Exploring Biogeochemistry and Microbial Diversity of Extant Microbialites in Mexico and Cuba

PubMed Central

Valdespino-Castillo, Patricia M.; Hu, Ping; Merino-Ibarra, Martín; López-Gómez, Luz M.; Cerqueda-García, Daniel; González-De Zayas, Roberto; Pi-Puig, Teresa; Lestayo, Julio A.; Holman, Hoi-Ying; Falcón, Luisa I.

2018-01-01

Microbialites are modern analogs of ancient microbial consortia that date as far back as the Archaean Eon. Microbialites have contributed to the geochemical history of our planet through their diverse metabolic capacities that mediate mineral precipitation. These mineral-forming microbial assemblages accumulate major ions, trace elements and biomass from their ambient aquatic environments; their role in the resulting chemical structure of these lithifications needs clarification. We studied the biogeochemistry and microbial structure of microbialites collected from diverse locations in Mexico and in a previously undescribed microbialite in Cuba. We examined their structure, chemistry and mineralogy at different scales using an array of nested methods including 16S rRNA gene high-throughput sequencing, elemental analysis, X-Ray fluorescence (XRF), X-Ray diffraction (XRD), Scanning Electron Microscopy-Energy Dispersive Spectroscopy (SEM-EDS), Fourier Transformed Infrared (FTIR) spectroscopy and Synchrotron Radiation-based Fourier Transformed Infrared (SR-FTIR) spectromicroscopy. The resulting data revealed high biological and chemical diversity among microbialites and specific microbe to chemical correlations. Regardless of the sampling site, Proteobacteria had the most significant correlations with biogeochemical parameters such as organic carbon (Corg), nitrogen and Corg:Ca ratio. Biogeochemically relevant bacterial groups (dominant phototrophs and heterotrophs) showed significant correlations with major ion composition, mineral type and transition element content, such as cadmium, cobalt, chromium, copper and nickel. Microbial-chemical relationships were discussed in reference to microbialite formation, microbial metabolic capacities and the role of transition elements as enzyme cofactors. This paper provides an analytical baseline to drive our understanding of the links between microbial diversity with the chemistry of their lithified precipitations. PMID:29666607

Growing Rocks: Implications of Lithification for Microbial Communities and Nutrient Cycling

NASA Astrophysics Data System (ADS)

Corman, J. R.; Poret-Peterson, A. T.; Elser, J. J.

2014-12-01

Lithifying microbial communities ("microbialites") have left their signature on Earth's rock record for over 3.4 billion years and are regarded as important players in paleo-biogeochemical cycles. In this project, we study extant microbialites to understand the interactions between lithification and resource availability. All microbes need nutrients and energy for growth; indeed, nutrients are often a factor limiting microbial growth. We hypothesize that calcium carbonate deposition can sequester bioavailable phosphorus (P) and expect the growth of microbialites to be P-limited. To test our hypothesis, we first compared nutrient limitation in lithifying and non-lithifying microbial communities in Río Mesquites, Cuatro Ciénegas. Then, we experimentally manipulated calcification rates in the Río Mesquites microbialites. Our results suggest that lithifying microbialites are indeed P-limited, while non-lithifying, benthic microbial communities tend towards co-limitation by nitrogen (N) and P. Indeed, in microbialites, photosynthesis and aerobic respiration responded positively to P additions (P<0.05). Organic carbon (OC) additions caused shifts in bacterial community composition based on analysis of 16S rRNA genes. Unexpectedly, calcification rates increased with OC additions (P<0.05), but not with P additions, suggesting that sulfate reduction may be an important pathway for calcification. Experimental reductions in calcification rates caused changes to microbial biomass OC and P concentrations (P<0.01 and P<0.001, respectively), although shifts depended on whether calcification was decreased abiotically or biotically. These results show that resource availability does influence microbialite formation and that lithification may promote phosphorus limitation; however, further investigation is required to understand the mechanism by which the later occurs.

Exploring Biogeochemistry and Microbial Diversity of Extant Microbialites in Mexico and Cuba.

PubMed

Valdespino-Castillo, Patricia M; Hu, Ping; Merino-Ibarra, Martín; López-Gómez, Luz M; Cerqueda-García, Daniel; González-De Zayas, Roberto; Pi-Puig, Teresa; Lestayo, Julio A; Holman, Hoi-Ying; Falcón, Luisa I

2018-01-01

Microbialites are modern analogs of ancient microbial consortia that date as far back as the Archaean Eon. Microbialites have contributed to the geochemical history of our planet through their diverse metabolic capacities that mediate mineral precipitation. These mineral-forming microbial assemblages accumulate major ions, trace elements and biomass from their ambient aquatic environments; their role in the resulting chemical structure of these lithifications needs clarification. We studied the biogeochemistry and microbial structure of microbialites collected from diverse locations in Mexico and in a previously undescribed microbialite in Cuba. We examined their structure, chemistry and mineralogy at different scales using an array of nested methods including 16S rRNA gene high-throughput sequencing, elemental analysis, X-Ray fluorescence (XRF), X-Ray diffraction (XRD), Scanning Electron Microscopy-Energy Dispersive Spectroscopy (SEM-EDS), Fourier Transformed Infrared (FTIR) spectroscopy and Synchrotron Radiation-based Fourier Transformed Infrared (SR-FTIR) spectromicroscopy. The resulting data revealed high biological and chemical diversity among microbialites and specific microbe to chemical correlations. Regardless of the sampling site, Proteobacteria had the most significant correlations with biogeochemical parameters such as organic carbon (C org ), nitrogen and C org :Ca ratio. Biogeochemically relevant bacterial groups (dominant phototrophs and heterotrophs) showed significant correlations with major ion composition, mineral type and transition element content, such as cadmium, cobalt, chromium, copper and nickel. Microbial-chemical relationships were discussed in reference to microbialite formation, microbial metabolic capacities and the role of transition elements as enzyme cofactors. This paper provides an analytical baseline to drive our understanding of the links between microbial diversity with the chemistry of their lithified precipitations.

Permian-Triassic boundary microbialites at Zuodeng Section, Guangxi Province, South China: Geobiology and palaeoceanographic implications

NASA Astrophysics Data System (ADS)

Fang, Yuheng; Chen, Zhong-Qiang; Kershaw, Stephen; Yang, Hao; Luo, Mao

2017-05-01

A previously unknown microbialite bed in the Permian-Triassic (P-Tr) boundary beds of Zuodeng section, Tiandong County, Guangxi, South China comprises a thin (5 cm maximum thickness) stromatolite in the lower part and the remaining 6 m is thrombolite. The Zuodeng microbialite has a pronounced irregular contact between the latest Permian bioclastic limestone and microbialite, as in other sites in the region. The stromatolite comprises low-relief columnar and broad domal geometries, containing faint laminations. The thrombolite displays an irregular mixture of sparitic dark coloured altered microbial fabric and light coloured interstitial sediment in polished blocks. Abundant microproblematic calcimicrobe structures identified here as Gakhumella are preserved in dark coloured laminated areas of the stromatolite and sparitic areas in thrombolites (i.e. the calcimicrobial part, not the interstitial sediment) and are orientated perpendicular to stromatolitic laminae. Each Gakhumella individual has densely arranged segments, which form a column- to fan-shaped structure. Single segments are arch-shaped and form a thin chamber between segments. Gakhumella individuals in the stromatolite and thrombolite are slightly different from each other, but are readily distinguished from the Gakhumella- and Renalcis-like fossils reported from other P-Tr boundary microbialites in having a smaller size, unbranching columns and densely arranged, arch-shaped segments. Renalcids usually possess a larger body size and branching, lobate outlines. Filament sheath aggregates are also observed in the stromatolite and they are all orientated in one direction. Both Gakhumella and filament sheath aggregates may be photosynthetic algae, which may have played an important role in constructing the Zuodeng microbialites. Other calcimicrobes in the Zuodeng microbialite are spheroids, of which a total of five morphological types are recognized from both stromatolite and thrombolite: (1) sparry calcite spheroid without outer sheaths, (2) a large sparry calcite nucleus coated with a thin sparry calcite sheath, (3) a large nucleus of micrite framboid aggregates rimmed by a thin sparry calcite sheath (bacterial clump-like spheroids), (4) a large nucleus of micrite framboid aggregates coated with a thin micritic sheath, and (5) a small sparry nuclei rimmed by coarse-grained, radiated euhedral rays. The irregular contact beneath the Zuodeng microbialites is interpreted as a subaerial exposure surface due to regional regression in South China. The demise of the Zuodeng microbialites may have been due to rapid rise in sea-level because they grew in relatively shallow marine conditions and are overlain by muddy limestones containing pelagic conodonts. Also siliciclastic content increases above the microbialite, suggesting a possible climate-related increase in weathering as the transgression progressed.

Phylogenetic diversity of a microbialite reef in a cold alkaline freshwater lake.

PubMed

Chan, Olivia W; Bugler-Lacap, Donnabella C; Biddle, Jennifer F; Lim, Darlene S; McKay, Christopher P; Pointing, Stephen B

2014-06-01

A culture-independent multidomain survey of biodiversity in microbialite structures within the cold alkaline Pavilion Lake (British Columbia, Canada) revealed a largely homogenous community at depths from 10 to 30 m. Real-time quantitative PCR was used to demonstrate that bacteria comprised approximately 80%-95% of recoverable phylotypes. Archaeal phylotypes accounted for <5% of the community in microbialites exposed to the water column, while structures in sediment contact supported 4- to 5-fold higher archaeal abundance. Eukaryal phylotypes were rare and indicated common aquatic diatoms that were concluded not to be part of the microbialite community. Phylogenetic analysis of rRNA genes from clone libraries (N = 491) revealed that alphaproteobacterial phylotypes were most abundant. Cyanobacterial phylotypes were highly diverse but resolved into 4 dominant genera: Acaryochloris, Leptolyngbya, Microcoleus, and Pseudanabaena. Interestingly, microbialite cyanobacteria generally affiliated phylogenetically with aquatic and coral cyanobacterial groups rather than those from stromatolites. Other commonly encountered bacterial phylotypes were from members of the Acidobacteria, with relatively low abundance of the Betaproteobacteria, Chloroflexi, Nitrospirae, and Planctomycetes. Archaeal diversity (N = 53) was largely accounted for by Euryarchaeota, with most phylotypes affiliated with freshwater methanogenic taxa.

Ediacaran Seepage-related Cloudina-Microbialites from Southern Namibia

NASA Astrophysics Data System (ADS)

Reitner, Joachim, , Dr

2015-04-01

Little is known about the lifestyle of the calcified tube organisms of the Cloudina group. This late Proterozoic group, whose overall morphology slightly resembles modern calcified worm tubes, were the first animals with calcified skeletons. The modern seep-related vestimentiferan worm tubes of Escarpia are composed of chitin; in few cases we note the beginning of CaCO3 (aragonite) mineralisation on the chitin surfaces. The calcified skeleton of C. hartmannae exhibits a more complicated microstructure. The calcareous skeleton, which was probably originally aragonitic, appears to be produced by a probably enzymatically controlled biomineralisation. Seilacher (1999) reconstructed the Cloudina group as typical soft bottom dwellers. Some millimeter-sized C. riemkeae specimens are indeed common in soft micritic, lagoonal carbonates. However, we have observed large C. hartmannae tubes inside very large (5-8 meters high, 30-50 cm in diameter) pillar-like microbialites ("organ-pipes") from the Zaris Mountains/Zebra River (Omkyk Member, Kuibis Subgroup, Nama Group). These microbialiates have a complex structure. The inner portions of these microbialites are formed by large, cm-sized recrystallized aragonitic spherulites covered by calcified microbial matter exhibiting a typical thrombolitic structure. The outer portions of the microbialites exhibit a typical stromatolitic structure. These "organ-pipe" microbialites strongly resemble the modern ones known from Lake Van and Mono Lake. In both modern cases the microbialites grow in extremely alkaline water located at sites where Ca2+-rich ground water is seeping in the lake water. Geochemical data, from the still Sr-rich neomorphic former aragonitic spherulites and all other noted carbonate phases, suggest that the microbialites from the Zaris Mountains in Namibia formed under comparable conditions. Cloudina is very common within the thrombolitic portion of the microbialites and the occurrence is definitely autochthonous; Cloudina has probably filtered the seep fluids. A chemosynthetic life style cannot be excluded and will be the subject of further investigations. The occurrence of the heavy calcified metazoan skeletons in potentially Ca2+-rich seep fluid environments support the idea that Ca2+-detoxification was a driving force of the beginning of an enzymatically controlled biomineralisation. Seilacher, A. (1999) Biomat-related lifestyles in the Precambrian. Palaios 14:86-93.

Non-marine carbonate facies, facies models and palaeogeographies of the Purbeck Formation (Late Jurassic to Early Cretaceous) of Dorset (Southern England).

NASA Astrophysics Data System (ADS)

Gallois, Arnaud; Bosence, Dan; Burgess, Peter

2015-04-01

Non-marine carbonates are relatively poorly understood compared with their more abundant marine counterparts. Sedimentary facies and basin architecture are controlled by a range of environmental parameters such as climate, hydrology and tectonic setting but facies models are few and limited in their predictive value. Following the discovery of extensive Early Cretaceous, non-marine carbonate hydrocarbon reservoirs in the South Atlantic, the interest of understanding such complex deposits has increased during recent years. This study is developing a new depositional model for non-marine carbonates in a semi-arid climate setting in an extensional basin; the Purbeck Formation (Upper Jurassic - Lower Cretaceous) in Dorset (Southern England). Outcrop study coupled with subsurface data analysis and petrographic study (sedimentology and early diagenesis) aims to constrain and improve published models of depositional settings. Facies models for brackish water and hypersaline water conditions of these lacustrine to palustrine carbonates deposited in the syn-rift phase of the Wessex Basin will be presented. Particular attention focusses on the factors that control the accumulation of in-situ microbialite mounds that occur within bedded inter-mound packstones-grainstones in the lower Purbeck. The microbialite mounds are located in three units (locally known as the Skull Cap, the Hard Cap and the Soft Cap) separated by three fossil soils (locally known as the Basal, the Lower and the Great Dirt Beds) respectively within three shallowing upward lacustrine sequences. These complex microbialite mounds (up to 4m high), are composed of tabular small-scale mounds (flat and long, up to 50cm high) divided into four subfacies. Many of these small-scale mounds developed around trees and branches which are preserved as moulds (or silicified wood) which are surrounded by a burrowed mudstone-wackestone collar. Subsequently a thrombolite framework developed on the upper part only within bedded inter-mound packestones-grainstones. Finally a discontinuous basal laminated subfacies can be found overlaying the fossil soils. The overall control on facies and their distribution is the tectonic control as highlighted by the activity of the two main extensional faults during Purbeck times. The tectonic control on development of microbialite mounds is indicated by their relationship with the relay ramp. Their occurrence is controlled by palaeotopography generated on sub-aerial exposure surfaces, palaesols and early conifer trees and developed mainly on the shallowest area of the lake as indicated by their relationship with the inter-mound packstone-grainstone facies and the palaeosols. The new depositional models developed in this study integrate sedimentological facies models with the syn-rift setting of the Wessex Basin to explain the distribution of the microbialite mounds.

Molecular fossils of prokaryotes in ancient authigenic minerals: archives of microbial activity in reefs and mounds?

NASA Astrophysics Data System (ADS)

Heindel, Katrin; Birgel, Daniel; Richoz, Sylvain; Westphal, Hildegard; Peckmann, Jörn

2016-04-01

Molecular fossils (lipid biomarkers) are commonly used as proxies in organic-rich sediments of various sources, including eukaryotes and prokaryotes. Usually, molecular fossils of organisms transferred from the water column to the sediment are studied to monitor environmental changes (e.g., temperature, pH). Apart from these 'allochthonous' molecular fossils, prokaryotes are active in sediments and mats on the seafloor and leave behind 'autochthonous' molecular fossils in situ. In contrast to many phototrophic organisms, most benthic sedimentary prokaryotes are obtaining their energy from oxidation or reduction of organic or inorganic substrates. A peculiarity of some of the sediment-thriving prokaryotes is their ability to trigger in situ mineral precipitation, often but not only due to metabolic activity, resulting in authigenic rocks (microbialites). During that process, prokaryotes are rapidly entombed in the mineral matrix, where the molecular fossils are protected from early (bio)degradation. In contrast to other organic compounds (DNA, proteins etc.), molecular fossils can be preserved over very long time periods (millions of years). Thus, molecular fossils in authigenic mineral phases are perfectly suitable to trace microbial activity back in time. Among the best examples of molecular fossils, which are preserved in authigenic rocks are various microbialites, forming e.g. in phototrophic microbial mats and at cold seeps. Microbialite formation is reported throughout earth history. We here will focus on reefal microbialites form the Early Triassic and the Holocene. After the End-Permian mass extinction, microbialites covered wide areas on the ocean margins. In microbialites from the Griesbachian in Iran and Turkey (both Neotethys), molecular fossils of cyanobacteria, archaea, anoxygenic phototrophs, and sulphate-reducing bacteria indicate the presence of layered microbial mats on the seafloor, in which carbonate precipitation was induced. In association with metazoans other than corals (sponges, bivalves, gastropods, ostracods) and foraminifera, first metazoan-microbialite reefs developed on the Early Triassic seafloor. After the last glacial maximum, microbialites formed in coral reefs. Our evidence shows that sulphate-reducing bacteria played an intrinsic role in the precipitation of these microbialites during the Holocene sea-level rise. With more nutrients and organic matter distributed in the reef ecosystem, anoxic microenvironments preferentially developed. Such conditions favored heterotrophic bacteria, particularly, sulphate-reducing bacteria. It is suggested that matrix-solute interaction related to the activity of sulphate reducers induced carbonate precipitation in extracellular polymeric substances. Overall, authigenic mineral phases from various environments can be used as excellent archives to describe former microbial activity in sediments. The early entombment of the lipids in the mineral matrix avoids the loss of specific and important information, which may have been lost in soft sediments rather quick.

Questioning the Origin of the Great Salt Lake "Microbialites"

NASA Astrophysics Data System (ADS)

Frantz, C.; Matyjasik, M.; Newell, D. L.; Vanden Berg, M. D.; Park, C.

2017-12-01

The Great Salt Lake (GSL) of Utah contains abundant carbonate mounds that have been described in the literature as "biostromes", "bioherms", "stromatolites", and "microbialites". The structures are commonly cited as being rare examples of modern lacustrine microbialites, which implies that they are actively-forming and biogenic. Indeed, at least in some regions of the lake, the mounds are covered in a mixed community of cyanobacteria, algae, insect larval casings, microbial heterotrophs, and other organisms that is thought to contribute significantly to benthic primary productivity in GSL. However, the presence of a modern surface microbial community does not implicate a biogenic or modern origin for the mounds. The few studies to date GSL microbialites indicate that they are ancient, with radiocarbon calendar ages in the late Pleistocene and Holocene ( 13 - 3 cal ka). However, could they still be actively growing, and are the surface microbial communities playing a role? Here, we present results of a suite geochemical measurements used to constrain parameters—including groundwater seepage—influencing carbonate saturation and precipitation in the vicinity of one currently-submerged "microbialite reef" on the northern shore of Antelope Island in the South Arm of GSL. Our data suggests that calcium-charged brackish groundwater input to the lake through a permeable substratum in this location results in locally supersaturated conditions for aragonite, which could lead to modern, abiogenic mineralization. In addition, a series of laboratory experiments suggest that the modern surface microbial communities that coat the mounds do not appreciably facilitate carbonate precipitation in simulated GSL conditions, although they may serve as a template for precipitation when local waters become supersaturated.

Microbial and diagenetic steps leading to the mineralisation of Great Salt Lake microbialites

NASA Astrophysics Data System (ADS)

Pace, Aurélie; Bourillot, Raphaël; Bouton, Anthony; Vennin, Emmanuelle; Galaup, Serge; Bundeleva, Irina; Patrier, Patricia; Dupraz, Christophe; Thomazo, Christophe; Sansjofre, Pierre; Yokoyama, Yusuke; Franceschi, Michel; Anguy, Yannick; Pigot, Léa; Virgone, Aurélien; Visscher, Pieter T.

2016-08-01

Microbialites are widespread in modern and fossil hypersaline environments, where they provide a unique sedimentary archive. Authigenic mineral precipitation in modern microbialites results from a complex interplay between microbial metabolisms, organic matrices and environmental parameters. Here, we combined mineralogical and microscopic analyses with measurements of metabolic activity in order to characterise the mineralisation of microbial mats forming microbialites in the Great Salt Lake (Utah, USA). Our results show that the mineralisation process takes place in three steps progressing along geochemical gradients produced through microbial activity. First, a poorly crystallized Mg-Si phase precipitates on alveolar extracellular organic matrix due to a rise of the pH in the zone of active oxygenic photosynthesis. Second, aragonite patches nucleate in close proximity to sulfate reduction hotspots, as a result of the degradation of cyanobacteria and extracellular organic matrix mediated by, among others, sulfate reducing bacteria. A final step consists of partial replacement of aragonite by dolomite, possibly in neutral to slightly acidic porewater. This might occur due to dissolution-precipitation reactions when the most recalcitrant part of the organic matrix is degraded. The mineralisation pathways proposed here provide pivotal insight for the interpretation of microbial processes in past hypersaline environments.

Characterization of bacterial diversity associated with microbial mats, gypsum evaporites and carbonate microbialites in thalassic wetlands: Tebenquiche and La Brava, Salar de Atacama, Chile.

PubMed

Farías, M E; Contreras, M; Rasuk, M C; Kurth, D; Flores, M R; Poiré, D G; Novoa, F; Visscher, P T

2014-03-01

In this paper, we report the presence of sedimentary microbial ecosystems in wetlands of the Salar de Atacama. These laminated systems, which bind, trap and precipitate mineral include: microbial mats at Laguna Tebenquiche and Laguna La Brava, gypsum domes at Tebenquiche and carbonate microbialites at La Brava. Microbial diversity and key biogeochemical characteristics of both lakes (La Brava and Tebenquiche) and their various microbial ecosystems (non-lithifying mats, flat and domal microbialites) were determined. The composition and abundance of minerals ranged from trapped and bound halite in organic-rich non-lithifying mats to aragonite-dominated lithified flat microbialites and gypsum in lithified domal structures. Pyrosequencing of the V4 region of the 16s rDNA gene showed that Proteobacteria comprised a major phylum in all of the microbial ecosystems studied, with a marked lower abundance in the non-lithifying mats. A higher proportion of Bacteroidetes was present in Tebenquiche sediments compared to La Brava samples. The concentration of pigments, particularly that of Chlorophyll a, was higher in the Tebenquiche than in La Brava. Pigments typically associated with anoxygenic phototrophic bacteria were present in lower amounts. Organic-rich, non-lithifying microbial mats frequently formed snake-like, bulbous structures due to gas accumulation underneath the mat. We hypothesize that the lithified microbialites might have developed from these snake-like microbial mats following mineral precipitation in the surface layer, producing domes with endoevaporitic communities in Tebenquiche and carbonate platforms in La Brava. Whereas the potential role of microbes in carbonate platforms is well established, the contribution of endoevaporitic microbes to formation of gypsum domes needs further investigation.

Anachronistic facies from a drowned Lower Triassic carbonate platform: Lower member of the Alwa Formation (Ba'id Exotic), Oman Mountains

NASA Astrophysics Data System (ADS)

Woods, Adam D.; Baud, Aymon

2008-09-01

The lower member of the Alwa Formation (Lower Olenekian), found within the Ba'id Exotic in the Oman Mountains (Sultanate of Oman), consists of ammonoid-bearing, pelagic limestones that were deposited on an isolated, drowned carbonate platform on the Neotethyan Gondwana margin. The strata contain a variety of unusual carbonate textures and features, including thrombolites, Frutexites-bearing microbialites that contain synsedimentary cements, matrix-free breccias surrounded by isopachous calcite cement, and fissures and cavities filled with large botryoidal cements. Thrombolites are found throughout the study interval, and occur as 0.5-1.0 m thick lenses or beds that contain laterally laterally-linked stromatactis cavities. The Frutexites-bearing microbialites occur less frequently, and also form lenses or beds, up to 30 cm thick; the microbialites may be laminated, and often developed on hardgrounds. In addition, the Frutexites-bearing microbialites also contain synsedimentary calcite cement crusts and botryoids (typically < 1 cm thick) that harbour layers or pockets of what appear to be bacterial sheaths and coccoids, and are indicative of biologically mediated precipitation of the cement bodies. Slumping following lithification led to fracturing of the limestone and the precipitation of large, botryoidal aragonite cements in fissures that cut across the primary fabric. Environmental conditions, specifically palaeoxygenation and the degree of calcium carbonate supersaturation, likely controlled whether the thrombolites (high level of calcium carbonate supersaturation associated with vertical mixing of water masses and dysoxic conditions) or Frutexites-bearing microbialites (low level of calcium carbonate supersaturation associated with anoxic conditions and deposition below a stable chemocline) formed. The results of this study point to continued environmental stress in the region during the Early Triassic that likely contributed to the uneven recovery from the Permian-Triassic mass extinction.

Freshwater Microbialites of Pavilion Lake, British Columbia, Canada: A Limnological Investigation

NASA Technical Reports Server (NTRS)

Lim, D. S. S.; McKay, C. P.; Laval, B.; Bird, J.; Cady, S.

2004-01-01

Pavillion Lake is 5.7km long and an average of 0.8 km in width, and is located in Marble Canyon in the interior of British Columbia, Canada. It is a slightly alkaline, freshwater lake with a maximum-recorded depth of 65m. The basin walls of Pavilion Lake are lined with microbialite structures that are oriented perpendicularly to the shoreline, and which are found from depths of 5 meters to the bottom of the photic zone (light levels 1% of ambient; approximately 30m depth). These structures are speculated to have begun formation nearly 11,000 years ago, after the glacial retreat of the Cordilleran Ice Sheet. They are likely a distinctive assemblage of freshwater calcite microbialites, which display micromorphologies possibly related to the ancient Epiphyton and Girvanella classes of calcareous organosedimentary structures.

Life in Meridiani Planum. Mars. (Italian Title: Vita in Meridiani Planum, Marte)

NASA Astrophysics Data System (ADS)

Bianciardi, G.; Rizzo, V.; Cantasano, V.

2015-05-01

We performed a quantitative image analysis to compare microstructures of microbialites with the images photographed by the Rover Opportunity. Terrestrial and Martian textures present a multifractal aspect. Mean values and confidence intervals from the Martian images overlapped perfectly with those from the terrestrial samples (p<0.004). Our work shows the presumptive evidence of microbialites in the Martian outcroppings: the presence of unicellular life widespread on the ancient Mars.

Cyanobacterial fossils from 252 Ma old microbialites and their environmental significance.

PubMed

Wu, Ya Sheng; Yu, Gong Liang; Li, Ren Hui; Song, Li Rong; Jiang, Hong Xia; Riding, Robert; Liu, Li Jing; Liu, Dong Yan; Zhao, Rui

2014-01-22

The end-Permian mass extinction was followed by the formation of an enigmatic rock layer with a distinctive macroscopic spotted or dendroid fabric. This deposit has been interpreted as microbial reef rock, digitate dendrolite, digital thrombolite, dendritic thrombolite, or bacterial deposits. Agreement has been reached in considering them as microbialites, but not in their formation. This study has revealed that the spotted and dendroid microbialites were composed of numerous fossil casts formed by the planktic cyanobacterium, Microcystis, a coccoid genus that at the present-day commonly forms blooms in modern lakes, rivers, and reservoirs. The abundance of the fossils and the diagenesis they experienced has determined the macroscopic fabric: where they abundant, the rock appears as dendroid, otherwise, it appears as spotted. The ancient Microcystis bloom might produce toxin to kill other metazoans, and be responsible for the oceanic anoxia that has puzzled so many researchers for so many years.

Structural parallels between terrestrial microbialites and Martian sediments: are all cases of `Pareidolia'?

NASA Astrophysics Data System (ADS)

Rizzo, Vincenzo; Cantasano, Nicola

2017-10-01

The study analyses possible parallels of the microbialite-known structures with a set of similar settings selected by a systematic investigation from the wide record and data set of images shot by NASA rovers. Terrestrial cases involve structures both due to bio-mineralization processes and those induced by bacterial metabolism, that occur in a dimensional field longer than 0.1 mm, at micro, meso and macro scales. The study highlights occurrence on Martian sediments of widespread structures like microspherules, often organized into some higher-order settings. Such structures also occur on terrestrial stromatolites in a great variety of `Microscopic Induced Sedimentary Structures', such as voids, gas domes and layer deformations of microbial mats. We present a suite of analogies so compelling (i.e. different scales of morphological, structural and conceptual relevance), to make the case that similarities between Martian sediment structures and terrestrial microbialites are not all cases of `Pareidolia'.

Cyanobacterial fossils from 252 Ma old microbialites and their environmental significance

PubMed Central

Wu, Ya Sheng; Yu, Gong Liang; Li, Ren Hui; Song, Li Rong; Jiang, Hong Xia; Riding, Robert; Liu, Li Jing; Liu, Dong Yan; Zhao, Rui

2014-01-01

The end-Permian mass extinction was followed by the formation of an enigmatic rock layer with a distinctive macroscopic spotted or dendroid fabric. This deposit has been interpreted as microbial reef rock, digitate dendrolite, digital thrombolite, dendritic thrombolite, or bacterial deposits. Agreement has been reached in considering them as microbialites, but not in their formation. This study has revealed that the spotted and dendroid microbialites were composed of numerous fossil casts formed by the planktic cyanobacterium, Microcystis, a coccoid genus that at the present-day commonly forms blooms in modern lakes, rivers, and reservoirs. The abundance of the fossils and the diagenesis they experienced has determined the macroscopic fabric: where they abundant, the rock appears as dendroid, otherwise, it appears as spotted. The ancient Microcystis bloom might produce toxin to kill other metazoans, and be responsible for the oceanic anoxia that has puzzled so many researchers for so many years. PMID:24448025

Occurrence and genesis of Quaternary microbialitic tufa at Hammam Al Ali, Oman

NASA Astrophysics Data System (ADS)

Khalaf, Fikry I.

