Gibberellins and gravitropism in maize shoots: endogenous gibberellin-like substances and movement and metabolism of [3H]Gibberellin A20

NASA Technical Reports Server (NTRS)

Rood, S. B.; Kaufman, P. B.; Abe, H.; Pharis, R. P.

1987-01-01

[3H]Gibberellin A20 (GA20) of high specific radioactivity (49.9 gigabecquerel per millimole) was applied equilaterally in a ring of microdrops to the internodal pulvinus of shoots of 3-week-old gravistimulated and vertical normal maize (Zea mays L.), and to a pleiogravitropic (prostrate) maize mutant, lazy (la). All plants converted the [3H]GA20 to [3H]GA1- and [3H]GA29-like metabolites as well as to several metabolites with the partitioning and chromatographic behavior of glucosyl conjugates of [3H]GA1, [3H]GA29, and [3H]GA8. The tentative identification of these putative [3H]GA glucosyl conjugates was further supported by the release of the free [3H]GA moiety after cleavage with cellulase. Within 12 hours of the [3H]GA20 feed, there was a significantly higher proportion of total radioactivity in lower than in upper halves of internode and leaf sheath pulvini in gravistimulated normal maize. Further, there was a significantly higher proportion of putative free GA metabolites of [3H]GA20, especially [3H]GA1, in the lower halves of normal maize relative to upper halves. The differential localization of the metabolites between upper and lower halves was not apparent in the pleiogravitropic mutant, la. Endogenous GA-like substances were also examined in gravistimulated maize shoots. Forty-eight hours after gravistimulation of 3-week-old maize seedlings, endogenous free GA-like substances in upper and lower leaf sheath and internode pulvini halves were extracted, chromatographed, and bioassayed using the "Tanginbozu" dwarf rice microdrop assay. Lower halves contained consistently higher total levels of GA-like activity. The qualitative elution profile of GA-like substances differed consistently, upper halves containing principally a GA20-like substance and lower halves containing principally a GA20-like substance and lower halves containing mainly GA1-like and GA19-like substances. Gibberellins A1 (10 nanograms per gram) and A20 (5 nanograms per gram) were identified from these lower leaf sheath pulvini by capillary gas chromatography-selected ion monitoring. Results from all of these experiments are consistent with a role for GAs in the differential shoot growth that follows gravitropism, although the results do not eliminate the possibility that the redistribution of GAs results from the gravitropic response.

Evaluation of antioxidant drugs for the treatment of ochronotic alkaptonuria in an in vitro human cell model.

PubMed

Tinti, Laura; Spreafico, Adriano; Braconi, Daniela; Millucci, Lia; Bernardini, Giulia; Chellini, Federico; Cavallo, Giovanni; Selvi, Enrico; Galeazzi, Mauro; Marcolongo, Roberto; Gallagher, James A; Santucci, Annalisa

2010-10-01

Alkaptonuria (AKU) is a rare autosomal recessive disease, associated with deficiency of homogentisate 1,2-dioxygenase activity in the liver. This leads to an accumulation of homogentisic acid (HGA) and its oxidized derivatives in polymerized form in connective tissues especially in joints. Currently, AKU lacks an appropriate therapy. Hence, we propose a new treatment for AKU using the antioxidant N-acetylcysteine (NAC) administered in combinations with ascorbic acid (ASC) since it has been proven that NAC counteracts the side-effects of ASC. We established an in vitro cell model using human articular primary chondrocytes challenged with an excess of HGA (0.33 mM). We used this experimental model to undertake pre-clinical testing of potential antioxidative therapies for AKU, evaluating apoptosis, viability, proliferation, and metabolism of chondrocytes exposed to HGA and treated with NAC and ASC administered alone or in combination addition of both. NAC decreased apoptosis induced in chondrocytes by HGA, increased chondrocyte growth reduced by HGA, and partially restored proteoglycan release inhibited by HGA. A significantly improvement in efficacy was found with combined addition of the two antioxidants in comparison with NAC and ASC alone. Our novel in vitro AKU model allowed us to demonstrate the efficacy of the co-administration of NAC and ASC to counteract the negative effects of HGA for the treatment of ochronotic arthropathy. (c) 2010 Wiley-Liss, Inc.

Rapid and minimum invasive functional brain mapping by real-time visualization of high gamma activity during awake craniotomy.

PubMed

Ogawa, Hiroshi; Kamada, Kyousuke; Kapeller, Christoph; Hiroshima, Satoru; Prueckl, Robert; Guger, Christoph

2014-11-01

Electrocortical stimulation (ECS) is the gold standard for functional brain mapping during an awake craniotomy. The critical issue is to set aside enough time to identify eloquent cortices by ECS. High gamma activity (HGA) ranging between 80 and 120 Hz on electrocorticogram is assumed to reflect localized cortical processing. In this report, we used real-time HGA mapping and functional neuronavigation integrated with functional magnetic resonance imaging (fMRI) for rapid and reliable identification of motor and language functions. Four patients with intra-axial tumors in their dominant hemisphere underwent preoperative fMRI and lesion resection with an awake craniotomy. All patients showed significant fMRI activation evoked by motor and language tasks. During the craniotomy, we recorded electrocorticogram activity by placing subdural grids directly on the exposed brain surface. Each patient performed motor and language tasks and demonstrated real-time HGA dynamics in hand motor areas and parts of the inferior frontal gyrus. Sensitivity and specificity of HGA mapping were 100% compared with ECS mapping in the frontal lobe, which suggested HGA mapping precisely indicated eloquent cortices. We found different HGA dynamics of language tasks in frontal and temporal regions. Specificities of the motor and language-fMRI did not reach 85%. The results of HGA mapping was mostly consistent with those of ECS mapping, although fMRI tended to overestimate functional areas. This novel technique enables rapid and accurate identification of motor and frontal language areas. Furthermore, real-time HGA mapping sheds light on underlying physiological mechanisms related to human brain functions. Copyright © 2014 Elsevier Inc. All rights reserved.

A rare case of acquired methemoglobinemia associated with alkaptonuria.

PubMed

Isa, Yasuki; Nihei, Shun-ichi; Irifukuhama, Yuna; Ikeda, Tomoya; Matsumoto, Hiroyuki; Nagata, Keiji; Harayama, Nobuya; Aibara, Keiji; Kamochi, Masayuki

2014-01-01

We herein present a rare case of acquired methemoglobinemia associated with alkaptonuria. Alkaptonuria is a congenital error of metabolism caused by the deficiency of homogentisic acid oxidase, which subsequently results in the accumulation of homogentisic acid (HGA) in body tissues. As renal dysfunction progresses, the level of HGA excretion in the urine decreases and the blood concentration of HGA increases. HGA oxidizes oxyhemoglobin to methemoglobin, which can induce multiple organ failure accompanied by tissue hypoxia, intravascular hemolysis and metabolic acidosis. The mortality of this disease is high when alkaptonuria is associated with the presence of methemoglobinemia; therefore, treatment should be carefully planned in such cases.

Hypoglycin A Content in Blood and Urine Discriminates Horses with Atypical Myopathy from Clinically Normal Horses Grazing on the Same Pasture

PubMed Central

Bochnia, M.; Ziegler, J.; Sander, J.; Uhlig, A.; Schaefer, S.; Vollstedt, S.; Glatter, M.; Abel, S.; Recknagel, S.; Schusser, G. F.; Wensch-Dorendorf, M.; Zeyner, A.

2015-01-01

Hypoglycin A (HGA) in seeds of Acer spp. is suspected to cause seasonal pasture myopathy in North America and equine atypical myopathy (AM) in Europe, fatal diseases in horses on pasture. In previous studies, this suspicion was substantiated by the correlation of seed HGA content with the concentrations of toxic metabolites in urine and serum (MCPA-conjugates) of affected horses. However, seed sampling was conducted after rather than during an outbreak of the disease. The aim of this study was to further confirm the causality between HGA occurrence and disease outbreak by seed sampling during an outbreak and the determination of i) HGA in seeds and of ii) HGA and MCPA-conjugates in urine and serum of diseased horses. Furthermore, cograzing healthy horses, which were present on AM affected pastures, were also investigated. AM-pastures in Germany were visited to identify seeds of Acer pseudoplatanus and serum (n = 8) as well as urine (n = 6) from a total of 16 diseased horses were analyzed for amino acid composition by LC-ESI-MS/MS, with a special focus on the content of HGA. Additionally, the content of its toxic metabolite was measured in its conjugated form in body fluids (UPLC-MS/MS). The seeds contained 1.7–319.8 μg HGA/g seed. The content of HGA in serum of affected horses ranged from 387.8–8493.8 μg/L (controls < 10 μg/L), and in urine from 143.8–926.4 μg/L (controls < 10 μg/L), respectively. Healthy cograzing horses on AM-pastures showed higher serum (108.8 ± 83.76 μg/L) and urine concentrations (26.9 ± 7.39 μg/L) compared to control horses, but lower concentrations compared to diseased horses. The range of MCPA-carnitine and creatinine concentrations found in diseased horses in serum and urine were 0.17–0.65 mmol/L (controls < 0.01), and 0.34–2.05 μmol/mmoL (controls < 0.001), respectively. MCPA-glycine levels in urine of cograzing horses were higher compared to controls. Thus, the causal link between HGA intoxication and disease outbreak could be further substantiated, and the early detection of HGA in cograzing horses, which are clinically normal, might be a promising step in prophylaxis. PMID:26378918

Metabolism of [3H]Gibberellin A20 in Light- and Dark-grown Tobacco Callus Cultures 1

PubMed Central

Lance, Barbara; Durley, Richard C.; Reid, David M.; Thorpe, Trevor A.; Pharis, Richard P.

1976-01-01

The growth of tobacco callus in culture (previously shown to contain gibberellin [GA]-like substances), and its ability to metabolize [3H]-GA20 were examined. Growth rates, in the presence and absence of exogenously applied GA, were examined in light and dark conditions. Dark-grown callus grew at a much faster rate than light-grown and [3H]GA20 was metabolized much more rapidly in darkness than in light. [3H]GA1 was identified by combined gas-liquid chromatography/mass spectrometry as a major product of [3H]GA20, and was found to be a more potent promoter of tobacco callus growth than GA20. PMID:16659684

Reduced primary cilia length and altered Arl13b expression are associated with deregulated chondrocyte Hedgehog signaling in alkaptonuria

PubMed Central

Thorpe, Stephen D.; Gambassi, Silvia; Thompson, Clare L.; Chandrakumar, Charmilie

2017-01-01

Alkaptonuria (AKU) is a rare inherited disease resulting from a deficiency of the enzyme homogentisate 1,2‐dioxygenase which leads to the accumulation of homogentisic acid (HGA). AKU is characterized by severe cartilage degeneration, similar to that observed in osteoarthritis. Previous studies suggest that AKU is associated with alterations in cytoskeletal organization which could modulate primary cilia structure/function. This study investigated whether AKU is associated with changes in chondrocyte primary cilia and associated Hedgehog signaling which mediates cartilage degradation in osteoarthritis. Human articular chondrocytes were obtained from healthy and AKU donors. Additionally, healthy chondrocytes were treated with HGA to replicate AKU pathology (+HGA). Diseased cells exhibited shorter cilia with length reductions of 36% and 16% in AKU and +HGA chondrocytes respectively, when compared to healthy controls. Both AKU and +HGA chondrocytes demonstrated disruption of the usual cilia length regulation by actin contractility. Furthermore, the proportion of cilia with axoneme breaks and bulbous tips was increased in AKU chondrocytes consistent with defective regulation of ciliary trafficking. Distribution of the Hedgehog‐related protein Arl13b along the ciliary axoneme was altered such that its localization was increased at the distal tip in AKU and +HGA chondrocytes. These changes in cilia structure/trafficking in AKU and +HGA chondrocytes were associated with a complete inability to activate Hedgehog signaling in response to exogenous ligand. Thus, we suggest that altered responsiveness to Hedgehog, as a consequence of cilia dysfunction, may be a contributing factor in the development of arthropathy highlighting the cilium as a novel target in AKU. PMID:28158906

GPG-NH2 acts via the metabolite alphaHGA to target HIV-1 Env to the ER-associated protein degradation pathway.

PubMed

Jejcic, Alenka; Höglund, Stefan; Vahlne, Anders

2010-03-15

The synthetic peptide glycyl-prolyl-glycine amide (GPG-NH2) was previously shown to abolish the ability of HIV-1 particles to fuse with the target cells, by reducing the content of the viral envelope glycoprotein (Env) in progeny HIV-1 particles. The loss of Env was found to result from GPG-NH2 targeting the Env precursor protein gp160 to the ER-associated protein degradation (ERAD) pathway during its maturation. However, the anti-viral effect of GPG-NH2 has been shown to be mediated by its metabolite alpha-hydroxy-glycineamide (alphaHGA), which is produced in the presence of fetal bovine serum, but not human serum. In accordance, we wanted to investigate whether the targeting of gp160 to the ERAD pathway by GPG-NH2 was attributed to its metabolite alphaHGA. In the presence of fetal bovine serum, GPG-NH2, its intermediary metabolite glycine amide (G-NH2), and final metabolite alphaHGA all induced the degradation of gp160 through the ERAD pathway. However, when fetal bovine serum was replaced with human serum only alphaHGA showed an effect on gp160, and this activity was further shown to be completely independent of serum. This indicated that GPG-NH2 acts as a pro-drug, which was supported by the observation that it had to be added earlier to the cell cultures than alphaHGA to induce the degradation of gp160. Furthermore, the substantial reduction of Env incorporation into HIV-1 particles that occurs during GPG-NH2 treatment was also achieved by treating HIV-1 infected cells with alphaHGA. The previously observed specificity of GPG-NH2 towards gp160 in HIV-1 infected cells, resulting in the production of Env (gp120/gp41) deficient fusion incompetent HIV-1 particles, was most probably due to the action of the GPG-NH2 metabolite alphaHGA.

Quantification of Toxins in Soapberry (Sapindaceae) Arils: Hypoglycin A and Methylenecyclopropylglycine

PubMed Central

Isenberg, Samantha L.; Carter, Melissa D.; Hayes, Shelby R.; Graham, Leigh Ann; Johnson, Darryl; Mathews, Thomas P.; Harden, Leslie A.; Takeoka, Gary R.; Thomas, Jerry D.; Pirkle, James L.; Johnson, Rudolph C.

2016-01-01

Methylenecyclopropylglycine (MCPG) and hypoglycin A (HGA) are naturally-occurring amino acids found in some soapberry fruits. Fatalities have been reported worldwide as a result of HGA ingestion, and exposure to MCPG has been implicated recently in the Asian outbreaks of hypoglycemic encephalopathy. In response to an outbreak linked to soapberry ingestion, the authors developed the first method to simultaneously quantify MCPG and HGA in soapberry fruits from 1 to 10,000 ppm of both toxins in dried fruit aril. Further, this is the first report of HGA in litchi and longan arils. This method is presented to specifically address the laboratory needs of public health investigators in the hypoglycemic encephalitis outbreaks linked to soapberry fruit ingestion. PMID:27367968

Quantification of Toxins in Soapberry (Sapindaceae) Arils: Hypoglycin A and Methylenecyclopropylglycine.

PubMed

Isenberg, Samantha L; Carter, Melissa D; Hayes, Shelby R; Graham, Leigh Ann; Johnson, Darryl; Mathews, Thomas P; Harden, Leslie A; Takeoka, Gary R; Thomas, Jerry D; Pirkle, James L; Johnson, Rudolph C

2016-07-13

Methylenecyclopropylglycine (MCPG) and hypoglycin A (HGA) are naturally occurring amino acids found in some soapberry fruits. Fatalities have been reported worldwide as a result of HGA ingestion, and exposure to MCPG has been implicated recently in the Asian outbreaks of hypoglycemic encephalopathy. In response to an outbreak linked to soapberry ingestion, the authors developed the first method to simultaneously quantify MCPG and HGA in soapberry fruits from 1 to 10 000 ppm of both toxins in dried fruit aril. Further, this is the first report of HGA in litchi, longan, and mamoncillo arils. This method is presented to specifically address the laboratory needs of public-health investigators in the hypoglycemic encephalitis outbreaks linked to soapberry fruit ingestion.

Reduced primary cilia length and altered Arl13b expression are associated with deregulated chondrocyte Hedgehog signaling in alkaptonuria.

PubMed

Thorpe, Stephen D; Gambassi, Silvia; Thompson, Clare L; Chandrakumar, Charmilie; Santucci, Annalisa; Knight, Martin M

2017-09-01

Alkaptonuria (AKU) is a rare inherited disease resulting from a deficiency of the enzyme homogentisate 1,2-dioxygenase which leads to the accumulation of homogentisic acid (HGA). AKU is characterized by severe cartilage degeneration, similar to that observed in osteoarthritis. Previous studies suggest that AKU is associated with alterations in cytoskeletal organization which could modulate primary cilia structure/function. This study investigated whether AKU is associated with changes in chondrocyte primary cilia and associated Hedgehog signaling which mediates cartilage degradation in osteoarthritis. Human articular chondrocytes were obtained from healthy and AKU donors. Additionally, healthy chondrocytes were treated with HGA to replicate AKU pathology (+HGA). Diseased cells exhibited shorter cilia with length reductions of 36% and 16% in AKU and +HGA chondrocytes respectively, when compared to healthy controls. Both AKU and +HGA chondrocytes demonstrated disruption of the usual cilia length regulation by actin contractility. Furthermore, the proportion of cilia with axoneme breaks and bulbous tips was increased in AKU chondrocytes consistent with defective regulation of ciliary trafficking. Distribution of the Hedgehog-related protein Arl13b along the ciliary axoneme was altered such that its localization was increased at the distal tip in AKU and +HGA chondrocytes. These changes in cilia structure/trafficking in AKU and +HGA chondrocytes were associated with a complete inability to activate Hedgehog signaling in response to exogenous ligand. Thus, we suggest that altered responsiveness to Hedgehog, as a consequence of cilia dysfunction, may be a contributing factor in the development of arthropathy highlighting the cilium as a novel target in AKU. © 2017 The Authors. Journal of Cellular Physiology Published by Wiley Periodicals Inc.

Technicians assembly the Hubble Space Telescope (HST) mockup at JSC

NASA Technical Reports Server (NTRS)

1989-01-01

At JSC's Mockup and Integration Laboratory (MAIL) Bldg 9A, technicians install a high gain antenna (HGA) on the Hubble Space Telescope (HST) mockup. On the ground a technician operates the controls for the overhead crane that is lifting the HGA into place on the Support System Module (SSM) forward shell. Others in a cherry picker basket wait for the HGA to near its final position so they can secure it on the mockup.

Identification and Molecular Characterization of the Homogentisate Pathway Responsible for Pyomelanin Production, the Major Melanin Constituents in Aeromonas media WS

PubMed Central

Wang, He; Qiao, Yunqian; Chai, Baozhong; Qiu, Chenxi; Chen, Xiangdong

2015-01-01

The pigmentation of many Aeromonas species has been thought to be due to the production of a L-DOPA (L-3,4-dihydroxyphenylalanine) based melanin. However, in this study we found that although L-DOPA synthesis occurs in the high-melanin-yielding Aeromonas media strain WS, it plays a minor, if any, role in pigmentation. Instead, the pigmentation of A. media strain WS is due to the production of pyomelanin through HGA (homogentisate). Gene products of phhA (encodes phenylalanine hydroxylase), tyrB and aspC (both encode aromatic amino acid aminotransferase), and hppD (encodes 4-hydroxyphenylpyruvate dioxygenase) constitute a linear pathway of converting phenylalanine to HGA and disruption of any one of these genes impairs or blocks pigmentation of A. media strain WS. This HGA biosynthesis pathway is widely distributed in Aeromonas, but HGA is only detectable in the cultures of pigmented Aeromonas species. Heterologous expression of HppD from both pigmented and non-pigmented Aeromonas species in E. coli leads to the production of pyomelanin and thus pigmentation, suggesting that most Aeromonas species have the critical enzymes to produce pyomelanin through HGA. Taken together, we have identified a widely conserved biosynthesis pathway of HGA based pyomelanin in Aeromonas that may be responsible for pigmentation of many Aeromonas species. PMID:25793756

Transport and Metabolism of 3H-Gibberellin A1 in Dioecious Cucumber Seedlings 1

PubMed Central

Rudich, Jehoshua; Sell, Harold M.; Baker, Larry R.

1976-01-01

The transport of 3H-GA1 through hypocotyl segments of cucumber (Cucumis sativus L.) was found to be nonpolar. The transport of 3H-GA1 was increased by pretreatment with relatively high concentrations of either IAA or Ethephon (2-chloroethylphosphonic acid). Hypocotyl segments from plants of a gynoecious genotype transported more 3H-GA1 than those of an androecious. The metabolism of 3H-GA1 in hypocotyl segments was neither related to the sex genotype of the cucumber plant nor influenced by pretreatment with Ethephon. The primary metabolite of GA1 was suggested to be GA8. Two other suspected metabolites were not identified. Differences in the endogenous GA of gynoecious and androecious plants could not be accounted for by transport differences. PMID:16659561

Bilateral hypogastric artery occlusion in endovascular repair of abdominal aortic aneurysms and its clinical significance.

PubMed

Zander, Tobias; Baldi, Sebastian; Rabellino, Martin; Rostagno, Roman; Isaza, Baltasar; Llorens, Rafael; Carreira, Jose M; Maynar, Manuel

2007-12-01

Endovascular treatment of aortoiliac aneurysms near or involving the hypogastric artery (HGA) requires HGA occlusion before endografting to avoid retrograde filling of the aneurysm. The purpose of this study is to evaluate clinical outcomes of bilateral HGA occlusion and determine if benefits gained by endovascular aneurysm repair (EVAR) outweigh the morbidity associated with the procedure. Between 1999 and 2004, 128 patients with abdominal aortic aneurysm (AAA) were treated with bifurcated endograft placement. Bilateral coverage or embolization of HGAs was performed in 14 patients (10.9%). Embolization was achieved by deployment of coils and coverage was accomplished by extending the endoprosthesis into the external iliac artery. Clinical follow-up and computed tomographic angiography were performed at 1, 3, 6, 9, and 12 months and annually thereafter to detect potential aneurysm growth and endoleaks. During follow-up (range, 1-72 months), buttock claudication was noted in four patients (28.6%), including unilateral claudication in two and bilateral claudication in two. One patient experienced claudication longer than 12 months, which resolved within 18 months. De novo erectile dysfunction was seen in one patient, and pelvic ischemia was not found in any patient. There was no evidence of endoleak, aneurysm enlargement, or death associated with HGA occlusion. In our series, complications of bilateral HGA occlusion before EVAR were moderate and resolved over time. The benefits gained from EVAR outweigh the clinical problems caused by bilateral HGA occlusion, as there are no technical complications added to the EVAR procedure.

Recent advances in management of alkaptonuria (invited review; best practice article).

PubMed

Ranganath, Lakshminarayan R; Jarvis, Jonathan C; Gallagher, James A

2013-05-01

Alkaptonuria (AKU) is an autosomal recessive condition arising as a result of a genetic deficiency of the enzyme homogentisate 1,2 dioxygenase and characterised by accumulation of homogentisic acid (HGA). Oxidative conversion of HGA leads to production of a melanin-like polymer in a process termed ochronosis. The binding of ochronotic pigment to the connective tissues of the body leads to multisystem disorder dominated by premature severe spondylo-arthropathy. Other systemic features include stones (renal, prostatic, salivary, gall bladder), renal damage/failure, osteopenia/fractures, ruptures of tendons/muscle/ligaments, respiratory compromise, hearing loss and aortic valve disease. Detection of these features requires systematic investigation. Treatment in AKU patients is palliative and unsatisfactory. Ascorbic acid, low protein diet and physiotherapy have been tried but do not alter the underlying metabolic defect. Regular surveillance to detect and treat complications early is important. Palliative pain management is a crucial issue in AKU. Timely spinal surgery and arthroplasty are the major treatment approaches at present. A potential disease modifying drug, nitisinone, inhibits 4-hydroxy-phenyl-pyruvate-dioxygenase and decreases formation of HGA and could prevent or slow the progression of disease in AKU. If nitisinone therapy is able to complement the biochemical 'cure' with improved outcomes, it will completely alter the way we approach the management of this disease. Greater efforts to improve recognition and registration of the disease will be worthwhile. Improved laboratory diagnostics to monitor the tyrosine metabolic pathway that includes plasma metabolites including tyrosine to monitor efficacy, toxicity and safety postnitisinone will also be required.

A geranyl acetophenone targeting cysteinyl leukotriene synthesis prevents allergic airway inflammation in ovalbumin-sensitized mice

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

Ismail, Norazren; Jambari, Nuzul Nurahya; Zareen, Seema

Asthma is associated with increased pulmonary inflammation and airway hyperresponsiveness. The current use of corticosteroids in the management of asthma has recently raised issues regarding safety and lack of responsiveness in 5–10% of asthmatic individuals. The aim of the present study was to investigate the therapeutic effect of a non-steroidal small molecule that has cysteinyl leukotriene (cysLT) inhibitory activity, upon attenuation of allergic lung inflammation in an acute murine model. Mice were sensitized with ovalbumin (OVA) and treated with several intraperitoneal doses (100, 20, 2 and 0.2 mg/kg) of 2,4,6,-trihydroxy-3-geranylacetophenone (tHGA). Bronchoalveolar lavage was performed, blood and lung samples weremore » obtained and respiratory function was measured. OVA sensitization increased pulmonary inflammation and pulmonary allergic inflammation was significantly reduced at doses of 100, 20 and 2 mg/kg with no effect at the lowest dose of 0.2 mg/kg. The beneficial effects in the lung were associated with reduced eosinophilic infiltration and reduced secretion of Th2 cytokines and cysLTs. Peripheral blood reduction of total IgE was also a prominent feature. Treatment with tHGA significantly attenuated altered airway hyperresponsiveness as measured by the enhanced pause (Penh) response to incremental doses of methacholine. These data demonstrate that tHGA, a synthetic non-steroidal small molecule, can prevent acute allergic inflammation. This proof of concept opens further avenues of research and development of tHGA as an additional option to the current armamentarium of anti-asthma therapeutics. -- Highlights: ► Safer and effective anti-asthmatic drugs are in great demand. ► tHGA is a new 5-LO/cysLT inhibitor that inhibits allergic asthma in mice. ► tHGA is a natural compound that can be synthesized. ► Doses as low as 2 mg/kg alleviate lung pathology in experimental asthma. ► tHGA is a potential drug lead for the treatment of allergic asthma.« less

Verification of the Solar Dynamics Observatory High Gain Antenna Pointing Algorithm Using Flight Data

NASA Technical Reports Server (NTRS)

Bourkland, Kristin L.; Liu, Kuo-Chia

2011-01-01

The Solar Dynamics Observatory (SDO), launched in 2010, is a NASA-designed spacecraft built to study the Sun. SDO has tight pointing requirements and instruments that are sensitive to spacecraft jitter. Two High Gain Antennas (HGAs) are used to continuously send science data to a dedicated ground station. Preflight analysis showed that jitter resulting from motion of the HGAs was a cause for concern. Three jitter mitigation techniques were developed and implemented to overcome effects of jitter from different sources. These mitigation techniques include: the random step delay, stagger stepping, and the No Step Request (NSR). During the commissioning phase of the mission, a jitter test was performed onboard the spacecraft, in which various sources of jitter were examined to determine their level of effect on the instruments. During the HGA portion of the test, the jitter amplitudes from the single step of a gimbal were examined, as well as the amplitudes due to the execution of various gimbal rates. The jitter levels were compared with the gimbal jitter allocations for each instrument. The decision was made to consider implementing two of the jitter mitigating techniques on board the spacecraft: stagger stepping and the NSR. Flight data with and without jitter mitigation enabled was examined, and it is shown in this paper that HGA tracking is not negatively impacted with the addition of the jitter mitigation techniques. Additionally, the individual gimbal steps were examined, and it was confirmed that the stagger stepping and NSRs worked as designed. An Image Quality Test was performed to determine the amount of cumulative jitter from the reaction wheels, HGAs, and instruments during various combinations of typical operations. The HGA-induced jitter on the instruments is well within the jitter requirement when the stagger step and NSR mitigation options are enabled.

Electrocorticographic high gamma activity versus electrical cortical stimulation mapping of naming.

PubMed

Sinai, Alon; Bowers, Christopher W; Crainiceanu, Ciprian M; Boatman, Dana; Gordon, Barry; Lesser, Ronald P; Lenz, Frederick A; Crone, Nathan E

2005-07-01

Subdural electrocorticographic (ECoG) recordings in patients undergoing epilepsy surgery have shown that functional activation is associated with event-related broadband gamma activity in a higher frequency range (>70 Hz) than previously studied in human scalp EEG. To investigate the utility of this high gamma activity (HGA) for mapping language cortex, we compared its neuroanatomical distribution with functional maps derived from electrical cortical stimulation (ECS), which remains the gold standard for predicting functional impairment after surgery for epilepsy, tumours or vascular malformations. Thirteen patients had undergone subdural electrode implantation for the surgical management of intractable epilepsy. Subdural ECoG signals were recorded while each patient verbally named sequentially presented line drawings of objects, and estimates of event-related HGA (80-100 Hz) were made at each recording site. Routine clinical ECS mapping used a subset of the same naming stimuli at each cortical site. If ECS disrupted mouth-related motor function, i.e. if it affected the mouth, lips or tongue, naming could not be tested with ECS at the same cortical site. Because naming during ECoG involved these muscles of articulation, the sensitivity and specificity of ECoG HGA were estimated relative to both ECS-induced impairments of naming and ECS disruption of mouth-related motor function. When these estimates were made separately for 12 electrode sites per patient (the average number with significant HGA), the specificity of ECoG HGA with respect to ECS was 78% for naming and 81% for mouth-related motor function, and equivalent sensitivities were 38% and 46%, respectively. When ECS maps of naming and mouth-related motor function were combined, the specificity and sensitivity of ECoG HGA with respect to ECS were 84% and 43%, respectively. This study indicates that event-related ECoG HGA during confrontation naming predicts ECS interference with naming and mouth-related motor function with good specificity but relatively low sensitivity. Its favourable specificity suggests that ECoG HGA can be used to construct a preliminary functional map that may help identify cortical sites of lower priority for ECS mapping. Passive recordings of ECoG gamma activity may be done simultaneously at all electrode sites without the risk of after-discharges associated with ECS mapping, which must be done sequentially at pairs of electrodes. We discuss the relative merits of these two functional mapping techniques.

D-2-hydroxyglutaric aciduria: a case with an intermediate phenotype and prenatal diagnosis of two affected fetuses.

PubMed

Clarke, Nigel F; Andrews, Ian; Carpenter, Kevin; Jakobs, Cornelis; van der Knaap, Marjo S; Kirk, Edwin P

2003-08-01

D-2-hydroxyglutaric aciduria (D2HGA) is a rare autosomal recessive disorder with variable clinical expression. The biochemical defect is unknown at present. Previously reported cases have either followed a severe clinical course characterized by neonatal epileptic encephalopathy, cortical blindness, and profound developmental delay, or a mild course characterized by mild developmental delay, manageable epilepsy, and mild hypotonia. To date there has been a clear distinction between these two groups. We report the second case of a child with D2HGA who has followed an intermediate course. She presented in infancy with hypotonia, manageable epilepsy and developed moderate to severe developmental delay, and cortical visual impairment. The proposita had a coarse facial appearance, flat face, broad nasal bridge, up-turned nose, and simple, anteverted ears. These facial anomalies have been noted in other children with D2HGA and this case strengthens the proposed association between this facial phenotype and D2HGA. We also report the third and fourth instances of prenatal diagnosis for D2HGA. At each prenatal diagnosis, an affected fetus was diagnosed on the basis of markedly increased levels of D-2-hydroxyglutaric acid in amniotic fluid. Copyright 2003 Wiley-Liss, Inc.

A pediatric trial of radiation/cetuximab followed by irinotecan/cetuximab in newly diagnosed diffuse pontine gliomas and high-grade astrocytomas: A Pediatric Oncology Experimental Therapeutics Investigators' Consortium study.

PubMed

Macy, Margaret E; Kieran, Mark W; Chi, Susan N; Cohen, Kenneth J; MacDonald, Tobey J; Smith, Amy A; Etzl, Michael M; Kuei, Michele C; Donson, Andrew M; Gore, Lia; DiRenzo, Jennifer; Trippett, Tanya M; Ostrovnaya, Irina; Narendran, Aru; Foreman, Nicholas K; Dunkel, Ira J

2017-11-01

Diffuse intrinsic pontine gliomas (DIPGs) and high-grade astrocytomas (HGA) continue to have dismal prognoses. The combination of cetuximab and irinotecan was demonstrated to be safe and tolerable in a previous pediatric phase 1 combination study. We developed this phase 2 trial to investigate the safety and efficacy of cetuximab given with radiation therapy followed by adjuvant cetuximab and irinotecan. Eligible patients of age 3-21 years had newly diagnosed DIPG or HGA. Patients received radiation therapy (5,940 cGy) with concurrent cetuximab. Following radiation, patients received cetuximab weekly and irinotecan daily for 5 days per week for 2 weeks every 21 days for 30 weeks. Correlative studies were performed. The regimen was considered to be promising if the number of patients with 1-year progression-free survival (PFS) for DIPG and HGA was at least six of 25 and 14 of 26, respectively. Forty-five evaluable patients were enrolled (25 DIPG and 20 HGA). Six patients with DIPG and five with HGA were progression free at 1 year from the start of therapy with 1-year PFS of 29.6% and 18%, respectively. Fatigue, gastrointestinal complaints, electrolyte abnormalities, and rash were the most common adverse events and generally of grade 1 and 2. Increased epidermal growth factor receptor copy number but no K-ras mutations were identified in available samples. The trial did not meet the predetermined endpoint to deem this regimen successful for HGA. While the trial met the predetermined endpoint for DIPG, overall survival was not markedly improved from historical controls, therefore does not merit further study in this population. © 2017 Wiley Periodicals, Inc.

Passive language mapping combining real-time oscillation analysis with cortico-cortical evoked potentials for awake craniotomy.

PubMed

Tamura, Yukie; Ogawa, Hiroshi; Kapeller, Christoph; Prueckl, Robert; Takeuchi, Fumiya; Anei, Ryogo; Ritaccio, Anthony; Guger, Christoph; Kamada, Kyousuke

2016-12-01

OBJECTIVE Electrocortical stimulation (ECS) is the gold standard for functional brain mapping; however, precise functional mapping is still difficult in patients with language deficits. High gamma activity (HGA) between 80 and 140 Hz on electrocorticography is assumed to reflect localized cortical processing, whereas the cortico-cortical evoked potential (CCEP) can reflect bidirectional responses evoked by monophasic pulse stimuli to the language cortices when there is no patient cooperation. The authors propose the use of "passive" mapping by combining HGA mapping and CCEP recording without active tasks during conscious resections of brain tumors. METHODS Five patients, each with an intraaxial tumor in their dominant hemisphere, underwent conscious resection of their lesion with passive mapping. The authors performed functional localization for the receptive language area, using real-time HGA mapping, by listening passively to linguistic sounds. Furthermore, single electrical pulses were delivered to the identified receptive temporal language area to detect CCEPs in the frontal lobe. All mapping results were validated by ECS, and the sensitivity and specificity were evaluated. RESULTS Linguistic HGA mapping quickly identified the language area in the temporal lobe. Electrical stimulation by linguistic HGA mapping to the identified temporal receptive language area evoked CCEPs on the frontal lobe. The combination of linguistic HGA and frontal CCEPs needed no patient cooperation or effort. In this small case series, the sensitivity and specificity were 93.8% and 89%, respectively. CONCLUSIONS The described technique allows for simple and quick functional brain mapping with higher sensitivity and specificity than ECS mapping. The authors believe that this could improve the reliability of functional brain mapping and facilitate rational and objective operations. Passive mapping also sheds light on the underlying physiological mechanisms of language in the human brain.

Anisotropic carrier mobility in buckled two-dimensional GaN.

PubMed

Tong, Lijia; He, Junjie; Yang, Min; Chen, Zheng; Zhang, Jing; Lu, Yanli; Zhao, Ziyuan

2017-08-30

Developing nanoelectronic engineering requires two-dimensional (2d) materials with both usable carrier mobility and proper large band-gap. In this study, we present a detailed theoretical investigation of the intrinsic carrier mobilities of buckled 2d GaN. This buckled 2d GaN is accessed by hydrofluorination (FGaNH) and hydrogenation (HGaNH). We predict that the anisotropic carrier mobilities of buckled 2d GaN can exceed those of 2d MoS 2 and can be altered by an alterable surface chemical bond (convert from a Ga-F-Ga bond of FGaNH to a Ga-H bond of HGaNH). Moreover, converting FGaNH to HGaNH can significantly suppress hole mobility (even close to zero) and result in a transition from a p-type-like semiconductor (FGaNH) to an n-type-like semiconductor (HGaNH). These features make buckled 2d GaN a promising candidate for application in future conductivity-adjustable electronics.

Targeted modification of homogalacturonan by transgenic expression of a fungal polygalacturonase alters plant growth.

PubMed

Capodicasa, Cristina; Vairo, Donatella; Zabotina, Olga; McCartney, Lesley; Caprari, Claudio; Mattei, Benedetta; Manfredini, Cinzia; Aracri, Benedetto; Benen, Jacques; Knox, J Paul; De Lorenzo, Giulia; Cervone, Felice

2004-07-01

Pectins are a highly complex family of cell wall polysaccharides comprised of homogalacturonan (HGA), rhamnogalacturonan I and rhamnogalacturonan II. We have specifically modified HGA in both tobacco (Nicotiana tabacum) and Arabidopsis by expressing the endopolygalacturonase II of Aspergillus niger (AnPGII). Cell walls of transgenic tobacco plants showed a 25% reduction in GalUA content as compared with the wild type and a reduced content of deesterified HGA as detected by antibody labeling. Neutral sugars remained unchanged apart from a slight increase of Rha, Ara, and Gal. Both transgenic tobacco and Arabidopsis were dwarfed, indicating that unesterified HGA is a critical factor for plant cell growth. The dwarf phenotypes were associated with AnPGII activity as demonstrated by the observation that the mutant phenotype of tobacco was completely reverted by crossing the dwarfed plants with plants expressing PGIP2, a strong inhibitor of AnPGII. The mutant phenotype in Arabidopsis did not appear when transformation was performed with a gene encoding AnPGII inactivated by site directed mutagenesis.

Amyloidosis in alkaptonuria.

PubMed

Millucci, Lia; Braconi, Daniela; Bernardini, Giulia; Lupetti, Pietro; Rovensky, Josef; Ranganath, Lakshminaryan; Santucci, Annalisa

2015-09-01

Alkaptonuria (AKU) is an ultra-rare inborn error of metabolism developed from the lack of homogentisic acid oxidase activity, causing homogentisic acid (HGA) accumulation that produces an HGA-melanin ochronotic pigment, of hitherto unknown composition. Besides the accumulation of HGA, the potential role and presence of unidentified proteins has been hypothesized as additional causal factors involved in ochronotic pigment deposition. Evidence has been provided on the presence of serum amyloid A (SAA) in several AKU tissues, which allowed classifying AKU as a novel secondary amyloidosis. In this paper, we will briefly review all direct and indirect lines of evidence related to the presence of amyloidosis in AKU. We also report the first data on abnormal SAA serum levels in a cohort of AKU patients.

Targeted Modification of Homogalacturonan by Transgenic Expression of a Fungal Polygalacturonase Alters Plant Growth1

PubMed Central

Capodicasa, Cristina; Vairo, Donatella; Zabotina, Olga; McCartney, Lesley; Caprari, Claudio; Mattei, Benedetta; Manfredini, Cinzia; Aracri, Benedetto; Benen, Jacques; Knox, J. Paul; De Lorenzo, Giulia; Cervone, Felice

2004-01-01

Pectins are a highly complex family of cell wall polysaccharides comprised of homogalacturonan (HGA), rhamnogalacturonan I and rhamnogalacturonan II. We have specifically modified HGA in both tobacco (Nicotiana tabacum) and Arabidopsis by expressing the endopolygalacturonase II of Aspergillus niger (AnPGII). Cell walls of transgenic tobacco plants showed a 25% reduction in GalUA content as compared with the wild type and a reduced content of deesterified HGA as detected by antibody labeling. Neutral sugars remained unchanged apart from a slight increase of Rha, Ara, and Gal. Both transgenic tobacco and Arabidopsis were dwarfed, indicating that unesterified HGA is a critical factor for plant cell growth. The dwarf phenotypes were associated with AnPGII activity as demonstrated by the observation that the mutant phenotype of tobacco was completely reverted by crossing the dwarfed plants with plants expressing PGIP2, a strong inhibitor of AnPGII. The mutant phenotype in Arabidopsis did not appear when transformation was performed with a gene encoding AnPGII inactivated by site directed mutagenesis. PMID:15247378

Oxidative stress and mechanisms of ochronosis in alkaptonuria.

PubMed

Braconi, Daniela; Millucci, Lia; Bernardini, Giulia; Santucci, Annalisa

2015-11-01

Alkaptonuria (AKU) is a rare metabolic disease due to a deficient activity of the enzyme homogentisate 1,2-dioxygenase (HGD), involved in Phe and Tyr catabolism. Due to such a deficiency, AKU patients undergo accumulation of the metabolite homogentisic acid (HGA), which is prone to oxidation/polymerization reactions causing the production of a melanin-like pigment. Once the pigment is deposited onto connective tissues (mainly in joints, spine, and cardiac valves), a classical bluish-brown discoloration is imparted, leading to a phenomenon known as "ochronosis", the hallmark of AKU. A clarification of the molecular mechanisms for the production and deposition of the ochronotic pigment in AKU started only recently with a range of in vitro and ex vivo human models used for the study of HGA-induced effects. Thanks to redox-proteomic analyses, it was found that HGA could induce significant oxidation of a number of serum and chondrocyte proteins. Further investigations allowed highlighting how HGA-induced proteome alteration, lipid peroxidation, thiol depletion, and amyloid production could contribute to oxidative stress generation and protein oxidation in AKU. This review briefly summarizes the most recent findings on HGA-induced oxidative stress in AKU, helping in the clarification of the molecular mechanisms of ochronosis and potentially providing the basis for its pharmacological treatment. Future work should be undertaken in order to validate in vivo the results so far obtained in in vitro AKU models. Copyright © 2015 Elsevier Inc. All rights reserved.

Advanced glycation end-products and insulin signaling in granulosa cells

PubMed Central

Chatzigeorgiou, Antonios; Papageorgiou, Efstathia; Koundouras, Dimitrios; Koutsilieris, Michael

2016-01-01

Advanced glycation end-products (AGEs) may interfere with insulin intracellular signaling and glucose transport in human granulosa cells, potentially affecting ovarian function, follicular growth, linked with diminished fertility. The potential interaction of AGEs with insulin signaling pathways and glucose transport was investigated in human granulosa KGN cells. KGN cells were cultured with variable concentrations of human glycated albumin (HGA, 50–200 µg/mL) or insulin (100 ng/mL). Combined treatments of KGN cells with insulin (100 ng/mL) and HGA (200 µg/mL) were also performed. p-AKT levels and glucose transporter type 4 (Glut-4) translocation analysis were performed by Western blot. Phosphatidylinositol-3-kinase (PI3K)-specific signaling was checked by using the PI3K-inhibitor, LY294002. p-AKT levels were significantly increased following insulin treatment compared to basal levels or HGA exposure. This insulin-mediated AKT-phosphorylation was PI3K-specific and it was inhibited after combined treatment of insulin and HGA. Furthermore, Glut-4 translocation from the cytoplasm to the membrane compartments of KGN cells was remarkably reduced after the combined treatment of insulin and HGA. The present findings support that AGEs interfere with insulin signaling in granulosa cells and prevent Glut-4 membrane translocation suggesting that intra ovarian AGEs accumulation, from endogenous or exogenous sources, may contribute to the pathophysiology of states characterized with anovulation and insulin resistance such as polycystic ovary syndrome. PMID:25956684

Optimal timing for the oral administration of Da-Cheng-Qi decoction based on the pharmacokinetic and pharmacodynamic targeting of the pancreas in rats with acute pancreatitis

PubMed Central

Zhang, Yu-Mei; Zhu, Lin; Zhao, Xian-Lin; Chen, Huan; Kang, Hong-Xin; Zhao, Jian-Lei; Wan, Mei-Hua; Li, Juan; Zhu, Lv; Tang, Wen-Fu

2017-01-01

AIM To identify the optimal oral dosing time of Da-Cheng-Qi decoction (DCQD) in rats with acute pancreatitis (AP) based on the pharmacokinetic and pharmacodynamic parameters. METHODS First, 24 male Sprague-Dawley rats were divided into a sham-operated group [NG(a)] and three model groups [4hG(a), 12hG(a) and 24hG(a)]. The NG(a) and model groups were administered DCQD (10 g/kg.BW) intragastrically at 4 h, 4 h, 12 h and 24 h, respectively, after AP models induced by 3% sodium taurocholate. Plasma samples were collected from the tails at 10 min, 20 min, 40 min, 1 h, 2 h, 4 h, 8 h, 12 h and 24 h after a single dosing with DCQD. Plasma and pancreatic tissue concentrations of the major components of DCQD were determined by high-performance liquid chromatography tandem mass spectroscopy. The pharmacokinetic parameters and serum amylase were detected and compared. Second, rats were divided into a sham-operated group [NG(b)] and three treatment groups [4hG(b), 12hG(b) and 24hG(b)] with three corresponding control groups [MG(b)s]. Blood and pancreatic tissues were collected 24 h after a single dosing with DCQD. Serum amylase, inflammatory cytokines and pathological scores of pancreatic tissues were detected and compared. RESULTS The concentrations of emodin, naringin, honokiol, naringenin, aloe-emodin, chrysophanol and rheochrysidin in the 12hG(a) group were higher than those in the 4hG(a) group in the pancreatic tissues (P < 0.05). The area under the plasma concentration-time curve from time 0 to the time of the last measurable concentration values (AUC0→t) for rhein, chrysophanol, magnolol and naringin in the 12hG(a) group were larger than those in the 4hG(a) or 24hG(a) groups. The 12hG(a) group had a higher Cmax than the other two model groups. The IL-10 levels in the 12hG(b) and 24hG(b) groups were higher than in the MG(b)s (96.55 ± 7.84 vs 77.46 ± 7.42, 251.22 ± 16.15 vs 99.72 ± 4.7 respectively, P < 0.05), while in the 24hG(b) group, the IL-10 level was higher than in the other two treatment groups (251.22 ± 16.15 vs 154.41 ± 12.09/96.55 ± 7.84, P < 0.05). The IL-6 levels displayed a decrease in the 4hG(b) and 12hG(b) groups compared to the MG(b)s (89.99 ± 4.61 vs 147.91 ± 4.36, 90.82 ± 5.34 vs 171.44 ± 13.43, P < 0.05). CONCLUSION Late-time dosing may have higher concentrations of the most major components of DCQD, with better pharmacokinetics and pharmacodynamics of anti-inflammation than early-time dosing, which showed the late time to be the optimal dosing time of DCQD for AP. PMID:29093618

The GaOH-HGaO potential energy hypersurface and the necessity of correlating the 3d electrons

NASA Astrophysics Data System (ADS)

Richards, Claude A., Jr.; Yamaguchi, Yukio; Kim, Seung-Joon; Schaefer, Henry F., III

1996-06-01

The ground state potential energy hypersurface of the GaOH-HGaO system has been investigated using high level ab initio molecular electronic structure theory. The geometries and physical properties of two equilibrium structures, one isomerization transition state and one inversion transition state were determined at the self-consistent field (SCF), configuration interaction with single and double excitations (CISD), coupled cluster with single and double excitations (CCSD), and CCSD with perturbative triple excitations [CCSD(T)] levels of theory with four sets of basis functions. It has been found that freezing the 3d electrons of the Ga atom in the correlation procedures is not appropriate for this system. For the energy difference ΔE (GaOH-HGaO) the freezing of the 3d electrons results in an error of 25 kcal/mol! The dipole moments, harmonic vibrational frequencies, and infrared (IR) intensities are predicted for the four stationary points. At the highest level of theory employed in this study, CCSD(T) using triple zeta plus double polarization with higher angular momentum and diffuse functions [TZ2P(f,d)+diff] basis set, the bent GaOH was found to be 41.9 kcal/mol more stable than the linear HGaO species; with the zero-point vibrational energy (ZPVE) correction, the energy separation becomes 40.4 kcal/mol. The classical barrier height for the exothermic isomerization (1,2 hydrogen shift) reaction HGaO→GaOH is determined to be 44.5 kcal/mol and the barrier height with the ZPVE correction 42.3 kcal/mol. The classical barrier to linearity for the bent GaOH molecule is determined to be 1.7 kcal/mol and the barrier height with the ZPVE correction to be 1.2 kcal/mol. The predicted dipole moments of GaOH and HGaO are 1.41 and 4.45 Debye, respectively. The effects of electron correlation reduce the dipole moment of HGaO by the sizable amount of 1.2 Debye. The two equilibrium species may be suitable for microwave spectroscopic investigation. Furthermore, they may also be detectable by IR techniques due to the relatively large intensities of their vibrational modes. The geometrical and energetic features are compared with those of the valence isoelectronic HXO-XOH systems, where X is a group IIIA atom and the HXO+-XOH+ systems, where X is a group IVA atom.

Smoothened-antagonists reverse homogentisic acid-induced alterations of Hedgehog signaling and primary cilium length in alkaptonuria.

PubMed

Gambassi, Silvia; Geminiani, Michela; Thorpe, Stephen D; Bernardini, Giulia; Millucci, Lia; Braconi, Daniela; Orlandini, Maurizio; Thompson, Clare L; Petricci, Elena; Manetti, Fabrizio; Taddei, Maurizio; Knight, Martin M; Santucci, Annalisa

2017-11-01

Alkaptonuria (AKU) is an ultra-rare genetic disease, in which the accumulation of a toxic metabolite, homogentisic acid (HGA) leads to the systemic development of ochronotic aggregates. These aggregates cause severe complications mainly at the level of joints with extensive degradation of the articular cartilage. Primary cilia have been demonstrated to play an essential role in development and the maintenance of articular cartilage homeostasis, through their involvement in mechanosignaling and Hedgehog signaling pathways. Hedgehog signaling has been demonstrated to be activated in osteoarthritis (OA) and to drive cartilage degeneration in vivo. The numerous similarities between OA and AKU suggest that primary cilia Hedgehog signaling may also be altered in AKU. Thus, we characterized an AKU cellular model in which healthy chondrocytes were treated with HGA (66 µM) to replicate AKU cartilage pathology. We investigated the degree of activation of the Hedgehog signaling pathway and how treatment with inhibitors of the receptor Smoothened (Smo) influenced Hedgehog activation and primary cilia structure. The results obtained in this work provide a further step in the comprehension of the pathophysiological features of AKU, suggesting a potential therapeutic approach to modulate AKU cartilage degradation processes through manipulation of the Hedgehog pathway. © 2016 Wiley Periodicals, Inc.

Alkaptonuria: a very rare metabolic disorder.

PubMed

Aquaron, Robert

2013-10-01

Alkaptonuria (AKU) is a very rare autosomal recessive disorder of tyrosine metabolism in the liver due to deficiency of homogentisate 1,2 dioxygenase (HGD) activity, resulting in the accumulation of homogentisic acid (HGA). Circulating HGA pass into various tissues through-out the body, mainly in cartilage and connective tissues, where its oxidation products polymerize and deposit as a melanin-like pigment. Gram quantities of HGA are excreted in the urine. AKU is a progressive disease and the three main features, according the chronology of appearance, are: darkening of the urine at birth, then ochronosis (blue-dark pigmentation of the connective tissue) clinically visible at around 30 yrs in the ear and eye, and finally a severe ochronotic arthropathy at around 50 yrs with spine and large joints involvements. Cardiovascular and renal complications have been described in numerous case report studies. A treatment now is available in the form of a drug nitisinone, which decreases the production of HGA. The enzymatic defect in AKU is caused by the homozygous or compound heterozygous mutations within the HGD gene. This disease has a very low prevalence (1:100,000-250,000) in most of the ethnic groups, except Slovakia and Dominican Republic, where the incidence has shown increase up to 1:19,000. This review highlights classical and recent findings on this very rare disease.

Verification of the Solar Dynamics Observatory High Gain Antenna Pointing Algorithm Using Flight Data

NASA Technical Reports Server (NTRS)

Bourkland, Kristin L.; Liu, Kuo-Chia

2011-01-01

The Solar Dynamics Observatory (SDO) is a NASA spacecraft designed to study the Sun. It was launched on February 11, 2010 into a geosynchronous orbit, and uses a suite of attitude sensors and actuators to finely point the spacecraft at the Sun. SDO has three science instruments: the Atmospheric Imaging Assembly (AIA), the Helioseismic and Magnetic Imager (HMI), and the Extreme Ultraviolet Variability Experiment (EVE). SDO uses two High Gain Antennas (HGAs) to send science data to a dedicated ground station in White Sands, New Mexico. In order to meet the science data capture budget, the HGAs must be able to transmit data to the ground for a very large percentage of the time. Each HGA is a dual-axis antenna driven by stepper motors. Both antennas transmit data at all times, but only a single antenna is required in order to meet the transmission rate requirement. For portions of the year, one antenna or the other has an unobstructed view of the White Sands ground station. During other periods, however, the view from both antennas to the Earth is blocked for different portions of the day. During these times of blockage, the two HGAs take turns pointing to White Sands, with the other antenna pointing out to space. The HGAs handover White Sands transmission responsibilities to the unblocked antenna. There are two handover seasons per year, each lasting about 72 days, where the antennas hand off control every twelve hours. The non-tracking antenna slews back to the ground station by following a ground commanded trajectory and arrives approximately 5 minutes before the formerly tracking antenna slews away to point out into space. The SDO Attitude Control System (ACS) runs at 5 Hz, and the HGA Gimbal Control Electronics (GCE) run at 200 Hz. There are 40 opportunities for the gimbals to step each ACS cycle, with a hardware limitation of no more than one step every three GCE cycles. The ACS calculates the desired gimbal motion for tracking the ground station or for slewing, and sends the command to the GCE at 5 Hz. This command contains the number of gimbals steps for that ACS cycle, the direction of motion, the spacing of the steps, and the delay before taking the first step. The AIA and HMI instruments are sensitive to spacecraft jitter. Pre-flight analysis showed that jitter from the motion of the HGAs was a cause of concern. Three jitter mitigation techniques were developed to overcome the effects of jitter from different sources. The first method is the random step delay, which avoids gimbal steps hitting a cadence on a jitter-critical mode by pseudo-randomly delaying the first gimbal step in an ACS cycle. The second method of jitter mitigation is stagger stepping, which forbids the two antennas from taking steps during the same ACS cycle in order to avoid constructively adding jitter from two antennas. The third method is the inclusion of an instrument No Step Request (NSR), which allows the instruments to request a stoppage in gimbal stepping during the times when they are taking images. During the commissioning phase of the mission, a jitter test was performed onboard the spacecraft. Various sources of jitter, such as the reaction wheels, the High Gain Antenna motors, and the motion of the instrument filter wheels, were examined to determine the level of their effect on the instruments. During the HGA portion of the test, the jitter amplitudes from the single step of a gimbal were examined, as well as the amplitudes due to the execution of various gimbal rates. These jitter levels are compared with the gimbal jitter allocations for each instrument. Additionally, the jitter test provided insight into a readback delay that exists with the GCE. Pre-flight analysis suggested that gimbal steps scheduled to occur during the later portion of an ACS cycle would not be read during that cycle, resulting in a delay in the telemetered current gimbal position. Flight data from the jitter test confirmed this expectation. Analysis is presentehat shows the readback delay does not have a negative impact on gimbal control. The decision was made to consider implementing two of the jitter mitigation techniques on board the spacecraft: stagger stepping and the NSR. Flight data from two sets of handovers, one set without jitter mitigation and the other with mitigation enabled, were examined. The trajectory of the predicted handover was compared with the measured trajectory for the two cases, showing that tracking was not negatively impacted with the addition of the jitter mitigation techniques. Additionally, the individual gimbal steps were examined, and it was confirmed that the stagger stepping and NSRs worked as designed. An Image Quality Test was performed to determine the amount of cumulative jitter from the reaction wheels, HGAs, and instruments during various combinations of typical operations. In this paper, the flight results are examined from a test where the HGAs are following the path of a nominal handover with stagger stepping on and HMI NSRs enabled. In this case, the reaction wheels are moving at low speed and the instruments are taking pictures in their standard sequence. The flight data shows the level of jitter that the instruments see when their shutters are open. The HGA-induced jitter is well within the jitter requirement when the stagger step and NSR mitigation options are enabled. The SDO HGA pointing algorithm was designed to achieve nominal antenna pointing at the ground station, perform slews during handover season, and provide three HGA-induced jitter mitigation options without compromising pointing objectives. During the commissioning phase, flight data sets were collected to verify the HGA pointing algorithm and demonstrate its jitter mitigation capabilities.

Hubble Space Telescope (HST) grappled by OV-103's RMS during STS-31 checkout

NASA Image and Video Library

1990-04-25

The Hubble Space Telescope (HST), grappled by Discovery's, Orbiter Vehicle (OV) 103's, remote manipulator system (RMS), is held in a pre-deployment position. During STS-31 checkout procedures, the solar array (SA) panels and the high gain antennae (HGA) will be deployed. The starboard SA (center) and the two HGA are stowed along side the Support System Module (SSM) forward shell. The sun highlights HST against the blackness of space.

