for jumping out of local optimum in this paper. Secondly, CQCSO has been applied in the simulation of five different test functions, showing higher accuracy and less time consumption than CSO and QCSO. Finally, photovoltaic MPPT model and experimental platform are established and global maximum power point tracking control strategy is achieved by CQCSO algorithm, the effectiveness and efficiency of which have been verified by both simulation and experiment.

New Dandelion Algorithm Optimizes Extreme Learning Machine for Biomedical Classification Problems

PubMed Central

Li, Xiguang; Zhao, Liang; Gong, Changqing; Liu, Xiaojing

2017-01-01

Inspired by the behavior of dandelion sowing, a new novel swarm intelligence algorithm, namely, dandelion algorithm (DA), is proposed for global optimization of complex functions in this paper. In DA, the dandelion population will be divided into two subpopulations, and different subpopulations will undergo different sowing behaviors. Moreover, another sowing method is designed to jump out of local optimum. In order to demonstrate the validation of DA, we compare the proposed algorithm with other existing algorithms, including bat algorithm, particle swarm optimization, and enhanced fireworks algorithm. Simulations show that the proposed algorithm seems much superior to other algorithms. At the same time, the proposed algorithm can be applied to optimize extreme learning machine (ELM) for biomedical classification problems, and the effect is considerable. At last, we use different fusion methods to form different fusion classifiers, and the fusion classifiers can achieve higher accuracy and better stability to some extent. PMID:29085425

Improved mine blast algorithm for optimal cost design of water distribution systems

NASA Astrophysics Data System (ADS)

Sadollah, Ali; Guen Yoo, Do; Kim, Joong Hoon

2015-12-01

The design of water distribution systems is a large class of combinatorial, nonlinear optimization problems with complex constraints such as conservation of mass and energy equations. Since feasible solutions are often extremely complex, traditional optimization techniques are insufficient. Recently, metaheuristic algorithms have been applied to this class of problems because they are highly efficient. In this article, a recently developed optimizer called the mine blast algorithm (MBA) is considered. The MBA is improved and coupled with the hydraulic simulator EPANET to find the optimal cost design for water distribution systems. The performance of the improved mine blast algorithm (IMBA) is demonstrated using the well-known Hanoi, New York tunnels and Balerma benchmark networks. Optimization results obtained using IMBA are compared to those using MBA and other optimizers in terms of their minimum construction costs and convergence rates. For the complex Balerma network, IMBA offers the cheapest network design compared to other optimization algorithms.

Linear antenna array optimization using flower pollination algorithm.

PubMed

Saxena, Prerna; Kothari, Ashwin

2016-01-01

Flower pollination algorithm (FPA) is a new nature-inspired evolutionary algorithm used to solve multi-objective optimization problems. The aim of this paper is to introduce FPA to the electromagnetics and antenna community for the optimization of linear antenna arrays. FPA is applied for the first time to linear array so as to obtain optimized antenna positions in order to achieve an array pattern with minimum side lobe level along with placement of deep nulls in desired directions. Various design examples are presented that illustrate the use of FPA for linear antenna array optimization, and subsequently the results are validated by benchmarking along with results obtained using other state-of-the-art, nature-inspired evolutionary algorithms such as particle swarm optimization, ant colony optimization and cat swarm optimization. The results suggest that in most cases, FPA outperforms the other evolutionary algorithms and at times it yields a similar performance.

A new chaotic multi-verse optimization algorithm for solving engineering optimization problems

NASA Astrophysics Data System (ADS)

Sayed, Gehad Ismail; Darwish, Ashraf; Hassanien, Aboul Ella

2018-03-01

Multi-verse optimization algorithm (MVO) is one of the recent meta-heuristic optimization algorithms. The main inspiration of this algorithm came from multi-verse theory in physics. However, MVO like most optimization algorithms suffers from low convergence rate and entrapment in local optima. In this paper, a new chaotic multi-verse optimization algorithm (CMVO) is proposed to overcome these problems. The proposed CMVO is applied on 13 benchmark functions and 7 well-known design problems in the engineering and mechanical field; namely, three-bar trust, speed reduce design, pressure vessel problem, spring design, welded beam, rolling element-bearing and multiple disc clutch brake. In the current study, a modified feasible-based mechanism is employed to handle constraints. In this mechanism, four rules were used to handle the specific constraint problem through maintaining a balance between feasible and infeasible solutions. Moreover, 10 well-known chaotic maps are used to improve the performance of MVO. The experimental results showed that CMVO outperforms other meta-heuristic optimization algorithms on most of the optimization problems. Also, the results reveal that sine chaotic map is the most appropriate map to significantly boost MVO's performance.

Speed and convergence properties of gradient algorithms for optimization of IMRT.

PubMed

Zhang, Xiaodong; Liu, Helen; Wang, Xiaochun; Dong, Lei; Wu, Qiuwen; Mohan, Radhe

2004-05-01

Gradient algorithms are the most commonly employed search methods in the routine optimization of IMRT plans. It is well known that local minima can exist for dose-volume-based and biology-based objective functions. The purpose of this paper is to compare the relative speed of different gradient algorithms, to investigate the strategies for accelerating the optimization process, to assess the validity of these strategies, and to study the convergence properties of these algorithms for dose-volume and biological objective functions. With these aims in mind, we implemented Newton's, conjugate gradient (CG), and the steepest decent (SD) algorithms for dose-volume- and EUD-based objective functions. Our implementation of Newton's algorithm approximates the second derivative matrix (Hessian) by its diagonal. The standard SD algorithm and the CG algorithm with "line minimization" were also implemented. In addition, we investigated the use of a variation of the CG algorithm, called the "scaled conjugate gradient" (SCG) algorithm. To accelerate the optimization process, we investigated the validity of the use of a "hybrid optimization" strategy, in which approximations to calculated dose distributions are used during most of the iterations. Published studies have indicated that getting trapped in local minima is not a significant problem. To investigate this issue further, we first obtained, by trial and error, and starting with uniform intensity distributions, the parameters of the dose-volume- or EUD-based objective functions which produced IMRT plans that satisfied the clinical requirements. Using the resulting optimized intensity distributions as the initial guess, we investigated the possibility of getting trapped in a local minimum. For most of the results presented, we used a lung cancer case. To illustrate the generality of our methods, the results for a prostate case are also presented. For both dose-volume and EUD based objective functions, Newton's method far outperforms other algorithms in terms of speed. The SCG algorithm, which avoids expensive "line minimization," can speed up the standard CG algorithm by at least a factor of 2. For the same initial conditions, all algorithms converge essentially to the same plan. However, we demonstrate that for any of the algorithms studied, starting with previously optimized intensity distributions as the initial guess but for different objective function parameters, the solution frequently gets trapped in local minima. We found that the initial intensity distribution obtained from IMRT optimization utilizing objective function parameters, which favor a specific anatomic structure, would lead to a local minimum corresponding to that structure. Our results indicate that from among the gradient algorithms tested, Newton's method appears to be the fastest by far. Different gradient algorithms have the same convergence properties for dose-volume- and EUD-based objective functions. The hybrid dose calculation strategy is valid and can significantly accelerate the optimization process. The degree of acceleration achieved depends on the type of optimization problem being addressed (e.g., IMRT optimization, intensity modulated beam configuration optimization, or objective function parameter optimization). Under special conditions, gradient algorithms will get trapped in local minima, and reoptimization, starting with the results of previous optimization, will lead to solutions that are generally not significantly different from the local minimum.

A Library of Optimization Algorithms for Organizational Design

DTIC Science & Technology

2005-01-01

N00014-98-1-0465 and #N00014-00-1-0101 A Library of Optimization Algorithms for Organizational Design Georgiy M. Levchuk Yuri N. Levchuk Jie Luo...E-mail: Krishna@engr.uconn.edu Abstract This paper presents a library of algorithms to solve a broad range of optimization problems arising in the...normative design of organizations to execute a specific mission. The use of specific optimization algorithms for different phases of the design process

Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization

NASA Technical Reports Server (NTRS)

Holst, Terry L.

2004-01-01

A genetic algorithm approach suitable for solving multi-objective optimization problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.

First-order convex feasibility algorithms for x-ray CT

PubMed Central

Sidky, Emil Y.; Jørgensen, Jakob S.; Pan, Xiaochuan

2013-01-01

Purpose: Iterative image reconstruction (IIR) algorithms in computed tomography (CT) are based on algorithms for solving a particular optimization problem. Design of the IIR algorithm, therefore, is aided by knowledge of the solution to the optimization problem on which it is based. Often times, however, it is impractical to achieve accurate solution to the optimization of interest, which complicates design of IIR algorithms. This issue is particularly acute for CT with a limited angular-range scan, which leads to poorly conditioned system matrices and difficult to solve optimization problems. In this paper, we develop IIR algorithms which solve a certain type of optimization called convex feasibility. The convex feasibility approach can provide alternatives to unconstrained optimization approaches and at the same time allow for rapidly convergent algorithms for their solution—thereby facilitating the IIR algorithm design process. Methods: An accelerated version of the Chambolle−Pock (CP) algorithm is adapted to various convex feasibility problems of potential interest to IIR in CT. One of the proposed problems is seen to be equivalent to least-squares minimization, and two other problems provide alternatives to penalized, least-squares minimization. Results: The accelerated CP algorithms are demonstrated on a simulation of circular fan-beam CT with a limited scanning arc of 144°. The CP algorithms are seen in the empirical results to converge to the solution of their respective convex feasibility problems. Conclusions: Formulation of convex feasibility problems can provide a useful alternative to unconstrained optimization when designing IIR algorithms for CT. The approach is amenable to recent methods for accelerating first-order algorithms which may be particularly useful for CT with limited angular-range scanning. The present paper demonstrates the methodology, and future work will illustrate its utility in actual CT application. PMID:23464295

A hybrid Jaya algorithm for reliability-redundancy allocation problems

NASA Astrophysics Data System (ADS)

Ghavidel, Sahand; Azizivahed, Ali; Li, Li

2018-04-01

This article proposes an efficient improved hybrid Jaya algorithm based on time-varying acceleration coefficients (TVACs) and the learning phase introduced in teaching-learning-based optimization (TLBO), named the LJaya-TVAC algorithm, for solving various types of nonlinear mixed-integer reliability-redundancy allocation problems (RRAPs) and standard real-parameter test functions. RRAPs include series, series-parallel, complex (bridge) and overspeed protection systems. The search power of the proposed LJaya-TVAC algorithm for finding the optimal solutions is first tested on the standard real-parameter unimodal and multi-modal functions with dimensions of 30-100, and then tested on various types of nonlinear mixed-integer RRAPs. The results are compared with the original Jaya algorithm and the best results reported in the recent literature. The optimal results obtained with the proposed LJaya-TVAC algorithm provide evidence for its better and acceptable optimization performance compared to the original Jaya algorithm and other reported optimal results.

New human-centered linear and nonlinear motion cueing algorithms for control of simulator motion systems

NASA Astrophysics Data System (ADS)

Telban, Robert J.

While the performance of flight simulator motion system hardware has advanced substantially, the development of the motion cueing algorithm, the software that transforms simulated aircraft dynamics into realizable motion commands, has not kept pace. To address this, new human-centered motion cueing algorithms were developed. A revised "optimal algorithm" uses time-invariant filters developed by optimal control, incorporating human vestibular system models. The "nonlinear algorithm" is a novel approach that is also formulated by optimal control, but can also be updated in real time. It incorporates a new integrated visual-vestibular perception model that includes both visual and vestibular sensation and the interaction between the stimuli. A time-varying control law requires the matrix Riccati equation to be solved in real time by a neurocomputing approach. Preliminary pilot testing resulted in the optimal algorithm incorporating a new otolith model, producing improved motion cues. The nonlinear algorithm vertical mode produced a motion cue with a time-varying washout, sustaining small cues for longer durations and washing out large cues more quickly compared to the optimal algorithm. The inclusion of the integrated perception model improved the responses to longitudinal and lateral cues. False cues observed with the NASA adaptive algorithm were absent. As a result of unsatisfactory sensation, an augmented turbulence cue was added to the vertical mode for both the optimal and nonlinear algorithms. The relative effectiveness of the algorithms, in simulating aircraft maneuvers, was assessed with an eleven-subject piloted performance test conducted on the NASA Langley Visual Motion Simulator (VMS). Two methods, the quasi-objective NASA Task Load Index (TLX), and power spectral density analysis of pilot control, were used to assess pilot workload. TLX analysis reveals, in most cases, less workload and variation among pilots with the nonlinear algorithm. Control input analysis shows pilot-induced oscillations on a straight-in approach are less prevalent compared to the optimal algorithm. The augmented turbulence cues increased workload on an offset approach that the pilots deemed more realistic compared to the NASA adaptive algorithm. The takeoff with engine failure showed the least roll activity for the nonlinear algorithm, with the least rudder pedal activity for the optimal algorithm.

Using Animal Instincts to Design Efficient Biomedical Studies via Particle Swarm Optimization.

PubMed

Qiu, Jiaheng; Chen, Ray-Bing; Wang, Weichung; Wong, Weng Kee

2014-10-01

Particle swarm optimization (PSO) is an increasingly popular metaheuristic algorithm for solving complex optimization problems. Its popularity is due to its repeated successes in finding an optimum or a near optimal solution for problems in many applied disciplines. The algorithm makes no assumption of the function to be optimized and for biomedical experiments like those presented here, PSO typically finds the optimal solutions in a few seconds of CPU time on a garden-variety laptop. We apply PSO to find various types of optimal designs for several problems in the biological sciences and compare PSO performance relative to the differential evolution algorithm, another popular metaheuristic algorithm in the engineering literature.

A global optimization algorithm inspired in the behavior of selfish herds.

PubMed

Fausto, Fernando; Cuevas, Erik; Valdivia, Arturo; González, Adrián

2017-10-01

In this paper, a novel swarm optimization algorithm called the Selfish Herd Optimizer (SHO) is proposed for solving global optimization problems. SHO is based on the simulation of the widely observed selfish herd behavior manifested by individuals within a herd of animals subjected to some form of predation risk. In SHO, individuals emulate the predatory interactions between groups of prey and predators by two types of search agents: the members of a selfish herd (the prey) and a pack of hungry predators. Depending on their classification as either a prey or a predator, each individual is conducted by a set of unique evolutionary operators inspired by such prey-predator relationship. These unique traits allow SHO to improve the balance between exploration and exploitation without altering the population size. To illustrate the proficiency and robustness of the proposed method, it is compared to other well-known evolutionary optimization approaches such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Firefly Algorithm (FA), Differential Evolution (DE), Genetic Algorithms (GA), Crow Search Algorithm (CSA), Dragonfly Algorithm (DA), Moth-flame Optimization Algorithm (MOA) and Sine Cosine Algorithm (SCA). The comparison examines several standard benchmark functions, commonly considered within the literature of evolutionary algorithms. The experimental results show the remarkable performance of our proposed approach against those of the other compared methods, and as such SHO is proven to be an excellent alternative to solve global optimization problems. Copyright © 2017 Elsevier B.V. All rights reserved.

Optimal Budget Allocation for Sample Average Approximation

DTIC Science & Technology

2011-06-01

an optimization algorithm applied to the sample average problem. We examine the convergence rate of the estimator as the computing budget tends to...regime for the optimization algorithm . 1 Introduction Sample average approximation (SAA) is a frequently used approach to solving stochastic programs...appealing due to its simplicity and the fact that a large number of standard optimization algorithms are often available to optimize the resulting sample

Techniques for shuttle trajectory optimization

NASA Technical Reports Server (NTRS)

Edge, E. R.; Shieh, C. J.; Powers, W. F.

1973-01-01

The application of recently developed function-space Davidon-type techniques to the shuttle ascent trajectory optimization problem is discussed along with an investigation of the recently developed PRAXIS algorithm for parameter optimization. At the outset of this analysis, the major deficiency of the function-space algorithms was their potential storage problems. Since most previous analyses of the methods were with relatively low-dimension problems, no storage problems were encountered. However, in shuttle trajectory optimization, storage is a problem, and this problem was handled efficiently. Topics discussed include: the shuttle ascent model and the development of the particular optimization equations; the function-space algorithms; the operation of the algorithm and typical simulations; variable final-time problem considerations; and a modification of Powell's algorithm.

Blood pressure and calf muscle oxygen extraction during plantar flexion exercise in peripheral artery disease.

PubMed

Luck, J Carter; Miller, Amanda J; Aziz, Faisal; Radtka, John F; Proctor, David N; Leuenberger, Urs A; Sinoway, Lawrence I; Muller, Matthew D

2017-07-01

Peripheral artery disease (PAD) is an atherosclerotic vascular disease that affects 200 million people worldwide. Although PAD primarily affects large arteries, it is also associated with microvascular dysfunction, an exaggerated blood pressure (BP) response to exercise, and high cardiovascular mortality. We hypothesized that fatiguing plantar flexion exercise that evokes claudication elicits a greater reduction in skeletal muscle oxygenation (SmO 2 ) and a higher rise in BP in PAD compared with age-matched healthy subjects, but low-intensity steady-state plantar flexion elicits similar responses between groups. In the first experiment, eight patients with PAD and eight healthy controls performed fatiguing plantar flexion exercise (from 0.5 to 7 kg for up to 14 min). In the second experiment, seven patients with PAD and seven healthy controls performed low-intensity plantar flexion exercise (2.0 kg for 14 min). BP, heart rate (HR), and SmO 2 were measured continuously using near-infrared spectroscopy (NIRS). SmO 2 is the ratio of oxygenated hemoglobin to total hemoglobin, expressed as a percent. At fatigue, patients with PAD had a greater increase in mean arterial BP (18 ± 2 vs. vs. 10 ± 2 mmHg, P = 0.029) and HR (14 ± 2 vs. 6 ± 2 beats/min, P = 0.033) and a greater reduction in SmO 2 (-54 ± 10 vs. -12 ± 4%, P = 0.001). However, both groups had similar physiological responses to low-intensity, nonpainful plantar flexion exercise. These data suggest that patients with PAD have altered oxygen uptake and/or utilization during fatiguing exercise coincident with an augmented BP response. NEW & NOTEWORTHY In this laboratory study, patients with peripheral artery disease performed plantar flexion exercise in the supine posture until symptoms of claudication occurred. Relative to age- and sex-matched healthy subjects we found that patients had a higher blood pressure response, a higher heart rate response, and a greater reduction in skeletal muscle oxygenation as determined by near-infrared spectroscopy. Our data suggest that muscle ischemia contributes to the augmented exercise pressor reflex in peripheral artery disease. Copyright © 2017 the American Physiological Society.

Optimal design of the rotor geometry of line-start permanent magnet synchronous motor using the bat algorithm

NASA Astrophysics Data System (ADS)

Knypiński, Łukasz

2017-12-01

In this paper an algorithm for the optimization of excitation system of line-start permanent magnet synchronous motors will be presented. For the basis of this algorithm, software was developed in the Borland Delphi environment. The software consists of two independent modules: an optimization solver, and a module including the mathematical model of a synchronous motor with a self-start ability. The optimization module contains the bat algorithm procedure. The mathematical model of the motor has been developed in an Ansys Maxwell environment. In order to determine the functional parameters of the motor, additional scripts in Visual Basic language were developed. Selected results of the optimization calculation are presented and compared with results for the particle swarm optimization algorithm.

Developing a Shuffled Complex-Self Adaptive Hybrid Evolution (SC-SAHEL) Framework for Water Resources Management and Water-Energy System Optimization

NASA Astrophysics Data System (ADS)

Rahnamay Naeini, M.; Sadegh, M.; AghaKouchak, A.; Hsu, K. L.; Sorooshian, S.; Yang, T.

2017-12-01

Meta-Heuristic optimization algorithms have gained a great deal of attention in a wide variety of fields. Simplicity and flexibility of these algorithms, along with their robustness, make them attractive tools for solving optimization problems. Different optimization methods, however, hold algorithm-specific strengths and limitations. Performance of each individual algorithm obeys the "No-Free-Lunch" theorem, which means a single algorithm cannot consistently outperform all possible optimization problems over a variety of problems. From users' perspective, it is a tedious process to compare, validate, and select the best-performing algorithm for a specific problem or a set of test cases. In this study, we introduce a new hybrid optimization framework, entitled Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL), which combines the strengths of different evolutionary algorithms (EAs) in a parallel computing scheme, and allows users to select the most suitable algorithm tailored to the problem at hand. The concept of SC-SAHEL is to execute different EAs as separate parallel search cores, and let all participating EAs to compete during the course of the search. The newly developed SC-SAHEL algorithm is designed to automatically select, the best performing algorithm for the given optimization problem. This algorithm is rigorously effective in finding the global optimum for several strenuous benchmark test functions, and computationally efficient as compared to individual EAs. We benchmark the proposed SC-SAHEL algorithm over 29 conceptual test functions, and two real-world case studies - one hydropower reservoir model and one hydrological model (SAC-SMA). Results show that the proposed framework outperforms individual EAs in an absolute majority of the test problems, and can provide competitive results to the fittest EA algorithm with more comprehensive information during the search. The proposed framework is also flexible for merging additional EAs, boundary-handling techniques, and sampling schemes, and has good potential to be used in Water-Energy system optimal operation and management.

Load Frequency Control of AC Microgrid Interconnected Thermal Power System

NASA Astrophysics Data System (ADS)

Lal, Deepak Kumar; Barisal, Ajit Kumar

2017-08-01

In this paper, a microgrid (MG) power generation system is interconnected with a single area reheat thermal power system for load frequency control study. A new meta-heuristic optimization algorithm i.e. Moth-Flame Optimization (MFO) algorithm is applied to evaluate optimal gains of the fuzzy based proportional, integral and derivative (PID) controllers. The system dynamic performance is studied by comparing the results with MFO optimized classical PI/PID controllers. Also the system performance is investigated with fuzzy PID controller optimized by recently developed grey wolf optimizer (GWO) algorithm, which has proven its superiority over other previously developed algorithm in many interconnected power systems.

Direct adaptive performance optimization of subsonic transports: A periodic perturbation technique

NASA Technical Reports Server (NTRS)

Espana, Martin D.; Gilyard, Glenn

1995-01-01

Aircraft performance can be optimized at the flight condition by using available redundancy among actuators. Effective use of this potential allows improved performance beyond limits imposed by design compromises. Optimization based on nominal models does not result in the best performance of the actual aircraft at the actual flight condition. An adaptive algorithm for optimizing performance parameters, such as speed or fuel flow, in flight based exclusively on flight data is proposed. The algorithm is inherently insensitive to model inaccuracies and measurement noise and biases and can optimize several decision variables at the same time. An adaptive constraint controller integrated into the algorithm regulates the optimization constraints, such as altitude or speed, without requiring and prior knowledge of the autopilot design. The algorithm has a modular structure which allows easy incorporation (or removal) of optimization constraints or decision variables to the optimization problem. An important part of the contribution is the development of analytical tools enabling convergence analysis of the algorithm and the establishment of simple design rules. The fuel-flow minimization and velocity maximization modes of the algorithm are demonstrated on the NASA Dryden B-720 nonlinear flight simulator for the single- and multi-effector optimization cases.

An Effective Hybrid Routing Algorithm in WSN: Ant Colony Optimization in combination with Hop Count Minimization.

PubMed

Jiang, Ailian; Zheng, Lihong

2018-03-29

Low cost, high reliability and easy maintenance are key criteria in the design of routing protocols for wireless sensor networks (WSNs). This paper investigates the existing ant colony optimization (ACO)-based WSN routing algorithms and the minimum hop count WSN routing algorithms by reviewing their strengths and weaknesses. We also consider the critical factors of WSNs, such as energy constraint of sensor nodes, network load balancing and dynamic network topology. Then we propose a hybrid routing algorithm that integrates ACO and a minimum hop count scheme. The proposed algorithm is able to find the optimal routing path with minimal total energy consumption and balanced energy consumption on each node. The algorithm has unique superiority in terms of searching for the optimal path, balancing the network load and the network topology maintenance. The WSN model and the proposed algorithm have been implemented using C++. Extensive simulation experimental results have shown that our algorithm outperforms several other WSN routing algorithms on such aspects that include the rate of convergence, the success rate in searching for global optimal solution, and the network lifetime.

An Effective Hybrid Routing Algorithm in WSN: Ant Colony Optimization in combination with Hop Count Minimization

PubMed Central

2018-01-01

Low cost, high reliability and easy maintenance are key criteria in the design of routing protocols for wireless sensor networks (WSNs). This paper investigates the existing ant colony optimization (ACO)-based WSN routing algorithms and the minimum hop count WSN routing algorithms by reviewing their strengths and weaknesses. We also consider the critical factors of WSNs, such as energy constraint of sensor nodes, network load balancing and dynamic network topology. Then we propose a hybrid routing algorithm that integrates ACO and a minimum hop count scheme. The proposed algorithm is able to find the optimal routing path with minimal total energy consumption and balanced energy consumption on each node. The algorithm has unique superiority in terms of searching for the optimal path, balancing the network load and the network topology maintenance. The WSN model and the proposed algorithm have been implemented using C++. Extensive simulation experimental results have shown that our algorithm outperforms several other WSN routing algorithms on such aspects that include the rate of convergence, the success rate in searching for global optimal solution, and the network lifetime. PMID:29596336

An Improved DINEOF Algorithm for Filling Missing Values in Spatio-Temporal Sea Surface Temperature Data.

PubMed

Ping, Bo; Su, Fenzhen; Meng, Yunshan

2016-01-01

In this study, an improved Data INterpolating Empirical Orthogonal Functions (DINEOF) algorithm for determination of missing values in a spatio-temporal dataset is presented. Compared with the ordinary DINEOF algorithm, the iterative reconstruction procedure until convergence based on every fixed EOF to determine the optimal EOF mode is not necessary and the convergence criterion is only reached once in the improved DINEOF algorithm. Moreover, in the ordinary DINEOF algorithm, after optimal EOF mode determination, the initial matrix with missing data will be iteratively reconstructed based on the optimal EOF mode until the reconstruction is convergent. However, the optimal EOF mode may be not the best EOF for some reconstructed matrices generated in the intermediate steps. Hence, instead of using asingle EOF to fill in the missing data, in the improved algorithm, the optimal EOFs for reconstruction are variable (because the optimal EOFs are variable, the improved algorithm is called VE-DINEOF algorithm in this study). To validate the accuracy of the VE-DINEOF algorithm, a sea surface temperature (SST) data set is reconstructed by using the DINEOF, I-DINEOF (proposed in 2015) and VE-DINEOF algorithms. Four parameters (Pearson correlation coefficient, signal-to-noise ratio, root-mean-square error, and mean absolute difference) are used as a measure of reconstructed accuracy. Compared with the DINEOF and I-DINEOF algorithms, the VE-DINEOF algorithm can significantly enhance the accuracy of reconstruction and shorten the computational time.

From Determinism and Probability to Chaos: Chaotic Evolution towards Philosophy and Methodology of Chaotic Optimization

PubMed Central

2015-01-01

We present and discuss philosophy and methodology of chaotic evolution that is theoretically supported by chaos theory. We introduce four chaotic systems, that is, logistic map, tent map, Gaussian map, and Hénon map, in a well-designed chaotic evolution algorithm framework to implement several chaotic evolution (CE) algorithms. By comparing our previous proposed CE algorithm with logistic map and two canonical differential evolution (DE) algorithms, we analyse and discuss optimization performance of CE algorithm. An investigation on the relationship between optimization capability of CE algorithm and distribution characteristic of chaotic system is conducted and analysed. From evaluation result, we find that distribution of chaotic system is an essential factor to influence optimization performance of CE algorithm. We propose a new interactive EC (IEC) algorithm, interactive chaotic evolution (ICE) that replaces fitness function with a real human in CE algorithm framework. There is a paired comparison-based mechanism behind CE search scheme in nature. A simulation experimental evaluation is conducted with a pseudo-IEC user to evaluate our proposed ICE algorithm. The evaluation result indicates that ICE algorithm can obtain a significant better performance than or the same performance as interactive DE. Some open topics on CE, ICE, fusion of these optimization techniques, algorithmic notation, and others are presented and discussed. PMID:25879067

From determinism and probability to chaos: chaotic evolution towards philosophy and methodology of chaotic optimization.

PubMed

Pei, Yan

2015-01-01

We present and discuss philosophy and methodology of chaotic evolution that is theoretically supported by chaos theory. We introduce four chaotic systems, that is, logistic map, tent map, Gaussian map, and Hénon map, in a well-designed chaotic evolution algorithm framework to implement several chaotic evolution (CE) algorithms. By comparing our previous proposed CE algorithm with logistic map and two canonical differential evolution (DE) algorithms, we analyse and discuss optimization performance of CE algorithm. An investigation on the relationship between optimization capability of CE algorithm and distribution characteristic of chaotic system is conducted and analysed. From evaluation result, we find that distribution of chaotic system is an essential factor to influence optimization performance of CE algorithm. We propose a new interactive EC (IEC) algorithm, interactive chaotic evolution (ICE) that replaces fitness function with a real human in CE algorithm framework. There is a paired comparison-based mechanism behind CE search scheme in nature. A simulation experimental evaluation is conducted with a pseudo-IEC user to evaluate our proposed ICE algorithm. The evaluation result indicates that ICE algorithm can obtain a significant better performance than or the same performance as interactive DE. Some open topics on CE, ICE, fusion of these optimization techniques, algorithmic notation, and others are presented and discussed.

Optimizing the Shunting Schedule of Electric Multiple Units Depot Using an Enhanced Particle Swarm Optimization Algorithm

PubMed Central

Jin, Junchen

2016-01-01

The shunting schedule of electric multiple units depot (SSED) is one of the essential plans for high-speed train maintenance activities. This paper presents a 0-1 programming model to address the problem of determining an optimal SSED through automatic computing. The objective of the model is to minimize the number of shunting movements and the constraints include track occupation conflicts, shunting routes conflicts, time durations of maintenance processes, and shunting running time. An enhanced particle swarm optimization (EPSO) algorithm is proposed to solve the optimization problem. Finally, an empirical study from Shanghai South EMU Depot is carried out to illustrate the model and EPSO algorithm. The optimization results indicate that the proposed method is valid for the SSED problem and that the EPSO algorithm outperforms the traditional PSO algorithm on the aspect of optimality. PMID:27436998

Investigations of quantum heuristics for optimization

NASA Astrophysics Data System (ADS)

Rieffel, Eleanor; Hadfield, Stuart; Jiang, Zhang; Mandra, Salvatore; Venturelli, Davide; Wang, Zhihui

We explore the design of quantum heuristics for optimization, focusing on the quantum approximate optimization algorithm, a metaheuristic developed by Farhi, Goldstone, and Gutmann. We develop specific instantiations of the of quantum approximate optimization algorithm for a variety of challenging combinatorial optimization problems. Through theoretical analyses and numeric investigations of select problems, we provide insight into parameter setting and Hamiltonian design for quantum approximate optimization algorithms and related quantum heuristics, and into their implementation on hardware realizable in the near term.