2017-05-01

Remnants of late Quaternary microbialitic tufa occurs within a shallow depression in the Hammam Al Ali hot spring area, which is located approximately 14.5 km to the southwest of Muscat, Oman. The tufa precipitated from hot spring water supersaturated with respect to calcium carbonate and is mostly of a porous phytogenic type, with occasional detrital and stromatolitic types. Microscopic and nanoscopic examination revealed that the tufa deposits developed through two successive processes of calcite precipitation, biotic and abiotic, preceded by limited precipitation of unstable aragonite. It is suggested that biologically mediated precipitation results in the construction of incomplete skeletal calcite crystals. The latter provide a base for classical physiochemical precipitation and, eventually, the development of complete sparry calcite crystals. The initiation of dendritic calcite crystals in the stromatolitic tufa as incomplete biogenic skeletal crystals and their characteristic growth pattern indicates that the tufa represents a clear example of hot spring calcitic microbialite.

Structure, mineralogy, and microbial diversity of geothermal spring microbialites associated with a deep oil drilling in Romania.

PubMed

Coman, Cristian; Chiriac, Cecilia M; Robeson, Michael S; Ionescu, Corina; Dragos, Nicolae; Barbu-Tudoran, Lucian; Andrei, Adrian-Ştefan; Banciu, Horia L; Sicora, Cosmin; Podar, Mircea

2015-01-01

Modern mineral deposits play an important role in evolutionary studies by providing clues to the formation of ancient lithified microbial communities. Here we report the presence of microbialite-forming microbial mats in different microenvironments at 32°C, 49°C, and 65°C around the geothermal spring from an abandoned oil drill in Ciocaia, Romania. The mineralogy and the macro- and microstructure of the microbialites were investigated, together with their microbial diversity based on a 16S rRNA gene amplicon sequencing approach. The calcium carbonate is deposited mainly in the form of calcite. At 32°C and 49°C, the microbialites show a laminated structure with visible microbial mat-carbonate crystal interactions. At 65°C, the mineral deposit is clotted, without obvious organic residues. Partial 16S rRNA gene amplicon sequencing showed that the relative abundance of the phylum Archaea was low at 32°C (<0.5%) but increased significantly at 65°C (36%). The bacterial diversity was either similar to other microbialites described in literature (the 32°C sample) or displayed a specific combination of phyla and classes (the 49°C and 65°C samples). Bacterial taxa were distributed among 39 phyla, out of which 14 had inferred abundances >1%. The dominant bacterial groups at 32°C were Cyanobacteria, Gammaproteobacteria, Firmicutes, Bacteroidetes, Chloroflexi, Thermi, Actinobacteria, Planctomycetes, and Defferibacteres. At 49°C, there was a striking dominance of the Gammaproteobacteria, followed by Firmicutes, Bacteroidetes, and Armantimonadetes. The 65°C sample was dominated by Betaproteobacteria, Firmicutes, [OP1], Defferibacteres, Thermi, Thermotogae, [EM3], and Nitrospirae. Several groups from Proteobacteria and Firmicutes, together with Halobacteria and Melainabacteria were described for the first time in calcium carbonate deposits. Overall, the spring from Ciocaia emerges as a valuable site to probe microbes-minerals interrelationships along thermal and geochemical gradients.

Structure, mineralogy, and microbial diversity of geothermal spring microbialites associated with a deep oil drilling in Romania

PubMed Central

Coman, Cristian; Chiriac, Cecilia M.; Robeson, Michael S.; Ionescu, Corina; Dragos, Nicolae; Barbu-Tudoran, Lucian; Andrei, Adrian-Ştefan; Banciu, Horia L.; Sicora, Cosmin; Podar, Mircea

2015-01-01

Modern mineral deposits play an important role in evolutionary studies by providing clues to the formation of ancient lithified microbial communities. Here we report the presence of microbialite-forming microbial mats in different microenvironments at 32°C, 49°C, and 65°C around the geothermal spring from an abandoned oil drill in Ciocaia, Romania. The mineralogy and the macro- and microstructure of the microbialites were investigated, together with their microbial diversity based on a 16S rRNA gene amplicon sequencing approach. The calcium carbonate is deposited mainly in the form of calcite. At 32°C and 49°C, the microbialites show a laminated structure with visible microbial mat-carbonate crystal interactions. At 65°C, the mineral deposit is clotted, without obvious organic residues. Partial 16S rRNA gene amplicon sequencing showed that the relative abundance of the phylum Archaea was low at 32°C (<0.5%) but increased significantly at 65°C (36%). The bacterial diversity was either similar to other microbialites described in literature (the 32°C sample) or displayed a specific combination of phyla and classes (the 49°C and 65°C samples). Bacterial taxa were distributed among 39 phyla, out of which 14 had inferred abundances >1%. The dominant bacterial groups at 32°C were Cyanobacteria, Gammaproteobacteria, Firmicutes, Bacteroidetes, Chloroflexi, Thermi, Actinobacteria, Planctomycetes, and Defferibacteres. At 49°C, there was a striking dominance of the Gammaproteobacteria, followed by Firmicutes, Bacteroidetes, and Armantimonadetes. The 65°C sample was dominated by Betaproteobacteria, Firmicutes, [OP1], Defferibacteres, Thermi, Thermotogae, [EM3], and Nitrospirae. Several groups from Proteobacteria and Firmicutes, together with Halobacteria and Melainabacteria were described for the first time in calcium carbonate deposits. Overall, the spring from Ciocaia emerges as a valuable site to probe microbes-minerals interrelationships along thermal and geochemical gradients. PMID:25870594

Structure, mineralogy, and microbial diversity of geothermal spring microbialites associated with a deep oil drilling in Romania

DOE PAGES

Coman, Cristian; Chiriac, Cecilia M.; Robeson, Michael S.; ...

2015-03-30

Modern mineral deposits play an important role in evolutionary studies by providing clues to the formation of ancient lithified microbial communities. Here we report the presence of microbialite-forming microbial mats in different microenvironments at 32°C, 49°C, and 65°C around the geothermal spring from an abandoned oil drill in Ciocaia, Romania. The mineralogy and the macro- and microstructure of the microbialites were investigated, together with their microbial diversity based on a 16S rRNA gene amplicon sequencing approach. The calcium carbonate is deposited mainly in the form of calcite. At 32°C and 49°C, the microbialites show a laminated structure with visible microbialmore » mat-carbonate crystal interactions. At 65°C, the mineral deposit is clotted, without obvious organic residues. Partial 16S rRNA gene amplicon sequencing showed that the relative abundance of the phylum Archaea was low at 32°C (<0.5%) but increased significantly at 65°C (36%). The bacterial diversity was either similar to other microbialites described in literature (the 32°C sample) or displayed a specific combination of phyla and classes (the 49°C and 65°C samples). Bacterial taxa were distributed among 39 phyla, out of which 14 had inferred abundances >1%. The dominant bacterial groups at 32°C were Cyanobacteria, Gammaproteobacteria, Firmicutes, Bacteroidetes, Chloroflexi, Thermi, Actinobacteria, Planctomycetes, and Defferibacteres. At 49°C, there was a striking dominance of the Gammaproteobacteria, followed by Firmicutes, Bacteroidetes, and Armantimonadetes. The 65°C sample was dominated by Betaproteobacteria, Firmicutes, [OP1], Defferibacteres, Thermi, Thermotogae, [EM3], and Nitrospirae. Lastly, several groups from Proteobacteria and Firmicutes, together with Halobacteria and Melainabacteria were described for the first time in calcium carbonate deposits. Overall, the spring from Ciocaia emerges as a valuable site to probe microbes-minerals interrelationships along thermal and geochemical gradients.« less

Looking for Biosignatures in Carbonate Microbialites from the Laguna Negra, Argentinian Andes

NASA Astrophysics Data System (ADS)

Boidi, F. J.; Gomez, F. J.; Fike, D. A.; Bradley, A. S.; Farías, M. E.; Beeler, S.

2015-12-01

The distinction between biotic and abiotic control on microbialites formation and its signatures is relevant since stromatolites are considered the oldest evidence for life on Earth and a target for astrobiological research. The Laguna Negra is a shallow hypersaline lake placed at the Andes, Northwest Argentina, where carbonate microbialites and microbial mats develop. It is a unique system where microbial influence on carbonate precipitation and potential preserved biosignatures in the microbialites can be studied. Here we compare three distinct microbialites systems: carbonate laminar crusts with no visible microbial mats, stromatolites and dm-size oncoids, both related with different microbial mats. Our goal is to unravel the biotic controls on their formation, and the biosignatures there recorded. Laminar crusts are composed of stacked regular and isopachous carbonate lamina. Oncoids laminae are typically characterized by irregular hybrid micro-textures, composed of alternating micritic and botryoidal laminae, and the stromatolites are mostly composed by irregular micritic laminae. Sulfur isotopes of carbonate associated sulphate show similar values but they show differences in the pyrite sulfur isotopes suggesting differences in the fractionation degree, possibly related to sulphate reducing bacteria and variable sulphate reservoirs in the case of stromatolites and oncoids. δ13C fractionation between organic carbon and carbonates suggests photosynthesis, but other metabolisms cannot yet be discarded. 16S rDNA data of the microbial communities associated with the carbonate structures indicate the presence of these taxonomic groups and those that are known to influence carbonate precipitation, particularly in the stromatolites associated microbial community. Our data indicate significant differences between the three systems in terms of stable isotopes, textures and associated microbial diversity, suggesting a microbial control on stromatolites and oncoids formation in the Laguna Negra. Further studies may provide a valuable contribution on biosignatures preservation and recognition that can shed light on understanding the early biosphere record.

Carbonate microbialites and hardgrounds from Manito Lake, an alkaline, hypersaline lake in the northern Great Plains of Canada

NASA Astrophysics Data System (ADS)

Last, Fawn M.; Last, William M.; Halden, Norman M.

2010-03-01

Manito Lake is a large, perennial, Na-SO 4 dominated saline to hypersaline lake located in the northern Great Plains of western Canada. Significant water level decrease over the past several decades has led to reduction in volume and surface area, as well as an increase in salinity. The salinity has increased from 10 ppt to about 50 ppt TDS. This decrease in water level has exposed large areas of nearshore microbialites. These organogenic structures range in size from several cm to over a meter and often form large bioherms several meters high. They have various external morphologies, vary in mineralogical composition, and show a variety of internal fabrics from finely laminated to massive. In addition to microbiolities and bioherms, the littoral zone of Manito Lake contains a variety of carbonate hardgrounds, pavements, and cemented clastic sediments. Dolomite and aragonite are the most common minerals found in these shoreline structures, however, calcite after ikaite, monohydrocalcite, magnesian calcite, and hydromagnesite are also present. The dolomite is nonstoichiometric and calcium-rich; the magnesian calcite has about 17 mol% MgCO 3. AMS radiocarbon dating of paired organic matter and endogenic carbonate material confirms little or no reservoir affect. Although there is abundant evidence for modern carbonate mineral precipitation and microbialite formation, most of the larger microbialites formed between about 2300 and 1000 cal BP, whereas the hardgrounds, cements, and laminated crusts formed about 1000-500 cal BP.

Tracing biosignatures from the Recent to the Jurassic in sabkha-associated microbial mats

NASA Astrophysics Data System (ADS)

van der Land, Cees; Dutton, Kirsten; Andrade, Luiza; Paul, Andreas; Sherry, Angela; Fender, Tom; Hewett, Guy; Jones, Martin; Lokier, Stephen W.; Head, Ian M.

2017-04-01

Microbial mat ecosystems have been operating at the sediment-fluid interface for over 3400 million years, influencing the flux, transformation and preservation of carbon from the biosphere to the physical environment. These ecosystems are excellent recorders of rapid and profound changes in earth surface environments and biota as they often survive crisis-induced extreme paleoenvironmental conditions. Their biosignatures, captured in the preserved organic matter and the biominerals that form the microbialite rock, constitute a significant tool in understanding geobiological processes and the interactions of the microbial communities with sediments and with the prevailing physical chemical parameters, as well as the environmental conditions at a local and global scale. Nevertheless, the exact pathways of diagenetic organic matter transformation and early-lithification, essential for the accretion and preservation in the geological record as microbialites, are not well understood. The Abu Dhabi coastal sabkha system contains a vast microbial mat belt that is dominated by continuous polygonal and internally-laminated microbial mats across the upper and middle intertidal zones. This modern system is believed to be the best analogue for the Upper Jurassic Arab Formation, which is both a prolific hydrocarbon reservoir and source rock facies in the United Arab Emirates and in neighbouring countries. In order to characterise the processes that lead to the formation of microbialites we investigated the modern and Jurassic system using a multidisciplinary approach, including growth of field-sampled microbial mats under controlled conditions in the laboratory and field-based analysis of microbial communities, mat mineralogy and organic biomarker analysis. In this study, we focus on hydrocarbon biomarker data obtained from the surface of microbial mats actively growing in the intertidal zone of the modern system. By comparing these findings to data obtained from recently-buried, unlithified mats and fully lithified Jurassic mats we are able to identify those biochemical signatures of organic matter preserved in microbialites which survived diagenetic disintegration and represent the primary microbial production. Biomarkers, in the form of alkanes, mono-, di- and trimethylalkanes (MMA, DMA, TMA) were identified in surface and buried mats. Previous studies reported a bimodal distribution of n-Alkanes in the buried mats due to the relatively rapid decline in the abundance of MMAs and DMAs in the C16-C22 range with C24-C45 exclusively found in buried mats, however, this bimodal distribution was not found in our samples. Furthermore, we were able to improve the subsurface facies model for the Jurassic microbialites with our biomarker data as it shows that microbial mats growing in tidal pools or lagoons within the sabkha system form the most prolific hydrocarbon source rocks.

The Rise of Complexity: Do the Pavilion Lake Microbialites Suggest a Way to Build a Macroorganism?

NASA Astrophysics Data System (ADS)

Schulze-Makuch, D.; Laval, B.; Lim, D. S.; Irwin, L. N.

2005-12-01

The distinctive assemblage of freshwater calcite microbialites discovered at Pavilion Lake, BC, has been associated with organisms such as Epiphyton and Girvanella, fossils from just before the Cambrian explosion about 550 million years ago (Laval et al., 2000). The presence of the microbialite structures in a dimictic mid-latitude lake and their establishment after the last ice age about 10,000 years ago is puzzling. Their distinctive morphologies include cone-shaped seepage structures 2 m or more in height with hollow internal conduits that open at the top of the cones, and dense artichoke-like structures with calcite "leaves" greater than 1 m in height. These structures are astounding as they imply functional properties. In principle, this is not unlike the interaction of individual cells in a macroorganism, in which many different types of specialized cells interact with each other to the benefit of the whole organism (e.g. interaction of blood, integument, and organ cells within animals). Certainly, the complex interaction of these microbial cells is not equivalent to the collaboration of cells within an individual multicellular organism, where each cell has the same genetic information but differential gene expression provides well-defined cellular specializations. However, the microbialites raise the question of how much complexity and structure can be achieved by a high degree of communication within a multitude of microbial cells. Our findings indicate a complex and interacting microbial consortium associated with the structures at Pavilion Lake, and revealed biomarkers for proteobacteria, sulphur reducing bacteria, and firmicutes (possibly photosynthetic heliobacteria), among others. Types of genetic exchange among these microbial cells may include lateral gene transfer via conjugation, transformation, and transduction, or other mechanisms. This finding may have significant implications for the evolution of life on Earth and possible life on other planets. References: Laval, B., Cady, S.L., Pollack, J.C., McKay, C.P., Bird, J.S., Grotzinger, J.P., Ford, D.C., and Bohm, H.R. (2000) Modern freshwater microbialite analogues for ancient dendritic reef structures. Nature 407, 626-629.

An early-branching microbialite cyanobacterium forms intracellular carbonates.

PubMed

Couradeau, Estelle; Benzerara, Karim; Gérard, Emmanuelle; Moreira, David; Bernard, Sylvain; Brown, Gordon E; López-García, Purificación

2012-04-27

Cyanobacteria have affected major geochemical cycles (carbon, nitrogen, and oxygen) on Earth for billions of years. In particular, they have played a major role in the formation of calcium carbonates (i.e., calcification), which has been considered to be an extracellular process. We identified a cyanobacterium in modern microbialites in Lake Alchichica (Mexico) that forms intracellular amorphous calcium-magnesium-strontium-barium carbonate inclusions about 270 nanometers in average diameter, revealing an unexplored pathway for calcification. Phylogenetic analyses place this cyanobacterium within the deeply divergent order Gloeobacterales. The chemical composition and structure of the intracellular precipitates suggest some level of cellular control on the biomineralization process. This discovery expands the diversity of organisms capable of forming amorphous calcium carbonates.

A year in the life of a thrombolite: comparative metatranscriptomics reveals dynamic metabolic changes over diel and seasonal cycles.

PubMed

Louyakis, Artemis S; Gourlé, Hadrien; Casaburi, Giorgio; Bonjawo, Rachelle M E; Duscher, Alexandrea A; Foster, Jamie S

2018-02-01

Microbialites are one of the oldest known ecosystems on Earth and the coordinated metabolisms and activities of these mineral-depositing communities have had a profound impact on the habitability of the planet. Despite efforts to understand the diversity and metabolic potential of these systems, there has not been a systematic molecular analysis of the transcriptional changes that occur within a living microbialite over time. In this study, we generated metatranscriptomic libraries from actively growing thrombolites, a type of microbialite, throughout diel and seasonal cycles and observed dynamic shifts in the population and metabolic transcriptional activity. The most transcribed genes in all seasons were associated with photosynthesis, but only transcripts associated with photosystem II exhibited diel cycling. Photosystem I transcripts were constitutively expressed at all time points including midnight and sunrise. Transcripts associated with nitrogen fixation, methanogenesis and dissimilatory sulfate reduction exhibited diel cycling, and variability between seasons. Networking analysis of the metatranscriptomes showed correlated expression patterns helping to elucidate how metabolic interactions are coordinated within the thrombolite community. These findings have identified distinctive temporal patterns within the thrombolites and will serve an important foundation to understand the mechanisms by which these communities form and respond to changes in their environment. © 2017 Society for Applied Microbiology and John Wiley & Sons Ltd.

A comparative SEM morphological study of biogenic and abiogenic carbonates for the search for biostructures on Mars.

NASA Astrophysics Data System (ADS)

D'Elia, M.; Blanco, A.; Galiano, A.; Orofino, V.; Fonti, S.; Mancarella, F.; Guido, A.

Next space missions will investigate the possibility of extinct or extant life on Mars. In previous laboratory works by studying the infrared spectral modifications induced by thermal processing on different carbonate samples (recent shells and fossils of different ages), we developed a method able to discriminate biogenic carbonates from their abiogenic counterparts. The method has been successfully applied to microbialites, i.e. bio-induced carbonates deposits, and particularly to stromatolites, the laminated fabric of microbialites, some of which can be ascribed among the oldest traces of biological activity known on Earth. These results are of valuable importance since such carbonates are linked to primitive living organisms which can be considered as good analogues for putative Martian life forms. Due to the fact that the microstructures of biogenic carbonate may be different from those of abiogenic origin, we have recently investigated the microscopic morphology at different scales of our samples (shells, skeletal grains, microbialites and stromatolites) using a scanning electron microscope (SEM). In this paper we present some preliminary results that can be of valuable interest in view of the high resolution imaging systems that in the near future will explore the surface of Mars in the search for biological traces of life.

Prokaryotic diversity and biogeochemical characteristics of benthic microbial ecosystems at La Brava, a hypersaline lake at Salar de Atacama, Chile.

PubMed

Farias, Maria Eugenia; Rasuk, Maria Cecilia; Gallagher, Kimberley L; Contreras, Manuel; Kurth, Daniel; Fernandez, Ana Beatriz; Poiré, Daniel; Novoa, Fernando; Visscher, Pieter T

2017-01-01

Benthic microbial ecosystems of Laguna La Brava, Salar de Atacama, a high altitude hypersaline lake, were characterized in terms of bacterial and archaeal diversity, biogeochemistry, (including O2 and sulfide depth profiles and mineralogy), and physicochemical characteristics. La Brava is one of several lakes in the Salar de Atacama where microbial communities are growing in extreme conditions, including high salinity, high solar insolation, and high levels of metals such as lithium, arsenic, magnesium, and calcium. Evaporation creates hypersaline conditions in these lakes and mineral precipitation is a characteristic geomicrobiological feature of these benthic ecosystems. In this study, the La Brava non-lithifying microbial mats, microbialites, and rhizome-associated concretions were compared to each other and their diversity was related to their environmental conditions. All the ecosystems revealed an unusual community where Euryarchaeota, Crenarchaeota, Acetothermia, Firmicutes and Planctomycetes were the most abundant groups, and cyanobacteria, typically an important primary producer in microbial mats, were relatively insignificant or absent. This suggests that other microorganisms, and possibly novel pathways unique to this system, are responsible for carbon fixation. Depth profiles of O2 and sulfide showed active production and respiration. The mineralogy composition was calcium carbonate (as aragonite) and increased from mats to microbialites and rhizome-associated concretions. Halite was also present. Further analyses were performed on representative microbial mats and microbialites by layer. Different taxonomic compositions were observed in the upper layers, with Archaea dominating the non-lithifying mat, and Planctomycetes the microbialite. The bottom layers were similar, with Euryarchaeota, Crenarchaeota and Planctomycetes as dominant phyla. Sequences related to Cyanobacteria were very scarce. These systems may contain previously uncharacterized community metabolisms, some of which may be contributing to net mineral precipitation. Further work on these sites might reveal novel organisms and metabolisms of biotechnological interest.

High-resolution (SIMS) versus bulk sulfur isotope patterns of pyrite in Proterozoic microbialites with diverse mat textures

NASA Astrophysics Data System (ADS)

Gomes, M. L.; Fike, D. A.; Bergmann, K.; Knoll, A. H.

2015-12-01

Sulfur (S) isotope signatures of sedimentary pyrite preserved in marine rocks provide a rich suite of information about changes in biogeochemical cycling associated with the evolution of microbial metabolisms and oxygenation of Earth surface environments. Conventionally, these S isotope records are based on bulk rock measurements. Yet, in modern microbial mat environments, S isotope compositions of sulfide can vary by up to 40‰ over a spatial range of ~ 1 mm. Similar ranges of S isotope variability have been found in Archean pyrite grains using both Secondary Ion Mass Spectrometry and other micro-analytical techniques. These micron-scale patterns have been linked to changes in rates of microbial sulfate reduction and/or sulfide oxidation, isotopic distillation of the sulfate reservoir due to microbial sulfate reduction, and post-depositional alteration. Fine-scale mapping of S isotope compositions of pyrite can thus be used to differentiate primary environmental signals from post-depositional overprinting - improving our understanding of both. Here, we examine micron-scale S isotope patterns of pyrite in microbialites from the Mesoproterozoic-Neoproterozoic Sukhaya Tunguska Formation and Neoproterozoic Draken Formation in order to explore S isotope variability associated with different mat textures and pyrite grain morphologies. A primary goal is to link modern observations of how sulfide spatial isotope distributions reflect active microbial communities present at given depths in the mats to ancient processes driving fine-sale pyrite variability in microbialites. We find large (up to 60‰) S isotope variability within a spatial range of less than 2.5cm. The micron-scale S isotope measurements converge around the S isotope composition of pyrite extracted from bulk samples of the same microbialites. These micron-scale pyrite S isotope patterns have the potential to reveal important information about ancient biogeochemical cycling in Proterozoic mat environments with implications for interpreting S isotope signatures from the geological record.

Prokaryotic diversity and biogeochemical characteristics of benthic microbial ecosystems at La Brava, a hypersaline lake at Salar de Atacama, Chile

PubMed Central

Rasuk, Maria Cecilia; Gallagher, Kimberley L.; Contreras, Manuel; Kurth, Daniel; Fernandez, Ana Beatriz; Poiré, Daniel; Novoa, Fernando; Visscher, Pieter T.

2017-01-01

Benthic microbial ecosystems of Laguna La Brava, Salar de Atacama, a high altitude hypersaline lake, were characterized in terms of bacterial and archaeal diversity, biogeochemistry, (including O2 and sulfide depth profiles and mineralogy), and physicochemical characteristics. La Brava is one of several lakes in the Salar de Atacama where microbial communities are growing in extreme conditions, including high salinity, high solar insolation, and high levels of metals such as lithium, arsenic, magnesium, and calcium. Evaporation creates hypersaline conditions in these lakes and mineral precipitation is a characteristic geomicrobiological feature of these benthic ecosystems. In this study, the La Brava non-lithifying microbial mats, microbialites, and rhizome-associated concretions were compared to each other and their diversity was related to their environmental conditions. All the ecosystems revealed an unusual community where Euryarchaeota, Crenarchaeota, Acetothermia, Firmicutes and Planctomycetes were the most abundant groups, and cyanobacteria, typically an important primary producer in microbial mats, were relatively insignificant or absent. This suggests that other microorganisms, and possibly novel pathways unique to this system, are responsible for carbon fixation. Depth profiles of O2 and sulfide showed active production and respiration. The mineralogy composition was calcium carbonate (as aragonite) and increased from mats to microbialites and rhizome-associated concretions. Halite was also present. Further analyses were performed on representative microbial mats and microbialites by layer. Different taxonomic compositions were observed in the upper layers, with Archaea dominating the non-lithifying mat, and Planctomycetes the microbialite. The bottom layers were similar, with Euryarchaeota, Crenarchaeota and Planctomycetes as dominant phyla. Sequences related to Cyanobacteria were very scarce. These systems may contain previously uncharacterized community metabolisms, some of which may be contributing to net mineral precipitation. Further work on these sites might reveal novel organisms and metabolisms of biotechnological interest. PMID:29140980

Origin and diagenetic evolution of gypsum and microbialitic carbonates in the Late Sag of the Namibe Basin (SW Angola)

NASA Astrophysics Data System (ADS)

Laurent, Gindre-Chanu; Edoardo, Perri; Ian, Sharp R.; Peacock, D. C. P.; Roger, Swart; Ragnar, Poulsen; Hercinda, Ferreira; Vladimir, Machado

2016-08-01

Ephemeral evaporitic conditions developed within the uppermost part of the transgressive Late Sag sequence in the Namibe Basin (SW Angola), leading to the formation of extensive centimetre- to metre-thick sulphate-bearing deposits and correlative microbialitic carbonates rich in pseudomorphs after evaporite crystals. The onshore pre-salt beds examined in this study are located up to 25 m underneath the major mid-Aptian evaporitic succession, which is typified at the outcrop by gypsiferous Bambata Formation and in the subsurface by the halite-dominated Loeme Formation. Carbonate-evaporite cycles mostly occur at the top of metre-thick regressive parasequences, which progressively onlap and overstep landward the former faulted (rift) topography, or fill major pre-salt palaeo-valleys. The sulphate beds are made up of alabastrine gypsum associated with embedded botryoidal nodules, dissolution-related gypsum breccia, and are cross-cut by thin satin-spar gypsum veins. Nodular and fine-grained fabrics are interpreted as being diagenetic gypsum deposits resulting from the dissolution and recrystallisation of former depositional subaqueous sulphates, whereas gypsum veins and breccia result from telogenetic processes. The carbonates display a broader diversity of facies, characterised by rapid lateral variations along strike. Thin dolomitic and calcitic bacterial-mediated filamentous microbialitic boundstones enclose a broad variety of evaporite pseudomorphs and can pass laterally over a few metres into sulphate beds. Dissolution-related depositional breccias are also common and indicate early dissolution of former evaporite layers embedded within the microbialites. Sulphate and carbonate units are interpreted as being concomitantly deposited along a tide-dominated coastal supra- to intertidal- sabkha and constitute high-frequency hypersaline precursor events, prior to the accumulation of the giant saline mid-Aptian Bambata and Loeme Formations. Petrographic and geochemical analyses reveal successive dissolution, recrystallisation and cementation phases that occurred during burial, uplift and exhumation, implying a complex diagenetic evolution of both gypsum and carbonates, influenced by pore fluids of diverse composition which distinctly varied from meso- to telogenetic domains.

Phylogenetic Analysis of a Microbialite-Forming Microbial Mat from a Hypersaline Lake of the Kiritimati Atoll, Central Pacific

PubMed Central

Schneider, Dominik; Arp, Gernot; Reimer, Andreas; Reitner, Joachim; Daniel, Rolf

2013-01-01

On the Kiritimati atoll, several lakes exhibit microbial mat-formation under different hydrochemical conditions. Some of these lakes trigger microbialite formation such as Lake 21, which is an evaporitic, hypersaline lake (salinity of approximately 170‰). Lake 21 is completely covered with a thick multilayered microbial mat. This mat is associated with the formation of decimeter-thick highly porous microbialites, which are composed of aragonite and gypsum crystals. We assessed the bacterial and archaeal community composition and its alteration along the vertical stratification by large-scale analysis of 16S rRNA gene sequences of the nine different mat layers. The surface layers are dominated by aerobic, phototrophic, and halotolerant microbes. The bacterial community of these layers harbored Cyanobacteria (Halothece cluster), which were accompanied with known phototrophic members of the Bacteroidetes and Alphaproteobacteria. In deeper anaerobic layers more diverse communities than in the upper layers were present. The deeper layers were dominated by Spirochaetes, sulfate-reducing bacteria (Deltaproteobacteria), Chloroflexi (Anaerolineae and Caldilineae), purple non-sulfur bacteria (Alphaproteobacteria), purple sulfur bacteria (Chromatiales), anaerobic Bacteroidetes (Marinilabiacae), Nitrospirae (OPB95), Planctomycetes and several candidate divisions. The archaeal community, including numerous uncultured taxonomic lineages, generally changed from Euryarchaeota (mainly Halobacteria and Thermoplasmata) to uncultured members of the Thaumarchaeota (mainly Marine Benthic Group B) with increasing depth. PMID:23762495

SEM morphological studies of carbonates and the search for ancient life on Mars

NASA Astrophysics Data System (ADS)

D'Elia, M.; Blanco, A.; Galiano, A.; Orofino, V.; Fonti, S.; Mancarella, F.; Guido, A.; Russo, F.; Mastandrea, A.