Dynamic Changes of Pectin Epitopes in Cell Walls during the Development of the Procambium–Cambium Continuum in Poplar

PubMed Central

Liu, Jundi; Hou, Jie; Chen, Huimin; Pei, Keliang; Li, Yi; He, Xin-Qiang

2017-01-01

The change of pectin epitopes during procambium–cambium continuum development was investigated by immunolocalization in poplar. The monoclonal antibody JIM5 labels homogalacturonan (HGA) with a low degree of esterification, and the monoclonal antibody JIM7 labels HGA with a high degree of methyl-esterification. Arabinan, rather than galactan, and HGA with low degree of esterification were located in the cell walls of procambial, while HGA with a low degree of esterification was located in the tangential walls, and galactan was located in both the tangential and radial walls of procambial, yet nearly no arabinan was located in the tangential walls of the cambial cells. The changes in pectin distribution took place when periclinal divisions appeared within a procambial trace. The distribution difference of pectin epitopes was also present in procambium–cambium derivatives. The arabinan existed in all cell walls of primary xylem, but was absent from the tangential walls of secondary xylem cells. The galactan existed only in mature primary phloem. Furthermore, 19 pectin methylesterases (PMEs) genes were identified by RNA sequencing, six genes presented highly differentially and were supposed to be involved in the cell wall esterification process. The results provide direct evidence of the dynamic changes of pectin epitopes during the development of the procambium–cambium continuum in poplar. PMID:28783076

Alkaptonuria is a novel human secondary amyloidogenic disease

PubMed Central

Millucci, Lia; Spreafico, Adriano; Tinti, Laura; Braconi, Daniela; Ghezzi, Lorenzo; Paccagnini, Eugenio; Bernardini, Giulia; Amato, Loredana; Laschi, Marcella; Selvi, Enrico; Galeazzi, Mauro; Mannoni, Alessandro; Benucci, Maurizio; Lupetti, Pietro; Chellini, Federico; Orlandini, Maurizio; Santucci, Annalisa

2012-01-01

Alkaptonuria (AKU) is an ultra-rare disease developed from the lack of homogentisic acid oxidase activity, causing homogentisic acid (HGA) accumulation that produces a HGA-melanin ochronotic pigment, of unknown composition. There is no therapy for AKU. Our aim was to verify if AKU implied a secondary amyloidosis. Congo Red, Thioflavin-T staining and TEM were performed to assess amyloid presence in AKU specimens (cartilage, synovia, periumbelical fat, salivary gland) and in HGA-treated human chondrocytes and cartilage. SAA and SAP deposition was examined using immunofluorescence and their levels were evaluated in the patients' plasma by ELISA. 2D electrophoresis was undertaken in AKU cells to evaluate the levels of proteins involved in amyloidogenesis. AKU osteoarticular tissues contained SAA-amyloid in 7/7 patients. Ochronotic pigment and amyloid co-localized in AKU osteoarticular tissues. SAA and SAP composition of the deposits assessed secondary type of amyloidosis. High levels of SAA and SAP were found in AKU patients' plasma. Systemic amyloidosis was assessed by Congo Red staining of patients' abdominal fat and salivary gland. AKU is the second pathology after Parkinson's disease where amyloid is associated with a form of melanin. Aberrant expression of proteins involved in amyloidogenesis has been found in AKU cells. Our findings on alkaptonuria as a novel type II AA amyloidosis open new important perspectives for its therapy, since methotrexate treatment proved to significantly reduce in vitro HGA-induced A-amyloid aggregates. PMID:22850426

Borrelia miyamotoi infection presenting as human granulocytic anaplasmosis: a case report.

PubMed

Chowdri, Hanumara Ram; Gugliotta, Joseph L; Berardi, Victor P; Goethert, Heidi K; Molloy, Philip J; Sterling, Sherri L; Telford, Sam R

2013-07-02

The diverse tickborne infections of the northeastern United States can present as undifferentiated flu-like illnesses. In areas endemic for Lyme and other tickborne diseases, patients presenting with acute febrile illness with myalgia, headache, neutropenia, thrombocytopenia, and elevated hepatic aminotransferase levels are presumptively diagnosed as having human granulocytic anaplasmosis (HGA). To assign a cause for illness experienced by 2 case patients who were initially diagnosed with HGA but did not rapidly defervesce with doxycycline treatment and had no laboratory evidence of Anaplasma phagocytophilum infection. Case report. 2 primary care medical centers in Massachusetts and New Jersey. 2 case patients acutely presenting with fever. Identification of the causative agent by polymerase chain reaction and DNA sequencing. Molecular diagnostic assays detected Borrelia miyamotoi in the peripheral blood of both patients. There was no evidence of infection with other tickborne pathogens commonly diagnosed in the referral areas. One of the case patients may have had concurrent Lyme disease. The presence of B. miyamotoi DNA in the peripheral blood and the patients' eventual therapeutic response to doxycycline are consistent with the hypothesis that their illness was due to this newly recognized spirochete. Samples from tick-exposed patients acutely presenting with signs of HGA but who have a delayed response to doxycycline therapy or negative confirmatory test results for HGA should be analyzed carefully for evidence of B. miyamotoi infection.

Inflight calibration technique for onboard high-gain antenna pointing. [of Mariner 10 spacecraft in Venus and Mercury flyby mission

NASA Technical Reports Server (NTRS)

Ohtakay, H.; Hardman, J. M.

1975-01-01

The X-band radio frequency communication system was used for the first time in deep space planetary exploration by the Mariner 10 Venus and Mercury flyby mission. This paper presents the technique utilized for and the results of inflight calibration of high-gain antenna (HGA) pointing. Also discussed is pointing accuracy to maintain a high data transmission rate throughout the mission, including the performance of HGA pointing during the critical period of Mercury encounter.

The status of diabetes control in Kurdistan province, west of Iran.

PubMed

Esmailnasab, Nader; Afkhamzadeh, Abdorrahim; Roshani, Daem; Moradi, Ghobad

2013-09-17

Based on some estimation more than two million peoples in Iran are affected by Type 2 diabetes. The present study was designed to evaluate the status of diabetes control among Type 2 diabetes patients in Kurdistan, west of Iran and its associated factors. In our cross sectional study conducted in 2010, 411 Type 2 diabetes patients were randomly recruited from Sanandaj, Capital of Kurdistan. Chi square test was used in univariate analysis to address the association between HgAlc and FBS status and other variables. The significant results from Univariate analysis were entered in multivariate analysis and multinomial logistic regression model. In 38% of patients, FBS was in normal range (70-130) and in 47% HgA1c was <7% which is normal range for HgA1c. In univariate analysis, FBS level was associated with educational levels (P=0.001), referral style (P=0.001), referral time (P=0.009), and insulin injection (P=0.016). In addition, HgA1c had a relationship with sex (P=0.023), age (P=0.035), education (P=0.001), referral style (P=0.001), and insulin injection (P=0.008). After using multinomial logistic regression for significant results of univariate analysis, it was found that FBS was significantly associated with referral style. In addition HgA1c was significantly associated with referral style and Insulin injection. Although some of patients were under the coverage of specialized cares, but their diabetes were not properly controlled.

Alkaptonuria is a novel human secondary amyloidogenic disease.

PubMed

Millucci, Lia; Spreafico, Adriano; Tinti, Laura; Braconi, Daniela; Ghezzi, Lorenzo; Paccagnini, Eugenio; Bernardini, Giulia; Amato, Loredana; Laschi, Marcella; Selvi, Enrico; Galeazzi, Mauro; Mannoni, Alessandro; Benucci, Maurizio; Lupetti, Pietro; Chellini, Federico; Orlandini, Maurizio; Santucci, Annalisa

2012-11-01

Alkaptonuria (AKU) is an ultra-rare disease developed from the lack of homogentisic acid oxidase activity, causing homogentisic acid (HGA) accumulation that produces a HGA-melanin ochronotic pigment, of unknown composition. There is no therapy for AKU. Our aim was to verify if AKU implied a secondary amyloidosis. Congo Red, Thioflavin-T staining and TEM were performed to assess amyloid presence in AKU specimens (cartilage, synovia, periumbelical fat, salivary gland) and in HGA-treated human chondrocytes and cartilage. SAA and SAP deposition was examined using immunofluorescence and their levels were evaluated in the patients' plasma by ELISA. 2D electrophoresis was undertaken in AKU cells to evaluate the levels of proteins involved in amyloidogenesis. AKU osteoarticular tissues contained SAA-amyloid in 7/7 patients. Ochronotic pigment and amyloid co-localized in AKU osteoarticular tissues. SAA and SAP composition of the deposits assessed secondary type of amyloidosis. High levels of SAA and SAP were found in AKU patients' plasma. Systemic amyloidosis was assessed by Congo Red staining of patients' abdominal fat and salivary gland. AKU is the second pathology after Parkinson's disease where amyloid is associated with a form of melanin. Aberrant expression of proteins involved in amyloidogenesis has been found in AKU cells. Our findings on alkaptonuria as a novel type II AA amyloidosis open new important perspectives for its therapy, since methotrexate treatment proved to significantly reduce in vitro HGA-induced A-amyloid aggregates. Copyright © 2012 Elsevier B.V. All rights reserved.

Antioxidant profile of strawberry tree honey and its marker homogentisic acid in several models of oxidative stress.

PubMed

Rosa, Antonella; Tuberoso, Carlo Ignazio Giovanni; Atzeri, Angela; Melis, Maria Paola; Bifulco, Ersilia; Dessì, Maria Assunta

2011-12-01

The antioxidant activity of several honeys was evaluated considering the different contribution of entire samples. The strawberry tree honey emerged as the richest in total phenols and the most active honey in the DPPH and FRAP tests, and could protect cholesterol against oxidative degradation (140°C). Homogentisic acid (2,5-dihydroxyphenylacetic acid, HGA), the main phenolic compound from strawberry tree honey, showed interesting antioxidant and antiradical activities, and protective effect against thermal-cholesterol degradation, comparable to those of well known antioxidants. Moreover, the pre-treatment with HGA significantly preserved liposomes and LDL from Cu(2+)-induced oxidative damage at 37°C for 2h, inhibiting the reduction of polyunsaturated fatty acids and cholesterol and the increase of their oxidative products. This phenol had no toxic effect in human intestinal epithelial Caco-2 cells within the concentration range tested (5-1000μM). HGA was able to pass through the Caco-2 monolayers, the apparent permeability coefficients (Papp) in the apical-to-basolateral and basolateral-to-apical direction were 3.48±1.22×10(-6) and 2.18±0.34×10(-6)cm/s, respectively, suggesting a passive diffusion pathway as the dominating process. The results of the work qualify HGA as natural antioxidant, able to exert a significant in vitro protective effect and to contribute to the strawberry tree honey antioxidant activity. Copyright © 2011 Elsevier Ltd. All rights reserved.

Novel techniques of real-time blood flow and functional mapping: technical note.

PubMed

Kamada, Kyousuke; Ogawa, Hiroshi; Saito, Masato; Tamura, Yukie; Anei, Ryogo; Kapeller, Christoph; Hayashi, Hideaki; Prueckl, Robert; Guger, Christoph

2014-01-01

There are two main approaches to intraoperative monitoring in neurosurgery. One approach is related to fluorescent phenomena and the other is related to oscillatory neuronal activity. We developed novel techniques to visualize blood flow (BF) conditions in real time, based on indocyanine green videography (ICG-VG) and the electrophysiological phenomenon of high gamma activity (HGA). We investigated the use of ICG-VG in four patients with moyamoya disease and two with arteriovenous malformation (AVM), and we investigated the use of real-time HGA mapping in four patients with brain tumors who underwent lesion resection with awake craniotomy. Real-time data processing of ICG-VG was based on perfusion imaging, which generated parameters including arrival time (AT), mean transit time (MTT), and BF of brain surface vessels. During awake craniotomy, we analyzed the frequency components of brain oscillation and performed real-time HGA mapping to identify functional areas. Processed results were projected on a wireless monitor linked to the operating microscope. After revascularization for moyamoya disease, AT and BF were significantly shortened and increased, respectively, suggesting hyperperfusion. Real-time fusion images on the wireless monitor provided anatomical, BF, and functional information simultaneously, and allowed the resection of AVMs under the microscope. Real-time HGA mapping during awake craniotomy rapidly indicated the eloquent areas of motor and language function and significantly shortened the operation time. These novel techniques, which we introduced might improve the reliability of intraoperative monitoring and enable the development of rational and objective surgical strategies.

Novel Techniques of Real-time Blood Flow and Functional Mapping: Technical Note

PubMed Central

KAMADA, Kyousuke; OGAWA, Hiroshi; SAITO, Masato; TAMURA, Yukie; ANEI, Ryogo; KAPELLER, Christoph; HAYASHI, Hideaki; PRUECKL, Robert; GUGER, Christoph

2014-01-01

There are two main approaches to intraoperative monitoring in neurosurgery. One approach is related to fluorescent phenomena and the other is related to oscillatory neuronal activity. We developed novel techniques to visualize blood flow (BF) conditions in real time, based on indocyanine green videography (ICG-VG) and the electrophysiological phenomenon of high gamma activity (HGA). We investigated the use of ICG-VG in four patients with moyamoya disease and two with arteriovenous malformation (AVM), and we investigated the use of real-time HGA mapping in four patients with brain tumors who underwent lesion resection with awake craniotomy. Real-time data processing of ICG-VG was based on perfusion imaging, which generated parameters including arrival time (AT), mean transit time (MTT), and BF of brain surface vessels. During awake craniotomy, we analyzed the frequency components of brain oscillation and performed real-time HGA mapping to identify functional areas. Processed results were projected on a wireless monitor linked to the operating microscope. After revascularization for moyamoya disease, AT and BF were significantly shortened and increased, respectively, suggesting hyperperfusion. Real-time fusion images on the wireless monitor provided anatomical, BF, and functional information simultaneously, and allowed the resection of AVMs under the microscope. Real-time HGA mapping during awake craniotomy rapidly indicated the eloquent areas of motor and language function and significantly shortened the operation time. These novel techniques, which we introduced might improve the reliability of intraoperative monitoring and enable the development of rational and objective surgical strategies. PMID:25263624

A Clinician-Centered Evaluation of the Usability of AHLTA and Automated Clinical Practice Guidelines at TAMC

DTIC Science & Technology

2011-03-31

enclave and receives care at a civilian facility during the later months of her pregnancy. During that time she develops gestational diabetes . Upon... Diabetic ” collection as those patients with a HgA1c > 9. Once the basic collection is defined, it can be used to create a rule such as “if high-risk... diabetic = true, then order HgA1c very six months, else every 12 months”. Using patient Cohort allows rule authors to efficiently manage rule systems

GIOTTO's antenna de-spin mechanism: Ots lubrication and thermal vacuum performance

NASA Technical Reports Server (NTRS)

Todd, M. J.; Parker, K.

1987-01-01

Except in the near Earth phase of GIOTTO's mission to Comet Halley, the HGA (high gain antenna) on board GIOTTO was the only designed means of up/down communications. The spacecraft spin stabilization required that the HGA be despun at the same rotational rate of nominally 15 rpm in order to keep the HGA pointing accurately to a Earth. A dual servomotor despin mechanism was designed and built by SEP of France for this purpose. The expected thermal environment suggested that dry lubrication was preferable to wet for the ball bearings but there existed no relevant data on the torque noise spectrum of candidate solid lubricants. Therefore ad hoc torque noise tests were run with two solid lubricants: ion plated lead film plus lead bronze cage (retainer) and a PTFE composite cage only. The lead lubrication showed the better spectrum up to the mission lifetime point so it was selected for continued test over some 20 times the Halley mission life, with periodic torque spectrum monitoring. The spectrum remained well within the pointing error budget over the 100 million revolutions covered.

Spontaneous Achilles tendon rupture in alkaptonuria

PubMed Central

Alajoulin, Omar A.; Alsbou, Mohammed S.; Ja’afreh, Somayya O.; Kalbouneh, Heba M.

2015-01-01

Alkaptonuria (AKU) is a rare inborn metabolic disease characterized by accumulation of homogentisic acid (HGA). Excretion of HGA in urine causes darkening of urine and its deposition in connective tissues causes dark pigmentation (ochronosis), early degeneration of articular cartilage, weakening of the tendons, and subsequent rupture. In this case report, we present a rare case of a patient presented with unilateral spontaneous rupture of Achilles tendon due to AKU. The patient developed most of the orthopedic manifestations of the disease earlier than typical presentations. Alkaptonuria patients should avoid strenuous exercises and foot straining especially in patients developing early orthopedic manifestations. PMID:26620992

Spontaneous Achilles tendon rupture in alkaptonuria.

PubMed

Alajoulin, Omar A; Alsbou, Mohammed S; Ja'afreh, Somayya O; Kalbouneh, Heba M

2015-12-01

Alkaptonuria (AKU) is a rare inborn metabolic disease characterized by accumulation of homogentisic acid (HGA). Excretion of HGA in urine causes darkening of urine and its deposition in connective tissues causes dark pigmentation (ochronosis), early degeneration of articular cartilage, weakening of the tendons, and subsequent rupture. In this case report, we present a rare case of a patient presented with unilateral spontaneous rupture of Achilles tendon due to AKU. The patient developed most of the orthopedic manifestations of the disease earlier than typical presentations. Alkaptonuria patients should avoid strenuous exercises and foot straining especially in patients developing early orthopedic manifestations.

On-board ephemeris representation for Topex/Poseidon

NASA Technical Reports Server (NTRS)

Salama, Ahmed H.

1990-01-01

The Topex/Poseidon satellite requires real-time on-board knowledge of the satellite and TDRS ephemeris for attitude determination and control and High-Gain Antenna (HGA) pointing. The ephemeris representation concept for the MMS (Multimission Modular Spacecraft) satellites has shown that compressing the predicted ephemeris in a Fourier Power Series (FPS) before uplinking in conjunction with the On-Board Computer (OBC) ephemeris reconstruction algorithms is an efficient technique for ephemeris representation. As an MMS-based satellite, Topex/Poseidon has inherited the Landsat ephemeris representation concept including a daily FPS upload. This paper presents the Topex/Poseidon concept, analysis, and results including the conclusion that the ephemeris representation duration could be extended to 10 days or more and convenient weekly uploading is adopted without an increase in OBC memory requirements.

Simultaneous detection of diagnostic biomarkers of alkaptonuria, ornithine carbamoyltransferase deficiency, and neuroblastoma disease by high-performance liquid chromatography/tandem mass spectrometry.

PubMed

Hsu, Wei-Yi; Chen, Ching-Ming; Tsai, Fuu-Jen; Lai, Chien-Chen

2013-05-01

Urinary homovanillic acid (HVA)/vanillylmandelic acid (VMA), orotic acid (OA), and homogentisic acid (HGA) are diagnostic biomarkers of neuroblastoma, ornithine carbamoyl transferase deficiency (OCTD), and alkaptonuria (AKU), respectively. In this study, a high-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was developed for simultaneous quantification of HVA, VMA, OA, and HGA in urine. After sample preparation, which involved only the dilution procedure, samples were quantified by LC-MS/MS. Full-scan MS/MS mode enabled the urinary markers to be quantified with a high degree of specificity and sensitivity. Rather than using a separate enzymatic method to normalize the concentration of creatinine in urine, we quantified the level of creatinine in urine in one LC-MS run. The limits of detection were 10 μg/l for HGA, 25 μg/l for HVA/VMA, and 50 μg/l for OA with a single-to-noise ratio of 3; the limits of quantification were 50 μg/l for HVA and HGA, 100 μg/l for VMA, and 250 μg/l for OA. The linear dynamic range for quantification of the analytes covered 2 to 3 orders of magnitude, depending on the analyte. The relative standard deviation of the developed LC-MS/MS method was less than 4% for the intra-day validation and 10% for the inter-day validation. The results show that our LC-MS/MS technique is a highly sensitive and rapid method for screening for biomarkers that are diagnostic of three metabolic diseases. Copyright © 2012 Elsevier B.V. All rights reserved.

Solar Dynamics Observatory High Gain Antenna Handover Planning

NASA Technical Reports Server (NTRS)

Hashmall, Joseph A.; Mann, Laurie

2007-01-01

The Solar Dynamics Observatory (SDO) is planned to launch in early 2009 as a mission to study the solar variability and its impact on Earth. To best satisfy its science goal, SDO will fly in a geosynchronous orbit with an inclination of approximately 29 deg. The spacecraft attitude is designed so that the science instruments point directly at the Sun with high accuracy. One of SDO s principal requirements is to obtain long periods of uninterrupted observations. The observations have an extremely high data volume so SDO must be in continuous contact with the ground during the observation periods. To maintain this contact, SDO is equipped with a pair of high gain antennas (HGAs) transmitting to a pair of ground antennas at the SDO ground station (SDOGS) located in White Sands, New Mexico. Either HGA can transmit to either SDOGS antenna. Neither HGA can be powered down. During a portion of each year, each of the HGA beams will intersect with the SDO body for a portion of the orbit. The original SDO antenna contact plan used each HGA for the half of each year during which its beam would not intersect the spacecraft. No data would be lost except, possibly, when switching from one antenna to another. After this plan was adopted, further analysis showed that daily handovers would be necessary for significant periods of the year. This unexpected need for extensive handovers necessitated that a handover design be developed to minimize the impact on the mission. This antenna handover design was developed and successfully tested with simulated data using the slew rate limits from preliminary jitter analysis. Subsequent analysis provided significant revision of allowed rates requiring modification of the handover plans.

Solar Dynamics Observatory High Gain Antenna Handover Planning

NASA Technical Reports Server (NTRS)

Hashmall, Joseph A.; Mann, Laurie

2007-01-01

The Solar Dynamics Observatory (SDO) is planned to launch in early 2009 as a mission to study the solar variability and its impact on Earth. To best satisfy its science goal, SDO will fly in a geosynchronous orbit with an inclination of approximately 29 deg. The spacecraft attitude is designed so that the science instruments point directly at the Sun with high accuracy. One of SDO's principal requirements is to obtain long periods of uninterrupted observations. The observations have an extremely high data volume so SDO must be in continuous contact with the ground during the observation periods. To maintain this contact, SDO is equipped with a pair of high gain antennas (HGAs) transmitting to a pair of ground antennas at the SDO ground station (SDOGS) located in White Sands, New Mexico. Either HGA can transmit to either SDOGS antenna. Neither HGA can be powered down. During a portion of each year, each of the HGA beams will intersect with the SDO body for a portion of the orbit. The original SDO antenna contact plan used each HGA for the half of each year during which its beam would not intersect the spacecraft. No data would be lost except, possibly, when switching from one antenna to another. After this plan was adopted, further analysis showed that daily handovers would be necessary for significant periods of the year. This unexpected need for extensive handovers necessitated that a handover design be developed to minimize the impact on the mission. This antenna handover design was developed and successfully tested with simulated data using the slew rate limits from preliminary jitter analysis. Subsequent analysis provided significant revision of allowed rates requiring modification of the handover plans.

Gibberellin A3 Is Biosynthesized from Gibberellin A20 via Gibberellin A5 in Shoots of Zea mays L. 1

PubMed Central

Fujioka, Shozo; Yamane, Hisakazu; Spray, Clive R.; Phinney, Bernard O.; Gaskin, Paul; MacMillan, Jake; Takahashi, Nobutaka

1990-01-01

[17-13C,3H]-Labeled gibberellin A20 (GA20), GA5, and GA1 were fed to homozygous normal (+/+), heterozygous dominant dwarf (D8/+), and homozygous dominant dwarf (D8/D8) seedlings of Zea mays L. (maize). 13C-Labeled GA29, GA8, GA5, GA1, and 3-epi-GA1, as well as unmetabolized [13C]GA20, were identified by gas chromatography-selected ion monitoring (GC-SIM) from feeds of [17-13C, 3H]GA20 to all three genotypes. 13C-Labeled GA8 and 3-epi-G1, as well as unmetabolized [13C]GA1, were identified by GC-SIM from feeds of [17-13C, 3H]GA1 to all three genotypes. From feeds of [17-13C, 3H]GA5, 13C-labeled GA3 and the GA3-isolactone, as well as unmetabolized [13C]GA5, were identified by GC-SIM from +/+ and D8/D8, and by full scan GC-MS from D8/+. No evidence was found for the metabolism of [17-13C, 3H]GA5 to [13C]GA1, either by full scan GC-mass spectrometry or by GC-SIM. The results demonstrate the presence in maize seedlings of three separate branches from GA20, as follows: (a) GA20 → GA1 → GA8; (b) GA20 → GA5 → GA3; and (c) GA20 → GA29. The in vivo biogenesis of GA3 from GA5, as well as the origin of GA5 from GA20, are conclusively established for the first time in a higher plant (maize shoots). PMID:16667678

Local differentiation of cell wall matrix polysaccharides in sinuous pavement cells: its possible involvement in the flexibility of cell shape.

PubMed

Sotiriou, P; Giannoutsou, E; Panteris, E; Galatis, B; Apostolakos, P

2018-03-01

The distribution of homogalacturonans (HGAs) displaying different degrees of esterification as well as of callose was examined in cell walls of mature pavement cells in two angiosperm and two fern species. We investigated whether local cell wall matrix differentiation may enable pavement cells to respond to mechanical tension forces by transiently altering their shape. HGA epitopes, identified with 2F4, JIM5 and JIM7 antibodies, and callose were immunolocalised in hand-made or semithin leaf sections. Callose was also stained with aniline blue. The structure of pavement cells was studied with light and transmission electron microscopy (TEM). In all species examined, pavement cells displayed wavy anticlinal cell walls, but the waviness pattern differed between angiosperms and ferns. The angiosperm pavement cells were tightly interconnected throughout their whole depth, while in ferns they were interconnected only close to the external periclinal cell wall and intercellular spaces were developed between them close to the mesophyll. Although the HGA epitopes examined were located along the whole cell wall surface, the 2F4- and JIM5- epitopes were especially localised at cell lobe tips. In fern pavement cells, the contact sites were impregnated with callose and JIM5-HGA epitopes. When tension forces were applied on leaf regions, the pavement cells elongated along the stretching axis, due to a decrease in waviness of anticlinal cell walls. After removal of tension forces, the original cell shape was resumed. The presented data support that HGA epitopes make the anticlinal pavement cell walls flexible, in order to reversibly alter their shape. Furthermore, callose seems to offer stability to cell contacts between pavement cells, as already suggested in photosynthetic mesophyll cells. © 2017 German Society for Plant Sciences and The Royal Botanical Society of the Netherlands.

The effect of a community garden on HgA1c in diabetics of Marshallese descent.

PubMed

Weltin, Ann M; Lavin, Roberta P

2012-01-01

A mixed-convergent parallell designed intervention study was created to learn whether a community garden could provide improved diabetes control for members of a Midwest community of immigrants from the Marshall Islands. Qualitative data gathered through field observations on cultural norms and beliefs, food perceptions, and barriers to health care corrobrorated data gained at medical appointments for diabetes follow-up. Marshallese clients from a local community health center were recruited to participate in a community garden. Persons who participated in a community garden had significant reduction in their HgA1c postintervention, compared to persons who did not participate actively.

A small molecule inhibitor of mutant IDH2 rescues cardiomyopathy in a D-2-hydroxyglutaric aciduria type II mouse model.

PubMed

Wang, Fang; Travins, Jeremy; Lin, Zhizhong; Si, Yaguang; Chen, Yue; Powe, Josh; Murray, Stuart; Zhu, Dongwei; Artin, Erin; Gross, Stefan; Santiago, Stephanie; Steadman, Mya; Kernytsky, Andrew; Straley, Kimberly; Lu, Chenming; Pop, Ana; Struys, Eduard A; Jansen, Erwin E W; Salomons, Gajja S; David, Muriel D; Quivoron, Cyril; Penard-Lacronique, Virginie; Regan, Karen S; Liu, Wei; Dang, Lenny; Yang, Hua; Silverman, Lee; Agresta, Samuel; Dorsch, Marion; Biller, Scott; Yen, Katharine; Cang, Yong; Su, Shin-San Michael; Jin, Shengfang

2016-11-01

D-2-hydroxyglutaric aciduria (D2HGA) type II is a rare neurometabolic disorder caused by germline gain-of-function mutations in isocitrate dehydrogenase 2 (IDH2), resulting in accumulation of D-2-hydroxyglutarate (D2HG). Patients exhibit a wide spectrum of symptoms including cardiomyopathy, epilepsy, developmental delay and limited life span. Currently, there are no effective therapeutic interventions. We generated a D2HGA type II mouse model by introducing the Idh2R140Q mutation at the native chromosomal locus. Idh2R140Q mice displayed significantly elevated 2HG levels and recapitulated multiple defects seen in patients. AGI-026, a potent, selective inhibitor of the human IDH2R140Q-mutant enzyme, suppressed 2HG production, rescued cardiomyopathy, and provided a survival benefit in Idh2R140Q mice; treatment withdrawal resulted in deterioration of cardiac function. We observed differential expression of multiple genes and metabolites that are associated with cardiomyopathy, which were largely reversed by AGI-026. These findings demonstrate the potential therapeutic benefit of an IDH2R140Q inhibitor in patients with D2HGA type II.

Osteoarticular cells tolerate short-term exposure to nitisinone-implications in alkaptonuria.

PubMed

Mistry, J B; Jackson, D J; Bukhari, M; Taylor, A M

2016-02-01

Alkaptonuria (AKU) is a rare genetic disease resulting in severe, rapidly progressing, early onset multi-joint osteoarthropathy. A potential therapy, nitisinone, is being trialled that reduces the causative agent; homogentisic acid (HGA) and in a murine model has shown to prevent ochronosis. Little is currently known about the effect nitisinone has on osteoarticular cells; these cells suffer most from the presence of HGA and its polymeric derivatives. This led us to investigate nitisinone's effect on chondrocytes and osteoblast-like cells in an in vitro model. Human C20/A4 immortalized chondrocytes, and osteosarcoma cells MG63 cultured in DMEM, as previously described. Confluent cells were then plated into 24-well plates at 4 × 10(4) cells per well in varying concentrations of nitisinone. Cells were cultured for 7 days with medium changes every third day. Trypan blue assay was used to determine viability and the effect of nitisinone concentration on cells. Statistical analysis was performed using analysis of variance, and differences between groups were determined by Newman-Keuls post-test. Analysis of C20/A4 chondrocyte and MG63 osteoblast-like cell viability when cultured in different concentrations of nitisinone demonstrates that there is no statistically significant difference in cell viability compared to control cultures. There is currently no literature surrounding the use of nitisinone in human in vitro models, or its effect on chondrocytes or osteoblast like cells. Our results show that nitisinone does not appear detrimental to cell viability of chondrocytes or osteoblast-like cells, which adds to the evidence that this therapy could be useful in treating AKU.

A 3-year Randomized Therapeutic Trial of Nitisinone in Alkaptonuria

PubMed Central

Introne, Wendy J.; Perry, Monique B.; Troendle, James; Tsilou, Ekaterini; Kayser, Michael A.; Suwannarat, Pim; O’Brien, Kevin E.; Bryant, Joy; Sachdev, Vandana; Reynolds, James C.; Moylan, Elizabeth; Bernardini, Isa; Gahl, William A.

2011-01-01

Alkaptonuria is a rare, autosomal recessive disorder of tyrosine degradation due to deficiency of the third enzyme in the catabolic pathway. As a result, homogentisic acid (HGA) accumulates and is excreted in gram quantities in the urine, which turns dark upon alkalization. The first symptoms, occurring in early adulthood, involve a painful, progressively debilitating arthritis of the spine and large joints. Cardiac valvular disease and renal and prostate stones occur later. Previously suggested therapies have failed to show benefit, and management remains symptomatic. Nitisinone, a potent inhibitor of the second enzyme in the tyrosine catabolic pathway, is considered a potential therapy; proof-of-principle studies showed 95% reduction in urinary HGA. Based on those findings, a prospective, randomized clinical trial was initiated in 2005 to evaluate 40 patients over a 36-month period. The primary outcome parameter was hip total range of motion with measures of musculoskeletal function serving as secondary parameters. Biochemically, this study consistently demonstrated 95% reduction of HGA in urine and plasma over the course of 3 years. Clinically, primary and secondary parameters did not prove benefit from the medication. Side effects were infrequent. This trial illustrates the remarkable tolerability of nitisinone, its biochemical efficacy, and the need to investigate its use in younger individuals prior to development of debilitating arthritis. PMID:21620748

A 3-year randomized therapeutic trial of nitisinone in alkaptonuria.

PubMed

Introne, Wendy J; Perry, Monique B; Troendle, James; Tsilou, Ekaterini; Kayser, Michael A; Suwannarat, Pim; O'Brien, Kevin E; Bryant, Joy; Sachdev, Vandana; Reynolds, James C; Moylan, Elizabeth; Bernardini, Isa; Gahl, William A

2011-08-01

Alkaptonuria is a rare, autosomal recessive disorder of tyrosine degradation due to deficiency of the third enzyme in the catabolic pathway. As a result, homogentisic acid (HGA) accumulates and is excreted in gram quantities in the urine, which turns dark upon alkalization. The first symptoms, occurring in early adulthood, involve a painful, progressively debilitating arthritis of the spine and large joints. Cardiac valvular disease and renal and prostate stones occur later. Previously suggested therapies have failed to show benefit, and management remains symptomatic. Nitisinone, a potent inhibitor of the second enzyme in the tyrosine catabolic pathway, is considered a potential therapy; proof-of-principle studies showed 95% reduction in urinary HGA. Based on those findings, a prospective, randomized clinical trial was initiated in 2005 to evaluate 40 patients over a 36-month period. The primary outcome parameter was hip total range of motion with measures of musculoskeletal function serving as secondary parameters. Biochemically, this study consistently demonstrated 95% reduction of HGA in urine and plasma over the course of 3 years. Clinically, primary and secondary parameters did not prove benefit from the medication. Side effects were infrequent. This trial illustrates the remarkable tolerability of nitisinone, its biochemical efficacy, and the need to investigate its use in younger individuals prior to development of debilitating arthritis. Published by Elsevier Inc.

Effect of intensive glycaemic control on endothelial progenitor cells in patients with long-standing uncontrolled type 2 diabetes.

PubMed

Lev, Eli I; Singer, Joel; Leshem-Lev, Dorit; Rigler, Merav; Dadush, Oshrat; Vaduganathan, Muthiah; Battler, Alexander; Kornowski, Ran

2014-09-01

Endothelial progenitor cells (EPCs) have an important role in repair following vascular injury. However, in patients with diabetes, EPC number and function are markedly reduced. It is unclear whether intensive glycaemic control can modify EPC properties in diabetic patients. We aimed to examine whether glycaemic control can improve EPC number and function in patients with long-standing uncontrolled type 2 diabetes. Thirty-five patients with treated type 2 diabetes and HgA1c ≥ 8.5% were included. Patients were tested at baseline and after 3-4 months of an intensive glycaemic control programme, with the aim of achieving HgA1c of 7%. The diabetes group was compared to 20 patients without diabetes (control). Circulating EPC levels were assessed by flow cytometry for expression of VEGFR2, CD133, and CD34. The capacity of the cells to form colony-forming units (CFUs), and their migration and viability were quantified after 1 week of culture. Patients with diabetes (mean age 61.1 ± 7 years, 28.6% women, disease duration of 19.2 ± 8 years) had a baseline HgA1c of 9.4 ± 0.8%. After the glycaemic control period, HgA1c decreased to 8 ± 0.8%. Circulating EPC levels increased significantly after the intensive control period and reached a level similar to the control group. The number of EPC CFUs also increased significantly after glycaemic control but remained lower than the control group. All EPC functional assays improved following the glycaemic control. In patients with uncontrolled long-standing type 2 diabetes, intensive glycaemic control was associated with an increase in the levels of circulating EPCs, and improvement in their functional properties. © The European Society of Cardiology 2013 Reprints and permissions:sagepub.co.uk/journalsPermissions.nav.

Detection of novel visible-light region absorbance peaks in the urine after alkalization in patients with alkaptonuria.

PubMed

Tokuhara, Yasunori; Shukuya, Kenichi; Tanaka, Masami; Mouri, Mariko; Ohkawa, Ryunosuke; Fujishiro, Midori; Takahashi, Tomoo; Okubo, Shigeo; Yokota, Hiromitsu; Kurano, Makoto; Ikeda, Hitoshi; Yamaguchi, Seiji; Inagaki, Shinobu; Ishige-Wada, Mika; Usui, Hiromi; Yatomi, Yutaka; Shimosawa, Tatsuo

2014-01-01

Alkaptonuria, caused by a deficiency of homogentisate 1,2-dioxygenase, results in the accumulation of homogentisic acid (2,5-dihydroxyphenylacetic acid, HGA) in the urine. Alkaptonuria is suspected when the urine changes color after it is left to stand at room temperature for several hours to days; oxidation of homogentisic acid to benzoquinone acetic acid underlies this color change, which is accelerated by the addition of alkali. In an attempt to develop a facile screening test for alkaptonuria, we added alkali to urine samples obtained from patients with alkaptonuria and measured the absorbance spectra in the visible light region. We evaluated the characteristics of the absorption spectra of urine samples obtained from patients with alkaptonuria (n = 2) and compared them with those of urine specimens obtained from healthy volunteers (n = 5) and patients with phenylketonuria (n = 3), and also of synthetic homogentisic acid solution after alkalization. Alkalization of the urine samples and HGA solution was carried out by the addition of NaOH, KOH or NH4OH. The sample solutions were incubated at room temperature for 1 min, followed by measurement of the absorption spectra. Addition of alkali to alkaptonuric urine yielded characteristic absorption peaks at 406 nm and 430 nm; an identical result was obtained from HGA solution after alkalization. The absorbance values at both 406 nm and 430 nm increased in a time-dependent manner. In addition, the absorbance values at these peaks were greater in strongly alkaline samples (NaOH- KOH-added) as compared with those in weakly alkaline samples (NH4OH-added). In addition, the peaks disappeared following the addition of ascorbic acid to the samples. We found two characteristic peaks at 406 nm and 430 nm in both alkaptonuric urine and HGA solution after alkalization. This new quick and easy method may pave the way for the development of an easy method for the diagnosis of alkaptonuria.

Detection of Novel Visible-Light Region Absorbance Peaks in the Urine after Alkalization in Patients with Alkaptonuria

PubMed Central

Tokuhara, Yasunori; Shukuya, Kenichi; Tanaka, Masami; Mouri, Mariko; Ohkawa, Ryunosuke; Fujishiro, Midori; Takahashi, Tomoo; Okubo, Shigeo; Yokota, Hiromitsu; Kurano, Makoto; Ikeda, Hitoshi; Yamaguchi, Seiji; Inagaki, Shinobu; Ishige-Wada, Mika; Usui, Hiromi; Yatomi, Yutaka; Shimosawa, Tatsuo

2014-01-01

Background Alkaptonuria, caused by a deficiency of homogentisate 1,2-dioxygenase, results in the accumulation of homogentisic acid (2,5-dihydroxyphenylacetic acid, HGA) in the urine. Alkaptonuria is suspected when the urine changes color after it is left to stand at room temperature for several hours to days; oxidation of homogentisic acid to benzoquinone acetic acid underlies this color change, which is accelerated by the addition of alkali. In an attempt to develop a facile screening test for alkaptonuria, we added alkali to urine samples obtained from patients with alkaptonuria and measured the absorbance spectra in the visible light region. Methods We evaluated the characteristics of the absorption spectra of urine samples obtained from patients with alkaptonuria (n = 2) and compared them with those of urine specimens obtained from healthy volunteers (n = 5) and patients with phenylketonuria (n = 3), and also of synthetic homogentisic acid solution after alkalization. Alkalization of the urine samples and HGA solution was carried out by the addition of NaOH, KOH or NH4OH. The sample solutions were incubated at room temperature for 1 min, followed by measurement of the absorption spectra. Results Addition of alkali to alkaptonuric urine yielded characteristic absorption peaks at 406 nm and 430 nm; an identical result was obtained from HGA solution after alkalization. The absorbance values at both 406 nm and 430 nm increased in a time-dependent manner. In addition, the absorbance values at these peaks were greater in strongly alkaline samples (NaOH- KOH-added) as compared with those in weakly alkaline samples (NH4OH-added). In addition, the peaks disappeared following the addition of ascorbic acid to the samples. Conclusions We found two characteristic peaks at 406 nm and 430 nm in both alkaptonuric urine and HGA solution after alkalization. This new quick and easy method may pave the way for the development of an easy method for the diagnosis of alkaptonuria. PMID:24466168

Time to clinical investigation for Indigenous and non-Indigenous Queensland women after a high grade abnormal Pap smear, 2000-2009.

PubMed

Whop, Lisa J; Baade, Peter D; Brotherton, Julia Ml; Canfell, Karen; Cunningham, Joan; Gertig, Dorota; Lokuge, Kamalini; Garvey, Gail; Moore, Suzanne P; Diaz, Abbey; O'Connell, Dianne L; Valery, Patricia; Roder, David M; Condon, John R

2017-02-06

To investigate time to follow-up (clinical investigation) for Indigenous and non-Indigenous women in Queensland after a high grade abnormality (HGA) being detected by Pap smear. Population-based retrospective cohort analysis of linked data from the Queensland Pap Smear Register (PSR), the Queensland Hospital Admitted Patient Data Collection, and the Queensland Cancer Registry. 34 980 women aged 20-68 years (including 1592 Indigenous women) with their first HGA Pap smear result recorded on the PSR (index smear) during 2000-2009 were included and followed to the end of 2010. Time from the index smear to clinical investigation (histology test or cancer diagnosis date), censored at 12 months. The proportion of women who had a clinical investigation within 2 months of a HGA finding was lower for Indigenous (34.1%; 95% CI, 31.8-36.4%) than for non-Indigenous women (46.5%; 95% CI, 46.0-47.0%; unadjusted incidence rate ratio [IRR], 0.65; 95% CI, 0.60-0.71). This difference remained after adjusting for place of residence, area-level disadvantage, and age group (adjusted IRR, 0.74; 95% CI, 0.68-0.81). However, Indigenous women who had not been followed up within 2 months were subsequently more likely to have a clinical investigation than non-Indigenous women (adjusted IRR for 2-4 month interval, 1.21; 95% CI, 1.08-1.36); by 6 months, a similar proportion of Indigenous (62.2%; 95% CI, 59.8-64.6%) and non-Indigenous women (62.8%; 95% CI, 62.2-63.3%) had been followed up. Prompt follow-up after a HGA Pap smear finding needs to improve for Indigenous women. Nevertheless, slow follow-up is a smaller contributor to their higher cervical cancer incidence and mortality than their lower participation in cervical screening.

ARM-based control system for terry rapier loom

NASA Astrophysics Data System (ADS)

Shi, Weimin; Gu, Yeqing; Wu, Zhenyu; Wang, Fan

2007-12-01

In this paper, a novel ARM-based mechatronics control technique applied in terry rapier loom was presented. Electronic weft selection, electronic fluff, electronic let-off and take-up motions system, which consists of position and speedcontrolled servomechanisms, were studied. The control system configuration, operation principle, and mathematical models of electronic drives system were analyzed. The synchronism among all mechanical motions and an improved intelligent control algorithm for the warp let-off tension control was discussed. The result indict that, by applying electronic and embedded control techniques and the individual servomechanisms, the electronic weft selection, electronic let-off device and electronic take-up device in HGA732T terry rapier loom have greatly simplified the initial complicated mechanism, kept the warp tension constant from full to empty beam, set the variable weft density, eliminated the start mark effectively, promoted its flexibility, reliability and properties, and improved the fabric quality.

A Community Health Worker Intervention for Diabetes Self-Management Among the Tz'utujil Maya of Guatemala.

PubMed

Micikas, Mary; Foster, Jennifer; Weis, Allison; Lopez-Salm, Alyse; Lungelow, Danielle; Mendez, Pedro; Micikas, Ashley

2015-07-01

Despite the high prevalence of diabetes in rural Guatemala, there is little education in diabetes self-management, particularly among the indigenous population. To address this need, a culturally relevant education intervention for diabetic patients was developed and implemented in two rural communities in Guatemala. An evaluative research project was designed to investigate if the structured, community-led diabetes self-management intervention improved selected health outcomes for participants. A one-group, pretest-posttest design was used to evaluate the effectiveness of the educational intervention by comparing measures of health, knowledge, and behavior in patients pre- and postintervention. A survey instrument assessed health beliefs and practices and hemoglobin A1c (HgA1c) measured blood glucose levels at baseline and 4 months post initiation of intervention (n = 52). There was a significant decrease (1.2%) in the main outcome measure, mean HgA1c from baseline (10.1%) and follow-up (8.9%; p = .001). Other survey findings were not statistically significant. This study illustrates that a culturally specific, diabetes self-management program led by community health workers may reduce HgA1c levels in rural populations of Guatemala. However, as a random sample was not feasible for this study, this finding should be interpreted with caution. Limitations unique to the setting and patient population are discussed in this article. © 2014 Society for Public Health Education.

Improved anti-tumor activity and safety profile of a paclitaxel-loaded glycyrrhetinic acid-graft-hyaluronic acid conjugate as a synergistically targeted drug delivery system.

PubMed

Zhang, Li; Zhou, Jian-Ping; Yao, Jing

2015-12-01

The present study was designed to develop and evaluate glycyrrhetinic acid-graft-hyaluronic acid (HGA) conjugate for intravenous paclitaxel (PTX) delivery. Lyophilized PTX-loaded self-assembled HGA nanoparticles (PTX/HGAs) were prepared and characterized by dynamic light scattering measurements. Hemolysis test, intravenous irritation assessment, and in vitro and in vivo pharmacodynamic studies were carried out. B16F10 and HepG2 cells were used in the cell apoptosis analysis. The mouse MDA-MB-231 xenograft model was used for the evaluation of in vivo anticancer activity of the drugs, by the analysis of tumor growth and side effects on other tissues. PTX/HGAs showed high stability and good biocompability. Compared with PTX plus GA plus HA solution, PTX/HGAs displayed obvious superiority in inducing the apoptosis of the cancer cells. Following systemic administration, PTX/HGAs efficiently suppressed tumor growth, with mean tumor inhibition ratio (TIR) being 65.08%, which was significantly higher than that of PTX plus GA plus HA treatment. In conclusion, PTX/HGAs demonstrated inhibitory effects tumor growth without unwanted side effects, suggesting that HGA conjugates hold a great potential as a delivery carrier for cancer chemotherapeutics to improve therapeutic efficacy and minimize adverse effects. Copyright © 2015 China Pharmaceutical University. Published by Elsevier B.V. All rights reserved.

Industry Partners at CBE

Science.gov Websites

* Charles M. Salter Associates DIALOG HGA Architects and Engineers HOK Integral Group Interface Engineering + Will SERA Architects Taylor Engineering Team: Atelier Ten Taylor Engineering TRC Energy Services

40 CFR 52.2309 - Emissions inventories.

Code of Federal Regulations, 2013 CFR

2013-07-01

.../Galveston (HGA), Beaumont/Port Arthur (BPA), El Paso (ELP), and Dallas/Fort Worth (DFW) ozone nonattainment... Counties; the BPA nonattainment area is classified as Serious and includes Hardin, Jefferson, and Orange...

40 CFR 52.2309 - Emissions inventories.

Code of Federal Regulations, 2011 CFR

2011-07-01

.../Galveston (HGA), Beaumont/Port Arthur (BPA), El Paso (ELP), and Dallas/Fort Worth (DFW) ozone nonattainment... Counties; the BPA nonattainment area is classified as Serious and includes Hardin, Jefferson, and Orange...

40 CFR 52.2309 - Emissions inventories.

Code of Federal Regulations, 2010 CFR

2010-07-01

.../Galveston (HGA), Beaumont/Port Arthur (BPA), El Paso (ELP), and Dallas/Fort Worth (DFW) ozone nonattainment... Counties; the BPA nonattainment area is classified as Serious and includes Hardin, Jefferson, and Orange...

40 CFR 52.2309 - Emissions inventories.

Code of Federal Regulations, 2012 CFR

2012-07-01

.../Galveston (HGA), Beaumont/Port Arthur (BPA), El Paso (ELP), and Dallas/Fort Worth (DFW) ozone nonattainment... Counties; the BPA nonattainment area is classified as Serious and includes Hardin, Jefferson, and Orange...

40 CFR 52.2309 - Emissions inventories.

Code of Federal Regulations, 2014 CFR

2014-07-01

.../Galveston (HGA), Beaumont/Port Arthur (BPA), El Paso (ELP), and Dallas/Fort Worth (DFW) ozone nonattainment... Counties; the BPA nonattainment area is classified as Serious and includes Hardin, Jefferson, and Orange...

Formaldehyde and its relation to CO, PAN, and SO2 in the Houston-Galveston airshed

NASA Astrophysics Data System (ADS)

Rappenglück, B.; Dasgupta, P. K.; Leuchner, M.; Li, Q.; Luke, W.

2010-03-01

The Houston-Galveston Airshed (HGA) is one of the major metropolitan areas in the US that is classified as a nonattainment area of federal ozone standards. Formaldehyde (HCHO) is a key species in understanding ozone related air pollution; some of the highest HCHO concentrations in North America have been reported for the HGA. We report on HCHO measurements in the HGA from summer 2006. Among several sites, maximum HCHO mixing ratios were observed in the Houston Ship Channel (HSC), a region with a very high density of industrial/petrochemical operations. HCHO levels at the Moody Tower (MT) site close to downtown were dependent on the wind direction: southerly maritime winds brought in background levels (0.5-1 ppbv) while trajectories originating in the HSC resulted in high HCHO (up to 31.5 ppbv). Based on the best multiparametric linear regression model fit, the HCHO levels at the MT site can be accounted for as follows: 38.5±12.3% from primary vehicular emissions (using CO as an index of vehicular emission), 24.1±17.7% formed photochemically (using peroxyacetic nitric anhydride (PAN) as an index of photochemical activity) and 8.9±11.2% from industrial emissions (using SO2 as an index of industrial emissions). The balance 28.5±12.7% constituted the residual which cannot be easily ascribed to the above categories and/or which is transported into the HGA. The CO related HCHO fraction is dominant during the morning rush hour (06:00-09:00 h, all times are given in CDT); on a carbon basis, HCHO emissions are up to 0.7% of the CO emissions. The SO2 related HCHO fraction is significant between 09:00-12:00 h. After 12:00 h HCHO is largely formed through secondary processes. The HCHO/PAN ratios are dependent on the SO2 levels. The SO2 related HCHO fraction at the downtown site originates in the ship channel. Aside from traffic-related primary HCHO emissions, HCHO of industrial origin serves as an appreciable source for OH in the morning.

Formaldehyde and its relation to CO, PAN, and SO2 in the Houston-Galveston airshed

NASA Astrophysics Data System (ADS)

Rappenglück, B.; Dasgupta, P. K.; Leuchner, M.; Li, Q.; Luke, W.

2009-11-01

The Houston-Galveston Airshed (HGA) is one of the major metropolitan areas in the US that is classified as a nonattainment area of Federal ozone standards. Formaldehyde (HCHO) is a key species in understanding ozone related air pollution; some of the highest HCHO concentrations in North America have been reported for the HGA. We report on HCHO measurements in the HGA from summer 2006. Among several sites, maximum HCHO mixing ratios were observed in the Houston Ship Channel (HSC), a region with a very high density of industrial/petrochemical operations. HCHO levels at the Moody Tower (MT) site close to downtown were dependent on the wind direction: southerly maritime winds brought in background levels (0.5-1 ppbv) while trajectories originating in the HSC resulted in high HCH (up to 31.5 ppbv). Based on the best multiparametric linear regression model fit, the HCHO levels at the MT site can be accounted for as follows: 38.5±12.3% from primary vehicular emissions (using CO as an index of vehicular emission), 24.1±17.7% formed photochemically (using peroxyacetic nitric anhydride (PAN) as an index of photochemical activity) and 8.9±11.2% from industrial emissions (using SO2 as an index of industrial emissions). The balance 28.5±12.7% constituted the residual which cannot be easily ascribed to the above categories and/or which is transported into the HGA. The CO related HCHO fraction is dominant during the morning rush hour (06:00-09:00 h, all times are given in CDT); on a carbon basis, HCHO emissions are up to 0.7% of the CO emissions. The SO2 related HCHO fraction is significant between 09:00-12:00 h. After 12:00 h HCHO is largely formed through secondary processes. The HCHO/PAN ratios are dependent on the SO2 levels. The SO2 related HCHO fraction at the downtown site originates in the ship channel. Aside from traffic-related primary HCHO emissions, HCHO of industrial origin serves as an appreciable source for OH in the morning.

Diagnosis, Treatment, and Prevention of Lyme Disease, Human Granulocytic Anaplasmosis, and Babesiosis: A Review.

PubMed

Sanchez, Edgar; Vannier, Edouard; Wormser, Gary P; Hu, Linden T

2016-04-26

Lyme disease, human granulocytic anaplasmosis (HGA), and babesiosis are emerging tick-borne infections. To provide an update on diagnosis, treatment, and prevention of tick-borne infections. Search of PubMed and Scopus for articles on diagnosis, treatment, and prevention of tick-borne infections published in English from January 2005 through December 2015. The search yielded 3550 articles for diagnosis and treatment and 752 articles for prevention. Of these articles, 361 were reviewed in depth. Evidence supports the use of US Food and Drug Administration-approved serologic tests, such as an enzyme immunoassay (EIA), followed by Western blot testing, to diagnose extracutaneous manifestations of Lyme disease. Microscopy and polymerase chain reaction assay of blood specimens are used to diagnose active HGA and babesiosis. The efficacy of oral doxycycline, amoxicillin, and cefuroxime axetil for treating Lyme disease has been established in multiple trials. Ceftriaxone is recommended when parenteral antibiotic therapy is recommended. Multiple trials have shown efficacy for a 10-day course of oral doxycycline for treatment of erythema migrans and for a 14-day course for treatment of early neurologic Lyme disease in ambulatory patients. Evidence indicates that a 10-day course of oral doxycycline is effective for HGA and that a 7- to 10-day course of azithromycin plus atovaquone is effective for mild babesiosis. Based on multiple case reports, a 7- to 10-day course of clindamycin plus quinine is often used to treat severe babesiosis. A recent study supports a minimum of 6 weeks of antibiotics for highly immunocompromised patients with babesiosis, with no parasites detected on blood smear for at least the final 2 weeks of treatment. Evidence is evolving regarding the diagnosis, treatment, and prevention of Lyme disease, HGA, and babesiosis. Recent evidence supports treating patients with erythema migrans for no longer than 10 days when doxycycline is used and prescription of a 14-day course of oral doxycycline for early neurologic Lyme disease in ambulatory patients. The duration of antimicrobial therapy for babesiosis in severely immunocompromised patients should be extended to 6 weeks or longer.

[First report of alkaptonuria in Peru].

PubMed

Guillén-Mendoza, Daniel; Quiroga de Michelena, María

2014-01-01

Alkaptonuria is an inborn error of metabolism caused by deficiency of homogentisate 1,2-dioxygenase (HGD) which produces an excess of homogentisic acid (HGA). A case is presented of a 57 year old woman whose urine has turned black since birth. For 9 years she presented a greenish pigmentation in her nail beds that did not improve with antifungal treatments, and in the last 9 months she showed worsening large joint osteoarthritis. This situation forced her to use a wheelchair due to the intense pain caused by osteoarthritis in her hips and lumbar spine. From the description of symptoms, her urinary HGA was measured which confirmed the diagnosis of alkaptonuria. Analgesics and a diet without tyrosine-containing products were suggested. The patient was also referred for hip replacement surgery. This is the first reported case of alkaptonuria in Peru.

Clinical features and disease progression of L-2-hydroxyglutaric aciduria in 27 Staffordshire bull terriers.

PubMed

Shea, A; De Risio, L; Carruthers, H; Ekiri, A; Beltran, E

2016-11-26

To describe the development of clinical signs (CS) and outcome of L-2-hydroxyglutaric aciduria (L-2-HGA), owners of 119 Staffordshire bull terriers positive for the known L-2-hydroxyglutarate dehydrogenase autosomal-recessive mutations were requested to complete a questionnaire regarding their pet's CS. Questionnaires were returned for 27 dogs, all with neurological abnormalities-not all questions were answered in all cases. The mean age of CS onset was 12 months (range 2.5-60). Gait dysfunction was reported in 26/26 dogs, with stiffness of all four limbs the most common (24/26) and earliest recognised abnormality. Kyphosis (19/26), body and/or head tremors (19/26) and hypermetria (15/26) were frequent. Behavioural changes were present in 24/27 dogs; most commonly staring into space (21/24), signs of dementia (17/24) and loss of training (15/24). Eighteen dogs demonstrated paroxysmal seizure-like/dyskinetic episodes. Nineteen (70 per cent) dogs were alive at a mean survival time of 76.6 months (12-170) after onset of CS. L-2-HGA was the cause of euthanasia in six dogs. Euthanasia occurred at a mean survival time of 44 months (8.5-93) after onset of CS, with 2/8 dogs euthanased within 12 months. L-2-HGA is considered a progressive neurological disease; however, CS can be successfully managed with affected dogs potentially living a normal lifespan. British Veterinary Association.

Genetic algorithms

NASA Technical Reports Server (NTRS)

Wang, Lui; Bayer, Steven E.

1991-01-01

Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology.

Genetic Algorithms and Local Search

NASA Technical Reports Server (NTRS)

Whitley, Darrell

1996-01-01

The first part of this presentation is a tutorial level introduction to the principles of genetic search and models of simple genetic algorithms. The second half covers the combination of genetic algorithms with local search methods to produce hybrid genetic algorithms. Hybrid algorithms can be modeled within the existing theoretical framework developed for simple genetic algorithms. An application of a hybrid to geometric model matching is given. The hybrid algorithm yields results that improve on the current state-of-the-art for this problem.

Hemolysis in a patient with alkaptonuria and chronic kidney failure.

PubMed

Heng, Anne-Elisabeth; Courbebaisse, Marie; Kemeny, Jean Louis; Matesan, Raluca; Bonniol, Claude; Deteix, Patrice; Souweine, Bertrand

2010-07-01

In alkaptonuria, the absence of homogentisic acid oxidase results in the accumulation of homogentisic acid (HGA) in the body. Fatal disease cases are infrequent, and death often results from kidney or cardiac complications. We report a 24-year-old alkaptonuric man with severe decreased kidney function who developed fatal metabolic acidosis and intravascular hemolysis. Hemolysis may have been caused by rapid and extensive accumulation of HGA and subsequent accumulation of plasma soluble melanins. Toxic effects of plasma soluble melanins, their intermediates, and reactive oxygen side products are increased when antioxidant mechanisms are overwhelmed. A decrease in serum antioxidative activity has been reported in patients with chronic decreased kidney function. However, despite administration of large doses of an antioxidant agent and ascorbic acid and intensive kidney support, hemolysis and acidosis could not be brought under control and hemolysis led to the death of the patient.