Optimization of multi-objective micro-grid based on improved particle swarm optimization algorithm

NASA Astrophysics Data System (ADS)

Zhang, Jian; Gan, Yang

2018-04-01

The paper presents a multi-objective optimal configuration model for independent micro-grid with the aim of economy and environmental protection. The Pareto solution set can be obtained by solving the multi-objective optimization configuration model of micro-grid with the improved particle swarm algorithm. The feasibility of the improved particle swarm optimization algorithm for multi-objective model is verified, which provides an important reference for multi-objective optimization of independent micro-grid.

Multiobjective Optimization Using a Pareto Differential Evolution Approach

NASA Technical Reports Server (NTRS)

Madavan, Nateri K.; Biegel, Bryan A. (Technical Monitor)

2002-01-01

Differential Evolution is a simple, fast, and robust evolutionary algorithm that has proven effective in determining the global optimum for several difficult single-objective optimization problems. In this paper, the Differential Evolution algorithm is extended to multiobjective optimization problems by using a Pareto-based approach. The algorithm performs well when applied to several test optimization problems from the literature.

Enhanced Particle Swarm Optimization Algorithm: Efficient Training of ReaxFF Reactive Force Fields.

PubMed

Furman, David; Carmeli, Benny; Zeiri, Yehuda; Kosloff, Ronnie

2018-06-12

Particle swarm optimization (PSO) is a powerful metaheuristic population-based global optimization algorithm. However, when it is applied to nonseparable objective functions, its performance on multimodal landscapes is significantly degraded. Here we show that a significant improvement in the search quality and efficiency on multimodal functions can be achieved by enhancing the basic rotation-invariant PSO algorithm with isotropic Gaussian mutation operators. The new algorithm demonstrates superior performance across several nonlinear, multimodal benchmark functions compared with the rotation-invariant PSO algorithm and the well-established simulated annealing and sequential one-parameter parabolic interpolation methods. A search for the optimal set of parameters for the dispersion interaction model in the ReaxFF- lg reactive force field was carried out with respect to accurate DFT-TS calculations. The resulting optimized force field accurately describes the equations of state of several high-energy molecular crystals where such interactions are of crucial importance. The improved algorithm also presents better performance compared to a genetic algorithm optimization method in the optimization of the parameters of a ReaxFF- lg correction model. The computational framework is implemented in a stand-alone C++ code that allows the straightforward development of ReaxFF reactive force fields.

Optimal Doppler centroid estimation for SAR data from a quasi-homogeneous source

NASA Technical Reports Server (NTRS)

Jin, M. Y.

1986-01-01

This correspondence briefly describes two Doppler centroid estimation (DCE) algorithms, provides a performance summary for these algorithms, and presents the experimental results. These algorithms include that of Li et al. (1985) and a newly developed one that is optimized for quasi-homogeneous sources. The performance enhancement achieved by the optimal DCE algorithm is clearly demonstrated by the experimental results.

Hybrid Nested Partitions and Math Programming Framework for Large-scale Combinatorial Optimization

DTIC Science & Technology

2010-03-31

optimization problems: 1) exact algorithms and 2) metaheuristic algorithms . This project will integrate concepts from these two technologies to develop...optimal solutions within an acceptable amount of computation time, and 2) metaheuristic algorithms such as genetic algorithms , tabu search, and the...integer programming decomposition approaches, such as Dantzig Wolfe decomposition and Lagrangian relaxation, and metaheuristics such as the Nested

Hull Form Design and Optimization Tool Development

DTIC Science & Technology

2012-07-01

global minimum. The algorithm accomplishes this by using a method known as metaheuristics which allows the algorithm to examine a large area by...further development of these tools including the implementation and testing of a new optimization algorithm , the improvement of a rapid hull form...under the 2012 Naval Research Enterprise Intern Program. 15. SUBJECT TERMS hydrodynamic, hull form, generation, optimization, algorithm

Impact of Chaos Functions on Modern Swarm Optimizers.

PubMed

Emary, E; Zawbaa, Hossam M

2016-01-01

Exploration and exploitation are two essential components for any optimization algorithm. Much exploration leads to oscillation and premature convergence while too much exploitation slows down the optimization algorithm and the optimizer may be stuck in local minima. Therefore, balancing the rates of exploration and exploitation at the optimization lifetime is a challenge. This study evaluates the impact of using chaos-based control of exploration/exploitation rates against using the systematic native control. Three modern algorithms were used in the study namely grey wolf optimizer (GWO), antlion optimizer (ALO) and moth-flame optimizer (MFO) in the domain of machine learning for feature selection. Results on a set of standard machine learning data using a set of assessment indicators prove advance in optimization algorithm performance when using variational repeated periods of declined exploration rates over using systematically decreased exploration rates.

PS-FW: A Hybrid Algorithm Based on Particle Swarm and Fireworks for Global Optimization

PubMed Central

Chen, Shuangqing; Wei, Lixin; Guan, Bing

2018-01-01

Particle swarm optimization (PSO) and fireworks algorithm (FWA) are two recently developed optimization methods which have been applied in various areas due to their simplicity and efficiency. However, when being applied to high-dimensional optimization problems, PSO algorithm may be trapped in the local optima owing to the lack of powerful global exploration capability, and fireworks algorithm is difficult to converge in some cases because of its relatively low local exploitation efficiency for noncore fireworks. In this paper, a hybrid algorithm called PS-FW is presented, in which the modified operators of FWA are embedded into the solving process of PSO. In the iteration process, the abandonment and supplement mechanism is adopted to balance the exploration and exploitation ability of PS-FW, and the modified explosion operator and the novel mutation operator are proposed to speed up the global convergence and to avoid prematurity. To verify the performance of the proposed PS-FW algorithm, 22 high-dimensional benchmark functions have been employed, and it is compared with PSO, FWA, stdPSO, CPSO, CLPSO, FIPS, Frankenstein, and ALWPSO algorithms. Results show that the PS-FW algorithm is an efficient, robust, and fast converging optimization method for solving global optimization problems. PMID:29675036

An algorithmic framework for multiobjective optimization.

PubMed

Ganesan, T; Elamvazuthi, I; Shaari, Ku Zilati Ku; Vasant, P

2013-01-01

Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization.

An Algorithmic Framework for Multiobjective Optimization

PubMed Central

Ganesan, T.; Elamvazuthi, I.; Shaari, Ku Zilati Ku; Vasant, P.

2013-01-01

Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization. PMID:24470795

Time-optimal trajectory planning for underactuated spacecraft using a hybrid particle swarm optimization algorithm

NASA Astrophysics Data System (ADS)

Zhuang, Yufei; Huang, Haibin

2014-02-01

A hybrid algorithm combining particle swarm optimization (PSO) algorithm with the Legendre pseudospectral method (LPM) is proposed for solving time-optimal trajectory planning problem of underactuated spacecrafts. At the beginning phase of the searching process, an initialization generator is constructed by the PSO algorithm due to its strong global searching ability and robustness to random initial values, however, PSO algorithm has a disadvantage that its convergence rate around the global optimum is slow. Then, when the change in fitness function is smaller than a predefined value, the searching algorithm is switched to the LPM to accelerate the searching process. Thus, with the obtained solutions by the PSO algorithm as a set of proper initial guesses, the hybrid algorithm can find a global optimum more quickly and accurately. 200 Monte Carlo simulations results demonstrate that the proposed hybrid PSO-LPM algorithm has greater advantages in terms of global searching capability and convergence rate than both single PSO algorithm and LPM algorithm. Moreover, the PSO-LPM algorithm is also robust to random initial values.

A hybrid artificial bee colony algorithm for numerical function optimization

NASA Astrophysics Data System (ADS)

Alqattan, Zakaria N.; Abdullah, Rosni

2015-02-01

Artificial Bee Colony (ABC) algorithm is one of the swarm intelligence algorithms; it has been introduced by Karaboga in 2005. It is a meta-heuristic optimization search algorithm inspired from the intelligent foraging behavior of the honey bees in nature. Its unique search process made it as one of the most competitive algorithm with some other search algorithms in the area of optimization, such as Genetic algorithm (GA) and Particle Swarm Optimization (PSO). However, the ABC performance of the local search process and the bee movement or the solution improvement equation still has some weaknesses. The ABC is good in avoiding trapping at the local optimum but it spends its time searching around unpromising random selected solutions. Inspired by the PSO, we propose a Hybrid Particle-movement ABC algorithm called HPABC, which adapts the particle movement process to improve the exploration of the original ABC algorithm. Numerical benchmark functions were used in order to experimentally test the HPABC algorithm. The results illustrate that the HPABC algorithm can outperform the ABC algorithm in most of the experiments (75% better in accuracy and over 3 times faster).

Seven-spot ladybird optimization: a novel and efficient metaheuristic algorithm for numerical optimization.

PubMed

Wang, Peng; Zhu, Zhouquan; Huang, Shuai

2013-01-01

This paper presents a novel biologically inspired metaheuristic algorithm called seven-spot ladybird optimization (SLO). The SLO is inspired by recent discoveries on the foraging behavior of a seven-spot ladybird. In this paper, the performance of the SLO is compared with that of the genetic algorithm, particle swarm optimization, and artificial bee colony algorithms by using five numerical benchmark functions with multimodality. The results show that SLO has the ability to find the best solution with a comparatively small population size and is suitable for solving optimization problems with lower dimensions.

Seven-Spot Ladybird Optimization: A Novel and Efficient Metaheuristic Algorithm for Numerical Optimization

PubMed Central

Zhu, Zhouquan

2013-01-01

This paper presents a novel biologically inspired metaheuristic algorithm called seven-spot ladybird optimization (SLO). The SLO is inspired by recent discoveries on the foraging behavior of a seven-spot ladybird. In this paper, the performance of the SLO is compared with that of the genetic algorithm, particle swarm optimization, and artificial bee colony algorithms by using five numerical benchmark functions with multimodality. The results show that SLO has the ability to find the best solution with a comparatively small population size and is suitable for solving optimization problems with lower dimensions. PMID:24385879

Optimal cost design of water distribution networks using a decomposition approach

NASA Astrophysics Data System (ADS)

Lee, Ho Min; Yoo, Do Guen; Sadollah, Ali; Kim, Joong Hoon

2016-12-01

Water distribution network decomposition, which is an engineering approach, is adopted to increase the efficiency of obtaining the optimal cost design of a water distribution network using an optimization algorithm. This study applied the source tracing tool in EPANET, which is a hydraulic and water quality analysis model, to the decomposition of a network to improve the efficiency of the optimal design process. The proposed approach was tested by carrying out the optimal cost design of two water distribution networks, and the results were compared with other optimal cost designs derived from previously proposed optimization algorithms. The proposed decomposition approach using the source tracing technique enables the efficient decomposition of an actual large-scale network, and the results can be combined with the optimal cost design process using an optimization algorithm. This proves that the final design in this study is better than those obtained with other previously proposed optimization algorithms.

Analysis of an Optimized MLOS Tomographic Reconstruction Algorithm and Comparison to the MART Reconstruction Algorithm

NASA Astrophysics Data System (ADS)

La Foy, Roderick; Vlachos, Pavlos

2011-11-01

An optimally designed MLOS tomographic reconstruction algorithm for use in 3D PIV and PTV applications is analyzed. Using a set of optimized reconstruction parameters, the reconstructions produced by the MLOS algorithm are shown to be comparable to reconstructions produced by the MART algorithm for a range of camera geometries, camera numbers, and particle seeding densities. The resultant velocity field error calculated using PIV and PTV algorithms is further minimized by applying both pre and post processing to the reconstructed data sets.

Application of Improved APO Algorithm in Vulnerability Assessment and Reconstruction of Microgrid

NASA Astrophysics Data System (ADS)

Xie, Jili; Ma, Hailing

2018-01-01

Artificial Physics Optimization (APO) has good global search ability and can avoid the premature convergence phenomenon in PSO algorithm, which has good stability of fast convergence and robustness. On the basis of APO of the vector model, a reactive power optimization algorithm based on improved APO algorithm is proposed for the static structure and dynamic operation characteristics of microgrid. The simulation test is carried out through the IEEE 30-bus system and the result shows that the algorithm has better efficiency and accuracy compared with other optimization algorithms.

Focusing light through random photonic layers by four-element division algorithm

NASA Astrophysics Data System (ADS)

Fang, Longjie; Zhang, Xicheng; Zuo, Haoyi; Pang, Lin

2018-02-01

The propagation of waves in turbid media is a fundamental problem of optics with vast applications. Optical phase optimization approaches for focusing light through turbid media using phase control algorithm have been widely studied in recent years due to the rapid development of spatial light modulator. The existing approaches include element-based algorithms - stepwise sequential algorithm, continuous sequential algorithm and whole element optimization approaches - partitioning algorithm, transmission matrix approach and genetic algorithm. The advantage of element-based approaches is that the phase contribution of each element is very clear; however, because the intensity contribution of each element to the focal point is small especially for the case of large number of elements, the determination of the optimal phase for a single element would be difficult. In other words, the signal to noise ratio of the measurement is weak, leading to possibly local maximal during the optimization. As for whole element optimization approaches, all elements are employed for the optimization. Of course, signal to noise ratio during the optimization is improved. However, because more random processings are introduced into the processing, optimizations take more time to converge than the single element based approaches. Based on the advantages of both single element based approaches and whole element optimization approaches, we propose FEDA approach. Comparisons with the existing approaches show that FEDA only takes one third of measurement time to reach the optimization, which means that FEDA is promising in practical application such as for deep tissue imaging.

Prognostic value of hedgehog signaling pathway in digestive system cancers: A systematic review and meta-analysis.

PubMed

Wang, Yihan; Peng, Qian; Jia, Hongyuan; Du, Xiao

2016-01-01

The Hedgehog (Hh) signaling pathway has recently been reported to be associated with the prognosis of digestive system cancers. However, the results are inconsistent. This study aimed to investigate the association between Hh pathway components and survival outcomes in patients with digestive system cancers. We conducted a comprehensive retrieval in PubMed, EMBASE and Cochrane library for relevant literatures until May 1st, 2015. The pooled hazard ratios (HRs) for overall survival (OS) and disease-free survival (DFS) with 95% confidence intervals (CIs) were calculated to clarify the prognostic value of Hh pathway components, including Shh, Gli1, Gli2, Smo and Ptch1. A total of 16 eligible articles with 3222 patients were included in the meta-analysis. Pooled HR suggested that over-expression of Shh and Gli1 were both associated with poor OS (HR = 1.87, 95% CI: 1.14-3.07 and HR = 1.96, 95% CI: 1.66-2.32, respectively) and DFS (HR = 2.37, 95% CI: 1.19-4.72 and HR = 2.18, 95% CI: 1.61-2.96, respectively). In addition, over-expression of Smo was associated with poor DFS (HR = 1.38, 95% CI: 1.08-1.75). This study reveals that over-expressed Hh pathway components, including Shh, Gli1 and Smo, are associated with poor prognosis in digestive system cancer patients. Hh signaling pathway may become a potential therapeutic target in digestive system cancers.

GlobiPack v. 1.0

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bartlett, Roscoe

2010-03-31

GlobiPack contains a small collection of optimization globalization algorithms. These algorithms are used by optimization and various nonlinear equation solver algorithms.Used as the line-search procedure with Newton and Quasi-Newton optimization and nonlinear equation solver methods. These are standard published 1-D line search algorithms such as are described in the book Nocedal and Wright Numerical Optimization: 2nd edition, 2006. One set of algorithms were copied and refactored from the existing open-source Trilinos package MOOCHO where the linear search code is used to globalize SQP methods. This software is generic to any mathematical optimization problem where smooth derivatives exist. There is nomore » specific connection or mention whatsoever to any specific application, period. You cannot find more general mathematical software.« less

Direct position determination for digital modulation signals based on improved particle swarm optimization algorithm

NASA Astrophysics Data System (ADS)

Yu, Wan-Ting; Yu, Hong-yi; Du, Jian-Ping; Wang, Ding

2018-04-01

The Direct Position Determination (DPD) algorithm has been demonstrated to achieve a better accuracy with known signal waveforms. However, the signal waveform is difficult to be completely known in the actual positioning process. To solve the problem, we proposed a DPD method for digital modulation signals based on improved particle swarm optimization algorithm. First, a DPD model is established for known modulation signals and a cost function is obtained on symbol estimation. Second, as the optimization of the cost function is a nonlinear integer optimization problem, an improved Particle Swarm Optimization (PSO) algorithm is considered for the optimal symbol search. Simulations are carried out to show the higher position accuracy of the proposed DPD method and the convergence of the fitness function under different inertia weight and population size. On the one hand, the proposed algorithm can take full advantage of the signal feature to improve the positioning accuracy. On the other hand, the improved PSO algorithm can improve the efficiency of symbol search by nearly one hundred times to achieve a global optimal solution.

An Optimizing Space Data-Communications Scheduling Method and Algorithm with Interference Mitigation, Generalized for a Broad Class of Optimization Problems

NASA Technical Reports Server (NTRS)

Rash, James L.

2010-01-01

NASA's space data-communications infrastructure, the Space Network and the Ground Network, provide scheduled (as well as some limited types of unscheduled) data-communications services to user spacecraft via orbiting relay satellites and ground stations. An implementation of the methods and algorithms disclosed herein will be a system that produces globally optimized schedules with not only optimized service delivery by the space data-communications infrastructure but also optimized satisfaction of all user requirements and prescribed constraints, including radio frequency interference (RFI) constraints. Evolutionary search, a class of probabilistic strategies for searching large solution spaces, constitutes the essential technology in this disclosure. Also disclosed are methods and algorithms for optimizing the execution efficiency of the schedule-generation algorithm itself. The scheduling methods and algorithms as presented are adaptable to accommodate the complexity of scheduling the civilian and/or military data-communications infrastructure. Finally, the problem itself, and the methods and algorithms, are generalized and specified formally, with applicability to a very broad class of combinatorial optimization problems.

Multi-AUV autonomous task planning based on the scroll time domain quantum bee colony optimization algorithm in uncertain environment

PubMed Central

Zhang, Rubo; Yang, Yu

2017-01-01

Research on distributed task planning model for multi-autonomous underwater vehicle (MAUV). A scroll time domain quantum artificial bee colony (STDQABC) optimization algorithm is proposed to solve the multi-AUV optimal task planning scheme. In the uncertain marine environment, the rolling time domain control technique is used to realize a numerical optimization in a narrowed time range. Rolling time domain control is one of the better task planning techniques, which can greatly reduce the computational workload and realize the tradeoff between AUV dynamics, environment and cost. Finally, a simulation experiment was performed to evaluate the distributed task planning performance of the scroll time domain quantum bee colony optimization algorithm. The simulation results demonstrate that the STDQABC algorithm converges faster than the QABC and ABC algorithms in terms of both iterations and running time. The STDQABC algorithm can effectively improve MAUV distributed tasking planning performance, complete the task goal and get the approximate optimal solution. PMID:29186166

Multi-AUV autonomous task planning based on the scroll time domain quantum bee colony optimization algorithm in uncertain environment.

PubMed

Li, Jianjun; Zhang, Rubo; Yang, Yu

2017-01-01

Research on distributed task planning model for multi-autonomous underwater vehicle (MAUV). A scroll time domain quantum artificial bee colony (STDQABC) optimization algorithm is proposed to solve the multi-AUV optimal task planning scheme. In the uncertain marine environment, the rolling time domain control technique is used to realize a numerical optimization in a narrowed time range. Rolling time domain control is one of the better task planning techniques, which can greatly reduce the computational workload and realize the tradeoff between AUV dynamics, environment and cost. Finally, a simulation experiment was performed to evaluate the distributed task planning performance of the scroll time domain quantum bee colony optimization algorithm. The simulation results demonstrate that the STDQABC algorithm converges faster than the QABC and ABC algorithms in terms of both iterations and running time. The STDQABC algorithm can effectively improve MAUV distributed tasking planning performance, complete the task goal and get the approximate optimal solution.

Evaluation of Genetic Algorithm Concepts Using Model Problems. Part 2; Multi-Objective Optimization

NASA Technical Reports Server (NTRS)

Holst, Terry L.; Pulliam, Thomas H.

2003-01-01

A genetic algorithm approach suitable for solving multi-objective optimization problems is described and evaluated using a series of simple model problems. Several new features including a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. Results indicate that the genetic algorithm optimization approach is flexible in application and extremely reliable, providing optimal results for all optimization problems attempted. The binning algorithm generally provides pareto front quality enhancements and moderate convergence efficiency improvements for most of the model problems. The gene-space transformation procedure provides a large convergence efficiency enhancement for problems with non-convoluted pareto fronts and a degradation in efficiency for problems with convoluted pareto fronts. The most difficult problems --multi-mode search spaces with a large number of genes and convoluted pareto fronts-- require a large number of function evaluations for GA convergence, but always converge.

A new improved artificial bee colony algorithm for ship hull form optimization

NASA Astrophysics Data System (ADS)

Huang, Fuxin; Wang, Lijue; Yang, Chi

2016-04-01

The artificial bee colony (ABC) algorithm is a relatively new swarm intelligence-based optimization algorithm. Its simplicity of implementation, relatively few parameter settings and promising optimization capability make it widely used in different fields. However, it has problems of slow convergence due to its solution search equation. Here, a new solution search equation based on a combination of the elite solution pool and the block perturbation scheme is proposed to improve the performance of the algorithm. In addition, two different solution search equations are used by employed bees and onlooker bees to balance the exploration and exploitation of the algorithm. The developed algorithm is validated by a set of well-known numerical benchmark functions. It is then applied to optimize two ship hull forms with minimum resistance. The tested results show that the proposed new improved ABC algorithm can outperform the ABC algorithm in most of the tested problems.

Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization.

PubMed

Cui, Huanqing; Shu, Minglei; Song, Min; Wang, Yinglong

2017-03-01

Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors' memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm.

Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization

PubMed Central

Cui, Huanqing; Shu, Minglei; Song, Min; Wang, Yinglong

2017-01-01

Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm. PMID:28257060

Classification of Medical Datasets Using SVMs with Hybrid Evolutionary Algorithms Based on Endocrine-Based Particle Swarm Optimization and Artificial Bee Colony Algorithms.

PubMed

Lin, Kuan-Cheng; Hsieh, Yi-Hsiu

2015-10-01

The classification and analysis of data is an important issue in today's research. Selecting a suitable set of features makes it possible to classify an enormous quantity of data quickly and efficiently. Feature selection is generally viewed as a problem of feature subset selection, such as combination optimization problems. Evolutionary algorithms using random search methods have proven highly effective in obtaining solutions to problems of optimization in a diversity of applications. In this study, we developed a hybrid evolutionary algorithm based on endocrine-based particle swarm optimization (EPSO) and artificial bee colony (ABC) algorithms in conjunction with a support vector machine (SVM) for the selection of optimal feature subsets for the classification of datasets. The results of experiments using specific UCI medical datasets demonstrate that the accuracy of the proposed hybrid evolutionary algorithm is superior to that of basic PSO, EPSO and ABC algorithms, with regard to classification accuracy using subsets with a reduced number of features.

A Swarm Optimization Genetic Algorithm Based on Quantum-Behaved Particle Swarm Optimization.

PubMed

Sun, Tao; Xu, Ming-Hai

2017-01-01

Quantum-behaved particle swarm optimization (QPSO) algorithm is a variant of the traditional particle swarm optimization (PSO). The QPSO that was originally developed for continuous search spaces outperforms the traditional PSO in search ability. This paper analyzes the main factors that impact the search ability of QPSO and converts the particle movement formula to the mutation condition by introducing the rejection region, thus proposing a new binary algorithm, named swarm optimization genetic algorithm (SOGA), because it is more like genetic algorithm (GA) than PSO in form. SOGA has crossover and mutation operator as GA but does not need to set the crossover and mutation probability, so it has fewer parameters to control. The proposed algorithm was tested with several nonlinear high-dimension functions in the binary search space, and the results were compared with those from BPSO, BQPSO, and GA. The experimental results show that SOGA is distinctly superior to the other three algorithms in terms of solution accuracy and convergence.

An improved grey wolf optimizer algorithm for the inversion of geoelectrical data

NASA Astrophysics Data System (ADS)

Li, Si-Yu; Wang, Shu-Ming; Wang, Peng-Fei; Su, Xiao-Lu; Zhang, Xin-Song; Dong, Zhi-Hui

2018-05-01

The grey wolf optimizer (GWO) is a novel bionics algorithm inspired by the social rank and prey-seeking behaviors of grey wolves. The GWO algorithm is easy to implement because of its basic concept, simple formula, and small number of parameters. This paper develops a GWO algorithm with a nonlinear convergence factor and an adaptive location updating strategy and applies this improved grey wolf optimizer (improved grey wolf optimizer, IGWO) algorithm to geophysical inversion problems using magnetotelluric (MT), DC resistivity and induced polarization (IP) methods. Numerical tests in MATLAB 2010b for the forward modeling data and the observed data show that the IGWO algorithm can find the global minimum and rarely sinks to the local minima. For further study, inverted results using the IGWO are contrasted with particle swarm optimization (PSO) and the simulated annealing (SA) algorithm. The outcomes of the comparison reveal that the IGWO and PSO similarly perform better in counterpoising exploration and exploitation with a given number of iterations than the SA.

Transonic Wing Shape Optimization Using a Genetic Algorithm

NASA Technical Reports Server (NTRS)

Holst, Terry L.; Pulliam, Thomas H.; Kwak, Dochan (Technical Monitor)

2002-01-01

A method for aerodynamic shape optimization based on a genetic algorithm approach is demonstrated. The algorithm is coupled with a transonic full potential flow solver and is used to optimize the flow about transonic wings including multi-objective solutions that lead to the generation of pareto fronts. The results indicate that the genetic algorithm is easy to implement, flexible in application and extremely reliable.

Solving TSP problem with improved genetic algorithm

NASA Astrophysics Data System (ADS)

Fu, Chunhua; Zhang, Lijun; Wang, Xiaojing; Qiao, Liying

2018-05-01

The TSP is a typical NP problem. The optimization of vehicle routing problem (VRP) and city pipeline optimization can use TSP to solve; therefore it is very important to the optimization for solving TSP problem. The genetic algorithm (GA) is one of ideal methods in solving it. The standard genetic algorithm has some limitations. Improving the selection operator of genetic algorithm, and importing elite retention strategy can ensure the select operation of quality, In mutation operation, using the adaptive algorithm selection can improve the quality of search results and variation, after the chromosome evolved one-way evolution reverse operation is added which can make the offspring inherit gene of parental quality improvement opportunities, and improve the ability of searching the optimal solution algorithm.

System Design under Uncertainty: Evolutionary Optimization of the Gravity Probe-B Spacecraft

NASA Technical Reports Server (NTRS)

Pullen, Samuel P.; Parkinson, Bradford W.

1994-01-01

This paper discusses the application of evolutionary random-search algorithms (Simulated Annealing and Genetic Algorithms) to the problem of spacecraft design under performance uncertainty. Traditionally, spacecraft performance uncertainty has been measured by reliability. Published algorithms for reliability optimization are seldom used in practice because they oversimplify reality. The algorithm developed here uses random-search optimization to allow us to model the problem more realistically. Monte Carlo simulations are used to evaluate the objective function for each trial design solution. These methods have been applied to the Gravity Probe-B (GP-B) spacecraft being developed at Stanford University for launch in 1999, Results of the algorithm developed here for GP-13 are shown, and their implications for design optimization by evolutionary algorithms are discussed.

An Improved Cuckoo Search Optimization Algorithm for the Problem of Chaotic Systems Parameter Estimation

PubMed Central

Wang, Jun; Zhou, Bihua; Zhou, Shudao

2016-01-01

This paper proposes an improved cuckoo search (ICS) algorithm to establish the parameters of chaotic systems. In order to improve the optimization capability of the basic cuckoo search (CS) algorithm, the orthogonal design and simulated annealing operation are incorporated in the CS algorithm to enhance the exploitation search ability. Then the proposed algorithm is used to establish parameters of the Lorenz chaotic system and Chen chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the algorithm can estimate parameters with high accuracy and reliability. Finally, the results are compared with the CS algorithm, genetic algorithm, and particle swarm optimization algorithm, and the compared results demonstrate the method is energy-efficient and superior. PMID:26880874

Optimal design of minimum mean-square error noise reduction algorithms using the simulated annealing technique.

PubMed

Bai, Mingsian R; Hsieh, Ping-Ju; Hur, Kur-Nan

2009-02-01

The performance of the minimum mean-square error noise reduction (MMSE-NR) algorithm in conjunction with time-recursive averaging (TRA) for noise estimation is found to be very sensitive to the choice of two recursion parameters. To address this problem in a more systematic manner, this paper proposes an optimization method to efficiently search the optimal parameters of the MMSE-TRA-NR algorithms. The objective function is based on a regression model, whereas the optimization process is carried out with the simulated annealing algorithm that is well suited for problems with many local optima. Another NR algorithm proposed in the paper employs linear prediction coding as a preprocessor for extracting the correlated portion of human speech. Objective and subjective tests were undertaken to compare the optimized MMSE-TRA-NR algorithm with several conventional NR algorithms. The results of subjective tests were processed by using analysis of variance to justify the statistic significance. A post hoc test, Tukey's Honestly Significant Difference, was conducted to further assess the pairwise difference between the NR algorithms.

Evolutionary Beamforming Optimization for Radio Frequency Charging in Wireless Rechargeable Sensor Networks.

PubMed

Yao, Ke-Han; Jiang, Jehn-Ruey; Tsai, Chung-Hsien; Wu, Zong-Syun

2017-08-20

This paper investigates how to efficiently charge sensor nodes in a wireless rechargeable sensor network (WRSN) with radio frequency (RF) chargers to make the network sustainable. An RF charger is assumed to be equipped with a uniform circular array (UCA) of 12 antennas with the radius λ , where λ is the RF wavelength. The UCA can steer most RF energy in a target direction to charge a specific WRSN node by the beamforming technology. Two evolutionary algorithms (EAs) using the evolution strategy (ES), namely the Evolutionary Beamforming Optimization (EBO) algorithm and the Evolutionary Beamforming Optimization Reseeding (EBO-R) algorithm, are proposed to nearly optimize the power ratio of the UCA beamforming peak side lobe (PSL) and the main lobe (ML) aimed at the given target direction. The proposed algorithms are simulated for performance evaluation and are compared with a related algorithm, called Particle Swarm Optimization Gravitational Search Algorithm-Explore (PSOGSA-Explore), to show their superiority.

Feed-Forward Neural Network Soft-Sensor Modeling of Flotation Process Based on Particle Swarm Optimization and Gravitational Search Algorithm

PubMed Central

Wang, Jie-Sheng; Han, Shuang

2015-01-01

For predicting the key technology indicators (concentrate grade and tailings recovery rate) of flotation process, a feed-forward neural network (FNN) based soft-sensor model optimized by the hybrid algorithm combining particle swarm optimization (PSO) algorithm and gravitational search algorithm (GSA) is proposed. Although GSA has better optimization capability, it has slow convergence velocity and is easy to fall into local optimum. So in this paper, the velocity vector and position vector of GSA are adjusted by PSO algorithm in order to improve its convergence speed and prediction accuracy. Finally, the proposed hybrid algorithm is adopted to optimize the parameters of FNN soft-sensor model. Simulation results show that the model has better generalization and prediction accuracy for the concentrate grade and tailings recovery rate to meet the online soft-sensor requirements of the real-time control in the flotation process. PMID:26583034

Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization

NASA Technical Reports Server (NTRS)

Holst, Terry L.