2017-04-01

Next space missions will investigate the possibility of extinct or extant life on Mars. Studying the infrared spectral modifications, induced by thermal processing on different carbonate samples (recent shells and fossils of different ages), we developed a method able to discriminate biogenic carbonates from their abiogenic counterparts. The method has been successfully applied to microbialites, i.e. bio-induced carbonates deposits, and particularly to stromatolites, the laminated fabric of microbialites, some of which can be ascribed to among the oldest traces of biological activity known on Earth. These results are of valuable importance since such carbonates are linked to primitive living organisms that can be considered as good analogues for putative Martian life forms. Considering that the microstructures of biogenic carbonate are different from those of abiogenic origin, we investigated the micromorphology of shells, skeletal grains and microbialites at different scale with a scanning electron microscope. The results show that this line of research may provide an alternative and complementary approach to other techniques developed in the past by our group to distinguish biotic from abiotic carbonates. In this paper, we present some results that can be of valuable interest since they demonstrate the utility for a database of images concerning the structures and textures of relevant carbonate minerals. Such data may be useful for the analysis of Martian samples, coming from sample return missions or investigated by future in situ explorations, aimed to characterize the near-subsurface of Mars in search for past or present life.

Microbialites vs detrital micrites: Degree of biogenicity, parameter suitable for Mars analogues

NASA Astrophysics Data System (ADS)

Blanco, Armando; D'Elia, Marcella; Orofino, Vincenzo; Mancarella, Francesca; Fonti, Sergio; Mastandrea, Adelaide; Guido, Adriano; Tosti, Fabio; Russo, Franco

2014-07-01

In upcoming years several space missions will investigate the habitability of Mars and the possibility of extinct or extant life on the planet. In previous laboratory works we have investigated the infrared spectral modifications induced by thermal processing on different carbonate samples, in the form of recent shells and fossils of different ages, whose biogenic origin is indisputable. The goal was to develop a method able to discriminate biogenic carbonate samples from their abiogenic counterparts. The method has been successfully applied to microbialites, i.e. bio-induced microcrystalline carbonate deposits, and particularly to stromatolites, the laminated fabric of microbialites, some of which can be ascribed among the oldest traces of biological activity known on Earth. In this work we show that, by applying our method to different parts of the same carbonate rock, we are able to discriminate the presence, nature and biogenicity of various micrite types (i.e. detrital vs autochthonous) and to distinguish them from the skeletal grains. To test our methodology we preliminarily used the epifluorescence technique to select on polished samples, skeletal grains, autochthonous and allochthonous micrites, each one characterized by different organic matter content. The results on the various components show that, applying the infrared spectral modifications induced by thermal processing, it is possible to determine the degree of biogenicity of the different carbonate samples. The results are of valuable importance since such carbonates are linked to primitive living organisms that can be considered as good analogues for putative Martian life forms.

Phylotype Dynamics of Bacterial P Utilization Genes in Microbialites and Bacterioplankton of a Monomictic Endorheic Lake.

PubMed

Valdespino-Castillo, Patricia M; Alcántara-Hernández, Rocío J; Merino-Ibarra, Martín; Alcocer, Javier; Macek, Miroslav; Moreno-Guillén, Octavio A; Falcón, Luisa I

2017-02-01

Microbes can modulate ecosystem function since they harbor a vast genetic potential for biogeochemical cycling. The spatial and temporal dynamics of this genetic diversity should be acknowledged to establish a link between ecosystem function and community structure. In this study, we analyzed the genetic diversity of bacterial phosphorus utilization genes in two microbial assemblages, microbialites and bacterioplankton of Lake Alchichica, a semiclosed (i.e., endorheic) system with marked seasonality that varies in nutrient conditions, temperature, dissolved oxygen, and water column stability. We focused on dissolved organic phosphorus (DOP) utilization gene dynamics during contrasting mixing and stratification periods. Bacterial alkaline phosphatases (phoX and phoD) and alkaline beta-propeller phytases (bpp) were surveyed. DOP utilization genes showed different dynamics evidenced by a marked change within an intra-annual period and a differential circadian pattern of expression. Although Lake Alchichica is a semiclosed system, this dynamic turnover of phylotypes (from lake circulation to stratification) points to a different potential of DOP utilization by the microbial communities within periods. DOP utilization gene dynamics was different among genetic markers and among assemblages (microbialite vs. bacterioplankton). As estimated by the system's P mass balance, P inputs and outputs were similar in magnitude (difference was <10 %). A theoretical estimation of water column P monoesters was used to calculate the potential P fraction that can be remineralized on an annual basis. Overall, bacterial groups including Proteobacteria (Alpha and Gamma) and Bacteroidetes seem to be key participants in DOP utilization responses.

Microbialite Morphologies and Distributions-Geoacoustic Survey with an AUV of Pavilion Lake, British Columbia, Canada

NASA Astrophysics Data System (ADS)

Gutsche, J. R.; Trembanis, A. C.

2010-12-01

With advances in lake bottom mapping it has been observed that modern microbialites, much like the ancient stromatolites, thrive in freshwater lake environments. Previously collected data shows that a diverse community of living stromatolites are present within Pavilion Lake (Laval et al., 2000, Lim et al., 2009). An additional comprehensive data set was collected in June-July 2010. By building on the previous dataset it is possible to compare two high-resolution geoacoustic datasets. Using Autonomous Underwater Vehicles (AUVs) as exploration platforms to conduct surveys of the lake bottom, very high-resolution sonar data has been collected. The data collected in June-July 2010 is composed of 125 km of AUV trackline. This length of trackline allowed for survey coverage of nearly the entire lake bottom. The Gavia AUV used for this survey collected bathymetry data collocated with backscatter information. The data has been processed and gridded to 1m, with specific high value areas gridded to a finer 0.5m. The bathymetric data was compiled to create a base map of the floor of Pavilion Lake. Backscatter data was also collected and processed using the same 1m grid resolution. After the backscatter data was processed, it was draped over the bathymetry map of Pavilion Lake. The tools offered within the Fledermaus software package allow for the bathymetry data to be analyzed with respect to slope and rugosity. By analyzing this dense phase measuring bathymetric sonar of the lake bottom, with respect to slope and rugosity, it is possible to map the morphological trends of the stromatolites. Additionally, the ability to compare two datasets allows for erosional changes in the lake bottom to be identified. The bathymetry data allows for the quantitative analysis of bed forms within Pavilion Lake, allowing for a better understanding of microbialite morphologies. The backscatter data is increasingly important to the Pavilion Lake project because of the location and general surroundings of the lake. The lake itself is located in a limestone canyon, which frequently sustains erosional episodes. The backscatter data allows for the differentiation between erosional deposits and microbial mounds. The combination of backscatter and bathymetry allows for a further understanding of bedforms and microbialite growth patterns.

Endogenic carbonate sedimentation in Bear Lake, Utah and Idaho, over the last two glacial-interglacial cycles

USGS Publications Warehouse

Dean, W.E.

2009-01-01

Sediments deposited over the past 220,000 years in Bear Lake, Utah and Idaho, are predominantly calcareous silty clay, with calcite as the dominant carbonate mineral. The abundance of siliciclastic sediment indicates that the Bear River usually was connected to Bear Lake. However, three marl intervals containing more than 50% CaCO3 were deposited during the Holocene and the last two interglacial intervals, equivalent to marine oxygen isotope stages (MIS) 5 and 7, indicating times when the Bear River was not connected to the lake. Aragonite is the dominant mineral in two of these three high-carbonate intervals. The high-carbonate, aragonitic intervals coincide with warm interglacial continental climates and warm Pacific sea-surface temperatures. Aragonite also is the dominant mineral in a carbonate-cemented microbialite mound that formed in the southwestern part of the lake over the last several thousand years. The history of carbonate sedimentation in Bear Lake is documented through the study of isotopic ratios of oxygen, carbon, and strontium, organic carbon content, CaCO3 content, X-ray diffraction mineralogy, and HCl-leach chemistry on samples from sediment traps, gravity cores, piston cores, drill cores, and microbialites. Sediment-trap studies show that the carbonate mineral that precipitates in the surface waters of the lake today is high-Mg calcite. The lake began to precipitate high-Mg calcite sometime in the mid-twentieth century after the artificial diversion of Bear River into Bear Lake that began in 1911. This diversion drastically reduced the salinity and Mg2+:Ca2+ of the lake water and changed the primary carbonate precipitate from aragonite to high-Mg calcite. However, sediment-trap and core studies show that aragonite is the dominant mineral accumulating on the lake floor today, even though it is not precipitating in surface waters. The isotopic studies show that this aragonite is derived from reworking and redistribution of shallow-water sediment that is at least 50 yr old, and probably older. Apparently, the microbialite mound also stopped forming aragonite cement sometime after Bear River diversion. Because of reworking of old aragonite, the bulk mineralogy of carbonate in bottom sediments has not changed very much since the diversion. However, the diversion is marked by very distinct changes in the chemical and isotopic composition of the bulk carbonate. After the last glacial interval (LGI), a large amount of endogenic carbonate began to precipitate in Bear Lake when the Pacific moisture that filled the large pluvial lakes of the Great Basin during the LGI diminished, and Bear River apparently abandoned Bear Lake. At first, the carbonate that formed was low-Mg calcite, but ???11,000 years ago, salinity and Mg2+:Ca2+ thresholds must have been crossed because the amount of aragonite gradually increased. Aragonite is the dominant carbonate mineral that has accumulated in the lake for the past 7000 years, with the addition of high-Mg calcite after the diversion of Bear River into the lake at the beginning of the twentieth century. Copyright ?? 2009 The Geological Society of America.

Patterns of Nitrogen Fixation and Related Genetic Diversity (nifH) in Microbial Mats and Stromatolites from Different Environments

NASA Astrophysics Data System (ADS)

Beltrán, Y. Y.; Centeno, C.; Falcón, L. I.

2010-04-01

We want to estimate the patterns of nitrogen fixation and the related genetic diversity (nifH) of microbial mats and microbialites on dial and temporal scales along a physicochemical and geographical gradient.

Heterotrophic Protists in Hypersaline Microbial Mats and Deep Hypersaline Basin Water Columns

PubMed Central

Edgcomb, Virginia P.; Bernhard, Joan M.

2013-01-01

Although hypersaline environments pose challenges to life because of the low water content (water activity), many such habitats appear to support eukaryotic microbes. This contribution presents brief reviews of our current knowledge on eukaryotes of water-column haloclines and brines from Deep Hypersaline Anoxic Basins (DHABs) of the Eastern Mediterranean, as well as shallow-water hypersaline microbial mats in solar salterns of Guerrero Negro, Mexico and benthic microbialite communities from Hamelin Pool, Shark Bay, Western Australia. New data on eukaryotic diversity from Shark Bay microbialites indicates eukaryotes are more diverse than previously reported. Although this comparison shows that eukaryotic communities in hypersaline habitats with varying physicochemical characteristics are unique, several groups are commonly found, including diverse alveolates, strameonopiles, and fungi, as well as radiolaria. Many eukaryote sequences (SSU) in both regions also have no close homologues in public databases, suggesting that these environments host unique microbial eukaryote assemblages with the potential to enhance our understanding of the capacity of eukaryotes to adapt to hypersaline conditions. PMID:25369746

Brucite microbialites in living coral skeletons: Indicators of extreme microenvironments in shallow-marine settings

USGS Publications Warehouse

Nothdurft, L.D.; Webb, G.E.; Buster, N.A.; Holmes, C.W.; Sorauf, J.E.; Kloprogge, J.T.

2005-01-01

Brucite [Mg(OH)2] microbialites occur in vacated interseptal spaces of living scleractinian coral colonies (Acropora, Pocillopora, Porites) from subtidal and intertidal settings in the Great Barrier Reef, Australia, and subtidal Montastraea from the Florida Keys, United States. Brucite encrusts microbial filaments of endobionts (i.e., fungi, green algae, cyanobacteria) growing under organic biofilms; the brucite distribution is patchy both within interseptal spaces and within coralla. Although brucite is undersaturated in seawater, its precipitation was apparently induced in the corals by lowered pCO 2 and increased pH within microenvironments protected by microbial biofilms. The occurrence of brucite in shallow-marine settings highlights the importance of microenvironments in the formation and early diagenesis of marine carbonates. Significantly, the brucite precipitates discovered in microenvironments in these corals show that early diagenetic products do not necessarily reflect ambient seawater chemistry. Errors in environmental interpretation may arise where unidentified precipitates occur in microenvironments in skeletal carbonates that are subsequently utilized as geochemical seawater proxies. ?? 2005 Geological Society of America.

Modern and late Holocene dolomite formation: Manito Lake, Saskatchewan, Canada

NASA Astrophysics Data System (ADS)

Last, Fawn M.; Last, William M.; Halden, Norman M.

2012-12-01

Major advances have occurred in our understanding of modern dolomite formation and penecontemporaneous dolomitization over the past several decades. Manito Lake, located in west-central Saskatchewan, Canada, is a large (65 km2), deep (zmax: 22 m) perennial saline (~ 45 ppt TDS) lake in which modern and late Holocene dolomite coexists with other endogenic and authigenic carbonate precipitates, including aragonite, monohydrocalcite, calcite, and Mg-calcite. Like many other lacustrine dolomites, Manito Lake dolomite is microcrystalline (less than 1 μm to 5 μm), Ca-rich and poor to moderately ordered. It occurs as relatively pure hardgrounds and as a component of nearshore microbialites. It also forms isopachous cements in consolidated siliciclastic shoreline sediments. Manito Lake dolomite is most likely forming by mainly biomediated precipitation at or near the sediment-water interface (i) in pore spaces of coarse siliciclastic sediments (i.e., beachrock), (ii) as fine laminae associated with microbialites, and (iii) as a major component of mudstone hardgrounds and pavements.

Clumped isotope calibration data for lacustrine carbonates: A progress report

NASA Astrophysics Data System (ADS)

Tripati, A.

2015-12-01

Our capacity to understand Earth's environmental history is highly dependent on the accuracy of reconstructions of past climates. Lake sediments provide important archives of terrestrial climate change, and represent an important tool for reconstructing paleohydrology, paleoclimate, paleoenvironment, and paleoaltimetry. Unfortunately, while multiple methods for constraining marine temperature exist, quantitative terrestrial proxies are scarcer - tree rings, speleothems, and leaf margin analyses have all been used with varying degrees of accuracy. Clumped isotope thermometry has the potential to be a useful instrument for determining terrestrial climates: multiple studies have shown the fraction of 13C—18O bonds in carbonates is inversely related to the temperature at which the rocks formed. We have been measuring the abundance of 13C18O16O in the CO2 produced by the dissolution of carbonate minerals in phosphoric acid in modern lake samples and comparing results to independently known estimates of lake water temperature. Here we discuss an extensive calibration dataset comprised of 132 analyses of 97 samples from 44 localities, including microbialites, tufas, and micrites endogenic carbonates, freshwater gastropods, bivalves, microbialites, and ooids.

Recognition of Fossil Prokaryotes in Cretaceous Methane Seep Carbonates: Relevance to Astrobiology

NASA Astrophysics Data System (ADS)

Shapiro, Russell Scott

2004-12-01

Recovery of prokaryotic body fossils from methane seep carbonates such as those of the Cretaceous Tepee Buttes of Colorado serves as a model for sampling in future astrobiological missions. The fossils, found primarily at the interface between paragenetic fabrics, suggest a sharp physicochemical gradient. Evidence of these microbial fossils occurs at a variety of scales. In the field, microbialite is found as meter-scale thrombolitic zones and centimeterscale stromatolitic crusts lining voids inferred to be the sites of ancient methane seepage. Petrographic fabrics suggestive of microbialite include indistinct peloids (0.1-1 mm in diameter) and crusts of authigenic micrite. Primary evidence obtained from scanning electron microscopy coupled with energy-dispersive x-ray spectroscopy analysis comprises pinnate bacteria (0.3 µm in diameter and 1-1.5 µm long), sheaths (2-4 µm in diameter), coccoids (0.5-1 µm in diameter, up to 40 per cluster), and the presence of framboidal pyrite (6-8 µm in diameter). These results are in agreement with studies of other ancient and modern seeps and suggest a morphological conservatism of microbial form that can be incorporated into studies of extraterrestrial environments where it is presumed that reduced gases drive the metabolic activity of prokaryote-like organisms. Target areas that could serve as conduits for reduced gas seeps include tectonic or impact-driven faulting, zones of cryosphere melting, or other disruptions in crustal coherence. Ancient seeps, preserved as localized anomalous evaporite deposits in the sedimentary cover, could be detected by remote sensing. Astrobiology 4, 438-449.

Lateral Comparative Investigation of Stromatolites: Astrobiological Implications and Assessment of Scales of Control

NASA Astrophysics Data System (ADS)

Ibarra, Yadira; Corsetti, Frank A.

2016-04-01

The processes that govern the formation of stromatolites, structures that may represent macroscopic manifestation of microbial processes and a clear target for astrobiological investigation, occur at various scales (local versus regional), yet determining their relative importance remains a challenge, particularly for ancient deposits and/or if similar deposits are discovered elsewhere in the Solar System. We build upon the traditional multiscale level approach of investigation (micro-, meso-, macro-, mega-) by including a lateral comparative investigational component of fine- to large-scale features to determine the relative significance of local and/or nonlocal controls on stromatolite morphology, and in the process, help constrain the dominant influences on microbialite formation. In one example of lateral comparative investigation, lacustrine microbialites from the Miocene Barstow Formation (California) display two main mesofabrics: (1) micritic bands that drastically change in thickness and cannot directly be traced between adjacent decimeter-scale subunits and (2) sparry fibrous layers that are strikingly consistent across subunits, suggesting the formation of sparry fibrous layers was influenced by a process larger than the length scale between the subunits (likely lake chemistry). Microbialites from the uppermost Triassic Cotham Member, United Kingdom, occur as meter-scale mounds and contain a characteristic succession of laminated and dendrolitic mesofabrics. The same succession of laminated/dendrolitic couplets can be traced, not only from mound to mound, but over 100 km, indicating a regional-scale influence on very small structures (microns to centimeters) that would otherwise not be apparent without the lateral comparative approach, and demonstrating that the scale of the feature does not necessarily scale with the scope of the process. Thus, the combination of lateral comparative investigations and multiscale analyses can provide an effective approach for evaluating the dominant controls on stromatolite texture and morphology throughout the rock record and potentially on other planets via rover-scale analyses (e.g., Mars).

High-Up: A Remote Reservoir of Microbial Extremophiles in Central Andean Wetlands.

PubMed

Albarracín, Virginia H; Kurth, Daniel; Ordoñez, Omar F; Belfiore, Carolina; Luccini, Eduardo; Salum, Graciela M; Piacentini, Ruben D; Farías, María E

2015-01-01

The Central Andes region displays unexplored ecosystems of shallow lakes and salt flats at mean altitudes of 3700 m. Being isolated and hostile, these so-called "High-Altitude Andean Lakes" (HAAL) are pristine and have been exposed to little human influence. HAAL proved to be a rich source of microbes showing interesting adaptations to life in extreme settings (poly-extremophiles) such as alkalinity, high concentrations of arsenic and dissolved salts, intense dryness, large daily ambient thermal amplitude, and extreme solar radiation levels. This work reviews HAAL microbiodiversity, taking into account different microbial niches, such as plankton, benthos, microbial mats and microbialites. The modern stromatolites and other microbialites discovered recently at HAAL are highlighted, as they provide unique modern-though quite imperfect-analogs of environments proxy for an earlier time in Earth's history (volcanic setting and profuse hydrothermal activity, low atmospheric O2 pressure, thin ozone layer and high UV exposure). Likewise, we stress the importance of HAAL microbes as model poly-extremophiles in the study of the molecular mechanisms underlying their resistance ability against UV and toxic or deleterious chemicals using genome mining and functional genomics. In future research directions, it will be necessary to exploit the full potential of HAAL poly-extremophiles in terms of their biotechnological applications. Current projects heading this way have yielded detailed molecular information and functional proof on novel extremoenzymes: i.e., DNA repair enzymes and arsenic efflux pumps for which medical and bioremediation applications, respectively, are envisaged. But still, much effort is required to unravel novel functions for this and other molecules that dwell in a unique biological treasure despite its being hidden high up, in the remote Andes.

High-Up: A Remote Reservoir of Microbial Extremophiles in Central Andean Wetlands

PubMed Central

Albarracín, Virginia H.; Kurth, Daniel; Ordoñez, Omar F.; Belfiore, Carolina; Luccini, Eduardo; Salum, Graciela M.; Piacentini, Ruben D.; Farías, María E.

2015-01-01

The Central Andes region displays unexplored ecosystems of shallow lakes and salt flats at mean altitudes of 3700 m. Being isolated and hostile, these so-called “High-Altitude Andean Lakes” (HAAL) are pristine and have been exposed to little human influence. HAAL proved to be a rich source of microbes showing interesting adaptations to life in extreme settings (poly-extremophiles) such as alkalinity, high concentrations of arsenic and dissolved salts, intense dryness, large daily ambient thermal amplitude, and extreme solar radiation levels. This work reviews HAAL microbiodiversity, taking into account different microbial niches, such as plankton, benthos, microbial mats and microbialites. The modern stromatolites and other microbialites discovered recently at HAAL are highlighted, as they provide unique modern—though quite imperfect—analogs of environments proxy for an earlier time in Earth's history (volcanic setting and profuse hydrothermal activity, low atmospheric O2 pressure, thin ozone layer and high UV exposure). Likewise, we stress the importance of HAAL microbes as model poly-extremophiles in the study of the molecular mechanisms underlying their resistance ability against UV and toxic or deleterious chemicals using genome mining and functional genomics. In future research directions, it will be necessary to exploit the full potential of HAAL poly-extremophiles in terms of their biotechnological applications. Current projects heading this way have yielded detailed molecular information and functional proof on novel extremoenzymes: i.e., DNA repair enzymes and arsenic efflux pumps for which medical and bioremediation applications, respectively, are envisaged. But still, much effort is required to unravel novel functions for this and other molecules that dwell in a unique biological treasure despite its being hidden high up, in the remote Andes. PMID:26733008

Biofilm exopolymers control microbialite formation at thermal springs discharging into the alkaline Pyramid Lake, Nevada, USA

NASA Astrophysics Data System (ADS)

Arp, Gernot; Thiel, Volker; Reimer, Andreas; Michaelis, Walter; Reitner, Joachim

1999-07-01

Calcium carbonate precipitation and microbialite formation at highly supersaturated mixing zones of thermal spring waters and alkaline lake water have been investigated at Pyramid Lake, Nevada. Without precipitation, pure mixing should lead to a nearly 100-fold supersaturation at 40°C. Physicochemical precipitation is modified or even inhibited by the properties of biofilms, dependent on the extent of biofilm development and the current precipitation rate. Mucus substances (extracellular polymeric substances, EPS, e.g., of cyanobacteria) serve as effective Ca 2+-buffers, thus preventing seed crystal nucleation even in a highly supersaturated macroenvironment. Carbonate is then preferentially precipitated in mucus-free areas such as empty diatom tests or voids. After the buffer capacity of the EPS is surpassed, precipitation is observed at the margins of mucus areas. Hydrocarbon biomarkers extracted from (1) a calcifying Phormidium-biofilm, (2) the stromatolitic carbonate below, and (3) a fossil `tufa' of the Pleistocene pinnacles, indicate that the cyanobacterial primary producers have been subject to significant temporal changes in their species distribution. Accordingly, the species composition of cyanobacterial biofilms does not appear to be relevant for the formation of microbial carbonates in Pyramid Lake. The results demonstrate the crucial influence of mucus substances on carbonate precipitation in highly supersaturated natural environments.

The degree of biogenicity of micrites and terrestrial Mars analogues .

NASA Astrophysics Data System (ADS)

D'Elia, M.; Blanco, A.; Orofino, V.; Fonti, S.; Mastandrea, A.; Guido, A.; Tosti, F.; Russo, F.

A number of indications, as the past presence of water, a denser atmosphere and a mild climate on early Mars, suggest that environmental conditions favorable to the emergence of life must have been present on that planet in the first hundred million years, or even more recently. If life actually existed on Mars, biomarkers could be still preserved with some degree of degradation. In previous laboratory works we have investigated the infrared spectral modifications induced by thermal processing on different carbonate samples, in the form of recent shells and fossils of different ages, whose biogenic origin is indisputable. The goal was to develop a method able to discriminate carbonate biogenic samples from their abiogenic counterparts. The method has been successfully applied to microbialites, i.e. bio-induced carbonates deposits, and particularly to stromatolites, the laminated fabric of microbialites, some of which can be ascribed among the oldest traces of biological activity known on Earth. This result is of valuable importance since such carbonates are linked to primitive living organisms which can be considered as good analogues for putative Martian life forms. In this work we show that, studying different parts of the same carbonate rock sample, we are able to distinguish, on the base of the degree of biogenicity, the various micrite types (i.e. detrital vs autochthonous).

Proceedings of the Astrobiology Science Conference 2010. Evolution and Life: Surviving Catastrophes and Extremes on Earth and Beyond

NASA Technical Reports Server (NTRS)

2010-01-01

The Program of the 2010 Astrobiology Science Conference: Evolution and Life: Surviving Catastrophes and Extremes on Earth and Beyond, included sessions on: 50 Years of Exobiology and Astrobiology: Greatest Hits; Extraterrestrial Molecular Evolution and Pre-Biological Chemistry: From the Interstellar Medium to the Solar System I; Human Exploration, Astronaut Health; Diversity in Astrobiology Research and Education; Titan: Past, Present, and Future; Energy Flow in Microbial Ecosystems; Extraterrestrial Molecular Evolution and Prebiological Chemistry: From the Interstellar Medium to the Solar System II; Astrobiology in Orbit; Astrobiology and Interdisciplinary Communication; Science from Rio Tinto: An Acidic Environment; Can We Rule Out Spontaneous Generation of RNA as the Key Step in the Origin of Life?; How Hellish Was the Hadean Earth?; Results from ASTEP and Other Astrobiology Field Campaigns I; Prebiotic Evolution: From Chemistry to Life I; Adaptation of Life in Hostile Space Environments; Extrasolar Terrestrial Planets I: Formation and Composition; Collaborative Tools and Technology for Astrobiology; Results from ASTEP and Other Astrobiology Field Campaigns II; Prebiotic Evolution: From Chemistry to Life II; Survival, Growth, and Evolution of Microrganisms in Model Extraterrestrial Environments; Extrasolar Terrestrial Planets II: Habitability and Life; Planetary Science Decadal Survey Update; Astrobiology Research Funding; Bioessential Elements Through Space and Time I; State of the Art in Life Detection; Terrestrial Evolution: Implications for the Past, Present, and Future of Life on Earth; Psychrophiles and Polar Environments; Life in Volcanic Environments: On Earth and Beyond; Geochronology and Astrobiology On and Off the Earth; Bioessential Elements Through Space and Time II; Origins and Evolution of Genetic Systems; Evolution of Advanced Life; Water-rich Asteroids and Moons: Composition and Astrobiological Potential; Impact Events and Evolution; A Warm, Wet Mars?; Titan Versus Europa - Potential for Astrobiology; Habitability Potential of Mars; Biosignatures: Tools and Development I; Origins of Molecular Asymmetry, Homochirality, and Life Detection; Deserts and Evaporite Basins and Associated Microbialite Systems; Ancient Life and Synthetic Biology: Crossroad of the Past and Future; Biosignatures: Tools and Development II; Free Oxygen: Proxies, Causes, and Consequences; Life in Modern Microbialite Systems - Function and Adaptation; Hydrothermal Systems and Organosynthesis Processes: Origin and Evolution of Life; Where Should We Go on Mars to Seek Signs of Life?; Search for Intelligent Life I. Innovative SETI Observing Programs and Future Directions; Integrating Astrobiology Research Across and Beyond the Community; Education in Astrobiology in K-12; Search for Intelligent Life II. Global Engagement and Interstellar Message Construction; Poster sessions included: Extraterrestrial Molecular Evolution and Pre-Biological Chemistry; Prebiotic Evolution: From Chemistry to Life; RNA World; Terrestrial Evolution: Implications for the Past, Present, and Future of Life on Earth; Hydrothermal Systems and Organosynthesis Processes: Origin and Evolution of Life; Virology and Astrobiology; Horizontal Genetic Transfer and Properties of Ancestral Organisms; Life in Volcanic Environments: On Earth and Beyond; Impact Events and Evolution; Evolution of Advanced Life; Evolution of Intelligent Life; Education in Astrobiology in K-12; Origins of Molecular Asymmetry, Homochirality, and Life Detection; Astrobiology and Interdisciplinary Communication; Diversity in Astrobiology Research and Education; Integrating Astrobiology Research Across and Beyond the Community; Policy and Societal Issues: Dealing with Potential Bumps in the Astrobiology Road Ahead; Results from ASTEP and Other Astrobiology Field Campaigns; Energy Flow in Microbial Ecosystems; Psychrophiles and Polar Environments; Deserts and Evaporite Basins and Associated Microbialite stems; Life in Modern Microbialite Systems - Function and Adaptation; Free Oxygen: Proxies, Causes, and Consequences; Bioessential Elements Through Space and Time; Water-rich Asteroids and Moons: Composition and Astrobiological Potential; Biosignatures: Tools and Developments; Robotics and Instrumentation for Astrobiology; State of the Art in Life Detection; Astrobiology in Orbit; Survival, Growth, and Evolution of Microrganisms in Model Extraterrestrial Evolution; Search for Intelligent Life; Habitability Potential of Mars; How and Where Should We Seek Signs of Life on Mars?; Titan: Past, Present, and Future; Extrasolar Terrestrial Planets: Formation, Composition, Diversity, Habitability and Life; Human Exploration, Astronaut Health; Science from Rio Tinto: An Acidic Environment and Adaptation of Life in Hostile Space Environments;

Lagoon microbialites on Isla Angel de la Guarda and associated peninsular shores, Gulf of California (Mexico)

NASA Astrophysics Data System (ADS)

Johnson, Markes E.; Ledesma-Vázquez, Jorge; Backus, David H.; González, Maria R.