Homogentisate 1,2 dioxygenase is expressed in brain: implications in alkaptonuria.

PubMed

Bernardini, Giulia; Laschi, Marcella; Geminiani, Michela; Braconi, Daniela; Vannuccini, Elisa; Lupetti, Pietro; Manetti, Fabrizio; Millucci, Lia; Santucci, Annalisa

2015-09-01

Alkaptonuria is an ultra-rare autosomal recessive disease developed from the lack of homogentisate 1,2-dioxygenase (HGD) activity, causing an accumulation in connective tissues of homogentisic acid (HGA) and its oxidized derivatives in polymerized form. The deposition of ochronotic pigment has been so far attributed to homogentisic acid produced by the liver, circulating in the blood, and accumulating locally. In the present paper, we report the expression of HGD in the brain. Mouse and human brain tissues were positively tested for HGD gene expression by western blotting. Furthermore, HGD expression was confirmed in human neuronal cells that also revealed the presence of six HGD molecular species. Moreover, once cultured in HGA excess, human neuronal cells produced ochronotic pigment and amyloid. Our findings indicate that alkaptonuric brain cells produce the ochronotic pigment in loco and this may contribute to induction of neurological complications.

Geogagnetic Activity and Effectiveness of Millimeter Electromagnetic Radiation in Unstable Angina Treatment

NASA Astrophysics Data System (ADS)

Parshina, S. S.; Samsonov, S. N.; Afanasiyeva, T. N.; Tokayeva, L. K.; Petrova, V. D.; Dolgova, E. M.; Manykina, V. I.; Vodolagina, E. S.

There had been performed a research of an effectiveness of millimeter electromagnetic radiation (MM EMR) use in patients with an unstable angina (UA) at periods of a lower (daily value of Kp-index 16,19±0,18) and a higher (daily value of Kp-index 17,25±0,21, p<0,05) gemagnetic activity (GA). It was found that involving of the MM EMR (the wave length 7.1 mm) into the treatment of the patients with an UA, enhances an antianginal effect of a drug therapy independently on the period of GA. The MM EMR at the period of a lower geomagnetic activity (LGA) enhances the decrease of diastolic blood pressure (BP), and at the period of a higher geomagnetic activity (HGA) - the decrease of systolic BP. At a HGA there were noted: a quick and more serious antianginal effect, maximal antihypertensive effect was achieved quicker, but (as opposed to the period of a LGA) there was no a pulse slowing effect of a MM EMR. Including the MM EMR into the treatment accelerates stabilization of the patients' condition only at a LGA. Positive effect on blood rheological properties is an independent effect of MM EMR, and it is in blood viscosity reduce in microcirculatory at both of the periods of GA. Normalization of blood viscosity under the MM EMR is only at the period of a LGA. So, the effect of MM EMR on a clinical condition of the patients is more evident at the period of a HGA, blood viscosity - at the period of a LGA.

Equine atypical myopathy caused by hypoglycin A intoxication associated with ingestion of sycamore maple tree seeds.

PubMed

Żuraw, A; Dietert, K; Kühnel, S; Sander, J; Klopfleisch, R

2016-07-01

Evidence suggest there is a link between equine atypical myopathy (EAM) and ingestion of sycamore maple tree seeds. To further evaluate the hypothesis that the ingestion of hypoglycin A (HGA) containing sycamore maple tree seeds causes acquired multiple acyl-CoA dehydrogenase deficiency and might be associated with the clinical and pathological signs of EAM. Case report. Necropsy and histopathology, using hematoxylin and eosin and Sudan III stains, were performed on a 2.5-year-old mare that died following the development of clinical signs of progressive muscle stiffness and recumbency. Prior to death, the animal ingested sycamore maple tree seeds (Acer pseudoplatanus). Detection of metabolites in blood and urine obtained post mortem was performed by rapid ultra-performance liquid chromatography-tandem mass spectrometry. Data from this case were compared with 3 geldings with no clinical history of myopathy. Macroscopic examination revealed fragments of maple tree seeds in the stomach and severe myopathy of several muscle groups including Mm. intercostales, deltoidei and trapezii. Histologically, the affected muscles showed severe, acute rhabdomyolysis with extensive accumulation of finely dispersed fat droplets in the cytoplasm of degenerated skeletal muscle cells not present in controls. Urine and serum concentrations of several acyl carnitines and acyl glycines were increased, and both contained metabolites of HGA, a toxic amino acid present in sycamore maple tree seeds. The study supports the hypothesis that ingestion of HGA-containing maple tree seeds may cause EAM due to acquired multiple acyl-CoA dehydrogenase deficiency. © 2015 EVJ Ltd.

Problem solving with genetic algorithms and Splicer

NASA Technical Reports Server (NTRS)

Bayer, Steven E.; Wang, Lui

1991-01-01

Genetic algorithms are highly parallel, adaptive search procedures (i.e., problem-solving methods) loosely based on the processes of population genetics and Darwinian survival of the fittest. Genetic algorithms have proven useful in domains where other optimization techniques perform poorly. The main purpose of the paper is to discuss a NASA-sponsored software development project to develop a general-purpose tool for using genetic algorithms. The tool, called Splicer, can be used to solve a wide variety of optimization problems and is currently available from NASA and COSMIC. This discussion is preceded by an introduction to basic genetic algorithm concepts and a discussion of genetic algorithm applications.

Genetic algorithms using SISAL parallel programming language

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

Tejada, S.

1994-05-06

Genetic algorithms are a mathematical optimization technique developed by John Holland at the University of Michigan [1]. The SISAL programming language possesses many of the characteristics desired to implement genetic algorithms. SISAL is a deterministic, functional programming language which is inherently parallel. Because SISAL is functional and based on mathematical concepts, genetic algorithms can be efficiently translated into the language. Several of the steps involved in genetic algorithms, such as mutation, crossover, and fitness evaluation, can be parallelized using SISAL. In this paper I will l discuss the implementation and performance of parallel genetic algorithms in SISAL.

Quantitation of isocitrate dehydrogenase (IDH)-induced D and L enantiomers of 2-hydroxyglutaric acid in biological fluids by a fully validated liquid tandem mass spectrometry method, suitable for clinical applications.

PubMed

Poinsignon, Vianney; Mercier, Lionel; Nakabayashi, Koïchi; David, Muriel D; Lalli, Alexandre; Penard-Lacronique, Virginie; Quivoron, Cyril; Saada, Véronique; De Botton, Stéphane; Broutin, Sophie; Paci, Angelo

2016-06-01

A recent update of the hallmarks of cancer includes metabolism with deregulating cellular energetics. Activating mutations in isocitrate dehydrogenase (IDH) metabolic enzymes leading to the abnormal accumulation of 2-hydroxyglutaric acid (2-HGA) have been described in hematologic malignancies and solid tumours. The diagnostic value of 2-HGA levels in blood to identify IDH mutations and its prognostic significance have been reported. We developed a liquid chromatography tandem mass spectrometry method allowing a rapid, accurate and precise simultaneous quantification of both L and D enantiomers of 2-HGA in blood samples from acute myeloid leukaemia (AML) patients, suitable for clinical applications. The method was also develop for preclinical applications from cellular and tissues samples. Deuterated (R,S)-2-hydroxyglutaric acid, disodium salt was used as internal standard and added to samples before a solid phase extraction on Phenomenex STRATA™-XL-A (200mg-3mL) 33μm cartridges. A derivatization step with (+)- o,o'-diacetyl-l-tartaric anhydride permitted to separate the two resulting diastereoisomers without chiral stationary phase, on a C18 column combined to a Xevo TQ-MS Waters mass spectrometer with an electrospray ionization (ESI) source. This method allows standard curves to be linear over the range 0.34-135.04μM with r(2) values>0.999 and low matrix effects (<11.7%). This method, which was validated according to current EMA guidelines, is accurate between-run (<3.1%) and within-run (<7.9%) and precise between-run (<5.3CV%) and within-run (<6.2CV%), and is suitable for clinical and preclinical applications. Copyright © 2016 Elsevier B.V. All rights reserved.

Quantification of toxins in soapberry (Sapindaceae) arils: Hypoglycin A and Methylenecyclopropylglycine

USDA-ARS?s Scientific Manuscript database

Methylenecyclopropylglycine (MCPG) and hypoglycin A (HGA) are cyclopropyl amino acids that are known to be in some soapberry fruits and can induce hypoglycemia, encephalopathy and sometimes death. Recent outbreaks linked to the ingestion of soapberry fruits include Jamaican Vomiting Sickness (JVS) a...

Engineering the cell wall by reducing de-methyl-esterified homogalacturonan improves saccharification of plant tissues for bioconversion.

PubMed

Lionetti, Vincenzo; Francocci, Fedra; Ferrari, Simone; Volpi, Chiara; Bellincampi, Daniela; Galletti, Roberta; D'Ovidio, Renato; De Lorenzo, Giulia; Cervone, Felice

2010-01-12

Plant cell walls represent an abundant, renewable source of biofuel and other useful products. The major bottleneck for the industrial scale-up of their conversion to simple sugars (saccharification), to be subsequently converted by microorganisms into ethanol or other products, is their recalcitrance to enzymatic saccharification. We investigated whether the structure of pectin that embeds the cellulose-hemicellulose network affects the exposure of cellulose to enzymes and consequently the process of saccharification. Reduction of de-methyl-esterified homogalacturonan (HGA) in Arabidopsis plants through the expression of a fungal polygalacturonase (PG) or an inhibitor of pectin methylesterase (PMEI) increased the efficiency of enzymatic saccharification. The improved enzymatic saccharification efficiency observed in transformed plants could also reduce the need for acid pretreatment. Similar results were obtained in PG-expressing tobacco plants and in PMEI-expressing wheat plants, indicating that reduction of de-methyl-esterified HGA may be used in crop species to facilitate the process of biomass saccharification.

Comparative proteomics in alkaptonuria provides insights into inflammation and oxidative stress.

PubMed

Braconi, Daniela; Bernardini, Giulia; Paffetti, Alessandro; Millucci, Lia; Geminiani, Michela; Laschi, Marcella; Frediani, Bruno; Marzocchi, Barbara; Santucci, Annalisa

2016-12-01

Alkaptonuria (AKU) is an ultra-rare inborn error of metabolism associated with a defective catabolism of phenylalanine and tyrosine leading to increased systemic levels of homogentisic acid (HGA). Excess HGA is partly excreted in the urine, partly accumulated within the body and deposited onto connective tissues under the form of an ochronotic pigment, leading to a range of clinical manifestations. No clear genotype/phenotype correlation was found in AKU, and today there is the urgent need to identify biomarkers able to monitor AKU progression and evaluate response to treatment. With this aim, we provided the first proteomic study on serum and plasma samples from alkaptonuric individuals showing pathological SAA, CRP and Advanced Oxidation Protein Products (AOPP) levels. Interesting similarities with proteomic studies on other rheumatic diseases were highlighted together with proteome alterations supporting the existence of oxidative stress and inflammation in AKU. Potential candidate biomarkers to assess disease severity, monitor disease progression and evaluate response to treatment were identified as well. Copyright © 2016 Elsevier Ltd. All rights reserved.

Optimal Design of Passive Power Filters Based on Pseudo-parallel Genetic Algorithm

NASA Astrophysics Data System (ADS)

Li, Pei; Li, Hongbo; Gao, Nannan; Niu, Lin; Guo, Liangfeng; Pei, Ying; Zhang, Yanyan; Xu, Minmin; Chen, Kerui

2017-05-01

The economic costs together with filter efficiency are taken as targets to optimize the parameter of passive filter. Furthermore, the method of combining pseudo-parallel genetic algorithm with adaptive genetic algorithm is adopted in this paper. In the early stages pseudo-parallel genetic algorithm is introduced to increase the population diversity, and adaptive genetic algorithm is used in the late stages to reduce the workload. At the same time, the migration rate of pseudo-parallel genetic algorithm is improved to change with population diversity adaptively. Simulation results show that the filter designed by the proposed method has better filtering effect with lower economic cost, and can be used in engineering.

G/SPLINES: A hybrid of Friedman's Multivariate Adaptive Regression Splines (MARS) algorithm with Holland's genetic algorithm

NASA Technical Reports Server (NTRS)

Rogers, David

1991-01-01

G/SPLINES are a hybrid of Friedman's Multivariable Adaptive Regression Splines (MARS) algorithm with Holland's Genetic Algorithm. In this hybrid, the incremental search is replaced by a genetic search. The G/SPLINE algorithm exhibits performance comparable to that of the MARS algorithm, requires fewer least squares computations, and allows significantly larger problems to be considered.

Comparison of genetic algorithms with conjugate gradient methods

NASA Technical Reports Server (NTRS)

Bosworth, J. L.; Foo, N. Y.; Zeigler, B. P.

1972-01-01

Genetic algorithms for mathematical function optimization are modeled on search strategies employed in natural adaptation. Comparisons of genetic algorithms with conjugate gradient methods, which were made on an IBM 1800 digital computer, show that genetic algorithms display superior performance over gradient methods for functions which are poorly behaved mathematically, for multimodal functions, and for functions obscured by additive random noise. Genetic methods offer performance comparable to gradient methods for many of the standard functions.

Software For Genetic Algorithms

NASA Technical Reports Server (NTRS)

Wang, Lui; Bayer, Steve E.

1992-01-01

SPLICER computer program is genetic-algorithm software tool used to solve search and optimization problems. Provides underlying framework and structure for building genetic-algorithm application program. Written in Think C.

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.

Ensemble of hybrid genetic algorithm for two-dimensional phase unwrapping

NASA Astrophysics Data System (ADS)

Balakrishnan, D.; Quan, C.; Tay, C. J.

2013-06-01

The phase unwrapping is the final and trickiest step in any phase retrieval technique. Phase unwrapping by artificial intelligence methods (optimization algorithms) such as hybrid genetic algorithm, reverse simulated annealing, particle swarm optimization, minimum cost matching showed better results than conventional phase unwrapping methods. In this paper, Ensemble of hybrid genetic algorithm with parallel populations is proposed to solve the branch-cut phase unwrapping problem. In a single populated hybrid genetic algorithm, the selection, cross-over and mutation operators are applied to obtain new population in every generation. The parameters and choice of operators will affect the performance of the hybrid genetic algorithm. The ensemble of hybrid genetic algorithm will facilitate to have different parameters set and different choice of operators simultaneously. Each population will use different set of parameters and the offspring of each population will compete against the offspring of all other populations, which use different set of parameters. The effectiveness of proposed algorithm is demonstrated by phase unwrapping examples and advantages of the proposed method are discussed.

Mobile robot dynamic path planning based on improved genetic algorithm

NASA Astrophysics Data System (ADS)

Wang, Yong; Zhou, Heng; Wang, Ying

2017-08-01

In dynamic unknown environment, the dynamic path planning of mobile robots is a difficult problem. In this paper, a dynamic path planning method based on genetic algorithm is proposed, and a reward value model is designed to estimate the probability of dynamic obstacles on the path, and the reward value function is applied to the genetic algorithm. Unique coding techniques reduce the computational complexity of the algorithm. The fitness function of the genetic algorithm fully considers three factors: the security of the path, the shortest distance of the path and the reward value of the path. The simulation results show that the proposed genetic algorithm is efficient in all kinds of complex dynamic environments.

An Efficient Rank Based Approach for Closest String and Closest Substring

PubMed Central

2012-01-01

This paper aims to present a new genetic approach that uses rank distance for solving two known NP-hard problems, and to compare rank distance with other distance measures for strings. The two NP-hard problems we are trying to solve are closest string and closest substring. For each problem we build a genetic algorithm and we describe the genetic operations involved. Both genetic algorithms use a fitness function based on rank distance. We compare our algorithms with other genetic algorithms that use different distance measures, such as Hamming distance or Levenshtein distance, on real DNA sequences. Our experiments show that the genetic algorithms based on rank distance have the best results. PMID:22675483

A hybrid genetic algorithm for resolving closely spaced objects

NASA Technical Reports Server (NTRS)

Abbott, R. J.; Lillo, W. E.; Schulenburg, N.

1995-01-01

A hybrid genetic algorithm is described for performing the difficult optimization task of resolving closely spaced objects appearing in space based and ground based surveillance data. This application of genetic algorithms is unusual in that it uses a powerful domain-specific operation as a genetic operator. Results of applying the algorithm to real data from telescopic observations of a star field are presented.

Genetic Algorithm Tuned Fuzzy Logic for Gliding Return Trajectories

NASA Technical Reports Server (NTRS)

Burchett, Bradley T.

2003-01-01

The problem of designing and flying a trajectory for successful recovery of a reusable launch vehicle is tackled using fuzzy logic control with genetic algorithm optimization. The plant is approximated by a simplified three degree of freedom non-linear model. A baseline trajectory design and guidance algorithm consisting of several Mamdani type fuzzy controllers is tuned using a simple genetic algorithm. Preliminary results show that the performance of the overall system is shown to improve with genetic algorithm tuning.

Learning Intelligent Genetic Algorithms Using Japanese Nonograms

ERIC Educational Resources Information Center

Tsai, Jinn-Tsong; Chou, Ping-Yi; Fang, Jia-Cen

2012-01-01

An intelligent genetic algorithm (IGA) is proposed to solve Japanese nonograms and is used as a method in a university course to learn evolutionary algorithms. The IGA combines the global exploration capabilities of a canonical genetic algorithm (CGA) with effective condensed encoding, improved fitness function, and modified crossover and…

Genetic algorithms with memory- and elitism-based immigrants in dynamic environments.

PubMed

Yang, Shengxiang

2008-01-01

In recent years the genetic algorithm community has shown a growing interest in studying dynamic optimization problems. Several approaches have been devised. The random immigrants and memory schemes are two major ones. The random immigrants scheme addresses dynamic environments by maintaining the population diversity while the memory scheme aims to adapt genetic algorithms quickly to new environments by reusing historical information. This paper investigates a hybrid memory and random immigrants scheme, called memory-based immigrants, and a hybrid elitism and random immigrants scheme, called elitism-based immigrants, for genetic algorithms in dynamic environments. In these schemes, the best individual from memory or the elite from the previous generation is retrieved as the base to create immigrants into the population by mutation. This way, not only can diversity be maintained but it is done more efficiently to adapt genetic algorithms to the current environment. Based on a series of systematically constructed dynamic problems, experiments are carried out to compare genetic algorithms with the memory-based and elitism-based immigrants schemes against genetic algorithms with traditional memory and random immigrants schemes and a hybrid memory and multi-population scheme. The sensitivity analysis regarding some key parameters is also carried out. Experimental results show that the memory-based and elitism-based immigrants schemes efficiently improve the performance of genetic algorithms in dynamic environments.

Boiler-turbine control system design using a genetic algorithm

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

Dimeo, R.; Lee, K.Y.

1995-12-01

This paper discusses the application of a genetic algorithm to control system design for a boiler-turbine plant. In particular the authors study the ability of the genetic algorithm to develop a proportional-integral (PI) controller and a state feedback controller for a non-linear multi-input/multi-output (MIMO) plant model. The plant model is presented along with a discussion of the inherent difficulties in such controller development. A sketch of the genetic algorithm (GA) is presented and its strategy as a method of control system design is discussed. Results are presented for two different control systems that have been designed with the genetic algorithm.

Method for hyperspectral imagery exploitation and pixel spectral unmixing

NASA Technical Reports Server (NTRS)

Lin, Ching-Fang (Inventor)

2003-01-01

An efficiently hybrid approach to exploit hyperspectral imagery and unmix spectral pixels. This hybrid approach uses a genetic algorithm to solve the abundance vector for the first pixel of a hyperspectral image cube. This abundance vector is used as initial state in a robust filter to derive the abundance estimate for the next pixel. By using Kalman filter, the abundance estimate for a pixel can be obtained in one iteration procedure which is much fast than genetic algorithm. The output of the robust filter is fed to genetic algorithm again to derive accurate abundance estimate for the current pixel. The using of robust filter solution as starting point of the genetic algorithm speeds up the evolution of the genetic algorithm. After obtaining the accurate abundance estimate, the procedure goes to next pixel, and uses the output of genetic algorithm as the previous state estimate to derive abundance estimate for this pixel using robust filter. And again use the genetic algorithm to derive accurate abundance estimate efficiently based on the robust filter solution. This iteration continues until pixels in a hyperspectral image cube end.

Genetics-based control of a mimo boiler-turbine plant

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

Dimeo, R.M.; Lee, K.Y.

1994-12-31

A genetic algorithm is used to develop an optimal controller for a non-linear, multi-input/multi-output boiler-turbine plant. The algorithm is used to train a control system for the plant over a wide operating range in an effort to obtain better performance. The results of the genetic algorithm`s controller designed from the linearized plant model at a nominal operating point. Because the genetic algorithm is well-suited to solving traditionally difficult optimization problems it is found that the algorithm is capable of developing the controller based on input/output information only. This controller achieves a performance comparable to the standard linear quadratic regulator.

Improved classification accuracy by feature extraction using genetic algorithms

NASA Astrophysics Data System (ADS)

Patriarche, Julia; Manduca, Armando; Erickson, Bradley J.

2003-05-01

A feature extraction algorithm has been developed for the purposes of improving classification accuracy. The algorithm uses a genetic algorithm / hill-climber hybrid to generate a set of linearly recombined features, which may be of reduced dimensionality compared with the original set. The genetic algorithm performs the global exploration, and a hill climber explores local neighborhoods. Hybridizing the genetic algorithm with a hill climber improves both the rate of convergence, and the final overall cost function value; it also reduces the sensitivity of the genetic algorithm to parameter selection. The genetic algorithm includes the operators: crossover, mutation, and deletion / reactivation - the last of these effects dimensionality reduction. The feature extractor is supervised, and is capable of deriving a separate feature space for each tissue (which are reintegrated during classification). A non-anatomical digital phantom was developed as a gold standard for testing purposes. In tests with the phantom, and with images of multiple sclerosis patients, classification with feature extractor derived features yielded lower error rates than using standard pulse sequences, and with features derived using principal components analysis. Using the multiple sclerosis patient data, the algorithm resulted in a mean 31% reduction in classification error of pure tissues.

Cell wall matrix polysaccharide distribution and cortical microtubule organization: two factors controlling mesophyll cell morphogenesis in land plants

PubMed Central

Sotiriou, P.; Giannoutsou, E.; Panteris, E.; Apostolakos, P.; Galatis, B.

2016-01-01

Background and aims This work investigates the involvement of local differentiation of cell wall matrix polysaccharides and the role of microtubules in the morphogenesis of mesophyll cells (MCs) of three types (lobed, branched and palisade) in the dicotyledon Vigna sinensis and the fern Asplenium nidus. Methods Homogalacturonan (HGA) epitopes recognized by the 2F4, JIM5 and JIM7 antibodies and callose were immunolocalized in hand-made leaf sections. Callose was also stained with aniline blue. We studied microtubule organization by tubulin immunofluorescence and transmission electron microscopy. Results In both plants, the matrix cell wall polysaccharide distribution underwent definite changes during MC differentiation. Callose constantly defined the sites of MC contacts. The 2F4 HGA epitope in V. sinensis first appeared in MC contacts but gradually moved towards the cell wall regions facing the intercellular spaces, while in A. nidus it was initially localized at the cell walls delimiting the intercellular spaces, but finally shifted to MC contacts. In V. sinensis, the JIM5 and JIM7 HGA epitopes initially marked the cell walls delimiting the intercellular spaces and gradually shifted in MC contacts, while in A. nidus they constantly enriched MC contacts. In all MC types examined, the cortical microtubules played a crucial role in their morphogenesis. In particular, in palisade MCs, cortical microtubule helices, by controlling cellulose microfibril orientation, forced these MCs to acquire a truncated cone-like shape. Unexpectedly in V. sinensis, the differentiation of colchicine-affected MCs deviated completely, since they developed a cell wall ingrowth labyrinth, becoming transfer-like cells. Conclusions The results of this work and previous studies on Zea mays (Giannoutsou et al., Annals of Botany 2013; 112: 1067–1081) revealed highly controlled local cell wall matrix differentiation in MCs of species belonging to different plant groups. This, in coordination with microtubule-dependent cellulose microfibril alignment, spatially controlled cell wall expansion, allowing MCs to acquire their particular shape. PMID:26802013

Diagnosis of glutaric aciduria type 1 by measuring 3-hydroxyglutaric acid in dried urine spots by liquid chromatography tandem mass spectrometry.

PubMed

Al-Dirbashi, Osama Y; Kölker, Stefan; Ng, Dione; Fisher, Lawrence; Rupar, Tony; Lepage, Nathalie; Rashed, Mohamed S; Santa, Tomofumi; Goodman, Stephen I; Geraghty, Michael T; Zschocke, Johannes; Christensen, Ernst; Hoffmann, Georg F; Chakraborty, Pranesh

2011-02-01

Accumulation of glutaric acid (GA) and 3-hydroxyglutaric acid (3HGA) in body fluids is the biochemical hallmark of type 1 glutaric aciduria (GA1), a disorder characterized by acute striatal degeneration and a subsequent dystonia. To date, methods for quantification of 3HGA are mainly based on stable isotope dilution gas chromatography mass spectrometry (GC-MS) and require extensive sample preparation. Here we describe a simple liquid chromatography tandem MS (LC-MS/MS) method to quantify this important metabolite in dried urine spots (DUS). This method is based on derivatization with 4-[2-(N,N-dimethylamino)ethylaminosulfonyl]-7-(2-aminoethylamino)-2,1,3-benzoxadiazole (DAABD-AE). Derivatization was adopted to improve the chromatographic and mass spectrometric properties of the studied analytes. Derivatization was performed directly on a 3.2-mm disc of DUS as a sample without extraction. Sample mixture was heated at 60°C for 45 min, and 5 μl of the reaction solution was analyzed by LC-MS/MS. Reference ranges obtained were in excellent agreement with the literature. The method was applied retrospectively for the analysis of DUS samples from established low- and high-excreter GA1 patients as well as controls (n = 100). Comparison of results obtained versus those obtained by GC-MS was satisfactory (n = 14). In populations with a high risk of GA1, this approach will be useful as a primary screening method for high- or low-excreter variants. In these populations, however, DUS analysis should not be implemented before completing a parallel comparative study with the standard screening method (i.e., molecular testing). In addition, follow-up DUS GA and 3HGA testing of babies with elevated dried blood spot C5DC acylcarnitines will be useful as a first-tier diagnostic test, thus reducing the number of cases requiring enzymatic and molecular analyses to establish or refute the diagnosis of GA1.

IDH1 R132H Mutation Is Accompanied with Malignant Progression of Paired Primary-Recurrent Astrocytic Tumours.

PubMed

Mu, Luyan; Xu, Wanzhen; Li, Qingla; Ge, Haitao; Bao, Hongbo; Xia, Songsong; Ji, Jingjing; Jiang, Jie; Song, Yuwen; Gao, Qiang

2017-01-01

IDH1 R132H mutation is an important marker of survival in patients with gliomas. Although there are many changes of genes in tumour malignant progression, IDH1 R132H mutation status in glioma progression remained unclear. Here, an in-depth characterization of IDH1 R132H mutations were assessed by immunohistochemistry in 55 paired primary-recurrent astrocytomas tissues, including 5 paired primary pilocytic astrocytoma (pPA, WHO grade I), 35 paired primary low grade astrocytoma (pLGA, WHO grade II and III) and 15 paired primary high grade astrocytoma (pHGA/ Glioblastoma, WHO grade IV). Meanwhile, the DNA was isolated from paired samples, and PCR amplification was used for IDH1 exon4 sequencing. Nonparametric test, KM and Cox models were used to examine the statistical difference and survival function. We found that the percent of IDH1 R132H mutation was 68.6% (24/35) in pLGA group, but no IDH1 mutation was found in pPA and pHGA groups. Meanwhile, the results from immunohistochemistry and DNA sequencing showed that, compared with primary astrocytoma, there was no change of IDH1 status in recurrent astrocytoma whatever tumour pathological grade raise or indolent. The pPA group has the longest recurrence-free period (RFP) and overall survival (OS) in three groups ( p<0.01 ), while the pHGA group has the shortest ones ( p<0.01 ). In pLGA group, the IDH1 R132H mutation subgroup has longer RFP than IDH1 wild type subgroup ( p<0.01 ), but the OS has no statistical difference between two subgroups ( p>0.6 ). Additionally, IDH1 R132H mutation independently predicted a long RFP in patients with pLGA (HR 1.073, 95% CI 0.151-0.775, p<0.01 ).

IDH1 R132H Mutation Is Accompanied with Malignant Progression of Paired Primary-Recurrent Astrocytic Tumours

PubMed Central

Mu, Luyan; Xu, Wanzhen; Li, Qingla; Ge, Haitao; Bao, Hongbo; Xia, Songsong; Ji, Jingjing; Jiang, Jie; Song, Yuwen; Gao, Qiang

2017-01-01

IDH1 R132H mutation is an important marker of survival in patients with gliomas. Although there are many changes of genes in tumour malignant progression, IDH1 R132H mutation status in glioma progression remained unclear. Here, an in-depth characterization of IDH1 R132H mutations were assessed by immunohistochemistry in 55 paired primary-recurrent astrocytomas tissues, including 5 paired primary pilocytic astrocytoma (pPA, WHO grade I), 35 paired primary low grade astrocytoma (pLGA, WHO grade II and III) and 15 paired primary high grade astrocytoma (pHGA/ Glioblastoma, WHO grade IV). Meanwhile, the DNA was isolated from paired samples, and PCR amplification was used for IDH1 exon4 sequencing. Nonparametric test, KM and Cox models were used to examine the statistical difference and survival function. We found that the percent of IDH1 R132H mutation was 68.6% (24/35) in pLGA group, but no IDH1 mutation was found in pPA and pHGA groups. Meanwhile, the results from immunohistochemistry and DNA sequencing showed that, compared with primary astrocytoma, there was no change of IDH1 status in recurrent astrocytoma whatever tumour pathological grade raise or indolent. The pPA group has the longest recurrence-free period (RFP) and overall survival (OS) in three groups (p<0.01), while the pHGA group has the shortest ones (p<0.01). In pLGA group, the IDH1 R132H mutation subgroup has longer RFP than IDH1 wild type subgroup (p<0.01), but the OS has no statistical difference between two subgroups (p>0.6). Additionally, IDH1 R132H mutation independently predicted a long RFP in patients with pLGA (HR 1.073, 95% CI 0.151-0.775, p<0.01). PMID:28928859

Study of parameter identification using hybrid neural-genetic algorithm in electro-hydraulic servo system

NASA Astrophysics Data System (ADS)

Moon, Byung-Young

2005-12-01

The hybrid neural-genetic multi-model parameter estimation algorithm was demonstrated. This method can be applied to structured system identification of electro-hydraulic servo system. This algorithms consist of a recurrent incremental credit assignment(ICRA) neural network and a genetic algorithm. The ICRA neural network evaluates each member of a generation of model and genetic algorithm produces new generation of model. To evaluate the proposed method, electro-hydraulic servo system was designed and manufactured. The experiment was carried out to figure out the hybrid neural-genetic multi-model parameter estimation algorithm. As a result, the dynamic characteristics were obtained such as the parameters(mass, damping coefficient, bulk modulus, spring coefficient), which minimize total square error. The result of this study can be applied to hydraulic systems in industrial fields.

Advanced glycation end products alter steroidogenic gene expression by granulosa cells: an effect partially reversible by vitamin D.

PubMed

Merhi, Z; Buyuk, E; Cipolla, M J

2018-06-01

Does vitamin D attenuate the adverse effects of advanced glycation end products (AGEs) on steroidogenesis by human granulosa cells (GCs)? AGEs alter the expression of genes important in steroidogenesis while 1,25-dihydroxyvitamin D3 (vit D3) in vitro attenuates some of the actions of AGEs on steroidogenic gene expression, possibly by downregulating the expression of the pro-inflammatory cell membrane receptor for AGEs (RAGE). Vitamin D attenuates the pro-inflammatory effects of AGEs in non-ovarian tissues. Women who were undergoing IVF were enrolled. Follicular fluid samples (n = 71) were collected and cumulus GCs (n = 12) were treated in culture. Follicular fluid levels of the anti-inflammatory soluble RAGE (sRAGE), AGEs and 25-hydroxyvitamin D (25-OHD) were quantified for possible correlations. GCs of each participant were split equally and treated with either media alone (control) or with human glycated albumin (HGA as a precursor for AGEs) with or without vit D3 after which RT-PCR and immunofluorescence were performed and cell culture media estradiol (E2) levels were compared. In follicular fluid, sRAGE levels were positively correlated with 25-OHD levels. HGA treatment (i) increased CYP11A1 (by 48%), 3β-HSD (by 38%), StAR (by 42%), CYP17A1 (by 30%) and LHR (by 37%) mRNA expression levels (P < 0.05 for all) but did not alter CYP19A1 or FSHR mRNA expression levels; and (ii) increased E2 release in cell culture media (P = 0.02). Vit D3 treatment (i) downregulated RAGE mRNA expression by 33% and RAGE protein levels by 44% (P < 0.05); (ii) inhibited the HGA-induced increase in CYP11A1, StAR, CYP17A1 and LHR mRNA levels, but not the increase in 3β-HSD mRNA levels; and (iii) did not inhibit the HGA-induced E2 release in cell culture media. This study used luteinized GCs that were collected from women who received gonadotropins thus the results obtained may not fully extrapolate to non-luteinized GCs in vivo. This study suggests that there is a relationship between AGEs and their receptors (RAGE and sRAGE) with vitamin D. Understanding the interaction between AGEs and vitamin D in ovarian physiology could lead to a more targeted therapy for the treatment of ovarian dysfunction. Funding was received from NIH (R01 NS045940), American Society for Reproductive Medicine, Ferring Pharmaceuticals Inc., and University of Vermont College of Medicine Bridge Funds. All authors have nothing to disclose.

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.

Training product unit neural networks with genetic algorithms

NASA Technical Reports Server (NTRS)

Janson, D. J.; Frenzel, J. F.; Thelen, D. C.

1991-01-01

The training of product neural networks using genetic algorithms is discussed. Two unusual neural network techniques are combined; product units are employed instead of the traditional summing units and genetic algorithms train the network rather than backpropagation. As an example, a neural netork is trained to calculate the optimum width of transistors in a CMOS switch. It is shown how local minima affect the performance of a genetic algorithm, and one method of overcoming this is presented.

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.

Nonlinear inversion of potential-field data using a hybrid-encoding genetic algorithm

USGS Publications Warehouse

Chen, C.; Xia, J.; Liu, J.; Feng, G.

2006-01-01

Using a genetic algorithm to solve an inverse problem of complex nonlinear geophysical equations is advantageous because it does not require computer gradients of models or "good" initial models. The multi-point search of a genetic algorithm makes it easier to find the globally optimal solution while avoiding falling into a local extremum. As is the case in other optimization approaches, the search efficiency for a genetic algorithm is vital in finding desired solutions successfully in a multi-dimensional model space. A binary-encoding genetic algorithm is hardly ever used to resolve an optimization problem such as a simple geophysical inversion with only three unknowns. The encoding mechanism, genetic operators, and population size of the genetic algorithm greatly affect search processes in the evolution. It is clear that improved operators and proper population size promote the convergence. Nevertheless, not all genetic operations perform perfectly while searching under either a uniform binary or a decimal encoding system. With the binary encoding mechanism, the crossover scheme may produce more new individuals than with the decimal encoding. On the other hand, the mutation scheme in a decimal encoding system will create new genes larger in scope than those in the binary encoding. This paper discusses approaches of exploiting the search potential of genetic operations in the two encoding systems and presents an approach with a hybrid-encoding mechanism, multi-point crossover, and dynamic population size for geophysical inversion. We present a method that is based on the routine in which the mutation operation is conducted in the decimal code and multi-point crossover operation in the binary code. The mix-encoding algorithm is called the hybrid-encoding genetic algorithm (HEGA). HEGA provides better genes with a higher probability by a mutation operator and improves genetic algorithms in resolving complicated geophysical inverse problems. Another significant result is that final solution is determined by the average model derived from multiple trials instead of one computation due to the randomness in a genetic algorithm procedure. These advantages were demonstrated by synthetic and real-world examples of inversion of potential-field data. ?? 2005 Elsevier Ltd. All rights reserved.

Aerodynamic Optimization of a Supersonic Bending Body Projectile by a Vector-Evaluated Genetic Algorithm

DTIC Science & Technology

2016-12-01

Evaluated Genetic Algorithm prepared by Justin L Paul Academy of Applied Science 24 Warren Street Concord, NH 03301 under contract W911SR...Supersonic Bending Body Projectile by a Vector-Evaluated Genetic Algorithm prepared by Justin L Paul Academy of Applied Science 24 Warren Street... Genetic Algorithm 5a. CONTRACT NUMBER W199SR-15-2-001 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Justin L Paul 5d. PROJECT

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.

Genetic Algorithm for Initial Orbit Determination with Too Short Arc (Continued)

NASA Astrophysics Data System (ADS)

Li, X. R.; Wang, X.

2016-03-01

When using the genetic algorithm to solve the problem of too-short-arc (TSA) determination, due to the difference of computing processes between the genetic algorithm and classical method, the methods for outliers editing are no longer applicable. In the genetic algorithm, the robust estimation is acquired by means of using different loss functions in the fitness function, then the outlier problem of TSAs is solved. Compared with the classical method, the application of loss functions in the genetic algorithm is greatly simplified. Through the comparison of results of different loss functions, it is clear that the methods of least median square and least trimmed square can greatly improve the robustness of TSAs, and have a high breakdown point.

Bio-Inspired Genetic Algorithms with Formalized Crossover Operators for Robotic Applications.

PubMed

Zhang, Jie; Kang, Man; Li, Xiaojuan; Liu, Geng-Yang

2017-01-01

Genetic algorithms are widely adopted to solve optimization problems in robotic applications. In such safety-critical systems, it is vitally important to formally prove the correctness when genetic algorithms are applied. This paper focuses on formal modeling of crossover operations that are one of most important operations in genetic algorithms. Specially, we for the first time formalize crossover operations with higher-order logic based on HOL4 that is easy to be deployed with its user-friendly programing environment. With correctness-guaranteed formalized crossover operations, we can safely apply them in robotic applications. We implement our technique to solve a path planning problem using a genetic algorithm with our formalized crossover operations, and the results show the effectiveness of our technique.

Estimating the solute transport parameters of the spatial fractional advection-dispersion equation using Bees Algorithm

NASA Astrophysics Data System (ADS)

Mehdinejadiani, Behrouz

2017-08-01

This study represents the first attempt to estimate the solute transport parameters of the spatial fractional advection-dispersion equation using Bees Algorithm. The numerical studies as well as the experimental studies were performed to certify the integrity of Bees Algorithm. The experimental ones were conducted in a sandbox for homogeneous and heterogeneous soils. A detailed comparative study was carried out between the results obtained from Bees Algorithm and those from Genetic Algorithm and LSQNONLIN routines in FracFit toolbox. The results indicated that, in general, the Bees Algorithm much more accurately appraised the sFADE parameters in comparison with Genetic Algorithm and LSQNONLIN, especially in the heterogeneous soil and for α values near to 1 in the numerical study. Also, the results obtained from Bees Algorithm were more reliable than those from Genetic Algorithm. The Bees Algorithm showed the relative similar performances for all cases, while the Genetic Algorithm and the LSQNONLIN yielded different performances for various cases. The performance of LSQNONLIN strongly depends on the initial guess values so that, compared to the Genetic Algorithm, it can more accurately estimate the sFADE parameters by taking into consideration the suitable initial guess values. To sum up, the Bees Algorithm was found to be very simple, robust and accurate approach to estimate the transport parameters of the spatial fractional advection-dispersion equation.

Estimating the solute transport parameters of the spatial fractional advection-dispersion equation using Bees Algorithm.

PubMed

Mehdinejadiani, Behrouz

2017-08-01

This study represents the first attempt to estimate the solute transport parameters of the spatial fractional advection-dispersion equation using Bees Algorithm. The numerical studies as well as the experimental studies were performed to certify the integrity of Bees Algorithm. The experimental ones were conducted in a sandbox for homogeneous and heterogeneous soils. A detailed comparative study was carried out between the results obtained from Bees Algorithm and those from Genetic Algorithm and LSQNONLIN routines in FracFit toolbox. The results indicated that, in general, the Bees Algorithm much more accurately appraised the sFADE parameters in comparison with Genetic Algorithm and LSQNONLIN, especially in the heterogeneous soil and for α values near to 1 in the numerical study. Also, the results obtained from Bees Algorithm were more reliable than those from Genetic Algorithm. The Bees Algorithm showed the relative similar performances for all cases, while the Genetic Algorithm and the LSQNONLIN yielded different performances for various cases. The performance of LSQNONLIN strongly depends on the initial guess values so that, compared to the Genetic Algorithm, it can more accurately estimate the sFADE parameters by taking into consideration the suitable initial guess values. To sum up, the Bees Algorithm was found to be very simple, robust and accurate approach to estimate the transport parameters of the spatial fractional advection-dispersion equation. Copyright © 2017 Elsevier B.V. All rights reserved.

A Test of Genetic Algorithms in Relevance Feedback.

ERIC Educational Resources Information Center

Lopez-Pujalte, Cristina; Guerrero Bote, Vicente P.; Moya Anegon, Felix de

2002-01-01

Discussion of information retrieval, query optimization techniques, and relevance feedback focuses on genetic algorithms, which are derived from artificial intelligence techniques. Describes an evaluation of different genetic algorithms using a residual collection method and compares results with the Ide dec-hi method (Salton and Buckley, 1990…

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.

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.

An improved genetic algorithm and its application in the TSP problem

NASA Astrophysics Data System (ADS)

Li, Zheng; Qin, Jinlei

2011-12-01

Concept and research actuality of genetic algorithm are introduced in detail in the paper. Under this condition, the simple genetic algorithm and an improved algorithm are described and applied in an example of TSP problem, where the advantage of genetic algorithm is adequately shown in solving the NP-hard problem. In addition, based on partial matching crossover operator, the crossover operator method is improved into extended crossover operator in order to advance the efficiency when solving the TSP. In the extended crossover method, crossover operator can be performed between random positions of two random individuals, which will not be restricted by the position of chromosome. Finally, the nine-city TSP is solved using the improved genetic algorithm with extended crossover method, the efficiency of whose solution process is much higher, besides, the solving speed of the optimal solution is much faster.

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.

Genetic algorithm based fuzzy control of spacecraft autonomous rendezvous

NASA Technical Reports Server (NTRS)

Karr, C. L.; Freeman, L. M.; Meredith, D. L.

1990-01-01

The U.S. Bureau of Mines is currently investigating ways to combine the control capabilities of fuzzy logic with the learning capabilities of genetic algorithms. Fuzzy logic allows for the uncertainty inherent in most control problems to be incorporated into conventional expert systems. Although fuzzy logic based expert systems have been used successfully for controlling a number of physical systems, the selection of acceptable fuzzy membership functions has generally been a subjective decision. High performance fuzzy membership functions for a fuzzy logic controller that manipulates a mathematical model simulating the autonomous rendezvous of spacecraft are learned using a genetic algorithm, a search technique based on the mechanics of natural genetics. The membership functions learned by the genetic algorithm provide for a more efficient fuzzy logic controller than membership functions selected by the authors for the rendezvous problem. Thus, genetic algorithms are potentially an effective and structured approach for learning fuzzy membership functions.

A "Hands on" Strategy for Teaching Genetic Algorithms to Undergraduates

ERIC Educational Resources Information Center

Venables, Anne; Tan, Grace

2007-01-01

Genetic algorithms (GAs) are a problem solving strategy that uses stochastic search. Since their introduction (Holland, 1975), GAs have proven to be particularly useful for solving problems that are "intractable" using classical methods. The language of genetic algorithms (GAs) is heavily laced with biological metaphors from evolutionary…

The potential of genetic algorithms for conceptual design of rotor systems

NASA Technical Reports Server (NTRS)

Crossley, William A.; Wells, Valana L.; Laananen, David H.

1993-01-01

The capabilities of genetic algorithms as a non-calculus based, global search method make them potentially useful in the conceptual design of rotor systems. Coupling reasonably simple analysis tools to the genetic algorithm was accomplished, and the resulting program was used to generate designs for rotor systems to match requirements similar to those of both an existing helicopter and a proposed helicopter design. This provides a comparison with the existing design and also provides insight into the potential of genetic algorithms in design of new rotors.

Genetic Algorithm for Initial Orbit Determination with Too Short Arc (Continued)

NASA Astrophysics Data System (ADS)

Li, Xin-ran; Wang, Xin

2017-04-01

When the genetic algorithm is used to solve the problem of too short-arc (TSA) orbit determination, due to the difference of computing process between the genetic algorithm and the classical method, the original method for outlier deletion is no longer applicable. In the genetic algorithm, the robust estimation is realized by introducing different loss functions for the fitness function, then the outlier problem of the TSA orbit determination is solved. Compared with the classical method, the genetic algorithm is greatly simplified by introducing in different loss functions. Through the comparison on the calculations of multiple loss functions, it is found that the least median square (LMS) estimation and least trimmed square (LTS) estimation can greatly improve the robustness of the TSA orbit determination, and have a high breakdown point.

A Genetic Algorithm Tool (splicer) for Complex Scheduling Problems and the Space Station Freedom Resupply Problem

NASA Technical Reports Server (NTRS)

Wang, Lui; Valenzuela-Rendon, Manuel

1993-01-01

The Space Station Freedom will require the supply of items in a regular fashion. A schedule for the delivery of these items is not easy to design due to the large span of time involved and the possibility of cancellations and changes in shuttle flights. This paper presents the basic concepts of a genetic algorithm model, and also presents the results of an effort to apply genetic algorithms to the design of propellant resupply schedules. As part of this effort, a simple simulator and an encoding by which a genetic algorithm can find near optimal schedules have been developed. Additionally, this paper proposes ways in which robust schedules, i.e., schedules that can tolerate small changes, can be found using genetic algorithms.

An Improved Heuristic Method for Subgraph Isomorphism Problem

NASA Astrophysics Data System (ADS)

Xiang, Yingzhuo; Han, Jiesi; Xu, Haijiang; Guo, Xin

2017-09-01

This paper focus on the subgraph isomorphism (SI) problem. We present an improved genetic algorithm, a heuristic method to search the optimal solution. The contribution of this paper is that we design a dedicated crossover algorithm and a new fitness function to measure the evolution process. Experiments show our improved genetic algorithm performs better than other heuristic methods. For a large graph, such as a subgraph of 40 nodes, our algorithm outperforms the traditional tree search algorithms. We find that the performance of our improved genetic algorithm does not decrease as the number of nodes in prototype graphs.

Subminiature eddy current transducers for studying boride coatings

NASA Astrophysics Data System (ADS)

Dmitriev, S. F.; Ishkov, A. V.; Malikov, V. N.; Sagalakov, A. M.

2016-07-01

Strengthening of parts and units of machines, increased reliability and longer service life is an important task of modern mechanical engineering. The main objects of study in the work were selected steel 65G and 50HGA, wear-resistant boride coatings ternary system Fe-B-Fe n B which were investigated by scanning electron microscopy and eddy-current nondestructive methods.

A quantitative assessment of alkaptonuria: testing the reliability of two disease severity scoring systems.

PubMed

Cox, Trevor F; Ranganath, Lakshminarayan

2011-12-01

Alkaptonuria (AKU) is due to excessive homogentisic acid accumulation in body fluids due to lack of enzyme homogentisate dioxygenase leading in turn to varied clinical manifestations mainly by a process of conversion of HGA to a polymeric melanin-like pigment known as ochronosis. A potential treatment, a drug called nitisinone, to decrease formation of HGA is available. However, successful demonstration of its efficacy in modifying the natural history of AKU requires an effective quantitative assessment tool. We have described two potential tools that could be used to quantitate disease burden in AKU. One tool describes scoring the clinical features that includes clinical assessments, investigations and questionnaires in 15 patients with AKU. The second tool describes a scoring system that only includes items obtained from questionnaires used in 44 people with AKU. Statistical analyses were carried out on the two patient datasets to assess the AKU tools; these included the calculation of Chronbach's alpha, multidimensional scaling and simple linear regression analysis. The conclusion was that there was good evidence that the tools could be adopted as AKU assessment tools, but perhaps with further refinement before being used in the practical setting of a clinical trial.

Acute fatal metabolic complications in alkaptonuria.

PubMed

Davison, A S; Milan, A M; Gallagher, J A; Ranganath, L R

2016-03-01

Alkaptonuria (AKU) is a rare inherited metabolic disorder of tyrosine metabolism that results from a defect in an enzyme called homogentisate 1,2-dioxygenase. The result of this is that homogentisic acid (HGA) accumulates in the body. HGA is central to the pathophysiology of this disease and the consequences observed; these include spondyloarthropathy, rupture of ligaments/muscle/tendons, valvular heart disease including aortic stenosis and renal stones. While AKU is considered to be a chronic progressive disorder, it is clear from published case reports that fatal acute metabolic complications can also occur. These include oxidative haemolysis and methaemoglobinaemia. The exact mechanisms underlying the latter are not clear, but it is proposed that disordered metabolism within the red blood cell is responsible for favouring a pro-oxidant environment that leads to the life threatening complications observed. Herein the role of red blood cell in maintaining the redox state of the body is reviewed in the context of AKU. In addition previously reported therapeutic strategies are discussed, specifically with respect to why reported treatments had little therapeutic effect. The potential use of nitisinone for the management of patients suffering from the acute metabolic decompensation in AKU is proposed as an alternative strategy.

Impact of chronic kidney disease on the natural history of alkaptonuria

PubMed Central

Faria, Bernardo; Vidinha, Joana; Pêgo, Cátia; Correia, Hugo; Sousa, Tânia

2012-01-01

In alkaptonuria, deficiency of homogentisate 1,2-dioxygenase leads to the accumulation of homogentisic acid (HGA) and its metabolites in the body, resulting in ochronosis. Reports of patients with alkaptonuria who have decreased kidney function are rare, but this seems to play an important role in the natural history of the disease. We describe a 68-year-old female with chronic kidney disease (CKD) of unknown etiology who started peritoneal dialysis (PD) after 5 years of follow-up and who was diagnosed with alkaptonuria at this time. Progressive exacerbation of ochronotic manifestations had been noted during these last few years, as kidney function worsened. After PD initiation, the disease continued to progress, and death occurred after one year and a half, due to severe aortic stenosis-related complications. Her 70-year-old sister was evaluated and also diagnosed with alkaptonuria. She had no renal dysfunction. Higher HGA excretion and significantly milder ochronosis than that of her sister were found. We present two alkaptonuric sisters with similar comorbidities except for the presence of CKD, who turned out to have totally different evolutions of their disease. This report confirms that kidney dysfunction may be an important factor in determining the natural history of alkaptonuria. PMID:25874097

Multi-elemental composition and antioxidant properties of strawberry tree (Arbutus unedo L.) honey from the coastal region of Croatia: Risk-benefit analysis.

PubMed

Tariba Lovaković, Blanka; Lazarus, Maja; Brčić Karačonji, Irena; Jurica, Karlo; Živković Semren, Tanja; Lušić, Dražen; Brajenović, Nataša; Pelaić, Zdenka; Pizent, Alica

2018-01-01

The concentration of 23 major and trace elements, total phenolic content (TPC) and 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging activity were determined in nine samples of strawberry tree honey and compared to other types of unifloral honeys. The most abundant elements in strawberry tree honey were potassium, calcium, magnesium and sodium, ranging between 1276 and 2367, 95.2-154, 14.4-74.4 and 13.4-64.3mg/kg, respectively. Strawberry tree honey had generally higher TPC (range: 0.314-0.522g GA/kg) and DPPH (1.94-4.45mM TE/kg) compared to other analysed unifloral honeys. A strong positive relationship was found between TPC and DPPH, TPC and concentration of homogentisic acid (HGA), chemical marker of strawberry tree honey, and between DPPH and HGA. Regarding daily intake of essential elements, strawberry tree honey can be considered nutritionally richer than the majority of unifloral honeys available in Croatia, while contribution to tolerable intake set for potentially toxic elements was very low, corresponding to pristine areas. Copyright © 2017 Elsevier GmbH. All rights reserved.

Does Your Patient's Urine Turns Dark? Alkaptonuria and Low Back Ache: A Literature Review.

PubMed

Kanniyan, Kalaivanan; Pathak, Aditya C; Dhammi, Ish Kumar; Jain, Anil Kumar

2014-01-01

Alkaptonuria is a very rare inborn error of amino acid metabolism due to deficient homogentisic acid (HGA) oxidase enzyme leading to accumulation of HGA in plasma, cartilage, other tissues of human body and its excretion in urine. It has both systemic and peripheral signs and symptoms. Though low back is a common symptom of alkaptonuria but, in the absence of ochronosis it is rare. Alkaptonuria itself is very rare occurrence with no specific treatment option available to reverse the effect as yet. A 38-year-old male, embroidery worker presented with chronic low back ache with history of staining of clothes in infancy. Later on laboratory and the radiological investigation patient was diagnosed to have alkaptonuria without ochronosis. No other systemic manifestation was present. Patient was treated conservatively and responded well. Though alkaptonuria is a very rare disease, and the occurrence of low back-ache in absence of ochronosis is much rarer. One must be aware of this inborn error of metabolism. Early diagnosis though being "diagnosis of exclusion" for low back-ache, high index of suspicion is advantageous as symptomatic treatment of the alkaptonuria can be initiated and evaluation of other systemic organs can be done in early stages itself.

Genetic algorithms for adaptive real-time control in space systems

NASA Technical Reports Server (NTRS)

Vanderzijp, J.; Choudry, A.

1988-01-01

Genetic Algorithms that are used for learning as one way to control the combinational explosion associated with the generation of new rules are discussed. The Genetic Algorithm approach tends to work best when it can be applied to a domain independent knowledge representation. Applications to real time control in space systems are discussed.

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

A General Closed-Form Solution for the Lunar Reconnaissance Orbiter (LRO) Antenna Pointing System

NASA Technical Reports Server (NTRS)

Shah, Neerav; Chen, J. Roger; Hashmall, Joseph A.

2010-01-01

The National Aeronautics and Space Administration s (NASA) Lunar Reconnaissance Orbiter (LRO) launched on June 18, 2009 from the Cape Canaveral Air Force Station aboard an Atlas V launch vehicle into a direct insertion trajectory to the Moon LRO, designed, built, and operated by the NASA Goddard Space Flight Center in Greenbelt, MD, is gathering crucial data on the lunar environment that will help astronauts prepare for long-duration lunar expeditions. During the mission s nominal life of one year its six instruments and one technology demonstrator will find safe landing site, locate potential resources, characterize the radiation environment and test new technology. To date, LRO has been operating well within the bounds of its requirements and has been collecting excellent science data images taken from the LRO Camera Narrow Angle Camera (LROC NAC) of the Apollo landing sites have appeared on cable news networks. A significant amount of information on LRO s science instruments is provided at the LRO mission webpage. LRO s Attitude Control System (ACS), in addition to controlling the orientation of the spacecraft is also responsible for pointing the High Gain Antenna (HGA). A dual-axis (or double-gimbaled) antenna, deployed on a meter-long boom, is required to point at a selected Earth ground station. Due to signal loss over the distance from the Moon to Earth, pointing precision for the antenna system is very tight. Since the HGA has to be deployed in spaceflight, its exact geometry relative to the spacecraft body is uncertain. In addition, thermal distortions and mechanical errors/tolerances must be characterized and removed to realize the greatest gain from the antenna system. These reasons necessitate the need for an in-flight calibration. Once in orbit around the moon, a series of attitude maneuvers was conducted to provide data needed to determine optimal parameters to load onboard, which would account for the environmental and mechanical errors at any antenna orientation. The nominal geometry for the HGA involves an outer gimbal axis that is exactly perpendicular to the inner gimbal axis, and a target direction that is exactly perpendicular to the outer gimbal axis. For this nominal geometry, closed-form solutions of the desired gimbal angles are simple to get for a desired target direction specified in the spacecraft body fame. If the gimbal axes and the antenna boresight are slightly misaligned, the nominal closed-form solution is not sufficiently accurate for computing the gimbal angles needed to point at a target. In this situation, either a general closed-form solution has to be developed for a mechanism with general geometries, or a correction scheme has to be applied to the nominal closed-form solutions. The latter has been adopted for Solar Dynamics Observatory (SDO) as can be seen in Reference 1, and the former has been used for LRO. The advantage of the general closed-form solution is the use of a small number of parameters for the correction of nominal solutions, especially in the regions near singularities. Singularities here refer to cases when the nominal closed-form solutions have two or more solutions. Algorithm complexity, however, is the disadvantage of the general closed-form solution.

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.

A controlled genetic algorithm by fuzzy logic and belief functions for job-shop scheduling.

PubMed

Hajri, S; Liouane, N; Hammadi, S; Borne, P

2000-01-01

Most scheduling problems are highly complex combinatorial problems. However, stochastic methods such as genetic algorithm yield good solutions. In this paper, we present a controlled genetic algorithm (CGA) based on fuzzy logic and belief functions to solve job-shop scheduling problems. For better performance, we propose an efficient representational scheme, heuristic rules for creating the initial population, and a new methodology for mixing and computing genetic operator probabilities.

Experimental Performance of a Genetic Algorithm for Airborne Strategic Conflict Resolution

NASA Technical Reports Server (NTRS)

Karr, David A.; Vivona, Robert A.; Roscoe, David A.; DePascale, Stephen M.; Consiglio, Maria

2009-01-01

The Autonomous Operations Planner, a research prototype flight-deck decision support tool to enable airborne self-separation, uses a pattern-based genetic algorithm to resolve predicted conflicts between the ownship and traffic aircraft. Conflicts are resolved by modifying the active route within the ownship s flight management system according to a predefined set of maneuver pattern templates. The performance of this pattern-based genetic algorithm was evaluated in the context of batch-mode Monte Carlo simulations running over 3600 flight hours of autonomous aircraft in en-route airspace under conditions ranging from typical current traffic densities to several times that level. Encountering over 8900 conflicts during two simulation experiments, the genetic algorithm was able to resolve all but three conflicts, while maintaining a required time of arrival constraint for most aircraft. Actual elapsed running time for the algorithm was consistent with conflict resolution in real time. The paper presents details of the genetic algorithm s design, along with mathematical models of the algorithm s performance and observations regarding the effectiveness of using complimentary maneuver patterns when multiple resolutions by the same aircraft were required.