2005-01-01

A genetic algorithm approach suitable for solving multi-objective problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding Pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the Pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide Pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.

A Modified Mean Gray Wolf Optimization Approach for Benchmark and Biomedical Problems.

PubMed

Singh, Narinder; Singh, S B

2017-01-01

A modified variant of gray wolf optimization algorithm, namely, mean gray wolf optimization algorithm has been developed by modifying the position update (encircling behavior) equations of gray wolf optimization algorithm. The proposed variant has been tested on 23 standard benchmark well-known test functions (unimodal, multimodal, and fixed-dimension multimodal), and the performance of modified variant has been compared with particle swarm optimization and gray wolf optimization. Proposed algorithm has also been applied to the classification of 5 data sets to check feasibility of the modified variant. The results obtained are compared with many other meta-heuristic approaches, ie, gray wolf optimization, particle swarm optimization, population-based incremental learning, ant colony optimization, etc. The results show that the performance of modified variant is able to find best solutions in terms of high level of accuracy in classification and improved local optima avoidance.

A Novel Hybrid Firefly Algorithm for Global Optimization.

PubMed

Zhang, Lina; Liu, Liqiang; Yang, Xin-She; Dai, Yuntao

Global optimization is challenging to solve due to its nonlinearity and multimodality. Traditional algorithms such as the gradient-based methods often struggle to deal with such problems and one of the current trends is to use metaheuristic algorithms. In this paper, a novel hybrid population-based global optimization algorithm, called hybrid firefly algorithm (HFA), is proposed by combining the advantages of both the firefly algorithm (FA) and differential evolution (DE). FA and DE are executed in parallel to promote information sharing among the population and thus enhance searching efficiency. In order to evaluate the performance and efficiency of the proposed algorithm, a diverse set of selected benchmark functions are employed and these functions fall into two groups: unimodal and multimodal. The experimental results show better performance of the proposed algorithm compared to the original version of the firefly algorithm (FA), differential evolution (DE) and particle swarm optimization (PSO) in the sense of avoiding local minima and increasing the convergence rate.

A Novel Hybrid Firefly Algorithm for Global Optimization

PubMed Central

Zhang, Lina; Liu, Liqiang; Yang, Xin-She; Dai, Yuntao

2016-01-01

Global optimization is challenging to solve due to its nonlinearity and multimodality. Traditional algorithms such as the gradient-based methods often struggle to deal with such problems and one of the current trends is to use metaheuristic algorithms. In this paper, a novel hybrid population-based global optimization algorithm, called hybrid firefly algorithm (HFA), is proposed by combining the advantages of both the firefly algorithm (FA) and differential evolution (DE). FA and DE are executed in parallel to promote information sharing among the population and thus enhance searching efficiency. In order to evaluate the performance and efficiency of the proposed algorithm, a diverse set of selected benchmark functions are employed and these functions fall into two groups: unimodal and multimodal. The experimental results show better performance of the proposed algorithm compared to the original version of the firefly algorithm (FA), differential evolution (DE) and particle swarm optimization (PSO) in the sense of avoiding local minima and increasing the convergence rate. PMID:27685869

A preliminary study to metaheuristic approach in multilayer radiation shielding optimization

NASA Astrophysics Data System (ADS)

Arif Sazali, Muhammad; Rashid, Nahrul Khair Alang Md; Hamzah, Khaidzir

2018-01-01

Metaheuristics are high-level algorithmic concepts that can be used to develop heuristic optimization algorithms. One of their applications is to find optimal or near optimal solutions to combinatorial optimization problems (COPs) such as scheduling, vehicle routing, and timetabling. Combinatorial optimization deals with finding optimal combinations or permutations in a given set of problem components when exhaustive search is not feasible. A radiation shield made of several layers of different materials can be regarded as a COP. The time taken to optimize the shield may be too high when several parameters are involved such as the number of materials, the thickness of layers, and the arrangement of materials. Metaheuristics can be applied to reduce the optimization time, trading guaranteed optimal solutions for near-optimal solutions in comparably short amount of time. The application of metaheuristics for radiation shield optimization is lacking. In this paper, we present a review on the suitability of using metaheuristics in multilayer shielding design, specifically the genetic algorithm and ant colony optimization algorithm (ACO). We would also like to propose an optimization model based on the ACO method.

Seeding the initial population with feasible solutions in metaheuristic optimization of steel trusses

NASA Astrophysics Data System (ADS)

Kazemzadeh Azad, Saeid

2018-01-01

In spite of considerable research work on the development of efficient algorithms for discrete sizing optimization of steel truss structures, only a few studies have addressed non-algorithmic issues affecting the general performance of algorithms. For instance, an important question is whether starting the design optimization from a feasible solution is fruitful or not. This study is an attempt to investigate the effect of seeding the initial population with feasible solutions on the general performance of metaheuristic techniques. To this end, the sensitivity of recently proposed metaheuristic algorithms to the feasibility of initial candidate designs is evaluated through practical discrete sizing of real-size steel truss structures. The numerical experiments indicate that seeding the initial population with feasible solutions can improve the computational efficiency of metaheuristic structural optimization algorithms, especially in the early stages of the optimization. This paves the way for efficient metaheuristic optimization of large-scale structural systems.

Joint Optimization of Receiver Placement and Illuminator Selection for a Multiband Passive Radar Network.

PubMed

Xie, Rui; Wan, Xianrong; Hong, Sheng; Yi, Jianxin

2017-06-14

The performance of a passive radar network can be greatly improved by an optimal radar network structure. Generally, radar network structure optimization consists of two aspects, namely the placement of receivers in suitable places and selection of appropriate illuminators. The present study investigates issues concerning the joint optimization of receiver placement and illuminator selection for a passive radar network. Firstly, the required radar cross section (RCS) for target detection is chosen as the performance metric, and the joint optimization model boils down to the partition p -center problem (PPCP). The PPCP is then solved by a proposed bisection algorithm. The key of the bisection algorithm lies in solving the partition set covering problem (PSCP), which can be solved by a hybrid algorithm developed by coupling the convex optimization with the greedy dropping algorithm. In the end, the performance of the proposed algorithm is validated via numerical simulations.

Modified Shuffled Frog Leaping Optimization Algorithm Based Distributed Generation Rescheduling for Loss Minimization

NASA Astrophysics Data System (ADS)

Arya, L. D.; Koshti, Atul

2018-05-01

This paper investigates the Distributed Generation (DG) capacity optimization at location based on the incremental voltage sensitivity criteria for sub-transmission network. The Modified Shuffled Frog Leaping optimization Algorithm (MSFLA) has been used to optimize the DG capacity. Induction generator model of DG (wind based generating units) has been considered for study. Standard test system IEEE-30 bus has been considered for the above study. The obtained results are also validated by shuffled frog leaping algorithm and modified version of bare bones particle swarm optimization (BBExp). The performance of MSFLA has been found more efficient than the other two algorithms for real power loss minimization problem.

Multiobjective Aerodynamic Shape Optimization Using Pareto Differential Evolution and Generalized Response Surface Metamodels

NASA Technical Reports Server (NTRS)

Madavan, Nateri K.

2004-01-01

Differential Evolution (DE) is a simple, fast, and robust evolutionary algorithm that has proven effective in determining the global optimum for several difficult single-objective optimization problems. The DE algorithm has been recently extended to multiobjective optimization problem by using a Pareto-based approach. In this paper, a Pareto DE algorithm is applied to multiobjective aerodynamic shape optimization problems that are characterized by computationally expensive objective function evaluations. To improve computational expensive the algorithm is coupled with generalized response surface meta-models based on artificial neural networks. Results are presented for some test optimization problems from the literature to demonstrate the capabilities of the method.

Modified harmony search

NASA Astrophysics Data System (ADS)

Mohamed, Najihah; Lutfi Amri Ramli, Ahmad; Majid, Ahmad Abd; Piah, Abd Rahni Mt

2017-09-01

A metaheuristic algorithm, called Harmony Search is quite highly applied in optimizing parameters in many areas. HS is a derivative-free real parameter optimization algorithm, and draws an inspiration from the musical improvisation process of searching for a perfect state of harmony. Propose in this paper Modified Harmony Search for solving optimization problems, which employs a concept from genetic algorithm method and particle swarm optimization for generating new solution vectors that enhances the performance of HS algorithm. The performances of MHS and HS are investigated on ten benchmark optimization problems in order to make a comparison to reflect the efficiency of the MHS in terms of final accuracy, convergence speed and robustness.

MULTI-OBJECTIVE OPTIMAL DESIGN OF GROUNDWATER REMEDIATION SYSTEMS: APPLICATION OF THE NICHED PARETO GENETIC ALGORITHM (NPGA). (R826614)

EPA Science Inventory

A multiobjective optimization algorithm is applied to a groundwater quality management problem involving remediation by pump-and-treat (PAT). The multiobjective optimization framework uses the niched Pareto genetic algorithm (NPGA) and is applied to simultaneously minimize the...

Structural damage identification using an enhanced thermal exchange optimization algorithm

NASA Astrophysics Data System (ADS)

Kaveh, A.; Dadras, A.

2018-03-01

The recently developed optimization algorithm-the so-called thermal exchange optimization (TEO) algorithm-is enhanced and applied to a damage detection problem. An offline parameter tuning approach is utilized to set the internal parameters of the TEO, resulting in the enhanced heat transfer optimization (ETEO) algorithm. The damage detection problem is defined as an inverse problem, and ETEO is applied to a wide range of structures. Several scenarios with noise and noise-free modal data are tested and the locations and extents of damages are identified with good accuracy.

HEURISTIC OPTIMIZATION AND ALGORITHM TUNING APPLIED TO SORPTIVE BARRIER DESIGN

EPA Science Inventory

While heuristic optimization is applied in environmental applications, ad-hoc algorithm configuration is typical. We use a multi-layer sorptive barrier design problem as a benchmark for an algorithm-tuning procedure, as applied to three heuristics (genetic algorithms, simulated ...

An optimized BP neural network based on genetic algorithm for static decoupling of a six-axis force/torque sensor

NASA Astrophysics Data System (ADS)

Fu, Liyue; Song, Aiguo

2018-02-01

In order to improve the measurement precision of 6-axis force/torque sensor for robot, BP decoupling algorithm optimized by GA (GA-BP algorithm) is proposed in this paper. The weights and thresholds of a BP neural network with 6-10-6 topology are optimized by GA to develop decouple a six-axis force/torque sensor. By comparison with other traditional decoupling algorithm, calculating the pseudo-inverse matrix of calibration and classical BP algorithm, the decoupling results validate the good decoupling performance of GA-BP algorithm and the coupling errors are reduced.

A novel metaheuristic for continuous optimization problems: Virus optimization algorithm

NASA Astrophysics Data System (ADS)

Liang, Yun-Chia; Rodolfo Cuevas Juarez, Josue

2016-01-01

A novel metaheuristic for continuous optimization problems, named the virus optimization algorithm (VOA), is introduced and investigated. VOA is an iteratively population-based method that imitates the behaviour of viruses attacking a living cell. The number of viruses grows at each replication and is controlled by an immune system (a so-called 'antivirus') to prevent the explosive growth of the virus population. The viruses are divided into two classes (strong and common) to balance the exploitation and exploration effects. The performance of the VOA is validated through a set of eight benchmark functions, which are also subject to rotation and shifting effects to test its robustness. Extensive comparisons were conducted with over 40 well-known metaheuristic algorithms and their variations, such as artificial bee colony, artificial immune system, differential evolution, evolutionary programming, evolutionary strategy, genetic algorithm, harmony search, invasive weed optimization, memetic algorithm, particle swarm optimization and simulated annealing. The results showed that the VOA is a viable solution for continuous optimization.

Cost optimization of reinforced concrete cantilever retaining walls under seismic loading using a biogeography-based optimization algorithm with Levy flights

NASA Astrophysics Data System (ADS)

Aydogdu, Ibrahim

2017-03-01

In this article, a new version of a biogeography-based optimization algorithm with Levy flight distribution (LFBBO) is introduced and used for the optimum design of reinforced concrete cantilever retaining walls under seismic loading. The cost of the wall is taken as an objective function, which is minimized under the constraints implemented by the American Concrete Institute (ACI 318-05) design code and geometric limitations. The influence of peak ground acceleration (PGA) on optimal cost is also investigated. The solution of the problem is attained by the LFBBO algorithm, which is developed by adding Levy flight distribution to the mutation part of the biogeography-based optimization (BBO) algorithm. Five design examples, of which two are used in literature studies, are optimized in the study. The results are compared to test the performance of the LFBBO and BBO algorithms, to determine the influence of the seismic load and PGA on the optimal cost of the wall.

Optimal pattern synthesis for speech recognition based on principal component analysis

NASA Astrophysics Data System (ADS)

Korsun, O. N.; Poliyev, A. V.

2018-02-01

The algorithm for building an optimal pattern for the purpose of automatic speech recognition, which increases the probability of correct recognition, is developed and presented in this work. The optimal pattern forming is based on the decomposition of an initial pattern to principal components, which enables to reduce the dimension of multi-parameter optimization problem. At the next step the training samples are introduced and the optimal estimates for principal components decomposition coefficients are obtained by a numeric parameter optimization algorithm. Finally, we consider the experiment results that show the improvement in speech recognition introduced by the proposed optimization algorithm.

Pulmonary Nodule Recognition Based on Multiple Kernel Learning Support Vector Machine-PSO

PubMed Central

Zhu, Zhichuan; Zhao, Qingdong; Liu, Liwei; Zhang, Lijuan

2018-01-01

Pulmonary nodule recognition is the core module of lung CAD. The Support Vector Machine (SVM) algorithm has been widely used in pulmonary nodule recognition, and the algorithm of Multiple Kernel Learning Support Vector Machine (MKL-SVM) has achieved good results therein. Based on grid search, however, the MKL-SVM algorithm needs long optimization time in course of parameter optimization; also its identification accuracy depends on the fineness of grid. In the paper, swarm intelligence is introduced and the Particle Swarm Optimization (PSO) is combined with MKL-SVM algorithm to be MKL-SVM-PSO algorithm so as to realize global optimization of parameters rapidly. In order to obtain the global optimal solution, different inertia weights such as constant inertia weight, linear inertia weight, and nonlinear inertia weight are applied to pulmonary nodules recognition. The experimental results show that the model training time of the proposed MKL-SVM-PSO algorithm is only 1/7 of the training time of the MKL-SVM grid search algorithm, achieving better recognition effect. Moreover, Euclidean norm of normalized error vector is proposed to measure the proximity between the average fitness curve and the optimal fitness curve after convergence. Through statistical analysis of the average of 20 times operation results with different inertial weights, it can be seen that the dynamic inertial weight is superior to the constant inertia weight in the MKL-SVM-PSO algorithm. In the dynamic inertial weight algorithm, the parameter optimization time of nonlinear inertia weight is shorter; the average fitness value after convergence is much closer to the optimal fitness value, which is better than the linear inertial weight. Besides, a better nonlinear inertial weight is verified. PMID:29853983

Pulmonary Nodule Recognition Based on Multiple Kernel Learning Support Vector Machine-PSO.

PubMed

Li, Yang; Zhu, Zhichuan; Hou, Alin; Zhao, Qingdong; Liu, Liwei; Zhang, Lijuan

2018-01-01

Pulmonary nodule recognition is the core module of lung CAD. The Support Vector Machine (SVM) algorithm has been widely used in pulmonary nodule recognition, and the algorithm of Multiple Kernel Learning Support Vector Machine (MKL-SVM) has achieved good results therein. Based on grid search, however, the MKL-SVM algorithm needs long optimization time in course of parameter optimization; also its identification accuracy depends on the fineness of grid. In the paper, swarm intelligence is introduced and the Particle Swarm Optimization (PSO) is combined with MKL-SVM algorithm to be MKL-SVM-PSO algorithm so as to realize global optimization of parameters rapidly. In order to obtain the global optimal solution, different inertia weights such as constant inertia weight, linear inertia weight, and nonlinear inertia weight are applied to pulmonary nodules recognition. The experimental results show that the model training time of the proposed MKL-SVM-PSO algorithm is only 1/7 of the training time of the MKL-SVM grid search algorithm, achieving better recognition effect. Moreover, Euclidean norm of normalized error vector is proposed to measure the proximity between the average fitness curve and the optimal fitness curve after convergence. Through statistical analysis of the average of 20 times operation results with different inertial weights, it can be seen that the dynamic inertial weight is superior to the constant inertia weight in the MKL-SVM-PSO algorithm. In the dynamic inertial weight algorithm, the parameter optimization time of nonlinear inertia weight is shorter; the average fitness value after convergence is much closer to the optimal fitness value, which is better than the linear inertial weight. Besides, a better nonlinear inertial weight is verified.

Research on cutting path optimization of sheet metal parts based on ant colony algorithm

NASA Astrophysics Data System (ADS)

Wu, Z. Y.; Ling, H.; Li, L.; Wu, L. H.; Liu, N. B.

2017-09-01

In view of the disadvantages of the current cutting path optimization methods of sheet metal parts, a new method based on ant colony algorithm was proposed in this paper. The cutting path optimization problem of sheet metal parts was taken as the research object. The essence and optimization goal of the optimization problem were presented. The traditional serial cutting constraint rule was improved. The cutting constraint rule with cross cutting was proposed. The contour lines of parts were discretized and the mathematical model of cutting path optimization was established. Thus the problem was converted into the selection problem of contour lines of parts. Ant colony algorithm was used to solve the problem. The principle and steps of the algorithm were analyzed.

Algorithms for the optimization of RBE-weighted dose in particle therapy.

PubMed

Horcicka, M; Meyer, C; Buschbacher, A; Durante, M; Krämer, M

2013-01-21

We report on various algorithms used for the nonlinear optimization of RBE-weighted dose in particle therapy. Concerning the dose calculation carbon ions are considered and biological effects are calculated by the Local Effect Model. Taking biological effects fully into account requires iterative methods to solve the optimization problem. We implemented several additional algorithms into GSI's treatment planning system TRiP98, like the BFGS-algorithm and the method of conjugated gradients, in order to investigate their computational performance. We modified textbook iteration procedures to improve the convergence speed. The performance of the algorithms is presented by convergence in terms of iterations and computation time. We found that the Fletcher-Reeves variant of the method of conjugated gradients is the algorithm with the best computational performance. With this algorithm we could speed up computation times by a factor of 4 compared to the method of steepest descent, which was used before. With our new methods it is possible to optimize complex treatment plans in a few minutes leading to good dose distributions. At the end we discuss future goals concerning dose optimization issues in particle therapy which might benefit from fast optimization solvers.

Algorithms for the optimization of RBE-weighted dose in particle therapy

NASA Astrophysics Data System (ADS)

Horcicka, M.; Meyer, C.; Buschbacher, A.; Durante, M.; Krämer, M.

2013-01-01

We report on various algorithms used for the nonlinear optimization of RBE-weighted dose in particle therapy. Concerning the dose calculation carbon ions are considered and biological effects are calculated by the Local Effect Model. Taking biological effects fully into account requires iterative methods to solve the optimization problem. We implemented several additional algorithms into GSI's treatment planning system TRiP98, like the BFGS-algorithm and the method of conjugated gradients, in order to investigate their computational performance. We modified textbook iteration procedures to improve the convergence speed. The performance of the algorithms is presented by convergence in terms of iterations and computation time. We found that the Fletcher-Reeves variant of the method of conjugated gradients is the algorithm with the best computational performance. With this algorithm we could speed up computation times by a factor of 4 compared to the method of steepest descent, which was used before. With our new methods it is possible to optimize complex treatment plans in a few minutes leading to good dose distributions. At the end we discuss future goals concerning dose optimization issues in particle therapy which might benefit from fast optimization solvers.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tumuluru, Jaya Shankar; McCulloch, Richard Chet James

In this work a new hybrid genetic algorithm was developed which combines a rudimentary adaptive steepest ascent hill climbing algorithm with a sophisticated evolutionary algorithm in order to optimize complex multivariate design problems. By combining a highly stochastic algorithm (evolutionary) with a simple deterministic optimization algorithm (adaptive steepest ascent) computational resources are conserved and the solution converges rapidly when compared to either algorithm alone. In genetic algorithms natural selection is mimicked by random events such as breeding and mutation. In the adaptive steepest ascent algorithm each variable is perturbed by a small amount and the variable that caused the mostmore » improvement is incremented by a small step. If the direction of most benefit is exactly opposite of the previous direction with the most benefit then the step size is reduced by a factor of 2, thus the step size adapts to the terrain. A graphical user interface was created in MATLAB to provide an interface between the hybrid genetic algorithm and the user. Additional features such as bounding the solution space and weighting the objective functions individually are also built into the interface. The algorithm developed was tested to optimize the functions developed for a wood pelleting process. Using process variables (such as feedstock moisture content, die speed, and preheating temperature) pellet properties were appropriately optimized. Specifically, variables were found which maximized unit density, bulk density, tapped density, and durability while minimizing pellet moisture content and specific energy consumption. The time and computational resources required for the optimization were dramatically decreased using the hybrid genetic algorithm when compared to MATLAB's native evolutionary optimization tool.« less

Discrete size optimization of steel trusses using a refined big bang-big crunch algorithm

NASA Astrophysics Data System (ADS)

Hasançebi, O.; Kazemzadeh Azad, S.

2014-01-01

This article presents a methodology that provides a method for design optimization of steel truss structures based on a refined big bang-big crunch (BB-BC) algorithm. It is shown that a standard formulation of the BB-BC algorithm occasionally falls short of producing acceptable solutions to problems from discrete size optimum design of steel trusses. A reformulation of the algorithm is proposed and implemented for design optimization of various discrete truss structures according to American Institute of Steel Construction Allowable Stress Design (AISC-ASD) specifications. Furthermore, the performance of the proposed BB-BC algorithm is compared to its standard version as well as other well-known metaheuristic techniques. The numerical results confirm the efficiency of the proposed algorithm in practical design optimization of truss structures.

Cognitive Nonlinear Radar

DTIC Science & Technology

2013-01-01

intelligently selecting waveform parameters using adaptive algorithms. The adaptive algorithms optimize the waveform parameters based on (1) the EM...the environment. 15. SUBJECT TERMS cognitive radar, adaptive sensing, spectrum sensing, multi-objective optimization, genetic algorithms, machine...detection and classification block diagram. .........................................................6 Figure 5. Genetic algorithm block diagram

Integrated controls design optimization

DOEpatents

Lou, Xinsheng; Neuschaefer, Carl H.

2015-09-01

A control system (207) for optimizing a chemical looping process of a power plant includes an optimizer (420), an income algorithm (230) and a cost algorithm (225) and a chemical looping process models. The process models are used to predict the process outputs from process input variables. Some of the process in puts and output variables are related to the income of the plant; and some others are related to the cost of the plant operations. The income algorithm (230) provides an income input to the optimizer (420) based on a plurality of input parameters (215) of the power plant. The cost algorithm (225) provides a cost input to the optimizer (420) based on a plurality of output parameters (220) of the power plant. The optimizer (420) determines an optimized operating parameter solution based on at least one of the income input and the cost input, and supplies the optimized operating parameter solution to the power plant.

Particle Swarm Optimization

NASA Technical Reports Server (NTRS)

Venter, Gerhard; Sobieszczanski-Sobieski Jaroslaw

2002-01-01

The purpose of this paper is to show how the search algorithm known as particle swarm optimization performs. Here, particle swarm optimization is applied to structural design problems, but the method has a much wider range of possible applications. The paper's new contributions are improvements to the particle swarm optimization algorithm and conclusions and recommendations as to the utility of the algorithm, Results of numerical experiments for both continuous and discrete applications are presented in the paper. The results indicate that the particle swarm optimization algorithm does locate the constrained minimum design in continuous applications with very good precision, albeit at a much higher computational cost than that of a typical gradient based optimizer. However, the true potential of particle swarm optimization is primarily in applications with discrete and/or discontinuous functions and variables. Additionally, particle swarm optimization has the potential of efficient computation with very large numbers of concurrently operating processors.

A Danger-Theory-Based Immune Network Optimization Algorithm

PubMed Central

Li, Tao; Xiao, Xin; Shi, Yuanquan

2013-01-01

Existing artificial immune optimization algorithms reflect a number of shortcomings, such as premature convergence and poor local search ability. This paper proposes a danger-theory-based immune network optimization algorithm, named dt-aiNet. The danger theory emphasizes that danger signals generated from changes of environments will guide different levels of immune responses, and the areas around danger signals are called danger zones. By defining the danger zone to calculate danger signals for each antibody, the algorithm adjusts antibodies' concentrations through its own danger signals and then triggers immune responses of self-regulation. So the population diversity can be maintained. Experimental results show that the algorithm has more advantages in the solution quality and diversity of the population. Compared with influential optimization algorithms, CLONALG, opt-aiNet, and dopt-aiNet, the algorithm has smaller error values and higher success rates and can find solutions to meet the accuracies within the specified function evaluation times. PMID:23483853

Algorithm comparison for schedule optimization in MR fingerprinting.

PubMed

Cohen, Ouri; Rosen, Matthew S

2017-09-01

In MR Fingerprinting, the flip angles and repetition times are chosen according to a pseudorandom schedule. In previous work, we have shown that maximizing the discrimination between different tissue types by optimizing the acquisition schedule allows reductions in the number of measurements required. The ideal optimization algorithm for this application remains unknown, however. In this work we examine several different optimization algorithms to determine the one best suited for optimizing MR Fingerprinting acquisition schedules. Copyright © 2017 Elsevier Inc. All rights reserved.

A Particle Swarm Optimization-Based Approach with Local Search for Predicting Protein Folding.

PubMed

Yang, Cheng-Hong; Lin, Yu-Shiun; Chuang, Li-Yeh; Chang, Hsueh-Wei

2017-10-01

The hydrophobic-polar (HP) model is commonly used for predicting protein folding structures and hydrophobic interactions. This study developed a particle swarm optimization (PSO)-based algorithm combined with local search algorithms; specifically, the high exploration PSO (HEPSO) algorithm (which can execute global search processes) was combined with three local search algorithms (hill-climbing algorithm, greedy algorithm, and Tabu table), yielding the proposed HE-L-PSO algorithm. By using 20 known protein structures, we evaluated the performance of the HE-L-PSO algorithm in predicting protein folding in the HP model. The proposed HE-L-PSO algorithm exhibited favorable performance in predicting both short and long amino acid sequences with high reproducibility and stability, compared with seven reported algorithms. The HE-L-PSO algorithm yielded optimal solutions for all predicted protein folding structures. All HE-L-PSO-predicted protein folding structures possessed a hydrophobic core that is similar to normal protein folding.

Chaotic particle swarm optimization with mutation for classification.

PubMed

Assarzadeh, Zahra; Naghsh-Nilchi, Ahmad Reza

2015-01-01

In this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classify patterns of different classes in the feature space. The introduced mutation operators and chaotic sequences allows us to overcome the problem of early convergence into a local minima associated with particle swarm optimization algorithms. That is, the mutation operator sharpens the convergence and it tunes the best possible solution. Furthermore, to remove the irrelevant data and reduce the dimensionality of medical datasets, a feature selection approach using binary version of the proposed particle swarm optimization is introduced. In order to demonstrate the effectiveness of our proposed classifier, mutation-based classifier particle swarm optimization, it is checked out with three sets of data classifications namely, Wisconsin diagnostic breast cancer, Wisconsin breast cancer and heart-statlog, with different feature vector dimensions. The proposed algorithm is compared with different classifier algorithms including k-nearest neighbor, as a conventional classifier, particle swarm-classifier, genetic algorithm, and Imperialist competitive algorithm-classifier, as more sophisticated ones. The performance of each classifier was evaluated by calculating the accuracy, sensitivity, specificity and Matthews's correlation coefficient. The experimental results show that the mutation-based classifier particle swarm optimization unequivocally performs better than all the compared algorithms.

A theoretical comparison of evolutionary algorithms and simulated annealing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hart, W.E.

1995-08-28

This paper theoretically compares the performance of simulated annealing and evolutionary algorithms. Our main result is that under mild conditions a wide variety of evolutionary algorithms can be shown to have greater performance than simulated annealing after a sufficiently large number of function evaluations. This class of EAs includes variants of evolutionary strategie and evolutionary programming, the canonical genetic algorithm, as well as a variety of genetic algorithms that have been applied to combinatorial optimization problems. The proof of this result is based on a performance analysis of a very general class of stochastic optimization algorithms, which has implications formore » the performance of a variety of other optimization algorithm.« less

Design optimization of single mixed refrigerant LNG process using a hybrid modified coordinate descent algorithm

NASA Astrophysics Data System (ADS)

Qyyum, Muhammad Abdul; Long, Nguyen Van Duc; Minh, Le Quang; Lee, Moonyong

2018-01-01

Design optimization of the single mixed refrigerant (SMR) natural gas liquefaction (LNG) process involves highly non-linear interactions between decision variables, constraints, and the objective function. These non-linear interactions lead to an irreversibility, which deteriorates the energy efficiency of the LNG process. In this study, a simple and highly efficient hybrid modified coordinate descent (HMCD) algorithm was proposed to cope with the optimization of the natural gas liquefaction process. The single mixed refrigerant process was modeled in Aspen Hysys® and then connected to a Microsoft Visual Studio environment. The proposed optimization algorithm provided an improved result compared to the other existing methodologies to find the optimal condition of the complex mixed refrigerant natural gas liquefaction process. By applying the proposed optimization algorithm, the SMR process can be designed with the 0.2555 kW specific compression power which is equivalent to 44.3% energy saving as compared to the base case. Furthermore, in terms of coefficient of performance (COP), it can be enhanced up to 34.7% as compared to the base case. The proposed optimization algorithm provides a deep understanding of the optimization of the liquefaction process in both technical and numerical perspectives. In addition, the HMCD algorithm can be employed to any mixed refrigerant based liquefaction process in the natural gas industry.

Gradient Optimization for Analytic conTrols - GOAT

NASA Astrophysics Data System (ADS)

Assémat, Elie; Machnes, Shai; Tannor, David; Wilhelm-Mauch, Frank

Quantum optimal control becomes a necessary step in a number of studies in the quantum realm. Recent experimental advances showed that superconducting qubits can be controlled with an impressive accuracy. However, most of the standard optimal control algorithms are not designed to manage such high accuracy. To tackle this issue, a novel quantum optimal control algorithm have been introduced: the Gradient Optimization for Analytic conTrols (GOAT). It avoids the piecewise constant approximation of the control pulse used by standard algorithms. This allows an efficient implementation of very high accuracy optimization. It also includes a novel method to compute the gradient that provides many advantages, e.g. the absence of backpropagation or the natural route to optimize the robustness of the control pulses. This talk will present the GOAT algorithm and a few applications to transmons systems.

Evolutionary Dynamic Multiobjective Optimization Via Kalman Filter Prediction.