2012-07-01

Examples of two closed lagoons with extensive growth of Recent microbialites showing variable surface morphology and internal structure are found on Isla Angel de la Guarda in the Gulf of California. Comparable lagoonal microbialites also occur ashore from Ensenada El Quemado on the adjacent peninsular mainland of Baja California. The perimeters of all three lagoons feature crusted structures indicative of thrombolites with a knobby surface morphology 2 cm to 3 cm in relief and internal clotting without any sign of laminations. Outward from this zone, thrombolitic construction thins to merge with a white calcified crust below which a soft substratum of dark organic material 4 cm to 6 cm in thickness is concealed. The substratum is laminated and heavily mucilaginous, as observed along the edges of extensive shrinkage cracks in the overlying crust. The thrombolitic crust is anchored to the shore, while the thinner crust and associated stromatolitic mats float on the surface of the lagoons. Laboratory cultures of the dark organic material yielded the solitary cyanobacterium Chroococcidiopsis as the predominant taxon interspersed with filamentous forms. In decreasing order of abundance, other morphotypes present include Phormidium, Oscillatoria, Geitlerinema, Chroococus, and probably Spirulina. The larger of the two island lagoons follows an east-west azimuth and covers 0.225 km2, while the smaller lagoon has a roughly north-south axis and covers only 0.023 km2. The salinity of water in the smaller lagoon was measured as148 ppt. Pliocene strata along the edge of the smaller modern lagoon include siltstone bearing calcified platelets suggestive of a microbial origin. Dry lagoons abandoned during the later Quaternary occur inland at higher elevations on the island, but retain no fossils except for sporadic white crusts cemented on cobbles around distinct margins. Raised Quaternary lagoons parallel to the big lagoon on Isla Angel de la Guarda are partly obscured by flood damage, but still easily mapped from aerial photos. These features suggest that Isla Angel de la Guarda experienced Quaternary uplift similar in scale to many other gulf islands on which marine terraces are preserved. Closed lagoons around the Gulf of California represent a stable oligotrophic ecosystem affected by extreme aridity and hypersalinity, punctuated episodically by the injection of floodwater from tropical storms. The taxonomic and geographic ranges of microbial communities throughout the larger region remain to be explored.

Cool episode and platform demise in the Early Aptian: New insights on the links between climate and carbonate production

NASA Astrophysics Data System (ADS)

Bonin, Aurélie; Pucéat, Emmanuelle; Vennin, Emmanuelle; Mattioli, Emanuela; Aurell, Marcos; Joachimski, Michael; Barbarin, Nicolas; Laffont, Rémi

2016-01-01

The Early Aptian encountered several crises in neritic and pelagic carbonate production, major perturbations in the carbon cycle, and an oceanic anoxic event (OAE1a). Yet the causal links between these perturbations and climate changes remain poorly understood, partly because temperature records spanning the Early Aptian interval are still scant. We present new δ18O data from well-preserved bivalves from a carbonate platform of the Galve subbasin (Spain) that document a major cooling event postdating most of OAE1a. Our data show that cooling postdates the global platform demise and cannot have triggered this event that occurred during the warmest interval. The warmest temperatures coincide with the time equivalent of OAE1a and with platform biotic assemblages dominated by microbialites at Aliaga as well as on other Tethyan platforms. Coral-dominated assemblages then replace microbialites during the subsequent cooling. Nannoconids are absent during most of the time equivalent of the OAE1a, probably related to the well-known crisis affecting this group. Yet they present a transient recovery in the upper part of this interval with an increase in both size and abundance during the cool interval portion that postdates OAE1a. An evolution toward cooler and drier climatic conditions may have induced the regional change from microbial to coral assemblages as well as nannoconids size and abundance increase by limiting continent-derived input of nutrients.

Microbialites in the shallow-water marine environments of the Holy Cross Mountains (Poland) in the aftermath of the Frasnian-Famennian biotic crisis

NASA Astrophysics Data System (ADS)

Rakociński, Michał; Racki, Grzegorz

2016-01-01

Microbial carbonates, consisting of abundant girvanellid oncoids, are described from cephalopod-crinoid and crinoid-brachiopod coquinas (rudstones) occurring in the lowermost Famennian of the Holy Cross Mountains, Poland. A Girvanella-bearing horizon (consist with numerous girvanellid oncoids) has been recognised at the Psie Górki section, and represents the northern slope succession of the drowned Dyminy Reef. This occurrence of microbialites in the aftermath of the Frasnian-Famennian event is interpreted as the result of opportunistic cyanobacteria blooms, which, as 'disaster forms', colonised empty shallow-water ecological niches during the survival phase following the Frasnian metazoan reef collapse, due to collapsed activity of epifaunal, grazing, and/or burrowing animals. The anachronistic lithofacies at Psie Górki is linked with catastrophic mass mortality of the cephalopod and crinoid-brachiopod communities during the heavy storm events. This mass occurrence of girvanellid oncoids, along with Frutexites-like microbial shrubs and, at least partly, common micritisation of some skeletal grains, records an overall increase in microbial activity in eutrophic normal marine environments. Microbial communities in the Holy Cross Mountains are not very diverse, being mainly represented by girvanellid oncoids, and stand in contrast to the very rich microbial communities known from the Guilin area (China), Canning Basin (Australia) and the Timan-northern Ural area (Russia). The association from Poland is similar to more diverse microbial communities represented by oncoids, trombolites and stromatolites, well known from the Canadian Alberta basin.

Microbialite Biosignature Analysis by Mesoscale X-ray Fluorescence (μXRF) Mapping

NASA Astrophysics Data System (ADS)

Tice, Michael M.; Quezergue, Kimbra; Pope, Michael C.

2017-11-01

As part of its biosignature detection package, the Mars 2020 rover will carry PIXL, the Planetary Instrument for X-ray Lithochemistry, a spatially resolved X-ray fluorescence (μXRF) spectrometer. Understanding the types of biosignatures detectable by μXRF and the rock types μXRF is most effective at analyzing is therefore an important goal in preparation for in situ Mars 2020 science and sample selection. We tested mesoscale chemical mapping for biosignature interpretation in microbialites. In particular, we used μXRF to identify spatial distributions and associations between various elements ("fluorescence microfacies") to infer the physical, biological, and chemical processes that produced the observed compositional distributions. As a test case, elemental distributions from μXRF scans of stromatolites from the Mesoarchean Nsuze Group (2.98 Ga) were analyzed. We included five fluorescence microfacies: laminated dolostone, laminated chert, clotted dolostone and chert, stromatolite clast breccia, and cavity fill. Laminated dolostone was formed primarily by microbial mats that trapped and bound loose sediment and likely precipitated carbonate mud at a shallow depth below the mat surface. Laminated chert was produced by the secondary silicification of microbial mats. Clotted dolostone and chert grew as cauliform, cryptically laminated mounds similar to younger thrombolites and was likely formed by a combination of mat growth and patchy precipitation of early-formed carbonate. Stromatolite clast breccias formed as lag deposits filling erosional scours and interstromatolite spaces. Cavities were filled by microquartz, Mn-rich dolomite, and partially dolomitized calcite. Overall, we concluded that μXRF is effective for inferring genetic processes and identifying biosignatures in compositionally heterogeneous rocks.

Processes of carbonate precipitation in modern microbial mats

NASA Astrophysics Data System (ADS)

Dupraz, Christophe; Reid, R. Pamela; Braissant, Olivier; Decho, Alan W.; Norman, R. Sean; Visscher, Pieter T.

2009-10-01

Microbial mats are ecosystems that arguably greatly affected the conditions of the biosphere on Earth through geological time. These laminated organosedimentary systems, which date back to > 3.4 Ga bp, are characterized by high metabolic rates, and coupled to this, rapid cycling of major elements on very small (mm-µm) scales. The activity of the mat communities has changed Earth's redox conditions (i.e. oxidation state) through oxygen and hydrogen production. Interpretation of fossil microbial mats and their potential role in alteration of the Earth's geochemical environment is challenging because these mats are generally not well preserved. Preservation of microbial mats in the fossil record can be enhanced through carbonate precipitation, resulting in the formation of lithified mats, or microbialites. Several types of microbially-mediated mineralization can be distinguished, including biologically-induced and biologically influenced mineralization. Biologically-induced mineralization results from the interaction between biological activity and the environment. Biologically-influenced mineralization is defined as passive mineralization of organic matter (biogenic or abiogenic in origin), whose properties influence crystal morphology and composition. We propose to use the term organomineralization sensu lato as an umbrella term encompassing biologically influenced and biologically induced mineralization. Key components of organomineralization sensu lato are the "alkalinity" engine (microbial metabolism and environmental conditions impacting the calcium carbonate saturation index) and an organic matrix comprised of extracellular polymeric substances (EPS), which may provide a template for carbonate nucleation. Here we review the specific role of microbes and the EPS matrix in various mineralization processes and discuss examples of modern aquatic (freshwater, marine and hypersaline) and terrestrial microbialites.

Rare Earth Elements of the Permian-Triassic Conodonts from Shelf Basin to Shallow Platform: Implications for Oceanic Redox Conditions immediately After the End-Permian Mass Extinction

NASA Astrophysics Data System (ADS)

Li, Y.; Zhao, L.; Chen, Z.; Chen, J.; Chen, Y.

2013-12-01

Rare-earth elements (REEs) can provide information regarding the influence of weathering fluxes and hydrothermal inputs on seawater chemistry as well as processes that fractionate REEs between solid and aqueous phases. Of these, cerium (Ce) distributions may provide information about variations in dissolved oxygen in seawater, and thus assess the redox conditions. The short residence times of REEs in seawater (~300-1,000 yr) can result in unique REE signatures in local watermasses. REE patterns preserved in biogenic apatite such as conodonts are ideal proxies for revealing original seawater chemistry. Here, we measured the REE content of in-situ, single albid crowns using laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) in combination with an ArF (λ=193 nm) excimer laser (Lambda Physiks GeoLas 2005) and quadrupole ICP-MS (Agilent 7500a). LA-ICP-MS is ideally suited for analyzing conodonts due to its ability to measure compositional variation within single conodont elements. It has the capability to determine, with high spatial resolution, continuous compositional depth profiles through the concentric layered structure of component histologies. To evaluate paleoceanographic conditions immediately after the Permian-Triassic (P-Tr) mass extinction in various depositional settings, we sampled a nearly contemporaneous strata unit, the P-Tr boundary bed, just above the extinction horizon from six sections in South China. They represent various depositional settings from shelf basin (Chaohu and Daxiakou sections), lower part of ramp (Meishan section), normal shallow platform (Yangou section), and platform microbialite (Chongyang and Xiushui sections). The sampled unit is constrained by conodonts Hindeodus changxingensis, H. parvus, and H. staeschei Zones in Meishan. REE results obtained from conodont albid crowns show that the seawater in lower ramp and shelf basin settings contains much higher REE concentrations than that in shallow platform. Ce/Ce* ratios in shelf basin and lower ramp are similar to one another, ranging from 0.7-1.0. The same ratios, however, are much lower in shallow platform and microbialite settings, ranging from 0.17-0.22 and 0.2-0.45, respectively. Eu/Eu+ ratios also show similar patterns: 0.7-1.0 in shelf basin and lower ramp and 0.3-0.7 in shallow platform. If the Ce/Ce* was truly influenced by environmental redox conditions, then Ce/Ce* values of 0.7-1.0 in shelf basin and lower ramp settings are indicative of a suboxic to anoxic depositional system, while the same proxy of 0.17-0.45 in shallow platform and microbialite points to a well-oxygenated setting immediately after the P-Tr mass extinction.

Microbialite Biosignature Analysis by Mesoscale X-ray Fluorescence (μXRF) Mapping.

PubMed

Tice, Michael M; Quezergue, Kimbra; Pope, Michael C

2017-11-01

As part of its biosignature detection package, the Mars 2020 rover will carry PIXL, the Planetary Instrument for X-ray Lithochemistry, a spatially resolved X-ray fluorescence (μXRF) spectrometer. Understanding the types of biosignatures detectable by μXRF and the rock types μXRF is most effective at analyzing is therefore an important goal in preparation for in situ Mars 2020 science and sample selection. We tested mesoscale chemical mapping for biosignature interpretation in microbialites. In particular, we used μXRF to identify spatial distributions and associations between various elements ("fluorescence microfacies") to infer the physical, biological, and chemical processes that produced the observed compositional distributions. As a test case, elemental distributions from μXRF scans of stromatolites from the Mesoarchean Nsuze Group (2.98 Ga) were analyzed. We included five fluorescence microfacies: laminated dolostone, laminated chert, clotted dolostone and chert, stromatolite clast breccia, and cavity fill. Laminated dolostone was formed primarily by microbial mats that trapped and bound loose sediment and likely precipitated carbonate mud at a shallow depth below the mat surface. Laminated chert was produced by the secondary silicification of microbial mats. Clotted dolostone and chert grew as cauliform, cryptically laminated mounds similar to younger thrombolites and was likely formed by a combination of mat growth and patchy precipitation of early-formed carbonate. Stromatolite clast breccias formed as lag deposits filling erosional scours and interstromatolite spaces. Cavities were filled by microquartz, Mn-rich dolomite, and partially dolomitized calcite. Overall, we concluded that μXRF is effective for inferring genetic processes and identifying biosignatures in compositionally heterogeneous rocks. Key Words: Stromatolites-Biosignatures-Spectroscopy-Archean. Astrobiology 17, 1161-1172.

Microbialites and global environmental change across the Permian-Triassic boundary: a synthesis.

PubMed

Kershaw, S; Crasquin, S; Li, Y; Collin, P-Y; Forel, M-B; Mu, X; Baud, A; Wang, Y; Xie, S; Maurer, F; Guo, L

2012-01-01

Permian-Triassic boundary microbialites (PTBMs) are thin (0.05-15 m) carbonates formed after the end-Permian mass extinction. They comprise Renalcis-group calcimicrobes, microbially mediated micrite, presumed inorganic micrite, calcite cement (some may be microbially influenced) and shelly faunas. PTBMs are abundant in low-latitude shallow-marine carbonate shelves in central Tethyan continents but are rare in higher latitudes, likely inhibited by clastic supply on Pangaea margins. PTBMs occupied broadly similar environments to Late Permian reefs in Tethys, but extended into deeper waters. Late Permian reefs are also rich in microbes (and cements), so post-extinction seawater carbonate saturation was likely similar to the Late Permian. However, PTBMs lack widespread abundant inorganic carbonate cement fans, so a previous interpretation that anoxic bicarbonate-rich water upwelled to rapidly increase carbonate saturation of shallow seawater, post-extinction, is problematic. Preliminary pyrite framboid evidence shows anoxia in PTBM facies, but interbedded shelly faunas indicate oxygenated water, perhaps there was short-term pulsing of normally saturated anoxic water from the oxygen-minimum zone to surface waters. In Tethys, PTBMs show geographic variations: (i) in south China, PTBMs are mostly thrombolites in open shelf settings, largely recrystallised, with remnant structure of Renalcis-group calcimicrobes; (ii) in south Turkey, in shallow waters, stromatolites and thrombolites, lacking calcimicrobes, are interbedded, likely depth-controlled; and (iii) in the Middle East, especially Iran, stromatolites and thrombolites (calcimicrobes uncommon) occur in different sites on open shelves, where controls are unclear. Thus, PTBMs were under more complex control than previously portrayed, with local facies control playing a significant role in their structure and composition. © 2011 Blackwell Publishing Ltd.

On the Effects of Artificial Feeding on Bee Colony Dynamics: A Mathematical Model

PubMed Central

Paiva, Juliana Pereira Lisboa Mohallem; Paiva, Henrique Mohallem; Esposito, Elisa; Morais, Michelle Manfrini

2016-01-01

This paper proposes a new mathematical model to evaluate the effects of artificial feeding on bee colony population dynamics. The proposed model is based on a classical framework and contains differential equations that describe the changes in the number of hive bees, forager bees, and brood cells, as a function of amounts of natural and artificial food. The model includes the following elements to characterize the artificial feeding scenario: a function to model the preference of the bees for natural food over artificial food; parameters to quantify the quality and palatability of artificial diets; a function to account for the efficiency of the foragers in gathering food under different environmental conditions; and a function to represent different approaches used by the beekeeper to feed the hive with artificial food. Simulated results are presented to illustrate the main characteristics of the model and its behavior under different scenarios. The model results are validated with experimental data from the literature involving four different artificial diets. A good match between simulated and experimental results was achieved. PMID:27875589

Experimental Validation of Model Updating and Damage Detection via Eigenvalue Sensitivity Methods with Artificial Boundary Conditions

DTIC Science & Technology

2017-09-01

VALIDATION OF MODEL UPDATING AND DAMAGE DETECTION VIA EIGENVALUE SENSITIVITY METHODS WITH ARTIFICIAL BOUNDARY CONDITIONS by Matthew D. Bouwense...VALIDATION OF MODEL UPDATING AND DAMAGE DETECTION VIA EIGENVALUE SENSITIVITY METHODS WITH ARTIFICIAL BOUNDARY CONDITIONS 5. FUNDING NUMBERS 6. AUTHOR...unlimited. EXPERIMENTAL VALIDATION OF MODEL UPDATING AND DAMAGE DETECTION VIA EIGENVALUE SENSITIVITY METHODS WITH ARTIFICIAL BOUNDARY

Artificial Exo-Society Modeling: a New Tool for SETI Research

NASA Astrophysics Data System (ADS)

Gardner, James N.

2002-01-01

One of the newest fields of complexity research is artificial society modeling. Methodologically related to artificial life research, artificial society modeling utilizes agent-based computer simulation tools like SWARM and SUGARSCAPE developed by the Santa Fe Institute, Los Alamos National Laboratory and the Bookings Institution in an effort to introduce an unprecedented degree of rigor and quantitative sophistication into social science research. The broad aim of artificial society modeling is to begin the development of a more unified social science that embeds cultural evolutionary processes in a computational environment that simulates demographics, the transmission of culture, conflict, economics, disease, the emergence of groups and coadaptation with an environment in a bottom-up fashion. When an artificial society computer model is run, artificial societal patterns emerge from the interaction of autonomous software agents (the "inhabitants" of the artificial society). Artificial society modeling invites the interpretation of society as a distributed computational system and the interpretation of social dynamics as a specialized category of computation. Artificial society modeling techniques offer the potential of computational simulation of hypothetical alien societies in much the same way that artificial life modeling techniques offer the potential to model hypothetical exobiological phenomena. NASA recently announced its intention to begin exploring the possibility of including artificial life research within the broad portfolio of scientific fields comprised by the interdisciplinary astrobiology research endeavor. It may be appropriate for SETI researchers to likewise commence an exploration of the possible inclusion of artificial exo-society modeling within the SETI research endeavor. Artificial exo-society modeling might be particularly useful in a post-detection environment by (1) coherently organizing the set of data points derived from a detected ETI signal, (2) mapping trends in the data points over time (assuming receipt of an extended ETI signal), and (3) projecting such trends forward to derive alternative cultural evolutionary scenarios for the exo-society under analysis. The latter exercise might be particularly useful to compensate for the inevitable time lag between generation of an ETI signal and receipt of an ETI signal on Earth. For this reason, such an exercise might be a helpful adjunct to the decisional process contemplated by Paragraph 9 of the Declaration of Principles Concerning Activities Following the Detection of Extraterrestrial Intelligence.

The giant stromatolite field at Santa Rosa de Viterbo, Brazil (Paraná Basin) - A new paleoenvironmental overview and the consequences of the Irati Sea closure in the Permian

NASA Astrophysics Data System (ADS)

Callefo, F.; Arduin, D. H.; Ricardi-Branco, F.; Galante, D.; Rodrigues, F.; Branco, F. C.

2018-07-01

The city of Santa Rosa de Viterbo, São Paulo State, Brazil, contains an important geological and paleontological site that is part of the Paraná Basin and presents one of the most significant records of Permian microbial life of southwestern Gondwana: the giant stromatolite field, with structures reaching 3 m high and 6 m wide. This work proposes a new overview of the paleoenvironment, focusing on the growth and development of microbial mats and microbialites. The characterization and association of nine facies were performed: intraformational breccia with wackestone matrix, intraformational breccia with grainstone matrix, packstone, wackestone, microbial mats, peloidal wackestone, laminated peloidal wackestone, laminated peloidal packstone and stromatolites. Four lithologic logs were correlated and used to interpret the history of the establishment of microbial communities during the episode of the Irati Sea closure. Field studies were carried out, thin sections were analyzed, and techniques such as SEM/EDS and Raman spectroscopy were applied for compositional analysis and to aid in the identification of associated fossils. This moment in the Paraná Basin's evolution was important due to the noteworthy increase in rates of organic matter deposition and the significant proliferation of microbial life brought about by the closure of the Irati Sea. One hypothesis is that there was more than one attempt to establish microbial communities during the paleoenvironmental evolution, and success was achieved only after a decrease in water depth and energy of the depositional system, as well as after an increase in salinity. As a result of a reconfiguration of paleogeography in the Gondwana supercontinent, the closure of the Irati Sea significantly affected the development and establishment of microbial communities, which gave rise to extensive microbialitic structures such as those at Santa Rosa de Viterbo.

Ancient sedimentary structures in the <3.7 Ga Gillespie Lake Member, Mars, that resemble macroscopic morphology, spatial associations, and temporal succession in terrestrial microbialites.

PubMed

Noffke, Nora

2015-02-01

Sandstone beds of the <3.7 Ga Gillespie Lake Member on Mars have been interpreted as evidence of an ancient playa lake environment. On Earth, such environments have been sites of colonization by microbial mats from the early Archean to the present time. Terrestrial microbial mats in playa lake environments form microbialites known as microbially induced sedimentary structures (MISS). On Mars, three lithofacies of the Gillespie Lake Member sandstone display centimeter- to meter-scale structures similar in macroscopic morphology to terrestrial MISS that include "erosional remnants and pockets," "mat chips," "roll-ups," "desiccation cracks," and "gas domes." The microbially induced sedimentary-like structures identified in Curiosity rover mission images do not have a random distribution. Rather, they were found to be arranged in spatial associations and temporal successions that indicate they changed over time. On Earth, if such MISS occurred with this type of spatial association and temporal succession, they would be interpreted as having recorded the growth of a microbially dominated ecosystem that thrived in pools that later dried completely: erosional pockets, mat chips, and roll-ups resulted from water eroding an ancient microbial mat-covered sedimentary surface; during the course of subsequent water recess, channels would have cut deep into the microbial mats, leaving erosional remnants behind; desiccation cracks and gas domes would have occurred during a final period of subaerial exposure of the microbial mats. In this paper, the similarities of the macroscopic morphologies, spatial associations, and temporal succession of sedimentary structures on Mars to MISS preserved on Earth has led to the following hypothesis: The sedimentary structures in the <3.7 Ga Gillespie Lake Member on Mars are ancient MISS produced by interactions between microbial mats and their environment. Proposed here is a strategy for detecting, identifying, confirming, and differentiating possible MISS during current and future Mars missions.

Application of artificial intelligence to the management of urological cancer.

PubMed

Abbod, Maysam F; Catto, James W F; Linkens, Derek A; Hamdy, Freddie C

2007-10-01

Artificial intelligence techniques, such as artificial neural networks, Bayesian belief networks and neuro-fuzzy modeling systems, are complex mathematical models based on the human neuronal structure and thinking. Such tools are capable of generating data driven models of biological systems without making assumptions based on statistical distributions. A large amount of study has been reported of the use of artificial intelligence in urology. We reviewed the basic concepts behind artificial intelligence techniques and explored the applications of this new dynamic technology in various aspects of urological cancer management. A detailed and systematic review of the literature was performed using the MEDLINE and Inspec databases to discover reports using artificial intelligence in urological cancer. The characteristics of machine learning and their implementation were described and reports of artificial intelligence use in urological cancer were reviewed. While most researchers in this field were found to focus on artificial neural networks to improve the diagnosis, staging and prognostic prediction of urological cancers, some groups are exploring other techniques, such as expert systems and neuro-fuzzy modeling systems. Compared to traditional regression statistics artificial intelligence methods appear to be accurate and more explorative for analyzing large data cohorts. Furthermore, they allow individualized prediction of disease behavior. Each artificial intelligence method has characteristics that make it suitable for different tasks. The lack of transparency of artificial neural networks hinders global scientific community acceptance of this method but this can be overcome by neuro-fuzzy modeling systems.

Mushroom speleothems: Stromatolites that formed in the absence of phototrophs

NASA Astrophysics Data System (ADS)

Bontognali, Tomaso; D'Angeli, Ilenia; Tisato, Nicola; Vasconcelos, Crisogono; Bernasconi, Stefano; Gonzales, Esteban; DeWaele, Jo

2016-04-01

Unusual speleothems resembling giant mushrooms occur in Santa Catalina Cave, Cuba. Although these mineral buildups are considered a natural heritage, their composition and formation mechanism remain poorly understood. Here we characterize their morphology and mineralogy and present a model for their genesis. We propose that the mushrooms, which are mainly comprised of calcite and aragonite, formed during four different phases within an evolving cave environment. The stipe of the mushroom is an assemblage of three well-known speleothems: a stalagmite surrounded by calcite rafts that were subsequently encrusted by cave clouds (mammilaries). More peculiar is the cap of the mushroom, which is morphologically similar to cerebroid stromatolites and thrombolites of microbial origin occurring in marine environments. Scanning electron microscopy investigations of this last unit revealed the presence of fossilized extracellular polymeric substances (EPS) - the constituents of biofilms and microbial mats. These organic microstructures are mineralized with Ca-carbonate, suggesting that the mushroom cap formed through a microbially-influenced mineralization process. The existence of cerebroid Ca-carbonate buildups forming in dark caves (i.e., in the absence of phototrophs) has interesting implications for the study of fossil microbialites preserved in ancient rocks, which are today considered as one of the earliest evidence for life on Earth.

An Evaluation of Artificial Neural Network Modeling for Manpower Analysis

DTIC Science & Technology

1993-09-01

NAVAL POSTGRADUATE SCHOOL Monterey, California 0- I 1 ’(ft ADV "’r-"A THESIS AN EVALUATION OF ARTIFICIAL NEURAL NETWORK MODELING FOR MANPOWER...AGENCY USE ONLY (Leave blank) 2. REPORT DATE 3. REPORT TYPE AND DATES COVERED September, 1993 4. TITLE AND SUBTITLE An Evaluation Of Artificial Neural Network 5...unlimited. An Evaluation of Artificial Neural Network Modeling for Manpower Analysis by Brian J. Byrne Captain, United States Marine Corps B.S

Evaluation of simulations to understand effects of groundwater development and artificial recharge on the surface water and riparian vegetation Sierra Vista subwatershed, Upper San Pedro Basin, Arizona

USGS Publications Warehouse

Leake, Stanley A.; Gungle, Bruce

2012-01-01

In 2007, the U.S. Geological Survey documented a five-layer groundwater flow model of the Sierra Vista and Sonoran subwatersheds of the Upper San Pedro Basin. The model has been applied by a private consultant to evaluate the effects of projected groundwater pumping through 2105 and effects of artificial recharge at three near-stream sites for 2012-2111. The main concern regarding simulations of long-term groundwater pumping is the effect of artificial model boundaries on modeled response, particularly for pumping near Cananea, Sonora, Mexico, which is adjacent to an artificial no-flow boundary. Concerns regarding the simulations of the effects of artificial recharge near streams include the resolution of the model and the representation of the model properties at the site scale; a possible limited ability of the model to correctly apportion recharge response between increased streamflow and increased evapotranspiration; a limited ability of the model to simulate detailed geometries of artificial recharge areas and evapotranspiration areas; and stream locations with the 820-foot grid spacing of the basin-scale model. In spite of these concerns, use of the U.S. Geological Survey five-layer groundwater flow model by the consultant are reasonable and valid.

Generalized Adaptive Artificial Neural Networks

NASA Technical Reports Server (NTRS)

Tawel, Raoul

1993-01-01

Mathematical model of supervised learning by artificial neural network provides for simultaneous adjustments of both temperatures of neurons and synaptic weights, and includes feedback as well as feedforward synaptic connections. Extension of mathematical model described in "Adaptive Neurons For Artificial Neural Networks" (NPO-17803). Dynamics of neural network represented in new model by less-restrictive continuous formalism.

Modeling Training Site Vegetation Coverage Probability with a Random Optimizing Procedure: An Artificial Neural Network Approach.

DTIC Science & Technology

1998-05-01

Coverage Probability with a Random Optimization Procedure: An Artificial Neural Network Approach by Biing T. Guan, George Z. Gertner, and Alan B...Modeling Training Site Vegetation Coverage Probability with a Random Optimizing Procedure: An Artificial Neural Network Approach 6. AUTHOR(S) Biing...coverage based on past coverage. Approach A literature survey was conducted to identify artificial neural network analysis techniques applicable for

Three-dimensional hysteresis compensation enhances accuracy of robotic artificial muscles

NASA Astrophysics Data System (ADS)

Zhang, Jun; Simeonov, Anthony; Yip, Michael C.