Experimental Performance of a Genetic Algorithm for Airborne Strategic Conflict Resolution

NASA Technical Reports Server (NTRS)

Karr, David A.; Vivona, Robert A.; Roscoe, David A.; DePascale, Stephen M.; Consiglio, Maria

2009-01-01

The Autonomous Operations Planner, a research prototype flight-deck decision support tool to enable airborne self-separation, uses a pattern-based genetic algorithm to resolve predicted conflicts between the ownship and traffic aircraft. Conflicts are resolved by modifying the active route within the ownship's flight management system according to a predefined set of maneuver pattern templates. The performance of this pattern-based genetic algorithm was evaluated in the context of batch-mode Monte Carlo simulations running over 3600 flight hours of autonomous aircraft in en-route airspace under conditions ranging from typical current traffic densities to several times that level. Encountering over 8900 conflicts during two simulation experiments, the genetic algorithm was able to resolve all but three conflicts, while maintaining a required time of arrival constraint for most aircraft. Actual elapsed running time for the algorithm was consistent with conflict resolution in real time. The paper presents details of the genetic algorithm's design, along with mathematical models of the algorithm's performance and observations regarding the effectiveness of using complimentary maneuver patterns when multiple resolutions by the same aircraft were required.

Forecasting Nonlinear Chaotic Time Series with Function Expression Method Based on an Improved Genetic-Simulated Annealing Algorithm

PubMed Central

Wang, Jun; Zhou, Bi-hua; Zhou, Shu-dao; Sheng, Zheng

2015-01-01

The paper proposes a novel function expression method to forecast chaotic time series, using an improved genetic-simulated annealing (IGSA) algorithm to establish the optimum function expression that describes the behavior of time series. In order to deal with the weakness associated with the genetic algorithm, the proposed algorithm incorporates the simulated annealing operation which has the strong local search ability into the genetic algorithm to enhance the performance of optimization; besides, the fitness function and genetic operators are also improved. Finally, the method is applied to the chaotic time series of Quadratic and Rossler maps for validation. The effect of noise in the chaotic time series is also studied numerically. The numerical results verify that the method can forecast chaotic time series with high precision and effectiveness, and the forecasting precision with certain noise is also satisfactory. It can be concluded that the IGSA algorithm is energy-efficient and superior. PMID:26000011

Scalability problems of simple genetic algorithms.

PubMed

Thierens, D

1999-01-01

Scalable evolutionary computation has become an intensively studied research topic in recent years. The issue of scalability is predominant in any field of algorithmic design, but it became particularly relevant for the design of competent genetic algorithms once the scalability problems of simple genetic algorithms were understood. Here we present some of the work that has aided in getting a clear insight in the scalability problems of simple genetic algorithms. Particularly, we discuss the important issue of building block mixing. We show how the need for mixing places a boundary in the GA parameter space that, together with the boundary from the schema theorem, delimits the region where the GA converges reliably to the optimum in problems of bounded difficulty. This region shrinks rapidly with increasing problem size unless the building blocks are tightly linked in the problem coding structure. In addition, we look at how straightforward extensions of the simple genetic algorithm-namely elitism, niching, and restricted mating are not significantly improving the scalability problems.

Forecasting nonlinear chaotic time series with function expression method based on an improved genetic-simulated annealing algorithm.

PubMed

Wang, Jun; Zhou, Bi-hua; Zhou, Shu-dao; Sheng, Zheng

2015-01-01

The paper proposes a novel function expression method to forecast chaotic time series, using an improved genetic-simulated annealing (IGSA) algorithm to establish the optimum function expression that describes the behavior of time series. In order to deal with the weakness associated with the genetic algorithm, the proposed algorithm incorporates the simulated annealing operation which has the strong local search ability into the genetic algorithm to enhance the performance of optimization; besides, the fitness function and genetic operators are also improved. Finally, the method is applied to the chaotic time series of Quadratic and Rossler maps for validation. The effect of noise in the chaotic time series is also studied numerically. The numerical results verify that the method can forecast chaotic time series with high precision and effectiveness, and the forecasting precision with certain noise is also satisfactory. It can be concluded that the IGSA algorithm is energy-efficient and superior.

An investigation of messy genetic algorithms

NASA Technical Reports Server (NTRS)

Goldberg, David E.; Deb, Kalyanmoy; Korb, Bradley

1990-01-01

Genetic algorithms (GAs) are search procedures based on the mechanics of natural selection and natural genetics. They combine the use of string codings or artificial chromosomes and populations with the selective and juxtapositional power of reproduction and recombination to motivate a surprisingly powerful search heuristic in many problems. Despite their empirical success, there has been a long standing objection to the use of GAs in arbitrarily difficult problems. A new approach was launched. Results to a 30-bit, order-three-deception problem were obtained using a new type of genetic algorithm called a messy genetic algorithm (mGAs). Messy genetic algorithms combine the use of variable-length strings, a two-phase selection scheme, and messy genetic operators to effect a solution to the fixed-coding problem of standard simple GAs. The results of the study of mGAs in problems with nonuniform subfunction scale and size are presented. The mGA approach is summarized, both its operation and the theory of its use. Experiments on problems of varying scale, varying building-block size, and combined varying scale and size are presented.

Global Optimization of a Periodic System using a Genetic Algorithm

NASA Astrophysics Data System (ADS)

Stucke, David; Crespi, Vincent

2001-03-01

We use a novel application of a genetic algorithm global optimizatin technique to find the lowest energy structures for periodic systems. We apply this technique to colloidal crystals for several different stoichiometries of binary and trinary colloidal crystals. This application of a genetic algorithm is decribed and results of likely candidate structures are presented.

Research and application of multi-agent genetic algorithm in tower defense game

NASA Astrophysics Data System (ADS)

Jin, Shaohua

2018-04-01

In this paper, a new multi-agent genetic algorithm based on orthogonal experiment is proposed, which is based on multi-agent system, genetic algorithm and orthogonal experimental design. The design of neighborhood competition operator, orthogonal crossover operator, Son and self-learning operator. The new algorithm is applied to mobile tower defense game, according to the characteristics of the game, the establishment of mathematical models, and finally increases the value of the game's monster.

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.

Genetic algorithms as global random search methods

NASA Technical Reports Server (NTRS)

Peck, Charles C.; Dhawan, Atam P.

1995-01-01

Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that the schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solutions and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.

Genetic algorithms as global random search methods

NASA Technical Reports Server (NTRS)

Peck, Charles C.; Dhawan, Atam P.

1995-01-01

Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that that schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solution and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.

Genetic Algorithm Calibration of Probabilistic Cellular Automata for Modeling Mining Permit Activity

USGS Publications Warehouse

Louis, S.J.; Raines, G.L.

2003-01-01

We use a genetic algorithm to calibrate a spatially and temporally resolved cellular automata to model mining activity on public land in Idaho and western Montana. The genetic algorithm searches through a space of transition rule parameters of a two dimensional cellular automata model to find rule parameters that fit observed mining activity data. Previous work by one of the authors in calibrating the cellular automaton took weeks - the genetic algorithm takes a day and produces rules leading to about the same (or better) fit to observed data. These preliminary results indicate that genetic algorithms are a viable tool in calibrating cellular automata for this application. Experience gained during the calibration of this cellular automata suggests that mineral resource information is a critical factor in the quality of the results. With automated calibration, further refinements of how the mineral-resource information is provided to the cellular automaton will probably improve our model.

Hybrid genetic algorithm in the Hopfield network for maximum 2-satisfiability problem

NASA Astrophysics Data System (ADS)

Kasihmuddin, Mohd Shareduwan Mohd; Sathasivam, Saratha; Mansor, Mohd. Asyraf

2017-08-01

Heuristic method was designed for finding optimal solution more quickly compared to classical methods which are too complex to comprehend. In this study, a hybrid approach that utilizes Hopfield network and genetic algorithm in doing maximum 2-Satisfiability problem (MAX-2SAT) was proposed. Hopfield neural network was used to minimize logical inconsistency in interpretations of logic clauses or program. Genetic algorithm (GA) has pioneered the implementation of methods that exploit the idea of combination and reproduce a better solution. The simulation incorporated with and without genetic algorithm will be examined by using Microsoft Visual 2013 C++ Express software. The performance of both searching techniques in doing MAX-2SAT was evaluate based on global minima ratio, ratio of satisfied clause and computation time. The result obtained form the computer simulation demonstrates the effectiveness and acceleration features of genetic algorithm in doing MAX-2SAT in Hopfield network.

Genetic Algorithm for Traveling Salesman Problem with Modified Cycle Crossover Operator

PubMed Central

Mohamd Shoukry, Alaa; Gani, Showkat

2017-01-01

Genetic algorithms are evolutionary techniques used for optimization purposes according to survival of the fittest idea. These methods do not ensure optimal solutions; however, they give good approximation usually in time. The genetic algorithms are useful for NP-hard problems, especially the traveling salesman problem. The genetic algorithm depends on selection criteria, crossover, and mutation operators. To tackle the traveling salesman problem using genetic algorithms, there are various representations such as binary, path, adjacency, ordinal, and matrix representations. In this article, we propose a new crossover operator for traveling salesman problem to minimize the total distance. This approach has been linked with path representation, which is the most natural way to represent a legal tour. Computational results are also reported with some traditional path representation methods like partially mapped and order crossovers along with new cycle crossover operator for some benchmark TSPLIB instances and found improvements. PMID:29209364

Genetic Algorithm for Traveling Salesman Problem with Modified Cycle Crossover Operator.

PubMed

Hussain, Abid; Muhammad, Yousaf Shad; Nauman Sajid, M; Hussain, Ijaz; Mohamd Shoukry, Alaa; Gani, Showkat

2017-01-01

Genetic algorithms are evolutionary techniques used for optimization purposes according to survival of the fittest idea. These methods do not ensure optimal solutions; however, they give good approximation usually in time. The genetic algorithms are useful for NP-hard problems, especially the traveling salesman problem. The genetic algorithm depends on selection criteria, crossover, and mutation operators. To tackle the traveling salesman problem using genetic algorithms, there are various representations such as binary, path, adjacency, ordinal, and matrix representations. In this article, we propose a new crossover operator for traveling salesman problem to minimize the total distance. This approach has been linked with path representation, which is the most natural way to represent a legal tour. Computational results are also reported with some traditional path representation methods like partially mapped and order crossovers along with new cycle crossover operator for some benchmark TSPLIB instances and found improvements.

A modified genetic algorithm with fuzzy roulette wheel selection for job-shop scheduling problems

NASA Astrophysics Data System (ADS)

Thammano, Arit; Teekeng, Wannaporn

2015-05-01

The job-shop scheduling problem is one of the most difficult production planning problems. Since it is in the NP-hard class, a recent trend in solving the job-shop scheduling problem is shifting towards the use of heuristic and metaheuristic algorithms. This paper proposes a novel metaheuristic algorithm, which is a modification of the genetic algorithm. This proposed algorithm introduces two new concepts to the standard genetic algorithm: (1) fuzzy roulette wheel selection and (2) the mutation operation with tabu list. The proposed algorithm has been evaluated and compared with several state-of-the-art algorithms in the literature. The experimental results on 53 JSSPs show that the proposed algorithm is very effective in solving the combinatorial optimization problems. It outperforms all state-of-the-art algorithms on all benchmark problems in terms of the ability to achieve the optimal solution and the computational time.

A New Challenge for Compression Algorithms: Genetic Sequences.

ERIC Educational Resources Information Center

Grumbach, Stephane; Tahi, Fariza

1994-01-01

Analyzes the properties of genetic sequences that cause the failure of classical algorithms used for data compression. A lossless algorithm, which compresses the information contained in DNA and RNA sequences by detecting regularities such as palindromes, is presented. This algorithm combines substitutional and statistical methods and appears to…

Firefly algorithm versus genetic algorithm as powerful variable selection tools and their effect on different multivariate calibration models in spectroscopy: A comparative study

NASA Astrophysics Data System (ADS)

Attia, Khalid A. M.; Nassar, Mohammed W. I.; El-Zeiny, Mohamed B.; Serag, Ahmed

2017-01-01

For the first time, a new variable selection method based on swarm intelligence namely firefly algorithm is coupled with three different multivariate calibration models namely, concentration residual augmented classical least squares, artificial neural network and support vector regression in UV spectral data. A comparative study between the firefly algorithm and the well-known genetic algorithm was developed. The discussion revealed the superiority of using this new powerful algorithm over the well-known genetic algorithm. Moreover, different statistical tests were performed and no significant differences were found between all the models regarding their predictabilities. This ensures that simpler and faster models were obtained without any deterioration of the quality of the calibration.

Refined genetic algorithm -- Economic dispatch example

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

Sheble, G.B.; Brittig, K.

1995-02-01

A genetic-based algorithm is used to solve an economic dispatch (ED) problem. The algorithm utilizes payoff information of perspective solutions to evaluate optimality. Thus, the constraints of classical LaGrangian techniques on unit curves are eliminated. Using an economic dispatch problem as a basis for comparison, several different techniques which enhance program efficiency and accuracy, such as mutation prediction, elitism, interval approximation and penalty factors, are explored. Two unique genetic algorithms are also compared. The results are verified for a sample problem using a classical technique.

Immune allied genetic algorithm for Bayesian network structure learning

NASA Astrophysics Data System (ADS)

Song, Qin; Lin, Feng; Sun, Wei; Chang, KC

2012-06-01

Bayesian network (BN) structure learning is a NP-hard problem. In this paper, we present an improved approach to enhance efficiency of BN structure learning. To avoid premature convergence in traditional single-group genetic algorithm (GA), we propose an immune allied genetic algorithm (IAGA) in which the multiple-population and allied strategy are introduced. Moreover, in the algorithm, we apply prior knowledge by injecting immune operator to individuals which can effectively prevent degeneration. To illustrate the effectiveness of the proposed technique, we present some experimental results.

Flexible Space-Filling Designs for Complex System Simulations

DTIC Science & Technology

2013-06-01

interior of the experimental region and cannot fit higher-order models. We present a genetic algorithm that constructs space-filling designs with...Computer Experiments, Design of Experiments, Genetic Algorithm , Latin Hypercube, Response Surface Methodology, Nearly Orthogonal 15. NUMBER OF PAGES 147...experimental region and cannot fit higher-order models. We present a genetic algorithm that constructs space-filling designs with minimal correlations

Genetic algorithms in conceptual design of a light-weight, low-noise, tilt-rotor aircraft

NASA Technical Reports Server (NTRS)

Wells, Valana L.

1996-01-01

This report outlines research accomplishments in the area of using genetic algorithms (GA) for the design and optimization of rotorcraft. It discusses the genetic algorithm as a search and optimization tool, outlines a procedure for using the GA in the conceptual design of helicopters, and applies the GA method to the acoustic design of rotors.

Self-calibration of a noisy multiple-sensor system with genetic algorithms

NASA Astrophysics Data System (ADS)

Brooks, Richard R.; Iyengar, S. Sitharama; Chen, Jianhua

1996-01-01

This paper explores an image processing application of optimization techniques which entails interpreting noisy sensor data. The application is a generalization of image correlation; we attempt to find the optimal gruence which matches two overlapping gray-scale images corrupted with noise. Both taboo search and genetic algorithms are used to find the parameters which match the two images. A genetic algorithm approach using an elitist reproduction scheme is found to provide significantly superior results. The presentation includes a graphic presentation of the paths taken by tabu search and genetic algorithms when trying to find the best possible match between two corrupted images.

Increasing Prediction the Original Final Year Project of Student Using Genetic Algorithm

NASA Astrophysics Data System (ADS)

Saragih, Rijois Iboy Erwin; Turnip, Mardi; Sitanggang, Delima; Aritonang, Mendarissan; Harianja, Eva

2018-04-01

Final year project is very important forgraduation study of a student. Unfortunately, many students are not seriouslydidtheir final projects. Many of studentsask for someone to do it for them. In this paper, an application of genetic algorithms to predict the original final year project of a studentis proposed. In the simulation, the data of the final project for the last 5 years is collected. The genetic algorithm has several operators namely population, selection, crossover, and mutation. The result suggest that genetic algorithm can do better prediction than other comparable model. Experimental results of predicting showed that 70% was more accurate than the previous researched.

Cell wall matrix polysaccharide distribution and cortical microtubule organization: two factors controlling mesophyll cell morphogenesis in land plants.

PubMed

Sotiriou, P; Giannoutsou, E; Panteris, E; Apostolakos, P; Galatis, B

2016-03-01

This work investigates the involvement of local differentiation of cell wall matrix polysaccharides and the role of microtubules in the morphogenesis of mesophyll cells (MCs) of three types (lobed, branched and palisade) in the dicotyledon Vigna sinensis and the fern Asplenium nidus. Homogalacturonan (HGA) epitopes recognized by the 2F4, JIM5 and JIM7 antibodies and callose were immunolocalized in hand-made leaf sections. Callose was also stained with aniline blue. We studied microtubule organization by tubulin immunofluorescence and transmission electron microscopy. In both plants, the matrix cell wall polysaccharide distribution underwent definite changes during MC differentiation. Callose constantly defined the sites of MC contacts. The 2F4 HGA epitope in V. sinensis first appeared in MC contacts but gradually moved towards the cell wall regions facing the intercellular spaces, while in A. nidus it was initially localized at the cell walls delimiting the intercellular spaces, but finally shifted to MC contacts. In V. sinensis, the JIM5 and JIM7 HGA epitopes initially marked the cell walls delimiting the intercellular spaces and gradually shifted in MC contacts, while in A. nidus they constantly enriched MC contacts. In all MC types examined, the cortical microtubules played a crucial role in their morphogenesis. In particular, in palisade MCs, cortical microtubule helices, by controlling cellulose microfibril orientation, forced these MCs to acquire a truncated cone-like shape. Unexpectedly in V. sinensis, the differentiation of colchicine-affected MCs deviated completely, since they developed a cell wall ingrowth labyrinth, becoming transfer-like cells. The results of this work and previous studies on Zea mays (Giannoutsou et al., Annals of Botany 2013; 112: : 1067-1081) revealed highly controlled local cell wall matrix differentiation in MCs of species belonging to different plant groups. This, in coordination with microtubule-dependent cellulose microfibril alignment, spatially controlled cell wall expansion, allowing MCs to acquire their particular shape. © The Author 2016. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

PCI is Not Predictive of Survival After Complete CRS/HIPEC in Peritoneal Dissemination from High-Grade Appendiceal Primaries.

PubMed

Votanopoulos, Konstantinos Ioannis; Bartlett, David; Moran, Brendan; Haroon, Choudry M; Russell, Greg; Pingpank, James F; Ramalingam, Lekshmi; Kandiah, Chandrakumaran; Chouliaras, Konstantinos; Shen, Perry; Levine, Edward A

2018-03-01

Cytoreductive surgery and hyperthermic intraperitoneal chemotherapy (CRS/HIPEC) is a treatment option in patients with carcinomatosis from high-grade appendiceal (HGA) primaries. It is unknown if there is a Peritoneal Carcinomatosis Index (PCI) upper limit above which a complete CRS/HIPEC does not assure long-term survival. Retrospective analysis from three centers was performed. The PCI was used to grade volume of of disease. Survival in relation to PCI was studied on patients with complete cytoreduction. Overall, 521 HGA patients underwent CRS/HIPEC from 1993 to 2015, with complete CRS being achieved in 50% (260/622). Mean PCI was 14.8 (standard deviation 8.7, range 0-36). Median survival for the complete CRS cohort was 6.1 years, while 5- and 10-year survival was 51.7% (standard error [SE] 4.6) and 36.1% (SE 6.3), respectively. Arbitrary cut-off PCI limits with 5-point splits (p = 0.63) were not predictive of a detrimental effect on survival as long as a complete CRS was achieved. A linear effect of the PCI on survival (p = 0.62) was not observed, and single-point PCI cohort splits within a PCI range of < 5 to > 10 were not predictive of survival for complete CRS patients. The PCI correlated with the ability to achieve a complete CRS, with a mean PCI of 14.7 (8.7) for completeness of cytoreduction (CC)0, 22.3 (7.8) for CC1 and 26.1 (9.5) for CC2/3 resections (p = 0.0001, hazard ratio 1.12, 95% confidence interval 1.09), with an HR of 1.15 for each 1-unit increase in the PCI score. Only 21% of the cohort achieved a complete CRS with a PCI ≥ 21. The PCI correlates with the ability to achieve a complete CRS in carcinomatosis from HGA. PCI is not associated with survival as long as a complete CRS can be achieved.

3D Protein structure prediction with genetic tabu search algorithm

PubMed Central

2010-01-01

Background Protein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task. Results In order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods. Conclusions The hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill-climbing capability in the conventional genetic algorithm by using the flexible memory functions of TS. Compared with some previous algorithms, GATS algorithm has better performance in global optimization and can predict 3D protein structure more effectively. PMID:20522256

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.

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.

Genetic algorithm dynamics on a rugged landscape

NASA Astrophysics Data System (ADS)

Bornholdt, Stefan

1998-04-01

The genetic algorithm is an optimization procedure motivated by biological evolution and is successfully applied to optimization problems in different areas. A statistical mechanics model for its dynamics is proposed based on the parent-child fitness correlation of the genetic operators, making it applicable to general fitness landscapes. It is compared to a recent model based on a maximum entropy ansatz. Finally it is applied to modeling the dynamics of a genetic algorithm on the rugged fitness landscape of the NK model.

Sequential and Mixed Genetic Algorithm and Learning Automata (SGALA, MGALA) for Feature Selection in QSAR

PubMed Central

MotieGhader, Habib; Gharaghani, Sajjad; Masoudi-Sobhanzadeh, Yosef; Masoudi-Nejad, Ali

2017-01-01

Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as GA, PSO, ACO and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR feature selection are proposed. SGALA algorithm uses advantages of Genetic algorithm and Learning Automata sequentially and the MGALA algorithm uses advantages of Genetic Algorithm and Learning Automata simultaneously. We applied our proposed algorithms to select the minimum possible number of features from three different datasets and also we observed that the MGALA and SGALA algorithms had the best outcome independently and in average compared to other feature selection algorithms. Through comparison of our proposed algorithms, we deduced that the rate of convergence to optimal result in MGALA and SGALA algorithms were better than the rate of GA, ACO, PSO and LA algorithms. In the end, the results of GA, ACO, PSO, LA, SGALA, and MGALA algorithms were applied as the input of LS-SVR model and the results from LS-SVR models showed that the LS-SVR model had more predictive ability with the input from SGALA and MGALA algorithms than the input from all other mentioned algorithms. Therefore, the results have corroborated that not only is the predictive efficiency of proposed algorithms better, but their rate of convergence is also superior to the all other mentioned algorithms. PMID:28979308

Sequential and Mixed Genetic Algorithm and Learning Automata (SGALA, MGALA) for Feature Selection in QSAR.

PubMed

MotieGhader, Habib; Gharaghani, Sajjad; Masoudi-Sobhanzadeh, Yosef; Masoudi-Nejad, Ali

2017-01-01

Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as GA, PSO, ACO and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR feature selection are proposed. SGALA algorithm uses advantages of Genetic algorithm and Learning Automata sequentially and the MGALA algorithm uses advantages of Genetic Algorithm and Learning Automata simultaneously. We applied our proposed algorithms to select the minimum possible number of features from three different datasets and also we observed that the MGALA and SGALA algorithms had the best outcome independently and in average compared to other feature selection algorithms. Through comparison of our proposed algorithms, we deduced that the rate of convergence to optimal result in MGALA and SGALA algorithms were better than the rate of GA, ACO, PSO and LA algorithms. In the end, the results of GA, ACO, PSO, LA, SGALA, and MGALA algorithms were applied as the input of LS-SVR model and the results from LS-SVR models showed that the LS-SVR model had more predictive ability with the input from SGALA and MGALA algorithms than the input from all other mentioned algorithms. Therefore, the results have corroborated that not only is the predictive efficiency of proposed algorithms better, but their rate of convergence is also superior to the all other mentioned algorithms.

An Improved Hierarchical Genetic Algorithm for Sheet Cutting Scheduling with Process Constraints

PubMed Central

Rao, Yunqing; Qi, Dezhong; Li, Jinling

2013-01-01

For the first time, an improved hierarchical genetic algorithm for sheet cutting problem which involves n cutting patterns for m non-identical parallel machines with process constraints has been proposed in the integrated cutting stock model. The objective of the cutting scheduling problem is minimizing the weighted completed time. A mathematical model for this problem is presented, an improved hierarchical genetic algorithm (ant colony—hierarchical genetic algorithm) is developed for better solution, and a hierarchical coding method is used based on the characteristics of the problem. Furthermore, to speed up convergence rates and resolve local convergence issues, a kind of adaptive crossover probability and mutation probability is used in this algorithm. The computational result and comparison prove that the presented approach is quite effective for the considered problem. PMID:24489491

An improved hierarchical genetic algorithm for sheet cutting scheduling with process constraints.

PubMed

Rao, Yunqing; Qi, Dezhong; Li, Jinling

2013-01-01

For the first time, an improved hierarchical genetic algorithm for sheet cutting problem which involves n cutting patterns for m non-identical parallel machines with process constraints has been proposed in the integrated cutting stock model. The objective of the cutting scheduling problem is minimizing the weighted completed time. A mathematical model for this problem is presented, an improved hierarchical genetic algorithm (ant colony--hierarchical genetic algorithm) is developed for better solution, and a hierarchical coding method is used based on the characteristics of the problem. Furthermore, to speed up convergence rates and resolve local convergence issues, a kind of adaptive crossover probability and mutation probability is used in this algorithm. The computational result and comparison prove that the presented approach is quite effective for the considered problem.

Pose estimation for augmented reality applications using genetic algorithm.

PubMed

Yu, Ying Kin; Wong, Kin Hong; Chang, Michael Ming Yuen

2005-12-01

This paper describes a genetic algorithm that tackles the pose-estimation problem in computer vision. Our genetic algorithm can find the rotation and translation of an object accurately when the three-dimensional structure of the object is given. In our implementation, each chromosome encodes both the pose and the indexes to the selected point features of the object. Instead of only searching for the pose as in the existing work, our algorithm, at the same time, searches for a set containing the most reliable feature points in the process. This mismatch filtering strategy successfully makes the algorithm more robust under the presence of point mismatches and outliers in the images. Our algorithm has been tested with both synthetic and real data with good results. The accuracy of the recovered pose is compared to the existing algorithms. Our approach outperformed the Lowe's method and the other two genetic algorithms under the presence of point mismatches and outliers. In addition, it has been used to estimate the pose of a real object. It is shown that the proposed method is applicable to augmented reality applications.

Optimization of laminated stacking sequence for buckling load maximization by genetic algorithm

NASA Technical Reports Server (NTRS)

Le Riche, Rodolphe; Haftka, Raphael T.

1992-01-01

The use of a genetic algorithm to optimize the stacking sequence of a composite laminate for buckling load maximization is studied. Various genetic parameters including the population size, the probability of mutation, and the probability of crossover are optimized by numerical experiments. A new genetic operator - permutation - is proposed and shown to be effective in reducing the cost of the genetic search. Results are obtained for a graphite-epoxy plate, first when only the buckling load is considered, and then when constraints on ply contiguity and strain failure are added. The influence on the genetic search of the penalty parameter enforcing the contiguity constraint is studied. The advantage of the genetic algorithm in producing several near-optimal designs is discussed.

Development of a Tool for an Efficient Calibration of CORSIM Models

DOT National Transportation Integrated Search

2014-08-01

This project proposes a Memetic Algorithm (MA) for the calibration of microscopic traffic flow simulation models. The proposed MA includes a combination of genetic and simulated annealing algorithms. The genetic algorithm performs the exploration of ...

Engineered Intrinsic Bioremediation of Ammonium Perchlorate in Groundwater

DTIC Science & Technology

2010-12-01

German Collection of Microorganisms and Cell Cultures) GA Genetic Algorithms GA-ANN Genetic Algorithm Artificial Neural Network GMO genetically...for in situ treatment of perchlorate in groundwater. This is accomplished without the addition of genetically engineered microorganisms ( GMOs ) to the...perchlorate, even in the presence of oxygen and without the addition of genetically engineered microorganisms ( GMOs ) to the environment. This approach

[Algorithm of toxigenic genetically altered Vibrio cholerae El Tor biovar strain identification].

PubMed

Smirnova, N I; Agafonov, D A; Zadnova, S P; Cherkasov, A V; Kutyrev, V V

2014-01-01

Development of an algorithm of genetically altered Vibrio cholerae biovar El Tor strai identification that ensures determination of serogroup, serovar and biovar of the studied isolate based on pheno- and genotypic properties, detection of genetically altered cholera El Tor causative agents, their differentiation by epidemic potential as well as evaluation of variability of key pathogenicity genes. Complex analysis of 28 natural V. cholerae strains was carried out by using traditional microbiological methods, PCR and fragmentary sequencing. An algorithm of toxigenic genetically altered V. cholerae biovar El Tor strain identification was developed that includes 4 stages: determination of serogroup, serovar and biovar based on phenotypic properties, confirmation of serogroup and biovar based on molecular-genetic properties determination of strains as genetically altered, differentiation of genetically altered strains by their epidemic potential and detection of ctxB and tcpA key pathogenicity gene polymorphism. The algorithm is based on the use of traditional microbiological methods, PCR and sequencing of gene fragments. The use of the developed algorithm will increase the effectiveness of detection of genetically altered variants of the cholera El Tor causative agent, their differentiation by epidemic potential and will ensure establishment of polymorphism of genes that code key pathogenicity factors for determination of origins of the strains and possible routes of introduction of the infection.

Firefly algorithm versus genetic algorithm as powerful variable selection tools and their effect on different multivariate calibration models in spectroscopy: A comparative study.

PubMed

Attia, Khalid A M; Nassar, Mohammed W I; El-Zeiny, Mohamed B; Serag, Ahmed

2017-01-05

For the first time, a new variable selection method based on swarm intelligence namely firefly algorithm is coupled with three different multivariate calibration models namely, concentration residual augmented classical least squares, artificial neural network and support vector regression in UV spectral data. A comparative study between the firefly algorithm and the well-known genetic algorithm was developed. The discussion revealed the superiority of using this new powerful algorithm over the well-known genetic algorithm. Moreover, different statistical tests were performed and no significant differences were found between all the models regarding their predictabilities. This ensures that simpler and faster models were obtained without any deterioration of the quality of the calibration. Copyright © 2016 Elsevier B.V. All rights reserved.

Distributed genetic algorithms for the floorplan design problem

NASA Technical Reports Server (NTRS)

Cohoon, James P.; Hegde, Shailesh U.; Martin, Worthy N.; Richards, Dana S.

1991-01-01

Designing a VLSI floorplan calls for arranging a given set of modules in the plane to minimize the weighted sum of area and wire-length measures. A method of solving the floorplan design problem using distributed genetic algorithms is presented. Distributed genetic algorithms, based on the paleontological theory of punctuated equilibria, offer a conceptual modification to the traditional genetic algorithms. Experimental results on several problem instances demonstrate the efficacy of this method and indicate the advantages of this method over other methods, such as simulated annealing. The method has performed better than the simulated annealing approach, both in terms of the average cost of the solutions found and the best-found solution, in almost all the problem instances tried.

Evolving aerodynamic airfoils for wind turbines through a genetic algorithm

NASA Astrophysics Data System (ADS)

Hernández, J. J.; Gómez, E.; Grageda, J. I.; Couder, C.; Solís, A.; Hanotel, C. L.; Ledesma, JI

2017-01-01

Nowadays, genetic algorithms stand out for airfoil optimisation, due to the virtues of mutation and crossing-over techniques. In this work we propose a genetic algorithm with arithmetic crossover rules. The optimisation criteria are taken to be the maximisation of both aerodynamic efficiency and lift coefficient, while minimising drag coefficient. Such algorithm shows greatly improvements in computational costs, as well as a high performance by obtaining optimised airfoils for Mexico City's specific wind conditions from generic wind turbines designed for higher Reynolds numbers, in few iterations.

An Agent Inspired Reconfigurable Computing Implementation of a Genetic Algorithm

NASA Technical Reports Server (NTRS)

Weir, John M.; Wells, B. Earl

2003-01-01

Many software systems have been successfully implemented using an agent paradigm which employs a number of independent entities that communicate with one another to achieve a common goal. The distributed nature of such a paradigm makes it an excellent candidate for use in high speed reconfigurable computing hardware environments such as those present in modem FPGA's. In this paper, a distributed genetic algorithm that can be applied to the agent based reconfigurable hardware model is introduced. The effectiveness of this new algorithm is evaluated by comparing the quality of the solutions found by the new algorithm with those found by traditional genetic algorithms. The performance of a reconfigurable hardware implementation of the new algorithm on an FPGA is compared to traditional single processor implementations.

Phase Reconstruction from FROG Using Genetic Algorithms[Frequency-Resolved Optical Gating

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

Omenetto, F.G.; Nicholson, J.W.; Funk, D.J.

1999-04-12

The authors describe a new technique for obtaining the phase and electric field from FROG measurements using genetic algorithms. Frequency-Resolved Optical Gating (FROG) has gained prominence as a technique for characterizing ultrashort pulses. FROG consists of a spectrally resolved autocorrelation of the pulse to be measured. Typically a combination of iterative algorithms is used, applying constraints from experimental data, and alternating between the time and frequency domain, in order to retrieve an optical pulse. The authors have developed a new approach to retrieving the intensity and phase from FROG data using a genetic algorithm (GA). A GA is a generalmore » parallel search technique that operates on a population of potential solutions simultaneously. Operators in a genetic algorithm, such as crossover, selection, and mutation are based on ideas taken from evolution.« less

Defense Small Business Innovation Research Program (SBIR). Volume 3. Air Force Abstracts of Phase 1 Awards 1993

DTIC Science & Technology

1993-01-01

design and centrifugation protocols. A validated model of the cardiovascular and vestibular response to High Gradient Acceleration (HGA) is vital to...hermetically sealed compressors for long life Stirling and Pulse Tube Cryocoolers for spacecraft. State-of-the art compressors use unlubricated flexure...displacement and vibration cancellation. The inexpensive compressor proposed for Stirling and Pulse Tube cycle spacecraft cryocoolers makes use of

Report on International Collaboration Involving the FE Heater and HG-A Tests at Mont Terri

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

Houseworth, Jim; Rutqvist, Jonny; Asahina, Daisuke

Nuclear waste programs outside of the US have focused on different host rock types for geological disposal of high-level radioactive waste. Several countries, including France, Switzerland, Belgium, and Japan are exploring the possibility of waste disposal in shale and other clay-rich rock that fall within the general classification of argillaceous rock. This rock type is also of interest for the US program because the US has extensive sedimentary basins containing large deposits of argillaceous rock. LBNL, as part of the DOE-NE Used Fuel Disposition Campaign, is collaborating on some of the underground research laboratory (URL) activities at the Mont Terrimore » URL near Saint-Ursanne, Switzerland. The Mont Terri project, which began in 1995, has developed a URL at a depth of about 300 m in a stiff clay formation called the Opalinus Clay. Our current collaboration efforts include two test modeling activities for the FE heater test and the HG-A leak-off test. This report documents results concerning our current modeling of these field tests. The overall objectives of these activities include an improved understanding of and advanced relevant modeling capabilities for EDZ evolution in clay repositories and the associated coupled processes, and to develop a technical basis for the maximum allowable temperature for a clay repository.« less

Aortic Stenosis and Vascular Calcifications in Alkaptonuria

PubMed Central

Hannoush, Hwaida; Introne, Wendy J.; Chen, Marcus Y.; Lee, Sook-Jin; O'Brien, Kevin; Suwannarat, Pim; Kayser, Michael A.; Gahl, William A.; Sachdev, Vandana

2011-01-01

Alkaptonuria is a rare metabolic disorder of tyrosine catabolism in which homogentisic acid (HGA) accumulates and is deposited throughout the spine, large joints, cardiovascular system, and various tissues throughout the body. In the cardiovascular system, pigment deposition has been described in the heart valves, endocardium, pericardium, aortic intima and coronary arteries. The prevalence of cardiovascular disease in patients with alkaptonuria varies in previous reports . We present a series of 76 consecutive adult patients with alkaptonuria who underwent transthoracic echocardiography between 2000 and 2009. A subgroup of 40 patients enrolled in a treatment study underwent non-contrast CT scans and these were assessed for vascular calcifications. Six of the 76 patients had aortic valve replacement. In the remaining 70 patients, 12 patients had aortic sclerosis and 7 patients had aortic stenosis. Unlike degenerative aortic valve disease, we found no correlation with standard cardiac risk factors. There was a modest association between the severity of aortic valve disease and joint involvement, however, we saw no correlation with urine HGA levels. Vascular calcifications were seen in the coronaries, cardiac valves, aortic root, descending aorta and iliac arteries. These findings suggest an important role for echocardiographic screening of alkaptonuria patients to detect valvular heart disease and cardiac CT to detect coronary artery calcifications. PMID:22100375

Radio Thermal Emission from Pluto and Charon during the New Horizons Encounter

NASA Astrophysics Data System (ADS)

Bird, Michael; Linscott, Ivan; Hinson, David; Tyler, G. L.; Strobel, Darrell F.; New Horizons Science Team

2017-10-01

As part of the New Horizons Radio-Science Experiment REX, radio thermal emission from Pluto and Charon (wavelength: 4.2 cm) was observed during the encounter on 14 July 2015. The primary REX measurement, a determination of the atmospheric height profile from the surface up to about 100 km, was conducted during an uplink radio occultation at both ingress and egress (Hinson et al., Icarus 290, 96-111, 2017). During the interval between ingress and egress, when the Earth and the REX uplink signals were occulted by the Pluto disk, the spacecraft antenna continued to point toward Earth and thus scanned diametrically across the Pluto nightside. The average diameter of the HGA 3 dB beam was ≈1100 km at the surface during this opportunity, thereby providing crudely resolved measurements of the radio brightness temperature across Pluto. The best resolution for the REX radiometry observations occurred shortly after closest approach, when the HGA was scanned twice across Pluto. These observations will be reported elsewhere (Linscott et al., Icarus, submitted, 2017). In addition to the resolved observations, full disk brightness temperature measurements of both bodies were performed during the approach (dayside) and departure (nightside) phases of the encounter. We present the results of these observations and provide a preliminary interpretation of the measured brightness temperatures.

Inhibition of para-Hydroxyphenylpyruvate Dioxygenase by Analogues of the Herbicide Nitisinone As a Strategy to Decrease Homogentisic Acid Levels, the Causative Agent of Alkaptonuria.

PubMed

Laschi, Marcella; Bernardini, Giulia; Dreassi, Elena; Millucci, Lia; Geminiani, Michela; Braconi, Daniela; Marzocchi, Barbara; Botta, Maurizio; Manetti, Fabrizio; Santucci, Annalisa

2016-04-05

Alkaptonuria (AKU) is a rare multisystem metabolic disease caused by deficient activity of homogentisate 1,2-dioxygenase (HGD), which leads to the accumulation of homogentisic acid (HGA). Currently, there is no treatment for AKU. The sole drug with some beneficial effects is the herbicide nitisinone (1), an inhibitor of p-hydroxyphenylpyruvate dioxygenase (4-HPPD). 1 has been used as a life-saving drug in infants with type I tyrosinemia despite severe side effects due to the buildup of tyrosine. Four clinical trials of nitisinone to treat AKU have shown that 1 consistently decreases HGA levels, but also caused the accumulation of tyrosine in blood serum. Moreover, the human preclinical toxicological data for 1 are incomplete. In this work, we performed pharmacodynamics and toxicological evaluations of 1, providing the first report of LD50 values in human cells. Intracellular tyrosinemia was also evaluated. Three additional 4-HPPD inhibitors with a more favorable profile than that of 1 in terms of IC50, LD50, and tyrosine accumulation were also identified among commercially available compounds. These may be promising starting points for the development of new therapeutic strategies for the treatment of AKU. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Aortic stenosis and vascular calcifications in alkaptonuria.

PubMed

Hannoush, Hwaida; Introne, Wendy J; Chen, Marcus Y; Lee, Sook-Jin; O'Brien, Kevin; Suwannarat, Pim; Kayser, Michael A; Gahl, William A; Sachdev, Vandana

2012-02-01

Alkaptonuria is a rare metabolic disorder of tyrosine catabolism in which homogentisic acid (HGA) accumulates and is deposited throughout the spine, large joints, cardiovascular system, and various tissues throughout the body. In the cardiovascular system, pigment deposition has been described in the heart valves, endocardium, pericardium, aortic intima and coronary arteries. The prevalence of cardiovascular disease in patients with alkaptonuria varies in previous reports. We present a series of 76 consecutive adult patients with alkaptonuria who underwent transthoracic echocardiography between 2000 and 2009. A subgroup of 40 patients enrolled in a treatment study underwent non-contrast CT scans and these were assessed for vascular calcifications. Six of the 76 patients had aortic valve replacement. In the remaining 70 patients, 12 patients had aortic sclerosis and 7 patients had aortic stenosis. Unlike degenerative aortic valve disease, we found no correlation with standard cardiac risk factors. There was a modest association between the severity of aortic valve disease and joint involvement, however, we saw no correlation with urine HGA levels. Vascular calcifications were seen in the coronaries, cardiac valves, aortic root, descending aorta and iliac arteries. These findings suggest an important role for echocardiographic screening of alkaptonuria patients to detect valvular heart disease and cardiac CT to detect coronary artery calcifications. Published by Elsevier Inc.

Population management, systems-based practice, and planned chronic illness care: integrating disease management competencies into primary care to improve composite diabetes quality measures.

PubMed

Kimura, Joe; DaSilva, Karen; Marshall, Richard

2008-02-01

The increasing prevalence of chronic illnesses in the United States requires a fundamental redesign of the primary care delivery system's structure and processes in order to meet the changing needs and expectations of patients. Population management, systems-based practice, and planned chronic illness care are 3 potential processes that can be integrated into primary care and are compatible with the Chronic Care Model. In 2003, Harvard Vanguard Medical Associates, a multispecialty ambulatory physician group practice based in Boston, Massachusetts, began implementing all 3 processes across its primary care practices. From 2004 to 2006, the overall diabetes composite quality measures improved from 51% to 58% for screening (HgA1c x 2, low-density lipoprotein, blood pressure in 12 months) and from 13% to 17% for intermediate outcomes (HgA1c

Does Your Patient’s Urine Turns Dark? Alkaptonuria and Low Back Ache: A Literature Review

PubMed Central

Kanniyan, Kalaivanan; Pathak, Aditya C; Dhammi, Ish Kumar; Jain, Anil Kumar

2014-01-01

Introduction: Alkaptonuria is a very rare inborn error of amino acid metabolism due to deficient homogentisic acid (HGA) oxidase enzyme leading to accumulation of HGA in plasma, cartilage, other tissues of human body and its excretion in urine. It has both systemic and peripheral signs and symptoms. Though low back is a common symptom of alkaptonuria but, in the absence of ochronosis it is rare. Alkaptonuria itself is very rare occurrence with no specific treatment option available to reverse the effect as yet. Case Report: A 38-year-old male, embroidery worker presented with chronic low back ache with history of staining of clothes in infancy. Later on laboratory and the radiological investigation patient was diagnosed to have alkaptonuria without ochronosis. No other systemic manifestation was present. Patient was treated conservatively and responded well. Conclusion: Though alkaptonuria is a very rare disease, and the occurrence of low back-ache in absence of ochronosis is much rarer. One must be aware of this inborn error of metabolism. Early diagnosis though being “diagnosis of exclusion” for low back-ache, high index of suspicion is advantageous as symptomatic treatment of the alkaptonuria can be initiated and evaluation of other systemic organs can be done in early stages itself. PMID:27298997

Performance Analysis of Combined Methods of Genetic Algorithm and K-Means Clustering in Determining the Value of Centroid

NASA Astrophysics Data System (ADS)

Adya Zizwan, Putra; Zarlis, Muhammad; Budhiarti Nababan, Erna

2017-12-01

The determination of Centroid on K-Means Algorithm directly affects the quality of the clustering results. Determination of centroid by using random numbers has many weaknesses. The GenClust algorithm that combines the use of Genetic Algorithms and K-Means uses a genetic algorithm to determine the centroid of each cluster. The use of the GenClust algorithm uses 50% chromosomes obtained through deterministic calculations and 50% is obtained from the generation of random numbers. This study will modify the use of the GenClust algorithm in which the chromosomes used are 100% obtained through deterministic calculations. The results of this study resulted in performance comparisons expressed in Mean Square Error influenced by centroid determination on K-Means method by using GenClust method, modified GenClust method and also classic K-Means.

Application of a single-objective, hybrid genetic algorithm approach to pharmacokinetic model building.

PubMed

Sherer, Eric A; Sale, Mark E; Pollock, Bruce G; Belani, Chandra P; Egorin, Merrill J; Ivy, Percy S; Lieberman, Jeffrey A; Manuck, Stephen B; Marder, Stephen R; Muldoon, Matthew F; Scher, Howard I; Solit, David B; Bies, Robert R

2012-08-01

A limitation in traditional stepwise population pharmacokinetic model building is the difficulty in handling interactions between model components. To address this issue, a method was previously introduced which couples NONMEM parameter estimation and model fitness evaluation to a single-objective, hybrid genetic algorithm for global optimization of the model structure. In this study, the generalizability of this approach for pharmacokinetic model building is evaluated by comparing (1) correct and spurious covariate relationships in a simulated dataset resulting from automated stepwise covariate modeling, Lasso methods, and single-objective hybrid genetic algorithm approaches to covariate identification and (2) information criteria values, model structures, convergence, and model parameter values resulting from manual stepwise versus single-objective, hybrid genetic algorithm approaches to model building for seven compounds. Both manual stepwise and single-objective, hybrid genetic algorithm approaches to model building were applied, blinded to the results of the other approach, for selection of the compartment structure as well as inclusion and model form of inter-individual and inter-occasion variability, residual error, and covariates from a common set of model options. For the simulated dataset, stepwise covariate modeling identified three of four true covariates and two spurious covariates; Lasso identified two of four true and 0 spurious covariates; and the single-objective, hybrid genetic algorithm identified three of four true covariates and one spurious covariate. For the clinical datasets, the Akaike information criterion was a median of 22.3 points lower (range of 470.5 point decrease to 0.1 point decrease) for the best single-objective hybrid genetic-algorithm candidate model versus the final manual stepwise model: the Akaike information criterion was lower by greater than 10 points for four compounds and differed by less than 10 points for three compounds. The root mean squared error and absolute mean prediction error of the best single-objective hybrid genetic algorithm candidates were a median of 0.2 points higher (range of 38.9 point decrease to 27.3 point increase) and 0.02 points lower (range of 0.98 point decrease to 0.74 point increase), respectively, than that of the final stepwise models. In addition, the best single-objective, hybrid genetic algorithm candidate models had successful convergence and covariance steps for each compound, used the same compartment structure as the manual stepwise approach for 6 of 7 (86 %) compounds, and identified 54 % (7 of 13) of covariates included by the manual stepwise approach and 16 covariate relationships not included by manual stepwise models. The model parameter values between the final manual stepwise and best single-objective, hybrid genetic algorithm models differed by a median of 26.7 % (q₁ = 4.9 % and q₃ = 57.1 %). Finally, the single-objective, hybrid genetic algorithm approach was able to identify models capable of estimating absorption rate parameters for four compounds that the manual stepwise approach did not identify. The single-objective, hybrid genetic algorithm represents a general pharmacokinetic model building methodology whose ability to rapidly search the feasible solution space leads to nearly equivalent or superior model fits to pharmacokinetic data.

Cloud computing-based TagSNP selection algorithm for human genome data.

PubMed

Hung, Che-Lun; Chen, Wen-Pei; Hua, Guan-Jie; Zheng, Huiru; Tsai, Suh-Jen Jane; Lin, Yaw-Ling

2015-01-05

Single nucleotide polymorphisms (SNPs) play a fundamental role in human genetic variation and are used in medical diagnostics, phylogeny construction, and drug design. They provide the highest-resolution genetic fingerprint for identifying disease associations and human features. Haplotypes are regions of linked genetic variants that are closely spaced on the genome and tend to be inherited together. Genetics research has revealed SNPs within certain haplotype blocks that introduce few distinct common haplotypes into most of the population. Haplotype block structures are used in association-based methods to map disease genes. In this paper, we propose an efficient algorithm for identifying haplotype blocks in the genome. In chromosomal haplotype data retrieved from the HapMap project website, the proposed algorithm identified longer haplotype blocks than an existing algorithm. To enhance its performance, we extended the proposed algorithm into a parallel algorithm that copies data in parallel via the Hadoop MapReduce framework. The proposed MapReduce-paralleled combinatorial algorithm performed well on real-world data obtained from the HapMap dataset; the improvement in computational efficiency was proportional to the number of processors used.

New optimization model for routing and spectrum assignment with nodes insecurity

NASA Astrophysics Data System (ADS)

Xuan, Hejun; Wang, Yuping; Xu, Zhanqi; Hao, Shanshan; Wang, Xiaoli

2017-04-01

By adopting the orthogonal frequency division multiplexing technology, elastic optical networks can provide the flexible and variable bandwidth allocation to each connection request and get higher spectrum utilization. The routing and spectrum assignment problem in elastic optical network is a well-known NP-hard problem. In addition, information security has received worldwide attention. We combine these two problems to investigate the routing and spectrum assignment problem with the guaranteed security in elastic optical network, and establish a new optimization model to minimize the maximum index of the used frequency slots, which is used to determine an optimal routing and spectrum assignment schemes. To solve the model effectively, a hybrid genetic algorithm framework integrating a heuristic algorithm into a genetic algorithm is proposed. The heuristic algorithm is first used to sort the connection requests and then the genetic algorithm is designed to look for an optimal routing and spectrum assignment scheme. In the genetic algorithm, tailor-made crossover, mutation and local search operators are designed. Moreover, simulation experiments are conducted with three heuristic strategies, and the experimental results indicate that the effectiveness of the proposed model and algorithm framework.

The Applications of Genetic Algorithms in Medicine.

PubMed

Ghaheri, Ali; Shoar, Saeed; Naderan, Mohammad; Hoseini, Sayed Shahabuddin

2015-11-01

A great wealth of information is hidden amid medical research data that in some cases cannot be easily analyzed, if at all, using classical statistical methods. Inspired by nature, metaheuristic algorithms have been developed to offer optimal or near-optimal solutions to complex data analysis and decision-making tasks in a reasonable time. Due to their powerful features, metaheuristic algorithms have frequently been used in other fields of sciences. In medicine, however, the use of these algorithms are not known by physicians who may well benefit by applying them to solve complex medical problems. Therefore, in this paper, we introduce the genetic algorithm and its applications in medicine. The use of the genetic algorithm has promising implications in various medical specialties including radiology, radiotherapy, oncology, pediatrics, cardiology, endocrinology, surgery, obstetrics and gynecology, pulmonology, infectious diseases, orthopedics, rehabilitation medicine, neurology, pharmacotherapy, and health care management. This review introduces the applications of the genetic algorithm in disease screening, diagnosis, treatment planning, pharmacovigilance, prognosis, and health care management, and enables physicians to envision possible applications of this metaheuristic method in their medical career.].

The Applications of Genetic Algorithms in Medicine

PubMed Central

Ghaheri, Ali; Shoar, Saeed; Naderan, Mohammad; Hoseini, Sayed Shahabuddin

2015-01-01

A great wealth of information is hidden amid medical research data that in some cases cannot be easily analyzed, if at all, using classical statistical methods. Inspired by nature, metaheuristic algorithms have been developed to offer optimal or near-optimal solutions to complex data analysis and decision-making tasks in a reasonable time. Due to their powerful features, metaheuristic algorithms have frequently been used in other fields of sciences. In medicine, however, the use of these algorithms are not known by physicians who may well benefit by applying them to solve complex medical problems. Therefore, in this paper, we introduce the genetic algorithm and its applications in medicine. The use of the genetic algorithm has promising implications in various medical specialties including radiology, radiotherapy, oncology, pediatrics, cardiology, endocrinology, surgery, obstetrics and gynecology, pulmonology, infectious diseases, orthopedics, rehabilitation medicine, neurology, pharmacotherapy, and health care management. This review introduces the applications of the genetic algorithm in disease screening, diagnosis, treatment planning, pharmacovigilance, prognosis, and health care management, and enables physicians to envision possible applications of this metaheuristic method in their medical career.] PMID:26676060

Cloud Computing-Based TagSNP Selection Algorithm for Human Genome Data

PubMed Central

Hung, Che-Lun; Chen, Wen-Pei; Hua, Guan-Jie; Zheng, Huiru; Tsai, Suh-Jen Jane; Lin, Yaw-Ling

2015-01-01

Single nucleotide polymorphisms (SNPs) play a fundamental role in human genetic variation and are used in medical diagnostics, phylogeny construction, and drug design. They provide the highest-resolution genetic fingerprint for identifying disease associations and human features. Haplotypes are regions of linked genetic variants that are closely spaced on the genome and tend to be inherited together. Genetics research has revealed SNPs within certain haplotype blocks that introduce few distinct common haplotypes into most of the population. Haplotype block structures are used in association-based methods to map disease genes. In this paper, we propose an efficient algorithm for identifying haplotype blocks in the genome. In chromosomal haplotype data retrieved from the HapMap project website, the proposed algorithm identified longer haplotype blocks than an existing algorithm. To enhance its performance, we extended the proposed algorithm into a parallel algorithm that copies data in parallel via the Hadoop MapReduce framework. The proposed MapReduce-paralleled combinatorial algorithm performed well on real-world data obtained from the HapMap dataset; the improvement in computational efficiency was proportional to the number of processors used. PMID:25569088

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 genetic algorithm for replica server placement

NASA Astrophysics Data System (ADS)

Eslami, Ghazaleh; Toroghi Haghighat, Abolfazl

2012-01-01

Modern distribution systems use replication to improve communication delay experienced by their clients. Some techniques have been developed for web server replica placement. One of the previous studies was Greedy algorithm proposed by Qiu et al, that needs knowledge about network topology. In This paper, first we introduce a genetic algorithm for web server replica placement. Second, we compare our algorithm with Greedy algorithm proposed by Qiu et al, and Optimum algorithm. We found that our approach can achieve better results than Greedy algorithm proposed by Qiu et al but it's computational time is more than Greedy algorithm.

A genetic algorithm for replica server placement

NASA Astrophysics Data System (ADS)

Eslami, Ghazaleh; Toroghi Haghighat, Abolfazl

2011-12-01

Modern distribution systems use replication to improve communication delay experienced by their clients. Some techniques have been developed for web server replica placement. One of the previous studies was Greedy algorithm proposed by Qiu et al, that needs knowledge about network topology. In This paper, first we introduce a genetic algorithm for web server replica placement. Second, we compare our algorithm with Greedy algorithm proposed by Qiu et al, and Optimum algorithm. We found that our approach can achieve better results than Greedy algorithm proposed by Qiu et al but it's computational time is more than Greedy algorithm.

MIP models and hybrid algorithms for simultaneous job splitting and scheduling on unrelated parallel machines.

PubMed

Eroglu, Duygu Yilmaz; Ozmutlu, H Cenk

2014-01-01

We developed mixed integer programming (MIP) models and hybrid genetic-local search algorithms for the scheduling problem of unrelated parallel machines with job sequence and machine-dependent setup times and with job splitting property. The first contribution of this paper is to introduce novel algorithms which make splitting and scheduling simultaneously with variable number of subjobs. We proposed simple chromosome structure which is constituted by random key numbers in hybrid genetic-local search algorithm (GAspLA). Random key numbers are used frequently in genetic algorithms, but it creates additional difficulty when hybrid factors in local search are implemented. We developed algorithms that satisfy the adaptation of results of local search into the genetic algorithms with minimum relocation operation of genes' random key numbers. This is the second contribution of the paper. The third contribution of this paper is three developed new MIP models which are making splitting and scheduling simultaneously. The fourth contribution of this paper is implementation of the GAspLAMIP. This implementation let us verify the optimality of GAspLA for the studied combinations. The proposed methods are tested on a set of problems taken from the literature and the results validate the effectiveness of the proposed algorithms.

Truss Optimization for a Manned Nuclear Electric Space Vehicle using Genetic Algorithms

NASA Technical Reports Server (NTRS)

Benford, Andrew; Tinker, Michael L.

2004-01-01

The purpose of this paper is to utilize the genetic algorithm (GA) optimization method for structural design of a nuclear propulsion vehicle. Genetic algorithms provide a guided, random search technique that mirrors biological adaptation. To verify the GA capabilities, other traditional optimization methods were used to generate results for comparison to the GA results, first for simple two-dimensional structures, and then for full-scale three-dimensional truss designs.

Superscattering of light optimized by a genetic algorithm

NASA Astrophysics Data System (ADS)

Mirzaei, Ali; Miroshnichenko, Andrey E.; Shadrivov, Ilya V.; Kivshar, Yuri S.

2014-07-01

We analyse scattering of light from multi-layer plasmonic nanowires and employ a genetic algorithm for optimizing the scattering cross section. We apply the mode-expansion method using experimental data for material parameters to demonstrate that our genetic algorithm allows designing realistic core-shell nanostructures with the superscattering effect achieved at any desired wavelength. This approach can be employed for optimizing both superscattering and cloaking at different wavelengths in the visible spectral range.

A High Fuel Consumption Efficiency Management Scheme for PHEVs Using an Adaptive Genetic Algorithm

PubMed Central

Lee, Wah Ching; Tsang, Kim Fung; Chi, Hao Ran; Hung, Faan Hei; Wu, Chung Kit; Chui, Kwok Tai; Lau, Wing Hong; Leung, Yat Wah

2015-01-01

A high fuel efficiency management scheme for plug-in hybrid electric vehicles (PHEVs) has been developed. In order to achieve fuel consumption reduction, an adaptive genetic algorithm scheme has been designed to adaptively manage the energy resource usage. The objective function of the genetic algorithm is implemented by designing a fuzzy logic controller which closely monitors and resembles the driving conditions and environment of PHEVs, thus trading off between petrol versus electricity for optimal driving efficiency. Comparison between calculated results and publicized data shows that the achieved efficiency of the fuzzified genetic algorithm is better by 10% than existing schemes. The developed scheme, if fully adopted, would help reduce over 600 tons of CO2 emissions worldwide every day. PMID:25587974

Neural-network-assisted genetic algorithm applied to silicon clusters

NASA Astrophysics Data System (ADS)

Marim, L. R.; Lemes, M. R.; dal Pino, A.