PubMed

Muruganantham, Arrchana; Tan, Kay Chen; Vadakkepat, Prahlad

2016-12-01

Evolutionary algorithms are effective in solving static multiobjective optimization problems resulting in the emergence of a number of state-of-the-art multiobjective evolutionary algorithms (MOEAs). Nevertheless, the interest in applying them to solve dynamic multiobjective optimization problems has only been tepid. Benchmark problems, appropriate performance metrics, as well as efficient algorithms are required to further the research in this field. One or more objectives may change with time in dynamic optimization problems. The optimization algorithm must be able to track the moving optima efficiently. A prediction model can learn the patterns from past experience and predict future changes. In this paper, a new dynamic MOEA using Kalman filter (KF) predictions in decision space is proposed to solve the aforementioned problems. The predictions help to guide the search toward the changed optima, thereby accelerating convergence. A scoring scheme is devised to hybridize the KF prediction with a random reinitialization method. Experimental results and performance comparisons with other state-of-the-art algorithms demonstrate that the proposed algorithm is capable of significantly improving the dynamic optimization performance.

An optimized algorithm for multiscale wideband deconvolution of radio astronomical images

NASA Astrophysics Data System (ADS)

Offringa, A. R.; Smirnov, O.

2017-10-01

We describe a new multiscale deconvolution algorithm that can also be used in a multifrequency mode. The algorithm only affects the minor clean loop. In single-frequency mode, the minor loop of our improved multiscale algorithm is over an order of magnitude faster than the casa multiscale algorithm, and produces results of similar quality. For multifrequency deconvolution, a technique named joined-channel cleaning is used. In this mode, the minor loop of our algorithm is two to three orders of magnitude faster than casa msmfs. We extend the multiscale mode with automated scale-dependent masking, which allows structures to be cleaned below the noise. We describe a new scale-bias function for use in multiscale cleaning. We test a second deconvolution method that is a variant of the moresane deconvolution technique, and uses a convex optimization technique with isotropic undecimated wavelets as dictionary. On simple well-calibrated data, the convex optimization algorithm produces visually more representative models. On complex or imperfect data, the convex optimization algorithm has stability issues.

Mobile geographic information system solution for pavement condition surveys [summary].

DOT National Transportation Integrated Search

2012-01-01

The State Materials Office (SMO) of the Florida : Department of Transportation (FDOT) performs : annual Pavement Condition Surveys (PCS) of : the Departments extensive pavement network. : This work is performed by single-person crews in : inertial...

Semiconducting Metal Oxide Based Sensors for Selective Gas Pollutant Detection

PubMed Central

Kanan, Sofian M.; El-Kadri, Oussama M.; Abu-Yousef, Imad A.; Kanan, Marsha C.

2009-01-01

A review of some papers published in the last fifty years that focus on the semiconducting metal oxide (SMO) based sensors for the selective and sensitive detection of various environmental pollutants is presented. PMID:22408500

DOE Office of Scientific and Technical Information (OSTI.GOV)

Beltran, C; Kamal, H

Purpose: To provide a multicriteria optimization algorithm for intensity modulated radiation therapy using pencil proton beam scanning. Methods: Intensity modulated radiation therapy using pencil proton beam scanning requires efficient optimization algorithms to overcome the uncertainties in the Bragg peaks locations. This work is focused on optimization algorithms that are based on Monte Carlo simulation of the treatment planning and use the weights and the dose volume histogram (DVH) control points to steer toward desired plans. The proton beam treatment planning process based on single objective optimization (representing a weighted sum of multiple objectives) usually leads to time-consuming iterations involving treatmentmore » planning team members. We proved a time efficient multicriteria optimization algorithm that is developed to run on NVIDIA GPU (Graphical Processing Units) cluster. The multicriteria optimization algorithm running time benefits from up-sampling of the CT voxel size of the calculations without loss of fidelity. Results: We will present preliminary results of Multicriteria optimization for intensity modulated proton therapy based on DVH control points. The results will show optimization results of a phantom case and a brain tumor case. Conclusion: The multicriteria optimization of the intensity modulated radiation therapy using pencil proton beam scanning provides a novel tool for treatment planning. Work support by a grant from Varian Inc.« less

New strain states and radical property tuning of metal oxides using a nanocomposite thin film approach

DOE PAGES

MacManus-Driscoll, Judith; Suwardi, Ady; Kursumovic, Ahmed; ...

2015-05-05

Auxetic-like strain states were generated in self-assembled nanocomposite thin films of (Ba 0.6Sr 0.4TiO 3) 1–x – (Sm 2O 3) x(BSTO – SmO). A switch from auxetic-like to elastic-like strain behavior was observed for x > 0.50, when the SmO switched from being nanopillars in the BSTO matrix to being the matrix with BSTO nanopillars embedded in it. A simple model was adopted to explain how in-plane strain varies with x. At high x (0.75), strongly enhanced ferroelectric properties were obtained compared to pure BSTO films. Furthermore, the nanocomposite method represents a powerful new way to tune the properties ofmore » a wide range of strongly correlated metal oxides whose properties are very sensitive to strain.« less

Janus Monolayer Transition-Metal Dichalcogenides.

PubMed

Zhang, Jing; Jia, Shuai; Kholmanov, Iskandar; Dong, Liang; Er, Dequan; Chen, Weibing; Guo, Hua; Jin, Zehua; Shenoy, Vivek B; Shi, Li; Lou, Jun

2017-08-22

The crystal configuration of sandwiched S-Mo-Se structure (Janus SMoSe) at the monolayer limit has been synthesized and carefully characterized in this work. By controlled sulfurization of monolayer MoSe 2 , the top layer of selenium atoms is substituted by sulfur atoms, while the bottom selenium layer remains intact. The structure of this material is systematically investigated by Raman, photoluminescence, transmission electron microscopy, and X-ray photoelectron spectroscopy and confirmed by time-of-flight secondary ion mass spectrometry. Density functional theory (DFT) calculations are performed to better understand the Raman vibration modes and electronic structures of the Janus SMoSe monolayer, which are found to correlate well with corresponding experimental results. Finally, high basal plane hydrogen evolution reaction activity is discovered for the Janus monolayer, and DFT calculation implies that the activity originates from the synergistic effect of the intrinsic defects and structural strain inherent in the Janus structure.

Crystal structure of a multi-domain human smoothened receptor in complex with a super stabilizing ligand

NASA Astrophysics Data System (ADS)

Zhang, Xianjun; Zhao, Fei; Wu, Yiran; Yang, Jun; Han, Gye Won; Zhao, Suwen; Ishchenko, Andrii; Ye, Lintao; Lin, Xi; Ding, Kang; Dharmarajan, Venkatasubramanian; Griffin, Patrick R.; Gati, Cornelius; Nelson, Garrett; Hunter, Mark S.; Hanson, Michael A.; Cherezov, Vadim; Stevens, Raymond C.; Tan, Wenfu; Tao, Houchao; Xu, Fei

2017-05-01

The Smoothened receptor (SMO) belongs to the Class Frizzled of the G protein-coupled receptor (GPCR) superfamily, constituting a key component of the Hedgehog signalling pathway. Here we report the crystal structure of the multi-domain human SMO, bound and stabilized by a designed tool ligand TC114, using an X-ray free-electron laser source at 2.9 Å. The structure reveals a precise arrangement of three distinct domains: a seven-transmembrane helices domain (TMD), a hinge domain (HD) and an intact extracellular cysteine-rich domain (CRD). This architecture enables allosteric interactions between the domains that are important for ligand recognition and receptor activation. By combining the structural data, molecular dynamics simulation, and hydrogen-deuterium-exchange analysis, we demonstrate that transmembrane helix VI, extracellular loop 3 and the HD play a central role in transmitting the signal employing a unique GPCR activation mechanism, distinct from other multi-domain GPCRs.

Janus Monolayer Transition-Metal Dichalcogenides

DOE PAGES

Zhang, Jing; Jia, Shuai; Kholmanov, Iskandar; ...

2017-08-03

In this work, the crystal configuration of sandwiched S–Mo–Se structure (Janus SMoSe) at the monolayer limit has been synthesized and carefully characterized. By controlled sulfurization of monolayer MoSe 2, the top layer of selenium atoms is substituted by sulfur atoms, while the bottom selenium layer remains intact. Furthermore, the structure of this material is systematically investigated by Raman, photoluminescence, transmission electron microscopy, and X-ray photoelectron spectroscopy and confirmed by time-of-flight secondary ion mass spectrometry. Density functional theory (DFT) calculations are performed to better understand the Raman vibration modes and electronic structures of the Janus SMoSe monolayer, which are found tomore » correlate well with corresponding experimental results. Finally, high basal plane hydrogen evolution reaction activity is discovered for the Janus monolayer, and DFT calculation implies that the activity originates from the synergistic effect of the intrinsic defects and structural strain inherent in the Janus structure.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Xianjun; Zhao, Fei; Wu, Yiran

Here, the Smoothened receptor (SMO) belongs to the Class Frizzled of the G protein-coupled receptor (GPCR) superfamily, constituting a key component of the Hedgehog signalling pathway. Here we report the crystal structure of the multi-domain human SMO, bound and stabilized by a designed tool ligand TC114, using an X-ray free-electron laser source at 2.9 Å. The structure reveals a precise arrangement of three distinct domains: a seven-transmembrane helices domain (TMD), a hinge domain (HD) and an intact extracellular cysteine-rich domain (CRD). This architecture enables allosteric interactions between the domains that are important for ligand recognition and receptor activation. By combiningmore » the structural data, molecular dynamics simulation, and hydrogen-deuterium-exchange analysis, we demonstrate that transmembrane helix VI, extracellular loop 3 and the HD play a central role in transmitting the signal employing a unique GPCR activation mechanism, distinct from other multi-domain GPCRs.« less

Beam-steering efficiency optimization method based on a rapid-search algorithm for liquid crystal optical phased array.

PubMed

Xiao, Feng; Kong, Lingjiang; Chen, Jian

2017-06-01

A rapid-search algorithm to improve the beam-steering efficiency for a liquid crystal optical phased array was proposed and experimentally demonstrated in this paper. This proposed algorithm, in which the value of steering efficiency is taken as the objective function and the controlling voltage codes are considered as the optimization variables, consisted of a detection stage and a construction stage. It optimized the steering efficiency in the detection stage and adjusted its search direction adaptively in the construction stage to avoid getting caught in a wrong search space. Simulations had been conducted to compare the proposed algorithm with the widely used pattern-search algorithm using criteria of convergence rate and optimized efficiency. Beam-steering optimization experiments had been performed to verify the validity of the proposed method.

An Improved Co-evolutionary Particle Swarm Optimization for Wireless Sensor Networks with Dynamic Deployment

PubMed Central

Wang, Xue; Wang, Sheng; Ma, Jun-Jie

2007-01-01

The effectiveness of wireless sensor networks (WSNs) depends on the coverage and target detection probability provided by dynamic deployment, which is usually supported by the virtual force (VF) algorithm. However, in the VF algorithm, the virtual force exerted by stationary sensor nodes will hinder the movement of mobile sensor nodes. Particle swarm optimization (PSO) is introduced as another dynamic deployment algorithm, but in this case the computation time required is the big bottleneck. This paper proposes a dynamic deployment algorithm which is named “virtual force directed co-evolutionary particle swarm optimization” (VFCPSO), since this algorithm combines the co-evolutionary particle swarm optimization (CPSO) with the VF algorithm, whereby the CPSO uses multiple swarms to optimize different components of the solution vectors for dynamic deployment cooperatively and the velocity of each particle is updated according to not only the historical local and global optimal solutions, but also the virtual forces of sensor nodes. Simulation results demonstrate that the proposed VFCPSO is competent for dynamic deployment in WSNs and has better performance with respect to computation time and effectiveness than the VF, PSO and VFPSO algorithms.

Continuous intensity map optimization (CIMO): A novel approach to leaf sequencing in step and shoot IMRT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cao Daliang; Earl, Matthew A.; Luan, Shuang

2006-04-15

A new leaf-sequencing approach has been developed that is designed to reduce the number of required beam segments for step-and-shoot intensity modulated radiation therapy (IMRT). This approach to leaf sequencing is called continuous-intensity-map-optimization (CIMO). Using a simulated annealing algorithm, CIMO seeks to minimize differences between the optimized and sequenced intensity maps. Two distinguishing features of the CIMO algorithm are (1) CIMO does not require that each optimized intensity map be clustered into discrete levels and (2) CIMO is not rule-based but rather simultaneously optimizes both the aperture shapes and weights. To test the CIMO algorithm, ten IMRT patient cases weremore » selected (four head-and-neck, two pancreas, two prostate, one brain, and one pelvis). For each case, the optimized intensity maps were extracted from the Pinnacle{sup 3} treatment planning system. The CIMO algorithm was applied, and the optimized aperture shapes and weights were loaded back into Pinnacle. A final dose calculation was performed using Pinnacle's convolution/superposition based dose calculation. On average, the CIMO algorithm provided a 54% reduction in the number of beam segments as compared with Pinnacle's leaf sequencer. The plans sequenced using the CIMO algorithm also provided improved target dose uniformity and a reduced discrepancy between the optimized and sequenced intensity maps. For ten clinical intensity maps, comparisons were performed between the CIMO algorithm and the power-of-two reduction algorithm of Xia and Verhey [Med. Phys. 25(8), 1424-1434 (1998)]. When the constraints of a Varian Millennium multileaf collimator were applied, the CIMO algorithm resulted in a 26% reduction in the number of segments. For an Elekta multileaf collimator, the CIMO algorithm resulted in a 67% reduction in the number of segments. An average leaf sequencing time of less than one minute per beam was observed.« less

Discrete Bat Algorithm for Optimal Problem of Permutation Flow Shop Scheduling

PubMed Central

Luo, Qifang; Zhou, Yongquan; Xie, Jian; Ma, Mingzhi; Li, Liangliang

2014-01-01

A discrete bat algorithm (DBA) is proposed for optimal permutation flow shop scheduling problem (PFSP). Firstly, the discrete bat algorithm is constructed based on the idea of basic bat algorithm, which divide whole scheduling problem into many subscheduling problems and then NEH heuristic be introduced to solve subscheduling problem. Secondly, some subsequences are operated with certain probability in the pulse emission and loudness phases. An intensive virtual population neighborhood search is integrated into the discrete bat algorithm to further improve the performance. Finally, the experimental results show the suitability and efficiency of the present discrete bat algorithm for optimal permutation flow shop scheduling problem. PMID:25243220

Discrete bat algorithm for optimal problem of permutation flow shop scheduling.

PubMed

Luo, Qifang; Zhou, Yongquan; Xie, Jian; Ma, Mingzhi; Li, Liangliang

2014-01-01

A discrete bat algorithm (DBA) is proposed for optimal permutation flow shop scheduling problem (PFSP). Firstly, the discrete bat algorithm is constructed based on the idea of basic bat algorithm, which divide whole scheduling problem into many subscheduling problems and then NEH heuristic be introduced to solve subscheduling problem. Secondly, some subsequences are operated with certain probability in the pulse emission and loudness phases. An intensive virtual population neighborhood search is integrated into the discrete bat algorithm to further improve the performance. Finally, the experimental results show the suitability and efficiency of the present discrete bat algorithm for optimal permutation flow shop scheduling problem.

Stochastic optimization algorithms for barrier dividend strategies

NASA Astrophysics Data System (ADS)

Yin, G.; Song, Q. S.; Yang, H.

2009-01-01

This work focuses on finding optimal barrier policy for an insurance risk model when the dividends are paid to the share holders according to a barrier strategy. A new approach based on stochastic optimization methods is developed. Compared with the existing results in the literature, more general surplus processes are considered. Precise models of the surplus need not be known; only noise-corrupted observations of the dividends are used. Using barrier-type strategies, a class of stochastic optimization algorithms are developed. Convergence of the algorithm is analyzed; rate of convergence is also provided. Numerical results are reported to demonstrate the performance of the algorithm.

A graph decomposition-based approach for water distribution network optimization

NASA Astrophysics Data System (ADS)

Zheng, Feifei; Simpson, Angus R.; Zecchin, Aaron C.; Deuerlein, Jochen W.

2013-04-01

A novel optimization approach for water distribution network design is proposed in this paper. Using graph theory algorithms, a full water network is first decomposed into different subnetworks based on the connectivity of the network's components. The original whole network is simplified to a directed augmented tree, in which the subnetworks are substituted by augmented nodes and directed links are created to connect them. Differential evolution (DE) is then employed to optimize each subnetwork based on the sequence specified by the assigned directed links in the augmented tree. Rather than optimizing the original network as a whole, the subnetworks are sequentially optimized by the DE algorithm. A solution choice table is established for each subnetwork (except for the subnetwork that includes a supply node) and the optimal solution of the original whole network is finally obtained by use of the solution choice tables. Furthermore, a preconditioning algorithm is applied to the subnetworks to produce an approximately optimal solution for the original whole network. This solution specifies promising regions for the final optimization algorithm to further optimize the subnetworks. Five water network case studies are used to demonstrate the effectiveness of the proposed optimization method. A standard DE algorithm (SDE) and a genetic algorithm (GA) are applied to each case study without network decomposition to enable a comparison with the proposed method. The results show that the proposed method consistently outperforms the SDE and GA (both with tuned parameters) in terms of both the solution quality and efficiency.

Variational Trajectory Optimization Tool Set: Technical description and user's manual

NASA Technical Reports Server (NTRS)

Bless, Robert R.; Queen, Eric M.; Cavanaugh, Michael D.; Wetzel, Todd A.; Moerder, Daniel D.

1993-01-01

The algorithms that comprise the Variational Trajectory Optimization Tool Set (VTOTS) package are briefly described. The VTOTS is a software package for solving nonlinear constrained optimal control problems from a wide range of engineering and scientific disciplines. The VTOTS package was specifically designed to minimize the amount of user programming; in fact, for problems that may be expressed in terms of analytical functions, the user needs only to define the problem in terms of symbolic variables. This version of the VTOTS does not support tabular data; thus, problems must be expressed in terms of analytical functions. The VTOTS package consists of two methods for solving nonlinear optimal control problems: a time-domain finite-element algorithm and a multiple shooting algorithm. These two algorithms, under the VTOTS package, may be run independently or jointly. The finite-element algorithm generates approximate solutions, whereas the shooting algorithm provides a more accurate solution to the optimization problem. A user's manual, some examples with results, and a brief description of the individual subroutines are included.

Self-adaptive multi-objective harmony search for optimal design of water distribution networks

NASA Astrophysics Data System (ADS)

Choi, Young Hwan; Lee, Ho Min; Yoo, Do Guen; Kim, Joong Hoon

2017-11-01

In multi-objective optimization computing, it is important to assign suitable parameters to each optimization problem to obtain better solutions. In this study, a self-adaptive multi-objective harmony search (SaMOHS) algorithm is developed to apply the parameter-setting-free technique, which is an example of a self-adaptive methodology. The SaMOHS algorithm attempts to remove some of the inconvenience from parameter setting and selects the most adaptive parameters during the iterative solution search process. To verify the proposed algorithm, an optimal least cost water distribution network design problem is applied to three different target networks. The results are compared with other well-known algorithms such as multi-objective harmony search and the non-dominated sorting genetic algorithm-II. The efficiency of the proposed algorithm is quantified by suitable performance indices. The results indicate that SaMOHS can be efficiently applied to the search for Pareto-optimal solutions in a multi-objective solution space.

Evolutionary Beamforming Optimization for Radio Frequency Charging in Wireless Rechargeable Sensor Networks

PubMed Central

Yao, Ke-Han; Jiang, Jehn-Ruey; Tsai, Chung-Hsien; Wu, Zong-Syun

2017-01-01

This paper investigates how to efficiently charge sensor nodes in a wireless rechargeable sensor network (WRSN) with radio frequency (RF) chargers to make the network sustainable. An RF charger is assumed to be equipped with a uniform circular array (UCA) of 12 antennas with the radius λ, where λ is the RF wavelength. The UCA can steer most RF energy in a target direction to charge a specific WRSN node by the beamforming technology. Two evolutionary algorithms (EAs) using the evolution strategy (ES), namely the Evolutionary Beamforming Optimization (EBO) algorithm and the Evolutionary Beamforming Optimization Reseeding (EBO-R) algorithm, are proposed to nearly optimize the power ratio of the UCA beamforming peak side lobe (PSL) and the main lobe (ML) aimed at the given target direction. The proposed algorithms are simulated for performance evaluation and are compared with a related algorithm, called Particle Swarm Optimization Gravitational Search Algorithm-Explore (PSOGSA-Explore), to show their superiority. PMID:28825648

Optimized data fusion for K-means Laplacian clustering

PubMed Central

Yu, Shi; Liu, Xinhai; Tranchevent, Léon-Charles; Glänzel, Wolfgang; Suykens, Johan A. K.; De Moor, Bart; Moreau, Yves

2011-01-01

Motivation: We propose a novel algorithm to combine multiple kernels and Laplacians for clustering analysis. The new algorithm is formulated on a Rayleigh quotient objective function and is solved as a bi-level alternating minimization procedure. Using the proposed algorithm, the coefficients of kernels and Laplacians can be optimized automatically. Results: Three variants of the algorithm are proposed. The performance is systematically validated on two real-life data fusion applications. The proposed Optimized Kernel Laplacian Clustering (OKLC) algorithms perform significantly better than other methods. Moreover, the coefficients of kernels and Laplacians optimized by OKLC show some correlation with the rank of performance of individual data source. Though in our evaluation the K values are predefined, in practical studies, the optimal cluster number can be consistently estimated from the eigenspectrum of the combined kernel Laplacian matrix. Availability: The MATLAB code of algorithms implemented in this paper is downloadable from http://homes.esat.kuleuven.be/~sistawww/bioi/syu/oklc.html. Contact: shiyu@uchicago.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:20980271

Particle swarm optimization - Genetic algorithm (PSOGA) on linear transportation problem

NASA Astrophysics Data System (ADS)

Rahmalia, Dinita

2017-08-01

Linear Transportation Problem (LTP) is the case of constrained optimization where we want to minimize cost subject to the balance of the number of supply and the number of demand. The exact method such as northwest corner, vogel, russel, minimal cost have been applied at approaching optimal solution. In this paper, we use heurisitic like Particle Swarm Optimization (PSO) for solving linear transportation problem at any size of decision variable. In addition, we combine mutation operator of Genetic Algorithm (GA) at PSO to improve optimal solution. This method is called Particle Swarm Optimization - Genetic Algorithm (PSOGA). The simulations show that PSOGA can improve optimal solution resulted by PSO.

Fast optimization of glide vehicle reentry trajectory based on genetic algorithm

NASA Astrophysics Data System (ADS)

Jia, Jun; Dong, Ruixing; Yuan, Xuejun; Wang, Chuangwei

2018-02-01

An optimization method of reentry trajectory based on genetic algorithm is presented to meet the need of reentry trajectory optimization for glide vehicle. The dynamic model for the glide vehicle during reentry period is established. Considering the constraints of heat flux, dynamic pressure, overload etc., the optimization of reentry trajectory is investigated by utilizing genetic algorithm. The simulation shows that the method presented by this paper is effective for the optimization of reentry trajectory of glide vehicle. The efficiency and speed of this method is comparative with the references. Optimization results meet all constraints, and the on-line fast optimization is potential by pre-processing the offline samples.

Adaptive reference update (ARU) algorithm. A stochastic search algorithm for efficient optimization of multi-drug cocktails

PubMed Central

2012-01-01

Background Multi-target therapeutics has been shown to be effective for treating complex diseases, and currently, it is a common practice to combine multiple drugs to treat such diseases to optimize the therapeutic outcomes. However, considering the huge number of possible ways to mix multiple drugs at different concentrations, it is practically difficult to identify the optimal drug combination through exhaustive testing. Results In this paper, we propose a novel stochastic search algorithm, called the adaptive reference update (ARU) algorithm, that can provide an efficient and systematic way for optimizing multi-drug cocktails. The ARU algorithm iteratively updates the drug combination to improve its response, where the update is made by comparing the response of the current combination with that of a reference combination, based on which the beneficial update direction is predicted. The reference combination is continuously updated based on the drug response values observed in the past, thereby adapting to the underlying drug response function. To demonstrate the effectiveness of the proposed algorithm, we evaluated its performance based on various multi-dimensional drug functions and compared it with existing algorithms. Conclusions Simulation results show that the ARU algorithm significantly outperforms existing stochastic search algorithms, including the Gur Game algorithm. In fact, the ARU algorithm can more effectively identify potent drug combinations and it typically spends fewer iterations for finding effective combinations. Furthermore, the ARU algorithm is robust to random fluctuations and noise in the measured drug response, which makes the algorithm well-suited for practical drug optimization applications. PMID:23134742

Firefly algorithm for cardinality constrained mean-variance portfolio optimization problem with entropy diversity constraint.

PubMed

Bacanin, Nebojsa; Tuba, Milan

2014-01-01

Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Nature-inspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few applications of nature-inspired metaheuristics to portfolio optimization problem. This is especially true for swarm intelligence algorithms which represent the newer branch of nature-inspired algorithms. No application of any swarm intelligence metaheuristics to cardinality constrained mean-variance (CCMV) portfolio problem with entropy constraint was found in the literature. This paper introduces modified firefly algorithm (FA) for the CCMV portfolio model with entropy constraint. Firefly algorithm is one of the latest, very successful swarm intelligence algorithm; however, it exhibits some deficiencies when applied to constrained problems. To overcome lack of exploration power during early iterations, we modified the algorithm and tested it on standard portfolio benchmark data sets used in the literature. Our proposed modified firefly algorithm proved to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results.

Firefly Algorithm for Cardinality Constrained Mean-Variance Portfolio Optimization Problem with Entropy Diversity Constraint

PubMed Central

2014-01-01

Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Nature-inspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few applications of nature-inspired metaheuristics to portfolio optimization problem. This is especially true for swarm intelligence algorithms which represent the newer branch of nature-inspired algorithms. No application of any swarm intelligence metaheuristics to cardinality constrained mean-variance (CCMV) portfolio problem with entropy constraint was found in the literature. This paper introduces modified firefly algorithm (FA) for the CCMV portfolio model with entropy constraint. Firefly algorithm is one of the latest, very successful swarm intelligence algorithm; however, it exhibits some deficiencies when applied to constrained problems. To overcome lack of exploration power during early iterations, we modified the algorithm and tested it on standard portfolio benchmark data sets used in the literature. Our proposed modified firefly algorithm proved to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results. PMID:24991645

Range image registration based on hash map and moth-flame optimization

NASA Astrophysics Data System (ADS)

Zou, Li; Ge, Baozhen; Chen, Lei

2018-03-01

Over the past decade, evolutionary algorithms (EAs) have been introduced to solve range image registration problems because of their robustness and high precision. However, EA-based range image registration algorithms are time-consuming. To reduce the computational time, an EA-based range image registration algorithm using hash map and moth-flame optimization is proposed. In this registration algorithm, a hash map is used to avoid over-exploitation in registration process. Additionally, we present a search equation that is better at exploration and a restart mechanism to avoid being trapped in local minima. We compare the proposed registration algorithm with the registration algorithms using moth-flame optimization and several state-of-the-art EA-based registration algorithms. The experimental results show that the proposed algorithm has a lower computational cost than other algorithms and achieves similar registration precision.

An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications.

PubMed

Ye, Fei; Lou, Xin Yuan; Sun, Lin Fu

2017-01-01

This paper proposes a new support vector machine (SVM) optimization scheme based on an improved chaotic fly optimization algorithm (FOA) with a mutation strategy to simultaneously perform parameter setting turning for the SVM and feature selection. In the improved FOA, the chaotic particle initializes the fruit fly swarm location and replaces the expression of distance for the fruit fly to find the food source. However, the proposed mutation strategy uses two distinct generative mechanisms for new food sources at the osphresis phase, allowing the algorithm procedure to search for the optimal solution in both the whole solution space and within the local solution space containing the fruit fly swarm location. In an evaluation based on a group of ten benchmark problems, the proposed algorithm's performance is compared with that of other well-known algorithms, and the results support the superiority of the proposed algorithm. Moreover, this algorithm is successfully applied in a SVM to perform both parameter setting turning for the SVM and feature selection to solve real-world classification problems. This method is called chaotic fruit fly optimization algorithm (CIFOA)-SVM and has been shown to be a more robust and effective optimization method than other well-known methods, particularly in terms of solving the medical diagnosis problem and the credit card problem.

Road screening and distribution route multi-objective robust optimization for hazardous materials based on neural network and genetic algorithm.

PubMed

Ma, Changxi; Hao, Wei; Pan, Fuquan; Xiang, Wang

2018-01-01

Route optimization of hazardous materials transportation is one of the basic steps in ensuring the safety of hazardous materials transportation. The optimization scheme may be a security risk if road screening is not completed before the distribution route is optimized. For road screening issues of hazardous materials transportation, a road screening algorithm of hazardous materials transportation is built based on genetic algorithm and Levenberg-Marquardt neural network (GA-LM-NN) by analyzing 15 attributes data of each road network section. A multi-objective robust optimization model with adjustable robustness is constructed for the hazardous materials transportation problem of single distribution center to minimize transportation risk and time. A multi-objective genetic algorithm is designed to solve the problem according to the characteristics of the model. The algorithm uses an improved strategy to complete the selection operation, applies partial matching cross shift and single ortho swap methods to complete the crossover and mutation operation, and employs an exclusive method to construct Pareto optimal solutions. Studies show that the sets of hazardous materials transportation road can be found quickly through the proposed road screening algorithm based on GA-LM-NN, whereas the distribution route Pareto solutions with different levels of robustness can be found rapidly through the proposed multi-objective robust optimization model and algorithm.

A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems

PubMed Central

Cao, Leilei; Xu, Lihong; Goodman, Erik D.

2016-01-01

A Guiding Evolutionary Algorithm (GEA) with greedy strategy for global optimization problems is proposed. Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each method while avoiding some disadvantages. In contrast to the usual Genetic Algorithm, each individual in GEA is crossed with the current global best one instead of a randomly selected individual. The current best individual served as a guide to attract offspring to its region of genotype space. Mutation was added to offspring according to a dynamic mutation probability. To increase the capability of exploitation, a local search mechanism was applied to new individuals according to a dynamic probability of local search. Experimental results show that GEA outperformed the other three typical global optimization algorithms with which it was compared. PMID:27293421

Structures vibration control via Tuned Mass Dampers using a co-evolution Coral Reefs Optimization algorithm

NASA Astrophysics Data System (ADS)

Salcedo-Sanz, S.; Camacho-Gómez, C.; Magdaleno, A.; Pereira, E.; Lorenzana, A.

2017-04-01

In this paper we tackle a problem of optimal design and location of Tuned Mass Dampers (TMDs) for structures subjected to earthquake ground motions, using a novel meta-heuristic algorithm. Specifically, the Coral Reefs Optimization (CRO) with Substrate Layer (CRO-SL) is proposed as a competitive co-evolution algorithm with different exploration procedures within a single population of solutions. The proposed approach is able to solve the TMD design and location problem, by exploiting the combination of different types of searching mechanisms. This promotes a powerful evolutionary-like algorithm for optimization problems, which is shown to be very effective in this particular problem of TMDs tuning. The proposed algorithm's performance has been evaluated and compared with several reference algorithms in two building models with two and four floors, respectively.

A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems.