2018-03-01

Robotic artificial muscles are compliant and can generate straight contractions. They are increasingly popular as driving mechanisms for robotic systems. However, their strain and tension force often vary simultaneously under varying loads and inputs, resulting in three-dimensional hysteretic relationships. The three-dimensional hysteresis in robotic artificial muscles poses difficulties in estimating how they work and how to make them perform designed motions. This study proposes an approach to driving robotic artificial muscles to generate designed motions and forces by modeling and compensating for their three-dimensional hysteresis. The proposed scheme captures the nonlinearity by embedding two hysteresis models. The effectiveness of the model is confirmed by testing three popular robotic artificial muscles. Inverting the proposed model allows us to compensate for the hysteresis among temperature surrogate, contraction length, and tension force of a shape memory alloy (SMA) actuator. Feedforward control of an SMA-actuated robotic bicep is demonstrated. This study can be generalized to other robotic artificial muscles, thus enabling muscle-powered machines to generate desired motions.

3D artificial bones for bone repair prepared by computed tomography-guided fused deposition modeling for bone repair.

PubMed

Xu, Ning; Ye, Xiaojian; Wei, Daixu; Zhong, Jian; Chen, Yuyun; Xu, Guohua; He, Dannong

2014-09-10

The medical community has expressed significant interest in the development of new types of artificial bones that mimic natural bones. In this study, computed tomography (CT)-guided fused deposition modeling (FDM) was employed to fabricate polycaprolactone (PCL)/hydroxyapatite (HA) and PCL 3D artificial bones to mimic natural goat femurs. The in vitro mechanical properties, in vitro cell biocompatibility, and in vivo performance of the artificial bones in a long load-bearing goat femur bone segmental defect model were studied. All of the results indicate that CT-guided FDM is a simple, convenient, relatively low-cost method that is suitable for fabricating natural bonelike artificial bones. Moreover, PCL/HA 3D artificial bones prepared by CT-guided FDM have more close mechanics to natural bone, good in vitro cell biocompatibility, biodegradation ability, and appropriate in vivo new bone formation ability. Therefore, PCL/HA 3D artificial bones could be potentially be of use in the treatment of patients with clinical bone defects.

Hyperviscosity for unstructured ALE meshes

NASA Astrophysics Data System (ADS)

Cook, Andrew W.; Ulitsky, Mark S.; Miller, Douglas S.

2013-01-01

An artificial viscosity, originally designed for Eulerian schemes, is adapted for use in arbitrary Lagrangian-Eulerian simulations. Changes to the Eulerian model (dubbed 'hyperviscosity') are discussed, which enable it to work within a Lagrangian framework. New features include a velocity-weighted grid scale and a generalised filtering procedure, applicable to either structured or unstructured grids. The model employs an artificial shear viscosity for treating small-scale vorticity and an artificial bulk viscosity for shock capturing. The model is based on the Navier-Stokes form of the viscous stress tensor, including the diagonal rate-of-expansion tensor. A second-order version of the model is presented, in which Laplacian operators act on the velocity divergence and the grid-weighted strain-rate magnitude to ensure that the velocity field remains smooth at the grid scale. Unlike sound-speed-based artificial viscosities, the hyperviscosity model is compatible with the low Mach number limit. The new model outperforms a commonly used Lagrangian artificial viscosity on a variety of test problems.

Intelligence: Real or artificial?

PubMed Central

Schlinger, Henry D.

1992-01-01

Throughout the history of the artificial intelligence movement, researchers have strived to create computers that could simulate general human intelligence. This paper argues that workers in artificial intelligence have failed to achieve this goal because they adopted the wrong model of human behavior and intelligence, namely a cognitive essentialist model with origins in the traditional philosophies of natural intelligence. An analysis of the word “intelligence” suggests that it originally referred to behavior-environment relations and not to inferred internal structures and processes. It is concluded that if workers in artificial intelligence are to succeed in their general goal, then they must design machines that are adaptive, that is, that can learn. Thus, artificial intelligence researchers must discard their essentialist model of natural intelligence and adopt a selectionist model instead. Such a strategic change should lead them to the science of behavior analysis. PMID:22477051

Rapid Simulation of Blast Wave Propagation in Built Environments Using Coarse-Grain Based Intelligent Modeling Methods

DTIC Science & Technology

2011-04-01

experiments was performed using an artificial neural network to try to capture the nonlinearities. The radial Gaussian artificial neural network system...Modeling Blast-Wave Propagation using Artificial Neural Network Methods‖, in International Journal of Advanced Engineering Informatics, Elsevier

Setup of a Parameterized FE Model for the Die Roll Prediction in Fine Blanking using Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Stanke, J.; Trauth, D.; Feuerhack, A.; Klocke, F.

2017-09-01

Die roll is a morphological feature of fine blanked sheared edges. The die roll reduces the functional part of the sheared edge. To compensate for the die roll thicker sheet metal strips and secondary machining must be used. However, in order to avoid this, the influence of various fine blanking process parameters on the die roll has been experimentally and numerically studied, but there is still a lack of knowledge on the effects of some factors and especially factor interactions on the die roll. Recent changes in the field of artificial intelligence motivate the hybrid use of the finite element method and artificial neural networks to account for these non-considered parameters. Therefore, a set of simulations using a validated finite element model of fine blanking is firstly used to train an artificial neural network. Then the artificial neural network is trained with thousands of experimental trials. Thus, the objective of this contribution is to develop an artificial neural network that reliably predicts the die roll. Therefore, in this contribution, the setup of a fully parameterized 2D FE model is presented that will be used for batch training of an artificial neural network. The FE model enables an automatic variation of the edge radii of blank punch and die plate, the counter and blank holder force, the sheet metal thickness and part diameter, V-ring height and position, cutting velocity as well as material parameters covered by the Hensel-Spittel model for 16MnCr5 (1.7131, AISI/SAE 5115). The FE model is validated using experimental trails. The results of this contribution is a FE model suitable to perform 9.623 simulations and to pass the simulated die roll width and height automatically to an artificial neural network.

Advanced Artificial Science. The development of an artificial science and engineering research infrastructure to facilitate innovative computational modeling, analysis, and application to interdisciplinary areas of scientific investigation.

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

Saffer, Shelley

2014-12-01

This is a final report of the DOE award DE-SC0001132, Advanced Artificial Science. The development of an artificial science and engineering research infrastructure to facilitate innovative computational modeling, analysis, and application to interdisciplinary areas of scientific investigation. This document describes the achievements of the goals, and resulting research made possible by this award.

Linking Rock Magnetic Parameters and Tropical Paleoclimate in Postglacial Carbonates of the Tahitian Coral Reef

NASA Astrophysics Data System (ADS)

Platzman, E. S.; Lund, S.; Camoin, G.; Thouveny, N.

2009-12-01

In areas far away from active plate boundaries and previously glaciated regions, ecologically sensitive coral reefs provide an ideal laboratory for studying the timing and extent of deglaciation events as well as climatic change/variability at sub-millennial timescales. We have studied the Post Last-Glacial-Maximum (Post-LGM) coral reef terrace sediments recovered from the island of Tahiti on IODP Expedition 310. Samples for magnetic analysis were obtained from 632 meters of core from three reef tracts (Maraa, Tiarei, Faaa) surrounding the island (37 holes at 22 sites). The Post-LGM sediments are composed of >95% carbonate residing in a mixture of macroscopic framework corals, encrusting coralline algae, and bacterial microbialites (60% of the total core volume). Detailed paleomagnetic and rock magnetic measurements indicate that the microbialites carry a strong and stable natural magnetic remanence residing almost entirely in titanomagnetite derived from the Tahitian volcanic edifice. Within each tract, paleomagnetic results (inclination, relative paleointensity) were correlated to build a composite magnetic stratigraphy, which we could then compile with radiocarbon dates to develop an absolute chronostratigraphy. At the Maraa tract, for example, we use 54 radiocarbon dates to date our composite section to 7,500 to 13,500 cal. ybp. and demonstrate that the reef developed in a smooth and coherent manner over this interval. Overlaying the chronostratigraphy on measurements of the variation in magnetic properties including susceptibility, ARM, and IRM we can monitor changes in concentration, composition and grainsize of the influx of volcanogenic sediment over time. The ARM, IRM, and CHI intensities (normalized to sample weight) show a single strong peak between~9-10,000 years ago. We also observe a ~500-yr cyclicity in magnetic grain size and a clear increase in grain size associated with the Younger Dryas that we interpret to be related to rainfall variability. The rainfall variability, driven on both a global and regional scale, ultimately results from changes in western Pacific sea-surface temperatures (SST) that drive the island monsoon. Comparison with other proxy data will allow us to build up a detailed climate picture of this key postglacial period.

Artificial Neural Networks: A New Approach to Predicting Application Behavior.

ERIC Educational Resources Information Center

Gonzalez, Julie M. Byers; DesJardins, Stephen L.

2002-01-01

Applied the technique of artificial neural networks to predict which students were likely to apply to one research university. Compared the results to the traditional analysis tool, logistic regression modeling. Found that the addition of artificial intelligence models was a useful new tool for predicting student application behavior. (EV)

Transforming Systems Engineering through Model-Centric Engineering

DTIC Science & Technology

2018-02-28

intelligence (e.g., Artificial Intelligence , etc.), because they provide a means for representing knowledge. We see these capabilities coming to use in both...level, including:  Performance is measured by degree of success of a mission  Artificial Intelligence (AI) is applied to counterparties so that they...Modeling, Artificial Intelligence , Simulation and Modeling, 1989. [140] SAE ARP4761. Guidelines and Methods for Conducting the Safety Assessment Process

Adaptive Modeling and Real-Time Simulation

DTIC Science & Technology

1984-01-01

34 Artificial Inteligence , Vol. 13, pp. 27-39 (1980). Describes circumscription which is just the assumption that everything that is known to have a particular... Artificial Intelligence Truth Maintenance Planning Resolution Modeling Wcrld Models ~ .. ~2.. ASSTR AT (Coninue n evrse sieIf necesaran Identfy by...represents a marriage of (1) the procedural-network st, planning technology developed in artificial intelligence with (2) the PERT/CPM technology developed in

A Multiagent Based Model for Tactical Planning

DTIC Science & Technology

2002-10-01

Pub. Co. 1985. [10] Castillo, J.M. Aproximación mediante procedimientos de Inteligencia Artificial al planeamiento táctico. Doctoral Thesis...been developed under the same conceptual model and using similar Artificial Intelligence Tools. We use four different stimulus/response agents in...The conceptual model is built on base of the Agents theory. To implement the different agents we have used Artificial Intelligence techniques such

Identification of mathematical model of human breathing in system “Artificial lungs – self-contained breathing apparatus”

NASA Astrophysics Data System (ADS)

Onevsky, P. M.; Onevsky, M. P.; Pogonin, V. A.

2018-03-01

The structure and mathematical models of the main subsystems of the control system of the “Artificial Lungs” are presented. This structure implements the process of imitation of human external respiration in the system “Artificial lungs - self-contained breathing apparatus”. A presented algorithm for parametric identification of the model is based on spectral operators, which allows using it in real time.

Plant Growth Models Using Artificial Neural Networks

NASA Technical Reports Server (NTRS)

Bubenheim, David

1997-01-01

In this paper, we descrive our motivation and approach to devloping models and the neural network architecture. Initial use of the artificial neural network for modeling the single plant process of transpiration is presented.

Modeling of mass transfer of Phospholipids in separation process with supercritical CO2 fluid by RBF artificial neural networks

USDA-ARS?s Scientific Manuscript database

An artificial Radial Basis Function (RBF) neural network model was developed for the prediction of mass transfer of the phospholipids from canola meal in supercritical CO2 fluid. The RBF kind of artificial neural networks (ANN) with orthogonal least squares (OLS) learning algorithm were used for mod...

Effects of artificial gravity on the cardiovascular system: Computational approach

NASA Astrophysics Data System (ADS)

Diaz Artiles, Ana; Heldt, Thomas; Young, Laurence R.

2016-09-01

Artificial gravity has been suggested as a multisystem countermeasure against the negative effects of weightlessness. However, many questions regarding the appropriate configuration are still unanswered, including optimal g-level, angular velocity, gravity gradient, and exercise protocol. Mathematical models can provide unique insight into these questions, particularly when experimental data is very expensive or difficult to obtain. In this research effort, a cardiovascular lumped-parameter model is developed to simulate the short-term transient hemodynamic response to artificial gravity exposure combined with ergometer exercise, using a bicycle mounted on a short-radius centrifuge. The model is thoroughly described and preliminary simulations are conducted to show the model capabilities and potential applications. The model consists of 21 compartments (including systemic circulation, pulmonary circulation, and a cardiac model), and it also includes the rapid cardiovascular control systems (arterial baroreflex and cardiopulmonary reflex). In addition, the pressure gradient resulting from short-radius centrifugation is captured in the model using hydrostatic pressure sources located at each compartment. The model also includes the cardiovascular effects resulting from exercise such as the muscle pump effect. An initial set of artificial gravity simulations were implemented using the Massachusetts Institute of Technology (MIT) Compact-Radius Centrifuge (CRC) configuration. Three centripetal acceleration (artificial gravity) levels were chosen: 1 g, 1.2 g, and 1.4 g, referenced to the subject's feet. Each simulation lasted 15.5 minutes and included a baseline period, the spin-up process, the ergometer exercise period (5 minutes of ergometer exercise at 30 W with a simulated pedal cadence of 60 RPM), and the spin-down process. Results showed that the cardiovascular model is able to predict the cardiovascular dynamics during gravity changes, as well as the expected steady-state cardiovascular behavior during sustained artificial gravity and exercise. Further validation of the model was performed using experimental data from the combined exercise and artificial gravity experiments conducted on the MIT CRC, and these results will be presented separately in future publications. This unique computational framework can be used to simulate a variety of centrifuge configuration and exercise intensities to improve understanding and inform decisions about future implementation of artificial gravity in space.

Computational Models of Neuron-Astrocyte Interactions Lead to Improved Efficacy in the Performance of Neural Networks

PubMed Central

Alvarellos-González, Alberto; Pazos, Alejandro; Porto-Pazos, Ana B.

2012-01-01

The importance of astrocytes, one part of the glial system, for information processing in the brain has recently been demonstrated. Regarding information processing in multilayer connectionist systems, it has been shown that systems which include artificial neurons and astrocytes (Artificial Neuron-Glia Networks) have well-known advantages over identical systems including only artificial neurons. Since the actual impact of astrocytes in neural network function is unknown, we have investigated, using computational models, different astrocyte-neuron interactions for information processing; different neuron-glia algorithms have been implemented for training and validation of multilayer Artificial Neuron-Glia Networks oriented toward classification problem resolution. The results of the tests performed suggest that all the algorithms modelling astrocyte-induced synaptic potentiation improved artificial neural network performance, but their efficacy depended on the complexity of the problem. PMID:22649480

Computational models of neuron-astrocyte interactions lead to improved efficacy in the performance of neural networks.

PubMed

Alvarellos-González, Alberto; Pazos, Alejandro; Porto-Pazos, Ana B

2012-01-01

The importance of astrocytes, one part of the glial system, for information processing in the brain has recently been demonstrated. Regarding information processing in multilayer connectionist systems, it has been shown that systems which include artificial neurons and astrocytes (Artificial Neuron-Glia Networks) have well-known advantages over identical systems including only artificial neurons. Since the actual impact of astrocytes in neural network function is unknown, we have investigated, using computational models, different astrocyte-neuron interactions for information processing; different neuron-glia algorithms have been implemented for training and validation of multilayer Artificial Neuron-Glia Networks oriented toward classification problem resolution. The results of the tests performed suggest that all the algorithms modelling astrocyte-induced synaptic potentiation improved artificial neural network performance, but their efficacy depended on the complexity of the problem.

Nondestructive pavement evaluation using ILLI-PAVE based artificial neural network models.

DOT National Transportation Integrated Search

2008-09-01

The overall objective in this research project is to develop advanced pavement structural analysis models for more accurate solutions with fast computation schemes. Soft computing and modeling approaches, specifically the Artificial Neural Network (A...

Modeling a thick unsaturated zone at San Gorgonio Pass, California: lessons learned after five years of artificial recharge

USGS Publications Warehouse

Flint, Alan L.; Ellett, Kevin M.; Christensen, Allen H.; Martin, Peter

2012-01-01

The information flow among the tasks of framework assessment, numerical modeling, model forecasting and hind casting, and system-performance monitoring is illustrated. Results provide an understanding of artificial recharge in high-altitude desert settings where large vertical distances may separate application ponds from their target aquifers.Approximately 3.8 million cubic meters of surface water was applied to spreading ponds from 2003–2007 to artificially recharge the underlying aquifer through a 200-meter thick unsaturated zone in the San Gorgonio Pass area in southern California. A study was conducted between 1997 and 2003, and a numerical model was developed to help determine the suitability of the site for artificial recharge. Ongoing monitoring results indicated that the existing model needed to be modified and recalibrated to more accurately predict artificial recharge at the site. The objective of this work was to recalibrate the model by using observation of the application rates, the rise and fall of the water level above a perching layer, and the approximate arrival time to the water table during the 5-yr monitoring period following initiation of long-term artificial recharge. Continuous monitoring of soil-matric potential, temperature, and water levels beneath the site indicated that artificial recharge reached the underlying water table between 3.75 and 4.5 yr after the initial application of the recharge water. The model was modified to allow the simulation to more adequately match the perching layer dynamics and the time of arrival at the water table. The instrumentation also showed that the lag time between changes in application of water at the surface and the response at the perching layer decreased from about 4 mo to less than 1 mo due to the wet-up of the unsaturated zone and the increase in relative permeability. The results of this study demonstrate the importance of iteratively monitoring and modeling the unsaturated zone in layered alluvial systems in the context of artificial recharge. They show that adequate geologic and hydraulic-property data on perching layers are critical to success. Continuous monitoring in the unsaturated and saturated zones beneath a site provides data to develop and constrain numerical models, better understand local unsaturated zone process, manage artificial recharge operations, and to determine the timing and volume of recoverable water for consumptive use.

Artificial retina model for the retinally blind based on wavelet transform

NASA Astrophysics Data System (ADS)

Zeng, Yan-an; Song, Xin-qiang; Jiang, Fa-gang; Chang, Da-ding

2007-01-01

Artificial retina is aimed for the stimulation of remained retinal neurons in the patients with degenerated photoreceptors. Microelectrode arrays have been developed for this as a part of stimulator. Design such microelectrode arrays first requires a suitable mathematical method for human retinal information processing. In this paper, a flexible and adjustable human visual information extracting model is presented, which is based on the wavelet transform. With the flexible of wavelet transform to image information processing and the consistent to human visual information extracting, wavelet transform theory is applied to the artificial retina model for the retinally blind. The response of the model to synthetic image is shown. The simulated experiment demonstrates that the model behaves in a manner qualitatively similar to biological retinas and thus may serve as a basis for the development of an artificial retina.

Cognitive computing and eScience in health and life science research: artificial intelligence and obesity intervention programs.

PubMed

Marshall, Thomas; Champagne-Langabeer, Tiffiany; Castelli, Darla; Hoelscher, Deanna

2017-12-01

To present research models based on artificial intelligence and discuss the concept of cognitive computing and eScience as disruptive factors in health and life science research methodologies. The paper identifies big data as a catalyst to innovation and the development of artificial intelligence, presents a framework for computer-supported human problem solving and describes a transformation of research support models. This framework includes traditional computer support; federated cognition using machine learning and cognitive agents to augment human intelligence; and a semi-autonomous/autonomous cognitive model, based on deep machine learning, which supports eScience. The paper provides a forward view of the impact of artificial intelligence on our human-computer support and research methods in health and life science research. By augmenting or amplifying human task performance with artificial intelligence, cognitive computing and eScience research models are discussed as novel and innovative systems for developing more effective adaptive obesity intervention programs.

Neural networks to predict exosphere temperature corrections

NASA Astrophysics Data System (ADS)

Choury, Anna; Bruinsma, Sean; Schaeffer, Philippe

2013-10-01

Precise orbit prediction requires a forecast of the atmospheric drag force with a high degree of accuracy. Artificial neural networks are universal approximators derived from artificial intelligence and are widely used for prediction. This paper presents a method of artificial neural networking for prediction of the thermosphere density by forecasting exospheric temperature, which will be used by the semiempirical thermosphere Drag Temperature Model (DTM) currently developed. Artificial neural network has shown to be an effective and robust forecasting model for temperature prediction. The proposed model can be used for any mission from which temperature can be deduced accurately, i.e., it does not require specific training. Although the primary goal of the study was to create a model for 1 day ahead forecast, the proposed architecture has been generalized to 2 and 3 days prediction as well. The impact of artificial neural network predictions has been quantified for the low-orbiting satellite Gravity Field and Steady-State Ocean Circulation Explorer in 2011, and an order of magnitude smaller orbit errors were found when compared with orbits propagated using the thermosphere model DTM2009.

Moderate hypothermia technique for chronic implantation of a total artificial heart in calves.

PubMed

Karimov, Jamshid H; Grady, Patrick; Sinkewich, Martin; Sunagawa, Gengo; Dessoffy, Raymond; Byram, Nicole; Moazami, Nader; Fukamachi, Kiyotaka

2017-06-01

The benefit of whole-body hypothermia in preventing ischemic injury during cardiac surgical operations is well documented. However, application of hypothermia during in vivo total artificial heart implantation has not become widespread because of limited understanding of the proper techniques and restrictions implied by constitutional and physiological characteristics specific to each animal model. Similarly, the literature on hypothermic set-up in total artificial heart implantation has also been limited. Herein we present our experience using hypothermia in bovine models implanted with the Cleveland Clinic continuous-flow total artificial heart.

Prediction of U-Mo dispersion nuclear fuels with Al-Si alloy using artificial neural network

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

Susmikanti, Mike, E-mail: mike@batan.go.id; Sulistyo, Jos, E-mail: soj@batan.go.id

2014-09-30

Dispersion nuclear fuels, consisting of U-Mo particles dispersed in an Al-Si matrix, are being developed as fuel for research reactors. The equilibrium relationship for a mixture component can be expressed in the phase diagram. It is important to analyze whether a mixture component is in equilibrium phase or another phase. The purpose of this research it is needed to built the model of the phase diagram, so the mixture component is in the stable or melting condition. Artificial neural network (ANN) is a modeling tool for processes involving multivariable non-linear relationships. The objective of the present work is to developmore » code based on artificial neural network models of system equilibrium relationship of U-Mo in Al-Si matrix. This model can be used for prediction of type of resulting mixture, and whether the point is on the equilibrium phase or in another phase region. The equilibrium model data for prediction and modeling generated from experimentally data. The artificial neural network with resilient backpropagation method was chosen to predict the dispersion of nuclear fuels U-Mo in Al-Si matrix. This developed code was built with some function in MATLAB. For simulations using ANN, the Levenberg-Marquardt method was also used for optimization. The artificial neural network is able to predict the equilibrium phase or in the phase region. The develop code based on artificial neural network models was built, for analyze equilibrium relationship of U-Mo in Al-Si matrix.« less

The use of artificial neural networks in experimental data acquisition and aerodynamic design

NASA Technical Reports Server (NTRS)

Meade, Andrew J., Jr.

1991-01-01

It is proposed that an artificial neural network be used to construct an intelligent data acquisition system. The artificial neural networks (ANN) model has a potential for replacing traditional procedures as well as for use in computational fluid dynamics validation. Potential advantages of the ANN model are listed. As a proof of concept, the author modeled a NACA 0012 airfoil at specific conditions, using the neural network simulator NETS, developed by James Baffes of the NASA Johnson Space Center. The neural network predictions were compared to the actual data. It is concluded that artificial neural networks can provide an elegant and valuable class of mathematical tools for data analysis.

Color regeneration from reflective color sensor using an artificial intelligent technique.

PubMed

Saracoglu, Ömer Galip; Altural, Hayriye

2010-01-01

A low-cost optical sensor based on reflective color sensing is presented. Artificial neural network models are used to improve the color regeneration from the sensor signals. Analog voltages of the sensor are successfully converted to RGB colors. The artificial intelligent models presented in this work enable color regeneration from analog outputs of the color sensor. Besides, inverse modeling supported by an intelligent technique enables the sensor probe for use of a colorimetric sensor that relates color changes to analog voltages.

Introduction to Concepts in Artificial Neural Networks

NASA Technical Reports Server (NTRS)

Niebur, Dagmar

1995-01-01

This introduction to artificial neural networks summarizes some basic concepts of computational neuroscience and the resulting models of artificial neurons. The terminology of biological and artificial neurons, biological and machine learning and neural processing is introduced. The concepts of supervised and unsupervised learning are explained with examples from the power system area. Finally, a taxonomy of different types of neurons and different classes of artificial neural networks is presented.

Standard representation and unified stability analysis for dynamic artificial neural network models.

PubMed

Kim, Kwang-Ki K; Patrón, Ernesto Ríos; Braatz, Richard D

2018-02-01

An overview is provided of dynamic artificial neural network models (DANNs) for nonlinear dynamical system identification and control problems, and convex stability conditions are proposed that are less conservative than past results. The three most popular classes of dynamic artificial neural network models are described, with their mathematical representations and architectures followed by transformations based on their block diagrams that are convenient for stability and performance analyses. Classes of nonlinear dynamical systems that are universally approximated by such models are characterized, which include rigorous upper bounds on the approximation errors. A unified framework and linear matrix inequality-based stability conditions are described for different classes of dynamic artificial neural network models that take additional information into account such as local slope restrictions and whether the nonlinearities within the DANNs are odd. A theoretical example shows reduced conservatism obtained by the conditions. Copyright © 2017. Published by Elsevier Ltd.

Model-Based Reasoning in the Detection of Satellite Anomalies

DTIC Science & Technology

1990-12-01

Conference on Artificial Intellegence . 1363-1368. Detroit, Michigan, August 89. Chu, Wei-Hai. "Generic Expert System Shell for Diagnostic Reasoning... Intellegence . 1324-1330. Detroit, Michigan, August 89. de Kleer, Johan and Brian C. Williams. "Diagnosing Multiple Faults," Artificial Intellegence , 32(1): 97...Benjamin Kuipers. "Model-Based Monitoring of Dynamic Systems," Proceedings of the Eleventh Intematianal Joint Conference on Artificial Intellegence . 1238

Development of Urban Inundation Warning Model at Cyclic Artificial Water Way in Song-do International City, Republic of Korea

NASA Astrophysics Data System (ADS)

Lee, T.; Lee, C.; Kim, H.

2016-12-01

Abstract Song-do international city was constructed by reclaiming land from the coastal waters of Yeonsu-gu, Incheon Metropolitan City, Republic of Korea. The □-shaped cyclic artificial water way has been considered for improving water quality, waterfront and internal drainage in Song-do international city. By improving water quality, various marine facilities, such as marina, artificial beach, marine terminal, and so on, will be set up around the artificial water way for the waterfront. Since the water stage of the artificial water way changes depending on water gates operations, it is necessary to develop an urban inundation warning model to evaluate safeties of the waterfront facilities and its passengers. By considering characteristics of urban watershed, we calculate discharge flowing into the water way using XP-SWMM model. As a result of estimating 100-year flood frequency, although there are slight differences in drainage sections, the maximum flood discharge occurs in 90-min rainfall duration. In order to consider impacts of tide and hydraulic structure, we establish Inland drainage plans through the analysis of unsteady flow using HEC-RAS. The urban inundation warning model is configured to issue a warning when the water plain elevation exceeds EL. 1.5m which is usually managed at EL. 1.0m. In this study, the design flood stage of artificial water way and urban inundation warning model are developed for Song-do international city, and therefore it is expected that a reliability of management and operation of the waterfront facilities is improved. Keywords : Artificial Water Way; Waterfront; Urban Inundation Warning Model. Acknowlegement This research was supported by a grant [MPSS-NH-2015-79] through the Disaster and Safety Management Institute funded by Ministry of Public Safety and Security of Korean government.

Artificial Neural Networks for Modeling Knowing and Learning in Science.

ERIC Educational Resources Information Center

Roth, Wolff-Michael

2000-01-01

Advocates artificial neural networks as models for cognition and development. Provides an example of how such models work in the context of a well-known Piagetian developmental task and school science activity: balance beam problems. (Contains 59 references.) (Author/WRM)

Artificial Neural Networks: an overview and their use in the analysis of the AMPHORA-3 dataset.

PubMed

Buscema, Paolo Massimo; Massini, Giulia; Maurelli, Guido

2014-10-01

The Artificial Adaptive Systems (AAS) are theories with which generative algebras are able to create artificial models simulating natural phenomenon. Artificial Neural Networks (ANNs) are the more diffused and best-known learning system models in the AAS. This article describes an overview of ANNs, noting its advantages and limitations for analyzing dynamic, complex, non-linear, multidimensional processes. An example of a specific ANN application to alcohol consumption in Spain, as part of the EU AMPHORA-3 project, during 1961-2006 is presented. Study's limitations are noted and future needed research using ANN methodologies are suggested.

Variable camber wing based on pneumatic artificial muscles

NASA Astrophysics Data System (ADS)

Yin, Weilong; Liu, Libo; Chen, Yijin; Leng, Jinsong

2009-07-01

As a novel bionic actuator, pneumatic artificial muscle has high power to weight ratio. In this paper, a variable camber wing with the pneumatic artificial muscle is developed. Firstly, the experimental setup to measure the static output force of pneumatic artificial muscle is designed. The relationship between the static output force and the air pressure is investigated. Experimental result shows the static output force of pneumatic artificial muscle decreases nonlinearly with increasing contraction ratio. Secondly, the finite element model of the variable camber wing is developed. Numerical results show that the tip displacement of the trailing-edge increases linearly with increasing external load and limited with the maximum static output force of pneumatic artificial muscles. Finally, the variable camber wing model is manufactured to validate the variable camber concept. Experimental result shows that the wing camber increases with increasing air pressure and that it compare very well with the FEM result.