2003-03-01

Recently, a new optimization procedure that combines the power of artificial neural-networks with the versatility of the genetic algorithm (GA) was introduced. This method, called neural-network-assisted genetic algorithm (NAGA), uses a neural network to restrict the search space and it is expected to speed up the solution of global optimization problems if some previous information is available. In this paper, we have tested NAGA to determine the ground-state geometry of Sin (10⩽n⩽15) according to a tight-binding total-energy method. Our results indicate that NAGA was able to find the desired global minimum of the potential energy for all the test cases and it was at least ten times faster than pure genetic algorithm.

Multiple Query Evaluation Based on an Enhanced Genetic Algorithm.

ERIC Educational Resources Information Center

Tamine, Lynda; Chrisment, Claude; Boughanem, Mohand

2003-01-01

Explains the use of genetic algorithms to combine results from multiple query evaluations to improve relevance in information retrieval. Discusses niching techniques, relevance feedback techniques, and evolution heuristics, and compares retrieval results obtained by both genetic multiple query evaluation and classical single query evaluation…

MOESHA: A genetic algorithm for automatic calibration and estimation of parameter uncertainty and sensitivity of hydrologic models

EPA Science Inventory

Characterization of uncertainty and sensitivity of model parameters is an essential and often overlooked facet of hydrological modeling. This paper introduces an algorithm called MOESHA that combines input parameter sensitivity analyses with a genetic algorithm calibration routin...

A genetic algorithm for solving supply chain network design model

NASA Astrophysics Data System (ADS)

Firoozi, Z.; Ismail, N.; Ariafar, S. H.; Tang, S. H.; Ariffin, M. K. M. A.

2013-09-01

Network design is by nature costly and optimization models play significant role in reducing the unnecessary cost components of a distribution network. This study proposes a genetic algorithm to solve a distribution network design model. The structure of the chromosome in the proposed algorithm is defined in a novel way that in addition to producing feasible solutions, it also reduces the computational complexity of the algorithm. Computational results are presented to show the algorithm performance.

The simulation method of chemical composition of vermicular graphite iron on the basis of genetic algorithm

NASA Astrophysics Data System (ADS)

Yusupov, L. R.; Klochkova, K. V.; Simonova, L. A.

2017-09-01

The paper presents a methodology of modeling the chemical composition of the composite material via genetic algorithm for optimization of the manufacturing process of products. The paper presents algorithms of methods based on intelligent system of vermicular graphite iron design

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...

Multi-Objective Constraint Satisfaction for Mobile Robot Area Defense

DTIC Science & Technology

2010-03-01

17 NSGA-II non-dominated sorting genetic algorithm II . . . . . . . . . . . . . . . . . . . 17 jMetal Metaheuristic Algorithms in...to alert the other agents and ensure trust in the system. This research presents an algorithm that tasks robots to meet the two specific goals of...problem is defined as a constraint satisfaction problem solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). Both goals of

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.

Combinatorial Multiobjective Optimization Using Genetic Algorithms

NASA Technical Reports Server (NTRS)

Crossley, William A.; Martin. Eric T.

2002-01-01

The research proposed in this document investigated multiobjective optimization approaches based upon the Genetic Algorithm (GA). Several versions of the GA have been adopted for multiobjective design, but, prior to this research, there had not been significant comparisons of the most popular strategies. The research effort first generalized the two-branch tournament genetic algorithm in to an N-branch genetic algorithm, then the N-branch GA was compared with a version of the popular Multi-Objective Genetic Algorithm (MOGA). Because the genetic algorithm is well suited to combinatorial (mixed discrete / continuous) optimization problems, the GA can be used in the conceptual phase of design to combine selection (discrete variable) and sizing (continuous variable) tasks. Using a multiobjective formulation for the design of a 50-passenger aircraft to meet the competing objectives of minimizing takeoff gross weight and minimizing trip time, the GA generated a range of tradeoff designs that illustrate which aircraft features change from a low-weight, slow trip-time aircraft design to a heavy-weight, short trip-time aircraft design. Given the objective formulation and analysis methods used, the results of this study identify where turboprop-powered aircraft and turbofan-powered aircraft become more desirable for the 50 seat passenger application. This aircraft design application also begins to suggest how a combinatorial multiobjective optimization technique could be used to assist in the design of morphing aircraft.

Hybrid artificial bee colony algorithm for parameter optimization of five-parameter bidirectional reflectance distribution function model.

PubMed

Wang, Qianqian; Zhao, Jing; Gong, Yong; Hao, Qun; Peng, Zhong

2017-11-20

A hybrid artificial bee colony (ABC) algorithm inspired by the best-so-far solution and bacterial chemotaxis was introduced to optimize the parameters of the five-parameter bidirectional reflectance distribution function (BRDF) model. To verify the performance of the hybrid ABC algorithm, we measured BRDF of three kinds of samples and simulated the undetermined parameters of the five-parameter BRDF model using the hybrid ABC algorithm and the genetic algorithm, respectively. The experimental results demonstrate that the hybrid ABC algorithm outperforms the genetic algorithm in convergence speed, accuracy, and time efficiency under the same conditions.

[Application of genetic algorithm in blending technology for extractions of Cortex Fraxini].

PubMed

Yang, Ming; Zhou, Yinmin; Chen, Jialei; Yu, Minying; Shi, Xiufeng; Gu, Xijun

2009-10-01

To explore the feasibility of genetic algorithm (GA) on multiple objective blending technology for extractions of Cortex Fraxini. According to that the optimization objective was the combination of fingerprint similarity and the root-mean-square error of multiple key constituents, a new multiple objective optimization model of 10 batches extractions of Cortex Fraxini was built. The blending coefficient was obtained by genetic algorithm. The quality of 10 batches extractions of Cortex Fraxini that after blending was evaluated with the finger print similarity and root-mean-square error as indexes. The quality of 10 batches extractions of Cortex Fraxini that after blending was well improved. Comparing with the fingerprint of the control sample, the similarity was up, but the degree of variation is down. The relative deviation of the key constituents was less than 10%. It is proved that genetic algorithm works well on multiple objective blending technology for extractions of Cortex Fraxini. This method can be a reference to control the quality of extractions of Cortex Fraxini. Genetic algorithm in blending technology for extractions of Chinese medicines is advisable.

Improving Brain Magnetic Resonance Image (MRI) Segmentation via a Novel Algorithm based on Genetic and Regional Growth

PubMed Central

A., Javadpour; A., Mohammadi

2016-01-01

Background Regarding the importance of right diagnosis in medical applications, various methods have been exploited for processing medical images solar. The method of segmentation is used to analyze anal to miscall structures in medical imaging. Objective This study describes a new method for brain Magnetic Resonance Image (MRI) segmentation via a novel algorithm based on genetic and regional growth. Methods Among medical imaging methods, brains MRI segmentation is important due to high contrast of non-intrusive soft tissue and high spatial resolution. Size variations of brain tissues are often accompanied by various diseases such as Alzheimer’s disease. As our knowledge about the relation between various brain diseases and deviation of brain anatomy increases, MRI segmentation is exploited as the first step in early diagnosis. In this paper, regional growth method and auto-mate selection of initial points by genetic algorithm is used to introduce a new method for MRI segmentation. Primary pixels and similarity criterion are automatically by genetic algorithms to maximize the accuracy and validity in image segmentation. Results By using genetic algorithms and defining the fixed function of image segmentation, the initial points for the algorithm were found. The proposed algorithms are applied to the images and results are manually selected by regional growth in which the initial points were compared. The results showed that the proposed algorithm could reduce segmentation error effectively. Conclusion The study concluded that the proposed algorithm could reduce segmentation error effectively and help us to diagnose brain diseases. PMID:27672629

Optimizing multiple sequence alignments using a genetic algorithm based on three objectives: structural information, non-gaps percentage and totally conserved columns.

PubMed

Ortuño, Francisco M; Valenzuela, Olga; Rojas, Fernando; Pomares, Hector; Florido, Javier P; Urquiza, Jose M; Rojas, Ignacio

2013-09-01

Multiple sequence alignments (MSAs) are widely used approaches in bioinformatics to carry out other tasks such as structure predictions, biological function analyses or phylogenetic modeling. However, current tools usually provide partially optimal alignments, as each one is focused on specific biological features. Thus, the same set of sequences can produce different alignments, above all when sequences are less similar. Consequently, researchers and biologists do not agree about which is the most suitable way to evaluate MSAs. Recent evaluations tend to use more complex scores including further biological features. Among them, 3D structures are increasingly being used to evaluate alignments. Because structures are more conserved in proteins than sequences, scores with structural information are better suited to evaluate more distant relationships between sequences. The proposed multiobjective algorithm, based on the non-dominated sorting genetic algorithm, aims to jointly optimize three objectives: STRIKE score, non-gaps percentage and totally conserved columns. It was significantly assessed on the BAliBASE benchmark according to the Kruskal-Wallis test (P < 0.01). This algorithm also outperforms other aligners, such as ClustalW, Multiple Sequence Alignment Genetic Algorithm (MSA-GA), PRRP, DIALIGN, Hidden Markov Model Training (HMMT), Pattern-Induced Multi-sequence Alignment (PIMA), MULTIALIGN, Sequence Alignment Genetic Algorithm (SAGA), PILEUP, Rubber Band Technique Genetic Algorithm (RBT-GA) and Vertical Decomposition Genetic Algorithm (VDGA), according to the Wilcoxon signed-rank test (P < 0.05), whereas it shows results not significantly different to 3D-COFFEE (P > 0.05) with the advantage of being able to use less structures. Structural information is included within the objective function to evaluate more accurately the obtained alignments. The source code is available at http://www.ugr.es/~fortuno/MOSAStrE/MO-SAStrE.zip.

Hubble Space Telescope (HST) above OV-103's PLB during STS-31 deployment

NASA Image and Video Library

1990-04-25

The Hubble Space Telescope (HST) is raised above the payload bay (PLB) in low hover position during STS-31 checkout and pre-deployment procedures aboard Discovery, Orbiter Vehicle (OV) 103. Stowed along the HST Support System Module (SSM) are the high gain antenna (HGA) (center) and the two solar arrays (one either side). In the background are the orbital maneuvering system (OMS) pods and the Earth's surface.

STS-31 Hubble Space Telescope (HST) (SA & HGA deployed) is grappled by RMS

NASA Image and Video Library

1990-04-24

STS031-76-026 (25 April 1990) --- Most of the giant Hubble Space Telescope (HST) can be seen as it is suspended in space by Discovery's Remote Manipulator System (RMS) following the deployment of part of its solar panels and antennae. The photo was taken with a handheld Hasselblad camera. This was among the first photos NASA released on April 30, 1990, from the five-day STS 31 mission.

STS-31 pre-deployment checkout of the Hubble Space Telescope (HST) on OV-103

NASA Image and Video Library

1990-04-25

The Hubble Space Telescope (HST), grappled by Discovery's, Orbiter Vehicle (OV) 103's, remote manipulator system (RMS), is oriented in a 90 degree pitch position during STS-31 pre-deployment checkout procedures. The solar array (SA) panel (center) and high gain antennae (HGA) (on either side) are stowed along the Support System Module (SSM) forward shell prior to deployment. The sun highlights HST against the blackness of space.

Technicians complete assembly of Hubble Space Telescope (HST) mockup at JSC

NASA Technical Reports Server (NTRS)

1989-01-01

Technicians complete assembly of the Hubble Space Telescope (HST) mockup at JSC's Mockup and Integration Laboratory (MAIL) Bldg 9A. In the foreground, a technician holds the controls for an overhead crane attached to one of the HST's high gain antennas (HGAs). Technicians on the ground prepare the HGA to be hoisted into position on the mockup's Support System Module (SSM) forward shell as others work on SSM from a cherry picker.

A multiobjective hybrid genetic algorithm for the capacitated multipoint network design problem.

PubMed

Lo, C C; Chang, W H

2000-01-01

The capacitated multipoint network design problem (CMNDP) is NP-complete. In this paper, a hybrid genetic algorithm for CMNDP is proposed. The multiobjective hybrid genetic algorithm (MOHGA) differs from other genetic algorithms (GAs) mainly in its selection procedure. The concept of subpopulation is used in MOHGA. Four subpopulations are generated according to the elitism reservation strategy, the shifting Prufer vector, the stochastic universal sampling, and the complete random method, respectively. Mixing these four subpopulations produces the next generation population. The MOHGA can effectively search the feasible solution space due to population diversity. The MOHGA has been applied to CMNDP. By examining computational and analytical results, we notice that the MOHGA can find most nondominated solutions and is much more effective and efficient than other multiobjective GAs.

A Hybrid Neural Network-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2001-01-01

In this paper, a model-based diagnostic method, which utilizes Neural Networks and Genetic Algorithms, is investigated. Neural networks are applied to estimate the engine internal health, and Genetic Algorithms are applied for sensor bias detection and estimation. This hybrid approach takes advantage of the nonlinear estimation capability provided by neural networks while improving the robustness to measurement uncertainty through the application of Genetic Algorithms. The hybrid diagnostic technique also has the ability to rank multiple potential solutions for a given set of anomalous sensor measurements in order to reduce false alarms and missed detections. The performance of the hybrid diagnostic technique is evaluated through some case studies derived from a turbofan engine simulation. The results show this approach is promising for reliable diagnostics of aircraft engines.

Genetic Algorithm Approaches for Actuator Placement

NASA Technical Reports Server (NTRS)

Crossley, William A.

2000-01-01

This research investigated genetic algorithm approaches for smart actuator placement to provide aircraft maneuverability without requiring hinged flaps or other control surfaces. The effort supported goals of the Multidisciplinary Design Optimization focus efforts in NASA's Aircraft au program. This work helped to properly identify various aspects of the genetic algorithm operators and parameters that allow for placement of discrete control actuators/effectors. An improved problem definition, including better definition of the objective function and constraints, resulted from this research effort. The work conducted for this research used a geometrically simple wing model; however, an increasing number of potential actuator placement locations were incorporated to illustrate the ability of the GA to determine promising actuator placement arrangements. This effort's major result is a useful genetic algorithm-based approach to assist in the discrete actuator/effector placement problem.

A pipelined FPGA implementation of an encryption algorithm based on genetic algorithm

NASA Astrophysics Data System (ADS)

Thirer, Nonel

2013-05-01

With the evolution of digital data storage and exchange, it is essential to protect the confidential information from every unauthorized access. High performance encryption algorithms were developed and implemented by software and hardware. Also many methods to attack the cipher text were developed. In the last years, the genetic algorithm has gained much interest in cryptanalysis of cipher texts and also in encryption ciphers. This paper analyses the possibility to use the genetic algorithm as a multiple key sequence generator for an AES (Advanced Encryption Standard) cryptographic system, and also to use a three stages pipeline (with four main blocks: Input data, AES Core, Key generator, Output data) to provide a fast encryption and storage/transmission of a large amount of data.

Image reconstruction through thin scattering media by simulated annealing algorithm

NASA Astrophysics Data System (ADS)

Fang, Longjie; Zuo, Haoyi; Pang, Lin; Yang, Zuogang; Zhang, Xicheng; Zhu, Jianhua

2018-07-01

An idea for reconstructing the image of an object behind thin scattering media is proposed by phase modulation. The optimized phase mask is achieved by modulating the scattered light using simulated annealing algorithm. The correlation coefficient is exploited as a fitness function to evaluate the quality of reconstructed image. The reconstructed images optimized from simulated annealing algorithm and genetic algorithm are compared in detail. The experimental results show that our proposed method has better definition and higher speed than genetic algorithm.

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.

Genetic algorithm for neural networks optimization

NASA Astrophysics Data System (ADS)

Setyawati, Bina R.; Creese, Robert C.; Sahirman, Sidharta

2004-11-01

This paper examines the forecasting performance of multi-layer feed forward neural networks in modeling a particular foreign exchange rates, i.e. Japanese Yen/US Dollar. The effects of two learning methods, Back Propagation and Genetic Algorithm, in which the neural network topology and other parameters fixed, were investigated. The early results indicate that the application of this hybrid system seems to be well suited for the forecasting of foreign exchange rates. The Neural Networks and Genetic Algorithm were programmed using MATLAB«.

MIP Models and Hybrid Algorithms for Simultaneous Job Splitting and Scheduling on Unrelated Parallel Machines

PubMed Central

Ozmutlu, H. Cenk

2014-01-01

We developed mixed integer programming (MIP) models and hybrid genetic-local search algorithms for the scheduling problem of unrelated parallel machines with job sequence and machine-dependent setup times and with job splitting property. The first contribution of this paper is to introduce novel algorithms which make splitting and scheduling simultaneously with variable number of subjobs. We proposed simple chromosome structure which is constituted by random key numbers in hybrid genetic-local search algorithm (GAspLA). Random key numbers are used frequently in genetic algorithms, but it creates additional difficulty when hybrid factors in local search are implemented. We developed algorithms that satisfy the adaptation of results of local search into the genetic algorithms with minimum relocation operation of genes' random key numbers. This is the second contribution of the paper. The third contribution of this paper is three developed new MIP models which are making splitting and scheduling simultaneously. The fourth contribution of this paper is implementation of the GAspLAMIP. This implementation let us verify the optimality of GAspLA for the studied combinations. The proposed methods are tested on a set of problems taken from the literature and the results validate the effectiveness of the proposed algorithms. PMID:24977204

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.

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.

Automatic page layout using genetic algorithms for electronic albuming

NASA Astrophysics Data System (ADS)

Geigel, Joe; Loui, Alexander C. P.

2000-12-01

In this paper, we describe a flexible system for automatic page layout that makes use of genetic algorithms for albuming applications. The system is divided into two modules, a page creator module which is responsible for distributing images amongst various album pages, and an image placement module which positions images on individual pages. Final page layouts are specified in a textual form using XML for printing or viewing over the Internet. The system makes use of genetic algorithms, a class of search and optimization algorithms that are based on the concepts of biological evolution, for generating solutions with fitness based on graphic design preferences supplied by the user. The genetic page layout algorithm has been incorporated into a web-based prototype system for interactive page layout over the Internet. The prototype system is built using client-server architecture and is implemented in java. The system described in this paper has demonstrated the feasibility of using genetic algorithms for automated page layout in albuming and web-based imaging applications. We believe that the system adequately proves the validity of the concept, providing creative layouts in a reasonable number of iterations. By optimizing the layout parameters of the fitness function, we hope to further improve the quality of the final layout in terms of user preference and computation speed.

Peak-to-average power ratio reduction in orthogonal frequency division multiplexing-based visible light communication systems using a modified partial transmit sequence technique

NASA Astrophysics Data System (ADS)

Liu, Yan; Deng, Honggui; Ren, Shuang; Tang, Chengying; Qian, Xuewen

2018-01-01

We propose an efficient partial transmit sequence technique based on genetic algorithm and peak-value optimization algorithm (GAPOA) to reduce high peak-to-average power ratio (PAPR) in visible light communication systems based on orthogonal frequency division multiplexing (VLC-OFDM). By analysis of hill-climbing algorithm's pros and cons, we propose the POA with excellent local search ability to further process the signals whose PAPR is still over the threshold after processed by genetic algorithm (GA). To verify the effectiveness of the proposed technique and algorithm, we evaluate the PAPR performance and the bit error rate (BER) performance and compare them with partial transmit sequence (PTS) technique based on GA (GA-PTS), PTS technique based on genetic and hill-climbing algorithm (GH-PTS), and PTS based on shuffled frog leaping algorithm and hill-climbing algorithm (SFLAHC-PTS). The results show that our technique and algorithm have not only better PAPR performance but also lower computational complexity and BER than GA-PTS, GH-PTS, and SFLAHC-PTS technique.

The McGill Interactive Pediatric OncoGenetic Guidelines: An approach to identifying pediatric oncology patients most likely to benefit from a genetic evaluation.

PubMed

Goudie, Catherine; Coltin, Hallie; Witkowski, Leora; Mourad, Stephanie; Malkin, David; Foulkes, William D

2017-08-01

Identifying cancer predisposition syndromes in children with tumors is crucial, yet few clinical guidelines exist to identify children at high risk of having germline mutations. The McGill Interactive Pediatric OncoGenetic Guidelines project aims to create a validated pediatric guideline in the form of a smartphone/tablet application using algorithms to process clinical data and help determine whether to refer a child for genetic assessment. This paper discusses the initial stages of the project, focusing on its overall structure, the methodology underpinning the algorithms, and the upcoming algorithm validation process. © 2017 Wiley Periodicals, Inc.

Optimization of genomic selection training populations with a genetic algorithm

USDA-ARS?s Scientific Manuscript database

In this article, we derive a computationally efficient statistic to measure the reliability of estimates of genetic breeding values for a fixed set of genotypes based on a given training set of genotypes and phenotypes. We adopt a genetic algorithm scheme to find a training set of certain size from ...

A Constrained Genetic Algorithm with Adaptively Defined Fitness Function in MRS Quantification

NASA Astrophysics Data System (ADS)

Papakostas, G. A.; Karras, D. A.; Mertzios, B. G.; Graveron-Demilly, D.; van Ormondt, D.

MRS Signal quantification is a rather involved procedure and has attracted the interest of the medical engineering community, regarding the development of computationally efficient methodologies. Significant contributions based on Computational Intelligence tools, such as Neural Networks (NNs), demonstrated a good performance but not without drawbacks already discussed by the authors. On the other hand preliminary application of Genetic Algorithms (GA) has already been reported in the literature by the authors regarding the peak detection problem encountered in MRS quantification using the Voigt line shape model. This paper investigates a novel constrained genetic algorithm involving a generic and adaptively defined fitness function which extends the simple genetic algorithm methodology in case of noisy signals. The applicability of this new algorithm is scrutinized through experimentation in artificial MRS signals interleaved with noise, regarding its signal fitting capabilities. Although extensive experiments with real world MRS signals are necessary, the herein shown performance illustrates the method's potential to be established as a generic MRS metabolites quantification procedure.

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.

Dynamic traffic assignment : genetic algorithms approach

DOT National Transportation Integrated Search

1997-01-01

Real-time route guidance is a promising approach to alleviating congestion on the nations highways. A dynamic traffic assignment model is central to the development of guidance strategies. The artificial intelligence technique of genetic algorithm...

Genetic algorithm based input selection for a neural network function approximator with applications to SSME health monitoring

NASA Technical Reports Server (NTRS)

Peck, Charles C.; Dhawan, Atam P.; Meyer, Claudia M.

1991-01-01

A genetic algorithm is used to select the inputs to a neural network function approximator. In the application considered, modeling critical parameters of the space shuttle main engine (SSME), the functional relationship between measured parameters is unknown and complex. Furthermore, the number of possible input parameters is quite large. Many approaches have been used for input selection, but they are either subjective or do not consider the complex multivariate relationships between parameters. Due to the optimization and space searching capabilities of genetic algorithms they were employed to systematize the input selection process. The results suggest that the genetic algorithm can generate parameter lists of high quality without the explicit use of problem domain knowledge. Suggestions for improving the performance of the input selection process are also provided.

Accelerating global optimization of aerodynamic shapes using a new surrogate-assisted parallel genetic algorithm

NASA Astrophysics Data System (ADS)

Ebrahimi, Mehdi; Jahangirian, Alireza

2017-12-01

An efficient strategy is presented for global shape optimization of wing sections with a parallel genetic algorithm. Several computational techniques are applied to increase the convergence rate and the efficiency of the method. A variable fidelity computational evaluation method is applied in which the expensive Navier-Stokes flow solver is complemented by an inexpensive multi-layer perceptron neural network for the objective function evaluations. A population dispersion method that consists of two phases, of exploration and refinement, is developed to improve the convergence rate and the robustness of the genetic algorithm. Owing to the nature of the optimization problem, a parallel framework based on the master/slave approach is used. The outcomes indicate that the method is able to find the global optimum with significantly lower computational time in comparison to the conventional genetic algorithm.

[Reconstruction of Vehicle-human Crash Accident and Injury Analysis Based on 3D Laser Scanning, Multi-rigid-body Reconstruction and Optimized Genetic Algorithm].

PubMed

Sun, J; Wang, T; Li, Z D; Shao, Y; Zhang, Z Y; Feng, H; Zou, D H; Chen, Y J

2017-12-01

To reconstruct a vehicle-bicycle-cyclist crash accident and analyse the injuries using 3D laser scanning technology, multi-rigid-body dynamics and optimized genetic algorithm, and to provide biomechanical basis for the forensic identification of death cause. The vehicle was measured by 3D laser scanning technology. The multi-rigid-body models of cyclist, bicycle and vehicle were developed based on the measurements. The value range of optimal variables was set. A multi-objective genetic algorithm and the nondominated sorting genetic algorithm were used to find the optimal solutions, which were compared to the record of the surveillance video around the accident scene. The reconstruction result of laser scanning on vehicle was satisfactory. In the optimal solutions found by optimization method of genetic algorithm, the dynamical behaviours of dummy, bicycle and vehicle corresponded to that recorded by the surveillance video. The injury parameters of dummy were consistent with the situation and position of the real injuries on the cyclist in accident. The motion status before accident, damage process by crash and mechanical analysis on the injury of the victim can be reconstructed using 3D laser scanning technology, multi-rigid-body dynamics and optimized genetic algorithm, which have application value in the identification of injury manner and analysis of death cause in traffic accidents. Copyright© by the Editorial Department of Journal of Forensic Medicine

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.

Research on laser marking speed optimization by using genetic algorithm.

PubMed

Wang, Dongyun; Yu, Qiwei; Zhang, Yu

2015-01-01

Laser Marking Machine is the most common coding equipment on product packaging lines. However, the speed of laser marking has become a bottleneck of production. In order to remove this bottleneck, a new method based on a genetic algorithm is designed. On the basis of this algorithm, a controller was designed and simulations and experiments were performed. The results show that using this algorithm could effectively improve laser marking efficiency by 25%.

Tag SNP selection via a genetic algorithm.

PubMed

Mahdevar, Ghasem; Zahiri, Javad; Sadeghi, Mehdi; Nowzari-Dalini, Abbas; Ahrabian, Hayedeh

2010-10-01

Single Nucleotide Polymorphisms (SNPs) provide valuable information on human evolutionary history and may lead us to identify genetic variants responsible for human complex diseases. Unfortunately, molecular haplotyping methods are costly, laborious, and time consuming; therefore, algorithms for constructing full haplotype patterns from small available data through computational methods, Tag SNP selection problem, are convenient and attractive. This problem is proved to be an NP-hard problem, so heuristic methods may be useful. In this paper we present a heuristic method based on genetic algorithm to find reasonable solution within acceptable time. The algorithm was tested on a variety of simulated and experimental data. In comparison with the exact algorithm, based on brute force approach, results show that our method can obtain optimal solutions in almost all cases and runs much faster than exact algorithm when the number of SNP sites is large. Our software is available upon request to the corresponding author.

Research on rolling element bearing fault diagnosis based on genetic algorithm matching pursuit

NASA Astrophysics Data System (ADS)

Rong, R. W.; Ming, T. F.

2017-12-01

In order to solve the problem of slow computation speed, matching pursuit algorithm is applied to rolling bearing fault diagnosis, and the improvement are conducted from two aspects that are the construction of dictionary and the way to search for atoms. To be specific, Gabor function which can reflect time-frequency localization characteristic well is used to construct the dictionary, and the genetic algorithm to improve the searching speed. A time-frequency analysis method based on genetic algorithm matching pursuit (GAMP) algorithm is proposed. The way to set property parameters for the improvement of the decomposition results is studied. Simulation and experimental results illustrate that the weak fault feature of rolling bearing can be extracted effectively by this proposed method, at the same time, the computation speed increases obviously.

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

Automated Test Assembly for Cognitive Diagnosis Models Using a Genetic Algorithm

ERIC Educational Resources Information Center

Finkelman, Matthew; Kim, Wonsuk; Roussos, Louis A.

2009-01-01

Much recent psychometric literature has focused on cognitive diagnosis models (CDMs), a promising class of instruments used to measure the strengths and weaknesses of examinees. This article introduces a genetic algorithm to perform automated test assembly alongside CDMs. The algorithm is flexible in that it can be applied whether the goal is to…

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…

A Parallel Genetic Algorithm to Discover Patterns in Genetic Markers that Indicate Predisposition to Multifactorial Disease

PubMed Central

Rausch, Tobias; Thomas, Alun; Camp, Nicola J.; Cannon-Albright, Lisa A.; Facelli, Julio C.

2008-01-01

This paper describes a novel algorithm to analyze genetic linkage data using pattern recognition techniques and genetic algorithms (GA). The method allows a search for regions of the chromosome that may contain genetic variations that jointly predispose individuals for a particular disease. The method uses correlation analysis, filtering theory and genetic algorithms (GA) to achieve this goal. Because current genome scans use from hundreds to hundreds of thousands of markers, two versions of the method have been implemented. The first is an exhaustive analysis version that can be used to visualize, explore, and analyze small genetic data sets for two marker correlations; the second is a GA version, which uses a parallel implementation allowing searches of higher-order correlations in large data sets. Results on simulated data sets indicate that the method can be informative in the identification of major disease loci and gene-gene interactions in genome-wide linkage data and that further exploration of these techniques is justified. The results presented for both variants of the method show that it can help genetic epidemiologists to identify promising combinations of genetic factors that might predispose to complex disorders. In particular, the correlation analysis of IBD expression patterns might hint to possible gene-gene interactions and the filtering might be a fruitful approach to distinguish true correlation signals from noise. PMID:18547558

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.

Performance impact of mutation operators of a subpopulation-based genetic algorithm for multi-robot task allocation problems.

PubMed

Liu, Chun; Kroll, Andreas

2016-01-01

Multi-robot task allocation determines the task sequence and distribution for a group of robots in multi-robot systems, which is one of constrained combinatorial optimization problems and more complex in case of cooperative tasks because they introduce additional spatial and temporal constraints. To solve multi-robot task allocation problems with cooperative tasks efficiently, a subpopulation-based genetic algorithm, a crossover-free genetic algorithm employing mutation operators and elitism selection in each subpopulation, is developed in this paper. Moreover, the impact of mutation operators (swap, insertion, inversion, displacement, and their various combinations) is analyzed when solving several industrial plant inspection problems. The experimental results show that: (1) the proposed genetic algorithm can obtain better solutions than the tested binary tournament genetic algorithm with partially mapped crossover; (2) inversion mutation performs better than other tested mutation operators when solving problems without cooperative tasks, and the swap-inversion combination performs better than other tested mutation operators/combinations when solving problems with cooperative tasks. As it is difficult to produce all desired effects with a single mutation operator, using multiple mutation operators (including both inversion and swap) is suggested when solving similar combinatorial optimization problems.

A Distributed Parallel Genetic Algorithm of Placement Strategy for Virtual Machines Deployment on Cloud Platform

PubMed Central

Dong, Yu-Shuang; Xu, Gao-Chao; Fu, Xiao-Dong

2014-01-01

The cloud platform provides various services to users. More and more cloud centers provide infrastructure as the main way of operating. To improve the utilization rate of the cloud center and to decrease the operating cost, the cloud center provides services according to requirements of users by sharding the resources with virtualization. Considering both QoS for users and cost saving for cloud computing providers, we try to maximize performance and minimize energy cost as well. In this paper, we propose a distributed parallel genetic algorithm (DPGA) of placement strategy for virtual machines deployment on cloud platform. It executes the genetic algorithm parallelly and distributedly on several selected physical hosts in the first stage. Then it continues to execute the genetic algorithm of the second stage with solutions obtained from the first stage as the initial population. The solution calculated by the genetic algorithm of the second stage is the optimal one of the proposed approach. The experimental results show that the proposed placement strategy of VM deployment can ensure QoS for users and it is more effective and more energy efficient than other placement strategies on the cloud platform. PMID:25097872

A distributed parallel genetic algorithm of placement strategy for virtual machines deployment on cloud platform.

PubMed

Dong, Yu-Shuang; Xu, Gao-Chao; Fu, Xiao-Dong

2014-01-01

The cloud platform provides various services to users. More and more cloud centers provide infrastructure as the main way of operating. To improve the utilization rate of the cloud center and to decrease the operating cost, the cloud center provides services according to requirements of users by sharding the resources with virtualization. Considering both QoS for users and cost saving for cloud computing providers, we try to maximize performance and minimize energy cost as well. In this paper, we propose a distributed parallel genetic algorithm (DPGA) of placement strategy for virtual machines deployment on cloud platform. It executes the genetic algorithm parallelly and distributedly on several selected physical hosts in the first stage. Then it continues to execute the genetic algorithm of the second stage with solutions obtained from the first stage as the initial population. The solution calculated by the genetic algorithm of the second stage is the optimal one of the proposed approach. The experimental results show that the proposed placement strategy of VM deployment can ensure QoS for users and it is more effective and more energy efficient than other placement strategies on the cloud platform.

STS-31 pre-deployment checkout of the Hubble Space Telescope (HST) on OV-103

NASA Image and Video Library

1990-04-25

View taken through overhead window W7 aboard Discovery, Orbiter Vehicle (OV) 103, shows the Hubble Space Telescope (HST) grappled by the remote manipulator system (RMS) and held in a 90 degree pitch position against the blackness of space. The solar array (SA) panel (center) and the high gain antennae (HGA) (on either side) are visible along the Support System Module (SSM) forward shell prior to deployment during STS-31.

Muscle Weakness, Cardiomyopathy, and L-2-Hydroxyglutaric Aciduria Associated with a Novel Recessive SLC25A4 Mutation.

PubMed

von Renesse, Anja; Morales-Gonzalez, Susanne; Gill, Esther; Salomons, Gajja S; Stenzel, Werner; Schuelke, Markus

2018-04-14

Mutations in SLC25A4 (syn. ANT1, Adenine nucleotide translocase, type 1) are known to cause either autosomal dominant progressive external ophthalmoplegia (adPEO) or recessive mitochondrial myopathy, hypertrophic cardiomyopathy, and lactic acidosis. Whole exome sequencing in a young man with myopathy, subsarcolemmal mitochondrial aggregations, cardiomyopathy, lactic acidosis, and L-2-hydroxyglutaric aciduria (L-2-HGA) revealed a new homozygous mutation in SLC25A4 [c.653A>C, NM_001151], leading to the replacement of a highly conserved glutamine by proline [p.(Q218P); NP_001142] that most likely affects the folding of the ANT1 protein. No pathogenic mutation was found in L2HGDH, which is associated with "classic" L-2-HGA. Furthermore, L-2-HGDH enzymatic activity in the patient fibroblasts was normal. Long-range PCR and Southern blot confirmed absence of mtDNA-deletions in blood and muscle. The disturbed ADP/ATP transport across the inner mitochondrial membrane may lead to an accumulation of different TCA-cycle intermediates such as 2-ketoglutarate (2-KG) in our patient. As L-2-HG is generated from 2-KG we hypothesize that the L-2-HG increase is a secondary effect of 2-KG accumulation. Hence, our report expands the spectrum of laboratory findings in ANT1-related diseases and hints towards a connection with organic acidurias.

Natural history of alkaptonuria revisited: analyses based on scoring systems.

PubMed

Ranganath, Lakshminarayan R; Cox, Trevor F

2011-12-01

Increased circulating homogentisic acid in body fluids occurs in alkaptonuria (AKU) due to lack of enzyme homogentisate dioxygenase leading in turn to conversion of HGA to a pigmented melanin-like polymer, known as ochronosis. The tissue damage in AKU is due to ochronosis. A potential treatment, a drug called nitisinone, to decrease formation of HGA is available. However, deploying nitisinone effectively requires its administration at the most optimal time in the natural history. AKU has a long apparent latent period before overt ochronosis develops. The rate of change of ochronosis and its consequences over time following its recognition has not been fully described in any quantitative manner. Two potential tools are described that were used to quantitate disease burden in AKU. One tool describes scoring the clinical features that includes clinical assessments, investigations and questionnaires in 15 patients with AKU. The second tool describes a scoring system that only includes items obtained from questionnaires in 44 people with AKU. Analysis of the data reveals distinct phases of the disease, a pre-ochronotic phase and an ochronotic phase. The ochronotic phase appears to demonstrate an earlier slower progression followed by a rapidly progressive phase. The rate of change of the disease will have implications for monitoring the course of the disease as well as decide on the most appropriate time that treatment should be started for it to be effective either in prevention or arrest of the disease.

Multiple turnovers of the nicotino-enzyme PdxB require α-keto acids as co-substrates

PubMed Central

Rudolph, Johannes; Kim, Juhan; Copley, Shelley D.

2012-01-01

PdxB catalyzes the second step in the biosynthesis of pyridoxal phosphate by oxidizing 4-phospho-D-erythronate (4PE) to 2-oxo-3-hydroxy-4-phospho-butanoate (OHPB) with concomitant reduction of NAD+ to NADH. PdxB is a nicotino-enzyme wherein the NAD(H) cofactor remains tightly bound to PdxB. It has been a mystery how PdxB performs multiple turnovers since addition of free NAD+ does not re-oxidize the enzyme-bound NADH following conversion of 4PE to OHPB. We have solved this mystery by demonstrating that a variety of physiologically available α-ketoacids serve as oxidants of PdxB to sustain multiple turnovers. In a coupled assay using the next two enzymes of the biosynthetic pathway for pyridoxal phosphate (SerC and PdxA), we have found that α-ketoglutarate, oxaloacetic acid, and pyruvate are equally good substrates for PdxB (kcat/Km values ~ 1 × 104 M-1s-1). The kinetic parameters for the substrate 4PE include a kcat of 1.4 s-1, a Km of 2.9 μM, and a kcat/Km of 6.7 × 106 M-1s-1. Additionally, we have characterized the stereochemistry of α-ketoglutarate reduction by showing that D-2-HGA, but not L-2-HGA, is a competitive inhibitor vs. 4PE and a noncompetitive inhibitor vs. α-ketoglutarate. PMID:20831184

Comparison of several analytical methods for the determination of tin in geochemical samples as a function of tin speciation

USGS Publications Warehouse

Kane, J.S.; Evans, J.R.; Jackson, J.C.

1989-01-01

Accurate and precise determinations of tin in geological materials are needed for fundamental studies of tin geochemistry, and for tin prospecting purposes. Achieving the required accuracy is difficult because of the different matrices in which Sn can occur (i.e. sulfides, silicates and cassiterite), and because of the variability of literature values for Sn concentrations in geochemical reference materials. We have evaluated three methods for the analysis of samples for Sn concentration: graphite furnace atomic absorption spectrometry (HGA-AAS) following iodide extraction, inductively coupled plasma atomic emission spectrometry (ICP-OES), and energy-dispersive X-ray fluorescence (EDXRF) spectrometry. Two of these methods (HGA-AAS and ICP-OES) required sample decomposition either by acid digestion or fusion, while the third (EDXRF) was performed directly on the powdered sample. Analytical details of all three methods, their potential errors, and the steps necessary to correct these errors were investigated. Results showed that similar accuracy was achieved from all methods for unmineralized samples, which contain no known Sn-bearing phase. For mineralized samples, which contain Sn-bearing minerals, either cassiterite or stannous sulfides, only EDXRF and fusion ICP-OES methods provided acceptable accuracy. This summary of our study provides information which helps to assure correct interpretation of data bases for underlying geochemical processes, regardless of method of data collection and its inherent limitations. ?? 1989.

Genetic algorithm for nuclear data evaluation

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

Arthur, Jennifer Ann

These are slides on genetic algorithm for nuclear data evaluation. The following is covered: initial population, fitness (outer loop), calculate fitness, selection (first part of inner loop), reproduction (second part of inner loop), solution, and examples.

Hybrid Model Based on Genetic Algorithms and SVM Applied to Variable Selection within Fruit Juice Classification

PubMed Central

Fernandez-Lozano, C.; Canto, C.; Gestal, M.; Andrade-Garda, J. M.; Rabuñal, J. R.; Dorado, J.; Pazos, A.

2013-01-01

Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM). Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA), the most representative variables for a specific classification problem can be selected. PMID:24453933

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.

Optimal placement of tuning masses on truss structures by genetic algorithms

NASA Technical Reports Server (NTRS)

Ponslet, Eric; Haftka, Raphael T.; Cudney, Harley H.

1993-01-01

Optimal placement of tuning masses, actuators and other peripherals on large space structures is a combinatorial optimization problem. This paper surveys several techniques for solving this problem. The genetic algorithm approach to the solution of the placement problem is described in detail. An example of minimizing the difference between the two lowest frequencies of a laboratory truss by adding tuning masses is used for demonstrating some of the advantages of genetic algorithms. The relative efficiencies of different codings are compared using the results of a large number of optimization runs.

Application of a Genetic Algorithm and Multi Agent System to Explore Emergent Patterns of Social Rationality and a Distress-Based Model for Deceit in the Workplace

DTIC Science & Technology

2008-06-01

postponed the fulfillment of her own Masters Degree by at least 18 months so that I would have the opportunity to earn mine. She is smart , lovely...GENETIC ALGORITHM AND MULTI AGENT SYSTEM TO EXPLORE EMERGENT PATTERNS OF SOCIAL RATIONALITY AND A DISTRESS-BASED MODEL FOR DECEIT IN THE WORKPLACE...of a Genetic Algorithm and Mutli Agent System to Explore Emergent Patterns of Social Rationality and a Distress-Based Model for Deceit in the

Multi-objective Optimization Design of Gear Reducer Based on Adaptive Genetic Algorithms

NASA Astrophysics Data System (ADS)

Li, Rui; Chang, Tian; Wang, Jianwei; Wei, Xiaopeng; Wang, Jinming

2008-11-01

An adaptive Genetic Algorithm (GA) is introduced to solve the multi-objective optimized design of the reducer. Firstly, according to the structure, strength, etc. in a reducer, a multi-objective optimized model of the helical gear reducer is established. And then an adaptive GA based on a fuzzy controller is introduced, aiming at the characteristics of multi-objective, multi-parameter, multi-constraint conditions. Finally, a numerical example is illustrated to show the advantages of this approach and the effectiveness of an adaptive genetic algorithm used in optimized design of a reducer.

Investigation on application of genetic algorithms to optimal reactive power dispatch of power systems

NASA Astrophysics Data System (ADS)

Wu, Q. H.; Ma, J. T.

1993-09-01

A primary investigation into application of genetic algorithms in optimal reactive power dispatch and voltage control is presented. The application was achieved, based on (the United Kingdom) National Grid 48 bus network model, using a novel genetic search approach. Simulation results, compared with that obtained using nonlinear programming methods, are included to show the potential of applications of the genetic search methodology in power system economical and secure operations.

Algorithme intelligent d'optimisation d'un design structurel de grande envergure

NASA Astrophysics Data System (ADS)

Dominique, Stephane

The implementation of an automated decision support system in the field of design and structural optimisation can give a significant advantage to any industry working on mechanical designs. Indeed, by providing solution ideas to a designer or by upgrading existing design solutions while the designer is not at work, the system may reduce the project cycle time, or allow more time to produce a better design. This thesis presents a new approach to automate a design process based on Case-Based Reasoning (CBR), in combination with a new genetic algorithm named Genetic Algorithm with Territorial core Evolution (GATE). This approach was developed in order to reduce the operating cost of the process. However, as the system implementation cost is quite expensive, the approach is better suited for large scale design problem, and particularly for design problems that the designer plans to solve for many different specification sets. First, the CBR process uses a databank filled with every known solution to similar design problems. Then, the closest solutions to the current problem in term of specifications are selected. After this, during the adaptation phase, an artificial neural network (ANN) interpolates amongst known solutions to produce an additional solution to the current problem using the current specifications as inputs. Each solution produced and selected by the CBR is then used to initialize the population of an island of the genetic algorithm. The algorithm will optimise the solution further during the refinement phase. Using progressive refinement, the algorithm starts using only the most important variables for the problem. Then, as the optimisation progress, the remaining variables are gradually introduced, layer by layer. The genetic algorithm that is used is a new algorithm specifically created during this thesis to solve optimisation problems from the field of mechanical device structural design. The algorithm is named GATE, and is essentially a real number genetic algorithm that prevents new individuals to be born too close to previously evaluated solutions. The restricted area becomes smaller or larger during the optimisation to allow global or local search when necessary. Also, a new search operator named Substitution Operator is incorporated in GATE. This operator allows an ANN surrogate model to guide the algorithm toward the most promising areas of the design space. The suggested CBR approach and GATE were tested on several simple test problems, as well as on the industrial problem of designing a gas turbine engine rotor's disc. These results are compared to other results obtained for the same problems by many other popular optimisation algorithms, such as (depending of the problem) gradient algorithms, binary genetic algorithm, real number genetic algorithm, genetic algorithm using multiple parents crossovers, differential evolution genetic algorithm, Hookes & Jeeves generalized pattern search method and POINTER from the software I-SIGHT 3.5. Results show that GATE is quite competitive, giving the best results for 5 of the 6 constrained optimisation problem. GATE also provided the best results of all on problem produced by a Maximum Set Gaussian landscape generator. Finally, GATE provided a disc 4.3% lighter than the best other tested algorithm (POINTER) for the gas turbine engine rotor's disc problem. One drawback of GATE is a lesser efficiency for highly multimodal unconstrained problems, for which he gave quite poor results with respect to its implementation cost. To conclude, according to the preliminary results obtained during this thesis, the suggested CBR process, combined with GATE, seems to be a very good candidate to automate and accelerate the structural design of mechanical devices, potentially reducing significantly the cost of industrial preliminary design processes.

Routing design and fleet allocation optimization of freeway service patrol: Improved results using genetic algorithm

NASA Astrophysics Data System (ADS)

Sun, Xiuqiao; Wang, Jian

2018-07-01

Freeway service patrol (FSP), is considered to be an effective method for incident management and can help transportation agency decision-makers alter existing route coverage and fleet allocation. This paper investigates the FSP problem of patrol routing design and fleet allocation, with the objective of minimizing the overall average incident response time. While the simulated annealing (SA) algorithm and its improvements have been applied to solve this problem, they often become trapped in local optimal solution. Moreover, the issue of searching efficiency remains to be further addressed. In this paper, we employ the genetic algorithm (GA) and SA to solve the FSP problem. To maintain population diversity and avoid premature convergence, niche strategy is incorporated into the traditional genetic algorithm. We also employ elitist strategy to speed up the convergence. Numerical experiments have been conducted with the help of the Sioux Falls network. Results show that the GA slightly outperforms the dual-based greedy (DBG) algorithm, the very large-scale neighborhood searching (VLNS) algorithm, the SA algorithm and the scenario algorithm.

Research on Laser Marking Speed Optimization by Using Genetic Algorithm

PubMed Central

Wang, Dongyun; Yu, Qiwei; Zhang, Yu

2015-01-01

Laser Marking Machine is the most common coding equipment on product packaging lines. However, the speed of laser marking has become a bottleneck of production. In order to remove this bottleneck, a new method based on a genetic algorithm is designed. On the basis of this algorithm, a controller was designed and simulations and experiments were performed. The results show that using this algorithm could effectively improve laser marking efficiency by 25%. PMID:25955831

Parana Basin Structure from Multi-Objective Inversion of Surface Wave and Receiver Function by Competent Genetic Algorithm

NASA Astrophysics Data System (ADS)

An, M.; Assumpcao, M.

2003-12-01

The joint inversion of receiver function and surface wave is an effective way to diminish the influences of the strong tradeoff among parameters and the different sensitivity to the model parameters in their respective inversions, but the inversion problem becomes more complex. Multi-objective problems can be much more complicated than single-objective inversion in the model selection and optimization. If objectives are involved and conflicting, models can be ordered only partially. In this case, Pareto-optimal preference should be used to select solutions. On the other hand, the inversion to get only a few optimal solutions can not deal properly with the strong tradeoff between parameters, the uncertainties in the observation, the geophysical complexities and even the incompetency of the inversion technique. The effective way is to retrieve the geophysical information statistically from many acceptable solutions, which requires more competent global algorithms. Competent genetic algorithms recently proposed are far superior to the conventional genetic algorithm and can solve hard problems quickly, reliably and accurately. In this work we used one of competent genetic algorithms, Bayesian Optimization Algorithm as the main inverse procedure. This algorithm uses Bayesian networks to draw out inherited information and can use Pareto-optimal preference in the inversion. With this algorithm, the lithospheric structure of Paran"› basin is inverted to fit both the observations of inter-station surface wave dispersion and receiver function.

A Genetic-Based Scheduling Algorithm to Minimize the Makespan of the Grid Applications

NASA Astrophysics Data System (ADS)

Entezari-Maleki, Reza; Movaghar, Ali

Task scheduling algorithms in grid environments strive to maximize the overall throughput of the grid. In order to maximize the throughput of the grid environments, the makespan of the grid tasks should be minimized. In this paper, a new task scheduling algorithm is proposed to assign tasks to the grid resources with goal of minimizing the total makespan of the tasks. The algorithm uses the genetic approach to find the suitable assignment within grid resources. The experimental results obtained from applying the proposed algorithm to schedule independent tasks within grid environments demonstrate the applicability of the algorithm in achieving schedules with comparatively lower makespan in comparison with other well-known scheduling algorithms such as, Min-min, Max-min, RASA and Sufferage algorithms.

Genetic Algorithms to Optimizatize Lecturer Assessment's Criteria

NASA Astrophysics Data System (ADS)

Jollyta, Deny; Johan; Hajjah, Alyauma

2017-12-01

The lecturer assessment criteria is used as a measurement of the lecturer's performance in a college environment. To determine the value for a criteriais complicated and often leads to doubt. The absence of a standard valuefor each assessment criteria will affect the final results of the assessment and become less presentational data for the leader of college in taking various policies relate to reward and punishment. The Genetic Algorithm comes as an algorithm capable of solving non-linear problems. Using chromosomes in the random initial population, one of the presentations is binary, evaluates the fitness function and uses crossover genetic operator and mutation to obtain the desired crossbreed. It aims to obtain the most optimum criteria values in terms of the fitness function of each chromosome. The training results show that Genetic Algorithm able to produce the optimal values of lecturer assessment criteria so that can be usedby the college as a standard value for lecturer assessment criteria.

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 of Genetic Algorithms for Topology Control of Unmanned Vehicles

DTIC Science & Technology

2010-01-01

decentralised topology control mechanism distributed among active running software agents to achieve a uniform spread of terrestrial unmanned vehicles...14. ABSTRACT We present genetic algorithms (GAs) as a decentralised topology control mechanism distributed among active running software agents to...inspired topology control algorithm. The topology control of UVs using a decentralised solution over an unknown geographical terrain is a challenging

Combinatorial optimization problem solution based on improved genetic algorithm

NASA Astrophysics Data System (ADS)

Zhang, Peng

2017-08-01

Traveling salesman problem (TSP) is a classic combinatorial optimization problem. It is a simplified form of many complex problems. In the process of study and research, it is understood that the parameters that affect the performance of genetic algorithm mainly include the quality of initial population, the population size, and crossover probability and mutation probability values. As a result, an improved genetic algorithm for solving TSP problems is put forward. The population is graded according to individual similarity, and different operations are performed to different levels of individuals. In addition, elitist retention strategy is adopted at each level, and the crossover operator and mutation operator are improved. Several experiments are designed to verify the feasibility of the algorithm. Through the experimental results analysis, it is proved that the improved algorithm can improve the accuracy and efficiency of the solution.

Multi-heuristic dynamic task allocation using genetic algorithms in a heterogeneous distributed system

PubMed Central

Page, Andrew J.; Keane, Thomas M.; Naughton, Thomas J.

2010-01-01

We present a multi-heuristic evolutionary task allocation algorithm to dynamically map tasks to processors in a heterogeneous distributed system. It utilizes a genetic algorithm, combined with eight common heuristics, in an effort to minimize the total execution time. It operates on batches of unmapped tasks and can preemptively remap tasks to processors. The algorithm has been implemented on a Java distributed system and evaluated with a set of six problems from the areas of bioinformatics, biomedical engineering, computer science and cryptography. Experiments using up to 150 heterogeneous processors show that the algorithm achieves better efficiency than other state-of-the-art heuristic algorithms. PMID:20862190

Using an improved association rules mining optimization algorithm in web-based mobile-learning system

NASA Astrophysics Data System (ADS)

Huang, Yin; Chen, Jianhua; Xiong, Shaojun

2009-07-01

Mobile-Learning (M-learning) makes many learners get the advantages of both traditional learning and E-learning. Currently, Web-based Mobile-Learning Systems have created many new ways and defined new relationships between educators and learners. Association rule mining is one of the most important fields in data mining and knowledge discovery in databases. Rules explosion is a serious problem which causes great concerns, as conventional mining algorithms often produce too many rules for decision makers to digest. Since Web-based Mobile-Learning System collects vast amounts of student profile data, data mining and knowledge discovery techniques can be applied to find interesting relationships between attributes of learners, assessments, the solution strategies adopted by learners and so on. Therefore ,this paper focus on a new data-mining algorithm, combined with the advantages of genetic algorithm and simulated annealing algorithm , called ARGSA(Association rules based on an improved Genetic Simulated Annealing Algorithm), to mine the association rules. This paper first takes advantage of the Parallel Genetic Algorithm and Simulated Algorithm designed specifically for discovering association rules. Moreover, the analysis and experiment are also made to show the proposed method is superior to the Apriori algorithm in this Mobile-Learning system.

Light-extraction enhancement for light-emitting diodes: a firefly-inspired structure refined by the genetic algorithm

NASA Astrophysics Data System (ADS)

Bay, Annick; Mayer, Alexandre

2014-09-01

The efficiency of light-emitting diodes (LED) has increased significantly over the past few years, but the overall efficiency is still limited by total internal reflections due to the high dielectric-constant contrast between the incident and emergent media. The bioluminescent organ of fireflies gave incentive for light-extraction enhance-ment studies. A specific factory-roof shaped structure was shown, by means of light-propagation simulations and measurements, to enhance light extraction significantly. In order to achieve a similar effect for light-emitting diodes, the structure needs to be adapted to the specific set-up of LEDs. In this context simulations were carried out to determine the best geometrical parameters. In the present work, the search for a geometry that maximizes the extraction of light has been conducted by using a genetic algorithm. The idealized structure considered previously was generalized to a broader variety of shapes. The genetic algorithm makes it possible to search simultaneously over a wider range of parameters. It is also significantly less time-consuming than the previous approach that was based on a systematic scan on parameters. The results of the genetic algorithm show that (1) the calculations can be performed in a smaller amount of time and (2) the light extraction can be enhanced even more significantly by using optimal parameters determined by the genetic algorithm for the generalized structure. The combination of the genetic algorithm with the Rigorous Coupled Waves Analysis method constitutes a strong simulation tool, which provides us with adapted designs for enhancing light extraction from light-emitting diodes.

Empirical valence bond models for reactive potential energy surfaces: a parallel multilevel genetic program approach.

PubMed

Bellucci, Michael A; Coker, David F

2011-07-28

We describe a new method for constructing empirical valence bond potential energy surfaces using a parallel multilevel genetic program (PMLGP). Genetic programs can be used to perform an efficient search through function space and parameter space to find the best functions and sets of parameters that fit energies obtained by ab initio electronic structure calculations. Building on the traditional genetic program approach, the PMLGP utilizes a hierarchy of genetic programming on two different levels. The lower level genetic programs are used to optimize coevolving populations in parallel while the higher level genetic program (HLGP) is used to optimize the genetic operator probabilities of the lower level genetic programs. The HLGP allows the algorithm to dynamically learn the mutation or combination of mutations that most effectively increase the fitness of the populations, causing a significant increase in the algorithm's accuracy and efficiency. The algorithm's accuracy and efficiency is tested against a standard parallel genetic program with a variety of one-dimensional test cases. Subsequently, the PMLGP is utilized to obtain an accurate empirical valence bond model for proton transfer in 3-hydroxy-gamma-pyrone in gas phase and protic solvent. © 2011 American Institute of Physics

MULTIOBJECTIVE PARALLEL GENETIC ALGORITHM FOR WASTE MINIMIZATION

EPA Science Inventory

In this research we have developed an efficient multiobjective parallel genetic algorithm (MOPGA) for waste minimization problems. This MOPGA integrates PGAPack (Levine, 1996) and NSGA-II (Deb, 2000) with novel modifications. PGAPack is a master-slave parallel implementation of a...

Strain gage selection in loads equations using a genetic algorithm

NASA Technical Reports Server (NTRS)

1994-01-01

Traditionally, structural loads are measured using strain gages. A loads calibration test must be done before loads can be accurately measured. In one measurement method, a series of point loads is applied to the structure, and loads equations are derived via the least squares curve fitting algorithm using the strain gage responses to the applied point loads. However, many research structures are highly instrumented with strain gages, and the number and selection of gages used in a loads equation can be problematic. This paper presents an improved technique using a genetic algorithm to choose the strain gages used in the loads equations. Also presented are a comparison of the genetic algorithm performance with the current T-value technique and a variant known as the Best Step-down technique. Examples are shown using aerospace vehicle wings of high and low aspect ratio. In addition, a significant limitation in the current methods is revealed. The genetic algorithm arrived at a comparable or superior set of gages with significantly less human effort, and could be applied in instances when the current methods could not.

A hybrid genetic algorithm for solving bi-objective traveling salesman problems

NASA Astrophysics Data System (ADS)

Ma, Mei; Li, Hecheng

2017-08-01

The traveling salesman problem (TSP) is a typical combinatorial optimization problem, in a traditional TSP only tour distance is taken as a unique objective to be minimized. When more than one optimization objective arises, the problem is known as a multi-objective TSP. In the present paper, a bi-objective traveling salesman problem (BOTSP) is taken into account, where both the distance and the cost are taken as optimization objectives. In order to efficiently solve the problem, a hybrid genetic algorithm is proposed. Firstly, two satisfaction degree indices are provided for each edge by considering the influences of the distance and the cost weight. The first satisfaction degree is used to select edges in a “rough” way, while the second satisfaction degree is executed for a more “refined” choice. Secondly, two satisfaction degrees are also applied to generate new individuals in the iteration process. Finally, based on genetic algorithm framework as well as 2-opt selection strategy, a hybrid genetic algorithm is proposed. The simulation illustrates the efficiency of the proposed algorithm.