PubMed

Cao, Leilei; Xu, Lihong; Goodman, Erik D

2016-01-01

A Guiding Evolutionary Algorithm (GEA) with greedy strategy for global optimization problems is proposed. Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each method while avoiding some disadvantages. In contrast to the usual Genetic Algorithm, each individual in GEA is crossed with the current global best one instead of a randomly selected individual. The current best individual served as a guide to attract offspring to its region of genotype space. Mutation was added to offspring according to a dynamic mutation probability. To increase the capability of exploitation, a local search mechanism was applied to new individuals according to a dynamic probability of local search. Experimental results show that GEA outperformed the other three typical global optimization algorithms with which it was compared.

Adaptive particle swarm optimization for optimal orbital elements of binary stars

NASA Astrophysics Data System (ADS)

Attia, Abdel-Fattah

2016-12-01

The paper presents an adaptive particle swarm optimization (APSO) as an alternative method to determine the optimal orbital elements of the star η Bootis of MK type G0 IV. The proposed algorithm transforms the problem of finding periodic orbits into the problem of detecting global minimizers as a function, to get a best fit of Keplerian and Phase curves. The experimental results demonstrate that the proposed approach of APSO generally more accurate than the standard particle swarm optimization (PSO) and other published optimization algorithms, in terms of solution accuracy, convergence speed and algorithm reliability.

Comparison of genetic algorithm methods for fuel management optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

DeChaine, M.D.; Feltus, M.A.

1995-12-31

The CIGARO system was developed for genetic algorithm fuel management optimization. Tests are performed to find the best fuel location swap mutation operator probability and to compare genetic algorithm to a truly random search method. Tests showed the fuel swap probability should be between 0% and 10%, and a 50% definitely hampered the optimization. The genetic algorithm performed significantly better than the random search method, which did not even satisfy the peak normalized power constraint.

Multiple shooting algorithms for jump-discontinuous problems in optimal control and estimation

NASA Technical Reports Server (NTRS)

Mook, D. J.; Lew, Jiann-Shiun

1991-01-01

Multiple shooting algorithms are developed for jump-discontinuous two-point boundary value problems arising in optimal control and optimal estimation. Examples illustrating the origin of such problems are given to motivate the development of the solution algorithms. The algorithms convert the necessary conditions, consisting of differential equations and transversality conditions, into algebraic equations. The solution of the algebraic equations provides exact solutions for linear problems. The existence and uniqueness of the solution are proved.

Aerodynamic Shape Optimization Using A Real-Number-Encoded Genetic Algorithm

NASA Technical Reports Server (NTRS)

Holst, Terry L.; Pulliam, Thomas H.

2001-01-01

A new method for aerodynamic shape optimization using a genetic algorithm with real number encoding is presented. The algorithm is used to optimize three different problems, a simple hill climbing problem, a quasi-one-dimensional nozzle problem using an Euler equation solver and a three-dimensional transonic wing problem using a nonlinear potential solver. Results indicate that the genetic algorithm is easy to implement and extremely reliable, being relatively insensitive to design space noise.

GAMBIT: A Parameterless Model-Based Evolutionary Algorithm for Mixed-Integer Problems.

PubMed

Sadowski, Krzysztof L; Thierens, Dirk; Bosman, Peter A N

2018-01-01

Learning and exploiting problem structure is one of the key challenges in optimization. This is especially important for black-box optimization (BBO) where prior structural knowledge of a problem is not available. Existing model-based Evolutionary Algorithms (EAs) are very efficient at learning structure in both the discrete, and in the continuous domain. In this article, discrete and continuous model-building mechanisms are integrated for the Mixed-Integer (MI) domain, comprising discrete and continuous variables. We revisit a recently introduced model-based evolutionary algorithm for the MI domain, the Genetic Algorithm for Model-Based mixed-Integer opTimization (GAMBIT). We extend GAMBIT with a parameterless scheme that allows for practical use of the algorithm without the need to explicitly specify any parameters. We furthermore contrast GAMBIT with other model-based alternatives. The ultimate goal of processing mixed dependences explicitly in GAMBIT is also addressed by introducing a new mechanism for the explicit exploitation of mixed dependences. We find that processing mixed dependences with this novel mechanism allows for more efficient optimization. We further contrast the parameterless GAMBIT with Mixed-Integer Evolution Strategies (MIES) and other state-of-the-art MI optimization algorithms from the General Algebraic Modeling System (GAMS) commercial algorithm suite on problems with and without constraints, and show that GAMBIT is capable of solving problems where variable dependences prevent many algorithms from successfully optimizing them.

A combined spectroscopic and plasma chemical kinetic analysis of ionospheric samarium releases

NASA Astrophysics Data System (ADS)

Holmes, Jeffrey M.; Dressler, Rainer A.; Pedersen, Todd R.; Caton, Ronald G.; Miller, Daniel

2017-05-01

Two rocket-borne releases of samarium vapor in the upper atmosphere occurred in May 2013, as part of the Metal Oxide Space Clouds experiment. The releases were characterized by a combination of optical and RF diagnostic instruments located at the Roi-Namur launch site and surrounding islands and atolls. The evolution of the optical spectrum of the solar-illuminated cloud was recorded with a spectrograph covering a 400-800 nm spectral range. The spectra exhibit two distinct spectral regions centered at 496 and 636 nm within which the relative intensities change insignificantly. The ratio between the integrated intensities within these regions, however, changes with time, suggesting that they are associated with different species. With the help of an equilibrium plasma spectral model we attribute the region centered at 496 nm to neutral samarium atoms (Sm I radiance) and features peaking at 649 nm to a molecular species. No evidence for structure due to Sm+ (Sm II) is identified. The persistence of the Sm I radiance suggests a high dissociative recombination rate for the chemi-ionization product, SmO+. A one-dimensional plasma chemical kinetic model of the evolution of the density ratio NSmO/NSm(t) demonstrates that the molecular feature peaking at 649 nm can be attributed to SmO radiance. SmO+ radiance is not identified. By adjusting the Sm vapor mass of the chemical kinetic model input to match the evolution of the total electron density determined by ionosonde data, we conclude that less than 5% of the payload samarium was vaporized.

Conditional loss of hepatocellular Hedgehog signaling in female mice leads to the persistence of hepatic steroidogenesis, androgenization and infertility.

PubMed

Rennert, Christiane; Eplinius, Franziska; Hofmann, Ute; Johänning, Janina; Rolfs, Franziska; Schmidt-Heck, Wolfgang; Guthke, Reinhardt; Gebhardt, Rolf; Ricken, Albert M; Matz-Soja, Madlen

2017-11-01

The Hedgehog signaling pathway is known to be involved in embryogenesis, tissue remodeling, and carcinogenesis. Because of its involvement in carcinogenesis, it seems an interesting target for cancer therapy. Indeed, Sonidegib, an approved inhibitor of the Hedgehog receptor Smoothened (Smo), is highly active against diverse carcinomas, but its use is also reported to be associated with several systemic side effects. Our former work in adult mice demonstrated hepatic Hedgehog signaling to play a key role in the insulin-like growth factor axis and lipid metabolism. The current work using mice with an embryonic and hepatocyte-specific Smo deletion describes an adverse impact of the hepatic Hedgehog pathway on female fertility. In female SAC-KO mice, we detected androgenization characterized by a 3.3-fold increase in testosterone at 12 weeks of age based on an impressive induction of steroidogenic gene expression in hepatocytes, but not in the classic steroidogenic organs (ovary and adrenal gland). Along with the elevated level of testosterone, the female SAC-KO mice showed infertility characterized by juvenile reproductive organs and acyclicity. The endocrine and reproductive alterations resembled polycystic ovarian syndrome and could be confirmed in a second mouse model with conditional deletion of Smo at 8 weeks of age after an extended period of 8 months. We conclude that the down-regulation of hepatic Hedgehog signaling leads to an impaired hormonal balance by the induction of steroidogenesis in the liver. These effects of Hedgehog signaling inhibition should be considered when using Hedgehog inhibitors as anti-cancer drugs.

Finite Set Control Transcription for Optimal Control Applications

DTIC Science & Technology

2009-05-01

Figures 1.1 The Parameters of x . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.1 Categories of Optimization Algorithms ...Programming (NLP) algorithm , such as SNOPT2 (hereafter, called the optimizer). The Finite Set Control Transcription (FSCT) method is essentially a...artificial neural networks, ge- netic algorithms , or combinations thereof for analysis.4,5 Indeed, an actual biological neural network is an example of

Design Genetic Algorithm Optimization Education Software Based Fuzzy Controller for a Tricopter Fly Path Planning

ERIC Educational Resources Information Center

Tran, Huu-Khoa; Chiou, Juing -Shian; Peng, Shou-Tao

2016-01-01

In this paper, the feasibility of a Genetic Algorithm Optimization (GAO) education software based Fuzzy Logic Controller (GAO-FLC) for simulating the flight motion control of Unmanned Aerial Vehicles (UAVs) is designed. The generated flight trajectories integrate the optimized Scaling Factors (SF) fuzzy controller gains by using GAO algorithm. The…

Comparison and optimization of radar-based hail detection algorithms in Slovenia

NASA Astrophysics Data System (ADS)

Stržinar, Gregor; Skok, Gregor

2018-05-01

Four commonly used radar-based hail detection algorithms are evaluated and optimized in Slovenia. The algorithms are verified against ground observations of hail at manned stations in the period between May and August, from 2002 to 2010. The algorithms are optimized by determining the optimal values of all possible algorithm parameters. A number of different contingency-table-based scores are evaluated with a combination of Critical Success Index and frequency bias proving to be the best choice for optimization. The best performance indexes are given by Waldvogel and the severe hail index, followed by vertically integrated liquid and maximum radar reflectivity. Using the optimal parameter values, a hail frequency climatology map for the whole of Slovenia is produced. The analysis shows that there is a considerable variability of hail occurrence within the Republic of Slovenia. The hail frequency ranges from almost 0 to 1.7 hail days per year with an average value of about 0.7 hail days per year.

A new technique based on Artificial Bee Colony Algorithm for optimal sizing of stand-alone photovoltaic system.

PubMed

Mohamed, Ahmed F; Elarini, Mahdi M; Othman, Ahmed M

2014-05-01

One of the most recent optimization techniques applied to the optimal design of photovoltaic system to supply an isolated load demand is the Artificial Bee Colony Algorithm (ABC). The proposed methodology is applied to optimize the cost of the PV system including photovoltaic, a battery bank, a battery charger controller, and inverter. Two objective functions are proposed: the first one is the PV module output power which is to be maximized and the second one is the life cycle cost (LCC) which is to be minimized. The analysis is performed based on measured solar radiation and ambient temperature measured at Helwan city, Egypt. A comparison between ABC algorithm and Genetic Algorithm (GA) optimal results is done. Another location is selected which is Zagazig city to check the validity of ABC algorithm in any location. The ABC is more optimal than GA. The results encouraged the use of the PV systems to electrify the rural sites of Egypt.

A new technique based on Artificial Bee Colony Algorithm for optimal sizing of stand-alone photovoltaic system

PubMed Central

Mohamed, Ahmed F.; Elarini, Mahdi M.; Othman, Ahmed M.

2013-01-01

One of the most recent optimization techniques applied to the optimal design of photovoltaic system to supply an isolated load demand is the Artificial Bee Colony Algorithm (ABC). The proposed methodology is applied to optimize the cost of the PV system including photovoltaic, a battery bank, a battery charger controller, and inverter. Two objective functions are proposed: the first one is the PV module output power which is to be maximized and the second one is the life cycle cost (LCC) which is to be minimized. The analysis is performed based on measured solar radiation and ambient temperature measured at Helwan city, Egypt. A comparison between ABC algorithm and Genetic Algorithm (GA) optimal results is done. Another location is selected which is Zagazig city to check the validity of ABC algorithm in any location. The ABC is more optimal than GA. The results encouraged the use of the PV systems to electrify the rural sites of Egypt. PMID:25685507

Hybrid-optimization strategy for the communication of large-scale Kinetic Monte Carlo simulation

NASA Astrophysics Data System (ADS)

Wu, Baodong; Li, Shigang; Zhang, Yunquan; Nie, Ningming

2017-02-01

The parallel Kinetic Monte Carlo (KMC) algorithm based on domain decomposition has been widely used in large-scale physical simulations. However, the communication overhead of the parallel KMC algorithm is critical, and severely degrades the overall performance and scalability. In this paper, we present a hybrid optimization strategy to reduce the communication overhead for the parallel KMC simulations. We first propose a communication aggregation algorithm to reduce the total number of messages and eliminate the communication redundancy. Then, we utilize the shared memory to reduce the memory copy overhead of the intra-node communication. Finally, we optimize the communication scheduling using the neighborhood collective operations. We demonstrate the scalability and high performance of our hybrid optimization strategy by both theoretical and experimental analysis. Results show that the optimized KMC algorithm exhibits better performance and scalability than the well-known open-source library-SPPARKS. On 32-node Xeon E5-2680 cluster (total 640 cores), the optimized algorithm reduces the communication time by 24.8% compared with SPPARKS.

Genetic algorithms for multicriteria shape optimization of induction furnace

NASA Astrophysics Data System (ADS)

Kůs, Pavel; Mach, František; Karban, Pavel; Doležel, Ivo

2012-09-01

In this contribution we deal with a multi-criteria shape optimization of an induction furnace. We want to find shape parameters of the furnace in such a way, that two different criteria are optimized. Since they cannot be optimized simultaneously, instead of one optimum we find set of partially optimal designs, so called Pareto front. We compare two different approaches to the optimization, one using nonlinear conjugate gradient method and second using variation of genetic algorithm. As can be seen from the numerical results, genetic algorithm seems to be the right choice for this problem. Solution of direct problem (coupled problem consisting of magnetic and heat field) is done using our own code Agros2D. It uses finite elements of higher order leading to fast and accurate solution of relatively complicated coupled problem. It also provides advanced scripting support, allowing us to prepare parametric model of the furnace and simply incorporate various types of optimization algorithms.

Automated Lead Optimization of MMP-12 Inhibitors Using a Genetic Algorithm.

PubMed

Pickett, Stephen D; Green, Darren V S; Hunt, David L; Pardoe, David A; Hughes, Ian

2011-01-13

Traditional lead optimization projects involve long synthesis and testing cycles, favoring extensive structure-activity relationship (SAR) analysis and molecular design steps, in an attempt to limit the number of cycles that a project must run to optimize a development candidate. Microfluidic-based chemistry and biology platforms, with cycle times of minutes rather than weeks, lend themselves to unattended autonomous operation. The bottleneck in the lead optimization process is therefore shifted from synthesis or test to SAR analysis and design. As such, the way is open to an algorithm-directed process, without the need for detailed user data analysis. Here, we present results of two synthesis and screening experiments, undertaken using traditional methodology, to validate a genetic algorithm optimization process for future application to a microfluidic system. The algorithm has several novel features that are important for the intended application. For example, it is robust to missing data and can suggest compounds for retest to ensure reliability of optimization. The algorithm is first validated on a retrospective analysis of an in-house library embedded in a larger virtual array of presumed inactive compounds. In a second, prospective experiment with MMP-12 as the target protein, 140 compounds are submitted for synthesis over 10 cycles of optimization. Comparison is made to the results from the full combinatorial library that was synthesized manually and tested independently. The results show that compounds selected by the algorithm are heavily biased toward the more active regions of the library, while the algorithm is robust to both missing data (compounds where synthesis failed) and inactive compounds. This publication places the full combinatorial library and biological data into the public domain with the intention of advancing research into algorithm-directed lead optimization methods.

Automated Lead Optimization of MMP-12 Inhibitors Using a Genetic Algorithm

PubMed Central

2010-01-01

Traditional lead optimization projects involve long synthesis and testing cycles, favoring extensive structure−activity relationship (SAR) analysis and molecular design steps, in an attempt to limit the number of cycles that a project must run to optimize a development candidate. Microfluidic-based chemistry and biology platforms, with cycle times of minutes rather than weeks, lend themselves to unattended autonomous operation. The bottleneck in the lead optimization process is therefore shifted from synthesis or test to SAR analysis and design. As such, the way is open to an algorithm-directed process, without the need for detailed user data analysis. Here, we present results of two synthesis and screening experiments, undertaken using traditional methodology, to validate a genetic algorithm optimization process for future application to a microfluidic system. The algorithm has several novel features that are important for the intended application. For example, it is robust to missing data and can suggest compounds for retest to ensure reliability of optimization. The algorithm is first validated on a retrospective analysis of an in-house library embedded in a larger virtual array of presumed inactive compounds. In a second, prospective experiment with MMP-12 as the target protein, 140 compounds are submitted for synthesis over 10 cycles of optimization. Comparison is made to the results from the full combinatorial library that was synthesized manually and tested independently. The results show that compounds selected by the algorithm are heavily biased toward the more active regions of the library, while the algorithm is robust to both missing data (compounds where synthesis failed) and inactive compounds. This publication places the full combinatorial library and biological data into the public domain with the intention of advancing research into algorithm-directed lead optimization methods. PMID:24900251

An Optimizing Space Data-Communications Scheduling Method and Algorithm with Interference Mitigation, Generalized for a Broad Class of Optimization Problems

NASA Technical Reports Server (NTRS)

Rash, James

2014-01-01

NASA's space data-communications infrastructure-the Space Network and the Ground Network-provide scheduled (as well as some limited types of unscheduled) data-communications services to user spacecraft. The Space Network operates several orbiting geostationary platforms (the Tracking and Data Relay Satellite System (TDRSS)), each with its own servicedelivery antennas onboard. The Ground Network operates service-delivery antennas at ground stations located around the world. Together, these networks enable data transfer between user spacecraft and their mission control centers on Earth. Scheduling data-communications events for spacecraft that use the NASA communications infrastructure-the relay satellites and the ground stations-can be accomplished today with software having an operational heritage dating from the 1980s or earlier. An implementation of the scheduling methods and algorithms disclosed and formally specified herein will produce globally optimized schedules with not only optimized service delivery by the space data-communications infrastructure but also optimized satisfaction of all user requirements and prescribed constraints, including radio frequency interference (RFI) constraints. Evolutionary algorithms, a class of probabilistic strategies for searching large solution spaces, is the essential technology invoked and exploited in this disclosure. Also disclosed are secondary methods and algorithms for optimizing the execution efficiency of the schedule-generation algorithms themselves. The scheduling methods and algorithms as presented are adaptable to accommodate the complexity of scheduling the civilian and/or military data-communications infrastructure within the expected range of future users and space- or ground-based service-delivery assets. Finally, the problem itself, and the methods and algorithms, are generalized and specified formally. The generalized methods and algorithms are applicable to a very broad class of combinatorial-optimization problems that encompasses, among many others, the problem of generating optimal space-data communications schedules.

An Efficient Optimization Method for Solving Unsupervised Data Classification Problems.

PubMed

Shabanzadeh, Parvaneh; Yusof, Rubiyah

2015-01-01

Unsupervised data classification (or clustering) analysis is one of the most useful tools and a descriptive task in data mining that seeks to classify homogeneous groups of objects based on similarity and is used in many medical disciplines and various applications. In general, there is no single algorithm that is suitable for all types of data, conditions, and applications. Each algorithm has its own advantages, limitations, and deficiencies. Hence, research for novel and effective approaches for unsupervised data classification is still active. In this paper a heuristic algorithm, Biogeography-Based Optimization (BBO) algorithm, was adapted for data clustering problems by modifying the main operators of BBO algorithm, which is inspired from the natural biogeography distribution of different species. Similar to other population-based algorithms, BBO algorithm starts with an initial population of candidate solutions to an optimization problem and an objective function that is calculated for them. To evaluate the performance of the proposed algorithm assessment was carried on six medical and real life datasets and was compared with eight well known and recent unsupervised data classification algorithms. Numerical results demonstrate that the proposed evolutionary optimization algorithm is efficient for unsupervised data classification.

Fuel management optimization using genetic algorithms and expert knowledge

DOE Office of Scientific and Technical Information (OSTI.GOV)

DeChaine, M.D.; Feltus, M.A.

1996-09-01

The CIGARO fuel management optimization code based on genetic algorithms is described and tested. The test problem optimized the core lifetime for a pressurized water reactor with a penalty function constraint on the peak normalized power. A bit-string genotype encoded the loading patterns, and genotype bias was reduced with additional bits. Expert knowledge about fuel management was incorporated into the genetic algorithm. Regional crossover exchanged physically adjacent fuel assemblies and improved the optimization slightly. Biasing the initial population toward a known priority table significantly improved the optimization.

Network-optimized congestion pricing : a parable, model and algorithm

DOT National Transportation Integrated Search

1995-05-31

This paper recites a parable, formulates a model and devises an algorithm for optimizing tolls on a road network. Such tolls induce an equilibrium traffic flow that is at once system-optimal and user-optimal. The parable introduces the network-wide c...

Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data

PubMed Central

2017-01-01

In this paper, we propose a new automatic hyperparameter selection approach for determining the optimal network configuration (network structure and hyperparameters) for deep neural networks using particle swarm optimization (PSO) in combination with a steepest gradient descent algorithm. In the proposed approach, network configurations were coded as a set of real-number m-dimensional vectors as the individuals of the PSO algorithm in the search procedure. During the search procedure, the PSO algorithm is employed to search for optimal network configurations via the particles moving in a finite search space, and the steepest gradient descent algorithm is used to train the DNN classifier with a few training epochs (to find a local optimal solution) during the population evaluation of PSO. After the optimization scheme, the steepest gradient descent algorithm is performed with more epochs and the final solutions (pbest and gbest) of the PSO algorithm to train a final ensemble model and individual DNN classifiers, respectively. The local search ability of the steepest gradient descent algorithm and the global search capabilities of the PSO algorithm are exploited to determine an optimal solution that is close to the global optimum. We constructed several experiments on hand-written characters and biological activity prediction datasets to show that the DNN classifiers trained by the network configurations expressed by the final solutions of the PSO algorithm, employed to construct an ensemble model and individual classifier, outperform the random approach in terms of the generalization performance. Therefore, the proposed approach can be regarded an alternative tool for automatic network structure and parameter selection for deep neural networks. PMID:29236718

Coastal hazards in a changing world: projecting and communicating future coastal flood risk at the local-scale using the Coastal Storm Modeling System (CoSMoS)

NASA Astrophysics Data System (ADS)

O'Neill, Andrea; Barnard, Patrick; Erikson, Li; Foxgrover, Amy; Limber, Patrick; Vitousek, Sean; Fitzgibbon, Michael; Wood, Nathan

2017-04-01

The risk of coastal flooding will increase for many low-lying coastal regions as predominant contributions to flooding, including sea level, storm surge, wave setup, and storm-related fluvial discharge, are altered with climate change. Community leaders and local governments therefore look to science to provide insight into how climate change may affect their areas. Many studies of future coastal flooding vulnerability consider sea level and tides, but ignore other important factors that elevate flood levels during storm events, such as waves, surge, and discharge. Here we present a modelling approach that considers a broad range of relevant processes contributing to elevated storm water levels for open coast and embayment settings along the U.S. West Coast. Additionally, we present online tools for communicating community-relevant projected vulnerabilities. The Coastal Storm Modeling System (CoSMoS) is a numerical modeling system developed to predict coastal flooding due to both sea-level rise (SLR) and plausible 21st century storms for active-margin settings like the U.S. West Coast. CoSMoS applies a predominantly deterministic framework of multi-scale models encompassing large geographic scales (100s to 1000s of kilometers) to small-scale features (10s to 1000s of meters), resulting in flood extents that can be projected at a local resolution (2 meters). In the latest iteration of CoSMoS applied to Southern California, U.S., efforts were made to incorporate water level fluctuations in response to regional storm impacts, locally wind-generated waves, coastal river discharge, and decadal-scale shoreline and cliff changes. Coastal hazard projections are available in a user-friendly web-based tool (www.prbo.org/ocof), where users can view variations in flood extent, maximum flood depth, current speeds, and wave heights in response to a range of potential SLR and storm combinations, providing direct support to adaptation and management decisions. In order to capture the societal aspect of the hazard, projections are combined with socioeconomic exposure to produce clear, actionable information (https://www.usgs.gov/apps/hera/); this integrated approach to hazard displays provides an example of how to effectively translate complex climate impacts projections into simple, societally-relevant information.

Optimally stopped variational quantum algorithms

NASA Astrophysics Data System (ADS)

Vinci, Walter; Shabani, Alireza

2018-04-01

Quantum processors promise a paradigm shift in high-performance computing which needs to be assessed by accurate benchmarking measures. In this article, we introduce a benchmark for the variational quantum algorithm (VQA), recently proposed as a heuristic algorithm for small-scale quantum processors. In VQA, a classical optimization algorithm guides the processor's quantum dynamics to yield the best solution for a given problem. A complete assessment of the scalability and competitiveness of VQA should take into account both the quality and the time of dynamics optimization. The method of optimal stopping, employed here, provides such an assessment by explicitly including time as a cost factor. Here, we showcase this measure for benchmarking VQA as a solver for some quadratic unconstrained binary optimization. Moreover, we show that a better choice for the cost function of the classical routine can significantly improve the performance of the VQA algorithm and even improve its scaling properties.

Voltage stability index based optimal placement of static VAR compensator and sizing using Cuckoo search algorithm

NASA Astrophysics Data System (ADS)

Venkateswara Rao, B.; Kumar, G. V. Nagesh; Chowdary, D. Deepak; Bharathi, M. Aruna; Patra, Stutee

2017-07-01

This paper furnish the new Metaheuristic algorithm called Cuckoo Search Algorithm (CSA) for solving optimal power flow (OPF) problem with minimization of real power generation cost. The CSA is found to be the most efficient algorithm for solving single objective optimal power flow problems. The CSA performance is tested on IEEE 57 bus test system with real power generation cost minimization as objective function. Static VAR Compensator (SVC) is one of the best shunt connected device in the Flexible Alternating Current Transmission System (FACTS) family. It has capable of controlling the voltage magnitudes of buses by injecting the reactive power to system. In this paper SVC is integrated in CSA based Optimal Power Flow to optimize the real power generation cost. SVC is used to improve the voltage profile of the system. CSA gives better results as compared to genetic algorithm (GA) in both without and with SVC conditions.

Application of Particle Swarm Optimization Algorithm in the Heating System Planning Problem

PubMed Central

Ma, Rong-Jiang; Yu, Nan-Yang; Hu, Jun-Yi

2013-01-01

Based on the life cycle cost (LCC) approach, this paper presents an integral mathematical model and particle swarm optimization (PSO) algorithm for the heating system planning (HSP) problem. The proposed mathematical model minimizes the cost of heating system as the objective for a given life cycle time. For the particularity of HSP problem, the general particle swarm optimization algorithm was improved. An actual case study was calculated to check its feasibility in practical use. The results show that the improved particle swarm optimization (IPSO) algorithm can more preferably solve the HSP problem than PSO algorithm. Moreover, the results also present the potential to provide useful information when making decisions in the practical planning process. Therefore, it is believed that if this approach is applied correctly and in combination with other elements, it can become a powerful and effective optimization tool for HSP problem. PMID:23935429

Optimization of wireless sensor networks based on chicken swarm optimization algorithm

NASA Astrophysics Data System (ADS)

Wang, Qingxi; Zhu, Lihua

2017-05-01

In order to reduce the energy consumption of wireless sensor network and improve the survival time of network, the clustering routing protocol of wireless sensor networks based on chicken swarm optimization algorithm was proposed. On the basis of LEACH agreement, it was improved and perfected that the points on the cluster and the selection of cluster head using the chicken group optimization algorithm, and update the location of chicken which fall into the local optimum by Levy flight, enhance population diversity, ensure the global search capability of the algorithm. The new protocol avoided the die of partial node of intensive using by making balanced use of the network nodes, improved the survival time of wireless sensor network. The simulation experiments proved that the protocol is better than LEACH protocol on energy consumption, also is better than that of clustering routing protocol based on particle swarm optimization algorithm.

Particle swarm optimization based space debris surveillance network scheduling

NASA Astrophysics Data System (ADS)

Jiang, Hai; Liu, Jing; Cheng, Hao-Wen; Zhang, Yao

2017-02-01

The increasing number of space debris has created an orbital debris environment that poses increasing impact risks to existing space systems and human space flights. For the safety of in-orbit spacecrafts, we should optimally schedule surveillance tasks for the existing facilities to allocate resources in a manner that most significantly improves the ability to predict and detect events involving affected spacecrafts. This paper analyzes two criteria that mainly affect the performance of a scheduling scheme and introduces an artificial intelligence algorithm into the scheduling of tasks of the space debris surveillance network. A new scheduling algorithm based on the particle swarm optimization algorithm is proposed, which can be implemented in two different ways: individual optimization and joint optimization. Numerical experiments with multiple facilities and objects are conducted based on the proposed algorithm, and simulation results have demonstrated the effectiveness of the proposed algorithm.

A general optimality criteria algorithm for a class of engineering optimization problems

NASA Astrophysics Data System (ADS)

Belegundu, Ashok D.

2015-05-01

An optimality criteria (OC)-based algorithm for optimization of a general class of nonlinear programming (NLP) problems is presented. The algorithm is only applicable to problems where the objective and constraint functions satisfy certain monotonicity properties. For multiply constrained problems which satisfy these assumptions, the algorithm is attractive compared with existing NLP methods as well as prevalent OC methods, as the latter involve computationally expensive active set and step-size control strategies. The fixed point algorithm presented here is applicable not only to structural optimization problems but also to certain problems as occur in resource allocation and inventory models. Convergence aspects are discussed. The fixed point update or resizing formula is given physical significance, which brings out a strength and trim feature. The number of function evaluations remains independent of the number of variables, allowing the efficient solution of problems with large number of variables.

Asymptotic analysis of SPTA-based algorithms for no-wait flow shop scheduling problem with release dates.

PubMed

Ren, Tao; Zhang, Chuan; Lin, Lin; Guo, Meiting; Xie, Xionghang

2014-01-01

We address the scheduling problem for a no-wait flow shop to optimize total completion time with release dates. With the tool of asymptotic analysis, we prove that the objective values of two SPTA-based algorithms converge to the optimal value for sufficiently large-sized problems. To further enhance the performance of the SPTA-based algorithms, an improvement scheme based on local search is provided for moderate scale problems. New lower bound is presented for evaluating the asymptotic optimality of the algorithms. Numerical simulations demonstrate the effectiveness of the proposed algorithms.

Asymptotic Analysis of SPTA-Based Algorithms for No-Wait Flow Shop Scheduling Problem with Release Dates

PubMed Central

Ren, Tao; Zhang, Chuan; Lin, Lin; Guo, Meiting; Xie, Xionghang

2014-01-01

We address the scheduling problem for a no-wait flow shop to optimize total completion time with release dates. With the tool of asymptotic analysis, we prove that the objective values of two SPTA-based algorithms converge to the optimal value for sufficiently large-sized problems. To further enhance the performance of the SPTA-based algorithms, an improvement scheme based on local search is provided for moderate scale problems. New lower bound is presented for evaluating the asymptotic optimality of the algorithms. Numerical simulations demonstrate the effectiveness of the proposed algorithms. PMID:24764774

Sensitivity Analysis of Genetic Algorithm Parameters for Optimal Groundwater Monitoring Network Design

NASA Astrophysics Data System (ADS)

Abdeh-Kolahchi, A.; Satish, M.; Datta, B.