Development and validation of deterioration models for concrete bridge decks - phase 1 : artificial intelligence models and bridge management system.

DOT National Transportation Integrated Search

2013-06-01

This research documents the development and evaluation of artificial neural network (ANN) models to predict the condition ratings of concrete highway bridge decks in Michigan. Historical condition assessments chronicled in the national bridge invento...

Systems in Science: Modeling Using Three Artificial Intelligence Concepts.

ERIC Educational Resources Information Center

Sunal, Cynthia Szymanski; Karr, Charles L.; Smith, Coralee; Sunal, Dennis W.

2003-01-01

Describes an interdisciplinary course focusing on modeling scientific systems. Investigates elementary education majors' applications of three artificial intelligence concepts used in modeling scientific systems before and after the course. Reveals a great increase in understanding of concepts presented but inconsistent application. (Author/KHR)

Artificial intelligence in the diagnosis of low back pain.

PubMed

Mann, N H; Brown, M D

1991-04-01

Computerized methods are used to recognize the characteristics of patient pain drawings. Artificial neural network (ANN) models are compared with expert predictions and traditional statistical classification methods when placing the pain drawings of low back pain patients into one of five clinically significant categories. A discussion is undertaken outlining the differences in these classifiers and the potential benefits of the ANN model as an artificial intelligence technique.

Actors: A Model of Concurrent Computation in Distributed Systems.

DTIC Science & Technology

1985-06-01

Artificial Intelligence Labora- tory of the Massachusetts Institute of Technology. Support for the labora- tory’s aritificial intelligence research is...RD-A157 917 ACTORS: A MODEL OF CONCURRENT COMPUTATION IN 1/3- DISTRIBUTED SY𔃿TEMS(U) MASSACHUSETTS INST OF TECH CRMBRIDGE ARTIFICIAL INTELLIGENCE ...Computation In Distributed Systems Gui A. Aghai MIT Artificial Intelligence Laboratory Thsdocument ha. been cipp-oved I= pblicrelease and sale; itsI

Generative Computer-Assisted Instruction and Artificial Intelligence. Report No. 5.

ERIC Educational Resources Information Center

Sinnott, Loraine T.

This paper reviews the state-of-the-art in generative computer-assisted instruction and artificial intelligence. It divides relevant research into three areas of instructional modeling: models of the subject matter; models of the learner's state of knowledge; and models of teaching strategies. Within these areas, work sponsored by Advanced…

Experience in using a numerical scheme with artificial viscosity at solving the Riemann problem for a multi-fluid model of multiphase flow

NASA Astrophysics Data System (ADS)

Bulovich, S. V.; Smirnov, E. M.

2018-05-01

The paper covers application of the artificial viscosity technique to numerical simulation of unsteady one-dimensional multiphase compressible flows on the base of the multi-fluid approach. The system of the governing equations is written under assumption of the pressure equilibrium between the "fluids" (phases). No interfacial exchange is taken into account. A model for evaluation of the artificial viscosity coefficient that (i) assumes identity of this coefficient for all interpenetrating phases and (ii) uses the multiphase-mixture Wood equation for evaluation of a scale speed of sound has been suggested. Performance of the artificial viscosity technique has been evaluated via numerical solution of a model problem of pressure discontinuity breakdown in a three-fluid medium. It has been shown that a relatively simple numerical scheme, explicit and first-order, combined with the suggested artificial viscosity model, predicts a physically correct behavior of the moving shock and expansion waves, and a subsequent refinement of the computational grid results in a monotonic approaching to an asymptotic time-dependent solution, without non-physical oscillations.

Investigation of an artificial intelligence technology--Model trees. Novel applications for an immediate release tablet formulation database.

PubMed

Shao, Q; Rowe, R C; York, P

2007-06-01

This study has investigated an artificial intelligence technology - model trees - as a modelling tool applied to an immediate release tablet formulation database. The modelling performance was compared with artificial neural networks that have been well established and widely applied in the pharmaceutical product formulation fields. The predictability of generated models was validated on unseen data and judged by correlation coefficient R(2). Output from the model tree analyses produced multivariate linear equations which predicted tablet tensile strength, disintegration time, and drug dissolution profiles of similar quality to neural network models. However, additional and valuable knowledge hidden in the formulation database was extracted from these equations. It is concluded that, as a transparent technology, model trees are useful tools to formulators.

Consumption of fast food, sugar-sweetened beverages, artificially-sweetened beverages and allostatic load among young adults.

PubMed

van Draanen, Jenna; Prelip, Michael; Upchurch, Dawn M

2018-06-01

This study investigates the associations between recent consumption of fast foods, sugar-sweetened beverages, and artificially-sweetened beverages on level of allostatic load, a measure of cumulative biological risk, in young adults in the US. Data from Wave IV of the National Longitudinal Study of Adolescent to Adult Health were analyzed. Negative binomial regression models were used to estimate the associations between consumption of fast foods, sugar-sweetened, and artificially-sweetened beverages and allostatic load. Poisson and logistic regression models were used to estimate the associations between these diet parameters and combined biomarkers of physiological subsystems that comprise our measure of allostatic load. All analyses were weighted and findings are representative of young adults in the US, ages 24-34 in 2008 (n = 11,562). Consumption of fast foods, sugar-sweetened, and artificially-sweetened beverages were associated with higher allostatic load at a bivariate level. Accounting for demographics and medication use, only artificially-sweetened beverages remained significantly associated with allostatic load. When all three dietary components were simultaneously included in a model, both sugar- and artificially-sweetened beverage consumption were associated with higher allostatic load. Differences in allostatic load emerge early in the life course and young adults consuming sugar- or artificially-sweetened beverages have higher allostatic load, net of demographics and medication use. Public health messages to young adults may need to include cautions about both sugar- and artificially-sweetened beverages.

Simulation of Blood flow in Artificial Heart Valve Design through Left heart

NASA Astrophysics Data System (ADS)

Hafizah Mokhtar, N.; Abas, Aizat

2018-05-01

In this work, an artificial heart valve is designed for use in real heart with further consideration on the effect of thrombosis, vorticity, and stress. The design of artificial heart valve model is constructed by Computer-aided design (CAD) modelling and simulated using Computational fluid dynamic (CFD) software. The effect of blood flow pattern, velocity and vorticity of the artificial heart valve design has been analysed in this research work. Based on the results, the artificial heart valve design shows that it has a Doppler velocity index that is less than the allowable standards for the left heart with values of more than 0.30 and less than 2.2. These values are safe to be used as replacement of the human heart valve.

Forecasting daily lake levels using artificial intelligence approaches

NASA Astrophysics Data System (ADS)

Kisi, Ozgur; Shiri, Jalal; Nikoofar, Bagher

2012-04-01

Accurate prediction of lake-level variations is important for planning, design, construction, and operation of lakeshore structures and also in the management of freshwater lakes for water supply purposes. In the present paper, three artificial intelligence approaches, namely artificial neural networks (ANNs), adaptive-neuro-fuzzy inference system (ANFIS), and gene expression programming (GEP), were applied to forecast daily lake-level variations up to 3-day ahead time intervals. The measurements at the Lake Iznik in Western Turkey, for the period of January 1961-December 1982, were used for training, testing, and validating the employed models. The results obtained by the GEP approach indicated that it performs better than ANFIS and ANNs in predicting lake-level variations. A comparison was also made between these artificial intelligence approaches and convenient autoregressive moving average (ARMA) models, which demonstrated the superiority of GEP, ANFIS, and ANN models over ARMA models.

Artificial intelligence in process control: Knowledge base for the shuttle ECS model

NASA Technical Reports Server (NTRS)

Stiffler, A. Kent

1989-01-01

The general operation of KATE, an artificial intelligence controller, is outlined. A shuttle environmental control system (ECS) demonstration system for KATE is explained. The knowledge base model for this system is derived. An experimental test procedure is given to verify parameters in the model.

Modelling Simple Experimental Platform for In Vitro Study of Drug Elution from Drug Eluting Stents (DES)

NASA Astrophysics Data System (ADS)

Kalachev, L. V.

2016-06-01

We present a simple model of experimental setup for in vitro study of drug release from drug eluting stents and drug propagation in artificial tissue samples representing blood vessels. The model is further reduced using the assumption on vastly different characteristic diffusion times in the stent coating and in the artificial tissue. The model is used to derive a relationship between the times at which the measurements have to be taken for two experimental platforms, with corresponding artificial tissue samples made of different materials with different drug diffusion coefficients, to properly compare the drug release characteristics of drug eluting stents.

STANFORD ARTIFICIAL INTELLIGENCE PROJECT.

DTIC Science & Technology

ARTIFICIAL INTELLIGENCE , GAME THEORY, DECISION MAKING, BIONICS, AUTOMATA, SPEECH RECOGNITION, GEOMETRIC FORMS, LEARNING MACHINES, MATHEMATICAL MODELS, PATTERN RECOGNITION, SERVOMECHANISMS, SIMULATION, BIBLIOGRAPHIES.

Possible Conflicts, ARRs, and Conflicts

DTIC Science & Technology

2002-05-04

Fourteenth European Conference on Artificial Intelligence Inteligencia Artificial , 41-53, (2001). (ECAI 2000), pp. 136-140, Berlin, Germany, (2000). [31] B...introduced), or proach to model-based diagnosis within the Artificial Intelligence backward (when a discrepancy is found, such as in CAEN [2, 21], community... Artificial Intelli- Relations (ARRs for short), for fault detection and localization [34]. gence community (usually known as DX). It is a research

Knowledge Based Simulation: An Artificial Intelligence Approach to System Modeling and Automating the Simulation Life Cycle.

DTIC Science & Technology

1988-04-13

Simulation: An Artificial Intelligence Approach to System Modeling and Automating the Simulation Life Cycle Mark S. Fox, Nizwer Husain, Malcolm...McRoberts and Y.V.Reddy CMU-RI-TR-88-5 Intelligent Systems Laboratory The Robotics Institute Carnegie Mellon University Pittsburgh, Pennsylvania D T T 13...years of research in the application of Artificial Intelligence to Simulation. Our focus has been in two areas: the use of Al knowledge representation

Development of a novel tissue-engineered nitinol frame artificial trachea with native-like physical characteristics.

PubMed

Sakaguchi, Yasuto; Sato, Toshihiko; Muranishi, Yusuke; Yutaka, Yojiro; Komatsu, Teruya; Omori, Koichi; Nakamura, Tatsuo; Date, Hiroshi

2018-04-24

Tracheal reconstruction is complicated by the short length to which a trachea can be resected. We previously developed a biocompatible polypropylene frame artificial trachea, but it lacked the strength and flexibility of the native trachea. In contrast, nitinol may provide these physical characteristics. We developed a novel nitinol frame artificial trachea and examined its biocompatibility and safety in canine models. We constructed several nitinol frame prototypes and selected the frame that most closely reproduced the strength of the native canine trachea. This frame was used to create a collagen-coated artificial trachea that was implanted into 5 adult beagle dogs. The artificial trachea was first implanted into the pedicled omentum and placed in the abdomen. Three weeks later, the omentum-wrapped artificial trachea was moved into the thoracic cavity. The thoracic trachea was then partially resected and reconstructed using the artificial trachea. Follow-up bronchoscopic evaluation was performed, and the artificial trachea was histologically examined after the dogs were sacrificed. Stenosis at the anastomosis sites was not observed in any dog. Survival for 18 months or longer was confirmed in all dogs but 1, which died after 9 months due to reasons unrelated to the artificial trachea. Histological examination confirmed respiratory epithelial regeneration on the artificial trachea's luminal surface. Severe foreign body reaction was not detected around the nitinol frame. The novel nitinol artificial trachea reproduced the physical characteristics of the native trachea. We have confirmed cell engraftment, good biocompatibility, and survival of 18 months or longer for this artificial trachea in canine models. Copyright © 2018 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.

Expanding the occupational health methodology: A concatenated artificial neural network approach to model the burnout process in Chinese nurses.

PubMed

Ladstätter, Felix; Garrosa, Eva; Moreno-Jiménez, Bernardo; Ponsoda, Vicente; Reales Aviles, José Manuel; Dai, Junming

2016-01-01

Artificial neural networks are sophisticated modelling and prediction tools capable of extracting complex, non-linear relationships between predictor (input) and predicted (output) variables. This study explores this capacity by modelling non-linearities in the hardiness-modulated burnout process with a neural network. Specifically, two multi-layer feed-forward artificial neural networks are concatenated in an attempt to model the composite non-linear burnout process. Sensitivity analysis, a Monte Carlo-based global simulation technique, is then utilised to examine the first-order effects of the predictor variables on the burnout sub-dimensions and consequences. Results show that (1) this concatenated artificial neural network approach is feasible to model the burnout process, (2) sensitivity analysis is a prolific method to study the relative importance of predictor variables and (3) the relationships among variables involved in the development of burnout and its consequences are to different degrees non-linear. Many relationships among variables (e.g., stressors and strains) are not linear, yet researchers use linear methods such as Pearson correlation or linear regression to analyse these relationships. Artificial neural network analysis is an innovative method to analyse non-linear relationships and in combination with sensitivity analysis superior to linear methods.

Evaluation of Physiologically-Based Artificial Neural Network Models to Detect Operator Workload in Remotely Piloted Aircraft Operations

DTIC Science & Technology

2016-07-13

to a computer via Bluetooth . Respiration is captured as a breathing waveform signal using a capacitive pressure sensor, sampled at 18 Hz. The...dropouts in the Bluetooth signal and artifacts caused by body movement. Workload models. Four artificial neural network models were created using

Antacid effects of Chinese herbal prescriptions assessed by a modified artificial stomach model

PubMed Central

Wu, Tsung-Hsiu; Chen, I-Chin; Chen, Lih-Chi

2010-01-01

AIM: To assess the antacid effects of the tonic Chinese herbal prescriptions, Si-Jun-Zi-Tang (SJZT) and Shen-Ling-Bai-Zhu-San (SLBZS). METHODS: Decoctions of the tonic Chinese herbal prescriptions, SJZT and SLBZS, were prepared according to Chinese original documents. The pH of the prescription decoctions and their neutralizing effects on artificial gastric acids were determined and compared with water and the active controls, sodium bicarbonate and colloidal aluminum phosphate. A modified model of Vatier’s artificial stomach was used to determine the duration of consistent neutralization effect on artificial gastric acids. The neutralization capacity in vitro was determined with the titration method of Fordtran’s model. RESULTS: The results showed that both SJZT and SLBZS have antacid effects in vitro. Compared with the water group, SJZT and SLBZS were found to possess significant gastric acid neutralizing effects. The duration for consistent neutralization of SLBZS was significantly longer than that of water. Also, SLBZS and SJZT exhibited significant antacid capacities compared to water. CONCLUSION: SJZT and SLBZS were consistently active in the artificial stomach model and are suggested to have antacid effects similar to the active control drugs. PMID:20845514

A Micro-Level Data-Calibrated Agent-Based Model: The Synergy between Microsimulation and Agent-Based Modeling.

PubMed

Singh, Karandeep; Ahn, Chang-Won; Paik, Euihyun; Bae, Jang Won; Lee, Chun-Hee

2018-01-01

Artificial life (ALife) examines systems related to natural life, its processes, and its evolution, using simulations with computer models, robotics, and biochemistry. In this article, we focus on the computer modeling, or "soft," aspects of ALife and prepare a framework for scientists and modelers to be able to support such experiments. The framework is designed and built to be a parallel as well as distributed agent-based modeling environment, and does not require end users to have expertise in parallel or distributed computing. Furthermore, we use this framework to implement a hybrid model using microsimulation and agent-based modeling techniques to generate an artificial society. We leverage this artificial society to simulate and analyze population dynamics using Korean population census data. The agents in this model derive their decisional behaviors from real data (microsimulation feature) and interact among themselves (agent-based modeling feature) to proceed in the simulation. The behaviors, interactions, and social scenarios of the agents are varied to perform an analysis of population dynamics. We also estimate the future cost of pension policies based on the future population structure of the artificial society. The proposed framework and model demonstrates how ALife techniques can be used by researchers in relation to social issues and policies.

Testing in artificial sweat - Is less more? Comparison of metal release in two different artificial sweat solutions.

PubMed

Midander, Klara; Julander, Anneli; Kettelarij, Jolinde; Lidén, Carola

2016-11-01

Metal release from materials immersed in artificial sweat can function as a measure of potential skin exposure. Several artificial sweat models exist that, to various degree, mimic realistic conditions. Study objective was to evaluate metal release from previously examined and well characterized materials in two different artificial sweat solutions; a comprehensive sweat model intended for use within research, based on the composition of human sweat; and the artificial sweat, EN1811, intended for testing compliance with the nickel restriction in REACH. The aim was to better understand whether there are advantages using either of the sweat solutions in bio-elution testing of materials. Metal release in two different artificial sweat solutions was compared for discs of a white gold alloy and two hard metals, and a rock drilling insert of tungsten carbide at 1 h, 24 h, 1 week and 1 month. The released amount of metal was analysed by means of inductively coupled plasma mass spectrometry. Similar levels of released metals were measured from test materials in the two different artificial sweat solutions. For purposes in relation to legislations, it was concluded that a metal release test using a simple artificial sweat composition may provide results that sufficiently indicate the degree of metal release at skin contact. Copyright © 2016 Elsevier Inc. All rights reserved.

Modelling and Predicting Backstroke Start Performance Using Non-Linear and Linear Models.

PubMed

de Jesus, Karla; Ayala, Helon V H; de Jesus, Kelly; Coelho, Leandro Dos S; Medeiros, Alexandre I A; Abraldes, José A; Vaz, Mário A P; Fernandes, Ricardo J; Vilas-Boas, João Paulo

2018-03-01

Our aim was to compare non-linear and linear mathematical model responses for backstroke start performance prediction. Ten swimmers randomly completed eight 15 m backstroke starts with feet over the wedge, four with hands on the highest horizontal and four on the vertical handgrip. Swimmers were videotaped using a dual media camera set-up, with the starts being performed over an instrumented block with four force plates. Artificial neural networks were applied to predict 5 m start time using kinematic and kinetic variables and to determine the accuracy of the mean absolute percentage error. Artificial neural networks predicted start time more robustly than the linear model with respect to changing training to the validation dataset for the vertical handgrip (3.95 ± 1.67 vs. 5.92 ± 3.27%). Artificial neural networks obtained a smaller mean absolute percentage error than the linear model in the horizontal (0.43 ± 0.19 vs. 0.98 ± 0.19%) and vertical handgrip (0.45 ± 0.19 vs. 1.38 ± 0.30%) using all input data. The best artificial neural network validation revealed a smaller mean absolute error than the linear model for the horizontal (0.007 vs. 0.04 s) and vertical handgrip (0.01 vs. 0.03 s). Artificial neural networks should be used for backstroke 5 m start time prediction due to the quite small differences among the elite level performances.

Computational properties of networks of synchronous groups of spiking neurons.

PubMed

Dayhoff, Judith E

2007-09-01

We demonstrate a model in which synchronously firing ensembles of neurons are networked to produce computational results. Each ensemble is a group of biological integrate-and-fire spiking neurons, with probabilistic interconnections between groups. An analogy is drawn in which each individual processing unit of an artificial neural network corresponds to a neuronal group in a biological model. The activation value of a unit in the artificial neural network corresponds to the fraction of active neurons, synchronously firing, in a biological neuronal group. Weights of the artificial neural network correspond to the product of the interconnection density between groups, the group size of the presynaptic group, and the postsynaptic potential heights in the synchronous group model. All three of these parameters can modulate connection strengths between neuronal groups in the synchronous group models. We give an example of nonlinear classification (XOR) and a function approximation example in which the capability of the artificial neural network can be captured by a neural network model with biological integrate-and-fire neurons configured as a network of synchronously firing ensembles of such neurons. We point out that the general function approximation capability proven for feedforward artificial neural networks appears to be approximated by networks of neuronal groups that fire in synchrony, where the groups comprise integrate-and-fire neurons. We discuss the advantages of this type of model for biological systems, its possible learning mechanisms, and the associated timing relationships.

Bayesian Model Averaging of Artificial Intelligence Models for Hydraulic Conductivity Estimation

NASA Astrophysics Data System (ADS)

Nadiri, A.; Chitsazan, N.; Tsai, F. T.; Asghari Moghaddam, A.

2012-12-01

This research presents a Bayesian artificial intelligence model averaging (BAIMA) method that incorporates multiple artificial intelligence (AI) models to estimate hydraulic conductivity and evaluate estimation uncertainties. Uncertainty in the AI model outputs stems from error in model input as well as non-uniqueness in selecting different AI methods. Using one single AI model tends to bias the estimation and underestimate uncertainty. BAIMA employs Bayesian model averaging (BMA) technique to address the issue of using one single AI model for estimation. BAIMA estimates hydraulic conductivity by averaging the outputs of AI models according to their model weights. In this study, the model weights were determined using the Bayesian information criterion (BIC) that follows the parsimony principle. BAIMA calculates the within-model variances to account for uncertainty propagation from input data to AI model output. Between-model variances are evaluated to account for uncertainty due to model non-uniqueness. We employed Takagi-Sugeno fuzzy logic (TS-FL), artificial neural network (ANN) and neurofuzzy (NF) to estimate hydraulic conductivity for the Tasuj plain aquifer, Iran. BAIMA combined three AI models and produced better fitting than individual models. While NF was expected to be the best AI model owing to its utilization of both TS-FL and ANN models, the NF model is nearly discarded by the parsimony principle. The TS-FL model and the ANN model showed equal importance although their hydraulic conductivity estimates were quite different. This resulted in significant between-model variances that are normally ignored by using one AI model.

Applications of artificial neural nets in clinical biomechanics.

PubMed

Schöllhorn, W I

2004-11-01

The purpose of this article is to provide an overview of current applications of artificial neural networks in the area of clinical biomechanics. The body of literature on artificial neural networks grew intractably vast during the last 15 years. Conventional statistical models may present certain limitations that can be overcome by neural networks. Artificial neural networks in general are introduced, some limitations, and some proven benefits are discussed.

Trimaran Resistance Artificial Neural Network

DTIC Science & Technology

2011-01-01

11th International Conference on Fast Sea Transportation FAST 2011, Honolulu, Hawaii, USA, September 2011 Trimaran Resistance Artificial Neural Network Richard...Trimaran Resistance Artificial Neural Network 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e... Artificial Neural Network and is restricted to the center and side-hull configurations tested. The value in the parametric model is that it is able to

Effects of heat conduction on artificial viscosity methods for shock capturing

DOE PAGES

Cook, Andrew W.

2013-12-01

Here we investigate the efficacy of artificial thermal conductivity for shock capturing. The conductivity model is derived from artificial bulk and shear viscosities, such that stagnation enthalpy remains constant across shocks. By thus fixing the Prandtl number, more physical shock profiles are obtained, only on a larger scale. The conductivity model does not contain any empirical constants. It increases the net dissipation of a computational algorithm but is found to better preserve symmetry and produce more robust solutions for strong-shock problems.

Expertise, Task Complexity, and Artificial Intelligence: A Conceptual Framework.

ERIC Educational Resources Information Center

Buckland, Michael K.; Florian, Doris

1991-01-01

Examines the relationship between users' expertise, task complexity of information system use, and artificial intelligence to provide the basis for a conceptual framework for considering the role that artificial intelligence might play in information systems. Cognitive and conceptual models are discussed, and cost effectiveness is considered. (27…

Combined Use of Tissue Morphology, Neural Network Analysis of Chromatin Texture and Clinical Variables to Predict Prostate Cancer Agressiveness from Biopsy Water

DTIC Science & Technology

2000-10-01

Purpose: To combine clinical, serum, pathologic and computer derived information into an artificial neural network to develop/validate a model to...Development of an artificial neural network (year 02). Prospective validation of this model (projected year 03). All models will be tested and

Combined Use of Tissue Morphology, Neural Network Analysis of Chromatin Texture & Clinical Variables to Predict Prostate Cancer Agressiveness from Biopsy Material

DTIC Science & Technology

1999-10-01

THE PURPOSE OF THIS REPORT IS TO COMBINE CLINICAL, SERUM, PATHOLOGICAL AND COMPUTER DERIVED INFORMATION INTO AN ARTIFICIAL NEURAL NETWORK TO DEVELOP...01). Development of a artificial neural network model (year 02). Prospective validation of this model (projected year 03). All models will be tested

Log-linear model based behavior selection method for artificial fish swarm algorithm.

PubMed

Huang, Zhehuang; Chen, Yidong

2015-01-01

Artificial fish swarm algorithm (AFSA) is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm.

Magnetically-actuated artificial cilia for microfluidic propulsion.

PubMed

Khaderi, S N; Craus, C B; Hussong, J; Schorr, N; Belardi, J; Westerweel, J; Prucker, O; Rühe, J; den Toonder, J M J; Onck, P R

2011-06-21

In this paper we quantitatively analyse the performance of magnetically-driven artificial cilia for lab-on-a-chip applications. The artificial cilia are fabricated using thin polymer films with embedded magnetic nano-particles and their deformation is studied under different external magnetic fields and flows. A coupled magneto-mechanical solid-fluid model that accurately captures the interaction between the magnetic field, cilia and fluid is used to simulate the cilia motion. The elastic and magnetic properties of the cilia are obtained by fitting the results of the computational model to the experimental data. The performance of the artificial cilia with a non-uniform cross-section is characterised using the numerical model for two channel configurations that are of practical importance: an open-loop and a closed-loop channel. We predict that the flow and pressure head generated by the artificial cilia can be as high as 18 microlitres per minute and 3 mm of water, respectively. We also study the effect of metachronal waves on the flow generated and show that the fluid propelled increases drastically compared to synchronously beating cilia, and is unidirectional. This increase is significant even when the phase difference between adjacent cilia is small. The obtained results provide guidelines for the optimal design of magnetically-driven artificial cilia for microfluidic propulsion.

Effects of artificial-recharge experiments at Ship Creek alluvial fan on water levels at Spring Acres Subdivision, Anchorage, Alaska

USGS Publications Warehouse

Meyer, William; Patrick, Leslie

1980-01-01

The effect of the artificial recharge experiments on water levels at Spring Acres subdivision, Anchorage, Alaska, was evaluated using two digital models constructed to simulate groundwater movement and water-level rises induced by the artificial recharge. The models predicted that the artificial recharge would have caused water levels in the aquifer immediately underlying Spring Acres subdivision to rise 0.2 foot from May 20 to August 7, 1975. The models also predicted a total rise in groundwater levels of 1.1 feet at this location from July 16, 1973 to August 7, 1975, as a result of the artificial-recharge experiments. Water-level data collected from auger holes in March 1975 by a consulting firm for the contractor indicated a depth to water of 6-7 feet below land surface at Spring Acres subdivision at this time. Water levels measured in and near Spring Acres subdivision several years before and after the 1973-75 artificial-recharge experiments showed seasonal rises of 2 to 12.4 feet. A depth to water below land surface of 2.6 feet was measured 600 feet from the subdivision in 1971 and in the subdivision in 1977. Average measured depth to water in the area was 7.0 feet from early 1976 to September 1979. (USGS)

A Symbolic Model of the Nonconscious Acquisition of Information.

ERIC Educational Resources Information Center

Ling, Charles X.; Marinov, Marin

1994-01-01

Challenges Smolensky's theory that human intuitive/nonconscious cognitive processes can only be accurately explained in terms of subsymbolic computations in artificial neural networks. Symbolic learning models of two cognitive tasks involving nonconscious acquisition of information are presented: learning production rules and artificial finite…

A Hermite-based lattice Boltzmann model with artificial viscosity for compressible viscous flows

NASA Astrophysics Data System (ADS)

Qiu, Ruofan; Chen, Rongqian; Zhu, Chenxiang; You, Yancheng

2018-05-01

A lattice Boltzmann model on Hermite basis for compressible viscous flows is presented in this paper. The model is developed in the framework of double-distribution-function approach, which has adjustable specific-heat ratio and Prandtl number. It contains a density distribution function for the flow field and a total energy distribution function for the temperature field. The equilibrium distribution function is determined by Hermite expansion, and the D3Q27 and D3Q39 three-dimensional (3D) discrete velocity models are used, in which the discrete velocity model can be replaced easily. Moreover, an artificial viscosity is introduced to enhance the model for capturing shock waves. The model is tested through several cases of compressible flows, including 3D supersonic viscous flows with boundary layer. The effect of artificial viscosity is estimated. Besides, D3Q27 and D3Q39 models are further compared in the present platform.

Application of Artificial Thunderstorm Cells for the Investigation of Lightning Initiation Problems between a Thundercloud and the Ground

NASA Astrophysics Data System (ADS)

Temnikov, A. G.; Chernensky, L. L.; Orlov, A. V.; Lysov, N. Y.; Zhuravkova, D. S.; Belova, O. S.; Gerastenok, T. K.

2017-12-01

The results of the experimental application of artificial thunderstorm cells of negative and positive polarities for the investigation of the lightning initiation problems between the thundercloud and the ground using model hydrometeor arrays are presented. Possible options of the initiation and development of a discharge between the charged cloud and the ground in the presence of model hydrometeors are established. It is experimentally shown that groups of large hydrometeors of various shapes significantly increase the probability of channel discharge initiation between the artificial thunderstorm cell and the ground, especially in the case of positive polarity of the cloud. The authors assume that large hail arrays in the thundercloud can initiate the preliminary breakdown stage in the lower part of the thundercloud or initiate and stimulate the propagation of positive lightning from its upper part. A significant effect of the shape of model hydrometeors and the way they are grouped on the processes of initiation and stimulation of the channel discharge propagation in the artificial thunderstorm cell of negative or positive polarity-ground gap is experimentally established. It is found that, in the case of negative polarity of a charged cloud, the group of conductive cylindrical hydrometeors connected by a dielectric string more effectively initiates the channel discharge between the artificial thunderstorm cell and the ground. In the case of positive polarity of the artificial thunderstorm cell, the best effect of the channel discharge initiation is achieved for model hydrometeors grouped together by the dielectric tape. The obtained results can be used in the development of the method for the directed artificial lightning initiation between the thundercloud and the ground.