STS-31 pre-deployment checkout of the Hubble Space Telescope (HST) on OV-103

NASA Image and Video Library

1990-04-25

During STS-31 checkout, the Hubble Space Telescope (HST) is held in a pre-deployment position by Discovery's, Orbiter Vehicle (OV) 103's, remote manipulator system (RMS). The view, taken from the crew cabin overhead window W7, shows the starboard solar array (SA) panel (center) and two high gain antennae (HGA) (on either side) stowed along side the Support System Module (SSM) forward shell. The sun highlights HST against the blackness of space.

Technicians complete assembly of Hubble Space Telescope (HST) mockup at JSC

NASA Technical Reports Server (NTRS)

1989-01-01

A technician listens to instructions as he operates the controls for the overhead crane that is lifting one of the Hubble Space Telescope (HST) high gain antennas (HGAs) into place on the HST Support System Module (SSM) forward shell. Others in a cherry picker basket wait to install the HGA on the SSM mockup. The HST mockup will be used for astronaut training and is being assembled in JSC's Mockup and Integration Laboratory (MAIL) Bldg 9A.

Using a genetic algorithm as an optimal band selector in the mid and thermal infrared (2.5-14 μm) to discriminate vegetation species.

PubMed

Ullah, Saleem; Groen, Thomas A; Schlerf, Martin; Skidmore, Andrew K; Nieuwenhuis, Willem; Vaiphasa, Chaichoke

2012-01-01

Genetic variation between various plant species determines differences in their physio-chemical makeup and ultimately in their hyperspectral emissivity signatures. The hyperspectral emissivity signatures, on the one hand, account for the subtle physio-chemical changes in the vegetation, but on the other hand, highlight the problem of high dimensionality. The aim of this paper is to investigate the performance of genetic algorithms coupled with the spectral angle mapper (SAM) to identify a meaningful subset of wavebands sensitive enough to discriminate thirteen broadleaved vegetation species from the laboratory measured hyperspectral emissivities. The performance was evaluated using an overall classification accuracy and Jeffries Matusita distance. For the multiple plant species, the targeted bands based on genetic algorithms resulted in a high overall classification accuracy (90%). Concentrating on the pairwise comparison results, the selected wavebands based on genetic algorithms resulted in higher Jeffries Matusita (J-M) distances than randomly selected wavebands did. This study concludes that targeted wavebands from leaf emissivity spectra are able to discriminate vegetation species.

Improved genetic algorithm for the protein folding problem by use of a Cartesian combination operator.

PubMed Central

Rabow, A. A.; Scheraga, H. A.

1996-01-01

We have devised a Cartesian combination operator and coding scheme for improving the performance of genetic algorithms applied to the protein folding problem. The genetic coding consists of the C alpha Cartesian coordinates of the protein chain. The recombination of the genes of the parents is accomplished by: (1) a rigid superposition of one parent chain on the other, to make the relation of Cartesian coordinates meaningful, then, (2) the chains of the children are formed through a linear combination of the coordinates of their parents. The children produced with this Cartesian combination operator scheme have similar topology and retain the long-range contacts of their parents. The new scheme is significantly more efficient than the standard genetic algorithm methods for locating low-energy conformations of proteins. The considerable superiority of genetic algorithms over Monte Carlo optimization methods is also demonstrated. We have also devised a new dynamic programming lattice fitting procedure for use with the Cartesian combination operator method. The procedure finds excellent fits of real-space chains to the lattice while satisfying bond-length, bond-angle, and overlap constraints. PMID:8880904

The genetic algorithm: A robust method for stress inversion

NASA Astrophysics Data System (ADS)

Thakur, Prithvi; Srivastava, Deepak C.; Gupta, Pravin K.

2017-01-01

The stress inversion of geological or geophysical observations is a nonlinear problem. In most existing methods, it is solved by linearization, under certain assumptions. These linear algorithms not only oversimplify the problem but also are vulnerable to entrapment of the solution in a local optimum. We propose the use of a nonlinear heuristic technique, the genetic algorithm, which searches the global optimum without making any linearizing assumption or simplification. The algorithm mimics the natural evolutionary processes of selection, crossover and mutation and, minimizes a composite misfit function for searching the global optimum, the fittest stress tensor. The validity and efficacy of the algorithm are demonstrated by a series of tests on synthetic and natural fault-slip observations in different tectonic settings and also in situations where the observations are noisy. It is shown that the genetic algorithm is superior to other commonly practised methods, in particular, in those tectonic settings where none of the principal stresses is directed vertically and/or the given data set is noisy.

USING GENETIC ALGORITHMS TO DESIGN ENVIRONMENTALLY FRIENDLY PROCESSES

EPA Science Inventory

Genetic algorithm calculations are applied to the design of chemical processes to achieve improvements in environmental and economic performance. By finding the set of Pareto (i.e., non-dominated) solutions one can see how different objectives, such as environmental and economic ...

The application of immune genetic algorithm in main steam temperature of PID control of BP network

NASA Astrophysics Data System (ADS)

Li, Han; Zhen-yu, Zhang

In order to overcome the uncertainties, large delay, large inertia and nonlinear property of the main steam temperature controlled object in the power plant, a neural network intelligent PID control system based on immune genetic algorithm and BP neural network is designed. Using the immune genetic algorithm global search optimization ability and good convergence, optimize the weights of the neural network, meanwhile adjusting PID parameters using BP network. The simulation result shows that the system is superior to conventional PID control system in the control of quality and robustness.

Optimization of multicast optical networks with genetic algorithm

NASA Astrophysics Data System (ADS)

Lv, Bo; Mao, Xiangqiao; Zhang, Feng; Qin, Xi; Lu, Dan; Chen, Ming; Chen, Yong; Cao, Jihong; Jian, Shuisheng

2007-11-01

In this letter, aiming to obtain the best multicast performance of optical network in which the video conference information is carried by specified wavelength, we extend the solutions of matrix games with the network coding theory and devise a new method to solve the complex problems of multicast network switching. In addition, an experimental optical network has been testified with best switching strategies by employing the novel numerical solution designed with an effective way of genetic algorithm. The result shows that optimal solutions with genetic algorithm are accordance with the ones with the traditional fictitious play method.

Real coded genetic algorithm for fuzzy time series prediction

NASA Astrophysics Data System (ADS)

Jain, Shilpa; Bisht, Dinesh C. S.; Singh, Phool; Mathpal, Prakash C.

2017-10-01

Genetic Algorithm (GA) forms a subset of evolutionary computing, rapidly growing area of Artificial Intelligence (A.I.). Some variants of GA are binary GA, real GA, messy GA, micro GA, saw tooth GA, differential evolution GA. This research article presents a real coded GA for predicting enrollments of University of Alabama. Data of Alabama University is a fuzzy time series. Here, fuzzy logic is used to predict enrollments of Alabama University and genetic algorithm optimizes fuzzy intervals. Results are compared to other eminent author works and found satisfactory, and states that real coded GA are fast and accurate.

Air data system optimization using a genetic algorithm

NASA Technical Reports Server (NTRS)

Deshpande, Samir M.; Kumar, Renjith R.; Seywald, Hans; Siemers, Paul M., III

1992-01-01

An optimization method for flush-orifice air data system design has been developed using the Genetic Algorithm approach. The optimization of the orifice array minimizes the effect of normally distributed random noise in the pressure readings on the calculation of air data parameters, namely, angle of attack, sideslip angle and freestream dynamic pressure. The optimization method is applied to the design of Pressure Distribution/Air Data System experiment (PD/ADS) proposed for inclusion in the Aeroassist Flight Experiment (AFE). Results obtained by the Genetic Algorithm method are compared to the results obtained by conventional gradient search method.

Simultaneous optimization of the cavity heat load and trip rates in linacs using a genetic algorithm

DOE PAGES

Terzić, Balša; Hofler, Alicia S.; Reeves, Cody J.; ...

2014-10-15

In this paper, a genetic algorithm-based optimization is used to simultaneously minimize two competing objectives guiding the operation of the Jefferson Lab's Continuous Electron Beam Accelerator Facility linacs: cavity heat load and radio frequency cavity trip rates. The results represent a significant improvement to the standard linac energy management tool and thereby could lead to a more efficient Continuous Electron Beam Accelerator Facility configuration. This study also serves as a proof of principle of how a genetic algorithm can be used for optimizing other linac-based machines.

A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection

PubMed Central

Thounaojam, Dalton Meitei; Khelchandra, Thongam; Singh, Kh. Manglem; Roy, Sudipta

2016-01-01

This paper proposed a shot boundary detection approach using Genetic Algorithm and Fuzzy Logic. In this, the membership functions of the fuzzy system are calculated using Genetic Algorithm by taking preobserved actual values for shot boundaries. The classification of the types of shot transitions is done by the fuzzy system. Experimental results show that the accuracy of the shot boundary detection increases with the increase in iterations or generations of the GA optimization process. The proposed system is compared to latest techniques and yields better result in terms of F1score parameter. PMID:27127500

Abdomen disease diagnosis in CT images using flexiscale curvelet transform and improved genetic algorithm.

PubMed

Sethi, Gaurav; Saini, B S

2015-12-01

This paper presents an abdomen disease diagnostic system based on the flexi-scale curvelet transform, which uses different optimal scales for extracting features from computed tomography (CT) images. To optimize the scale of the flexi-scale curvelet transform, we propose an improved genetic algorithm. The conventional genetic algorithm assumes that fit parents will likely produce the healthiest offspring that leads to the least fit parents accumulating at the bottom of the population, reducing the fitness of subsequent populations and delaying the optimal solution search. In our improved genetic algorithm, combining the chromosomes of a low-fitness and a high-fitness individual increases the probability of producing high-fitness offspring. Thereby, all of the least fit parent chromosomes are combined with high fit parent to produce offspring for the next population. In this way, the leftover weak chromosomes cannot damage the fitness of subsequent populations. To further facilitate the search for the optimal solution, our improved genetic algorithm adopts modified elitism. The proposed method was applied to 120 CT abdominal images; 30 images each of normal subjects, cysts, tumors and stones. The features extracted by the flexi-scale curvelet transform were more discriminative than conventional methods, demonstrating the potential of our method as a diagnostic tool for abdomen diseases.

Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification.

PubMed

Alshamlan, Hala M; Badr, Ghada H; Alohali, Yousef A

2015-06-01

Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene selection method, namely Genetic Bee Colony (GBC) algorithm. The proposed algorithm combines the used of a Genetic Algorithm (GA) along with Artificial Bee Colony (ABC) algorithm. The goal is to integrate the advantages of both algorithms. The proposed algorithm is applied to a microarray gene expression profile in order to select the most predictive and informative genes for cancer classification. In order to test the accuracy performance of the proposed algorithm, extensive experiments were conducted. Three binary microarray datasets are use, which include: colon, leukemia, and lung. In addition, another three multi-class microarray datasets are used, which are: SRBCT, lymphoma, and leukemia. Results of the GBC algorithm are compared with our recently proposed technique: mRMR when combined with the Artificial Bee Colony algorithm (mRMR-ABC). We also compared the combination of mRMR with GA (mRMR-GA) and Particle Swarm Optimization (mRMR-PSO) algorithms. In addition, we compared the GBC algorithm with other related algorithms that have been recently published in the literature, using all benchmark datasets. The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes. This proves that the GBC algorithm is a promising approach for solving the gene selection problem in both binary and multi-class cancer classification. Copyright © 2015 Elsevier Ltd. All rights reserved.

Image processing meta-algorithm development via genetic manipulation of existing algorithm graphs

NASA Astrophysics Data System (ADS)

Schalkoff, Robert J.; Shaaban, Khaled M.

1999-07-01

Automatic algorithm generation for image processing applications is not a new idea, however previous work is either restricted to morphological operates or impractical. In this paper, we show recent research result in the development and use of meta-algorithms, i.e. algorithms which lead to new algorithms. Although the concept is generally applicable, the application domain in this work is restricted to image processing. The meta-algorithm concept described in this paper is based upon out work in dynamic algorithm. The paper first present the concept of dynamic algorithms which, on the basis of training and archived algorithmic experience embedded in an algorithm graph (AG), dynamically adjust the sequence of operations applied to the input image data. Each node in the tree-based representation of a dynamic algorithm with out degree greater than 2 is a decision node. At these nodes, the algorithm examines the input data and determines which path will most likely achieve the desired results. This is currently done using nearest-neighbor classification. The details of this implementation are shown. The constrained perturbation of existing algorithm graphs, coupled with a suitable search strategy, is one mechanism to achieve meta-algorithm an doffers rich potential for the discovery of new algorithms. In our work, a meta-algorithm autonomously generates new dynamic algorithm graphs via genetic recombination of existing algorithm graphs. The AG representation is well suited to this genetic-like perturbation, using a commonly- employed technique in artificial neural network synthesis, namely the blueprint representation of graphs. A number of exam. One of the principal limitations of our current approach is the need for significant human input in the learning phase. Efforts to overcome this limitation are discussed. Future research directions are indicated.

Finite element analysis and genetic algorithm optimization design for the actuator placement on a large adaptive structure

NASA Astrophysics Data System (ADS)

Sheng, Lizeng

The dissertation focuses on one of the major research needs in the area of adaptive/intelligent/smart structures, the development and application of finite element analysis and genetic algorithms for optimal design of large-scale adaptive structures. We first review some basic concepts in finite element method and genetic algorithms, along with the research on smart structures. Then we propose a solution methodology for solving a critical problem in the design of a next generation of large-scale adaptive structures---optimal placements of a large number of actuators to control thermal deformations. After briefly reviewing the three most frequently used general approaches to derive a finite element formulation, the dissertation presents techniques associated with general shell finite element analysis using flat triangular laminated composite elements. The element used here has three nodes and eighteen degrees of freedom and is obtained by combining a triangular membrane element and a triangular plate bending element. The element includes the coupling effect between membrane deformation and bending deformation. The membrane element is derived from the linear strain triangular element using Cook's transformation. The discrete Kirchhoff triangular (DKT) element is used as the plate bending element. For completeness, a complete derivation of the DKT is presented. Geometrically nonlinear finite element formulation is derived for the analysis of adaptive structures under the combined thermal and electrical loads. Next, we solve the optimization problems of placing a large number of piezoelectric actuators to control thermal distortions in a large mirror in the presence of four different thermal loads. We then extend this to a multi-objective optimization problem of determining only one set of piezoelectric actuator locations that can be used to control the deformation in the same mirror under the action of any one of the four thermal loads. A series of genetic algorithms, GA Version 1, 2 and 3, were developed to find the optimal locations of piezoelectric actuators from the order of 1021 ˜ 1056 candidate placements. Introducing a variable population approach, we improve the flexibility of selection operation in genetic algorithms. Incorporating mutation and hill climbing into micro-genetic algorithms, we are able to develop a more efficient genetic algorithm. Through extensive numerical experiments, we find that the design search space for the optimal placements of a large number of actuators is highly multi-modal and that the most distinct nature of genetic algorithms is their robustness. They give results that are random but with only a slight variability. The genetic algorithms can be used to get adequate solution using a limited number of evaluations. To get the highest quality solution, multiple runs including different random seed generators are necessary. The investigation time can be significantly reduced using a very coarse grain parallel computing. Overall, the methodology of using finite element analysis and genetic algorithm optimization provides a robust solution approach for the challenging problem of optimal placements of a large number of actuators in the design of next generation of adaptive structures.

Selecting materialized views using random algorithm

NASA Astrophysics Data System (ADS)

Zhou, Lijuan; Hao, Zhongxiao; Liu, Chi

2007-04-01

The data warehouse is a repository of information collected from multiple possibly heterogeneous autonomous distributed databases. The information stored at the data warehouse is in form of views referred to as materialized views. The selection of the materialized views is one of the most important decisions in designing a data warehouse. Materialized views are stored in the data warehouse for the purpose of efficiently implementing on-line analytical processing queries. The first issue for the user to consider is query response time. So in this paper, we develop algorithms to select a set of views to materialize in data warehouse in order to minimize the total view maintenance cost under the constraint of a given query response time. We call it query_cost view_ selection problem. First, cost graph and cost model of query_cost view_ selection problem are presented. Second, the methods for selecting materialized views by using random algorithms are presented. The genetic algorithm is applied to the materialized views selection problem. But with the development of genetic process, the legal solution produced become more and more difficult, so a lot of solutions are eliminated and producing time of the solutions is lengthened in genetic algorithm. Therefore, improved algorithm has been presented in this paper, which is the combination of simulated annealing algorithm and genetic algorithm for the purpose of solving the query cost view selection problem. Finally, in order to test the function and efficiency of our algorithms experiment simulation is adopted. The experiments show that the given methods can provide near-optimal solutions in limited time and works better in practical cases. Randomized algorithms will become invaluable tools for data warehouse evolution.

Ortho Image and DTM Generation with Intelligent Methods

NASA Astrophysics Data System (ADS)

Bagheri, H.; Sadeghian, S.

2013-10-01

Nowadays the artificial intelligent algorithms has considered in GIS and remote sensing. Genetic algorithm and artificial neural network are two intelligent methods that are used for optimizing of image processing programs such as edge extraction and etc. these algorithms are very useful for solving of complex program. In this paper, the ability and application of genetic algorithm and artificial neural network in geospatial production process like geometric modelling of satellite images for ortho photo generation and height interpolation in raster Digital Terrain Model production process is discussed. In first, the geometric potential of Ikonos-2 and Worldview-2 with rational functions, 2D & 3D polynomials were tested. Also comprehensive experiments have been carried out to evaluate the viability of the genetic algorithm for optimization of rational function, 2D & 3D polynomials. Considering the quality of Ground Control Points, the accuracy (RMSE) with genetic algorithm and 3D polynomials method for Ikonos-2 Geo image was 0.508 pixel sizes and the accuracy (RMSE) with GA algorithm and rational function method for Worldview-2 image was 0.930 pixel sizes. For more another optimization artificial intelligent methods, neural networks were used. With the use of perceptron network in Worldview-2 image, a result of 0.84 pixel sizes with 4 neurons in middle layer was gained. The final conclusion was that with artificial intelligent algorithms it is possible to optimize the existing models and have better results than usual ones. Finally the artificial intelligence methods, like genetic algorithms as well as neural networks, were examined on sample data for optimizing interpolation and for generating Digital Terrain Models. The results then were compared with existing conventional methods and it appeared that these methods have a high capacity in heights interpolation and that using these networks for interpolating and optimizing the weighting methods based on inverse distance leads to a high accurate estimation of heights.

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.

JavaGenes and Condor: Cycle-Scavenging Genetic Algorithms

NASA Technical Reports Server (NTRS)

Globus, Al; Langhirt, Eric; Livny, Miron; Ramamurthy, Ravishankar; Soloman, Marvin; Traugott, Steve

2000-01-01

A genetic algorithm code, JavaGenes, was written in Java and used to evolve pharmaceutical drug molecules and digital circuits. JavaGenes was run under the Condor cycle-scavenging batch system managing 100-170 desktop SGI workstations. Genetic algorithms mimic biological evolution by evolving solutions to problems using crossover and mutation. While most genetic algorithms evolve strings or trees, JavaGenes evolves graphs representing (currently) molecules and circuits. Java was chosen as the implementation language because the genetic algorithm requires random splitting and recombining of graphs, a complex data structure manipulation with ample opportunities for memory leaks, loose pointers, out-of-bound indices, and other hard to find bugs. Java garbage-collection memory management, lack of pointer arithmetic, and array-bounds index checking prevents these bugs from occurring, substantially reducing development time. While a run-time performance penalty must be paid, the only unacceptable performance we encountered was using standard Java serialization to checkpoint and restart the code. This was fixed by a two-day implementation of custom checkpointing. JavaGenes is minimally integrated with Condor; in other words, JavaGenes must do its own checkpointing and I/O redirection. A prototype Java-aware version of Condor was developed using standard Java serialization for checkpointing. For the prototype to be useful, standard Java serialization must be significantly optimized. JavaGenes is approximately 8700 lines of code and a few thousand JavaGenes jobs have been run. Most jobs ran for a few days. Results include proof that genetic algorithms can evolve directed and undirected graphs, development of a novel crossover operator for graphs, a paper in the journal Nanotechnology, and another paper in preparation.

Incremental cost-effectiveness of algorithm-driven genetic testing versus no testing for Maturity Onset Diabetes of the Young (MODY) in Singapore.

PubMed

Nguyen, Hai Van; Finkelstein, Eric Andrew; Mital, Shweta; Gardner, Daphne Su-Lyn

2017-11-01

Offering genetic testing for Maturity Onset Diabetes of the Young (MODY) to all young patients with type 2 diabetes has been shown to be not cost-effective. This study tests whether a novel algorithm-driven genetic testing strategy for MODY is incrementally cost-effective relative to the setting of no testing. A decision tree was constructed to estimate the costs and effectiveness of the algorithm-driven MODY testing strategy and a strategy of no genetic testing over a 30-year time horizon from a payer's perspective. The algorithm uses glutamic acid decarboxylase (GAD) antibody testing (negative antibodies), age of onset of diabetes (<45 years) and body mass index (<25 kg/m 2 if diagnosed >30 years) to stratify the population of patients with diabetes into three subgroups, and testing for MODY only among the subgroup most likely to have the mutation. Singapore-specific costs and prevalence of MODY obtained from local studies and utility values sourced from the literature are used to populate the model. The algorithm-driven MODY testing strategy has an incremental cost-effectiveness ratio of US$93 663 per quality-adjusted life year relative to the no testing strategy. If the price of genetic testing falls from US$1050 to US$530 (a 50% decrease), it will become cost-effective. Our proposed algorithm-driven testing strategy for MODY is not yet cost-effective based on established benchmarks. However, as genetic testing prices continue to fall, this strategy is likely to become cost-effective in the near future. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Genetic Algorithms for Multiple-Choice Problems

NASA Astrophysics Data System (ADS)

Aickelin, Uwe

2010-04-01

This thesis investigates the use of problem-specific knowledge to enhance a genetic algorithm approach to multiple-choice optimisation problems.It shows that such information can significantly enhance performance, but that the choice of information and the way it is included are important factors for success.Two multiple-choice problems are considered.The first is constructing a feasible nurse roster that considers as many requests as possible.In the second problem, shops are allocated to locations in a mall subject to constraints and maximising the overall income.Genetic algorithms are chosen for their well-known robustness and ability to solve large and complex discrete optimisation problems.However, a survey of the literature reveals room for further research into generic ways to include constraints into a genetic algorithm framework.Hence, the main theme of this work is to balance feasibility and cost of solutions.In particular, co-operative co-evolution with hierarchical sub-populations, problem structure exploiting repair schemes and indirect genetic algorithms with self-adjusting decoder functions are identified as promising approaches.The research starts by applying standard genetic algorithms to the problems and explaining the failure of such approaches due to epistasis.To overcome this, problem-specific information is added in a variety of ways, some of which are designed to increase the number of feasible solutions found whilst others are intended to improve the quality of such solutions.As well as a theoretical discussion as to the underlying reasons for using each operator,extensive computational experiments are carried out on a variety of data.These show that the indirect approach relies less on problem structure and hence is easier to implement and superior in solution quality.

A synthetic genetic edge detection program.

PubMed

Tabor, Jeffrey J; Salis, Howard M; Simpson, Zachary Booth; Chevalier, Aaron A; Levskaya, Anselm; Marcotte, Edward M; Voigt, Christopher A; Ellington, Andrew D

2009-06-26

Edge detection is a signal processing algorithm common in artificial intelligence and image recognition programs. We have constructed a genetically encoded edge detection algorithm that programs an isogenic community of E. coli to sense an image of light, communicate to identify the light-dark edges, and visually present the result of the computation. The algorithm is implemented using multiple genetic circuits. An engineered light sensor enables cells to distinguish between light and dark regions. In the dark, cells produce a diffusible chemical signal that diffuses into light regions. Genetic logic gates are used so that only cells that sense light and the diffusible signal produce a positive output. A mathematical model constructed from first principles and parameterized with experimental measurements of the component circuits predicts the performance of the complete program. Quantitatively accurate models will facilitate the engineering of more complex biological behaviors and inform bottom-up studies of natural genetic regulatory networks.

A Synthetic Genetic Edge Detection Program

PubMed Central

Tabor, Jeffrey J.; Salis, Howard; Simpson, Zachary B.; Chevalier, Aaron A.; Levskaya, Anselm; Marcotte, Edward M.; Voigt, Christopher A.; Ellington, Andrew D.

2009-01-01

Summary Edge detection is a signal processing algorithm common in artificial intelligence and image recognition programs. We have constructed a genetically encoded edge detection algorithm that programs an isogenic community of E.coli to sense an image of light, communicate to identify the light-dark edges, and visually present the result of the computation. The algorithm is implemented using multiple genetic circuits. An engineered light sensor enables cells to distinguish between light and dark regions. In the dark, cells produce a diffusible chemical signal that diffuses into light regions. Genetic logic gates are used so that only cells that sense light and the diffusible signal produce a positive output. A mathematical model constructed from first principles and parameterized with experimental measurements of the component circuits predicts the performance of the complete program. Quantitatively accurate models will facilitate the engineering of more complex biological behaviors and inform bottom-up studies of natural genetic regulatory networks. PMID:19563759

Constrained minimization of smooth functions using a genetic algorithm

NASA Technical Reports Server (NTRS)

Moerder, Daniel D.; Pamadi, Bandu N.

1994-01-01

The use of genetic algorithms for minimization of differentiable functions that are subject to differentiable constraints is considered. A technique is demonstrated for converting the solution of the necessary conditions for a constrained minimum into an unconstrained function minimization. This technique is extended as a global constrained optimization algorithm. The theory is applied to calculating minimum-fuel ascent control settings for an energy state model of an aerospace plane.

Real Time Optima Tracking Using Harvesting Models of the Genetic Algorithm

NASA Technical Reports Server (NTRS)

Baskaran, Subbiah; Noever, D.

1999-01-01

Tracking optima in real time propulsion control, particularly for non-stationary optimization problems is a challenging task. Several approaches have been put forward for such a study including the numerical method called the genetic algorithm. In brief, this approach is built upon Darwinian-style competition between numerical alternatives displayed in the form of binary strings, or by analogy to 'pseudogenes'. Breeding of improved solution is an often cited parallel to natural selection in.evolutionary or soft computing. In this report we present our results of applying a novel model of a genetic algorithm for tracking optima in propulsion engineering and in real time control. We specialize the algorithm to mission profiling and planning optimizations, both to select reduced propulsion needs through trajectory planning and to explore time or fuel conservation strategies.

Study of genetic direct search algorithms for function optimization

NASA Technical Reports Server (NTRS)

Zeigler, B. P.

1974-01-01

The results are presented of a study to determine the performance of genetic direct search algorithms in solving function optimization problems arising in the optimal and adaptive control areas. The findings indicate that: (1) genetic algorithms can outperform standard algorithms in multimodal and/or noisy optimization situations, but suffer from lack of gradient exploitation facilities when gradient information can be utilized to guide the search. (2) For large populations, or low dimensional function spaces, mutation is a sufficient operator. However for small populations or high dimensional functions, crossover applied in about equal frequency with mutation is an optimum combination. (3) Complexity, in terms of storage space and running time, is significantly increased when population size is increased or the inversion operator, or the second level adaptation routine is added to the basic structure.

An Adaptive Immune Genetic Algorithm for Edge Detection

NASA Astrophysics Data System (ADS)

Li, Ying; Bai, Bendu; Zhang, Yanning

An adaptive immune genetic algorithm (AIGA) based on cost minimization technique method for edge detection is proposed. The proposed AIGA recommends the use of adaptive probabilities of crossover, mutation and immune operation, and a geometric annealing schedule in immune operator to realize the twin goals of maintaining diversity in the population and sustaining the fast convergence rate in solving the complex problems such as edge detection. Furthermore, AIGA can effectively exploit some prior knowledge and information of the local edge structure in the edge image to make vaccines, which results in much better local search ability of AIGA than that of the canonical genetic algorithm. Experimental results on gray-scale images show the proposed algorithm perform well in terms of quality of the final edge image, rate of convergence and robustness to noise.

Convergence properties of simple genetic algorithms

NASA Technical Reports Server (NTRS)

Bethke, A. D.; Zeigler, B. P.; Strauss, D. M.

1974-01-01

The essential parameters determining the behaviour of genetic algorithms were investigated. Computer runs were made while systematically varying the parameter values. Results based on the progress curves obtained from these runs are presented along with results based on the variability of the population as the run progresses.

A genetic algorithm approach in interface and surface structure optimization

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

Zhang, Jian

The thesis is divided into two parts. In the first part a global optimization method is developed for the interface and surface structures optimization. Two prototype systems are chosen to be studied. One is Si[001] symmetric tilted grain boundaries and the other is Ag/Au induced Si(111) surface. It is found that Genetic Algorithm is very efficient in finding lowest energy structures in both cases. Not only existing structures in the experiments can be reproduced, but also many new structures can be predicted using Genetic Algorithm. Thus it is shown that Genetic Algorithm is a extremely powerful tool for the materialmore » structures predictions. The second part of the thesis is devoted to the explanation of an experimental observation of thermal radiation from three-dimensional tungsten photonic crystal structures. The experimental results seems astounding and confusing, yet the theoretical models in the paper revealed the physics insight behind the phenomena and can well reproduced the experimental results.« less

Estimation of radiative and conductive properties of a semitransparent medium using genetic algorithms

NASA Astrophysics Data System (ADS)

Braiek, A.; Adili, A.; Albouchi, F.; Karkri, M.; Ben Nasrallah, S.

2016-06-01

The aim of this work is to simultaneously identify the conductive and radiative parameters of a semitransparent sample using a photothermal method associated with an inverse problem. The identification of the conductive and radiative proprieties is performed by the minimization of an objective function that represents the errors between calculated temperature and measured signal. The calculated temperature is obtained from a theoretical model built with the thermal quadrupole formalism. Measurement is obtained in the rear face of the sample whose front face is excited by a crenel of heat flux. For identification procedure, a genetic algorithm is developed and used. The genetic algorithm is a useful tool in the simultaneous estimation of correlated or nearly correlated parameters, which can be a limiting factor for the gradient-based methods. The results of the identification procedure show the efficiency and the stability of the genetic algorithm to simultaneously estimate the conductive and radiative properties of clear glass.

An application of CART algorithm in genetics: IGFs and cGH polymorphisms in Japanese quail

NASA Astrophysics Data System (ADS)

Kaplan, Selçuk

2017-04-01

The avian insulin-like growth factor-1 (IGFs) and avian growth hormone (cGH) genes are the most important genes that can affect bird performance traits because of its important function in growth and metabolism. Understanding the molecular genetic basis of variation in growth-related traits is of importance for continued improvement and increased rates of genetic gain. The objective of the present study was to identify polymorphisms of cGH and IGFs genes in Japanese quail using conventional least square method (LSM) and CART algorithm. Therefore, this study was aimed to demonstrate at determining the polymorphisms of two genes related growth characteristics via CART algorithm. A simulated data set was generated to analyze by adhering the results of some poultry genetic studies which it includes live weights at 5 weeks of age, 3 alleles and 6 genotypes of cGH and 2 alleles and 3 genotypes of IGFs. As a result, it has been determined that the CART algorithm has some advantages as for that LSM.

Application of artificial intelligence to search ground-state geometry of clusters

NASA Astrophysics Data System (ADS)

Lemes, Maurício Ruv; Marim, L. R.; dal Pino, A.

2002-08-01

We introduce a global optimization procedure, the neural-assisted genetic algorithm (NAGA). It combines the power of an artificial neural network (ANN) with the versatility of the genetic algorithm. This method is suitable to solve optimization problems that depend on some kind of heuristics to limit the search space. If a reasonable amount of data is available, the ANN can ``understand'' the problem and provide the genetic algorithm with a selected population of elements that will speed up the search for the optimum solution. We tested the method in a search for the ground-state geometry of silicon clusters. We trained the ANN with information about the geometry and energetics of small silicon clusters. Next, the ANN learned how to restrict the configurational space for larger silicon clusters. For Si10 and Si20, we noticed that the NAGA is at least three times faster than the ``pure'' genetic algorithm. As the size of the cluster increases, it is expected that the gain in terms of time will increase as well.

Application of genetic algorithms to focal mechanism determination

NASA Astrophysics Data System (ADS)

Kobayashi, Reiji; Nakanishi, Ichiro

1994-04-01

Genetic algorithms are a new class of methods for global optimization. They resemble Monte Carlo techniques, but search for solutions more efficiently than uniform Monte Carlo sampling. In the field of geophysics, genetic algorithms have recently been used to solve some non-linear inverse problems (e.g., earthquake location, waveform inversion, migration velocity estimation). We present an application of genetic algorithms to focal mechanism determination from first-motion polarities of P-waves and apply our method to two recent large events, the Kushiro-oki earthquake of January 15, 1993 and the SW Hokkaido (Japan Sea) earthquake of July 12, 1993. Initial solution and curvature information of the objective function that gradient methods need are not required in our approach. Moreover globally optimal solutions can be efficiently obtained. Calculation of polarities based on double-couple models is the most time-consuming part of the source mechanism determination. The amount of calculations required by the method designed in this study is much less than that of previous grid search methods.

Optimized design on condensing tubes high-speed TIG welding technology magnetic control based on genetic algorithm

NASA Astrophysics Data System (ADS)

Lu, Lin; Chang, Yunlong; Li, Yingmin; Lu, Ming

2013-05-01

An orthogonal experiment was conducted by the means of multivariate nonlinear regression equation to adjust the influence of external transverse magnetic field and Ar flow rate on welding quality in the process of welding condenser pipe by high-speed argon tungsten-arc welding (TIG for short). The magnetic induction and flow rate of Ar gas were used as optimum variables, and tensile strength of weld was set to objective function on the base of genetic algorithm theory, and then an optimal design was conducted. According to the request of physical production, the optimum variables were restrained. The genetic algorithm in the MATLAB was used for computing. A comparison between optimum results and experiment parameters was made. The results showed that the optimum technologic parameters could be chosen by the means of genetic algorithm with the conditions of excessive optimum variables in the process of high-speed welding. And optimum technologic parameters of welding coincided with experiment results.

Optimal sensor placement for spatial lattice structure based on genetic algorithms

NASA Astrophysics Data System (ADS)

Liu, Wei; Gao, Wei-cheng; Sun, Yi; Xu, Min-jian

2008-10-01

Optimal sensor placement technique plays a key role in structural health monitoring of spatial lattice structures. This paper considers the problem of locating sensors on a spatial lattice structure with the aim of maximizing the data information so that structural dynamic behavior can be fully characterized. Based on the criterion of optimal sensor placement for modal test, an improved genetic algorithm is introduced to find the optimal placement of sensors. The modal strain energy (MSE) and the modal assurance criterion (MAC) have been taken as the fitness function, respectively, so that three placement designs were produced. The decimal two-dimension array coding method instead of binary coding method is proposed to code the solution. Forced mutation operator is introduced when the identical genes appear via the crossover procedure. A computational simulation of a 12-bay plain truss model has been implemented to demonstrate the feasibility of the three optimal algorithms above. The obtained optimal sensor placements using the improved genetic algorithm are compared with those gained by exiting genetic algorithm using the binary coding method. Further the comparison criterion based on the mean square error between the finite element method (FEM) mode shapes and the Guyan expansion mode shapes identified by data-driven stochastic subspace identification (SSI-DATA) method are employed to demonstrate the advantage of the different fitness function. The results showed that some innovations in genetic algorithm proposed in this paper can enlarge the genes storage and improve the convergence of the algorithm. More importantly, the three optimal sensor placement methods can all provide the reliable results and identify the vibration characteristics of the 12-bay plain truss model accurately.

Neural system for heartbeats recognition using genetically integrated ensemble of classifiers.

PubMed

Osowski, Stanislaw; Siwek, Krzysztof; Siroic, Robert

2011-03-01

This paper presents the application of genetic algorithm for the integration of neural classifiers combined in the ensemble for the accurate recognition of heartbeat types on the basis of ECG registration. The idea presented in this paper is that using many classifiers arranged in the form of ensemble leads to the increased accuracy of the recognition. In such ensemble the important problem is the integration of all classifiers into one effective classification system. This paper proposes the use of genetic algorithm. It was shown that application of the genetic algorithm is very efficient and allows to reduce significantly the total error of heartbeat recognition. This was confirmed by the numerical experiments performed on the MIT BIH Arrhythmia Database. Copyright © 2011 Elsevier Ltd. All rights reserved.

Astrophysical data mining with GPU. A case study: Genetic classification of globular clusters

NASA Astrophysics Data System (ADS)

Cavuoti, S.; Garofalo, M.; Brescia, M.; Paolillo, M.; Pescape', A.; Longo, G.; Ventre, G.

2014-01-01

We present a multi-purpose genetic algorithm, designed and implemented with GPGPU/CUDA parallel computing technology. The model was derived from our CPU serial implementation, named GAME (Genetic Algorithm Model Experiment). It was successfully tested and validated on the detection of candidate Globular Clusters in deep, wide-field, single band HST images. The GPU version of GAME will be made available to the community by integrating it into the web application DAMEWARE (DAta Mining Web Application REsource, http://dame.dsf.unina.it/beta_info.html), a public data mining service specialized on massive astrophysical data. Since genetic algorithms are inherently parallel, the GPGPU computing paradigm leads to a speedup of a factor of 200× in the training phase with respect to the CPU based version.

Prioritizing the Components of Vulnerability: A Genetic Algorithm Minimization of Flood Risk

NASA Astrophysics Data System (ADS)

Bongolan, Vena Pearl; Ballesteros, Florencio; Baritua, Karessa Alexandra; Junne Santos, Marie

2013-04-01

We define a flood resistant city as an optimal arrangement of communities according to their traits, with the goal of minimizing the flooding vulnerability via a genetic algorithm. We prioritize the different components of flooding vulnerability, giving each component a weight, thus expressing vulnerability as a weighted sum. This serves as the fitness function for the genetic algorithm. We also allowed non-linear interactions among related but independent components, viz, poverty and mortality rate, and literacy and radio/ tv penetration. The designs produced reflect the relative importance of the components, and we observed a synchronicity between the interacting components, giving us a more consistent design.

Algorithmic Trading with Developmental and Linear Genetic Programming

NASA Astrophysics Data System (ADS)

Wilson, Garnett; Banzhaf, Wolfgang

A developmental co-evolutionary genetic programming approach (PAM DGP) and a standard linear genetic programming (LGP) stock trading systemare applied to a number of stocks across market sectors. Both GP techniques were found to be robust to market fluctuations and reactive to opportunities associated with stock price rise and fall, with PAMDGP generating notably greater profit in some stock trend scenarios. Both algorithms were very accurate at buying to achieve profit and selling to protect assets, while exhibiting bothmoderate trading activity and the ability to maximize or minimize investment as appropriate. The content of the trading rules produced by both algorithms are also examined in relation to stock price trend scenarios.

Supervised chaos genetic algorithm based state of charge determination for LiFePO4 batteries in electric vehicles

NASA Astrophysics Data System (ADS)

Shen, Yanqing

2018-04-01

LiFePO4 battery is developed rapidly in electric vehicle, whose safety and functional capabilities are influenced greatly by the evaluation of available cell capacity. Added with adaptive switch mechanism, this paper advances a supervised chaos genetic algorithm based state of charge determination method, where a combined state space model is employed to simulate battery dynamics. The method is validated by the experiment data collected from battery test system. Results indicate that the supervised chaos genetic algorithm based state of charge determination method shows great performance with less computation complexity and is little influenced by the unknown initial cell state.

Artificial intelligence programming with LabVIEW: genetic algorithms for instrumentation control and optimization.

PubMed

Moore, J H

1995-06-01

A genetic algorithm for instrumentation control and optimization was developed using the LabVIEW graphical programming environment. The usefulness of this methodology for the optimization of a closed loop control instrument is demonstrated with minimal complexity and the programming is presented in detail to facilitate its adaptation to other LabVIEW applications. Closed loop control instruments have variety of applications in the biomedical sciences including the regulation of physiological processes such as blood pressure. The program presented here should provide a useful starting point for those wishing to incorporate genetic algorithm approaches to LabVIEW mediated optimization of closed loop control instruments.

An Efficient Functional Test Generation Method For Processors Using Genetic Algorithms

NASA Astrophysics Data System (ADS)

Hudec, Ján; Gramatová, Elena

2015-07-01

The paper presents a new functional test generation method for processors testing based on genetic algorithms and evolutionary strategies. The tests are generated over an instruction set architecture and a processor description. Such functional tests belong to the software-oriented testing. Quality of the tests is evaluated by code coverage of the processor description using simulation. The presented test generation method uses VHDL models of processors and the professional simulator ModelSim. The rules, parameters and fitness functions were defined for various genetic algorithms used in automatic test generation. Functionality and effectiveness were evaluated using the RISC type processor DP32.

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.

On Directly Solving SCHRÖDINGER Equation for H+2 Ion by Genetic Algorithm

NASA Astrophysics Data System (ADS)

Saha, Rajendra; Bhattacharyya, S. P.

Schrödinger equation (SE) is sought to be solved directly for the ground state of H+2 ion by invoking genetic algorithm (GA). In one approach the internuclear distance (R) is kept fixed, the corresponding electronic SE for H+2 is solved by GA at each R and the full potential energy curve (PEC) is constructed. The minimum of the PEC is then located giving Ve and Re. Alternatively, Ve and Re are located in a single run by allowing R to vary simultaneously while solving the electronic SE by genetic algorithm. The performance patterns of the two strategies are compared.

Applying a Genetic Algorithm to Reconfigurable Hardware

NASA Technical Reports Server (NTRS)

Wells, B. Earl; Weir, John; Trevino, Luis; Patrick, Clint; Steincamp, Jim

2004-01-01

This paper investigates the feasibility of applying genetic algorithms to solve optimization problems that are implemented entirely in reconfgurable hardware. The paper highlights the pe$ormance/design space trade-offs that must be understood to effectively implement a standard genetic algorithm within a modem Field Programmable Gate Array, FPGA, reconfgurable hardware environment and presents a case-study where this stochastic search technique is applied to standard test-case problems taken from the technical literature. In this research, the targeted FPGA-based platform and high-level design environment was the Starbridge Hypercomputing platform, which incorporates multiple Xilinx Virtex II FPGAs, and the Viva TM graphical hardware description language.

Mobile transporter path planning

NASA Technical Reports Server (NTRS)

Baffes, Paul; Wang, Lui

1990-01-01

The use of a genetic algorithm (GA) for solving the mobile transporter path planning problem is investigated. The mobile transporter is a traveling robotic vehicle proposed for the space station which must be able to reach any point of the structure autonomously. Elements of the genetic algorithm are explored in both a theoretical and experimental sense. Specifically, double crossover, greedy crossover, and tournament selection techniques are examined. Additionally, the use of local optimization techniques working in concert with the GA are also explored. Recent developments in genetic algorithm theory are shown to be particularly effective in a path planning problem domain, though problem areas can be cited which require more research.

Mechanism-based inhibition of C5-cytosine DNA methyltransferases by 2-H pyrimidinone.

PubMed

Hurd, P J; Whitmarsh, A J; Baldwin, G S; Kelly, S M; Waltho, J P; Price, N C; Connolly, B A; Hornby, D P

1999-02-19

DNA duplexes in which the target cytosine base is replaced by 2-H pyrimidinone have previously been shown to bind with a significantly greater affinity to C5-cytosine DNA methyltransferases than unmodified DNA. Here, it is shown that 2-H pyrimidinone, when incorporated into DNA duplexes containing the recognition sites for M.HgaI-2 and M.MspI, elicits the formation of inhibitory covalent nucleoprotein complexes. We have found that although covalent complexes are formed between 2-H pyrimidinone-modified DNA and both M.HgaI-2 and M.MspI, the kinetics of complex formation are quite distinct in each case. Moreover, the formation of a covalent complex is still observed between 2-H pyrimidinone DNA and M.MspI in which the active-site cysteine residue is replaced by serine or threonine. Covalent complex formation between M.MspI and 2-H pyrimidinone occurs as a direct result of nucleophilic attack by the residue at the catalytic position, which is enhanced by the absence of the 4-amino function in the base. The substitution of the catalytic cysteine residue by tyrosine or chemical modification of the wild-type enzyme with N-ethylmaleimide, abolishes covalent interaction. Nevertheless the 2-H pyrimidinone-substituted duplex still binds to M.MspI with a greater affinity than a standard cognate duplex, since the 2-H pyrimidinone base is mis-paired with guanine. Copyright 1999 Academic Press.

Genetic algorithms and their use in Geophysical Problems

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

Parker, Paul B.

1999-04-01

Genetic algorithms (GAs), global optimization methods that mimic Darwinian evolution are well suited to the nonlinear inverse problems of geophysics. A standard genetic algorithm selects the best or ''fittest'' models from a ''population'' and then applies operators such as crossover and mutation in order to combine the most successful characteristics of each model and produce fitter models. More sophisticated operators have been developed, but the standard GA usually provides a robust and efficient search. Although the choice of parameter settings such as crossover and mutation rate may depend largely on the type of problem being solved, numerous results show thatmore » certain parameter settings produce optimal performance for a wide range of problems and difficulties. In particular, a low (about half of the inverse of the population size) mutation rate is crucial for optimal results, but the choice of crossover method and rate do not seem to affect performance appreciably. Optimal efficiency is usually achieved with smaller (< 50) populations. Lastly, tournament selection appears to be the best choice of selection methods due to its simplicity and its autoscaling properties. However, if a proportional selection method is used such as roulette wheel selection, fitness scaling is a necessity, and a high scaling factor (> 2.0) should be used for the best performance. Three case studies are presented in which genetic algorithms are used to invert for crustal parameters. The first is an inversion for basement depth at Yucca mountain using gravity data, the second an inversion for velocity structure in the crust of the south island of New Zealand using receiver functions derived from teleseismic events, and the third is a similar receiver function inversion for crustal velocities beneath the Mendocino Triple Junction region of Northern California. The inversions demonstrate that genetic algorithms are effective in solving problems with reasonably large numbers of free parameters and with computationally expensive objective function calculations. More sophisticated techniques are presented for special problems. Niching and island model algorithms are introduced as methods to find multiple, distinct solutions to the nonunique problems that are typically seen in geophysics. Finally, hybrid algorithms are investigated as a way to improve the efficiency of the standard genetic algorithm.« less

Genetic algorithms and their use in geophysical problems

NASA Astrophysics Data System (ADS)

Parker, Paul Bradley

Genetic algorithms (GAs), global optimization methods that mimic Darwinian evolution are well suited to the nonlinear inverse problems of geophysics. A standard genetic algorithm selects the best or "fittest" models from a "population" and then applies operators such as crossover and mutation in order to combine the most successful characteristics of each model and produce fitter models. More sophisticated operators have been developed, but the standard GA usually provides a robust and efficient search. Although the choice of parameter settings such as crossover and mutation rate may depend largely on the type of problem being solved, numerous results show that certain parameter settings produce optimal performance for a wide range of problems and difficulties. In particular, a low (about half of the inverse of the population size) mutation rate is crucial for optimal results, but the choice of crossover method and rate do not seem to affect performance appreciably. Also, optimal efficiency is usually achieved with smaller (<50) populations. Lastly, tournament selection appears to be the best choice of selection methods due to its simplicity and its autoscaling properties. However, if a proportional selection method is used such as roulette wheel selection, fitness scaling is a necessity, and a high scaling factor (>2.0) should be used for the best performance. Three case studies are presented in which genetic algorithms are used to invert for crustal parameters. The first is an inversion for basement depth at Yucca mountain using gravity data, the second an inversion for velocity structure in the crust of the south island of New Zealand using receiver functions derived from teleseismic events, and the third is a similar receiver function inversion for crustal velocities beneath the Mendocino Triple Junction region of Northern California. The inversions demonstrate that genetic algorithms are effective in solving problems with reasonably large numbers of free parameters and with computationally expensive objective function calculations. More sophisticated techniques are presented for special problems. Niching and island model algorithms are introduced as methods to find multiple, distinct solutions to the nonunique problems that are typically seen in geophysics. Finally, hybrid algorithms are investigated as a way to improve the efficiency of the standard genetic algorithm.

Using Genetic Algorithm and MODFLOW to Characterize Aquifer System of Northwest Florida

EPA Science Inventory

By integrating Genetic Algorithm and MODFLOW2005, an optimizing tool is developed to characterize the aquifer system of Region II, Northwest Florida. The history and the newest available observation data of the aquifer system is fitted automatically by using the numerical model c...

Longest jobs first algorithm in solving job shop scheduling using adaptive genetic algorithm (GA)

NASA Astrophysics Data System (ADS)

Alizadeh Sahzabi, Vahid; Karimi, Iman; Alizadeh Sahzabi, Navid; Mamaani Barnaghi, Peiman

2012-01-01

In this paper, genetic algorithm was used to solve job shop scheduling problems. One example discussed in JSSP (Job Shop Scheduling Problem) and I described how we can solve such these problems by genetic algorithm. The goal in JSSP is to gain the shortest process time. Furthermore I proposed a method to obtain best performance on performing all jobs in shortest time. The method mainly, is according to Genetic algorithm (GA) and crossing over between parents always follows the rule which the longest process is at the first in the job queue. In the other word chromosomes is suggested to sorts based on the longest processes to shortest i.e. "longest job first" says firstly look which machine contains most processing time during its performing all its jobs and that is the bottleneck. Secondly, start sort those jobs which are belonging to that specific machine descending. Based on the achieved results," longest jobs first" is the optimized status in job shop scheduling problems. In our results the accuracy would grow up to 94.7% for total processing time and the method improved 4% the accuracy of performing all jobs in the presented example.

Efficient computation of the joint probability of multiple inherited risk alleles from pedigree data.

PubMed

Madsen, Thomas; Braun, Danielle; Peng, Gang; Parmigiani, Giovanni; Trippa, Lorenzo

2018-06-25

The Elston-Stewart peeling algorithm enables estimation of an individual's probability of harboring germline risk alleles based on pedigree data, and serves as the computational backbone of important genetic counseling tools. However, it remains limited to the analysis of risk alleles at a small number of genetic loci because its computing time grows exponentially with the number of loci considered. We propose a novel, approximate version of this algorithm, dubbed the peeling and paring algorithm, which scales polynomially in the number of loci. This allows extending peeling-based models to include many genetic loci. The algorithm creates a trade-off between accuracy and speed, and allows the user to control this trade-off. We provide exact bounds on the approximation error and evaluate it in realistic simulations. Results show that the loss of accuracy due to the approximation is negligible in important applications. This algorithm will improve genetic counseling tools by increasing the number of pathogenic risk alleles that can be addressed. To illustrate we create an extended five genes version of BRCAPRO, a widely used model for estimating the carrier probabilities of BRCA1 and BRCA2 risk alleles and assess its computational properties. © 2018 WILEY PERIODICALS, INC.

Optimization of beam orientation in radiotherapy using planar geometry

NASA Astrophysics Data System (ADS)

Haas, O. C. L.; Burnham, K. J.; Mills, J. A.

1998-08-01

This paper proposes a new geometrical formulation of the coplanar beam orientation problem combined with a hybrid multiobjective genetic algorithm. The approach is demonstrated by optimizing the beam orientation in two dimensions, with the objectives being formulated using planar geometry. The traditional formulation of the objectives associated with the organs at risk has been modified to account for the use of complex dose delivery techniques such as beam intensity modulation. The new algorithm attempts to replicate the approach of a treatment planner whilst reducing the amount of computation required. Hybrid genetic search operators have been developed to improve the performance of the genetic algorithm by exploiting problem-specific features. The multiobjective genetic algorithm is formulated around the concept of Pareto optimality which enables the algorithm to search in parallel for different objectives. When the approach is applied without constraining the number of beams, the solution produces an indication of the minimum number of beams required. It is also possible to obtain non-dominated solutions for various numbers of beams, thereby giving the clinicians a choice in terms of the number of beams as well as in the orientation of these beams.

Distributed query plan generation using multiobjective genetic algorithm.

PubMed

Panicker, Shina; Kumar, T V Vijay

2014-01-01

A distributed query processing strategy, which is a key performance determinant in accessing distributed databases, aims to minimize the total query processing cost. One way to achieve this is by generating efficient distributed query plans that involve fewer sites for processing a query. In the case of distributed relational databases, the number of possible query plans increases exponentially with respect to the number of relations accessed by the query and the number of sites where these relations reside. Consequently, computing optimal distributed query plans becomes a complex problem. This distributed query plan generation (DQPG) problem has already been addressed using single objective genetic algorithm, where the objective is to minimize the total query processing cost comprising the local processing cost (LPC) and the site-to-site communication cost (CC). In this paper, this DQPG problem is formulated and solved as a biobjective optimization problem with the two objectives being minimize total LPC and minimize total CC. These objectives are simultaneously optimized using a multiobjective genetic algorithm NSGA-II. Experimental comparison of the proposed NSGA-II based DQPG algorithm with the single objective genetic algorithm shows that the former performs comparatively better and converges quickly towards optimal solutions for an observed crossover and mutation probability.

Acoustic Impedance Inversion of Seismic Data Using Genetic Algorithm

NASA Astrophysics Data System (ADS)

Eladj, Said; Djarfour, Noureddine; Ferahtia, Djalal; Ouadfeul, Sid-Ali

2013-04-01

The inversion of seismic data can be used to constrain estimates of the Earth's acoustic impedance structure. This kind of problem is usually known to be non-linear, high-dimensional, with a complex search space which may be riddled with many local minima, and results in irregular objective functions. We investigate here the performance and the application of a genetic algorithm, in the inversion of seismic data. The proposed algorithm has the advantage of being easily implemented without getting stuck in local minima. The effects of population size, Elitism strategy, uniform cross-over and lower mutation are examined. The optimum solution parameters and performance were decided as a function of the testing error convergence with respect to the generation number. To calculate the fitness function, we used L2 norm of the sample-to-sample difference between the reference and the inverted trace. The cross-over probability is of 0.9-0.95 and mutation has been tested at 0.01 probability. The application of such a genetic algorithm to synthetic data shows that the inverted acoustic impedance section was efficient. Keywords: Seismic, Inversion, acoustic impedance, genetic algorithm, fitness functions, cross-over, mutation.

Distributed Query Plan Generation Using Multiobjective Genetic Algorithm

PubMed Central

Panicker, Shina; Vijay Kumar, T. V.

2014-01-01

A distributed query processing strategy, which is a key performance determinant in accessing distributed databases, aims to minimize the total query processing cost. One way to achieve this is by generating efficient distributed query plans that involve fewer sites for processing a query. In the case of distributed relational databases, the number of possible query plans increases exponentially with respect to the number of relations accessed by the query and the number of sites where these relations reside. Consequently, computing optimal distributed query plans becomes a complex problem. This distributed query plan generation (DQPG) problem has already been addressed using single objective genetic algorithm, where the objective is to minimize the total query processing cost comprising the local processing cost (LPC) and the site-to-site communication cost (CC). In this paper, this DQPG problem is formulated and solved as a biobjective optimization problem with the two objectives being minimize total LPC and minimize total CC. These objectives are simultaneously optimized using a multiobjective genetic algorithm NSGA-II. Experimental comparison of the proposed NSGA-II based DQPG algorithm with the single objective genetic algorithm shows that the former performs comparatively better and converges quickly towards optimal solutions for an observed crossover and mutation probability. PMID:24963513

Genetic algorithm enhanced by machine learning in dynamic aperture optimization

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

Li, Yongjun; Cheng, Weixing; Yu, Li Hua

With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given “elite” status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitnessmore » of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. Furthermore, the machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.« less

Genetic algorithm enhanced by machine learning in dynamic aperture optimization

NASA Astrophysics Data System (ADS)

Li, Yongjun; Cheng, Weixing; Yu, Li Hua; Rainer, Robert

2018-05-01

With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given "elite" status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitness of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. The machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.

Genetic algorithm enhanced by machine learning in dynamic aperture optimization

DOE PAGES

Li, Yongjun; Cheng, Weixing; Yu, Li Hua; ...

2018-05-29

With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given “elite” status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitnessmore » of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. Furthermore, the machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.« less

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

An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks.

PubMed

Yoon, Yourim; Kim, Yong-Hyuk

2013-10-01

Sensor networks have a lot of applications such as battlefield surveillance, environmental monitoring, and industrial diagnostics. Coverage is one of the most important performance metrics for sensor networks since it reflects how well a sensor field is monitored. In this paper, we introduce the maximum coverage deployment problem in wireless sensor networks and analyze the properties of the problem and its solution space. Random deployment is the simplest way to deploy sensor nodes but may cause unbalanced deployment and therefore, we need a more intelligent way for sensor deployment. We found that the phenotype space of the problem is a quotient space of the genotype space in a mathematical view. Based on this property, we propose an efficient genetic algorithm using a novel normalization method. A Monte Carlo method is adopted to design an efficient evaluation function, and its computation time is decreased without loss of solution quality using a method that starts from a small number of random samples and gradually increases the number for subsequent generations. The proposed genetic algorithms could be further improved by combining with a well-designed local search. The performance of the proposed genetic algorithm is shown by a comparative experimental study. When compared with random deployment and existing methods, our genetic algorithm was not only about twice faster, but also showed significant performance improvement in quality.

Comparison of Genetic Algorithm and Hill Climbing for Shortest Path Optimization Mapping

NASA Astrophysics Data System (ADS)

Fronita, Mona; Gernowo, Rahmat; Gunawan, Vincencius

2018-02-01

Traveling Salesman Problem (TSP) is an optimization to find the shortest path to reach several destinations in one trip without passing through the same city and back again to the early departure city, the process is applied to the delivery systems. This comparison is done using two methods, namely optimization genetic algorithm and hill climbing. Hill Climbing works by directly selecting a new path that is exchanged with the neighbour's to get the track distance smaller than the previous track, without testing. Genetic algorithms depend on the input parameters, they are the number of population, the probability of crossover, mutation probability and the number of generations. To simplify the process of determining the shortest path supported by the development of software that uses the google map API. Tests carried out as much as 20 times with the number of city 8, 16, 24 and 32 to see which method is optimal in terms of distance and time computation. Based on experiments conducted with a number of cities 3, 4, 5 and 6 producing the same value and optimal distance for the genetic algorithm and hill climbing, the value of this distance begins to differ with the number of city 7. The overall results shows that these tests, hill climbing are more optimal to number of small cities and the number of cities over 30 optimized using genetic algorithms.