2004-05-01

A state art groundwater monitoring network design is introduced. The method combines groundwater flow and transport results with optimization Genetic Algorithm (GA) to identify optimal monitoring well locations. Optimization theory uses different techniques to find a set of parameter values that minimize or maximize objective functions. The suggested groundwater optimal monitoring network design is based on the objective of maximizing the probability of tracking a transient contamination plume by determining sequential monitoring locations. The MODFLOW and MT3DMS models included as separate modules within the Groundwater Modeling System (GMS) are used to develop three dimensional groundwater flow and contamination transport simulation. The groundwater flow and contamination simulation results are introduced as input to the optimization model, using Genetic Algorithm (GA) to identify the groundwater optimal monitoring network design, based on several candidate monitoring locations. The groundwater monitoring network design model is used Genetic Algorithms with binary variables representing potential monitoring location. As the number of decision variables and constraints increase, the non-linearity of the objective function also increases which make difficulty to obtain optimal solutions. The genetic algorithm is an evolutionary global optimization technique, which is capable of finding the optimal solution for many complex problems. In this study, the GA approach capable of finding the global optimal solution to a groundwater monitoring network design problem involving 18.4X 1018 feasible solutions will be discussed. However, to ensure the efficiency of the solution process and global optimality of the solution obtained using GA, it is necessary that appropriate GA parameter values be specified. The sensitivity analysis of genetic algorithms parameters such as random number, crossover probability, mutation probability, and elitism are discussed for solution of monitoring network design.

Comparison of evolutionary algorithms for LPDA antenna optimization

NASA Astrophysics Data System (ADS)

Lazaridis, Pavlos I.; Tziris, Emmanouil N.; Zaharis, Zaharias D.; Xenos, Thomas D.; Cosmas, John P.; Gallion, Philippe B.; Holmes, Violeta; Glover, Ian A.

2016-08-01

A novel approach to broadband log-periodic antenna design is presented, where some of the most powerful evolutionary algorithms are applied and compared for the optimal design of wire log-periodic dipole arrays (LPDA) using Numerical Electromagnetics Code. The target is to achieve an optimal antenna design with respect to maximum gain, gain flatness, front-to-rear ratio (F/R) and standing wave ratio. The parameters of the LPDA optimized are the dipole lengths, the spacing between the dipoles, and the dipole wire diameters. The evolutionary algorithms compared are the Differential Evolution (DE), Particle Swarm (PSO), Taguchi, Invasive Weed (IWO), and Adaptive Invasive Weed Optimization (ADIWO). Superior performance is achieved by the IWO (best results) and PSO (fast convergence) algorithms.

Optimal recombination in genetic algorithms for flowshop scheduling problems

NASA Astrophysics Data System (ADS)

Kovalenko, Julia

2016-10-01

The optimal recombination problem consists in finding the best possible offspring as a result of a recombination operator in a genetic algorithm, given two parent solutions. We prove NP-hardness of the optimal recombination for various variants of the flowshop scheduling problem with makespan criterion and criterion of maximum lateness. An algorithm for solving the optimal recombination problem for permutation flowshop problems is built, using enumeration of prefect matchings in a special bipartite graph. The algorithm is adopted for the classical flowshop scheduling problem and for the no-wait flowshop problem. It is shown that the optimal recombination problem for the permutation flowshop scheduling problem is solvable in polynomial time for almost all pairs of parent solutions as the number of jobs tends to infinity.

Application of genetic algorithm in modeling on-wafer inductors for up to 110 Ghz

NASA Astrophysics Data System (ADS)

Liu, Nianhong; Fu, Jun; Liu, Hui; Cui, Wenpu; Liu, Zhihong; Liu, Linlin; Zhou, Wei; Wang, Quan; Guo, Ao

2018-05-01

In this work, the genetic algorithm has been introducted into parameter extraction for on-wafer inductors for up to 110 GHz millimeter-wave operations, and nine independent parameters of the equivalent circuit model are optimized together. With the genetic algorithm, the model with the optimized parameters gives a better fitting accuracy than the preliminary parameters without optimization. Especially, the fitting accuracy of the Q value achieves a significant improvement after the optimization.

Social Emotional Optimization Algorithm for Nonlinear Constrained Optimization Problems

NASA Astrophysics Data System (ADS)

Xu, Yuechun; Cui, Zhihua; Zeng, Jianchao

Nonlinear programming problem is one important branch in operational research, and has been successfully applied to various real-life problems. In this paper, a new approach called Social emotional optimization algorithm (SEOA) is used to solve this problem which is a new swarm intelligent technique by simulating the human behavior guided by emotion. Simulation results show that the social emotional optimization algorithm proposed in this paper is effective and efficiency for the nonlinear constrained programming problems.

Chaotic Particle Swarm Optimization with Mutation for Classification

PubMed Central

Assarzadeh, Zahra; Naghsh-Nilchi, Ahmad Reza

2015-01-01

In this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classify patterns of different classes in the feature space. The introduced mutation operators and chaotic sequences allows us to overcome the problem of early convergence into a local minima associated with particle swarm optimization algorithms. That is, the mutation operator sharpens the convergence and it tunes the best possible solution. Furthermore, to remove the irrelevant data and reduce the dimensionality of medical datasets, a feature selection approach using binary version of the proposed particle swarm optimization is introduced. In order to demonstrate the effectiveness of our proposed classifier, mutation-based classifier particle swarm optimization, it is checked out with three sets of data classifications namely, Wisconsin diagnostic breast cancer, Wisconsin breast cancer and heart-statlog, with different feature vector dimensions. The proposed algorithm is compared with different classifier algorithms including k-nearest neighbor, as a conventional classifier, particle swarm-classifier, genetic algorithm, and Imperialist competitive algorithm-classifier, as more sophisticated ones. The performance of each classifier was evaluated by calculating the accuracy, sensitivity, specificity and Matthews's correlation coefficient. The experimental results show that the mutation-based classifier particle swarm optimization unequivocally performs better than all the compared algorithms. PMID:25709937

Cost versus life cycle assessment-based environmental impact optimization of drinking water production plants.

PubMed

Capitanescu, F; Rege, S; Marvuglia, A; Benetto, E; Ahmadi, A; Gutiérrez, T Navarrete; Tiruta-Barna, L

2016-07-15

Empowering decision makers with cost-effective solutions for reducing industrial processes environmental burden, at both design and operation stages, is nowadays a major worldwide concern. The paper addresses this issue for the sector of drinking water production plants (DWPPs), seeking for optimal solutions trading-off operation cost and life cycle assessment (LCA)-based environmental impact while satisfying outlet water quality criteria. This leads to a challenging bi-objective constrained optimization problem, which relies on a computationally expensive intricate process-modelling simulator of the DWPP and has to be solved with limited computational budget. Since mathematical programming methods are unusable in this case, the paper examines the performances in tackling these challenges of six off-the-shelf state-of-the-art global meta-heuristic optimization algorithms, suitable for such simulation-based optimization, namely Strength Pareto Evolutionary Algorithm (SPEA2), Non-dominated Sorting Genetic Algorithm (NSGA-II), Indicator-based Evolutionary Algorithm (IBEA), Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), Differential Evolution (DE), and Particle Swarm Optimization (PSO). The results of optimization reveal that good reduction in both operating cost and environmental impact of the DWPP can be obtained. Furthermore, NSGA-II outperforms the other competing algorithms while MOEA/D and DE perform unexpectedly poorly. Copyright © 2016 Elsevier Ltd. All rights reserved.

AI-BL1.0: a program for automatic on-line beamline optimization using the evolutionary algorithm.

PubMed

Xi, Shibo; Borgna, Lucas Santiago; Zheng, Lirong; Du, Yonghua; Hu, Tiandou

2017-01-01

In this report, AI-BL1.0, an open-source Labview-based program for automatic on-line beamline optimization, is presented. The optimization algorithms used in the program are Genetic Algorithm and Differential Evolution. Efficiency was improved by use of a strategy known as Observer Mode for Evolutionary Algorithm. The program was constructed and validated at the XAFCA beamline of the Singapore Synchrotron Light Source and 1W1B beamline of the Beijing Synchrotron Radiation Facility.

Belief Propagation Algorithm for Portfolio Optimization Problems

PubMed Central

2015-01-01

The typical behavior of optimal solutions to portfolio optimization problems with absolute deviation and expected shortfall models using replica analysis was pioneeringly estimated by S. Ciliberti et al. [Eur. Phys. B. 57, 175 (2007)]; however, they have not yet developed an approximate derivation method for finding the optimal portfolio with respect to a given return set. In this study, an approximation algorithm based on belief propagation for the portfolio optimization problem is presented using the Bethe free energy formalism, and the consistency of the numerical experimental results of the proposed algorithm with those of replica analysis is confirmed. Furthermore, the conjecture of H. Konno and H. Yamazaki, that the optimal solutions with the absolute deviation model and with the mean-variance model have the same typical behavior, is verified using replica analysis and the belief propagation algorithm. PMID:26305462

Nash equilibrium and multi criterion aerodynamic optimization

NASA Astrophysics Data System (ADS)

Tang, Zhili; Zhang, Lianhe

2016-06-01

Game theory and its particular Nash Equilibrium (NE) are gaining importance in solving Multi Criterion Optimization (MCO) in engineering problems over the past decade. The solution of a MCO problem can be viewed as a NE under the concept of competitive games. This paper surveyed/proposed four efficient algorithms for calculating a NE of a MCO problem. Existence and equivalence of the solution are analyzed and proved in the paper based on fixed point theorem. Specific virtual symmetric Nash game is also presented to set up an optimization strategy for single objective optimization problems. Two numerical examples are presented to verify proposed algorithms. One is mathematical functions' optimization to illustrate detailed numerical procedures of algorithms, the other is aerodynamic drag reduction of civil transport wing fuselage configuration by using virtual game. The successful application validates efficiency of algorithms in solving complex aerodynamic optimization problem.

Belief Propagation Algorithm for Portfolio Optimization Problems.

PubMed

Shinzato, Takashi; Yasuda, Muneki

2015-01-01

The typical behavior of optimal solutions to portfolio optimization problems with absolute deviation and expected shortfall models using replica analysis was pioneeringly estimated by S. Ciliberti et al. [Eur. Phys. B. 57, 175 (2007)]; however, they have not yet developed an approximate derivation method for finding the optimal portfolio with respect to a given return set. In this study, an approximation algorithm based on belief propagation for the portfolio optimization problem is presented using the Bethe free energy formalism, and the consistency of the numerical experimental results of the proposed algorithm with those of replica analysis is confirmed. Furthermore, the conjecture of H. Konno and H. Yamazaki, that the optimal solutions with the absolute deviation model and with the mean-variance model have the same typical behavior, is verified using replica analysis and the belief propagation algorithm.

Thrust stand evaluation of engine performance improvement algorithms in an F-15 airplane

NASA Technical Reports Server (NTRS)

Conners, Timothy R.

1992-01-01

Results are presented from the evaluation of the performance seeking control (PSC) optimization algorithm developed by Smith et al. (1990) for F-15 aircraft, which optimizes the quasi-steady-state performance of an F100 derivative turbofan engine for several modes of operation. The PSC algorithm uses onboard software engine model that calculates thrust, stall margin, and other unmeasured variables for use in the optimization. Comparisons are presented between the load cell measurements, PSC onboard model thrust calculations, and posttest state variable model computations. Actual performance improvements using the PSC algorithm are presented for its various modes. The results of using PSC algorithm are compared with similar test case results using the HIDEC algorithm.

An Effective Hybrid Evolutionary Algorithm for Solving the Numerical Optimization Problems

NASA Astrophysics Data System (ADS)

Qian, Xiaohong; Wang, Xumei; Su, Yonghong; He, Liu

2018-04-01

There are many different algorithms for solving complex optimization problems. Each algorithm has been applied successfully in solving some optimization problems, but not efficiently in other problems. In this paper the Cauchy mutation and the multi-parent hybrid operator are combined to propose a hybrid evolutionary algorithm based on the communication (Mixed Evolutionary Algorithm based on Communication), hereinafter referred to as CMEA. The basic idea of the CMEA algorithm is that the initial population is divided into two subpopulations. Cauchy mutation operators and multiple paternal crossover operators are used to perform two subpopulations parallelly to evolve recursively until the downtime conditions are met. While subpopulation is reorganized, the individual is exchanged together with information. The algorithm flow is given and the performance of the algorithm is compared using a number of standard test functions. Simulation results have shown that this algorithm converges significantly faster than FEP (Fast Evolutionary Programming) algorithm, has good performance in global convergence and stability and is superior to other compared algorithms.

Hybrid simulated annealing and its application to optimization of hidden Markov models for visual speech recognition.

PubMed

Lee, Jong-Seok; Park, Cheol Hoon

2010-08-01

We propose a novel stochastic optimization algorithm, hybrid simulated annealing (SA), to train hidden Markov models (HMMs) for visual speech recognition. In our algorithm, SA is combined with a local optimization operator that substitutes a better solution for the current one to improve the convergence speed and the quality of solutions. We mathematically prove that the sequence of the objective values converges in probability to the global optimum in the algorithm. The algorithm is applied to train HMMs that are used as visual speech recognizers. While the popular training method of HMMs, the expectation-maximization algorithm, achieves only local optima in the parameter space, the proposed method can perform global optimization of the parameters of HMMs and thereby obtain solutions yielding improved recognition performance. The superiority of the proposed algorithm to the conventional ones is demonstrated via isolated word recognition experiments.

Capacity improvement using simulation optimization approaches: A case study in the thermotechnology industry

NASA Astrophysics Data System (ADS)

Yelkenci Köse, Simge; Demir, Leyla; Tunalı, Semra; Türsel Eliiyi, Deniz

2015-02-01

In manufacturing systems, optimal buffer allocation has a considerable impact on capacity improvement. This study presents a simulation optimization procedure to solve the buffer allocation problem in a heat exchanger production plant so as to improve the capacity of the system. For optimization, three metaheuristic-based search algorithms, i.e. a binary-genetic algorithm (B-GA), a binary-simulated annealing algorithm (B-SA) and a binary-tabu search algorithm (B-TS), are proposed. These algorithms are integrated with the simulation model of the production line. The simulation model, which captures the stochastic and dynamic nature of the production line, is used as an evaluation function for the proposed metaheuristics. The experimental study with benchmark problem instances from the literature and the real-life problem show that the proposed B-TS algorithm outperforms B-GA and B-SA in terms of solution quality.

Design and Optimization Method of a Two-Disk Rotor System

NASA Astrophysics Data System (ADS)

Huang, Jingjing; Zheng, Longxi; Mei, Qing

2016-04-01

An integrated analytical method based on multidisciplinary optimization software Isight and general finite element software ANSYS was proposed in this paper. Firstly, a two-disk rotor system was established and the mode, humorous response and transient response at acceleration condition were analyzed with ANSYS. The dynamic characteristics of the two-disk rotor system were achieved. On this basis, the two-disk rotor model was integrated to the multidisciplinary design optimization software Isight. According to the design of experiment (DOE) and the dynamic characteristics, the optimization variables, optimization objectives and constraints were confirmed. After that, the multi-objective design optimization of the transient process was carried out with three different global optimization algorithms including Evolutionary Optimization Algorithm, Multi-Island Genetic Algorithm and Pointer Automatic Optimizer. The optimum position of the two-disk rotor system was obtained at the specified constraints. Meanwhile, the accuracy and calculation numbers of different optimization algorithms were compared. The optimization results indicated that the rotor vibration reached the minimum value and the design efficiency and quality were improved by the multidisciplinary design optimization in the case of meeting the design requirements, which provided the reference to improve the design efficiency and reliability of the aero-engine rotor.

A Novel Latin Hypercube Algorithm via Translational Propagation

PubMed Central

Pan, Guang; Ye, Pengcheng

2014-01-01

Metamodels have been widely used in engineering design to facilitate analysis and optimization of complex systems that involve computationally expensive simulation programs. The accuracy of metamodels is directly related to the experimental designs used. Optimal Latin hypercube designs are frequently used and have been shown to have good space-filling and projective properties. However, the high cost in constructing them limits their use. In this paper, a methodology for creating novel Latin hypercube designs via translational propagation and successive local enumeration algorithm (TPSLE) is developed without using formal optimization. TPSLE algorithm is based on the inspiration that a near optimal Latin Hypercube design can be constructed by a simple initial block with a few points generated by algorithm SLE as a building block. In fact, TPSLE algorithm offers a balanced trade-off between the efficiency and sampling performance. The proposed algorithm is compared to two existing algorithms and is found to be much more efficient in terms of the computation time and has acceptable space-filling and projective properties. PMID:25276844

A study on the performance comparison of metaheuristic algorithms on the learning of neural networks

NASA Astrophysics Data System (ADS)

Lai, Kee Huong; Zainuddin, Zarita; Ong, Pauline

2017-08-01

The learning or training process of neural networks entails the task of finding the most optimal set of parameters, which includes translation vectors, dilation parameter, synaptic weights, and bias terms. Apart from the traditional gradient descent-based methods, metaheuristic methods can also be used for this learning purpose. Since the inception of genetic algorithm half a century ago, the last decade witnessed the explosion of a variety of novel metaheuristic algorithms, such as harmony search algorithm, bat algorithm, and whale optimization algorithm. Despite the proof of the no free lunch theorem in the discipline of optimization, a survey in the literature of machine learning gives contrasting results. Some researchers report that certain metaheuristic algorithms are superior to the others, whereas some others argue that different metaheuristic algorithms give comparable performance. As such, this paper aims to investigate if a certain metaheuristic algorithm will outperform the other algorithms. In this work, three metaheuristic algorithms, namely genetic algorithms, particle swarm optimization, and harmony search algorithm are considered. The algorithms are incorporated in the learning of neural networks and their classification results on the benchmark UCI machine learning data sets are compared. It is found that all three metaheuristic algorithms give similar and comparable performance, as captured in the average overall classification accuracy. The results corroborate the findings reported in the works done by previous researchers. Several recommendations are given, which include the need of statistical analysis to verify the results and further theoretical works to support the obtained empirical results.

Planning Under Uncertainty: Methods and Applications

DTIC Science & Technology

2010-06-09

begun research into fundamental algorithms for optimization and re?optimization of continuous optimization problems (such as linear and quadratic... algorithm yields a 14.3% improvement over the original design while saving 68.2 % of the simulation evaluations compared to standard sample-path...They provide tools for building and justifying computational algorithms for such problems. Year. 2010 Month: 03 Final Research under this grant

Application of cellular automatons and ant algorithms in avionics

NASA Astrophysics Data System (ADS)

Kuznetsov, A. V.; Selvesiuk, N. I.; Platoshin, G. A.; Semenova, E. V.

2018-03-01

The paper considers two algorithms for searching quasi-optimal solutions of discrete optimization problems with regard to the tasks of avionics placing. The first one solves the problem of optimal placement of devices by installation locations, the second one is for the problem of finding the shortest route between devices. Solutions are constructed using a cellular automaton and the ant colony algorithm.

Genetic evolutionary taboo search for optimal marker placement in infrared patient setup

NASA Astrophysics Data System (ADS)

Riboldi, M.; Baroni, G.; Spadea, M. F.; Tagaste, B.; Garibaldi, C.; Cambria, R.; Orecchia, R.; Pedotti, A.

2007-09-01

In infrared patient setup adequate selection of the external fiducial configuration is required for compensating inner target displacements (target registration error, TRE). Genetic algorithms (GA) and taboo search (TS) were applied in a newly designed approach to optimal marker placement: the genetic evolutionary taboo search (GETS) algorithm. In the GETS paradigm, multiple solutions are simultaneously tested in a stochastic evolutionary scheme, where taboo-based decision making and adaptive memory guide the optimization process. The GETS algorithm was tested on a group of ten prostate patients, to be compared to standard optimization and to randomly selected configurations. The changes in the optimal marker configuration, when TRE is minimized for OARs, were specifically examined. Optimal GETS configurations ensured a 26.5% mean decrease in the TRE value, versus 19.4% for conventional quasi-Newton optimization. Common features in GETS marker configurations were highlighted in the dataset of ten patients, even when multiple runs of the stochastic algorithm were performed. Including OARs in TRE minimization did not considerably affect the spatial distribution of GETS marker configurations. In conclusion, the GETS algorithm proved to be highly effective in solving the optimal marker placement problem. Further work is needed to embed site-specific deformation models in the optimization process.

Application of the gravity search algorithm to multi-reservoir operation optimization

NASA Astrophysics Data System (ADS)

Bozorg-Haddad, Omid; Janbaz, Mahdieh; Loáiciga, Hugo A.

2016-12-01

Complexities in river discharge, variable rainfall regime, and drought severity merit the use of advanced optimization tools in multi-reservoir operation. The gravity search algorithm (GSA) is an evolutionary optimization algorithm based on the law of gravity and mass interactions. This paper explores the GSA's efficacy for solving benchmark functions, single reservoir, and four-reservoir operation optimization problems. The GSA's solutions are compared with those of the well-known genetic algorithm (GA) in three optimization problems. The results show that the GSA's results are closer to the optimal solutions than the GA's results in minimizing the benchmark functions. The average values of the objective function equal 1.218 and 1.746 with the GSA and GA, respectively, in solving the single-reservoir hydropower operation problem. The global solution equals 1.213 for this same problem. The GSA converged to 99.97% of the global solution in its average-performing history, while the GA converged to 97% of the global solution of the four-reservoir problem. Requiring fewer parameters for algorithmic implementation and reaching the optimal solution in fewer number of functional evaluations are additional advantages of the GSA over the GA. The results of the three optimization problems demonstrate a superior performance of the GSA for optimizing general mathematical problems and the operation of reservoir systems.

Ant algorithms for discrete optimization.

PubMed

Dorigo, M; Di Caro, G; Gambardella, L M

1999-01-01

This article presents an overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies' foraging behavior, and introduces the ant colony optimization (ACO) metaheuristic. In the first part of the article the basic biological findings on real ants are reviewed and their artificial counterparts as well as the ACO metaheuristic are defined. In the second part of the article a number of applications of ACO algorithms to combinatorial optimization and routing in communications networks are described. We conclude with a discussion of related work and of some of the most important aspects of the ACO metaheuristic.

Series Hybrid Electric Vehicle Power System Optimization Based on Genetic Algorithm

NASA Astrophysics Data System (ADS)

Zhu, Tianjun; Li, Bin; Zong, Changfu; Wu, Yang

2017-09-01

Hybrid electric vehicles (HEV), compared with conventional vehicles, have complex structures and more component parameters. If variables optimization designs are carried on all these parameters, it will increase the difficulty and the convergence of algorithm program, so this paper chooses the parameters which has a major influence on the vehicle fuel consumption to make it all work at maximum efficiency. First, HEV powertrain components modelling are built. Second, taking a tandem hybrid structure as an example, genetic algorithm is used in this paper to optimize fuel consumption and emissions. Simulation results in ADVISOR verify the feasibility of the proposed genetic optimization algorithm.

Cyclical parthenogenesis algorithm for layout optimization of truss structures with frequency constraints

NASA Astrophysics Data System (ADS)

Kaveh, A.; Zolghadr, A.

2017-08-01

Structural optimization with frequency constraints is seen as a challenging problem because it is associated with highly nonlinear, discontinuous and non-convex search spaces consisting of several local optima. Therefore, competent optimization algorithms are essential for addressing these problems. In this article, a newly developed metaheuristic method called the cyclical parthenogenesis algorithm (CPA) is used for layout optimization of truss structures subjected to frequency constraints. CPA is a nature-inspired, population-based metaheuristic algorithm, which imitates the reproductive and social behaviour of some animal species such as aphids, which alternate between sexual and asexual reproduction. The efficiency of the CPA is validated using four numerical examples.

Optimal Decentralized Protocol for Electric Vehicle Charging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gan, LW; Topcu, U; Low, SH

We propose a decentralized algorithm to optimally schedule electric vehicle (EV) charging. The algorithm exploits the elasticity of electric vehicle loads to fill the valleys in electric load profiles. We first formulate the EV charging scheduling problem as an optimal control problem, whose objective is to impose a generalized notion of valley-filling, and study properties of optimal charging profiles. We then give a decentralized algorithm to iteratively solve the optimal control problem. In each iteration, EVs update their charging profiles according to the control signal broadcast by the utility company, and the utility company alters the control signal to guidemore » their updates. The algorithm converges to optimal charging profiles (that are as "flat" as they can possibly be) irrespective of the specifications (e.g., maximum charging rate and deadline) of EVs, even if EVs do not necessarily update their charging profiles in every iteration, and use potentially outdated control signal when they update. Moreover, the algorithm only requires each EV solving its local problem, hence its implementation requires low computation capability. We also extend the algorithm to track a given load profile and to real-time implementation.« less

A solution quality assessment method for swarm intelligence optimization algorithms.

PubMed

Zhang, Zhaojun; Wang, Gai-Ge; Zou, Kuansheng; Zhang, Jianhua

2014-01-01

Nowadays, swarm intelligence optimization has become an important optimization tool and wildly used in many fields of application. In contrast to many successful applications, the theoretical foundation is rather weak. Therefore, there are still many problems to be solved. One problem is how to quantify the performance of algorithm in finite time, that is, how to evaluate the solution quality got by algorithm for practical problems. It greatly limits the application in practical problems. A solution quality assessment method for intelligent optimization is proposed in this paper. It is an experimental analysis method based on the analysis of search space and characteristic of algorithm itself. Instead of "value performance," the "ordinal performance" is used as evaluation criteria in this method. The feasible solutions were clustered according to distance to divide solution samples into several parts. Then, solution space and "good enough" set can be decomposed based on the clustering results. Last, using relative knowledge of statistics, the evaluation result can be got. To validate the proposed method, some intelligent algorithms such as ant colony optimization (ACO), particle swarm optimization (PSO), and artificial fish swarm algorithm (AFS) were taken to solve traveling salesman problem. Computational results indicate the feasibility of proposed method.

Beam orientation optimization for intensity-modulated radiation therapy using mixed integer programming

NASA Astrophysics Data System (ADS)

Yang, Ruijie; Dai, Jianrong; Yang, Yong; Hu, Yimin

2006-08-01

The purpose of this study is to extend an algorithm proposed for beam orientation optimization in classical conformal radiotherapy to intensity-modulated radiation therapy (IMRT) and to evaluate the algorithm's performance in IMRT scenarios. In addition, the effect of the candidate pool of beam orientations, in terms of beam orientation resolution and starting orientation, on the optimized beam configuration, plan quality and optimization time is also explored. The algorithm is based on the technique of mixed integer linear programming in which binary and positive float variables are employed to represent candidates for beam orientation and beamlet weights in beam intensity maps. Both beam orientations and beam intensity maps are simultaneously optimized in the algorithm with a deterministic method. Several different clinical cases were used to test the algorithm and the results show that both target coverage and critical structures sparing were significantly improved for the plans with optimized beam orientations compared to those with equi-spaced beam orientations. The calculation time was less than an hour for the cases with 36 binary variables on a PC with a Pentium IV 2.66 GHz processor. It is also found that decreasing beam orientation resolution to 10° greatly reduced the size of the candidate pool of beam orientations without significant influence on the optimized beam configuration and plan quality, while selecting different starting orientations had large influence. Our study demonstrates that the algorithm can be applied to IMRT scenarios, and better beam orientation configurations can be obtained using this algorithm. Furthermore, the optimization efficiency can be greatly increased through proper selection of beam orientation resolution and starting beam orientation while guaranteeing the optimized beam configurations and plan quality.

Mystic: Implementation of the Static Dynamic Optimal Control Algorithm for High-Fidelity, Low-Thrust Trajectory Design

NASA Technical Reports Server (NTRS)

Whiffen, Gregory J.

2006-01-01

Mystic software is designed to compute, analyze, and visualize optimal high-fidelity, low-thrust trajectories, The software can be used to analyze inter-planetary, planetocentric, and combination trajectories, Mystic also provides utilities to assist in the operation and navigation of low-thrust spacecraft. Mystic will be used to design and navigate the NASA's Dawn Discovery mission to orbit the two largest asteroids, The underlying optimization algorithm used in the Mystic software is called Static/Dynamic Optimal Control (SDC). SDC is a nonlinear optimal control method designed to optimize both 'static variables' (parameters) and dynamic variables (functions of time) simultaneously. SDC is a general nonlinear optimal control algorithm based on Bellman's principal.

Smell Detection Agent Based Optimization Algorithm

NASA Astrophysics Data System (ADS)

Vinod Chandra, S. S.

2016-09-01

In this paper, a novel nature-inspired optimization algorithm has been employed and the trained behaviour of dogs in detecting smell trails is adapted into computational agents for problem solving. The algorithm involves creation of a surface with smell trails and subsequent iteration of the agents in resolving a path. This algorithm can be applied in different computational constraints that incorporate path-based problems. Implementation of the algorithm can be treated as a shortest path problem for a variety of datasets. The simulated agents have been used to evolve the shortest path between two nodes in a graph. This algorithm is useful to solve NP-hard problems that are related to path discovery. This algorithm is also useful to solve many practical optimization problems. The extensive derivation of the algorithm can be enabled to solve shortest path problems.

On the Optimization of Aerospace Plane Ascent Trajectory

NASA Astrophysics Data System (ADS)

Al-Garni, Ahmed; Kassem, Ayman Hamdy

A hybrid heuristic optimization technique based on genetic algorithms and particle swarm optimization has been developed and tested for trajectory optimization problems with multi-constraints and a multi-objective cost function. The technique is used to calculate control settings for two types for ascending trajectories (constant dynamic pressure and minimum-fuel-minimum-heat) for a two-dimensional model of an aerospace plane. A thorough statistical analysis is done on the hybrid technique to make comparisons with both basic genetic algorithms and particle swarm optimization techniques with respect to convergence and execution time. Genetic algorithm optimization showed better execution time performance while particle swarm optimization showed better convergence performance. The hybrid optimization technique, benefiting from both techniques, showed superior robust performance compromising convergence trends and execution time.

Performance of Grey Wolf Optimizer on large scale problems

NASA Astrophysics Data System (ADS)

Gupta, Shubham; Deep, Kusum

2017-01-01

For solving nonlinear continuous problems of optimization numerous nature inspired optimization techniques are being proposed in literature which can be implemented to solve real life problems wherein the conventional techniques cannot be applied. Grey Wolf Optimizer is one of such technique which is gaining popularity since the last two years. The objective of this paper is to investigate the performance of Grey Wolf Optimization Algorithm on large scale optimization problems. The Algorithm is implemented on 5 common scalable problems appearing in literature namely Sphere, Rosenbrock, Rastrigin, Ackley and Griewank Functions. The dimensions of these problems are varied from 50 to 1000. The results indicate that Grey Wolf Optimizer is a powerful nature inspired Optimization Algorithm for large scale problems, except Rosenbrock which is a unimodal function.