Training for laparoscopic Nissen fundoplication with a newly designed model: a replacement for animal tissue models?

PubMed Central

Christie, Lorna; Goossens, Richard; Jakimowicz, Jack J.

2010-01-01

Background To bridge the early learning curve for laparoscopic Nissen fundoplication from the clinical setting to a safe environment, training models can be used. This study aimed to develop a reusable, low-cost model to be used for training in laparoscopic Nissen fundoplication procedure as an alternative to the use of animal tissue models. Methods From artificial organs and tissue, an anatomic model of the human upper abdomen was developed for training in performing laparoscopic Nissen fundoplication. The 20 participants and tutors in the European Association for Endoscopic Surgery (EAES) upper gastrointestinal surgery course completed four complementary tasks of laparoscopic Nissen fundoplication with the artificial model, then compared the realism, haptic feedback, and training properties of the model with those of animal tissue models. Results The main difference between the two training models was seen in the properties of the stomach. The wrapping of the stomach in the artificial model was rated significantly lower than that in the animal tissue model (mean, 3.6 vs. 4.2; p = 0.010). The main criticism of the stomach of the artificial model was that it was too rigid for making a proper wrap. The suturing of the stomach wall, however, was regarded as fairly realistic (mean, 3.6). The crura on the artificial model were rated better (mean, 4.3) than those on the animal tissue (mean, 4.0), although the difference was not significant. The participants regarded the model as a good to excellent (mean, 4.3) training tool. Conclusion The newly developed model is regarded as a good tool for training in laparoscopic Nissen fundoplication procedure. It is cheaper, more durable, and more readily available for training and can therefore be used in every training center. The stomach of this model, however, still needs improvement because it is too rigid for making the wrap. PMID:20526629

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

Wronkiewicz, David; Paul, Varum; Abousif, Alsedik

The most effective mechanism to limit CO 2 release from underground Geologic Carbon Sequestration (GCS) sites over multi-century time scales will be to convert the CO 2 into solid carbonate minerals. This report describes the results from four independent research investigations on carbonate mineralization: 1) Colloidal calcite particles forming in Maramec Spring, Missouri, provide a natural analog to evaluate reactions that may occur in a leaking GCS site. The calcite crystals form as a result of physiochemical changes that occur as the spring water rises from a depth of more than 190'. The resultant pressure decrease induces a loss ofmore » CO 2 from the water, rise in pH, lowering of the solubility of Ca 2+ and CO 3 2-, and calcite precipitation. Equilibrium modelling of the spring water resulted in a calculated undersaturated state with respect to calcite. The discontinuity between the observed occurrence of calcite and the model result predicting undersaturated conditions can be explained if bicarbonate ions (HCO 3 -) are directly involved in precipitation process rather than just carbonate ions (CO 3 2-). 2) Sedimentary rocks in the Oronto Group of the Midcontinent Rift (MCR) system contain an abundance of labile Ca-, Mg-, and Fe-silicate minerals that will neutralize carbonic acid and provide alkaline earth ions for carbonate mineralization. One of the challenges in using MCR rocks for GCS results from their low porosity and permeability. Oronto Group samples were reacted with both CO 2-saturated deionized water at 90°C, and a mildly acidic leachant solution in flow-through core-flooding reactor vessels at room temperature. Resulting leachate solutions often exceeded the saturation limit for calcite. Carbonate crystals were also detected in as little as six days of reaction with Oronto Group rocks at 90oC, as well as experiments with forsterite-olivine and augite, both being common minerals this sequence. The Oronto Group samples have poor reservoir rock characteristics, none ever exceeded a permeability value of 2.0 mD even after extensive dissolution of calcite cement during the experiments. The overlying Bayfield Group – Jacobsville Formation sandstones averaged 13.4 ± 4.3% porosity and a single sample tested by core-flooding revealed a permeability of ~340 mD. The high porosity-permeability characteristics of these sandstones will allow them to be used for GCS as a continuous aquifer unit with the overlying Mt. Simon Formation. 3) Anaerobic sulfate reducing bacteria (SRB) can enhance the conversion rate of CO 2 into solid minerals and thereby improve long-term storage. SRB accelerated carbonate mineralization reactions between pCO 2 values of 0.0059 and 14.7 psi. Hydrogen, lactate and formate served as suitable electron donors for SRB metabolism. The use of a 13CO 2 spiked gas source also produced carbonate minerals with ~53% of the carbon being derived from the gas phase. The sulfate reducing activity of the microbial community was limited, however, at 20 psi pCO 2 and carbonate mineralization did not occur. Inhibition of bacterial metabolism may have resulted from the acidic conditions or CO 2 toxicity. 4) Microbialite communities forming in the high turbidity and hypersaline water of Storrs’ Lake, San Salvador Island, The Bahamas, were investigated for their distribution, mineralogy and microbial diversity. Molecular analysis of the organic mats on the microbialites indicate only a trace amount of cyanobacteria, while anaerobic and photosynthetic non-sulfur bacteria of the phyla Chloroflexi and purple sulfur bacteria of class Gammaproteobacteria were abundant.« less

Applying artificial neural networks to predict communication risks in the emergency department.

PubMed

Bagnasco, Annamaria; Siri, Anna; Aleo, Giuseppe; Rocco, Gennaro; Sasso, Loredana

2015-10-01

To describe the utility of artificial neural networks in predicting communication risks. In health care, effective communication reduces the risk of error. Therefore, it is important to identify the predictive factors of effective communication. Non-technical skills are needed to achieve effective communication. This study explores how artificial neural networks can be applied to predict the risk of communication failures in emergency departments. A multicentre observational study. Data were collected between March-May 2011 by observing the communication interactions of 840 nurses with their patients during their routine activities in emergency departments. The tools used for our observation were a questionnaire to collect personal and descriptive data, level of training and experience and Guilbert's observation grid, applying the Situation-Background-Assessment-Recommendation technique to communication in emergency departments. A total of 840 observations were made on the nurses working in the emergency departments. Based on Guilbert's observation grid, the output variables is likely to influence the risk of communication failure were 'terminology'; 'listening'; 'attention' and 'clarity', whereas nurses' personal characteristics were used as input variables in the artificial neural network model. A model based on the multilayer perceptron topology was developed and trained. The receiver operator characteristic analysis confirmed that the artificial neural network model correctly predicted the performance of more than 80% of the communication failures. The application of the artificial neural network model could offer a valid tool to forecast and prevent harmful communication errors in the emergency department. © 2015 John Wiley & Sons Ltd.

Modeling the thermotaxis behavior of C.elegans based on the artificial neural network.

PubMed

Li, Mingxu; Deng, Xin; Wang, Jin; Chen, Qiaosong; Tang, Yun

2016-07-03

ASBTRACT This research aims at modeling the thermotaxis behavior of C.elegans which is a kind of nematode with full clarified neuronal connections. Firstly, this work establishes the motion model which can perform the undulatory locomotion with turning behavior. Secondly, the thermotaxis behavior is modeled by nonlinear functions and the nonlinear functions are learned by artificial neural network. Once the artificial neural networks have been well trained, they can perform the desired thermotaxis behavior. Last, several testing simulations are carried out to verify the effectiveness of the model for thermotaxis behavior. This work also analyzes the different performances of the model under different environments. The testing results reveal the essence of the thermotaxis of C.elegans to some extent, and theoretically support the research on the navigation of the crawling robots.

Daily water level forecasting using wavelet decomposition and artificial intelligence techniques

NASA Astrophysics Data System (ADS)

Seo, Youngmin; Kim, Sungwon; Kisi, Ozgur; Singh, Vijay P.

2015-01-01

Reliable water level forecasting for reservoir inflow is essential for reservoir operation. The objective of this paper is to develop and apply two hybrid models for daily water level forecasting and investigate their accuracy. These two hybrid models are wavelet-based artificial neural network (WANN) and wavelet-based adaptive neuro-fuzzy inference system (WANFIS). Wavelet decomposition is employed to decompose an input time series into approximation and detail components. The decomposed time series are used as inputs to artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for WANN and WANFIS models, respectively. Based on statistical performance indexes, the WANN and WANFIS models are found to produce better efficiency than the ANN and ANFIS models. WANFIS7-sym10 yields the best performance among all other models. It is found that wavelet decomposition improves the accuracy of ANN and ANFIS. This study evaluates the accuracy of the WANN and WANFIS models for different mother wavelets, including Daubechies, Symmlet and Coiflet wavelets. It is found that the model performance is dependent on input sets and mother wavelets, and the wavelet decomposition using mother wavelet, db10, can further improve the efficiency of ANN and ANFIS models. Results obtained from this study indicate that the conjunction of wavelet decomposition and artificial intelligence models can be a useful tool for accurate forecasting daily water level and can yield better efficiency than the conventional forecasting models.

An algebraic interpretation of PSP composition.

PubMed

Vaucher, G

1998-01-01

The introduction of time in artificial neurons is a delicate problem on which many groups are working. Our approach combines some properties of biological models and the algebraic properties of McCulloch and Pitts artificial neuron (AN) (McCulloch and Pitts, 1943) to produce a new model which links both characteristics. In this extended artificial neuron, postsynaptic potentials (PSPs) are considered as numerical elements, having two degrees of freedom, on which the neuron computes operations. Modelled in this manner, a group of neurons can be seen as a computer with an asynchronous architecture. To formalize the functioning of this computer, we propose an algebra of impulses. This approach might also be interesting in the modelling of the passive electrical properties in some biological neurons.

Artificial Boundary Conditions for Finite Element Model Update and Damage Detection

DTIC Science & Technology

2017-03-01

BOUNDARY CONDITIONS FOR FINITE ELEMENT MODEL UPDATE AND DAMAGE DETECTION by Emmanouil Damanakis March 2017 Thesis Advisor: Joshua H. Gordis...REPORT TYPE AND DATES COVERED Master’s thesis 4. TITLE AND SUBTITLE ARTIFICIAL BOUNDARY CONDITIONS FOR FINITE ELEMENT MODEL UPDATE AND DAMAGE DETECTION...release. Distribution is unlimited. 12b. DISTRIBUTION CODE 13. ABSTRACT (maximum 200 words) In structural engineering, a finite element model is often

Modelling and Predicting Backstroke Start Performance Using Non-Linear and Linear Models

PubMed Central

de Jesus, Karla; Ayala, Helon V. H.; de Jesus, Kelly; Coelho, Leandro dos S.; Medeiros, Alexandre I.A.; Abraldes, José A.; Vaz, Mário A.P.; Fernandes, Ricardo J.; Vilas-Boas, João Paulo

2018-01-01

Abstract Our aim was to compare non-linear and linear mathematical model responses for backstroke start performance prediction. Ten swimmers randomly completed eight 15 m backstroke starts with feet over the wedge, four with hands on the highest horizontal and four on the vertical handgrip. Swimmers were videotaped using a dual media camera set-up, with the starts being performed over an instrumented block with four force plates. Artificial neural networks were applied to predict 5 m start time using kinematic and kinetic variables and to determine the accuracy of the mean absolute percentage error. Artificial neural networks predicted start time more robustly than the linear model with respect to changing training to the validation dataset for the vertical handgrip (3.95 ± 1.67 vs. 5.92 ± 3.27%). Artificial neural networks obtained a smaller mean absolute percentage error than the linear model in the horizontal (0.43 ± 0.19 vs. 0.98 ± 0.19%) and vertical handgrip (0.45 ± 0.19 vs. 1.38 ± 0.30%) using all input data. The best artificial neural network validation revealed a smaller mean absolute error than the linear model for the horizontal (0.007 vs. 0.04 s) and vertical handgrip (0.01 vs. 0.03 s). Artificial neural networks should be used for backstroke 5 m start time prediction due to the quite small differences among the elite level performances. PMID:29599857

Gender Differences in Sexual Attraction and Moral Judgment: Research With Artificial Face Models.

PubMed

González-Álvarez, Julio; Cervera-Crespo, Teresa

2018-01-01

Sexual attraction in humans is influenced by cultural or moral factors, and some gender differences can emerge in this complex interaction. A previous study found that men dissociate sexual attraction from moral judgment more than women do. Two experiments consisting of giving attractiveness ratings to photos of real opposite-sex individuals showed that men, compared to women, were significantly less influenced by the moral valence of a description about the person shown in each photo. There is evidence of some processing differences between real and artificial computer-generated faces. The present study tests the robustness of González-Álvarez's findings and extends the research to an experimental design using artificial face models as stimuli. A sample of 88 young adults (61 females and 27 males, average age 19.32, SD = 2.38) rated the attractiveness of 80 3D artificial face models generated with the FaceGen Modeller 3.5 software. Each face model was paired with a "good" and a "bad" (from a moral point of view) sentence depicting a quality or activity of the person represented in the model (e.g., she/he is an altruistic nurse in Africa vs. she/he is a prominent drug dealer). Results were in line with the previous findings and showed that, with artificial faces as well, sexual attraction is less influenced by morality in men than in women. This gender difference is consistent with an evolutionary perspective on human sexuality.

Neurolinguistically constrained simulation of sentence comprehension: integrating artificial intelligence and brain theory

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

Gigley, H.M.

1982-01-01

An artificial intelligence approach to the simulation of neurolinguistically constrained processes in sentence comprehension is developed using control strategies for simulation of cooperative computation in associative networks. The desirability of this control strategy in contrast to ATN and production system strategies is explained. A first pass implementation of HOPE, an artificial intelligence simulation model of sentence comprehension, constrained by studies of aphasic performance, psycholinguistics, neurolinguistics, and linguistic theory is described. Claims that the model could serve as a basis for sentence production simulation and for a model of language acquisition as associative learning are discussed. HOPE is a model thatmore » performs in a normal state and includes a lesion simulation facility. HOPE is also a research tool. Its modifiability and use as a tool to investigate hypothesized causes of degradation in comprehension performance by aphasic patients are described. Issues of using behavioral constraints in modelling and obtaining appropriate data for simulated process modelling are discussed. Finally, problems of validation of the simulation results are raised; and issues of how to interpret clinical results to define the evolution of the model are discussed. Conclusions with respect to the feasibility of artificial intelligence simulation process modelling are discussed based on the current state of research.« less

Electrical Stimulation of Broca's Area Enhances Implicit Learning of an Artificial Grammar

ERIC Educational Resources Information Center

de Vries, Meinou H.; Barth, Andre C. R.; Maiworm, Sandra; Knecht, Stefan; Zwitserlood, Pienie; Floel, Agnes

2010-01-01

Artificial grammar learning constitutes a well-established model for the acquisition of grammatical knowledge in a natural setting. Previous neuroimaging studies demonstrated that Broca's area (left BA 44/45) is similarly activated by natural syntactic processing and artificial grammar learning. The current study was conducted to investigate the…

An artificial EMG generation model based on signal-dependent noise and related application to motion classification

PubMed Central

Hayashi, Hideaki; Nakamura, Go; Chin, Takaaki; Tsuji, Toshio

2017-01-01

This paper proposes an artificial electromyogram (EMG) signal generation model based on signal-dependent noise, which has been ignored in existing methods, by introducing the stochastic construction of the EMG signals. In the proposed model, an EMG signal variance value is first generated from a probability distribution with a shape determined by a commanded muscle force and signal-dependent noise. Artificial EMG signals are then generated from the associated Gaussian distribution with a zero mean and the generated variance. This facilitates representation of artificial EMG signals with signal-dependent noise superimposed according to the muscle activation levels. The frequency characteristics of the EMG signals are also simulated via a shaping filter with parameters determined by an autoregressive model. An estimation method to determine EMG variance distribution using rectified and smoothed EMG signals, thereby allowing model parameter estimation with a small number of samples, is also incorporated in the proposed model. Moreover, the prediction of variance distribution with strong muscle contraction from EMG signals with low muscle contraction and related artificial EMG generation are also described. The results of experiments conducted, in which the reproduction capability of the proposed model was evaluated through comparison with measured EMG signals in terms of amplitude, frequency content, and EMG distribution demonstrate that the proposed model can reproduce the features of measured EMG signals. Further, utilizing the generated EMG signals as training data for a neural network resulted in the classification of upper limb motion with a higher precision than by learning from only measured EMG signals. This indicates that the proposed model is also applicable to motion classification. PMID:28640883

Artificialized land characteristics and sediment connectivity explain muddy flood hazard in Wallonia

NASA Astrophysics Data System (ADS)

de Walque, Baptiste; Bielders, Charles; Degré, Aurore; Maugnard, Alexandre

2017-04-01

Muddy flood occurrence is an off-site erosion problem of growing interest in Europe and in particular in the loess belt and Condroz regions of Wallonia (Belgium). In order to assess the probability of occurrence of muddy floods in specific places, a muddy flood hazard prediction model has been built. It was used to test 11 different explanatory variables in simple and multiple logistic regressions approaches. A database of 442 muddy flood-affected sites and an equal number of homologous non flooded sites was used. For each site, relief, land use, sediment production and sediment connectivity of the contributing area were extracted. To assess the prediction quality of the model, we proceeded to a validation using 48 new pairs of homologous sites. Based on Akaïke Information Criterion (AIC), we determined that the best muddy flood hazard assessment model requires a total of 6 explanatory variable as inputs: the spatial aggregation of the artificialized land, the sediment connectivity, the artificialized land proximity to the outlet, the proportion of artificialized land, the mean slope and the Gravelius index of compactness of the contributive area. The artificialized land properties listed above showed to improve substantially the model quality (p-values from 10e-10 to 10e-4). All of the 3 properties showed negative correlation with the muddy flood hazard. These results highlight the importance of considering the artificialized land characteristics in the sediment transport assessment models. Indeed, artificialized land such as roads may dramatically deviate flows and influence the connectivity in the landscape. Besides the artificialized land properties, the sediment connectivity showed significant explanatory power (p-value of 10e-11). A positive correlation between the sediment connectivity and the muddy flood hazard was found, ranging from 0.3 to 0.45 depending on the sediment connectivity index. Several studies already have highlighted the importance of this parameter in the sediment transport characterization in the landscape. Using the best muddy flood probability of occurrence threshold value of 0.49, the validation of the best multiple logistic regression resulted in a prediction quality of 75.6% (original dataset) and 81.2% (secondary dataset). The developed statistical model could be used as a reliable tool to target muddy floods mitigation measures in sites resulting with the highest muddy floods hazard.

[An experimental study on the therapeutic effects of eustachian tube surfactant in barotitis media].

PubMed

Feng, Lining; Chen, Wenxian; Cong, Rui; Zheng, Guoxi; Gou, Lin; Guo, Qun

2002-11-01

To observe the effect of surfactant on eustachian tube (ET) on the opening of ET as well as it's therapeutic role in barotitis media (BM). 50 guinea pigs were successfully established as BM models by stimulated ascending in altitude chamber. Parts of the models were treated with by middle ear flushing with nature ETS, artificial ETS, artificial phospholipid and saline, after which the eustachian tube pressure opening level (POL) of each group was tested. Others were injected with 1 ml artificial ETS in on side of the middle ear, and 1 ml of saline in the other served as control. Natural ETS decreased the POL from 11.98 to 6.11 kPa (P < 0.01); Artificial ETS reduced the POL from 11.91 to 6.67 kPa (P < 0.01), there were no significant differences between the two groups. Artificial phospholipid decreased the POL from 11.86 to 8.61 kPa (P < 0.05), which was not as effective as natural ETS. While the POL of saline group remained unchanged. After one week of artificial ETS treatment, the congestion in drum membrane alleviated, the hearing threshold of ETS group improved and the effusion in tympanic cavity lessened. The results suggest that artificial ETS is as effective as nature ETS to facilitates the opening of eustachian tube. Artificial ETS may exert therapeutic effects on BM.

Introducing Artificial Neural Networks through a Spreadsheet Model

ERIC Educational Resources Information Center

Rienzo, Thomas F.; Athappilly, Kuriakose K.

2012-01-01

Business students taking data mining classes are often introduced to artificial neural networks (ANN) through point and click navigation exercises in application software. Even if correct outcomes are obtained, students frequently do not obtain a thorough understanding of ANN processes. This spreadsheet model was created to illuminate the roles of…

Artificial Intelligence and the High School Computer Curriculum.

ERIC Educational Resources Information Center

Dillon, Richard W.

1993-01-01

Describes a four-part curriculum that can serve as a model for incorporating artificial intelligence (AI) into the high school computer curriculum. The model includes examining questions fundamental to AI, creating and designing an expert system, language processing, and creating programs that integrate machine vision with robotics and…

Psychometric Measurement Models and Artificial Neural Networks

ERIC Educational Resources Information Center

Sese, Albert; Palmer, Alfonso L.; Montano, Juan J.

2004-01-01

The study of measurement models in psychometrics by means of dimensionality reduction techniques such as Principal Components Analysis (PCA) is a very common practice. In recent times, an upsurge of interest in the study of artificial neural networks apt to computing a principal component extraction has been observed. Despite this interest, the…

Using chaotic artificial neural networks to model memory in the brain

NASA Astrophysics Data System (ADS)

Aram, Zainab; Jafari, Sajad; Ma, Jun; Sprott, Julien C.; Zendehrouh, Sareh; Pham, Viet-Thanh

2017-03-01

In the current study, a novel model for human memory is proposed based on the chaotic dynamics of artificial neural networks. This new model explains a biological fact about memory which is not yet explained by any other model: There are theories that the brain normally works in a chaotic mode, while during attention it shows ordered behavior. This model uses the periodic windows observed in a previously proposed model for the brain to store and then recollect the information.

Effects of Menthol-Containing Artificial Tears on Tear Stimulation and Ocular Surface Integrity in Normal and Dry Eye Rat Models.

PubMed

Ahn, Somin; Eom, Youngsub; Kang, Boram; Park, Jungboung; Lee, Hyung Keun; Kim, Hyo Myung; Song, Jong Suk

2018-05-01

To evaluate the effects of menthol-containing artificial tears on tear stimulation and ocular surface integrity in normal and dry eye rat models. A total of 54 male Lewis rats were used. The levels of tear secretion and tear MUC5AC concentrations were compared between the menthol-containing artificial tear-treated group (menthol group) and the vehicle-treated group (vehicle group). The groups were compared after a single instillation to evaluate the immediate effects, and after repeated instillation (five times a day for 5 days) to evaluate the longer-term effects. Tear lactate dehydrogenase (LDH) activity was measured to evaluate eye drop instillation-induced ocular surface damage. The effects of menthol-containing artificial tears were also evaluated in a dry eye rat model. After a single instillation of menthol-containing artificial tears, tear secretion increased from 4.37 (±0.75) mm at baseline to 7.37 (±1.60) mm. However, after repeated instillations, the effects of tear stimulation decreased. The tear MUC5AC concentration was significantly lower in the menthol group than in the vehicle group after a single instillation, but not after repeated instillation. However, the tear LDH concentration was significantly increased in the menthol group after repeated instillation. In the dry eye rat model, the extent of menthol-induced tear stimulation was reduced. Menthol-containing artificial tears increased tear secretion, but lowered the tear MUC5AC concentration. Menthol-induced tear stimulation was reduced after repeated instillation for 5 days and in the dry eye rat model. Conversely, repeated instillation of menthol-induced ocular surface damage, resulting in increased tear LDH activity.

Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction.

PubMed

Park, Seong Ho; Han, Kyunghwa

2018-03-01

The use of artificial intelligence in medicine is currently an issue of great interest, especially with regard to the diagnostic or predictive analysis of medical images. Adoption of an artificial intelligence tool in clinical practice requires careful confirmation of its clinical utility. Herein, the authors explain key methodology points involved in a clinical evaluation of artificial intelligence technology for use in medicine, especially high-dimensional or overparameterized diagnostic or predictive models in which artificial deep neural networks are used, mainly from the standpoints of clinical epidemiology and biostatistics. First, statistical methods for assessing the discrimination and calibration performances of a diagnostic or predictive model are summarized. Next, the effects of disease manifestation spectrum and disease prevalence on the performance results are explained, followed by a discussion of the difference between evaluating the performance with use of internal and external datasets, the importance of using an adequate external dataset obtained from a well-defined clinical cohort to avoid overestimating the clinical performance as a result of overfitting in high-dimensional or overparameterized classification model and spectrum bias, and the essentials for achieving a more robust clinical evaluation. Finally, the authors review the role of clinical trials and observational outcome studies for ultimate clinical verification of diagnostic or predictive artificial intelligence tools through patient outcomes, beyond performance metrics, and how to design such studies. © RSNA, 2018.

Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing.

PubMed

Kriegeskorte, Nikolaus

2015-11-24

Recent advances in neural network modeling have enabled major strides in computer vision and other artificial intelligence applications. Human-level visual recognition abilities are coming within reach of artificial systems. Artificial neural networks are inspired by the brain, and their computations could be implemented in biological neurons. Convolutional feedforward networks, which now dominate computer vision, take further inspiration from the architecture of the primate visual hierarchy. However, the current models are designed with engineering goals, not to model brain computations. Nevertheless, initial studies comparing internal representations between these models and primate brains find surprisingly similar representational spaces. With human-level performance no longer out of reach, we are entering an exciting new era, in which we will be able to build biologically faithful feedforward and recurrent computational models of how biological brains perform high-level feats of intelligence, including vision.

Artificial intelligence may help in predicting the need for additional surgery after endoscopic resection of T1 colorectal cancer.

PubMed

Ichimasa, Katsuro; Kudo, Shin-Ei; Mori, Yuichi; Misawa, Masashi; Matsudaira, Shingo; Kouyama, Yuta; Baba, Toshiyuki; Hidaka, Eiji; Wakamura, Kunihiko; Hayashi, Takemasa; Kudo, Toyoki; Ishigaki, Tomoyuki; Yagawa, Yusuke; Nakamura, Hiroki; Takeda, Kenichi; Haji, Amyn; Hamatani, Shigeharu; Mori, Kensaku; Ishida, Fumio; Miyachi, Hideyuki

2018-03-01

 Decisions concerning additional surgery after endoscopic resection of T1 colorectal cancer (CRC) are difficult because preoperative prediction of lymph node metastasis (LNM) is problematic. We investigated whether artificial intelligence can predict LNM presence, thus minimizing the need for additional surgery.  Data on 690 consecutive patients with T1 CRCs that were surgically resected in 2001 - 2016 were retrospectively analyzed. We divided patients into two groups according to date: data from 590 patients were used for machine learning for the artificial intelligence model, and the remaining 100 patients were included for model validation. The artificial intelligence model analyzed 45 clinicopathological factors and then predicted positivity or negativity for LNM. Operative specimens were used as the gold standard for the presence of LNM. The artificial intelligence model was validated by calculating the sensitivity, specificity, and accuracy for predicting LNM, and comparing these data with those of the American, European, and Japanese guidelines.  Sensitivity was 100 % (95 % confidence interval [CI] 72 % to 100 %) in all models. Specificity of the artificial intelligence model and the American, European, and Japanese guidelines was 66 % (95 %CI 56 % to 76 %), 44 % (95 %CI 34 % to 55 %), 0 % (95 %CI 0 % to 3 %), and 0 % (95 %CI 0 % to 3 %), respectively; and accuracy was 69 % (95 %CI 59 % to 78 %), 49 % (95 %CI 39 % to 59 %), 9 % (95 %CI 4 % to 16 %), and 9 % (95 %CI 4 % - 16 %), respectively. The rates of unnecessary additional surgery attributable to misdiagnosing LNM-negative patients as having LNM were: 77 % (95 %CI 62 % to 89 %) for the artificial intelligence model, and 85 % (95 %CI 73 % to 93 %; P  < 0.001), 91 % (95 %CI 84 % to 96 %; P  < 0.001), and 91 % (95 %CI 84 % to 96 %; P  < 0.001) for the American, European, and Japanese guidelines, respectively.  Compared with current guidelines, artificial intelligence significantly reduced unnecessary additional surgery after endoscopic resection of T1 CRC without missing LNM positivity. © Georg Thieme Verlag KG Stuttgart · New York.

Modeling and Evaluating Emotions Impact on Cognition

DTIC Science & Technology

2013-07-01

Causality and Responsibility Judgment in Multi-Agent Interactions: Extended abstract. 23rd International Joint Conference on Artificial Inteligence ...responsibility judgment in multi-agent interactions." Journal of Artificial Intelligence Research v44(1), 223- 273. • Morteza Dehghani, Jonathan Gratch... Artificial Intelligence (AAAI’11). Grant related invited talks: • Keynote speaker, Workshop on Empathic and Emotional Agents at the International

Case-Based Planning: An Integrated Theory of Planning, Learning and Memory

DTIC Science & Technology

1986-10-01

rtvoeoo oldo II nocomtmry and Idonltly by block numbor) planning Case-based reasoning learning Artificial Intelligence 20. ABSTRACT (Conllnum...Computational Model of Analogical Prob- lem Solving, Proceedings of the Seventh International Joint Conference on Artificial Intelligence ...Understanding and Generalizing Plans., Proceedings of the Eight Interna- tional Joint Conference on Artificial Intelligence , IJCAI, Karlsrhue, Germany

Cultural Modelling: Literature review

DTIC Science & Technology

2006-09-01

of mood and/or emotions. Our review did show some evidence that artificial intelligence research has tended to depict human decision making as...pp. 72-79). The Society for the Study of Artificial Intelligence and the Simulation of Behaviour (AISB). Halfill, T., Sundstrom, E., Nielsen, T. M...M. & Thagard, P. (2005). Changing personalities: Towards realistic virtual characters. Journal of Experimental & Theoretical Artificial Intelligence

Organization-based Model-driven Development of High-assurance Multiagent Systems

DTIC Science & Technology

2009-02-27

based Model -driven Development of High-assurance Multiagent Systems " performed by Dr. Scott A . DeLoach and Dr Robby at Kansas State University... A Capabilities Based Model for Artificial Organizations. Journal of Autonomous Agents and Multiagent Systems . Volume 16, no. 1, February 2008, pp...Matson, E . T. (2007). A capabilities based theory of artificial organizations. Journal of Autonomous Agents and Multiagent Systems

A CFBPN Artificial Neural Network Model for Educational Qualitative Data Analyses: Example of Students' Attitudes Based on Kellerts' Typologies

ERIC Educational Resources Information Center

Yorek, Nurettin; Ugulu, Ilker

2015-01-01

In this study, artificial neural networks are suggested as a model that can be "trained" to yield qualitative results out of a huge amount of categorical data. It can be said that this is a new approach applied in educational qualitative data analysis. In this direction, a cascade-forward back-propagation neural network (CFBPN) model was…

Building a Decision Support System for Inpatient Admission Prediction With the Manchester Triage System and Administrative Check-in Variables.