Weather prediction using a genetic memory

NASA Technical Reports Server (NTRS)

Rogers, David

1990-01-01

Kanaerva's sparse distributed memory (SDM) is an associative memory model based on the mathematical properties of high dimensional binary address spaces. Holland's genetic algorithms are a search technique for high dimensional spaces inspired by evolutional processes of DNA. Genetic Memory is a hybrid of the above two systems, in which the memory uses a genetic algorithm to dynamically reconfigure its physical storage locations to reflect correlations between the stored addresses and data. This architecture is designed to maximize the ability of the system to scale-up to handle real world problems.

Assessing Seismic Hazards - Algorithms, Maps, and Emergency Scenarios

NASA Astrophysics Data System (ADS)

Ferriz, H.

2007-05-01

Public officials in charge of building codes, land use planning, and emergency response need sound estimates of seismic hazards. Sources may be well defined (e.g., active faults that have a surface trace) or diffuse (e.g., a subduction zone or a blind-thrust belt), but in both cases one can use a deterministic or worst-case scenario approach. For each scenario, a design earthquake is selected based on historic data or the known length of Holocene ruptures (as determined by geologic mapping). Horizontal ground accelerations (HGAs) can then be estimated at different distances from the earthquake epicenter using published attenuation relations (e.g., Seismological Res. Letters, v. 68, 1997) and estimates of the elastic properties of the substrate materials. No good algorithms are available to take into account reflection of elastic waves across other fault planes (e.g., a common effect in California, where there are many strands of the San Andreas fault), or amplification of waves in water-saturated alluvial and lacustrine basins (e.g., the Mexico City basin), but empirical relations can be developed by correlating historic damage patterns with predicted HGAs. The ultimate result is a map of HGAs. With this map, and with additional data on depth to groundwater and geotechnical properties of local soils, a liquefaction susceptibility map can be prepared, using published algorithms (e.g., J. of Geotech. Geoenv. Eng., v. 127, p. 817-833, 2001; Eng. Geology Practice in N. California, p. 579-594, 2001). Finally, the HGA estimates, digital elevation models, geologic structural data, and geotechnical properties of local geologic units can be used to prepare a slope failure susceptibility map (e.g., Eng. Geology Practice in N. California, p. 77-94, 2001). Seismic hazard maps are used by: (1) Building officials to determine areas of the city where special construction codes have to be implemented, and where existing buildings may need to be retrofitted. (2) Planning officials to evaluate plans for new growth (though in most cities land use patterns are historically established). (3) Emergency response officials to plan emergency operations. (4) Insurance commissioners to estimate losses and insurance claims (e.g., with FEMA's software HAZUS).

iNJclust: Iterative Neighbor-Joining Tree Clustering Framework for Inferring Population Structure.

PubMed

Limpiti, Tulaya; Amornbunchornvej, Chainarong; Intarapanich, Apichart; Assawamakin, Anunchai; Tongsima, Sissades

2014-01-01

Understanding genetic differences among populations is one of the most important issues in population genetics. Genetic variations, e.g., single nucleotide polymorphisms, are used to characterize commonality and difference of individuals from various populations. This paper presents an efficient graph-based clustering framework which operates iteratively on the Neighbor-Joining (NJ) tree called the iNJclust algorithm. The framework uses well-known genetic measurements, namely the allele-sharing distance, the neighbor-joining tree, and the fixation index. The behavior of the fixation index is utilized in the algorithm's stopping criterion. The algorithm provides an estimated number of populations, individual assignments, and relationships between populations as outputs. The clustering result is reported in the form of a binary tree, whose terminal nodes represent the final inferred populations and the tree structure preserves the genetic relationships among them. The clustering performance and the robustness of the proposed algorithm are tested extensively using simulated and real data sets from bovine, sheep, and human populations. The result indicates that the number of populations within each data set is reasonably estimated, the individual assignment is robust, and the structure of the inferred population tree corresponds to the intrinsic relationships among populations within the data.

Efficient experimental design of high-fidelity three-qubit quantum gates via genetic programming

NASA Astrophysics Data System (ADS)

Devra, Amit; Prabhu, Prithviraj; Singh, Harpreet; Arvind; Dorai, Kavita

2018-03-01

We have designed efficient quantum circuits for the three-qubit Toffoli (controlled-controlled-NOT) and the Fredkin (controlled-SWAP) gate, optimized via genetic programming methods. The gates thus obtained were experimentally implemented on a three-qubit NMR quantum information processor, with a high fidelity. Toffoli and Fredkin gates in conjunction with the single-qubit Hadamard gates form a universal gate set for quantum computing and are an essential component of several quantum algorithms. Genetic algorithms are stochastic search algorithms based on the logic of natural selection and biological genetics and have been widely used for quantum information processing applications. We devised a new selection mechanism within the genetic algorithm framework to select individuals from a population. We call this mechanism the "Luck-Choose" mechanism and were able to achieve faster convergence to a solution using this mechanism, as compared to existing selection mechanisms. The optimization was performed under the constraint that the experimentally implemented pulses are of short duration and can be implemented with high fidelity. We demonstrate the advantage of our pulse sequences by comparing our results with existing experimental schemes and other numerical optimization methods.

On the suitability of different representations of solid catalysts for combinatorial library design by genetic algorithms.

PubMed

Gobin, Oliver C; Schüth, Ferdi

2008-01-01

Genetic algorithms are widely used to solve and optimize combinatorial problems and are more often applied for library design in combinatorial chemistry. Because of their flexibility, however, their implementation can be challenging. In this study, the influence of the representation of solid catalysts on the performance of genetic algorithms was systematically investigated on the basis of a new, constrained, multiobjective, combinatorial test problem with properties common to problems in combinatorial materials science. Constraints were satisfied by penalty functions, repair algorithms, or special representations. The tests were performed using three state-of-the-art evolutionary multiobjective algorithms by performing 100 optimization runs for each algorithm and test case. Experimental data obtained during the optimization of a noble metal-free solid catalyst system active in the selective catalytic reduction of nitric oxide with propene was used to build up a predictive model to validate the results of the theoretical test problem. A significant influence of the representation on the optimization performance was observed. Binary encodings were found to be the preferred encoding in most of the cases, and depending on the experimental test unit, repair algorithms or penalty functions performed best.

Improved adaptive genetic algorithm with sparsity constraint applied to thermal neutron CT reconstruction of two-phase flow

NASA Astrophysics Data System (ADS)

Yan, Mingfei; Hu, Huasi; Otake, Yoshie; Taketani, Atsushi; Wakabayashi, Yasuo; Yanagimachi, Shinzo; Wang, Sheng; Pan, Ziheng; Hu, Guang

2018-05-01

Thermal neutron computer tomography (CT) is a useful tool for visualizing two-phase flow due to its high imaging contrast and strong penetrability of neutrons for tube walls constructed with metallic material. A novel approach for two-phase flow CT reconstruction based on an improved adaptive genetic algorithm with sparsity constraint (IAGA-SC) is proposed in this paper. In the algorithm, the neighborhood mutation operator is used to ensure the continuity of the reconstructed object. The adaptive crossover probability P c and mutation probability P m are improved to help the adaptive genetic algorithm (AGA) achieve the global optimum. The reconstructed results for projection data, obtained from Monte Carlo simulation, indicate that the comprehensive performance of the IAGA-SC algorithm exceeds the adaptive steepest descent-projection onto convex sets (ASD-POCS) algorithm in restoring typical and complex flow regimes. It especially shows great advantages in restoring the simply connected flow regimes and the shape of object. In addition, the CT experiment for two-phase flow phantoms was conducted on the accelerator-driven neutron source to verify the performance of the developed IAGA-SC algorithm.

Creating IRT-Based Parallel Test Forms Using the Genetic Algorithm Method

ERIC Educational Resources Information Center

Sun, Koun-Tem; Chen, Yu-Jen; Tsai, Shu-Yen; Cheng, Chien-Fen

2008-01-01

In educational measurement, the construction of parallel test forms is often a combinatorial optimization problem that involves the time-consuming selection of items to construct tests having approximately the same test information functions (TIFs) and constraints. This article proposes a novel method, genetic algorithm (GA), to construct parallel…

Genetic Algorithm Phase Retrieval for the Systematic Image-Based Optical Alignment Testbed

NASA Technical Reports Server (NTRS)

Rakoczy, John; Steincamp, James; Taylor, Jaime

2003-01-01

A reduced surrogate, one point crossover genetic algorithm with random rank-based selection was used successfully to estimate the multiple phases of a segmented optical system modeled on the seven-mirror Systematic Image-Based Optical Alignment testbed located at NASA's Marshall Space Flight Center.

Using Genetic Algorithm and MODFLOW to Characterize Aquifer System of Northwest Florida (Published Proceedings)

EPA Science Inventory

By integrating Genetic Algorithm and MODFLOW2005, an optimizing tool is developed to characterize the aquifer system of Region II, Northwest Florida. The history and the newest available observation data of the aquifer system is fitted automatically by using the numerical model c...

Genetic algorithm to solve the problems of lectures and practicums scheduling

NASA Astrophysics Data System (ADS)

Syahputra, M. F.; Apriani, R.; Sawaluddin; Abdullah, D.; Albra, W.; Heikal, M.; Abdurrahman, A.; Khaddafi, M.

2018-02-01

Generally, the scheduling process is done manually. However, this method has a low accuracy level, along with possibilities that a scheduled process collides with another scheduled process. When doing theory class and practicum timetable scheduling process, there are numerous problems, such as lecturer teaching schedule collision, schedule collision with another schedule, practicum lesson schedules that collides with theory class, and the number of classrooms available. In this research, genetic algorithm is implemented to perform theory class and practicum timetable scheduling process. The algorithm will be used to process the data containing lists of lecturers, courses, and class rooms, obtained from information technology department at University of Sumatera Utara. The result of scheduling process using genetic algorithm is the most optimal timetable that conforms to available time slots, class rooms, courses, and lecturer schedules.

Multiple feature fusion via covariance matrix for visual tracking

NASA Astrophysics Data System (ADS)

Jin, Zefenfen; Hou, Zhiqiang; Yu, Wangsheng; Wang, Xin; Sun, Hui

2018-04-01

Aiming at the problem of complicated dynamic scenes in visual target tracking, a multi-feature fusion tracking algorithm based on covariance matrix is proposed to improve the robustness of the tracking algorithm. In the frame-work of quantum genetic algorithm, this paper uses the region covariance descriptor to fuse the color, edge and texture features. It also uses a fast covariance intersection algorithm to update the model. The low dimension of region covariance descriptor, the fast convergence speed and strong global optimization ability of quantum genetic algorithm, and the fast computation of fast covariance intersection algorithm are used to improve the computational efficiency of fusion, matching, and updating process, so that the algorithm achieves a fast and effective multi-feature fusion tracking. The experiments prove that the proposed algorithm can not only achieve fast and robust tracking but also effectively handle interference of occlusion, rotation, deformation, motion blur and so on.

A Modified Decision Tree Algorithm Based on Genetic Algorithm for Mobile User Classification Problem

PubMed Central

Liu, Dong-sheng; Fan, Shu-jiang

2014-01-01

In order to offer mobile customers better service, we should classify the mobile user firstly. Aimed at the limitations of previous classification methods, this paper puts forward a modified decision tree algorithm for mobile user classification, which introduced genetic algorithm to optimize the results of the decision tree algorithm. We also take the context information as a classification attributes for the mobile user and we classify the context into public context and private context classes. Then we analyze the processes and operators of the algorithm. At last, we make an experiment on the mobile user with the algorithm, we can classify the mobile user into Basic service user, E-service user, Plus service user, and Total service user classes and we can also get some rules about the mobile user. Compared to C4.5 decision tree algorithm and SVM algorithm, the algorithm we proposed in this paper has higher accuracy and more simplicity. PMID:24688389

An improved self-adaptive ant colony algorithm based on genetic strategy for the traveling salesman problem

NASA Astrophysics Data System (ADS)

Wang, Pan; Zhang, Yi; Yan, Dong

2018-05-01

Ant Colony Algorithm (ACA) is a powerful and effective algorithm for solving the combination optimization problem. Moreover, it was successfully used in traveling salesman problem (TSP). But it is easy to prematurely converge to the non-global optimal solution and the calculation time is too long. To overcome those shortcomings, a new method is presented-An improved self-adaptive Ant Colony Algorithm based on genetic strategy. The proposed method adopts adaptive strategy to adjust the parameters dynamically. And new crossover operation and inversion operation in genetic strategy was used in this method. We also make an experiment using the well-known data in TSPLIB. The experiment results show that the performance of the proposed method is better than the basic Ant Colony Algorithm and some improved ACA in both the result and the convergence time. The numerical results obtained also show that the proposed optimization method can achieve results close to the theoretical best known solutions at present.

A Solution Method of Job-shop Scheduling Problems by the Idle Time Shortening Type Genetic Algorithm

NASA Astrophysics Data System (ADS)

Ida, Kenichi; Osawa, Akira

In this paper, we propose a new idle time shortening method for Job-shop scheduling problems (JSPs). We insert its method into a genetic algorithm (GA). The purpose of JSP is to find a schedule with the minimum makespan. We suppose that it is effective to reduce idle time of a machine in order to improve the makespan. The left shift is a famous algorithm in existing algorithms for shortening idle time. The left shift can not arrange the work to idle time. For that reason, some idle times are not shortened by the left shift. We propose two kinds of algorithms which shorten such idle time. Next, we combine these algorithms and the reversal of a schedule. We apply GA with its algorithm to benchmark problems and we show its effectiveness.

Packing Boxes into Multiple Containers Using Genetic Algorithm

NASA Astrophysics Data System (ADS)

Menghani, Deepak; Guha, Anirban

2016-07-01

Container loading problems have been studied extensively in the literature and various analytical, heuristic and metaheuristic methods have been proposed. This paper presents two different variants of a genetic algorithm framework for the three-dimensional container loading problem for optimally loading boxes into multiple containers with constraints. The algorithms are designed so that it is easy to incorporate various constraints found in real life problems. The algorithms are tested on data of standard test cases from literature and are found to compare well with the benchmark algorithms in terms of utilization of containers. This, along with the ability to easily incorporate a wide range of practical constraints, makes them attractive for implementation in real life scenarios.

Airport Flight Departure Delay Model on Improved BN Structure Learning

NASA Astrophysics Data System (ADS)

Cao, Weidong; Fang, Xiangnong

An high score prior genetic simulated annealing Bayesian network structure learning algorithm (HSPGSA) by combining genetic algorithm(GA) with simulated annealing algorithm(SAA) is developed. The new algorithm provides not only with strong global search capability of GA, but also with strong local hill climb search capability of SAA. The structure with the highest score is prior selected. In the mean time, structures with lower score are also could be choice. It can avoid efficiently prematurity problem by higher score individual wrong direct growing population. Algorithm is applied to flight departure delays analysis in a large hub airport. Based on the flight data a BN model is created. Experiments show that parameters learning can reflect departure delay.

Fuzzy Mixed Assembly Line Sequencing and Scheduling Optimization Model Using Multiobjective Dynamic Fuzzy GA

PubMed Central

Tahriri, Farzad; Dawal, Siti Zawiah Md; Taha, Zahari

2014-01-01

A new multiobjective dynamic fuzzy genetic algorithm is applied to solve a fuzzy mixed-model assembly line sequencing problem in which the primary goals are to minimize the total make-span and minimize the setup number simultaneously. Trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data. An improved genetic algorithm called fuzzy adaptive genetic algorithm (FAGA) is proposed in order to solve this optimization model. In establishing the FAGA, five dynamic fuzzy parameter controllers are devised in which fuzzy expert experience controller (FEEC) is integrated with automatic learning dynamic fuzzy controller (ALDFC) technique. The enhanced algorithm dynamically adjusts the population size, number of generations, tournament candidate, crossover rate, and mutation rate compared with using fixed control parameters. The main idea is to improve the performance and effectiveness of existing GAs by dynamic adjustment and control of the five parameters. Verification and validation of the dynamic fuzzy GA are carried out by developing test-beds and testing using a multiobjective fuzzy mixed production assembly line sequencing optimization problem. The simulation results highlight that the performance and efficacy of the proposed novel optimization algorithm are more efficient than the performance of the standard genetic algorithm in mixed assembly line sequencing model. PMID:24982962

Deadlock-free genetic scheduling algorithm for automated manufacturing systems based on deadlock control policy.

PubMed

Xing, KeYi; Han, LiBin; Zhou, MengChu; Wang, Feng

2012-06-01

Deadlock-free control and scheduling are vital for optimizing the performance of automated manufacturing systems (AMSs) with shared resources and route flexibility. Based on the Petri net models of AMSs, this paper embeds the optimal deadlock avoidance policy into the genetic algorithm and develops a novel deadlock-free genetic scheduling algorithm for AMSs. A possible solution of the scheduling problem is coded as a chromosome representation that is a permutation with repetition of parts. By using the one-step look-ahead method in the optimal deadlock control policy, the feasibility of a chromosome is checked, and infeasible chromosomes are amended into feasible ones, which can be easily decoded into a feasible deadlock-free schedule. The chromosome representation and polynomial complexity of checking and amending procedures together support the cooperative aspect of genetic search for scheduling problems strongly.

Nuclear fuel management optimization using genetic algorithms

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

DeChaine, M.D.; Feltus, M.A.

1995-07-01

The code independent genetic algorithm reactor optimization (CIGARO) system has been developed to optimize nuclear reactor loading patterns. It uses genetic algorithms (GAs) and a code-independent interface, so any reactor physics code (e.g., CASMO-3/SIMULATE-3) can be used to evaluate the loading patterns. The system is compared to other GA-based loading pattern optimizers. Tests were carried out to maximize the beginning of cycle k{sub eff} for a pressurized water reactor core loading with a penalty function to limit power peaking. The CIGARO system performed well, increasing the k{sub eff} after lowering the peak power. Tests of a prototype parallel evaluation methodmore » showed the potential for a significant speedup.« less

Medical image segmentation using genetic algorithms.

PubMed

Maulik, Ujjwal

2009-03-01

Genetic algorithms (GAs) have been found to be effective in the domain of medical image segmentation, since the problem can often be mapped to one of search in a complex and multimodal landscape. The challenges in medical image segmentation arise due to poor image contrast and artifacts that result in missing or diffuse organ/tissue boundaries. The resulting search space is therefore often noisy with a multitude of local optima. Not only does the genetic algorithmic framework prove to be effective in coming out of local optima, it also brings considerable flexibility into the segmentation procedure. In this paper, an attempt has been made to review the major applications of GAs to the domain of medical image segmentation.

Naturally selecting solutions: the use of genetic algorithms in bioinformatics.

PubMed

Manning, Timmy; Sleator, Roy D; Walsh, Paul

2013-01-01

For decades, computer scientists have looked to nature for biologically inspired solutions to computational problems; ranging from robotic control to scheduling optimization. Paradoxically, as we move deeper into the post-genomics era, the reverse is occurring, as biologists and bioinformaticians look to computational techniques, to solve a variety of biological problems. One of the most common biologically inspired techniques are genetic algorithms (GAs), which take the Darwinian concept of natural selection as the driving force behind systems for solving real world problems, including those in the bioinformatics domain. Herein, we provide an overview of genetic algorithms and survey some of the most recent applications of this approach to bioinformatics based problems.

Determination of thiamine HCl and pyridoxine HCl in pharmaceutical preparations using UV-visible spectrophotometry and genetic algorithm based multivariate calibration methods.

PubMed

Ozdemir, Durmus; Dinc, Erdal

2004-07-01

Simultaneous determination of binary mixtures pyridoxine hydrochloride and thiamine hydrochloride in a vitamin combination using UV-visible spectrophotometry and classical least squares (CLS) and three newly developed genetic algorithm (GA) based multivariate calibration methods was demonstrated. The three genetic multivariate calibration methods are Genetic Classical Least Squares (GCLS), Genetic Inverse Least Squares (GILS) and Genetic Regression (GR). The sample data set contains the UV-visible spectra of 30 synthetic mixtures (8 to 40 microg/ml) of these vitamins and 10 tablets containing 250 mg from each vitamin. The spectra cover the range from 200 to 330 nm in 0.1 nm intervals. Several calibration models were built with the four methods for the two components. Overall, the standard error of calibration (SEC) and the standard error of prediction (SEP) for the synthetic data were in the range of <0.01 and 0.43 microg/ml for all the four methods. The SEP values for the tablets were in the range of 2.91 and 11.51 mg/tablets. A comparison of genetic algorithm selected wavelengths for each component using GR method was also included.

Stochastic search in structural optimization - Genetic algorithms and simulated annealing

NASA Technical Reports Server (NTRS)

Hajela, Prabhat

1993-01-01

An account is given of illustrative applications of genetic algorithms and simulated annealing methods in structural optimization. The advantages of such stochastic search methods over traditional mathematical programming strategies are emphasized; it is noted that these methods offer a significantly higher probability of locating the global optimum in a multimodal design space. Both genetic-search and simulated annealing can be effectively used in problems with a mix of continuous, discrete, and integer design variables.

Unscented Sampling Techniques For Evolutionary Computation With Applications To Astrodynamic Optimization

DTIC Science & Technology

2016-09-01

to both genetic algorithms and evolution strategies to achieve these goals. The results of this research offer a promising new set of modified ...abs_all.jsp?arnumber=203904 [163] Z. Michalewicz, C. Z. Janikow, and J. B. Krawczyk, “A modified genetic algo- rithm for optimal control problems...Available: http://arc.aiaa.org/doi/abs/10.2514/ 2.7053 375 [166] N. Yokoyama and S. Suzuki, “ Modified genetic algorithm for constrained trajectory

A novel structure-aware sparse learning algorithm for brain imaging genetics.

PubMed

Du, Lei; Jingwen, Yan; Kim, Sungeun; Risacher, Shannon L; Huang, Heng; Inlow, Mark; Moore, Jason H; Saykin, Andrew J; Shen, Li

2014-01-01

Brain imaging genetics is an emergent research field where the association between genetic variations such as single nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs) is evaluated. Sparse canonical correlation analysis (SCCA) is a bi-multivariate analysis method that has the potential to reveal complex multi-SNP-multi-QT associations. Most existing SCCA algorithms are designed using the soft threshold strategy, which assumes that the features in the data are independent from each other. This independence assumption usually does not hold in imaging genetic data, and thus inevitably limits the capability of yielding optimal solutions. We propose a novel structure-aware SCCA (denoted as S2CCA) algorithm to not only eliminate the independence assumption for the input data, but also incorporate group-like structure in the model. Empirical comparison with a widely used SCCA implementation, on both simulated and real imaging genetic data, demonstrated that S2CCA could yield improved prediction performance and biologically meaningful findings.

Application of Hierarchical Goal Analysis to the Halifax Class Frigate Operations Room: A Case Study

DTIC Science & Technology

2007-11-01

actuelle en fonction de son niveau de soutien pour ces diverses dépendances. Les auteurs prévoient que la même méthode peut être utilisée pour décrire...analysis. The focus of this paper is on the practice rather than the theory of HGA. Therefore, the next section contains only a brief summary of the...is based on Perceptual Control Theory [17], which posits that humans operate as perceptually driven, goal referenced, feedback systems, in that all

STS-31 Hubble Space Telescope (HST) solar array (SA) deploy aboard OV-103

NASA Image and Video Library

1990-04-25

During STS-31, the Hubble Space Telescope (HST) is held in appendage deploy position by Discovery's, Orbiter Vehicle (OV) 103's, remote manipulator system (RMS) above the payload bay (PLB) and crew compartment cabin. While in this position the solar array (SA) wing bistem cassette (HST center) is deployed from its stowed location along side the Support System Module (SSM) forward shell. A high gain antenna (HGA) remains stowed along the SSM. The Earth's surface and the Earth limb creates a dramatic backdrop.

A Smart Itsy Bitsy Spider for the Web.

ERIC Educational Resources Information Center

Chen, Hsinchun; Chung, Yi-Ming; Ramsey, Marshall; Yang, Christopher C.

1998-01-01

This study tested two Web personal spiders (i.e., agents that take users' requests and perform real-time customized searches) based on best first-search and genetic-algorithm techniques. Both results were comparable and complementary, although the genetic algorithm obtained higher recall value. The Java-based interface was found to be necessary…

Genetic algorithm driven spectral shaping of supercontinuum radiation in a photonic crystal fiber

NASA Astrophysics Data System (ADS)

Michaeli, Linor; Bahabad, Alon

2018-05-01

We employ a genetic algorithm to control a pulse-shaping system pumping a nonlinear photonic crystal with ultrashort pulses. With this system, we are able to modify the spectrum of the generated supercontinuum (SC) radiation to yield narrow Gaussian-like features around pre-selected wavelengths over the whole SC spectrum.

MAC Protocol for Ad Hoc Networks Using a Genetic Algorithm

PubMed Central

Elizarraras, Omar; Panduro, Marco; Méndez, Aldo L.

2014-01-01

The problem of obtaining the transmission rate in an ad hoc network consists in adjusting the power of each node to ensure the signal to interference ratio (SIR) and the energy required to transmit from one node to another is obtained at the same time. Therefore, an optimal transmission rate for each node in a medium access control (MAC) protocol based on CSMA-CDMA (carrier sense multiple access-code division multiple access) for ad hoc networks can be obtained using evolutionary optimization. This work proposes a genetic algorithm for the transmission rate election considering a perfect power control, and our proposition achieves improvement of 10% compared with the scheme that handles the handshaking phase to adjust the transmission rate. Furthermore, this paper proposes a genetic algorithm that solves the problem of power combining, interference, data rate, and energy ensuring the signal to interference ratio in an ad hoc network. The result of the proposed genetic algorithm has a better performance (15%) compared to the CSMA-CDMA protocol without optimizing. Therefore, we show by simulation the effectiveness of the proposed protocol in terms of the throughput. PMID:25140339

A genetic-algorithm approach for assessing the liquefaction potential of sandy soils

NASA Astrophysics Data System (ADS)

Sen, G.; Akyol, E.

2010-04-01

The determination of liquefaction potential is required to take into account a large number of parameters, which creates a complex nonlinear structure of the liquefaction phenomenon. The conventional methods rely on simple statistical and empirical relations or charts. However, they cannot characterise these complexities. Genetic algorithms are suited to solve these types of problems. A genetic algorithm-based model has been developed to determine the liquefaction potential by confirming Cone Penetration Test datasets derived from case studies of sandy soils. Software has been developed that uses genetic algorithms for the parameter selection and assessment of liquefaction potential. Then several estimation functions for the assessment of a Liquefaction Index have been generated from the dataset. The generated Liquefaction Index estimation functions were evaluated by assessing the training and test data. The suggested formulation estimates the liquefaction occurrence with significant accuracy. Besides, the parametric study on the liquefaction index curves shows a good relation with the physical behaviour. The total number of misestimated cases was only 7.8% for the proposed method, which is quite low when compared to another commonly used method.

Complex motion measurement using genetic algorithm

NASA Astrophysics Data System (ADS)

Shen, Jianjun; Tu, Dan; Shen, Zhenkang

1997-12-01

Genetic algorithm (GA) is an optimization technique that provides an untraditional approach to deal with many nonlinear, complicated problems. The notion of motion measurement using genetic algorithm arises from the fact that the motion measurement is virtually an optimization process based on some criterions. In the paper, we propose a complex motion measurement method using genetic algorithm based on block-matching criterion. The following three problems are mainly discussed and solved in the paper: (1) apply an adaptive method to modify the control parameters of GA that are critical to itself, and offer an elitism strategy at the same time (2) derive an evaluate function of motion measurement for GA based on block-matching technique (3) employ hill-climbing (HC) method hybridly to assist GA's search for the global optimal solution. Some other related problems are also discussed. At the end of paper, experiments result is listed. We employ six motion parameters for measurement in our experiments. Experiments result shows that the performance of our GA is good. The GA can find the object motion accurately and rapidly.

Optimization of fuels from waste composition with application of genetic algorithm.

PubMed

Małgorzata, Wzorek

2014-05-01

The objective of this article is to elaborate a method to optimize the composition of the fuels from sewage sludge (PBS fuel - fuel based on sewage sludge and coal slime, PBM fuel - fuel based on sewage sludge and meat and bone meal, PBT fuel - fuel based on sewage sludge and sawdust). As a tool for an optimization procedure, the use of a genetic algorithm is proposed. The optimization task involves the maximization of mass fraction of sewage sludge in a fuel developed on the basis of quality-based criteria for the use as an alternative fuel used by the cement industry. The selection criteria of fuels composition concerned such parameters as: calorific value, content of chlorine, sulphur and heavy metals. Mathematical descriptions of fuel compositions and general forms of the genetic algorithm, as well as the obtained optimization results are presented. The results of this study indicate that the proposed genetic algorithm offers an optimization tool, which could be useful in the determination of the composition of fuels that are produced from waste.

Research on prediction of agricultural machinery total power based on grey model optimized by genetic algorithm

NASA Astrophysics Data System (ADS)

Xie, Yan; Li, Mu; Zhou, Jin; Zheng, Chang-zheng

2009-07-01

Agricultural machinery total power is an important index to reflex and evaluate the level of agricultural mechanization. It is the power source of agricultural production, and is the main factors to enhance the comprehensive agricultural production capacity expand production scale and increase the income of the farmers. Its demand is affected by natural, economic, technological and social and other "grey" factors. Therefore, grey system theory can be used to analyze the development of agricultural machinery total power. A method based on genetic algorithm optimizing grey modeling process is introduced in this paper. This method makes full use of the advantages of the grey prediction model and characteristics of genetic algorithm to find global optimization. So the prediction model is more accurate. According to data from a province, the GM (1, 1) model for predicting agricultural machinery total power was given based on the grey system theories and genetic algorithm. The result indicates that the model can be used as agricultural machinery total power an effective tool for prediction.

Logistic regression trees for initial selection of interesting loci in case-control studies

PubMed Central

Nickolov, Radoslav Z; Milanov, Valentin B

2007-01-01

Modern genetic epidemiology faces the challenge of dealing with hundreds of thousands of genetic markers. The selection of a small initial subset of interesting markers for further investigation can greatly facilitate genetic studies. In this contribution we suggest the use of a logistic regression tree algorithm known as logistic tree with unbiased selection. Using the simulated data provided for Genetic Analysis Workshop 15, we show how this algorithm, with incorporation of multifactor dimensionality reduction method, can reduce an initial large pool of markers to a small set that includes the interesting markers with high probability. PMID:18466557

Routine Discovery of Complex Genetic Models using Genetic Algorithms

PubMed Central

Moore, Jason H.; Hahn, Lance W.; Ritchie, Marylyn D.; Thornton, Tricia A.; White, Bill C.

2010-01-01

Simulation studies are useful in various disciplines for a number of reasons including the development and evaluation of new computational and statistical methods. This is particularly true in human genetics and genetic epidemiology where new analytical methods are needed for the detection and characterization of disease susceptibility genes whose effects are complex, nonlinear, and partially or solely dependent on the effects of other genes (i.e. epistasis or gene-gene interaction). Despite this need, the development of complex genetic models that can be used to simulate data is not always intuitive. In fact, only a few such models have been published. We have previously developed a genetic algorithm approach to discovering complex genetic models in which two single nucleotide polymorphisms (SNPs) influence disease risk solely through nonlinear interactions. In this paper, we extend this approach for the discovery of high-order epistasis models involving three to five SNPs. We demonstrate that the genetic algorithm is capable of routinely discovering interesting high-order epistasis models in which each SNP influences risk of disease only through interactions with the other SNPs in the model. This study opens the door for routine simulation of complex gene-gene interactions among SNPs for the development and evaluation of new statistical and computational approaches for identifying common, complex multifactorial disease susceptibility genes. PMID:20948983

A Genetic Algorithm That Exchanges Neighboring Centers for Fuzzy c-Means Clustering

ERIC Educational Resources Information Center

Chahine, Firas Safwan

2012-01-01

Clustering algorithms are widely used in pattern recognition and data mining applications. Due to their computational efficiency, partitional clustering algorithms are better suited for applications with large datasets than hierarchical clustering algorithms. K-means is among the most popular partitional clustering algorithm, but has a major…

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 ...

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.

Design of synthetic biological logic circuits based on evolutionary algorithm.

PubMed

Chuang, Chia-Hua; Lin, Chun-Liang; Chang, Yen-Chang; Jennawasin, Tanagorn; Chen, Po-Kuei

2013-08-01

The construction of an artificial biological logic circuit using systematic strategy is recognised as one of the most important topics for the development of synthetic biology. In this study, a real-structured genetic algorithm (RSGA), which combines general advantages of the traditional real genetic algorithm with those of the structured genetic algorithm, is proposed to deal with the biological logic circuit design problem. A general model with the cis-regulatory input function and appropriate promoter activity functions is proposed to synthesise a wide variety of fundamental logic gates such as NOT, Buffer, AND, OR, NAND, NOR and XOR. The results obtained can be extended to synthesise advanced combinational and sequential logic circuits by topologically distinct connections. The resulting optimal design of these logic gates and circuits are established via the RSGA. The in silico computer-based modelling technology has been verified showing its great advantages in the purpose.

Preliminary Structural Design Using Topology Optimization with a Comparison of Results from Gradient and Genetic Algorithm Methods

NASA Technical Reports Server (NTRS)

Burt, Adam O.; Tinker, Michael L.

2014-01-01

In this paper, genetic algorithm based and gradient-based topology optimization is presented in application to a real hardware design problem. Preliminary design of a planetary lander mockup structure is accomplished using these methods that prove to provide major weight savings by addressing the structural efficiency during the design cycle. This paper presents two alternative formulations of the topology optimization problem. The first is the widely-used gradient-based implementation using commercially available algorithms. The second is formulated using genetic algorithms and internally developed capabilities. These two approaches are applied to a practical design problem for hardware that has been built, tested and proven to be functional. Both formulations converged on similar solutions and therefore were proven to be equally valid implementations of the process. This paper discusses both of these formulations at a high level.

Inverting the parameters of an earthquake-ruptured fault with a genetic algorithm

NASA Astrophysics Data System (ADS)

Yu, Ting-To; Fernàndez, Josè; Rundle, John B.

1998-03-01

Natural selection is the spirit of the genetic algorithm (GA): by keeping the good genes in the current generation, thereby producing better offspring during evolution. The crossover function ensures the heritage of good genes from parent to offspring. Meanwhile, the process of mutation creates a special gene, the character of which does not exist in the parent generation. A program based on genetic algorithms using C language is constructed to invert the parameters of an earthquake-ruptured fault. The verification and application of this code is shown to demonstrate its capabilities. It is determined that this code is able to find the global extreme and can be used to solve more practical problems with constraints gathered from other sources. It is shown that GA is superior to other inverting schema in many aspects. This easy handling and yet powerful algorithm should have many suitable applications in the field of geosciences.

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.

An Improved SoC Test Scheduling Method Based on Simulated Annealing Algorithm

NASA Astrophysics Data System (ADS)

Zheng, Jingjing; Shen, Zhihang; Gao, Huaien; Chen, Bianna; Zheng, Weida; Xiong, Xiaoming

2017-02-01

In this paper, we propose an improved SoC test scheduling method based on simulated annealing algorithm (SA). It is our first to disorganize IP core assignment for each TAM to produce a new solution for SA, allocate TAM width for each TAM using greedy algorithm and calculate corresponding testing time. And accepting the core assignment according to the principle of simulated annealing algorithm and finally attain the optimum solution. Simultaneously, we run the test scheduling experiment with the international reference circuits provided by International Test Conference 2002(ITC’02) and the result shows that our algorithm is superior to the conventional integer linear programming algorithm (ILP), simulated annealing algorithm (SA) and genetic algorithm(GA). When TAM width reaches to 48,56 and 64, the testing time based on our algorithm is lesser than the classic methods and the optimization rates are 30.74%, 3.32%, 16.13% respectively. Moreover, the testing time based on our algorithm is very close to that of improved genetic algorithm (IGA), which is state-of-the-art at present.

A new compound arithmetic crossover-based genetic algorithm for constrained optimisation in enterprise systems

NASA Astrophysics Data System (ADS)

Jin, Chenxia; Li, Fachao; Tsang, Eric C. C.; Bulysheva, Larissa; Kataev, Mikhail Yu

2017-01-01

In many real industrial applications, the integration of raw data with a methodology can support economically sound decision-making. Furthermore, most of these tasks involve complex optimisation problems. Seeking better solutions is critical. As an intelligent search optimisation algorithm, genetic algorithm (GA) is an important technique for complex system optimisation, but it has internal drawbacks such as low computation efficiency and prematurity. Improving the performance of GA is a vital topic in academic and applications research. In this paper, a new real-coded crossover operator, called compound arithmetic crossover operator (CAC), is proposed. CAC is used in conjunction with a uniform mutation operator to define a new genetic algorithm CAC10-GA. This GA is compared with an existing genetic algorithm (AC10-GA) that comprises an arithmetic crossover operator and a uniform mutation operator. To judge the performance of CAC10-GA, two kinds of analysis are performed. First the analysis of the convergence of CAC10-GA is performed by the Markov chain theory; second, a pair-wise comparison is carried out between CAC10-GA and AC10-GA through two test problems available in the global optimisation literature. The overall comparative study shows that the CAC performs quite well and the CAC10-GA defined outperforms the AC10-GA.

Demonstrating the suitability of genetic algorithms for driving microbial ecosystems in desirable directions.

PubMed

Vandecasteele, Frederik P J; Hess, Thomas F; Crawford, Ronald L

2007-07-01

The functioning of natural microbial ecosystems is determined by biotic interactions, which are in turn influenced by abiotic environmental conditions. Direct experimental manipulation of such conditions can be used to purposefully drive ecosystems toward exhibiting desirable functions. When a set of environmental conditions can be manipulated to be present at a discrete number of levels, finding the right combination of conditions to obtain the optimal desired effect becomes a typical combinatorial optimisation problem. Genetic algorithms are a class of robust and flexible search and optimisation techniques from the field of computer science that may be very suitable for such a task. To verify this idea, datasets containing growth levels of the total microbial community of four different natural microbial ecosystems in response to all possible combinations of a set of five chemical supplements were obtained. Subsequently, the ability of a genetic algorithm to search this parameter space for combinations of supplements driving the microbial communities to high levels of growth was compared to that of a random search, a local search, and a hill-climbing algorithm, three intuitive alternative optimisation approaches. The results indicate that a genetic algorithm is very suitable for driving microbial ecosystems in desirable directions, which opens opportunities for both fundamental ecological research and industrial applications.

Comparative Analysis of Soft Computing Models in Prediction of Bending Rigidity of Cotton Woven Fabrics

NASA Astrophysics Data System (ADS)

Guruprasad, R.; Behera, B. K.

2015-10-01

Quantitative prediction of fabric mechanical properties is an essential requirement for design engineering of textile and apparel products. In this work, the possibility of prediction of bending rigidity of cotton woven fabrics has been explored with the application of Artificial Neural Network (ANN) and two hybrid methodologies, namely Neuro-genetic modeling and Adaptive Neuro-Fuzzy Inference System (ANFIS) modeling. For this purpose, a set of cotton woven grey fabrics was desized, scoured and relaxed. The fabrics were then conditioned and tested for bending properties. With the database thus created, a neural network model was first developed using back propagation as the learning algorithm. The second model was developed by applying a hybrid learning strategy, in which genetic algorithm was first used as a learning algorithm to optimize the number of neurons and connection weights of the neural network. The Genetic algorithm optimized network structure was further allowed to learn using back propagation algorithm. In the third model, an ANFIS modeling approach was attempted to map the input-output data. The prediction performances of the models were compared and a sensitivity analysis was reported. The results show that the prediction by neuro-genetic and ANFIS models were better in comparison with that of back propagation neural network model.

The mGA1.0: A common LISP implementation of a messy genetic algorithm

NASA Technical Reports Server (NTRS)

Goldberg, David E.; Kerzic, Travis

1990-01-01

Genetic algorithms (GAs) are finding increased application in difficult search, optimization, and machine learning problems in science and engineering. Increasing demands are being placed on algorithm performance, and the remaining challenges of genetic algorithm theory and practice are becoming increasingly unavoidable. Perhaps the most difficult of these challenges is the so-called linkage problem. Messy GAs were created to overcome the linkage problem of simple genetic algorithms by combining variable-length strings, gene expression, messy operators, and a nonhomogeneous phasing of evolutionary processing. Results on a number of difficult deceptive test functions are encouraging with the mGA always finding global optima in a polynomial number of function evaluations. Theoretical and empirical studies are continuing, and a first version of a messy GA is ready for testing by others. A Common LISP implementation called mGA1.0 is documented and related to the basic principles and operators developed by Goldberg et. al. (1989, 1990). Although the code was prepared with care, it is not a general-purpose code, only a research version. Important data structures and global variations are described. Thereafter brief function descriptions are given, and sample input data are presented together with sample program output. A source listing with comments is also included.

Genetic algorithms for protein threading.

PubMed

Yadgari, J; Amir, A; Unger, R

1998-01-01

Despite many years of efforts, a direct prediction of protein structure from sequence is still not possible. As a result, in the last few years researchers have started to address the "inverse folding problem": Identifying and aligning a sequence to the fold with which it is most compatible, a process known as "threading". In two meetings in which protein folding predictions were objectively evaluated, it became clear that threading as a concept promises a real breakthrough, but that much improvement is still needed in the technique itself. Threading is a NP-hard problem, and thus no general polynomial solution can be expected. Still a practical approach with demonstrated ability to find optimal solutions in many cases, and acceptable solutions in other cases, is needed. We applied the technique of Genetic Algorithms in order to significantly improve the ability of threading algorithms to find the optimal alignment of a sequence to a structure, i.e. the alignment with the minimum free energy. A major progress reported here is the design of a representation of the threading alignment as a string of fixed length. With this representation validation of alignments and genetic operators are effectively implemented. Appropriate data structure and parameters have been selected. It is shown that Genetic Algorithm threading is effective and is able to find the optimal alignment in a few test cases. Furthermore, the described algorithm is shown to perform well even without pre-definition of core elements. Existing threading methods are dependent on such constraints to make their calculations feasible. But the concept of core elements is inherently arbitrary and should be avoided if possible. While a rigorous proof is hard to submit yet an, we present indications that indeed Genetic Algorithm threading is capable of finding consistently good solutions of full alignments in search spaces of size up to 10(70).

A hybrid intelligent algorithm for portfolio selection problem with fuzzy returns

NASA Astrophysics Data System (ADS)

Li, Xiang; Zhang, Yang; Wong, Hau-San; Qin, Zhongfeng

2009-11-01

Portfolio selection theory with fuzzy returns has been well developed and widely applied. Within the framework of credibility theory, several fuzzy portfolio selection models have been proposed such as mean-variance model, entropy optimization model, chance constrained programming model and so on. In order to solve these nonlinear optimization models, a hybrid intelligent algorithm is designed by integrating simulated annealing algorithm, neural network and fuzzy simulation techniques, where the neural network is used to approximate the expected value and variance for fuzzy returns and the fuzzy simulation is used to generate the training data for neural network. Since these models are used to be solved by genetic algorithm, some comparisons between the hybrid intelligent algorithm and genetic algorithm are given in terms of numerical examples, which imply that the hybrid intelligent algorithm is robust and more effective. In particular, it reduces the running time significantly for large size problems.

Clustering for Binary Data Sets by Using Genetic Algorithm-Incremental K-means

NASA Astrophysics Data System (ADS)

Saharan, S.; Baragona, R.; Nor, M. E.; Salleh, R. M.; Asrah, N. M.

2018-04-01

This research was initially driven by the lack of clustering algorithms that specifically focus in binary data. To overcome this gap in knowledge, a promising technique for analysing this type of data became the main subject in this research, namely Genetic Algorithms (GA). For the purpose of this research, GA was combined with the Incremental K-means (IKM) algorithm to cluster the binary data streams. In GAIKM, the objective function was based on a few sufficient statistics that may be easily and quickly calculated on binary numbers. The implementation of IKM will give an advantage in terms of fast convergence. The results show that GAIKM is an efficient and effective new clustering algorithm compared to the clustering algorithms and to the IKM itself. In conclusion, the GAIKM outperformed other clustering algorithms such as GCUK, IKM, Scalable K-means (SKM) and K-means clustering and paves the way for future research involving missing data and outliers.

A High-Performance Genetic Algorithm: Using Traveling Salesman Problem as a Case

PubMed Central

Tsai, Chun-Wei; Tseng, Shih-Pang; Yang, Chu-Sing

2014-01-01

This paper presents a simple but efficient algorithm for reducing the computation time of genetic algorithm (GA) and its variants. The proposed algorithm is motivated by the observation that genes common to all the individuals of a GA have a high probability of surviving the evolution and ending up being part of the final solution; as such, they can be saved away to eliminate the redundant computations at the later generations of a GA. To evaluate the performance of the proposed algorithm, we use it not only to solve the traveling salesman problem but also to provide an extensive analysis on the impact it may have on the quality of the end result. Our experimental results indicate that the proposed algorithm can significantly reduce the computation time of GA and GA-based algorithms while limiting the degradation of the quality of the end result to a very small percentage compared to traditional GA. PMID:24892038

A high-performance genetic algorithm: using traveling salesman problem as a case.

PubMed

Tsai, Chun-Wei; Tseng, Shih-Pang; Chiang, Ming-Chao; Yang, Chu-Sing; Hong, Tzung-Pei

2014-01-01

This paper presents a simple but efficient algorithm for reducing the computation time of genetic algorithm (GA) and its variants. The proposed algorithm is motivated by the observation that genes common to all the individuals of a GA have a high probability of surviving the evolution and ending up being part of the final solution; as such, they can be saved away to eliminate the redundant computations at the later generations of a GA. To evaluate the performance of the proposed algorithm, we use it not only to solve the traveling salesman problem but also to provide an extensive analysis on the impact it may have on the quality of the end result. Our experimental results indicate that the proposed algorithm can significantly reduce the computation time of GA and GA-based algorithms while limiting the degradation of the quality of the end result to a very small percentage compared to traditional GA.

Genetic algorithm with maximum-minimum crossover (GA-MMC) applied in optimization of radiation pattern control of phased-array radars for rocket tracking systems.

PubMed

Silva, Leonardo W T; Barros, Vitor F; Silva, Sandro G

2014-08-18

In launching operations, Rocket Tracking Systems (RTS) process the trajectory data obtained by radar sensors. In order to improve functionality and maintenance, radars can be upgraded by replacing antennas with parabolic reflectors (PRs) with phased arrays (PAs). These arrays enable the electronic control of the radiation pattern by adjusting the signal supplied to each radiating element. However, in projects of phased array radars (PARs), the modeling of the problem is subject to various combinations of excitation signals producing a complex optimization problem. In this case, it is possible to calculate the problem solutions with optimization methods such as genetic algorithms (GAs). For this, the Genetic Algorithm with Maximum-Minimum Crossover (GA-MMC) method was developed to control the radiation pattern of PAs. The GA-MMC uses a reconfigurable algorithm with multiple objectives, differentiated coding and a new crossover genetic operator. This operator has a different approach from the conventional one, because it performs the crossover of the fittest individuals with the least fit individuals in order to enhance the genetic diversity. Thus, GA-MMC was successful in more than 90% of the tests for each application, increased the fitness of the final population by more than 20% and reduced the premature convergence.

Genetic Algorithm with Maximum-Minimum Crossover (GA-MMC) Applied in Optimization of Radiation Pattern Control of Phased-Array Radars for Rocket Tracking Systems

PubMed Central

Silva, Leonardo W. T.; Barros, Vitor F.; Silva, Sandro G.

2014-01-01

In launching operations, Rocket Tracking Systems (RTS) process the trajectory data obtained by radar sensors. In order to improve functionality and maintenance, radars can be upgraded by replacing antennas with parabolic reflectors (PRs) with phased arrays (PAs). These arrays enable the electronic control of the radiation pattern by adjusting the signal supplied to each radiating element. However, in projects of phased array radars (PARs), the modeling of the problem is subject to various combinations of excitation signals producing a complex optimization problem. In this case, it is possible to calculate the problem solutions with optimization methods such as genetic algorithms (GAs). For this, the Genetic Algorithm with Maximum-Minimum Crossover (GA-MMC) method was developed to control the radiation pattern of PAs. The GA-MMC uses a reconfigurable algorithm with multiple objectives, differentiated coding and a new crossover genetic operator. This operator has a different approach from the conventional one, because it performs the crossover of the fittest individuals with the least fit individuals in order to enhance the genetic diversity. Thus, GA-MMC was successful in more than 90% of the tests for each application, increased the fitness of the final population by more than 20% and reduced the premature convergence. PMID:25196013

Threshold matrix for digital halftoning by genetic algorithm optimization

NASA Astrophysics Data System (ADS)

Alander, Jarmo T.; Mantere, Timo J.; Pyylampi, Tero

1998-10-01

Digital halftoning is used both in low and high resolution high quality printing technologies. Our method is designed to be mainly used for low resolution ink jet marking machines to produce both gray tone and color images. The main problem with digital halftoning is pink noise caused by the human eye's visual transfer function. To compensate for this the random dot patterns used are optimized to contain more blue than pink noise. Several such dot pattern generator threshold matrices have been created automatically by using genetic algorithm optimization, a non-deterministic global optimization method imitating natural evolution and genetics. A hybrid of genetic algorithm with a search method based on local backtracking was developed together with several fitness functions evaluating dot patterns for rectangular grids. By modifying the fitness function, a family of dot generators results, each with its particular statistical features. Several versions of genetic algorithms, backtracking and fitness functions were tested to find a reasonable combination. The generated threshold matrices have been tested by simulating a set of test images using the Khoros image processing system. Even though the work was focused on developing low resolution marking technology, the resulting family of dot generators can be applied also in other halftoning application areas including high resolution printing technology.

Refined Genetic Algorithms for Polypeptide Structure Prediction.

DTIC Science & Technology

1996-12-01

16 I I I. Algorithm Analysis, Design , and Implemen tation : : : : : : : : : : : : : : : : : : : : : : : : : 18 3.1 Analysis...21 3.2 Algorithm Design and Implemen tation : : : : : : : : : : : : : : : : : : : : : : : : : 22 3.2.1...26 IV. Exp erimen t Design

Comparing genetic algorithm and particle swarm optimization for solving capacitated vehicle routing problem

NASA Astrophysics Data System (ADS)

Iswari, T.; Asih, A. M. S.

2018-04-01

In the logistics system, transportation plays an important role to connect every element in the supply chain, but it can produces the greatest cost. Therefore, it is important to make the transportation costs as minimum as possible. Reducing the transportation cost can be done in several ways. One of the ways to minimizing the transportation cost is by optimizing the routing of its vehicles. It refers to Vehicle Routing Problem (VRP). The most common type of VRP is Capacitated Vehicle Routing Problem (CVRP). In CVRP, the vehicles have their own capacity and the total demands from the customer should not exceed the capacity of the vehicle. CVRP belongs to the class of NP-hard problems. These NP-hard problems make it more complex to solve such that exact algorithms become highly time-consuming with the increases in problem sizes. Thus, for large-scale problem instances, as typically found in industrial applications, finding an optimal solution is not practicable. Therefore, this paper uses two kinds of metaheuristics approach to solving CVRP. Those are Genetic Algorithm and Particle Swarm Optimization. This paper compares the results of both algorithms and see the performance of each algorithm. The results show that both algorithms perform well in solving CVRP but still needs to be improved. From algorithm testing and numerical example, Genetic Algorithm yields a better solution than Particle Swarm Optimization in total distance travelled.

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.

A review of estimation of distribution algorithms in bioinformatics

PubMed Central

Armañanzas, Rubén; Inza, Iñaki; Santana, Roberto; Saeys, Yvan; Flores, Jose Luis; Lozano, Jose Antonio; Peer, Yves Van de; Blanco, Rosa; Robles, Víctor; Bielza, Concha; Larrañaga, Pedro

2008-01-01

Evolutionary search algorithms have become an essential asset in the algorithmic toolbox for solving high-dimensional optimization problems in across a broad range of bioinformatics problems. Genetic algorithms, the most well-known and representative evolutionary search technique, have been the subject of the major part of such applications. Estimation of distribution algorithms (EDAs) offer a novel evolutionary paradigm that constitutes a natural and attractive alternative to genetic algorithms. They make use of a probabilistic model, learnt from the promising solutions, to guide the search process. In this paper, we set out a basic taxonomy of EDA techniques, underlining the nature and complexity of the probabilistic model of each EDA variant. We review a set of innovative works that make use of EDA techniques to solve challenging bioinformatics problems, emphasizing the EDA paradigm's potential for further research in this domain. PMID:18822112

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

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.

Optimizing Support Vector Machine Parameters with Genetic Algorithm for Credit Risk Assessment

NASA Astrophysics Data System (ADS)

Manurung, Jonson; Mawengkang, Herman; Zamzami, Elviawaty

2017-12-01

Support vector machine (SVM) is a popular classification method known to have strong generalization capabilities. SVM can solve the problem of classification and linear regression or nonlinear kernel which can be a learning algorithm for the ability of classification and regression. However, SVM also has a weakness that is difficult to determine the optimal parameter value. SVM calculates the best linear separator on the input feature space according to the training data. To classify data which are non-linearly separable, SVM uses kernel tricks to transform the data into a linearly separable data on a higher dimension feature space. The kernel trick using various kinds of kernel functions, such as : linear kernel, polynomial, radial base function (RBF) and sigmoid. Each function has parameters which affect the accuracy of SVM classification. To solve the problem genetic algorithms are proposed to be applied as the optimal parameter value search algorithm thus increasing the best classification accuracy on SVM. Data taken from UCI repository of machine learning database: Australian Credit Approval. The results show that the combination of SVM and genetic algorithms is effective in improving classification accuracy. Genetic algorithms has been shown to be effective in systematically finding optimal kernel parameters for SVM, instead of randomly selected kernel parameters. The best accuracy for data has been upgraded from kernel Linear: 85.12%, polynomial: 81.76%, RBF: 77.22% Sigmoid: 78.70%. However, for bigger data sizes, this method is not practical because it takes a lot of time.

GAGA: a new algorithm for genomic inference of geographic ancestry reveals fine level population substructure in Europeans.

PubMed

Lao, Oscar; Liu, Fan; Wollstein, Andreas; Kayser, Manfred

2014-02-01

Attempts to detect genetic population substructure in humans are troubled by the fact that the vast majority of the total amount of observed genetic variation is present within populations rather than between populations. Here we introduce a new algorithm for transforming a genetic distance matrix that reduces the within-population variation considerably. Extensive computer simulations revealed that the transformed matrix captured the genetic population differentiation better than the original one which was based on the T1 statistic. In an empirical genomic data set comprising 2,457 individuals from 23 different European subpopulations, the proportion of individuals that were determined as a genetic neighbour to another individual from the same sampling location increased from 25% with the original matrix to 52% with the transformed matrix. Similarly, the percentage of genetic variation explained between populations by means of Analysis of Molecular Variance (AMOVA) increased from 1.62% to 7.98%. Furthermore, the first two dimensions of a classical multidimensional scaling (MDS) using the transformed matrix explained 15% of the variance, compared to 0.7% obtained with the original matrix. Application of MDS with Mclust, SPA with Mclust, and GemTools algorithms to the same dataset also showed that the transformed matrix gave a better association of the genetic clusters with the sampling locations, and particularly so when it was used in the AMOVA framework with a genetic algorithm. Overall, the new matrix transformation introduced here substantially reduces the within population genetic differentiation, and can be broadly applied to methods such as AMOVA to enhance their sensitivity to reveal population substructure. We herewith provide a publically available (http://www.erasmusmc.nl/fmb/resources/GAGA) model-free method for improved genetic population substructure detection that can be applied to human as well as any other species data in future studies relevant to evolutionary biology, behavioural ecology, medicine, and forensics.

Comparative Analysis of Rank Aggregation Techniques for Metasearch Using Genetic Algorithm

ERIC Educational Resources Information Center

Kaur, Parneet; Singh, Manpreet; Singh Josan, Gurpreet

2017-01-01

Rank Aggregation techniques have found wide applications for metasearch along with other streams such as Sports, Voting System, Stock Markets, and Reduction in Spam. This paper presents the optimization of rank lists for web queries put by the user on different MetaSearch engines. A metaheuristic approach such as Genetic algorithm based rank…

Finding Patterns of Emergence in Science and Technology

DTIC Science & Technology

2012-09-24

formal evaluation scheduled – Case Studies, Eight Examples: Tissue Engineering, Cold Fusion, RF Metamaterials, DNA Microarrays, Genetic Algorithms, RNAi...emerging capabilities Case Studies, Eight Examples: • Tissue Engineering, Cold Fusion, RF Metamaterials, DNA Microarrays, Genetic Algorithms...Evidence Quality (i.e., the rubric ) and deliver comprehensible evidential support for nomination • Demonstrate proof-of-concept nomination for Chinese

Validation Test Report for a Genetic Algorithm in the Glider Observation STrategies (GOST 1.0) Project: Sensitivity Studies

DTIC Science & Technology

2012-08-15

Environmental Model ( GDEM ) 72 levels) was conserved in the interpolated profiles and small variations in the vertical field may have lead to large...Planner ETKF Ensemble Transform Kalman Filter G8NCOM 1/8⁰ Global NCOM GA Genetic Algorithm GDEM Generalized Digital Environmental Model GOST

Fuzzy Logic, Neural Networks, Genetic Algorithms: Views of Three Artificial Intelligence Concepts Used in Modeling Scientific Systems

ERIC Educational Resources Information Center

Sunal, Cynthia Szymanski; Karr, Charles L.; Sunal, Dennis W.