Comparison of optimized algorithms in facility location allocation problems with different distance measures

NASA Astrophysics Data System (ADS)

Kumar, Rakesh; Chandrawat, Rajesh Kumar; Garg, B. P.; Joshi, Varun

2017-07-01

Opening the new firm or branch with desired execution is very relevant to facility location problem. Along the lines to locate the new ambulances and firehouses, the government desires to minimize average response time for emergencies from all residents of cities. So finding the best location is biggest challenge in day to day life. These type of problems were named as facility location problems. A lot of algorithms have been developed to handle these problems. In this paper, we review five algorithms that were applied to facility location problems. The significance of clustering in facility location problems is also presented. First we compare Fuzzy c-means clustering (FCM) algorithm with alternating heuristic (AH) algorithm, then with Particle Swarm Optimization (PSO) algorithms using different type of distance function. The data was clustered with the help of FCM and then we apply median model and min-max problem model on that data. After finding optimized locations using these algorithms we find the distance from optimized location point to the demanded point with different distance techniques and compare the results. At last, we design a general example to validate the feasibility of the five algorithms for facilities location optimization, and authenticate the advantages and drawbacks of them.

Algorithm design, user interface, and optimization procedure for a fuzzy logic ramp metering algorithm : a training manual for freeway operations engineers

DOT National Transportation Integrated Search

2000-02-01

This training manual describes the fuzzy logic ramp metering algorithm in detail, as implemented system-wide in the greater Seattle area. The method of defining the inputs to the controller and optimizing the performance of the algorithm is explained...

An analysis dictionary learning algorithm under a noisy data model with orthogonality constraint.

PubMed

Zhang, Ye; Yu, Tenglong; Wang, Wenwu

2014-01-01

Two common problems are often encountered in analysis dictionary learning (ADL) algorithms. The first one is that the original clean signals for learning the dictionary are assumed to be known, which otherwise need to be estimated from noisy measurements. This, however, renders a computationally slow optimization process and potentially unreliable estimation (if the noise level is high), as represented by the Analysis K-SVD (AK-SVD) algorithm. The other problem is the trivial solution to the dictionary, for example, the null dictionary matrix that may be given by a dictionary learning algorithm, as discussed in the learning overcomplete sparsifying transform (LOST) algorithm. Here we propose a novel optimization model and an iterative algorithm to learn the analysis dictionary, where we directly employ the observed data to compute the approximate analysis sparse representation of the original signals (leading to a fast optimization procedure) and enforce an orthogonality constraint on the optimization criterion to avoid the trivial solutions. Experiments demonstrate the competitive performance of the proposed algorithm as compared with three baselines, namely, the AK-SVD, LOST, and NAAOLA algorithms.

Optimal control and optimal trajectories of regional macroeconomic dynamics based on the Pontryagin maximum principle

NASA Astrophysics Data System (ADS)

Bulgakov, V. K.; Strigunov, V. V.

2009-05-01

The Pontryagin maximum principle is used to prove a theorem concerning optimal control in regional macroeconomics. A boundary value problem for optimal trajectories of the state and adjoint variables is formulated, and optimal curves are analyzed. An algorithm is proposed for solving the boundary value problem of optimal control. The performance of the algorithm is demonstrated by computing an optimal control and the corresponding optimal trajectories.

An Airway Network Flow Assignment Approach Based on an Efficient Multiobjective Optimization Framework

PubMed Central

Zhang, Xuejun; Lei, Jiaxing

2015-01-01

Considering reducing the airspace congestion and the flight delay simultaneously, this paper formulates the airway network flow assignment (ANFA) problem as a multiobjective optimization model and presents a new multiobjective optimization framework to solve it. Firstly, an effective multi-island parallel evolution algorithm with multiple evolution populations is employed to improve the optimization capability. Secondly, the nondominated sorting genetic algorithm II is applied for each population. In addition, a cooperative coevolution algorithm is adapted to divide the ANFA problem into several low-dimensional biobjective optimization problems which are easier to deal with. Finally, in order to maintain the diversity of solutions and to avoid prematurity, a dynamic adjustment operator based on solution congestion degree is specifically designed for the ANFA problem. Simulation results using the real traffic data from China air route network and daily flight plans demonstrate that the proposed approach can improve the solution quality effectively, showing superiority to the existing approaches such as the multiobjective genetic algorithm, the well-known multiobjective evolutionary algorithm based on decomposition, and a cooperative coevolution multiobjective algorithm as well as other parallel evolution algorithms with different migration topology. PMID:26180840

Optimal field-splitting algorithm in intensity-modulated radiotherapy: Evaluations using head-and-neck and female pelvic IMRT cases

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dou, Xin; Kim, Yusung, E-mail: yusung-kim@uiowa.edu; Bayouth, John E.

2013-04-01

To develop an optimal field-splitting algorithm of minimal complexity and verify the algorithm using head-and-neck (H and N) and female pelvic intensity-modulated radiotherapy (IMRT) cases. An optimal field-splitting algorithm was developed in which a large intensity map (IM) was split into multiple sub-IMs (≥2). The algorithm reduced the total complexity by minimizing the monitor units (MU) delivered and segment number of each sub-IM. The algorithm was verified through comparison studies with the algorithm as used in a commercial treatment planning system. Seven IMRT, H and N, and female pelvic cancer cases (54 IMs) were analyzed by MU, segment numbers, andmore » dose distributions. The optimal field-splitting algorithm was found to reduce both total MU and the total number of segments. We found on average a 7.9 ± 11.8% and 9.6 ± 18.2% reduction in MU and segment numbers for H and N IMRT cases with an 11.9 ± 17.4% and 11.1 ± 13.7% reduction for female pelvic cases. The overall percent (absolute) reduction in the numbers of MU and segments were found to be on average −9.7 ± 14.6% (−15 ± 25 MU) and −10.3 ± 16.3% (−3 ± 5), respectively. In addition, all dose distributions from the optimal field-splitting method showed improved dose distributions. The optimal field-splitting algorithm shows considerable improvements in both total MU and total segment number. The algorithm is expected to be beneficial for the radiotherapy treatment of large-field IMRT.« less

Research on Collection System Optimal Design of Wind Farm with Obstacles

NASA Astrophysics Data System (ADS)

Huang, W.; Yan, B. Y.; Tan, R. S.; Liu, L. F.

2017-05-01

To the collection system optimal design of offshore wind farm, the factors considered are not only the reasonable configuration of the cable and switch, but also the influence of the obstacles on the topology design of the offshore wind farm. This paper presents a concrete topology optimization algorithm with obstacles. The minimal area rectangle encasing box of the obstacle is obtained by using the method of minimal area encasing box. Then the optimization algorithm combining the advantages of Dijkstra algorithm and Prim algorithm is used to gain the scheme of avoidance obstacle path planning. Finally a fuzzy comprehensive evaluation model based on the analytic hierarchy process is constructed to compare the performance of the different topologies. Case studies demonstrate the feasibility of the proposed algorithm and model.

Simulated parallel annealing within a neighborhood for optimization of biomechanical systems.

PubMed

Higginson, J S; Neptune, R R; Anderson, F C

2005-09-01

Optimization problems for biomechanical systems have become extremely complex. Simulated annealing (SA) algorithms have performed well in a variety of test problems and biomechanical applications; however, despite advances in computer speed, convergence to optimal solutions for systems of even moderate complexity has remained prohibitive. The objective of this study was to develop a portable parallel version of a SA algorithm for solving optimization problems in biomechanics. The algorithm for simulated parallel annealing within a neighborhood (SPAN) was designed to minimize interprocessor communication time and closely retain the heuristics of the serial SA algorithm. The computational speed of the SPAN algorithm scaled linearly with the number of processors on different computer platforms for a simple quadratic test problem and for a more complex forward dynamic simulation of human pedaling.

Three-dimensional unstructured grid generation via incremental insertion and local optimization

NASA Technical Reports Server (NTRS)

Barth, Timothy J.; Wiltberger, N. Lyn; Gandhi, Amar S.

1992-01-01

Algorithms for the generation of 3D unstructured surface and volume grids are discussed. These algorithms are based on incremental insertion and local optimization. The present algorithms are very general and permit local grid optimization based on various measures of grid quality. This is very important; unlike the 2D Delaunay triangulation, the 3D Delaunay triangulation appears not to have a lexicographic characterization of angularity. (The Delaunay triangulation is known to minimize that maximum containment sphere, but unfortunately this is not true lexicographically). Consequently, Delaunay triangulations in three-space can result in poorly shaped tetrahedral elements. Using the present algorithms, 3D meshes can be constructed which optimize a certain angle measure, albeit locally. We also discuss the combinatorial aspects of the algorithm as well as implementational details.

Exact and heuristic algorithms for Space Information Flow.

PubMed

Uwitonze, Alfred; Huang, Jiaqing; Ye, Yuanqing; Cheng, Wenqing; Li, Zongpeng

2018-01-01

Space Information Flow (SIF) is a new promising research area that studies network coding in geometric space, such as Euclidean space. The design of algorithms that compute the optimal SIF solutions remains one of the key open problems in SIF. This work proposes the first exact SIF algorithm and a heuristic SIF algorithm that compute min-cost multicast network coding for N (N ≥ 3) given terminal nodes in 2-D Euclidean space. Furthermore, we find that the Butterfly network in Euclidean space is the second example besides the Pentagram network where SIF is strictly better than Euclidean Steiner minimal tree. The exact algorithm design is based on two key techniques: Delaunay triangulation and linear programming. Delaunay triangulation technique helps to find practically good candidate relay nodes, after which a min-cost multicast linear programming model is solved over the terminal nodes and the candidate relay nodes, to compute the optimal multicast network topology, including the optimal relay nodes selected by linear programming from all the candidate relay nodes and the flow rates on the connection links. The heuristic algorithm design is also based on Delaunay triangulation and linear programming techniques. The exact algorithm can achieve the optimal SIF solution with an exponential computational complexity, while the heuristic algorithm can achieve the sub-optimal SIF solution with a polynomial computational complexity. We prove the correctness of the exact SIF algorithm. The simulation results show the effectiveness of the heuristic SIF algorithm.

Mobile geographic information system (GIS) solution for pavement condition surveys.

DOT National Transportation Integrated Search

2012-06-28

This report discusses the design and implementation of a software-based solution that will improve the data collection processes during the Pavement Condition Surveys (PCS) conducted by the State Materials Office (SMO) of the Florida Department of Tr...

Foraging on the potential energy surface: a swarm intelligence-based optimizer for molecular geometry.

PubMed

Wehmeyer, Christoph; Falk von Rudorff, Guido; Wolf, Sebastian; Kabbe, Gabriel; Schärf, Daniel; Kühne, Thomas D; Sebastiani, Daniel

2012-11-21

We present a stochastic, swarm intelligence-based optimization algorithm for the prediction of global minima on potential energy surfaces of molecular cluster structures. Our optimization approach is a modification of the artificial bee colony (ABC) algorithm which is inspired by the foraging behavior of honey bees. We apply our modified ABC algorithm to the problem of global geometry optimization of molecular cluster structures and show its performance for clusters with 2-57 particles and different interatomic interaction potentials.

Low-thrust orbit transfer optimization with refined Q-law and multi-objective genetic algorithm

NASA Technical Reports Server (NTRS)

Lee, Seungwon; Petropoulos, Anastassios E.; von Allmen, Paul

2005-01-01

An optimization method for low-thrust orbit transfers around a central body is developed using the Q-law and a multi-objective genetic algorithm. in the hybrid method, the Q-law generates candidate orbit transfers, and the multi-objective genetic algorithm optimizes the Q-law control parameters in order to simultaneously minimize both the consumed propellant mass and flight time of the orbit tranfer. This paper addresses the problem of finding optimal orbit transfers for low-thrust spacecraft.

A Kind of Nonlinear Programming Problem Based on Mixed Fuzzy Relation Equations Constraints

NASA Astrophysics Data System (ADS)

Li, Jinquan; Feng, Shuang; Mi, Honghai

In this work, a kind of nonlinear programming problem with non-differential objective function and under the constraints expressed by a system of mixed fuzzy relation equations is investigated. First, some properties of this kind of optimization problem are obtained. Then, a polynomial-time algorithm for this kind of optimization problem is proposed based on these properties. Furthermore, we show that this algorithm is optimal for the considered optimization problem in this paper. Finally, numerical examples are provided to illustrate our algorithms.

Foraging on the potential energy surface: A swarm intelligence-based optimizer for molecular geometry

NASA Astrophysics Data System (ADS)

Wehmeyer, Christoph; Falk von Rudorff, Guido; Wolf, Sebastian; Kabbe, Gabriel; Schärf, Daniel; Kühne, Thomas D.; Sebastiani, Daniel

2012-11-01

We present a stochastic, swarm intelligence-based optimization algorithm for the prediction of global minima on potential energy surfaces of molecular cluster structures. Our optimization approach is a modification of the artificial bee colony (ABC) algorithm which is inspired by the foraging behavior of honey bees. We apply our modified ABC algorithm to the problem of global geometry optimization of molecular cluster structures and show its performance for clusters with 2-57 particles and different interatomic interaction potentials.

Full glowworm swarm optimization algorithm for whole-set orders scheduling in single machine.

PubMed

Yu, Zhang; Yang, Xiaomei

2013-01-01

By analyzing the characteristics of whole-set orders problem and combining the theory of glowworm swarm optimization, a new glowworm swarm optimization algorithm for scheduling is proposed. A new hybrid-encoding schema combining with two-dimensional encoding and random-key encoding is given. In order to enhance the capability of optimal searching and speed up the convergence rate, the dynamical changed step strategy is integrated into this algorithm. Furthermore, experimental results prove its feasibility and efficiency.

Optimal trajectories of aircraft and spacecraft

NASA Technical Reports Server (NTRS)

Miele, A.

1990-01-01

Work done on algorithms for the numerical solutions of optimal control problems and their application to the computation of optimal flight trajectories of aircraft and spacecraft is summarized. General considerations on calculus of variations, optimal control, numerical algorithms, and applications of these algorithms to real-world problems are presented. The sequential gradient-restoration algorithm (SGRA) is examined for the numerical solution of optimal control problems of the Bolza type. Both the primal formulation and the dual formulation are discussed. Aircraft trajectories, in particular, the application of the dual sequential gradient-restoration algorithm (DSGRA) to the determination of optimal flight trajectories in the presence of windshear are described. Both take-off trajectories and abort landing trajectories are discussed. Take-off trajectories are optimized by minimizing the peak deviation of the absolute path inclination from a reference value. Abort landing trajectories are optimized by minimizing the peak drop of altitude from a reference value. Abort landing trajectories are optimized by minimizing the peak drop of altitude from a reference value. The survival capability of an aircraft in a severe windshear is discussed, and the optimal trajectories are found to be superior to both constant pitch trajectories and maximum angle of attack trajectories. Spacecraft trajectories, in particular, the application of the primal sequential gradient-restoration algorithm (PSGRA) to the determination of optimal flight trajectories for aeroassisted orbital transfer are examined. Both the coplanar case and the noncoplanar case are discussed within the frame of three problems: minimization of the total characteristic velocity; minimization of the time integral of the square of the path inclination; and minimization of the peak heating rate. The solution of the second problem is called nearly-grazing solution, and its merits are pointed out as a useful engineering compromise between energy requirements and aerodynamics heating requirements.

Recursive Branching Simulated Annealing Algorithm

NASA Technical Reports Server (NTRS)

Bolcar, Matthew; Smith, J. Scott; Aronstein, David

2012-01-01

This innovation is a variation of a simulated-annealing optimization algorithm that uses a recursive-branching structure to parallelize the search of a parameter space for the globally optimal solution to an objective. The algorithm has been demonstrated to be more effective at searching a parameter space than traditional simulated-annealing methods for a particular problem of interest, and it can readily be applied to a wide variety of optimization problems, including those with a parameter space having both discrete-value parameters (combinatorial) and continuous-variable parameters. It can take the place of a conventional simulated- annealing, Monte-Carlo, or random- walk algorithm. In a conventional simulated-annealing (SA) algorithm, a starting configuration is randomly selected within the parameter space. The algorithm randomly selects another configuration from the parameter space and evaluates the objective function for that configuration. If the objective function value is better than the previous value, the new configuration is adopted as the new point of interest in the parameter space. If the objective function value is worse than the previous value, the new configuration may be adopted, with a probability determined by a temperature parameter, used in analogy to annealing in metals. As the optimization continues, the region of the parameter space from which new configurations can be selected shrinks, and in conjunction with lowering the annealing temperature (and thus lowering the probability for adopting configurations in parameter space with worse objective functions), the algorithm can converge on the globally optimal configuration. The Recursive Branching Simulated Annealing (RBSA) algorithm shares some features with the SA algorithm, notably including the basic principles that a starting configuration is randomly selected from within the parameter space, the algorithm tests other configurations with the goal of finding the globally optimal solution, and the region from which new configurations can be selected shrinks as the search continues. The key difference between these algorithms is that in the SA algorithm, a single path, or trajectory, is taken in parameter space, from the starting point to the globally optimal solution, while in the RBSA algorithm, many trajectories are taken; by exploring multiple regions of the parameter space simultaneously, the algorithm has been shown to converge on the globally optimal solution about an order of magnitude faster than when using conventional algorithms. Novel features of the RBSA algorithm include: 1. More efficient searching of the parameter space due to the branching structure, in which multiple random configurations are generated and multiple promising regions of the parameter space are explored; 2. The implementation of a trust region for each parameter in the parameter space, which provides a natural way of enforcing upper- and lower-bound constraints on the parameters; and 3. The optional use of a constrained gradient- search optimization, performed on the continuous variables around each branch s configuration in parameter space to improve search efficiency by allowing for fast fine-tuning of the continuous variables within the trust region at that configuration point.

On the problem of solving the optimization for continuous space based on information distribution function of ant colony algorithm

NASA Astrophysics Data System (ADS)

Min, Huang; Na, Cai

2017-06-01

These years, ant colony algorithm has been widely used in solving the domain of discrete space optimization, while the research on solving the continuous space optimization was relatively little. Based on the original optimization for continuous space, the article proposes the improved ant colony algorithm which is used to Solve the optimization for continuous space, so as to overcome the ant colony algorithm’s disadvantages of searching for a long time in continuous space. The article improves the solving way for the total amount of information of each interval and the due number of ants. The article also introduces a function of changes with the increase of the number of iterations in order to enhance the convergence rate of the improved ant colony algorithm. The simulation results show that compared with the result in literature[5], the suggested improved ant colony algorithm that based on the information distribution function has a better convergence performance. Thus, the article provides a new feasible and effective method for ant colony algorithm to solve this kind of problem.

[Application of an Adaptive Inertia Weight Particle Swarm Algorithm in the Magnetic Resonance Bias Field Correction].

PubMed

Wang, Chang; Qin, Xin; Liu, Yan; Zhang, Wenchao

2016-06-01

An adaptive inertia weight particle swarm algorithm is proposed in this study to solve the local optimal problem with the method of traditional particle swarm optimization in the process of estimating magnetic resonance(MR)image bias field.An indicator measuring the degree of premature convergence was designed for the defect of traditional particle swarm optimization algorithm.The inertia weight was adjusted adaptively based on this indicator to ensure particle swarm to be optimized globally and to avoid it from falling into local optimum.The Legendre polynomial was used to fit bias field,the polynomial parameters were optimized globally,and finally the bias field was estimated and corrected.Compared to those with the improved entropy minimum algorithm,the entropy of corrected image was smaller and the estimated bias field was more accurate in this study.Then the corrected image was segmented and the segmentation accuracy obtained in this research was 10% higher than that with improved entropy minimum algorithm.This algorithm can be applied to the correction of MR image bias field.

Evaluation of the selection methods used in the exIWO algorithm based on the optimization of multidimensional functions

NASA Astrophysics Data System (ADS)

Kostrzewa, Daniel; Josiński, Henryk

2016-06-01

The expanded Invasive Weed Optimization algorithm (exIWO) is an optimization metaheuristic modelled on the original IWO version inspired by dynamic growth of weeds colony. The authors of the present paper have modified the exIWO algorithm introducing a set of both deterministic and non-deterministic strategies of individuals' selection. The goal of the project was to evaluate the modified exIWO by testing its usefulness for multidimensional numerical functions optimization. The optimized functions: Griewank, Rastrigin, and Rosenbrock are frequently used as benchmarks because of their characteristics.

Parameter Estimation of Fractional-Order Chaotic Systems by Using Quantum Parallel Particle Swarm Optimization Algorithm

PubMed Central

Huang, Yu; Guo, Feng; Li, Yongling; Liu, Yufeng

2015-01-01

Parameter estimation for fractional-order chaotic systems is an important issue in fractional-order chaotic control and synchronization and could be essentially formulated as a multidimensional optimization problem. A novel algorithm called quantum parallel particle swarm optimization (QPPSO) is proposed to solve the parameter estimation for fractional-order chaotic systems. The parallel characteristic of quantum computing is used in QPPSO. This characteristic increases the calculation of each generation exponentially. The behavior of particles in quantum space is restrained by the quantum evolution equation, which consists of the current rotation angle, individual optimal quantum rotation angle, and global optimal quantum rotation angle. Numerical simulation based on several typical fractional-order systems and comparisons with some typical existing algorithms show the effectiveness and efficiency of the proposed algorithm. PMID:25603158

Search Parameter Optimization for Discrete, Bayesian, and Continuous Search Algorithms

DTIC Science & Technology

2017-09-01

NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS SEARCH PARAMETER OPTIMIZATION FOR DISCRETE , BAYESIAN, AND CONTINUOUS SEARCH ALGORITHMS by...to 09-22-2017 4. TITLE AND SUBTITLE SEARCH PARAMETER OPTIMIZATION FOR DISCRETE , BAYESIAN, AND CON- TINUOUS SEARCH ALGORITHMS 5. FUNDING NUMBERS 6...simple search and rescue acts to prosecuting aerial/surface/submersible targets on mission. This research looks at varying the known discrete and

Generalized gradient algorithm for trajectory optimization

NASA Technical Reports Server (NTRS)

Zhao, Yiyuan; Bryson, A. E.; Slattery, R.

1990-01-01

The generalized gradient algorithm presented and verified as a basis for the solution of trajectory optimization problems improves the performance index while reducing path equality constraints, and terminal equality constraints. The algorithm is conveniently divided into two phases, of which the first, 'feasibility' phase yields a solution satisfying both path and terminal constraints, while the second, 'optimization' phase uses the results of the first phase as initial guesses.

Discrete-State Simulated Annealing For Traveling-Wave Tube Slow-Wave Circuit Optimization

NASA Technical Reports Server (NTRS)

Wilson, Jeffrey D.; Bulson, Brian A.; Kory, Carol L.; Williams, W. Dan (Technical Monitor)

2001-01-01

Algorithms based on the global optimization technique of simulated annealing (SA) have proven useful in designing traveling-wave tube (TWT) slow-wave circuits for high RF power efficiency. The characteristic of SA that enables it to determine a globally optimized solution is its ability to accept non-improving moves in a controlled manner. In the initial stages of the optimization, the algorithm moves freely through configuration space, accepting most of the proposed designs. This freedom of movement allows non-intuitive designs to be explored rather than restricting the optimization to local improvement upon the initial configuration. As the optimization proceeds, the rate of acceptance of non-improving moves is gradually reduced until the algorithm converges to the optimized solution. The rate at which the freedom of movement is decreased is known as the annealing or cooling schedule of the SA algorithm. The main disadvantage of SA is that there is not a rigorous theoretical foundation for determining the parameters of the cooling schedule. The choice of these parameters is highly problem dependent and the designer needs to experiment in order to determine values that will provide a good optimization in a reasonable amount of computational time. This experimentation can absorb a large amount of time especially when the algorithm is being applied to a new type of design. In order to eliminate this disadvantage, a variation of SA known as discrete-state simulated annealing (DSSA), was recently developed. DSSA provides the theoretical foundation for a generic cooling schedule which is problem independent, Results of similar quality to SA can be obtained, but without the extra computational time required to tune the cooling parameters. Two algorithm variations based on DSSA were developed and programmed into a Microsoft Excel spreadsheet graphical user interface (GUI) to the two-dimensional nonlinear multisignal helix traveling-wave amplifier analysis program TWA3. The algorithms were used to optimize the computed RF efficiency of a TWT by determining the phase velocity profile of the slow-wave circuit. The mathematical theory and computational details of the DSSA algorithms will be presented and results will be compared to those obtained with a SA algorithm.

Integrative systems modeling and multi-objective optimization

EPA Science Inventory

This presentation presents a number of algorithms, tools, and methods for utilizing multi-objective optimization within integrated systems modeling frameworks. We first present innovative methods using a genetic algorithm to optimally calibrate the VELMA and SWAT ecohydrological ...

A parallel Jacobson-Oksman optimization algorithm. [parallel processing (computers)

NASA Technical Reports Server (NTRS)

Straeter, T. A.; Markos, A. T.

1975-01-01

A gradient-dependent optimization technique which exploits the vector-streaming or parallel-computing capabilities of some modern computers is presented. The algorithm, derived by assuming that the function to be minimized is homogeneous, is a modification of the Jacobson-Oksman serial minimization method. In addition to describing the algorithm, conditions insuring the convergence of the iterates of the algorithm and the results of numerical experiments on a group of sample test functions are presented. The results of these experiments indicate that this algorithm will solve optimization problems in less computing time than conventional serial methods on machines having vector-streaming or parallel-computing capabilities.

PSO Algorithm for an Optimal Power Controller in a Microgrid

NASA Astrophysics Data System (ADS)

Al-Saedi, W.; Lachowicz, S.; Habibi, D.; Bass, O.

2017-07-01

This paper presents the Particle Swarm Optimization (PSO) algorithm to improve the quality of the power supply in a microgrid. This algorithm is proposed for a real-time selftuning method that used in a power controller for an inverter based Distributed Generation (DG) unit. In such system, the voltage and frequency are the main control objectives, particularly when the microgrid is islanded or during load change. In this work, the PSO algorithm is implemented to find the optimal controller parameters to satisfy the control objectives. The results show high performance of the applied PSO algorithm of regulating the microgrid voltage and frequency.

Accelerating IMRT optimization by voxel sampling

NASA Astrophysics Data System (ADS)

Martin, Benjamin C.; Bortfeld, Thomas R.; Castañon, David A.

2007-12-01

This paper presents a new method for accelerating intensity-modulated radiation therapy (IMRT) optimization using voxel sampling. Rather than calculating the dose to the entire patient at each step in the optimization, the dose is only calculated for some randomly selected voxels. Those voxels are then used to calculate estimates of the objective and gradient which are used in a randomized version of a steepest descent algorithm. By selecting different voxels on each step, we are able to find an optimal solution to the full problem. We also present an algorithm to automatically choose the best sampling rate for each structure within the patient during the optimization. Seeking further improvements, we experimented with several other gradient-based optimization algorithms and found that the delta-bar-delta algorithm performs well despite the randomness. Overall, we were able to achieve approximately an order of magnitude speedup on our test case as compared to steepest descent.

Linear feasibility algorithms for treatment planning in interstitial photodynamic therapy

NASA Astrophysics Data System (ADS)

Rendon, A.; Beck, J. C.; Lilge, Lothar

2008-02-01

Interstitial Photodynamic therapy (IPDT) has been under intense investigation in recent years, with multiple clinical trials underway. This effort has demanded the development of optimization strategies that determine the best locations and output powers for light sources (cylindrical or point diffusers) to achieve an optimal light delivery. Furthermore, we have recently introduced cylindrical diffusers with customizable emission profiles, placing additional requirements on the optimization algorithms, particularly in terms of the stability of the inverse problem. Here, we present a general class of linear feasibility algorithms and their properties. Moreover, we compare two particular instances of these algorithms, which are been used in the context of IPDT: the Cimmino algorithm and a weighted gradient descent (WGD) algorithm. The algorithms were compared in terms of their convergence properties, the cost function they minimize in the infeasible case, their ability to regularize the inverse problem, and the resulting optimal light dose distributions. Our results show that the WGD algorithm overall performs slightly better than the Cimmino algorithm and that it converges to a minimizer of a clinically relevant cost function in the infeasible case. Interestingly however, treatment plans resulting from either algorithms were very similar in terms of the resulting fluence maps and dose volume histograms, once the diffuser powers adjusted to achieve equal prostate coverage.

Comparing, optimizing, and benchmarking quantum-control algorithms in a unifying programming framework

DOE Office of Scientific and Technical Information (OSTI.GOV)

Machnes, S.; Institute for Theoretical Physics, University of Ulm, D-89069 Ulm; Sander, U.

2011-08-15

For paving the way to novel applications in quantum simulation, computation, and technology, increasingly large quantum systems have to be steered with high precision. It is a typical task amenable to numerical optimal control to turn the time course of pulses, i.e., piecewise constant control amplitudes, iteratively into an optimized shape. Here, we present a comparative study of optimal-control algorithms for a wide range of finite-dimensional applications. We focus on the most commonly used algorithms: GRAPE methods which update all controls concurrently, and Krotov-type methods which do so sequentially. Guidelines for their use are given and open research questions aremore » pointed out. Moreover, we introduce a unifying algorithmic framework, DYNAMO (dynamic optimization platform), designed to provide the quantum-technology community with a convenient matlab-based tool set for optimal control. In addition, it gives researchers in optimal-control techniques a framework for benchmarking and comparing newly proposed algorithms with the state of the art. It allows a mix-and-match approach with various types of gradients, update and step-size methods as well as subspace choices. Open-source code including examples is made available at http://qlib.info.« less

Image reconstruction and scan configurations enabled by optimization-based algorithms in multispectral CT

NASA Astrophysics Data System (ADS)

Chen, Buxin; Zhang, Zheng; Sidky, Emil Y.; Xia, Dan; Pan, Xiaochuan

2017-11-01

Optimization-based algorithms for image reconstruction in multispectral (or photon-counting) computed tomography (MCT) remains a topic of active research. The challenge of optimization-based image reconstruction in MCT stems from the inherently non-linear data model that can lead to a non-convex optimization program for which no mathematically exact solver seems to exist for achieving globally optimal solutions. In this work, based upon a non-linear data model, we design a non-convex optimization program, derive its first-order-optimality conditions, and propose an algorithm to solve the program for image reconstruction in MCT. In addition to consideration of image reconstruction for the standard scan configuration, the emphasis is on investigating the algorithm’s potential for enabling non-standard scan configurations with no or minimum hardware modification to existing CT systems, which has potential practical implications for lowered hardware cost, enhanced scanning flexibility, and reduced imaging dose/time in MCT. Numerical studies are carried out for verification of the algorithm and its implementation, and for a preliminary demonstration and characterization of the algorithm in reconstructing images and in enabling non-standard configurations with varying scanning angular range and/or x-ray illumination coverage in MCT.

New Results in Astrodynamics Using Genetic Algorithms

NASA Technical Reports Server (NTRS)

Coverstone-Carroll, V.; Hartmann, J. W.; Williams, S. N.; Mason, W. J.

1998-01-01

Generic algorithms have gained popularity as an effective procedure for obtaining solutions to traditionally difficult space mission optimization problems. In this paper, a brief survey of the use of genetic algorithms to solve astrodynamics problems is presented and is followed by new results obtained from applying a Pareto genetic algorithm to the optimization of low-thrust interplanetary spacecraft missions.

PID controller tuning using metaheuristic optimization algorithms for benchmark problems

NASA Astrophysics Data System (ADS)

Gholap, Vishal; Naik Dessai, Chaitali; Bagyaveereswaran, V.

2017-11-01

This paper contributes to find the optimal PID controller parameters using particle swarm optimization (PSO), Genetic Algorithm (GA) and Simulated Annealing (SA) algorithm. The algorithms were developed through simulation of chemical process and electrical system and the PID controller is tuned. Here, two different fitness functions such as Integral Time Absolute Error and Time domain Specifications were chosen and applied on PSO, GA and SA while tuning the controller. The proposed Algorithms are implemented on two benchmark problems of coupled tank system and DC motor. Finally, comparative study has been done with different algorithms based on best cost, number of iterations and different objective functions. The closed loop process response for each set of tuned parameters is plotted for each system with each fitness function.

Two New PRP Conjugate Gradient Algorithms for Minimization Optimization Models.

PubMed

Yuan, Gonglin; Duan, Xiabin; Liu, Wenjie; Wang, Xiaoliang; Cui, Zengru; Sheng, Zhou

2015-01-01

Two new PRP conjugate Algorithms are proposed in this paper based on two modified PRP conjugate gradient methods: the first algorithm is proposed for solving unconstrained optimization problems, and the second algorithm is proposed for solving nonlinear equations. The first method contains two aspects of information: function value and gradient value. The two methods both possess some good properties, as follows: 1) βk ≥ 0 2) the search direction has the trust region property without the use of any line search method 3) the search direction has sufficient descent property without the use of any line search method. Under some suitable conditions, we establish the global convergence of the two algorithms. We conduct numerical experiments to evaluate our algorithms. The numerical results indicate that the first algorithm is effective and competitive for solving unconstrained optimization problems and that the second algorithm is effective for solving large-scale nonlinear equations.

Two New PRP Conjugate Gradient Algorithms for Minimization Optimization Models

PubMed Central

Yuan, Gonglin; Duan, Xiabin; Liu, Wenjie; Wang, Xiaoliang; Cui, Zengru; Sheng, Zhou

2015-01-01

Two new PRP conjugate Algorithms are proposed in this paper based on two modified PRP conjugate gradient methods: the first algorithm is proposed for solving unconstrained optimization problems, and the second algorithm is proposed for solving nonlinear equations. The first method contains two aspects of information: function value and gradient value. The two methods both possess some good properties, as follows: 1)β k ≥ 0 2) the search direction has the trust region property without the use of any line search method 3) the search direction has sufficient descent property without the use of any line search method. Under some suitable conditions, we establish the global convergence of the two algorithms. We conduct numerical experiments to evaluate our algorithms. The numerical results indicate that the first algorithm is effective and competitive for solving unconstrained optimization problems and that the second algorithm is effective for solving large-scale nonlinear equations. PMID:26502409

Design and pitch scaling for affordable node transition and EUV insertion scenario

NASA Astrophysics Data System (ADS)

Kim, Ryoung-han; Ryckaert, Julien; Raghavan, Praveen; Sherazi, Yasser; Debacker, Peter; Trivkovic, Darko; Gillijns, Werner; Tan, Ling Ee; Drissi, Youssef; Blanco, Victor; Bekaert, Joost; Mao, Ming; Larivière, Stephane; McIntyre, Greg

2017-04-01

imec's DTCO and EUV achievement toward imec 7nm (iN7) technology node which is industry 5nm node equivalent is reported with a focus on cost and scaling. Patterning-aware design methodology supports both iArF multiple patterning and EUV under one compliant design rule. FinFET device with contacted poly pitch of 42nm and metal pitch of 32nm with 7.5-track, 6.5-track, and 6-track standard cell library are explored. Scaling boosters are used to provide additional scaling and die cost benefit while lessening pitch shrink burden, and it makes EUV insertion more affordable. EUV pattern fidelity is optimized through OPC, SMO, M3D, mask sizing and SRAF. Processed wafers were characterized and edge-placement-error (EPE) variability is validated for EUV insertion. Scale-ability and cost of ownership of EUV patterning in aligned with iN7 standard cell design, integration and patterning specification are discussed.

Bio-inspired optimization algorithms for optical parameter extraction of dielectric materials: A comparative study

NASA Astrophysics Data System (ADS)

Ghulam Saber, Md; Arif Shahriar, Kh; Ahmed, Ashik; Hasan Sagor, Rakibul

2016-10-01

Particle swarm optimization (PSO) and invasive weed optimization (IWO) algorithms are used for extracting the modeling parameters of materials useful for optics and photonics research community. These two bio-inspired algorithms are used here for the first time in this particular field to the best of our knowledge. The algorithms are used for modeling graphene oxide and the performances of the two are compared. Two objective functions are used for different boundary values. Root mean square (RMS) deviation is determined and compared.

An Empirical Comparison of Seven Iterative and Evolutionary Function Optimization Heuristics

NASA Technical Reports Server (NTRS)

Baluja, Shumeet

1995-01-01

This report is a repository of the results obtained from a large scale empirical comparison of seven iterative and evolution-based optimization heuristics. Twenty-seven static optimization problems, spanning six sets of problem classes which are commonly explored in genetic algorithm literature, are examined. The problem sets include job-shop scheduling, traveling salesman, knapsack, binpacking, neural network weight optimization, and standard numerical optimization. The search spaces in these problems range from 2368 to 22040. The results indicate that using genetic algorithms for the optimization of static functions does not yield a benefit, in terms of the final answer obtained, over simpler optimization heuristics. Descriptions of the algorithms tested and the encodings of the problems are described in detail for reproducibility.

Concepts and applications of "natural computing" techniques in de novo drug and peptide design.

PubMed

Hiss, Jan A; Hartenfeller, Markus; Schneider, Gisbert

2010-05-01

Evolutionary algorithms, particle swarm optimization, and ant colony optimization have emerged as robust optimization methods for molecular modeling and peptide design. Such algorithms mimic combinatorial molecule assembly by using molecular fragments as building-blocks for compound construction, and relying on adaptation and emergence of desired pharmacological properties in a population of virtual molecules. Nature-inspired algorithms might be particularly suited for bioisosteric replacement or scaffold-hopping from complex natural products to synthetically more easily accessible compounds that are amenable to optimization by medicinal chemistry. The theory and applications of selected nature-inspired algorithms for drug design are reviewed, together with practical applications and a discussion of their advantages and limitations.

Bounded-Degree Approximations of Stochastic Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Quinn, Christopher J.; Pinar, Ali; Kiyavash, Negar

2017-06-01

We propose algorithms to approximate directed information graphs. Directed information graphs are probabilistic graphical models that depict causal dependencies between stochastic processes in a network. The proposed algorithms identify optimal and near-optimal approximations in terms of Kullback-Leibler divergence. The user-chosen sparsity trades off the quality of the approximation against visual conciseness and computational tractability. One class of approximations contains graphs with speci ed in-degrees. Another class additionally requires that the graph is connected. For both classes, we propose algorithms to identify the optimal approximations and also near-optimal approximations, using a novel relaxation of submodularity. We also propose algorithms to identifymore » the r-best approximations among these classes, enabling robust decision making.« less

Optimal tracking control for a class of nonlinear discrete-time systems with time delays based on heuristic dynamic programming.

PubMed

Zhang, Huaguang; Song, Ruizhuo; Wei, Qinglai; Zhang, Tieyan

2011-12-01

In this paper, a novel heuristic dynamic programming (HDP) iteration algorithm is proposed to solve the optimal tracking control problem for a class of nonlinear discrete-time systems with time delays. The novel algorithm contains state updating, control policy iteration, and performance index iteration. To get the optimal states, the states are also updated. Furthermore, the "backward iteration" is applied to state updating. Two neural networks are used to approximate the performance index function and compute the optimal control policy for facilitating the implementation of HDP iteration algorithm. At last, we present two examples to demonstrate the effectiveness of the proposed HDP iteration algorithm.

Research on robust optimization of emergency logistics network considering the time dependence characteristic

NASA Astrophysics Data System (ADS)

WANG, Qingrong; ZHU, Changfeng; LI, Ying; ZHANG, Zhengkun

2017-06-01

Considering the time dependence of emergency logistic network and complexity of the environment that the network exists in, in this paper the time dependent network optimization theory and robust discrete optimization theory are combined, and the emergency logistics dynamic network optimization model with characteristics of robustness is built to maximize the timeliness of emergency logistics. On this basis, considering the complexity of dynamic network and the time dependence of edge weight, an improved ant colony algorithm is proposed to realize the coupling of the optimization algorithm and the network time dependence and robustness. Finally, a case study has been carried out in order to testify validity of this robustness optimization model and its algorithm, and the value of different regulation factors was analyzed considering the importance of the value of the control factor in solving the optimal path. Analysis results show that this model and its algorithm above-mentioned have good timeliness and strong robustness.

An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications

PubMed Central

Lou, Xin Yuan; Sun, Lin Fu

2017-01-01

This paper proposes a new support vector machine (SVM) optimization scheme based on an improved chaotic fly optimization algorithm (FOA) with a mutation strategy to simultaneously perform parameter setting turning for the SVM and feature selection. In the improved FOA, the chaotic particle initializes the fruit fly swarm location and replaces the expression of distance for the fruit fly to find the food source. However, the proposed mutation strategy uses two distinct generative mechanisms for new food sources at the osphresis phase, allowing the algorithm procedure to search for the optimal solution in both the whole solution space and within the local solution space containing the fruit fly swarm location. In an evaluation based on a group of ten benchmark problems, the proposed algorithm’s performance is compared with that of other well-known algorithms, and the results support the superiority of the proposed algorithm. Moreover, this algorithm is successfully applied in a SVM to perform both parameter setting turning for the SVM and feature selection to solve real-world classification problems. This method is called chaotic fruit fly optimization algorithm (CIFOA)-SVM and has been shown to be a more robust and effective optimization method than other well-known methods, particularly in terms of solving the medical diagnosis problem and the credit card problem. PMID:28369096

A Study of Penalty Function Methods for Constraint Handling with Genetic Algorithm

NASA Technical Reports Server (NTRS)

Ortiz, Francisco

2004-01-01

COMETBOARDS (Comparative Evaluation Testbed of Optimization and Analysis Routines for Design of Structures) is a design optimization test bed that can evaluate the performance of several different optimization algorithms. A few of these optimization algorithms are the sequence of unconstrained minimization techniques (SUMT), sequential linear programming (SLP) and the sequential quadratic programming techniques (SQP). A genetic algorithm (GA) is a search technique that is based on the principles of natural selection or "survival of the fittest". Instead of using gradient information, the GA uses the objective function directly in the search. The GA searches the solution space by maintaining a population of potential solutions. Then, using evolving operations such as recombination, mutation and selection, the GA creates successive generations of solutions that will evolve and take on the positive characteristics of their parents and thus gradually approach optimal or near-optimal solutions. By using the objective function directly in the search, genetic algorithms can be effectively applied in non-convex, highly nonlinear, complex problems. The genetic algorithm is not guaranteed to find the global optimum, but it is less likely to get trapped at a local optimum than traditional gradient-based search methods when the objective function is not smooth and generally well behaved. The purpose of this research is to assist in the integration of genetic algorithm (GA) into COMETBOARDS. COMETBOARDS cast the design of structures as a constrained nonlinear optimization problem. One method used to solve constrained optimization problem with a GA to convert the constrained optimization problem into an unconstrained optimization problem by developing a penalty function that penalizes infeasible solutions. There have been several suggested penalty function in the literature each with there own strengths and weaknesses. A statistical analysis of some suggested penalty functions is performed in this study. Also, a response surface approach to robust design is used to develop a new penalty function approach. This new penalty function approach is then compared with the other existing penalty functions.

CURE-SMOTE algorithm and hybrid algorithm for feature selection and parameter optimization based on random forests.

PubMed

Ma, Li; Fan, Suohai

2017-03-14

The random forests algorithm is a type of classifier with prominent universality, a wide application range, and robustness for avoiding overfitting. But there are still some drawbacks to random forests. Therefore, to improve the performance of random forests, this paper seeks to improve imbalanced data processing, feature selection and parameter optimization. We propose the CURE-SMOTE algorithm for the imbalanced data classification problem. Experiments on imbalanced UCI data reveal that the combination of Clustering Using Representatives (CURE) enhances the original synthetic minority oversampling technique (SMOTE) algorithms effectively compared with the classification results on the original data using random sampling, Borderline-SMOTE1, safe-level SMOTE, C-SMOTE, and k-means-SMOTE. Additionally, the hybrid RF (random forests) algorithm has been proposed for feature selection and parameter optimization, which uses the minimum out of bag (OOB) data error as its objective function. Simulation results on binary and higher-dimensional data indicate that the proposed hybrid RF algorithms, hybrid genetic-random forests algorithm, hybrid particle swarm-random forests algorithm and hybrid fish swarm-random forests algorithm can achieve the minimum OOB error and show the best generalization ability. The training set produced from the proposed CURE-SMOTE algorithm is closer to the original data distribution because it contains minimal noise. Thus, better classification results are produced from this feasible and effective algorithm. Moreover, the hybrid algorithm's F-value, G-mean, AUC and OOB scores demonstrate that they surpass the performance of the original RF algorithm. Hence, this hybrid algorithm provides a new way to perform feature selection and parameter optimization.

A parallel adaptive quantum genetic algorithm for the controllability of arbitrary networks.

PubMed

Li, Yuhong; Gong, Guanghong; Li, Ni

2018-01-01

In this paper, we propose a novel algorithm-parallel adaptive quantum genetic algorithm-which can rapidly determine the minimum control nodes of arbitrary networks with both control nodes and state nodes. The corresponding network can be fully controlled with the obtained control scheme. We transformed the network controllability issue into a combinational optimization problem based on the Popov-Belevitch-Hautus rank condition. A set of canonical networks and a list of real-world networks were experimented. Comparison results demonstrated that the algorithm was more ideal to optimize the controllability of networks, especially those larger-size networks. We demonstrated subsequently that there were links between the optimal control nodes and some network statistical characteristics. The proposed algorithm provides an effective approach to improve the controllability optimization of large networks or even extra-large networks with hundreds of thousands nodes.

Guidance control of small UAV with energy and maneuverability limitations for a search and coverage mission

NASA Astrophysics Data System (ADS)

Gramajo, German G.

This thesis presents an algorithm for a search and coverage mission that has increased autonomy in generating an ideal trajectory while explicitly considering the available energy in the optimization. Further, current algorithms used to generate trajectories depend on the operator providing a discrete set of turning rate requirements to obtain an optimal solution. This work proposes an additional modification to the algorithm so that it optimizes the trajectory for a range of turning rates instead of a discrete set of turning rates. This thesis conducts an evaluation of the algorithm with variation in turn duration, entry-heading angle, and entry point. Comparative studies of the algorithm with existing method indicates improved autonomy in choosing the optimization parameters while producing trajectories with better coverage area and closer final distance to the desired terminal point.

Switching neuronal state: optimal stimuli revealed using a stochastically-seeded gradient algorithm.

PubMed

Chang, Joshua; Paydarfar, David

2014-12-01

Inducing a switch in neuronal state using energy optimal stimuli is relevant to a variety of problems in neuroscience. Analytical techniques from optimal control theory can identify such stimuli; however, solutions to the optimization problem using indirect variational approaches can be elusive in models that describe neuronal behavior. Here we develop and apply a direct gradient-based optimization algorithm to find stimulus waveforms that elicit a change in neuronal state while minimizing energy usage. We analyze standard models of neuronal behavior, the Hodgkin-Huxley and FitzHugh-Nagumo models, to show that the gradient-based algorithm: (1) enables automated exploration of a wide solution space, using stochastically generated initial waveforms that converge to multiple locally optimal solutions; and (2) finds optimal stimulus waveforms that achieve a physiological outcome condition, without a priori knowledge of the optimal terminal condition of all state variables. Analysis of biological systems using stochastically-seeded gradient methods can reveal salient dynamical mechanisms underlying the optimal control of system behavior. The gradient algorithm may also have practical applications in future work, for example, finding energy optimal waveforms for therapeutic neural stimulation that minimizes power usage and diminishes off-target effects and damage to neighboring tissue.

A novel comprehensive learning artificial bee colony optimizer for dynamic optimization biological problems.

PubMed

Su, Weixing; Chen, Hanning; Liu, Fang; Lin, Na; Jing, Shikai; Liang, Xiaodan; Liu, Wei

2017-03-01

There are many dynamic optimization problems in the real world, whose convergence and searching ability is cautiously desired, obviously different from static optimization cases. This requires an optimization algorithm adaptively seek the changing optima over dynamic environments, instead of only finding the global optimal solution in the static environment. This paper proposes a novel comprehensive learning artificial bee colony optimizer (CLABC) for optimization in dynamic environments problems, which employs a pool of optimal foraging strategies to balance the exploration and exploitation tradeoff. The main motive of CLABC is to enrich artificial bee foraging behaviors in the ABC model by combining Powell's pattern search method, life-cycle, and crossover-based social learning strategy. The proposed CLABC is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. The experiments for evaluating CLABC are conducted on the dynamic moving peak benchmarks. Furthermore, the proposed algorithm is applied to a real-world application of dynamic RFID network optimization. Statistical analysis of all these cases highlights the significant performance improvement due to the beneficial combination and demonstrates the performance superiority of the proposed algorithm.

Optimization of C4.5 algorithm-based particle swarm optimization for breast cancer diagnosis

NASA Astrophysics Data System (ADS)

Muslim, M. A.; Rukmana, S. H.; Sugiharti, E.; Prasetiyo, B.; Alimah, S.

2018-03-01

Data mining has become a basic methodology for computational applications in the field of medical domains. Data mining can be applied in the health field such as for diagnosis of breast cancer, heart disease, diabetes and others. Breast cancer is most common in women, with more than one million cases and nearly 600,000 deaths occurring worldwide each year. The most effective way to reduce breast cancer deaths was by early diagnosis. This study aims to determine the level of breast cancer diagnosis. This research data uses Wisconsin Breast Cancer dataset (WBC) from UCI machine learning. The method used in this research is the algorithm C4.5 and Particle Swarm Optimization (PSO) as a feature option and to optimize the algorithm. C4.5. Ten-fold cross-validation is used as a validation method and a confusion matrix. The result of this research is C4.5 algorithm. The particle swarm optimization C4.5 algorithm has increased by 0.88%.

Photoinjector optimization using a derivative-free, model-based trust-region algorithm for the Argonne Wakefield Accelerator

NASA Astrophysics Data System (ADS)

Neveu, N.; Larson, J.; Power, J. G.; Spentzouris, L.

2017-07-01

Model-based, derivative-free, trust-region algorithms are increasingly popular for optimizing computationally expensive numerical simulations. A strength of such methods is their efficient use of function evaluations. In this paper, we use one such algorithm to optimize the beam dynamics in two cases of interest at the Argonne Wakefield Accelerator (AWA) facility. First, we minimize the emittance of a 1 nC electron bunch produced by the AWA rf photocathode gun by adjusting three parameters: rf gun phase, solenoid strength, and laser radius. The algorithm converges to a set of parameters that yield an emittance of 1.08 μm. Second, we expand the number of optimization parameters to model the complete AWA rf photoinjector (the gun and six accelerating cavities) at 40 nC. The optimization algorithm is used in a Pareto study that compares the trade-off between emittance and bunch length for the AWA 70MeV photoinjector.

Multilevel algorithms for nonlinear optimization

NASA Technical Reports Server (NTRS)

Alexandrov, Natalia; Dennis, J. E., Jr.

1994-01-01

Multidisciplinary design optimization (MDO) gives rise to nonlinear optimization problems characterized by a large number of constraints that naturally occur in blocks. We propose a class of multilevel optimization methods motivated by the structure and number of constraints and by the expense of the derivative computations for MDO. The algorithms are an extension to the nonlinear programming problem of the successful class of local Brown-Brent algorithms for nonlinear equations. Our extensions allow the user to partition constraints into arbitrary blocks to fit the application, and they separately process each block and the objective function, restricted to certain subspaces. The methods use trust regions as a globalization strategy, and they have been shown to be globally convergent under reasonable assumptions. The multilevel algorithms can be applied to all classes of MDO formulations. Multilevel algorithms for solving nonlinear systems of equations are a special case of the multilevel optimization methods. In this case, they can be viewed as a trust-region globalization of the Brown-Brent class.

Design and implementation of intelligent electronic warfare decision making algorithm

NASA Astrophysics Data System (ADS)

Peng, Hsin-Hsien; Chen, Chang-Kuo; Hsueh, Chi-Shun

2017-05-01

Electromagnetic signals and the requirements of timely response have been a rapid growth in modern electronic warfare. Although jammers are limited resources, it is possible to achieve the best electronic warfare efficiency by tactical decisions. This paper proposes the intelligent electronic warfare decision support system. In this work, we develop a novel hybrid algorithm, Digital Pheromone Particle Swarm Optimization, based on Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Shuffled Frog Leaping Algorithm (SFLA). We use PSO to solve the problem and combine the concept of pheromones in ACO to accumulate more useful information in spatial solving process and speed up finding the optimal solution. The proposed algorithm finds the optimal solution in reasonable computation time by using the method of matrix conversion in SFLA. The results indicated that jammer allocation was more effective. The system based on the hybrid algorithm provides electronic warfare commanders with critical information to assist commanders in effectively managing the complex electromagnetic battlefield.

Joint Power Charging and Routing in Wireless Rechargeable Sensor Networks.

PubMed

Jia, Jie; Chen, Jian; Deng, Yansha; Wang, Xingwei; Aghvami, Abdol-Hamid

2017-10-09

The development of wireless power transfer (WPT) technology has inspired the transition from traditional battery-based wireless sensor networks (WSNs) towards wireless rechargeable sensor networks (WRSNs). While extensive efforts have been made to improve charging efficiency, little has been done for routing optimization. In this work, we present a joint optimization model to maximize both charging efficiency and routing structure. By analyzing the structure of the optimization model, we first decompose the problem and propose a heuristic algorithm to find the optimal charging efficiency for the predefined routing tree. Furthermore, by coding the many-to-one communication topology as an individual, we further propose to apply a genetic algorithm (GA) for the joint optimization of both routing and charging. The genetic operations, including tree-based recombination and mutation, are proposed to obtain a fast convergence. Our simulation results show that the heuristic algorithm reduces the number of resident locations and the total moving distance. We also show that our proposed algorithm achieves a higher charging efficiency compared with existing algorithms.

Joint Power Charging and Routing in Wireless Rechargeable Sensor Networks

PubMed Central

Jia, Jie; Chen, Jian; Deng, Yansha; Wang, Xingwei; Aghvami, Abdol-Hamid

2017-01-01

The development of wireless power transfer (WPT) technology has inspired the transition from traditional battery-based wireless sensor networks (WSNs) towards wireless rechargeable sensor networks (WRSNs). While extensive efforts have been made to improve charging efficiency, little has been done for routing optimization. In this work, we present a joint optimization model to maximize both charging efficiency and routing structure. By analyzing the structure of the optimization model, we first decompose the problem and propose a heuristic algorithm to find the optimal charging efficiency for the predefined routing tree. Furthermore, by coding the many-to-one communication topology as an individual, we further propose to apply a genetic algorithm (GA) for the joint optimization of both routing and charging. The genetic operations, including tree-based recombination and mutation, are proposed to obtain a fast convergence. Our simulation results show that the heuristic algorithm reduces the number of resident locations and the total moving distance. We also show that our proposed algorithm achieves a higher charging efficiency compared with existing algorithms. PMID:28991200

Bare-Bones Teaching-Learning-Based Optimization

PubMed Central

Zou, Feng; Wang, Lei; Hei, Xinhong; Chen, Debao; Jiang, Qiaoyong; Li, Hongye

2014-01-01

Teaching-learning-based optimization (TLBO) algorithm which simulates the teaching-learning process of the class room is one of the recently proposed swarm intelligent (SI) algorithms. In this paper, a new TLBO variant called bare-bones teaching-learning-based optimization (BBTLBO) is presented to solve the global optimization problems. In this method, each learner of teacher phase employs an interactive learning strategy, which is the hybridization of the learning strategy of teacher phase in the standard TLBO and Gaussian sampling learning based on neighborhood search, and each learner of learner phase employs the learning strategy of learner phase in the standard TLBO or the new neighborhood search strategy. To verify the performance of our approaches, 20 benchmark functions and two real-world problems are utilized. Conducted experiments can been observed that the BBTLBO performs significantly better than, or at least comparable to, TLBO and some existing bare-bones algorithms. The results indicate that the proposed algorithm is competitive to some other optimization algorithms. PMID:25013844

Bare-bones teaching-learning-based optimization.

PubMed

Zou, Feng; Wang, Lei; Hei, Xinhong; Chen, Debao; Jiang, Qiaoyong; Li, Hongye

2014-01-01

Teaching-learning-based optimization (TLBO) algorithm which simulates the teaching-learning process of the class room is one of the recently proposed swarm intelligent (SI) algorithms. In this paper, a new TLBO variant called bare-bones teaching-learning-based optimization (BBTLBO) is presented to solve the global optimization problems. In this method, each learner of teacher phase employs an interactive learning strategy, which is the hybridization of the learning strategy of teacher phase in the standard TLBO and Gaussian sampling learning based on neighborhood search, and each learner of learner phase employs the learning strategy of learner phase in the standard TLBO or the new neighborhood search strategy. To verify the performance of our approaches, 20 benchmark functions and two real-world problems are utilized. Conducted experiments can been observed that the BBTLBO performs significantly better than, or at least comparable to, TLBO and some existing bare-bones algorithms. The results indicate that the proposed algorithm is competitive to some other optimization algorithms.

A study of optimization techniques in HDR brachytherapy for the prostate

NASA Astrophysics Data System (ADS)

Pokharel, Ghana Shyam

Several studies carried out thus far are in favor of dose escalation to the prostate gland to have better local control of the disease. But optimal way of delivery of higher doses of radiation therapy to the prostate without hurting neighboring critical structures is still debatable. In this study, we proposed that real time high dose rate (HDR) brachytherapy with highly efficient and effective optimization could be an alternative means of precise delivery of such higher doses. This approach of delivery eliminates the critical issues such as treatment setup uncertainties and target localization as in external beam radiation therapy. Likewise, dosimetry in HDR brachytherapy is not influenced by organ edema and potential source migration as in permanent interstitial implants. Moreover, the recent report of radiobiological parameters further strengthen the argument of using hypofractionated HDR brachytherapy for the management of prostate cancer. Firstly, we studied the essential features and requirements of real time HDR brachytherapy treatment planning system. Automating catheter reconstruction with fast editing tools, fast yet accurate dose engine, robust and fast optimization and evaluation engine are some of the essential requirements for such procedures. Moreover, in most of the cases we performed, treatment plan optimization took significant amount of time of overall procedure. So, making treatment plan optimization automatic or semi-automatic with sufficient speed and accuracy was the goal of the remaining part of the project. Secondly, we studied the role of optimization function and constraints in overall quality of optimized plan. We have studied the gradient based deterministic algorithm with dose volume histogram (DVH) and more conventional variance based objective functions for optimization. In this optimization strategy, the relative weight of particular objective in aggregate objective function signifies its importance with respect to other objectives. Based on our study, DVH based objective function performed better than traditional variance based objective function in creating a clinically acceptable plan when executed under identical conditions. Thirdly, we studied the multiobjective optimization strategy using both DVH and variance based objective functions. The optimization strategy was to create several Pareto optimal solutions by scanning the clinically relevant part of the Pareto front. This strategy was adopted to decouple optimization from decision such that user could select final solution from the pool of alternative solutions based on his/her clinical goals. The overall quality of treatment plan improved using this approach compared to traditional class solution approach. In fact, the final optimized plan selected using decision engine with DVH based objective was comparable to typical clinical plan created by an experienced physicist. Next, we studied the hybrid technique comprising both stochastic and deterministic algorithm to optimize both dwell positions and dwell times. The simulated annealing algorithm was used to find optimal catheter distribution and the DVH based algorithm was used to optimize 3D dose distribution for given catheter distribution. This unique treatment planning and optimization tool was capable of producing clinically acceptable highly reproducible treatment plans in clinically reasonable time. As this algorithm was able to create clinically acceptable plans within clinically reasonable time automatically, it is really appealing for real time procedures. Next, we studied the feasibility of multiobjective optimization using evolutionary algorithm for real time HDR brachytherapy for the prostate. The algorithm with properly tuned algorithm specific parameters was able to create clinically acceptable plans within clinically reasonable time. However, the algorithm was let to run just for limited number of generations not considered optimal, in general, for such algorithms. This was done to keep time window desirable for real time procedures. Therefore, it requires further study with improved conditions to realize the full potential of the algorithm.

Motion Cueing Algorithm Development: Human-Centered Linear and Nonlinear Approaches

NASA Technical Reports Server (NTRS)

Houck, Jacob A. (Technical Monitor); Telban, Robert J.; Cardullo, Frank M.

2005-01-01

While the performance of flight simulator motion system hardware has advanced substantially, the development of the motion cueing algorithm, the software that transforms simulated aircraft dynamics into realizable motion commands, has not kept pace. Prior research identified viable features from two algorithms: the nonlinear "adaptive algorithm", and the "optimal algorithm" that incorporates human vestibular models. A novel approach to motion cueing, the "nonlinear algorithm" is introduced that combines features from both approaches. This algorithm is formulated by optimal control, and incorporates a new integrated perception model that includes both visual and vestibular sensation and the interaction between the stimuli. Using a time-varying control law, the matrix Riccati equation is updated in real time by a neurocomputing approach. Preliminary pilot testing resulted in the optimal algorithm incorporating a new otolith model, producing improved motion cues. The nonlinear algorithm vertical mode produced a motion cue with a time-varying washout, sustaining small cues for longer durations and washing out large cues more quickly compared to the optimal algorithm. The inclusion of the integrated perception model improved the responses to longitudinal and lateral cues. False cues observed with the NASA adaptive algorithm were absent. The neurocomputing approach was crucial in that the number of presentations of an input vector could be reduced to meet the real time requirement without degrading the quality of the motion cues.

Optimization of Contrast Detection Power with Probabilistic Behavioral Information

PubMed Central

Cordes, Dietmar; Herzmann, Grit; Nandy, Rajesh; Curran, Tim

2012-01-01

Recent progress in the experimental design for event-related fMRI experiments made it possible to find the optimal stimulus sequence for maximum contrast detection power using a genetic algorithm. In this study, a novel algorithm is proposed for optimization of contrast detection power by including probabilistic behavioral information, based on pilot data, in the genetic algorithm. As a particular application, a recognition memory task is studied and the design matrix optimized for contrasts involving the familiarity of individual items (pictures of objects) and the recollection of qualitative information associated with the items (left/right orientation). Optimization of contrast efficiency is a complicated issue whenever subjects’ responses are not deterministic but probabilistic. Contrast efficiencies are not predictable unless behavioral responses are included in the design optimization. However, available software for design optimization does not include options for probabilistic behavioral constraints. If the anticipated behavioral responses are included in the optimization algorithm, the design is optimal for the assumed behavioral responses, and the resulting contrast efficiency is greater than what either a block design or a random design can achieve. Furthermore, improvements of contrast detection power depend strongly on the behavioral probabilities, the perceived randomness, and the contrast of interest. The present genetic algorithm can be applied to any case in which fMRI contrasts are dependent on probabilistic responses that can be estimated from pilot data. PMID:22326984