2003-01-01

Students' conceptions of three major artificial intelligence concepts used in the modeling of systems in science, fuzzy logic, neural networks, and genetic algorithms were investigated before and after a higher education science course. Students initially explored their prior ideas related to the three concepts through active tasks. Then,…

Process optimization of rolling for zincked sheet technology using response surface methodology and genetic algorithm

NASA Astrophysics Data System (ADS)

Ji, Liang-Bo; Chen, Fang

2017-07-01

Numerical simulation and intelligent optimization technology were adopted for rolling and extrusion of zincked sheet. By response surface methodology (RSM), genetic algorithm (GA) and data processing technology, an efficient optimization of process parameters for rolling of zincked sheet was investigated. The influence trend of roller gap, rolling speed and friction factor effects on reduction rate and plate shortening rate were analyzed firstly. Then a predictive response surface model for comprehensive quality index of part was created using RSM. Simulated and predicted values were compared. Through genetic algorithm method, the optimal process parameters for the forming of rolling were solved. They were verified and the optimum process parameters of rolling were obtained. It is feasible and effective.

Genetic Algorithm Design of a 3D Printed Heat Sink

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

Wu, Tong; Ozpineci, Burak; Ayers, Curtis William

2016-01-01

In this paper, a genetic algorithm- (GA-) based approach is discussed for designing heat sinks based on total heat generation and dissipation for a pre-specified size andshape. This approach combines random iteration processesand genetic algorithms with finite element analysis (FEA) to design the optimized heat sink. With an approach that prefers survival of the fittest , a more powerful heat sink can bedesigned which can cool power electronics more efficiently. Some of the resulting designs can only be 3D printed due totheir complexity. In addition to describing the methodology, this paper also includes comparisons of different cases to evaluate themore » performance of the newly designed heat sinkcompared to commercially available heat sinks.« less

Logistics Distribution Center Location Evaluation Based on Genetic Algorithm and Fuzzy Neural Network

NASA Astrophysics Data System (ADS)

Shao, Yuxiang; Chen, Qing; Wei, Zhenhua

Logistics distribution center location evaluation is a dynamic, fuzzy, open and complicated nonlinear system, which makes it difficult to evaluate the distribution center location by the traditional analysis method. The paper proposes a distribution center location evaluation system which uses the fuzzy neural network combined with the genetic algorithm. In this model, the neural network is adopted to construct the fuzzy system. By using the genetic algorithm, the parameters of the neural network are optimized and trained so as to improve the fuzzy system’s abilities of self-study and self-adaptation. At last, the sampled data are trained and tested by Matlab software. The simulation results indicate that the proposed identification model has very small errors.

A genetic algorithm solution to the unit commitment problem

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

Kazarlis, S.A.; Bakirtzis, A.G.; Petridis, V.

1996-02-01

This paper presents a Genetic Algorithm (GA) solution to the Unit Commitment problem. GAs are general purpose optimization techniques based on principles inspired from the biological evolution using metaphors of mechanisms such as natural selection, genetic recombination and survival of the fittest. A simple Ga algorithm implementation using the standard crossover and mutation operators could locate near optimal solutions but in most cases failed to converge to the optimal solution. However, using the Varying Quality Function technique and adding problem specific operators, satisfactory solutions to the Unit Commitment problem were obtained. Test results for systems of up to 100 unitsmore » and comparisons with results obtained using Lagrangian Relaxation and Dynamic Programming are also reported.« less

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.

Preliminary Design of a Manned Nuclear Electric Propulsion Vehicle Using Genetic Algorithms

NASA Technical Reports Server (NTRS)

Irwin, Ryan W.; Tinker, Michael L.

2005-01-01

Nuclear electric propulsion (NEP) vehicles will be needed for future manned missions to Mars and beyond. Candidate designs must be identified for further detailed design from a large array of possibilities. Genetic algorithms have proven their utility in conceptual design studies by effectively searching a large design space to pinpoint unique optimal designs. This research combined analysis codes for NEP subsystems with a genetic algorithm. The use of penalty functions with scaling ratios was investigated to increase computational efficiency. Also, the selection of design variables for optimization was considered to reduce computation time without losing beneficial design search space. Finally, trend analysis of a reference mission to the asteroids yielded a group of candidate designs for further analysis.

Evaluation of Genetic Algorithm Concepts using Model Problems. Part 1; Single-Objective Optimization

NASA Technical Reports Server (NTRS)

Holst, Terry L.; Pulliam, Thomas H.

2003-01-01

A genetic-algorithm-based optimization approach is described and evaluated using a simple hill-climbing model problem. The model problem utilized herein allows for the broad specification of a large number of search spaces including spaces with an arbitrary number of genes or decision variables and an arbitrary number hills or modes. In the present study, only single objective problems are considered. Results indicate that the genetic algorithm optimization approach is flexible in application and extremely reliable, providing optimal results for all problems attempted. The most difficult problems - those with large hyper-volumes and multi-mode search spaces containing a large number of genes - require a large number of function evaluations for GA convergence, but they always converge.

Golden Ratio Genetic Algorithm Based Approach for Modelling and Analysis of the Capacity Expansion of Urban Road Traffic Network

PubMed Central

Zhang, Lun; Zhang, Meng; Yang, Wenchen; Dong, Decun

2015-01-01

This paper presents the modelling and analysis of the capacity expansion of urban road traffic network (ICURTN). Thebilevel programming model is first employed to model the ICURTN, in which the utility of the entire network is maximized with the optimal utility of travelers' route choice. Then, an improved hybrid genetic algorithm integrated with golden ratio (HGAGR) is developed to enhance the local search of simple genetic algorithms, and the proposed capacity expansion model is solved by the combination of the HGAGR and the Frank-Wolfe algorithm. Taking the traditional one-way network and bidirectional network as the study case, three numerical calculations are conducted to validate the presented model and algorithm, and the primary influencing factors on extended capacity model are analyzed. The calculation results indicate that capacity expansion of road network is an effective measure to enlarge the capacity of urban road network, especially on the condition of limited construction budget; the average computation time of the HGAGR is 122 seconds, which meets the real-time demand in the evaluation of the road network capacity. PMID:25802512

[Seroprevalence of Anaplasma phagocytophilum in patients with suspected Lyme borreliosis].

PubMed

Dvořáková Heroldová, M; Dvořáčková, M

2014-11-01

Human granulocytic anaplasmosis (HGA) is an emerging tick-borne zoonotic disease caused by an obligate intracellular bacterium, Anaplasma phagocytophilum. In Europe, A. phagocytophilum is transmitted by Ixodes ricinus ticks. After Lyme borreliosis and European tick-borne encephalitis, HGA is the third most common tick-borne infection in the USA and Europe. The clinical symptoms of anaplasmosis are non-specific and include malaise, fever, headache, myalgia, and arthralgia. In more severe cases, the gastrointestinal or respiratory tract may be affected. However, most infections are asymptomatic. The aim of our study was to determine the seroprevalence of A. phagocytophilum in patients with suspected Lyme borreliosis. A total of 314 sera from patients with suspected Lyme borreliosis were screened for IgG and IgM antibodies against A. phagocytophilum. The immunoblot assay was used to detect the antibodies. Anti-A. phagocytophilum antibodies were detected in 34 patients, i.e. in 10.82%. IgM antibodies were positive in 19 cases and IgG antibodies in 10 cases. Positivity to both IgM and IgG antibodies was revealed in five patients. Antibodies against Borrelia burgdorferi sensu lato were detected in 181 patients (57.64%). Co-seroprevalence of Borrelia burgdorferi s. l. and A. phagocytophilum was found in 26 patients (8.3%). Positivity for anti-A. phagocytophilum antibodies was most often seen in samples from the age group 60-69 years. Our results show that A. phagocytophilum infection is not uncommon in the Czech Republic and should be considered in patients with a history of a tick bite.

Spatio-temporal diversification of the cell wall matrix materials in the developing stomatal complexes of Zea mays.

PubMed

Giannoutsou, E; Apostolakos, P; Galatis, B

2016-11-01

The matrix cell wall materials, in developing Zea mays stomatal complexes are asymmetrically distributed, a phenomenon appearing related to the local cell wall expansion and deformation, the establishment of cell polarity, and determination of the cell division plane. In cells of developing Zea mays stomatal complexes, definite cell wall regions expand determinately and become locally deformed. This differential cell wall behavior is obvious in the guard cell mother cells (GMCs) and the subsidiary cell mother cells (SMCs) that locally protrude towards the adjacent GMCs. The latter, emitting a morphogenetic stimulus, induce polarization/asymmetrical division in SMCs. Examination of immunolabeled specimens revealed that homogalacturonans (HGAs) with a high degree of de-esterification (2F4- and JIM5-HGA epitopes) and arabinogalactan proteins are selectively distributed in the extending and deformed cell wall regions, while their margins are enriched with rhamnogalacturonans (RGAs) containing highly branched arabinans (LM6-RGA epitope). In SMCs, the local cell wall matrix differentiation constitutes the first structural event, indicating the establishment of cell polarity. Moreover, in the premitotic GMCs and SMCs, non-esterified HGAs (2F4-HGA epitope) are preferentially localized in the cell wall areas outlining the cytoplasm where the preprophase band is formed. In these areas, the forthcoming cell plate fuses with the parent cell walls. These data suggest that the described heterogeneity in matrix cell wall materials is probably involved in: (a) local cell wall expansion and deformation, (b) the transduction of the inductive GMC stimulus, and (c) the determination of the division plane in GMCs and SMCs.

Genetics in the art and art in genetics.

PubMed

Bukvic, Nenad; Elling, John W

2015-01-15

"Healing is best accomplished when art and science are conjoined, when body and spirit are probed together", says Bernard Lown, in his book "The Lost Art of Healing". Art has long been a witness to disease either through diseases which affected artists or diseases afflicting objects of their art. In particular, artists have often portrayed genetic disorders and malformations in their work. Sometimes genetic disorders have mystical significance; other times simply have intrinsic interest. Recognizing genetic disorders is also an art form. From the very beginning of my work as a Medical Geneticist I have composed personal "algorithms" to piece together evidence of genetics syndromes and diseases from the observable signs and symptoms. In this paper we apply some 'gestalt' Genetic Syndrome Diagnostic algorithms to virtual patients found in some art masterpieces. In some the diagnosis is clear and in others the artists' depiction only supports a speculative differential diagnosis. Copyright © 2014 Elsevier B.V. All rights reserved.

Production scheduling and rescheduling with genetic algorithms.

PubMed

Bierwirth, C; Mattfeld, D C

1999-01-01

A general model for job shop scheduling is described which applies to static, dynamic and non-deterministic production environments. Next, a Genetic Algorithm is presented which solves the job shop scheduling problem. This algorithm is tested in a dynamic environment under different workload situations. Thereby, a highly efficient decoding procedure is proposed which strongly improves the quality of schedules. Finally, this technique is tested for scheduling and rescheduling in a non-deterministic environment. It is shown by experiment that conventional methods of production control are clearly outperformed at reasonable run-time costs.

GA(M)E-QSAR: a novel, fully automatic genetic-algorithm-(meta)-ensembles approach for binary classification in ligand-based drug design.

PubMed

Pérez-Castillo, Yunierkis; Lazar, Cosmin; Taminau, Jonatan; Froeyen, Mathy; Cabrera-Pérez, Miguel Ángel; Nowé, Ann

2012-09-24

Computer-aided drug design has become an important component of the drug discovery process. Despite the advances in this field, there is not a unique modeling approach that can be successfully applied to solve the whole range of problems faced during QSAR modeling. Feature selection and ensemble modeling are active areas of research in ligand-based drug design. Here we introduce the GA(M)E-QSAR algorithm that combines the search and optimization capabilities of Genetic Algorithms with the simplicity of the Adaboost ensemble-based classification algorithm to solve binary classification problems. We also explore the usefulness of Meta-Ensembles trained with Adaboost and Voting schemes to further improve the accuracy, generalization, and robustness of the optimal Adaboost Single Ensemble derived from the Genetic Algorithm optimization. We evaluated the performance of our algorithm using five data sets from the literature and found that it is capable of yielding similar or better classification results to what has been reported for these data sets with a higher enrichment of active compounds relative to the whole actives subset when only the most active chemicals are considered. More important, we compared our methodology with state of the art feature selection and classification approaches and found that it can provide highly accurate, robust, and generalizable models. In the case of the Adaboost Ensembles derived from the Genetic Algorithm search, the final models are quite simple since they consist of a weighted sum of the output of single feature classifiers. Furthermore, the Adaboost scores can be used as ranking criterion to prioritize chemicals for synthesis and biological evaluation after virtual screening experiments.

Genetic algorithms for the vehicle routing problem

NASA Astrophysics Data System (ADS)

Volna, Eva

2016-06-01

The Vehicle Routing Problem (VRP) is one of the most challenging combinatorial optimization tasks. This problem consists in designing the optimal set of routes for fleet of vehicles in order to serve a given set of customers. Evolutionary algorithms are general iterative algorithms for combinatorial optimization. These algorithms have been found to be very effective and robust in solving numerous problems from a wide range of application domains. This problem is known to be NP-hard; hence many heuristic procedures for its solution have been suggested. For such problems it is often desirable to obtain approximate solutions, so they can be found fast enough and are sufficiently accurate for the purpose. In this paper we have performed an experimental study that indicates the suitable use of genetic algorithms for the vehicle routing problem.

Social Media: Menagerie of Metrics

DTIC Science & Technology

2010-01-27

intelligence, an evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm . An EA...Cloning - 22 Animals were cloned to date; genetic algorithms can help prediction (e.g. “elitism” - attempts to ensure selection by including performers...28, 2010 Evolutionary Algorithm • Evolutionary algorithm From Wikipedia, the free encyclopedia Artificial intelligence portal In artificial

A Parallel Genetic Algorithm for Automated Electronic Circuit Design

NASA Technical Reports Server (NTRS)

Lohn, Jason D.; Colombano, Silvano P.; Haith, Gary L.; Stassinopoulos, Dimitris; Norvig, Peter (Technical Monitor)

2000-01-01

We describe a parallel genetic algorithm (GA) that automatically generates circuit designs using evolutionary search. A circuit-construction programming language is introduced and we show how evolution can generate practical analog circuit designs. Our system allows circuit size (number of devices), circuit topology, and device values to be evolved. We present experimental results as applied to analog filter and amplifier design tasks.

Optimizing simulated fertilizer additions using a genetic algorithm with a nutrient uptake model

Treesearch

Wendell P. Cropper; N.B. Comerford

2005-01-01

Intensive management of pine plantations in the southeastern coastal plain typically involves weed and pest control, and the addition of fertilizer to meet the high nutrient demand of rapidly growing pines. In this study we coupled a mechanistic nutrient uptake model (SSAND, soil supply and nutrient demand) with a genetic algorithm (GA) in order to estimate the minimum...

Automatic Mexico Gulf Oil Spill Detection from Radarsat-2 SAR Satellite Data Using Genetic Algorithm

NASA Astrophysics Data System (ADS)

Marghany, Maged

2016-10-01

In this work, a genetic algorithm is exploited for automatic detection of oil spills of small and large size. The route is achieved using arrays of RADARSAT-2 SAR ScanSAR Narrow single beam data obtained in the Gulf of Mexico. The study shows that genetic algorithm has automatically segmented the dark spot patches related to small and large oil spill pixels. This conclusion is confirmed by the receiveroperating characteristic (ROC) curve and ground data which have been documented. The ROC curve indicates that the existence of oil slick footprints can be identified with the area under the curve between the ROC curve and the no-discrimination line of 90%, which is greater than that of other surrounding environmental features. The small oil spill sizes represented 30% of the discriminated oil spill pixels in ROC curve. In conclusion, the genetic algorithm can be used as a tool for the automatic detection of oil spills of either small or large size and the ScanSAR Narrow single beam mode serves as an excellent sensor for oil spill patterns detection and surveying in the Gulf of Mexico.

Improved Cost-Base Design of Water Distribution Networks using Genetic Algorithm

NASA Astrophysics Data System (ADS)

Moradzadeh Azar, Foad; Abghari, Hirad; Taghi Alami, Mohammad; Weijs, Steven

2010-05-01

Population growth and progressive extension of urbanization in different places of Iran cause an increasing demand for primary needs. The water, this vital liquid is the most important natural need for human life. Providing this natural need is requires the design and construction of water distribution networks, that incur enormous costs on the country's budget. Any reduction in these costs enable more people from society to access extreme profit least cost. Therefore, investment of Municipal councils need to maximize benefits or minimize expenditures. To achieve this purpose, the engineering design depends on the cost optimization techniques. This paper, presents optimization models based on genetic algorithm(GA) to find out the minimum design cost Mahabad City's (North West, Iran) water distribution network. By designing two models and comparing the resulting costs, the abilities of GA were determined. the GA based model could find optimum pipe diameters to reduce the design costs of network. Results show that the water distribution network design using Genetic Algorithm could lead to reduction of at least 7% in project costs in comparison to the classic model. Keywords: Genetic Algorithm, Optimum Design of Water Distribution Network, Mahabad City, Iran.

A Genetic Algorithm for the Bi-Level Topological Design of Local Area Networks

PubMed Central

Camacho-Vallejo, José-Fernando; Mar-Ortiz, Julio; López-Ramos, Francisco; Rodríguez, Ricardo Pedraza

2015-01-01

Local access networks (LAN) are commonly used as communication infrastructures which meet the demand of a set of users in the local environment. Usually these networks consist of several LAN segments connected by bridges. The topological LAN design bi-level problem consists on assigning users to clusters and the union of clusters by bridges in order to obtain a minimum response time network with minimum connection cost. Therefore, the decision of optimally assigning users to clusters will be made by the leader and the follower will make the decision of connecting all the clusters while forming a spanning tree. In this paper, we propose a genetic algorithm for solving the bi-level topological design of a Local Access Network. Our solution method considers the Stackelberg equilibrium to solve the bi-level problem. The Stackelberg-Genetic algorithm procedure deals with the fact that the follower’s problem cannot be optimally solved in a straightforward manner. The computational results obtained from two different sets of instances show that the performance of the developed algorithm is efficient and that it is more suitable for solving the bi-level problem than a previous Nash-Genetic approach. PMID:26102502

Prediction of road traffic death rate using neural networks optimised by genetic algorithm.

PubMed

Jafari, Seyed Ali; Jahandideh, Sepideh; Jahandideh, Mina; Asadabadi, Ebrahim Barzegari

2015-01-01

Road traffic injuries (RTIs) are realised as a main cause of public health problems at global, regional and national levels. Therefore, prediction of road traffic death rate will be helpful in its management. Based on this fact, we used an artificial neural network model optimised through Genetic algorithm to predict mortality. In this study, a five-fold cross-validation procedure on a data set containing total of 178 countries was used to verify the performance of models. The best-fit model was selected according to the root mean square errors (RMSE). Genetic algorithm, as a powerful model which has not been introduced in prediction of mortality to this extent in previous studies, showed high performance. The lowest RMSE obtained was 0.0808. Such satisfactory results could be attributed to the use of Genetic algorithm as a powerful optimiser which selects the best input feature set to be fed into the neural networks. Seven factors have been known as the most effective factors on the road traffic mortality rate by high accuracy. The gained results displayed that our model is very promising and may play a useful role in developing a better method for assessing the influence of road traffic mortality risk factors.

Genetic Algorithm Optimizes Q-LAW Control Parameters

NASA Technical Reports Server (NTRS)

Lee, Seungwon; von Allmen, Paul; Petropoulos, Anastassios; Terrile, Richard

2008-01-01

A document discusses a multi-objective, genetic algorithm designed to optimize Lyapunov feedback control law (Q-law) parameters in order to efficiently find Pareto-optimal solutions for low-thrust trajectories for electronic propulsion systems. These would be propellant-optimal solutions for a given flight time, or flight time optimal solutions for a given propellant requirement. The approximate solutions are used as good initial solutions for high-fidelity optimization tools. When the good initial solutions are used, the high-fidelity optimization tools quickly converge to a locally optimal solution near the initial solution. Q-law control parameters are represented as real-valued genes in the genetic algorithm. The performances of the Q-law control parameters are evaluated in the multi-objective space (flight time vs. propellant mass) and sorted by the non-dominated sorting method that assigns a better fitness value to the solutions that are dominated by a fewer number of other solutions. With the ranking result, the genetic algorithm encourages the solutions with higher fitness values to participate in the reproduction process, improving the solutions in the evolution process. The population of solutions converges to the Pareto front that is permitted within the Q-law control parameter space.

Genetic algorithms used for the optimization of light-emitting diodes and solar thermal collectors

NASA Astrophysics Data System (ADS)

Mayer, Alexandre; Bay, Annick; Gaouyat, Lucie; Nicolay, Delphine; Carletti, Timoteo; Deparis, Olivier

2014-09-01

We present a genetic algorithm (GA) we developed for the optimization of light-emitting diodes (LED) and solar thermal collectors. The surface of a LED can be covered by periodic structures whose geometrical and material parameters must be adjusted in order to maximize the extraction of light. The optimization of these parameters by the GA enabled us to get a light-extraction efficiency η of 11.0% from a GaN LED (for comparison, the flat material has a light-extraction efficiency η of only 3.7%). The solar thermal collector we considered consists of a waffle-shaped Al substrate with NiCrOx and SnO2 conformal coatings. We must in this case maximize the solar absorption α while minimizing the thermal emissivity ɛ in the infrared. A multi-objective genetic algorithm has to be implemented in this case in order to determine optimal geometrical parameters. The parameters we obtained using the multi-objective GA enable α~97.8% and ɛ~4.8%, which improves results achieved previously when considering a flat substrate. These two applications demonstrate the interest of genetic algorithms for addressing complex problems in physics.

Locating Critical Circular and Unconstrained Failure Surface in Slope Stability Analysis with Tailored Genetic Algorithm

NASA Astrophysics Data System (ADS)

Pasik, Tomasz; van der Meij, Raymond

2017-12-01

This article presents an efficient search method for representative circular and unconstrained slip surfaces with the use of the tailored genetic algorithm. Searches for unconstrained slip planes with rigid equilibrium methods are yet uncommon in engineering practice, and little publications regarding truly free slip planes exist. The proposed method presents an effective procedure being the result of the right combination of initial population type, selection, crossover and mutation method. The procedure needs little computational effort to find the optimum, unconstrained slip plane. The methodology described in this paper is implemented using Mathematica. The implementation, along with further explanations, is fully presented so the results can be reproduced. Sample slope stability calculations are performed for four cases, along with a detailed result interpretation. Two cases are compared with analyses described in earlier publications. The remaining two are practical cases of slope stability analyses of dikes in Netherlands. These four cases show the benefits of analyzing slope stability with a rigid equilibrium method combined with a genetic algorithm. The paper concludes by describing possibilities and limitations of using the genetic algorithm in the context of the slope stability problem.

Optimization of HAART with genetic algorithms and agent-based models of HIV infection.

PubMed

Castiglione, F; Pappalardo, F; Bernaschi, M; Motta, S

2007-12-15

Highly Active AntiRetroviral Therapies (HAART) can prolong life significantly to people infected by HIV since, although unable to eradicate the virus, they are quite effective in maintaining control of the infection. However, since HAART have several undesirable side effects, it is considered useful to suspend the therapy according to a suitable schedule of Structured Therapeutic Interruptions (STI). In the present article we describe an application of genetic algorithms (GA) aimed at finding the optimal schedule for a HAART simulated with an agent-based model (ABM) of the immune system that reproduces the most significant features of the response of an organism to the HIV-1 infection. The genetic algorithm helps in finding an optimal therapeutic schedule that maximizes immune restoration, minimizes the viral count and, through appropriate interruptions of the therapy, minimizes the dose of drug administered to the simulated patient. To validate the efficacy of the therapy that the genetic algorithm indicates as optimal, we ran simulations of opportunistic diseases and found that the selected therapy shows the best survival curve among the different simulated control groups. A version of the C-ImmSim simulator is available at http://www.iac.cnr.it/~filippo/c-ImmSim.html

Design of thrust vectoring exhaust nozzles for real-time applications using neural networks

NASA Technical Reports Server (NTRS)

Prasanth, Ravi K.; Markin, Robert E.; Whitaker, Kevin W.

1991-01-01

Thrust vectoring continues to be an important issue in military aircraft system designs. A recently developed concept of vectoring aircraft thrust makes use of flexible exhaust nozzles. Subtle modifications in the nozzle wall contours produce a non-uniform flow field containing a complex pattern of shock and expansion waves. The end result, due to the asymmetric velocity and pressure distributions, is vectored thrust. Specification of the nozzle contours required for a desired thrust vector angle (an inverse design problem) has been achieved with genetic algorithms. This approach is computationally intensive and prevents the nozzles from being designed in real-time, which is necessary for an operational aircraft system. An investigation was conducted into using genetic algorithms to train a neural network in an attempt to obtain, in real-time, two-dimensional nozzle contours. Results show that genetic algorithm trained neural networks provide a viable, real-time alternative for designing thrust vectoring nozzles contours. Thrust vector angles up to 20 deg were obtained within an average error of 0.0914 deg. The error surfaces encountered were highly degenerate and thus the robustness of genetic algorithms was well suited for minimizing global errors.

Flexible Job-Shop Scheduling with Dual-Resource Constraints to Minimize Tardiness Using Genetic Algorithm

NASA Astrophysics Data System (ADS)

Paksi, A. B. N.; Ma'ruf, A.

2016-02-01

In general, both machines and human resources are needed for processing a job on production floor. However, most classical scheduling problems have ignored the possible constraint caused by availability of workers and have considered only machines as a limited resource. In addition, along with production technology development, routing flexibility appears as a consequence of high product variety and medium demand for each product. Routing flexibility is caused by capability of machines that offers more than one machining process. This paper presents a method to address scheduling problem constrained by both machines and workers, considering routing flexibility. Scheduling in a Dual-Resource Constrained shop is categorized as NP-hard problem that needs long computational time. Meta-heuristic approach, based on Genetic Algorithm, is used due to its practical implementation in industry. Developed Genetic Algorithm uses indirect chromosome representative and procedure to transform chromosome into Gantt chart. Genetic operators, namely selection, elitism, crossover, and mutation are developed to search the best fitness value until steady state condition is achieved. A case study in a manufacturing SME is used to minimize tardiness as objective function. The algorithm has shown 25.6% reduction of tardiness, equal to 43.5 hours.

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.

A novel hybrid algorithm for the design of the phase diffractive optical elements for beam shaping

NASA Astrophysics Data System (ADS)

Jiang, Wenbo; Wang, Jun; Dong, Xiucheng

2013-02-01

In this paper, a novel hybrid algorithm for the design of a phase diffractive optical elements (PDOE) is proposed. It combines the genetic algorithm (GA) with the transformable scale BFGS (Broyden, Fletcher, Goldfarb, Shanno) algorithm, the penalty function was used in the cost function definition. The novel hybrid algorithm has the global merits of the genetic algorithm as well as the local improvement capabilities of the transformable scale BFGS algorithm. We designed the PDOE using the conventional simulated annealing algorithm and the novel hybrid algorithm. To compare the performance of two algorithms, three indexes of the diffractive efficiency, uniformity error and the signal-to-noise ratio are considered in numerical simulation. The results show that the novel hybrid algorithm has good convergence property and good stability. As an application example, the PDOE was used for the Gaussian beam shaping; high diffractive efficiency, low uniformity error and high signal-to-noise were obtained. The PDOE can be used for high quality beam shaping such as inertial confinement fusion (ICF), excimer laser lithography, fiber coupling laser diode array, laser welding, etc. It shows wide application value.

Application of artificial neural networks and genetic algorithms to modeling molecular electronic spectra in solution

NASA Astrophysics Data System (ADS)

Lilichenko, Mark; Kelley, Anne Myers

2001-04-01

A novel approach is presented for finding the vibrational frequencies, Franck-Condon factors, and vibronic linewidths that best reproduce typical, poorly resolved electronic absorption (or fluorescence) spectra of molecules in condensed phases. While calculation of the theoretical spectrum from the molecular parameters is straightforward within the harmonic oscillator approximation for the vibrations, "inversion" of an experimental spectrum to deduce these parameters is not. Standard nonlinear least-squares fitting methods such as Levenberg-Marquardt are highly susceptible to becoming trapped in local minima in the error function unless very good initial guesses for the molecular parameters are made. Here we employ a genetic algorithm to force a broad search through parameter space and couple it with the Levenberg-Marquardt method to speed convergence to each local minimum. In addition, a neural network trained on a large set of synthetic spectra is used to provide an initial guess for the fitting parameters and to narrow the range searched by the genetic algorithm. The combined algorithm provides excellent fits to a variety of single-mode absorption spectra with experimentally negligible errors in the parameters. It converges more rapidly than the genetic algorithm alone and more reliably than the Levenberg-Marquardt method alone, and is robust in the presence of spectral noise. Extensions to multimode systems, and/or to include other spectroscopic data such as resonance Raman intensities, are straightforward.

A novel pipeline based FPGA implementation of a genetic algorithm

NASA Astrophysics Data System (ADS)

Thirer, Nonel

2014-05-01

To solve problems when an analytical solution is not available, more and more bio-inspired computation techniques have been applied in the last years. Thus, an efficient algorithm is the Genetic Algorithm (GA), which imitates the biological evolution process, finding the solution by the mechanism of "natural selection", where the strong has higher chances to survive. A genetic algorithm is an iterative procedure which operates on a population of individuals called "chromosomes" or "possible solutions" (usually represented by a binary code). GA performs several processes with the population individuals to produce a new population, like in the biological evolution. To provide a high speed solution, pipelined based FPGA hardware implementations are used, with a nstages pipeline for a n-phases genetic algorithm. The FPGA pipeline implementations are constraints by the different execution time of each stage and by the FPGA chip resources. To minimize these difficulties, we propose a bio-inspired technique to modify the crossover step by using non identical twins. Thus two of the chosen chromosomes (parents) will build up two new chromosomes (children) not only one as in classical GA. We analyze the contribution of this method to reduce the execution time in the asynchronous and synchronous pipelines and also the possibility to a cheaper FPGA implementation, by using smaller populations. The full hardware architecture for a FPGA implementation to our target ALTERA development card is presented and analyzed.

Advancing X-ray scattering metrology using inverse genetic algorithms.

PubMed

Hannon, Adam F; Sunday, Daniel F; Windover, Donald; Kline, R Joseph

2016-01-01

We compare the speed and effectiveness of two genetic optimization algorithms to the results of statistical sampling via a Markov chain Monte Carlo algorithm to find which is the most robust method for determining real space structure in periodic gratings measured using critical dimension small angle X-ray scattering. Both a covariance matrix adaptation evolutionary strategy and differential evolution algorithm are implemented and compared using various objective functions. The algorithms and objective functions are used to minimize differences between diffraction simulations and measured diffraction data. These simulations are parameterized with an electron density model known to roughly correspond to the real space structure of our nanogratings. The study shows that for X-ray scattering data, the covariance matrix adaptation coupled with a mean-absolute error log objective function is the most efficient combination of algorithm and goodness of fit criterion for finding structures with little foreknowledge about the underlying fine scale structure features of the nanograting.

Learning Cue Phrase Patterns from Radiology Reports Using a Genetic Algorithm

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

Patton, Robert M; Beckerman, Barbara G; Potok, Thomas E

2009-01-01

Various computer-assisted technologies have been developed to assist radiologists in detecting cancer; however, the algorithms still lack high degrees of sensitivity and specificity, and must undergo machine learning against a training set with known pathologies in order to further refine the algorithms with higher validity of truth. This work describes an approach to learning cue phrase patterns in radiology reports that utilizes a genetic algorithm (GA) as the learning method. The approach described here successfully learned cue phrase patterns for two distinct classes of radiology reports. These patterns can then be used as a basis for automatically categorizing, clustering, ormore » retrieving relevant data for the user.« less

Branch-pipe-routing approach for ships using improved genetic algorithm

NASA Astrophysics Data System (ADS)

Sui, Haiteng; Niu, Wentie

2016-09-01

Branch-pipe routing plays fundamental and critical roles in ship-pipe design. The branch-pipe-routing problem is a complex combinatorial optimization problem and is thus difficult to solve when depending only on human experts. A modified genetic-algorithm-based approach is proposed in this paper to solve this problem. The simplified layout space is first divided into threedimensional (3D) grids to build its mathematical model. Branch pipes in layout space are regarded as a combination of several two-point pipes, and the pipe route between two connection points is generated using an improved maze algorithm. The coding of branch pipes is then defined, and the genetic operators are devised, especially the complete crossover strategy that greatly accelerates the convergence speed. Finally, simulation tests demonstrate the performance of proposed method.

A sustainable genetic algorithm for satellite resource allocation

NASA Technical Reports Server (NTRS)

Abbott, R. J.; Campbell, M. L.; Krenz, W. C.

1995-01-01

A hybrid genetic algorithm is used to schedule tasks for 8 satellites, which can be modelled as a robot whose task is to retrieve objects from a two dimensional field. The objective is to find a schedule that maximizes the value of objects retrieved. Typical of the real-world tasks to which this corresponds is the scheduling of ground contacts for a communications satellite. An important feature of our application is that the amount of time available for running the scheduler is not necessarily known in advance. This requires that the scheduler produce reasonably good results after a short period but that it also continue to improve its results if allowed to run for a longer period. We satisfy this requirement by developing what we call a sustainable genetic algorithm.

Modeling the Volcanic Source at Long Valley, CA, Using a Genetic Algorithm Technique

NASA Technical Reports Server (NTRS)

Tiampo, Kristy F.

1999-01-01

In this project, we attempted to model the deformation pattern due to the magmatic source at Long Valley caldera using a real-value coded genetic algorithm (GA) inversion similar to that found in Michalewicz, 1992. The project has been both successful and rewarding. The genetic algorithm, coded in the C programming language, performs stable inversions over repeated trials, with varying initial and boundary conditions. The original model used a GA in which the geophysical information was coded into the fitness function through the computation of surface displacements for a Mogi point source in an elastic half-space. The program was designed to invert for a spherical magmatic source - its depth, horizontal location and volume - using the known surface deformations. It also included the capability of inverting for multiple sources.

Evolutionary Optimization of Yagi-Uda Antennas

NASA Technical Reports Server (NTRS)

Lohn, Jason D.; Kraus, William F.; Linden, Derek S.; Colombano, Silvano P.

2001-01-01

Yagi-Uda antennas are known to be difficult to design and optimize due to their sensitivity at high gain, and the inclusion of numerous parasitic elements. We present a genetic algorithm-based automated antenna optimization system that uses a fixed Yagi-Uda topology and a byte-encoded antenna representation. The fitness calculation allows the implicit relationship between power gain and sidelobe/backlobe loss to emerge naturally, a technique that is less complex than previous approaches. The genetic operators used are also simpler. Our results include Yagi-Uda antennas that have excellent bandwidth and gain properties with very good impedance characteristics. Results exceeded previous Yagi-Uda antennas produced via evolutionary algorithms by at least 7.8% in mainlobe gain. We also present encouraging preliminary results where a coevolutionary genetic algorithm is used.

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.

Evolving neural networks with genetic algorithms to study the string landscape

NASA Astrophysics Data System (ADS)

Ruehle, Fabian

2017-08-01

We study possible applications of artificial neural networks to examine the string landscape. Since the field of application is rather versatile, we propose to dynamically evolve these networks via genetic algorithms. This means that we start from basic building blocks and combine them such that the neural network performs best for the application we are interested in. We study three areas in which neural networks can be applied: to classify models according to a fixed set of (physically) appealing features, to find a concrete realization for a computation for which the precise algorithm is known in principle but very tedious to actually implement, and to predict or approximate the outcome of some involved mathematical computation which performs too inefficient to apply it, e.g. in model scans within the string landscape. We present simple examples that arise in string phenomenology for all three types of problems and discuss how they can be addressed by evolving neural networks from genetic algorithms.

Design optimization of cold-formed steel portal frames taking into account the effect of building topology

NASA Astrophysics Data System (ADS)

Phan, Duoc T.; Lim, James B. P.; Sha, Wei; Siew, Calvin Y. M.; Tanyimboh, Tiku T.; Issa, Honar K.; Mohammad, Fouad A.

2013-04-01

Cold-formed steel portal frames are a popular form of construction for low-rise commercial, light industrial and agricultural buildings with spans of up to 20 m. In this article, a real-coded genetic algorithm is described that is used to minimize the cost of the main frame of such buildings. The key decision variables considered in this proposed algorithm consist of both the spacing and pitch of the frame as continuous variables, as well as the discrete section sizes. A routine taking the structural analysis and frame design for cold-formed steel sections is embedded into a genetic algorithm. The results show that the real-coded genetic algorithm handles effectively the mixture of design variables, with high robustness and consistency in achieving the optimum solution. All wind load combinations according to Australian code are considered in this research. Results for frames with knee braces are also included, for which the optimization achieved even larger savings in cost.

Optimizing management of the condensing heat and cooling of gases compression in oxy block using of a genetic algorithm

NASA Astrophysics Data System (ADS)

Brzęczek, Mateusz; Bartela, Łukasz

2013-12-01

This paper presents the parameters of the reference oxy combustion block operating with supercritical steam parameters, equipped with an air separation unit and a carbon dioxide capture and compression installation. The possibility to recover the heat in the analyzed power plant is discussed. The decision variables and the thermodynamic functions for the optimization algorithm were identified. The principles of operation of genetic algorithm and methodology of conducted calculations are presented. The sensitivity analysis was performed for the best solutions to determine the effects of the selected variables on the power and efficiency of the unit. Optimization of the heat recovery from the air separation unit, flue gas condition and CO2 capture and compression installation using genetic algorithm was designed to replace the low-pressure section of the regenerative water heaters of steam cycle in analyzed unit. The result was to increase the power and efficiency of the entire power plant.

A Novel Algorithm Combining Finite State Method and Genetic Algorithm for Solving Crude Oil Scheduling Problem

PubMed Central

Duan, Qian-Qian; Yang, Gen-Ke; Pan, Chang-Chun

2014-01-01

A hybrid optimization algorithm combining finite state method (FSM) and genetic algorithm (GA) is proposed to solve the crude oil scheduling problem. The FSM and GA are combined to take the advantage of each method and compensate deficiencies of individual methods. In the proposed algorithm, the finite state method makes up for the weakness of GA which is poor at local searching ability. The heuristic returned by the FSM can guide the GA algorithm towards good solutions. The idea behind this is that we can generate promising substructure or partial solution by using FSM. Furthermore, the FSM can guarantee that the entire solution space is uniformly covered. Therefore, the combination of the two algorithms has better global performance than the existing GA or FSM which is operated individually. Finally, a real-life crude oil scheduling problem from the literature is used for conducting simulation. The experimental results validate that the proposed method outperforms the state-of-art GA method. PMID:24772031

Optimised analytical models of the dielectric properties of biological tissue.

PubMed

Salahuddin, Saqib; Porter, Emily; Krewer, Finn; O' Halloran, Martin

2017-05-01

The interaction of electromagnetic fields with the human body is quantified by the dielectric properties of biological tissues. These properties are incorporated into complex numerical simulations using parametric models such as Debye and Cole-Cole, for the computational investigation of electromagnetic wave propagation within the body. These parameters can be acquired through a variety of optimisation algorithms to achieve an accurate fit to measured data sets. A number of different optimisation techniques have been proposed, but these are often limited by the requirement for initial value estimations or by the large overall error (often up to several percentage points). In this work, a novel two-stage genetic algorithm proposed by the authors is applied to optimise the multi-pole Debye parameters for 54 types of human tissues. The performance of the two-stage genetic algorithm has been examined through a comparison with five other existing algorithms. The experimental results demonstrate that the two-stage genetic algorithm produces an accurate fit to a range of experimental data and efficiently out-performs all other optimisation algorithms under consideration. Accurate values of the three-pole Debye models for 54 types of human tissues, over 500 MHz to 20 GHz, are also presented for reference. Copyright © 2017 IPEM. Published by Elsevier Ltd. All rights reserved.

STS-31 Hubble Space Telescope (HST) pre-deployment procedures aboard OV-103

NASA Image and Video Library

1990-04-24

During STS-31, the Hubble Space Telescope (HST) grappled by the remote manipulator system (RMS) end effector is held in appendage deploy position above Discovery, Orbiter Vehicle (OV) 103. The solar array (SA) bistem cassette has been released from its latch fittings. The bistem spreader bars begin to unfurl the SA wing. The secondary deployment mechanism (SDM) handle is visible at the SA end. Stowed against either side of the HST System Support Module (SSM) forward shell are the high-gain antennae (HGA). Puerto Rico and the Dominican Republic are recognizable at the left of the frame.

KSC-2009-4760

NASA Image and Video Library

2009-08-17

VANDENBERG AIR FORCE BASE, Calif. -- At Vandenberg Air Force Base's Astrotech processing facility in California, NASA's Wide-field Infrared Survey Explorer, or WISE, spacecraft is situated on a work stand. At left on the spacecraft is the fixed panel solar array. In front, the square is the HGA Slotted Array (Ku-Band). The satellite will survey the entire sky at infrared wavelengths, creating a cosmic clearinghouse of hundreds of millions of objects, which will be catalogued, providing a vast storehouse of knowledge about the solar system, the Milky Way, and the universe. Launch is scheduled no earlier than Dec. 10. Photo credit: NASA/Moore, VAFB

High Gain Antenna Gimbal for the 2003-2004 Mars Exploration Rover Program

NASA Technical Reports Server (NTRS)

Sokol, Jeff; Krishnan, Satish; Ayari, Laoucet

2004-01-01

The High Gain Antenna Assemblies built for the 2003-2004 Mars Exploration Rover (MER) missions provide the primary communication link for the Rovers once they arrive on Mars. The High Gain Antenna Gimbal (HGAG) portion of the assembly is a two-axis gimbal that provides the structural support, pointing, and tracking for the High Gain Antenna (HGA). The MER mission requirements provided some unique design challenges for the HGAG. This paper describes all the major subsystems of the HGAG that were developed to meet these challenges, and the requirements that drove their design.

A Food Chain Algorithm for Capacitated Vehicle Routing Problem with Recycling in Reverse Logistics

NASA Astrophysics Data System (ADS)

Song, Qiang; Gao, Xuexia; Santos, Emmanuel T.

2015-12-01

This paper introduces the capacitated vehicle routing problem with recycling in reverse logistics, and designs a food chain algorithm for it. Some illustrative examples are selected to conduct simulation and comparison. Numerical results show that the performance of the food chain algorithm is better than the genetic algorithm, particle swarm optimization as well as quantum evolutionary algorithm.

Data-Driven Property Estimation for Protective Clothing

DTIC Science & Technology

2014-09-01

reliable predictions falls under the rubric “machine learning”. Inspired by the applications of machine learning in pharmaceutical drug design and...using genetic algorithms, for instance— descriptor selection can be automated as well. A well-known structured learning technique—Artificial Neural...descriptors automatically, by iteration, e.g., using a genetic algorithm [49]. 4.2.4 Avoiding Overfitting A peril of all regression—least squares as

Genetic Algorithm Optimization of Phononic Bandgap Structures

DTIC Science & Technology

2006-09-01

a GA with a computational finite element method for solving the acoustic wave equation, and find optimal designs for both metal-matrix composite...systems consisting of Ti/SiC, and H2O-filled porous ceramic media, by maximizing the relative acoustic bandgap for these media. The term acoustic here...stress minimization, global optimization, phonon bandgap, genetic algorithm, periodic elastic media, inhomogeneity, inclusion, porous media, acoustic

Inferring genetic interactions via a nonlinear model and an optimization algorithm.

PubMed

Chen, Chung-Ming; Lee, Chih; Chuang, Cheng-Long; Wang, Chia-Chang; Shieh, Grace S

2010-02-26

Biochemical pathways are gradually becoming recognized as central to complex human diseases and recently genetic/transcriptional interactions have been shown to be able to predict partial pathways. With the abundant information made available by microarray gene expression data (MGED), nonlinear modeling of these interactions is now feasible. Two of the latest advances in nonlinear modeling used sigmoid models to depict transcriptional interaction of a transcription factor (TF) for a target gene, but do not model cooperative or competitive interactions of several TFs for a target. An S-shape model and an optimization algorithm (GASA) were developed to infer genetic interactions/transcriptional regulation of several genes simultaneously using MGED. GASA consists of a genetic algorithm (GA) and a simulated annealing (SA) algorithm, which is enhanced by a steepest gradient descent algorithm to avoid being trapped in local minimum. Using simulated data with various degrees of noise, we studied how GASA with two model selection criteria and two search spaces performed. Furthermore, GASA was shown to outperform network component analysis, the time series network inference algorithm (TSNI), GA with regular GA (GAGA) and GA with regular SA. Two applications are demonstrated. First, GASA is applied to infer a subnetwork of human T-cell apoptosis. Several of the predicted interactions are supported by the literature. Second, GASA was applied to infer the transcriptional factors of 34 cell cycle regulated targets in S. cerevisiae, and GASA performed better than one of the latest advances in nonlinear modeling, GAGA and TSNI. Moreover, GASA is able to predict multiple transcription factors for certain targets, and these results coincide with experiments confirmed data in YEASTRACT. GASA is shown to infer both genetic interactions and transcriptional regulatory interactions well. In particular, GASA seems able to characterize the nonlinear mechanism of transcriptional regulatory interactions (TIs) in yeast, and may be applied to infer TIs in other organisms. The predicted genetic interactions of a subnetwork of human T-cell apoptosis coincide with existing partial pathways, suggesting the potential of GASA on inferring biochemical pathways.

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.

Physics-Based Image Segmentation Using First Order Statistical Properties and Genetic Algorithm for Inductive Thermography Imaging.

PubMed

Gao, Bin; Li, Xiaoqing; Woo, Wai Lok; Tian, Gui Yun

2018-05-01

Thermographic inspection has been widely applied to non-destructive testing and evaluation with the capabilities of rapid, contactless, and large surface area detection. Image segmentation is considered essential for identifying and sizing defects. To attain a high-level performance, specific physics-based models that describe defects generation and enable the precise extraction of target region are of crucial importance. In this paper, an effective genetic first-order statistical image segmentation algorithm is proposed for quantitative crack detection. The proposed method automatically extracts valuable spatial-temporal patterns from unsupervised feature extraction algorithm and avoids a range of issues associated with human intervention in laborious manual selection of specific thermal video frames for processing. An internal genetic functionality is built into the proposed algorithm to automatically control the segmentation threshold to render enhanced accuracy in sizing the cracks. Eddy current pulsed thermography will be implemented as a platform to demonstrate surface crack detection. Experimental tests and comparisons have been conducted to verify the efficacy of the proposed method. In addition, a global quantitative assessment index F-score has been adopted to objectively evaluate the performance of different segmentation algorithms.

Threshold-selecting strategy for best possible ground state detection with genetic algorithms

NASA Astrophysics Data System (ADS)

Lässig, Jörg; Hoffmann, Karl Heinz

2009-04-01

Genetic algorithms are a standard heuristic to find states of low energy in complex state spaces as given by physical systems such as spin glasses but also in combinatorial optimization. The paper considers the problem of selecting individuals in the current population in genetic algorithms for crossover. Many schemes have been considered in literature as possible crossover selection strategies. We show for a large class of quality measures that the best possible probability distribution for selecting individuals in each generation of the algorithm execution is a rectangular distribution over the individuals sorted by their energy values. This means uniform probabilities have to be assigned to a group of the individuals with lowest energy in the population but probabilities equal to zero to individuals which are corresponding to energy values higher than a fixed cutoff, which is equal to a certain rank in the vector sorted by the energy of the states in the current population. The considered strategy is dubbed threshold selecting. The proof applies basic arguments of Markov chains and linear optimization and makes only a few assumptions on the underlying principles and hence applies to a large class of algorithms.

Biological engineering applications of feedforward neural networks designed and parameterized by genetic algorithms.

PubMed

Ferentinos, Konstantinos P

2005-09-01

Two neural network (NN) applications in the field of biological engineering are developed, designed and parameterized by an evolutionary method based on the evolutionary process of genetic algorithms. The developed systems are a fault detection NN model and a predictive modeling NN system. An indirect or 'weak specification' representation was used for the encoding of NN topologies and training parameters into genes of the genetic algorithm (GA). Some a priori knowledge of the demands in network topology for specific application cases is required by this approach, so that the infinite search space of the problem is limited to some reasonable degree. Both one-hidden-layer and two-hidden-layer network architectures were explored by the GA. Except for the network architecture, each gene of the GA also encoded the type of activation functions in both hidden and output nodes of the NN and the type of minimization algorithm that was used by the backpropagation algorithm for the training of the NN. Both models achieved satisfactory performance, while the GA system proved to be a powerful tool that can successfully replace the problematic trial-and-error approach that is usually used for these tasks.

How Crossover Speeds up Building Block Assembly in Genetic Algorithms.

PubMed

Sudholt, Dirk

2017-01-01

We reinvestigate a fundamental question: How effective is crossover in genetic algorithms in combining building blocks of good solutions? Although this has been discussed controversially for decades, we are still lacking a rigorous and intuitive answer. We provide such answers for royal road functions and OneMax, where every bit is a building block. For the latter, we show that using crossover makes every ([Formula: see text]+[Formula: see text]) genetic algorithm at least twice as fast as the fastest evolutionary algorithm using only standard bit mutation, up to small-order terms and for moderate [Formula: see text] and [Formula: see text]. Crossover is beneficial because it can capitalize on mutations that have both beneficial and disruptive effects on building blocks: crossover is able to repair the disruptive effects of mutation in later generations. Compared to mutation-based evolutionary algorithms, this makes multibit mutations more useful. Introducing crossover changes the optimal mutation rate on OneMax from [Formula: see text] to [Formula: see text]. This holds both for uniform crossover and k-point crossover. Experiments and statistical tests confirm that our findings apply to a broad class of building block functions.

Data mining for multiagent rules, strategies, and fuzzy decision tree structure

NASA Astrophysics Data System (ADS)

Smith, James F., III; Rhyne, Robert D., II; Fisher, Kristin

2002-03-01

A fuzzy logic based resource manager (RM) has been developed that automatically allocates electronic attack resources in real-time over many dissimilar platforms. Two different data mining algorithms have been developed to determine rules, strategies, and fuzzy decision tree structure. The first data mining algorithm uses a genetic algorithm as a data mining function and is called from an electronic game. The game allows a human expert to play against the resource manager in a simulated battlespace with each of the defending platforms being exclusively directed by the fuzzy resource manager and the attacking platforms being controlled by the human expert or operating autonomously under their own logic. This approach automates the data mining problem. The game automatically creates a database reflecting the domain expert's knowledge. It calls a data mining function, a genetic algorithm, for data mining of the database as required and allows easy evaluation of the information mined in the second step. The criterion for re- optimization is discussed as well as experimental results. Then a second data mining algorithm that uses a genetic program as a data mining function is introduced to automatically discover fuzzy decision tree structures. Finally, a fuzzy decision tree generated through this process is discussed.

High performance genetic algorithm for VLSI circuit partitioning

NASA Astrophysics Data System (ADS)

Dinu, Simona

2016-12-01

Partitioning is one of the biggest challenges in computer-aided design for VLSI circuits (very large-scale integrated circuits). This work address the min-cut balanced circuit partitioning problem- dividing the graph that models the circuit into almost equal sized k sub-graphs while minimizing the number of edges cut i.e. minimizing the number of edges connecting the sub-graphs. The problem may be formulated as a combinatorial optimization problem. Experimental studies in the literature have shown the problem to be NP-hard and thus it is important to design an efficient heuristic algorithm to solve it. The approach proposed in this study is a parallel implementation of a genetic algorithm, namely an island model. The information exchange between the evolving subpopulations is modeled using a fuzzy controller, which determines an optimal balance between exploration and exploitation of the solution space. The results of simulations show that the proposed algorithm outperforms the standard sequential genetic algorithm both in terms of solution quality and convergence speed. As a direction for future study, this research can be further extended to incorporate local search operators which should include problem-specific knowledge. In addition, the adaptive configuration of mutation and crossover rates is another guidance for future research.

iACP-GAEnsC: Evolutionary genetic algorithm based ensemble classification of anticancer peptides by utilizing hybrid feature space.

PubMed

Akbar, Shahid; Hayat, Maqsood; Iqbal, Muhammad; Jan, Mian Ahmad

2017-06-01

Cancer is a fatal disease, responsible for one-quarter of all deaths in developed countries. Traditional anticancer therapies such as, chemotherapy and radiation, are highly expensive, susceptible to errors and ineffective techniques. These conventional techniques induce severe side-effects on human cells. Due to perilous impact of cancer, the development of an accurate and highly efficient intelligent computational model is desirable for identification of anticancer peptides. In this paper, evolutionary intelligent genetic algorithm-based ensemble model, 'iACP-GAEnsC', is proposed for the identification of anticancer peptides. In this model, the protein sequences are formulated, using three different discrete feature representation methods, i.e., amphiphilic Pseudo amino acid composition, g-Gap dipeptide composition, and Reduce amino acid alphabet composition. The performance of the extracted feature spaces are investigated separately and then merged to exhibit the significance of hybridization. In addition, the predicted results of individual classifiers are combined together, using optimized genetic algorithm and simple majority technique in order to enhance the true classification rate. It is observed that genetic algorithm-based ensemble classification outperforms than individual classifiers as well as simple majority voting base ensemble. The performance of genetic algorithm-based ensemble classification is highly reported on hybrid feature space, with an accuracy of 96.45%. In comparison to the existing techniques, 'iACP-GAEnsC' model has achieved remarkable improvement in terms of various performance metrics. Based on the simulation results, it is observed that 'iACP-GAEnsC' model might be a leading tool in the field of drug design and proteomics for researchers. Copyright © 2017 Elsevier B.V. All rights reserved.

Optimal Refueling Pattern Search for a CANDU Reactor Using a Genetic Algorithm

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

Quang Binh, DO; Gyuhong, ROH; Hangbok, CHOI

2006-07-01

This paper presents the results from the application of genetic algorithms to a refueling optimization of a Canada deuterium uranium (CANDU) reactor. This work aims at making a mathematical model of the refueling optimization problem including the objective function and constraints and developing a method based on genetic algorithms to solve the problem. The model of the optimization problem and the proposed method comply with the key features of the refueling strategy of the CANDU reactor which adopts an on-power refueling operation. In this study, a genetic algorithm combined with an elitism strategy was used to automatically search for themore » refueling patterns. The objective of the optimization was to maximize the discharge burn-up of the refueling bundles, minimize the maximum channel power, or minimize the maximum change in the zone controller unit (ZCU) water levels. A combination of these objectives was also investigated. The constraints include the discharge burn-up, maximum channel power, maximum bundle power, channel power peaking factor and the ZCU water level. A refueling pattern that represents the refueling rate and channels was coded by a one-dimensional binary chromosome, which is a string of binary numbers 0 and 1. A computer program was developed in FORTRAN 90 running on an HP 9000 workstation to conduct the search for the optimal refueling patterns for a CANDU reactor at the equilibrium state. The results showed that it was possible to apply genetic algorithms to automatically search for the refueling channels of the CANDU reactor. The optimal refueling patterns were compared with the solutions obtained from the AUTOREFUEL program and the results were consistent with each other. (authors)« less

Assessment of genetic and nongenetic interactions for the prediction of depressive symptomatology: an analysis of the Wisconsin Longitudinal Study using machine learning algorithms.

PubMed

Roetker, Nicholas S; Page, C David; Yonker, James A; Chang, Vicky; Roan, Carol L; Herd, Pamela; Hauser, Taissa S; Hauser, Robert M; Atwood, Craig S

2013-10-01

We examined depression within a multidimensional framework consisting of genetic, environmental, and sociobehavioral factors and, using machine learning algorithms, explored interactions among these factors that might better explain the etiology of depressive symptoms. We measured current depressive symptoms using the Center for Epidemiologic Studies Depression Scale (n = 6378 participants in the Wisconsin Longitudinal Study). Genetic factors were 78 single nucleotide polymorphisms (SNPs); environmental factors-13 stressful life events (SLEs), plus a composite proportion of SLEs index; and sociobehavioral factors-18 personality, intelligence, and other health or behavioral measures. We performed traditional SNP associations via logistic regression likelihood ratio testing and explored interactions with support vector machines and Bayesian networks. After correction for multiple testing, we found no significant single genotypic associations with depressive symptoms. Machine learning algorithms showed no evidence of interactions. Naïve Bayes produced the best models in both subsets and included only environmental and sociobehavioral factors. We found no single or interactive associations with genetic factors and depressive symptoms. Various environmental and sociobehavioral factors were more predictive of depressive symptoms, yet their impacts were independent of one another. A genome-wide analysis of genetic alterations using machine learning methodologies will provide a framework for identifying genetic-environmental-sociobehavioral interactions in depressive symptoms.

A genetic algorithm based global search strategy for population pharmacokinetic/pharmacodynamic model selection

PubMed Central

Sale, Mark; Sherer, Eric A

2015-01-01

The current algorithm for selecting a population pharmacokinetic/pharmacodynamic model is based on the well-established forward addition/backward elimination method. A central strength of this approach is the opportunity for a modeller to continuously examine the data and postulate new hypotheses to explain observed biases. This algorithm has served the modelling community well, but the model selection process has essentially remained unchanged for the last 30 years. During this time, more robust approaches to model selection have been made feasible by new technology and dramatic increases in computation speed. We review these methods, with emphasis on genetic algorithm approaches and discuss the role these methods may play in population pharmacokinetic/pharmacodynamic model selection. PMID:23772792

Quasi-conformal mapping with genetic algorithms applied to coordinate transformations

NASA Astrophysics Data System (ADS)

González-Matesanz, F. J.; Malpica, J. A.

2006-11-01

In this paper, piecewise conformal mapping for the transformation of geodetic coordinates is studied. An algorithm, which is an improved version of a previous algorithm published by Lippus [2004a. On some properties of piecewise conformal mappings. Eesti NSV Teaduste Akademmia Toimetised Füüsika-Matemaakika 53, 92-98; 2004b. Transformation of coordinates using piecewise conformal mapping. Journal of Geodesy 78 (1-2), 40] is presented; the improvement comes from using a genetic algorithm to partition the complex plane into convex polygons, whereas the original one did so manually. As a case study, the method is applied to the transformation of the Spanish datum ED50 and ETRS89, and both its advantages and disadvantages are discussed herein.