PubMed

Zlotnik, Alexander; Alfaro, Miguel Cuchí; Pérez, María Carmen Pérez; Gallardo-Antolín, Ascensión; Martínez, Juan Manuel Montero

2016-05-01

The usage of decision support tools in emergency departments, based on predictive models, capable of estimating the probability of admission for patients in the emergency department may give nursing staff the possibility of allocating resources in advance. We present a methodology for developing and building one such system for a large specialized care hospital using a logistic regression and an artificial neural network model using nine routinely collected variables available right at the end of the triage process.A database of 255.668 triaged nonobstetric emergency department presentations from the Ramon y Cajal University Hospital of Madrid, from January 2011 to December 2012, was used to develop and test the models, with 66% of the data used for derivation and 34% for validation, with an ordered nonrandom partition. On the validation dataset areas under the receiver operating characteristic curve were 0.8568 (95% confidence interval, 0.8508-0.8583) for the logistic regression model and 0.8575 (95% confidence interval, 0.8540-0. 8610) for the artificial neural network model. χ Values for Hosmer-Lemeshow fixed "deciles of risk" were 65.32 for the logistic regression model and 17.28 for the artificial neural network model. A nomogram was generated upon the logistic regression model and an automated software decision support system with a Web interface was built based on the artificial neural network model.

Log-Linear Model Based Behavior Selection Method for Artificial Fish Swarm Algorithm

PubMed Central

Huang, Zhehuang; Chen, Yidong

2015-01-01

Artificial fish swarm algorithm (AFSA) is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm. PMID:25691895

A self-taught artificial agent for multi-physics computational model personalization.

PubMed

Neumann, Dominik; Mansi, Tommaso; Itu, Lucian; Georgescu, Bogdan; Kayvanpour, Elham; Sedaghat-Hamedani, Farbod; Amr, Ali; Haas, Jan; Katus, Hugo; Meder, Benjamin; Steidl, Stefan; Hornegger, Joachim; Comaniciu, Dorin

2016-12-01

Personalization is the process of fitting a model to patient data, a critical step towards application of multi-physics computational models in clinical practice. Designing robust personalization algorithms is often a tedious, time-consuming, model- and data-specific process. We propose to use artificial intelligence concepts to learn this task, inspired by how human experts manually perform it. The problem is reformulated in terms of reinforcement learning. In an off-line phase, Vito, our self-taught artificial agent, learns a representative decision process model through exploration of the computational model: it learns how the model behaves under change of parameters. The agent then automatically learns an optimal strategy for on-line personalization. The algorithm is model-independent; applying it to a new model requires only adjusting few hyper-parameters of the agent and defining the observations to match. The full knowledge of the model itself is not required. Vito was tested in a synthetic scenario, showing that it could learn how to optimize cost functions generically. Then Vito was applied to the inverse problem of cardiac electrophysiology and the personalization of a whole-body circulation model. The obtained results suggested that Vito could achieve equivalent, if not better goodness of fit than standard methods, while being more robust (up to 11% higher success rates) and with faster (up to seven times) convergence rate. Our artificial intelligence approach could thus make personalization algorithms generalizable and self-adaptable to any patient and any model. Copyright © 2016. Published by Elsevier B.V.

Optimization of an artificial-recharge-pumping system for water supply in the Maghaway Valley, Cebu, Philippines

NASA Astrophysics Data System (ADS)

Kawo, Nafyad Serre; Zhou, Yangxiao; Magalso, Ronnell; Salvacion, Lasaro

2018-05-01

A coupled simulation-optimization approach to optimize an artificial-recharge-pumping system for the water supply in the Maghaway Valley, Cebu, Philippines, is presented. The objective is to maximize the total pumping rate through a system of artificial recharge and pumping while meeting constraints such as groundwater-level drawdown and bounds on pumping rates at each well. The simulation models were coupled with groundwater management optimization to maximize production rates. Under steady-state natural conditions, the significant inflow to the aquifer comes from river leakage, whereas the natural discharge is mainly the subsurface outflow to the downstream area. Results from the steady artificial-recharge-pumping simulation model show that artificial recharge is about 20,587 m3/day and accounts for 77% of total inflow. Under transient artificial-recharge-pumping conditions, artificial recharge varies between 14,000 and 20,000 m3/day depending on the wet and dry seasons, respectively. The steady-state optimisation results show that the total optimal abstraction rate is 37,545 m3/day and artificial recharge is increased to 29,313 m3/day. The transient optimization results show that the average total optimal pumping rate is 36,969 m3/day for the current weir height. The transient optimization results for an increase in weir height by 1 and 2 m show that the average total optimal pumping rates are increased to 38,768 and 40,463 m3/day, respectively. It is concluded that the increase in the height of the weir can significantly increase the artificial recharge rate and production rate in Maghaway Valley.

Application of artificial neural networks in hydrological modeling: A case study of runoff simulation of a Himalayan glacier basin

NASA Technical Reports Server (NTRS)

Buch, A. M.; Narain, A.; Pandey, P. C.

1994-01-01

The simulation of runoff from a Himalayan Glacier basin using an Artificial Neural Network (ANN) is presented. The performance of the ANN model is found to be superior to the Energy Balance Model and the Multiple Regression model. The RMS Error is used as the figure of merit for judging the performance of the three models, and the RMS Error for the ANN model is the latest of the three models. The ANN is faster in learning and exhibits excellent system generalization characteristics.

A comparative study between nonlinear regression and artificial neural network approaches for modelling wild oat (Avena fatua) field emergence

USDA-ARS?s Scientific Manuscript database

Non-linear regression techniques are used widely to fit weed field emergence patterns to soil microclimatic indices using S-type functions. Artificial neural networks present interesting and alternative features for such modeling purposes. In this work, a univariate hydrothermal-time based Weibull m...

Numerical simulation of artificial hip joint motion based on human age factor

NASA Astrophysics Data System (ADS)

Ramdhani, Safarudin; Saputra, Eko; Jamari, J.

2018-05-01

Artificial hip joint is a prosthesis (synthetic body part) which usually consists of two or more components. Replacement of the hip joint due to the occurrence of arthritis, ordinarily patients aged or older. Numerical simulation models are used to observe the range of motion in the artificial hip joint, the range of motion of joints used as the basis of human age. Finite- element analysis (FEA) is used to calculate stress von mises in motion and observes a probability of prosthetic impingement. FEA uses a three-dimensional nonlinear model and considers the position variation of acetabular liner cups. The result of numerical simulation shows that FEA method can be used to analyze the performance calculation of the artificial hip joint at this time more accurate than conventional method.

Anatomy of biologically mediated opal speleothems in the World's largest sandstone cave: Cueva Charles Brewer, Chimantá Plateau, Venezuela

NASA Astrophysics Data System (ADS)

Aubrecht, R.; Brewer-Carías, Ch.; Šmída, B.; Audy, M.; Kováčik, Ľ.

2008-01-01

Siliceous speleothems can be formed in sandstone caves. Recently, opal "biospeleothems" have been found in the World's largest cave in Precambrian sandstones on the Chimantá Tepui in Venezuela. The speleothems, although reminiscent of normal stalactites and stalagmites from limestone caves, are in fact large microbialites. More than a dozen forms were distinguished, but they share a common structure and origin. They consist of two main types: 1. fine-laminated columnar stromatolite formed by silicified filamentous microbes (either heterotrophic filamentous bacteria or cyanobacteria) and 2. a porous peloidal stromatolite formed by Nostoc-type cyanobacteria. The first type usually forms the central part and the second type, the outer part, of speleothems. Fungal hyphae, metazoan and plant remains also subordinately contribute to speleothem construction. The speleothems occur out of the reach of flowing water; the main source of silica is the condensed cave moisture which is the main dissolution-reprecipitation agent. Speleothems which originated by encrustation of spider threads are unique.

Chemotrophic Microbial Mats and Their Potential for Preservation in the Rock Record

NASA Astrophysics Data System (ADS)

Bailey, Jake V.; Orphan, Victoria J.; Joye, Samantha B.; Corsetti, Frank A.

2009-11-01

Putative microbialites are commonly regarded to have formed in association with photosynthetic microorganisms, such as cyanobacteria. However, many modern microbial mat ecosystems are dominated by chemotrophic bacteria and archaea. Like phototrophs, filamentous sulfur-oxidizing bacteria form large mats at the sediment/water interface that can act to stabilize sediments, and their metabolic activities may mediate the formation of marine phosphorites. Similarly, bacteria and archaea associated with the anaerobic oxidation of methane (AOM) catalyze the precipitation of seafloor authigenic carbonates. When preserved, lipid biomarkers, isotopic signatures, body fossils, and lithological indicators of the local depositional environment may be used to identify chemotrophic mats in the rock record. The recognition of chemotrophic communities in the rock record has the potential to transform our understanding of ancient microbial ecologies, evolution, and geochemical conditions. Chemotrophic microbes on Earth occupy naturally occurring interfaces between oxidized and reduced chemical species and thus may provide a new set of search criteria to target life-detection efforts on other planets.

Collapse susceptibility mapping in karstified gypsum terrain (Sivas basin - Turkey) by conditional probability, logistic regression, artificial neural network models

NASA Astrophysics Data System (ADS)

Yilmaz, Isik; Keskin, Inan; Marschalko, Marian; Bednarik, Martin

2010-05-01

This study compares the GIS based collapse susceptibility mapping methods such as; conditional probability (CP), logistic regression (LR) and artificial neural networks (ANN) applied in gypsum rock masses in Sivas basin (Turkey). Digital Elevation Model (DEM) was first constructed using GIS software. Collapse-related factors, directly or indirectly related to the causes of collapse occurrence, such as distance from faults, slope angle and aspect, topographical elevation, distance from drainage, topographic wetness index- TWI, stream power index- SPI, Normalized Difference Vegetation Index (NDVI) by means of vegetation cover, distance from roads and settlements were used in the collapse susceptibility analyses. In the last stage of the analyses, collapse susceptibility maps were produced from CP, LR and ANN models, and they were then compared by means of their validations. Area Under Curve (AUC) values obtained from all three methodologies showed that the map obtained from ANN model looks like more accurate than the other models, and the results also showed that the artificial neural networks is a usefull tool in preparation of collapse susceptibility map and highly compatible with GIS operating features. Key words: Collapse; doline; susceptibility map; gypsum; GIS; conditional probability; logistic regression; artificial neural networks.

Artificial neural network modelling of a large-scale wastewater treatment plant operation.

PubMed

Güçlü, Dünyamin; Dursun, Sükrü

2010-11-01

Artificial Neural Networks (ANNs), a method of artificial intelligence method, provide effective predictive models for complex processes. Three independent ANN models trained with back-propagation algorithm were developed to predict effluent chemical oxygen demand (COD), suspended solids (SS) and aeration tank mixed liquor suspended solids (MLSS) concentrations of the Ankara central wastewater treatment plant. The appropriate architecture of ANN models was determined through several steps of training and testing of the models. ANN models yielded satisfactory predictions. Results of the root mean square error, mean absolute error and mean absolute percentage error were 3.23, 2.41 mg/L and 5.03% for COD; 1.59, 1.21 mg/L and 17.10% for SS; 52.51, 44.91 mg/L and 3.77% for MLSS, respectively, indicating that the developed model could be efficiently used. The results overall also confirm that ANN modelling approach may have a great implementation potential for simulation, precise performance prediction and process control of wastewater treatment plants.

Modeling rainfall-runoff process using soft computing techniques

NASA Astrophysics Data System (ADS)

Kisi, Ozgur; Shiri, Jalal; Tombul, Mustafa

2013-02-01

Rainfall-runoff process was modeled for a small catchment in Turkey, using 4 years (1987-1991) of measurements of independent variables of rainfall and runoff values. The models used in the study were Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Gene Expression Programming (GEP) which are Artificial Intelligence (AI) approaches. The applied models were trained and tested using various combinations of the independent variables. The goodness of fit for the model was evaluated in terms of the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), coefficient of efficiency (CE) and scatter index (SI). A comparison was also made between these models and traditional Multi Linear Regression (MLR) model. The study provides evidence that GEP (with RMSE=17.82 l/s, MAE=6.61 l/s, CE=0.72 and R2=0.978) is capable of modeling rainfall-runoff process and is a viable alternative to other applied artificial intelligence and MLR time-series methods.

Artificial neural network cardiopulmonary modeling and diagnosis

DOEpatents

Kangas, L.J.; Keller, P.E.

1997-10-28

The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis. 12 figs.

Artificial neural network cardiopulmonary modeling and diagnosis

DOEpatents

Kangas, Lars J.; Keller, Paul E.

1997-01-01

The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis.

Artificial organisms as tools for the development of psychological theory: Tolman's lesson.

PubMed

Miglino, Orazio; Gigliotta, Onofrio; Cardaci, Maurizio; Ponticorvo, Michela

2007-12-01

In the 1930s and 1940s, Edward Tolman developed a psychological theory of spatial orientation in rats and humans. He expressed his theory as an automaton (the "schematic sowbug") or what today we would call an "artificial organism." With the technology of the day, he could not implement his model. Nonetheless, he used it to develop empirical predictions which tested with animals in the laboratory. This way of proceeding was in line with scientific practice dating back to Galileo. The way psychologists use artificial organisms in their work today breaks with this tradition. Modern "artificial organisms" are constructed a posteriori, working from experimental or ethological observations. As a result, researchers can use them to confirm a theoretical model or to simulate its operation. But they make no contribution to the actual building of models. In this paper, we try to return to Tolman's original strategy: implementing his theory of "vicarious trial and error" in a simulated robot, forecasting the robot's behavior and conducting experiments that verify or falsify these predictions.

Modeling of dielectric elastomer oscillators for soft biomimetic applications.

PubMed

Henke, E-F M; Wilson, Katherine E; Anderson, I A

2018-06-26

Biomimetic, entirely soft robots with animal-like behavior and integrated artificial nervous systems will open up totally new perspectives and applications. However, until now, most presented studies on soft robots were limited to only partly soft designs, since all solutions at least needed conventional, stiff electronics to sense, process signals and activate actuators. We present a novel approach for a set up and the experimental validation of an artificial pace maker that is able to drive basic robotic structures and act as artificial central pattern generator. The structure is based on multi-functional dielectric elastomers (DEs). DE actuators, DE switches and DE resistors are combined to create complex DE oscillators (DEOs). Supplied with only one external DC voltage, the DEO autonomously generates oscillating signals that can be used to clock a robotic structure, control the cyclic motion of artificial muscles in bionic robots or make a whole robotic structure move. We present the basic functionality, derive a mathematical model for predicting the generated signal waveform and verify the model experimentally.

Permeability study of cancellous bone and its idealised structures.

PubMed

Syahrom, Ardiyansyah; Abdul Kadir, Mohammed Rafiq; Harun, Muhamad Nor; Öchsner, Andreas

2015-01-01

Artificial bone is a suitable alternative to autografts and allografts, however their use is still limited. Though there were numerous reports on their structural properties, permeability studies of artificial bones were comparably scarce. This study focused on the development of idealised, structured models of artificial cancellous bone and compared their permeability values with bone surface area and porosity. Cancellous bones from fresh bovine femur were extracted and cleaned following an established protocol. The samples were scanned using micro-computed tomography (μCT) and three-dimensional models of the cancellous bones were reconstructed for morphology study. Seven idealised and structured cancellous bone models were then developed and fabricated via rapid prototyping technique. A test-rig was developed and permeability tests were performed on the artificial and real cancellous bones. The results showed a linear correlation between the permeability and the porosity as well as the bone surface area. The plate-like idealised structure showed a similar value of permeability to the real cancellous bones. Copyright © 2014 IPEM. Published by Elsevier Ltd. All rights reserved.

Could Intelligent Tutors Anticipate Successfully User Reactions?

NASA Astrophysics Data System (ADS)

Kalisz, Eugenia; Florea, Adina Magda

2006-06-01

Emotions have been shown to have an important impact on several human processes such as decision-making, planning, cognition, and learning. In an e-learning system, an artificial tutor capable of effectively understanding and anticipating the student emotions during learning will have a significantly enhanced role. The paper presents a model of an artificial tutor endowed with synthesized emotions according to the BDE model, previously developed by the authors. It also analyzes possible student reactions while interacting with the learning material and the way the artificial tutor could anticipate and should respond to these reactions, with adequate actions.

Computational evolution: taking liberties.

PubMed

Correia, Luís

2010-09-01

Evolution has, for a long time, inspired computer scientists to produce computer models mimicking its behavior. Evolutionary algorithm (EA) is one of the areas where this approach has flourished. EAs have been used to model and study evolution, but they have been especially developed for their aptitude as optimization tools for engineering. Developed models are quite simple in comparison with their natural sources of inspiration. However, since EAs run on computers, we have the freedom, especially in optimization models, to test approaches both realistic and outright speculative, from the biological point of view. In this article, we discuss different common evolutionary algorithm models, and then present some alternatives of interest. These include biologically inspired models, such as co-evolution and, in particular, symbiogenetics and outright artificial operators and representations. In each case, the advantages of the modifications to the standard model are identified. The other area of computational evolution, which has allowed us to study basic principles of evolution and ecology dynamics, is the development of artificial life platforms for open-ended evolution of artificial organisms. With these platforms, biologists can test theories by directly manipulating individuals and operators, observing the resulting effects in a realistic way. An overview of the most prominent of such environments is also presented. If instead of artificial platforms we use the real world for evolving artificial life, then we are dealing with evolutionary robotics (ERs). A brief description of this area is presented, analyzing its relations to biology. Finally, we present the conclusions and identify future research avenues in the frontier of computation and biology. Hopefully, this will help to draw the attention of more biologists and computer scientists to the benefits of such interdisciplinary research.

A Cyber Situational Awareness Model for Network Administrators

DTIC Science & Technology

2017-03-01

environments, the Internet of Things, artificial intelligence , and so on. As users’ data requirements grow more complex, they demand information...security of systems of interest. Further, artificial intelligence is a powerful concept in information technology. Therefore, new research should...look into how to use artificial intelligence to develop CSA. Human interaction with cyber systems is not making networks and their components safer

Computing Visible-Surface Representations,

DTIC Science & Technology

1985-03-01

Terzopoulos N00014-75-C-0643 9. PERFORMING ORGANIZATION NAME AMC ADDRESS 10. PROGRAM ELEMENT. PROJECT, TASK Artificial Inteligence Laboratory AREA A...Massachusetts Institute of lechnolog,. Support lbr the laboratory’s Artificial Intelligence research is provided in part by the Advanced Rtccarcl Proj...dynamically maintaining visible surface representations. Whether the intention is to model human vision or to design competent artificial vision systems

The deconvolution of complex spectra by artificial immune system

NASA Astrophysics Data System (ADS)

Galiakhmetova, D. I.; Sibgatullin, M. E.; Galimullin, D. Z.; Kamalova, D. I.

2017-11-01

An application of the artificial immune system method for decomposition of complex spectra is presented. The results of decomposition of the model contour consisting of three components, Gaussian contours, are demonstrated. The method of artificial immune system is an optimization method, which is based on the behaviour of the immune system and refers to modern methods of search for the engine optimization.

A Memory-Process Model of Symbolic Assimilation

DTIC Science & Technology

1974-04-01

Systems: Final Report of a Study Group, published for Artificial Intellegence by North-Holland/Amorican...contribution of the methods is answered by evaluating the same program in the context of the field of artificial intelligence. The remainder of the...been widely demonstrated on a diversity of tasks in tha history of artificial intelligence. See [r.71], chapter 2. Given a particular task to be

Computational Modeling of Cultural Dimensions in Adversary Organizations

DTIC Science & Technology

2010-01-01

Nodes”, In the Proceedings of the 9th Conference on Uncertainty in Artificial Intelli - gence, 1993. [8] Pearl, J. Probabilistic Reasoning in...the artificial life simulations; in con- trast, models with only a few agents typically employ quite sophisticated cognitive agents capa- ble of...Model Construction 45 cisions as to how to allocate scarce ISR assets (two Unmanned Air Systems, UAS ) among the two Red activities while at the same

Application of Artificial Neural Network to Optical Fluid Analyzer

NASA Astrophysics Data System (ADS)

Kimura, Makoto; Nishida, Katsuhiko

1994-04-01

A three-layer artificial neural network has been applied to the presentation of optical fluid analyzer (OFA) raw data, and the accuracy of oil fraction determination has been significantly improved compared to previous approaches. To apply the artificial neural network approach to solving a problem, the first step is training to determine the appropriate weight set for calculating the target values. This involves using a series of data sets (each comprising a set of input values and an associated set of output values that the artificial neural network is required to determine) to tune artificial neural network weighting parameters so that the output of the neural network to the given set of input values is as close as possible to the required output. The physical model used to generate the series of learning data sets was the effective flow stream model, developed for OFA data presentation. The effectiveness of the training was verified by reprocessing the same input data as were used to determine the weighting parameters and then by comparing the results of the artificial neural network to the expected output values. The standard deviation of the expected and obtained values was approximately 10% (two sigma).

The use of artificially intelligent agents with bounded rationality in the study of economic markets

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

Rajan, V.; Slagle, J.R.

The concepts of {open_quote}knowledge{close_quote} and {open_quote}rationality{close_quote} are of central importance to fields of science that are interested in human behavior and learning, such as artificial intelligence, economics, and psychology. The similarity between artificial intelligence and economics - both are concerned with intelligent thought, rational behavior, and the use and acquisition of knowledge - has led to the use of economic models as a paradigm for solving problems in distributed artificial intelligence (DAI) and multi agent systems (MAS). What we propose is the opposite; the use of artificial intelligence in the study of economic markets. Over the centuries various theories ofmore » market behavior have been advanced. The prevailing theory holds that an asset`s current price converges to the risk adjusted value of the rationally expected dividend stream. While this rational expectations model holds in equilibrium or near-equilibrium conditions, it does not sufficiently explain conditions of market disequilibrium. An example of market disequilibrium is the phenomenon of a speculative bubble. We present an example of using artificially intelligent agents with bounded rationality in the study of speculative bubbles.« less

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,…

The autonomy of biological individuals and artificial models.

PubMed

Moreno, Alvaro; Etxeberria, Arantza; Umerez, Jon

2008-02-01

This paper aims to offer an overview of the meaning of autonomy for biological individuals and artificial models rooted in a specific perspective that pays attention to the historical and structural aspects of its origins and evolution. Taking autopoiesis and the recursivity characteristic of its circular logic as a starting point, we depart from some of its consequences to claim that the theory of autonomy should also take into account historical and structural features. Autonomy should not be considered only in internal or constitutive terms, the largely neglected interactive aspects stemming from it should be equally addressed. Artificial models contribute to get a better understanding of the role of autonomy for life and the varieties of its organization and phenomenological diversity.

Risk prediction model: Statistical and artificial neural network approach

NASA Astrophysics Data System (ADS)

Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim

2017-04-01

Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.

Doubly stochastic Poisson processes in artificial neural learning.

PubMed

Card, H C

1998-01-01

This paper investigates neuron activation statistics in artificial neural networks employing stochastic arithmetic. It is shown that a doubly stochastic Poisson process is an appropriate model for the signals in these circuits.

Mathematical modeling relevant to closed artificial ecosystems

USGS Publications Warehouse

DeAngelis, D.L.

2003-01-01

The mathematical modeling of ecosystems has contributed much to the understanding of the dynamics of such systems. Ecosystems can include not only the natural variety, but also artificial systems designed and controlled by humans. These can range from agricultural systems and activated sludge plants, down to mesocosms, microcosms, and aquaria, which may have practical or research applications. Some purposes may require the design of systems that are completely closed, as far as material cycling is concerned. In all cases, mathematical modeling can help not only to understand the dynamics of the system, but also to design methods of control to keep the system operating in desired ranges. This paper reviews mathematical modeling relevant to the simulation and control of closed or semi-closed artificial ecosystems designed for biological production and recycling in applications in space. Published by Elsevier Science Ltd on behalf of COSPAR.

Comparison of self-efficacy and its improvement after artificial simulator or live animal model emergency procedure training.

PubMed

Hall, Andrew B; Riojas, Ramon; Sharon, Danny

2014-03-01

The objective of this study is to compare post-training self-efficacy between artificial simulators and live animal training for the performance of emergency medical procedures. Volunteer airmen of the 81st Medical Group, without prior medical procedure training, were randomly assigned to two experimental arms consisting of identical lectures and training of diagnostic peritoneal lavage, thoracostomy (chest tube), and cricothyroidotomy on either the TraumaMan (Simulab Corp., Seattle, Washington) artificial simulator or a live pig (Sus scrofa domestica) model. Volunteers were given a postlecture and postskills training assessment of self-efficacy. Twenty-seven volunteers that initially performed artificial simulator training subsequently underwent live animal training and provided assessments comparing both modalities. The results were first, postskills training self-efficacy scores were significantly higher than postlecture scores for either training mode and for all procedures (p < 0.0001). Second, post-training self-efficacy scores were not statistically different between live animal and artificial simulator training for diagnostic peritoneal lavage (p = 0.555), chest tube (p = 0.486), and cricothyroidotomy (p = 0.329). Finally, volunteers undergoing both training modalities indicated preference for live animal training (p < 0.0001). We conclude that artificial simulator and live animal training produce equivalent levels of self-efficacy after initial training, but there is a preference in using a live animal model to achieve those skills. Reprint & Copyright © 2014 Association of Military Surgeons of the U.S.

Comprehensive heat transfer correlation for water/ethylene glycol-based graphene (nitrogen-doped graphene) nanofluids derived by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS)

NASA Astrophysics Data System (ADS)

Savari, Maryam; Moghaddam, Amin Hedayati; Amiri, Ahmad; Shanbedi, Mehdi; Ayub, Mohamad Nizam Bin

2017-10-01

Herein, artificial neural network and adaptive neuro-fuzzy inference system are employed for modeling the effects of important parameters on heat transfer and fluid flow characteristics of a car radiator and followed by comparing with those of the experimental results for testing data. To this end, two novel nanofluids (water/ethylene glycol-based graphene and nitrogen-doped graphene nanofluids) were experimentally synthesized. Then, Nusselt number was modeled with respect to the variation of inlet temperature, Reynolds number, Prandtl number and concentration, which were defined as the input (design) variables. To reach reliable results, we divided these data into train and test sections to accomplish modeling. Artificial networks were instructed by a major part of experimental data. The other part of primary data which had been considered for testing the appropriateness of the models was entered into artificial network models. Finally, predictad results were compared to the experimental data to evaluate validity. Confronted with high-level of validity confirmed that the proposed modeling procedure by BPNN with one hidden layer and five neurons is efficient and it can be expanded for all water/ethylene glycol-based carbon nanostructures nanofluids. Finally, we expanded our data collection from model and could present a fundamental correlation for calculating Nusselt number of the water/ethylene glycol-based nanofluids including graphene or nitrogen-doped graphene.

Microbially induced and microbially catalysed precipitation: two different carbonate factories

NASA Astrophysics Data System (ADS)

Meister, Patrick

2016-04-01

The landmark paper by Schlager (2003) has revealed three types of benthic carbonate production referred to as "carbonate factories", operative at different locations at different times in Earth history. The tropical or T-factory comprises the classical platforms and fringing reefs and is dominated by carbonate precipitation by autotrophic calcifying metazoans ("biotically controlled" precipitation). The cool or C-factory is also biotically controlled but via heterotrophic, calcifying metazoans in cold and deep waters at the continental margins. A further type is the mud-mound or M-factory, where carbonate precipitation is supported by microorganisms but not controlled by a specific enzymatic pathway ("biotically induced" precipitation). How exactly the microbes influence precipitation is still poorly understood. Based on recent experimental and field studies, the microbial influence on modern mud mound and microbialite growth includes two fundamentally different processes: (1) Metabolic activity of microbes may increase the saturation state with respect to a particular mineral phase, thereby indirectly driving the precipitation of the mineral phase: microbially induced precipitation. (2) In a situation, where a solution is already supersaturated but precipitation of the mineral is inhibited by a kinetic barrier, microbes may act as a catalyser, i.e. they lower the kinetic barrier: microbially catalysed precipitation. Such a catalytic effect can occur e.g. via secreted polymeric substances or specific chemical groups on the cell surface, at which the minerals nucleate or which facilitate mechanistically the bonding of new ions to the mineral surface. Based on these latest developments in microbialite formation, I propose to extend the scheme of benthic carbonate factories of Schlager et al. (2003) by introducing an additional branch distinguishing microbially induced from microbially catalysed precipitation. Although both mechanisms could be operative in a M-factory, and it is difficult to distinguish their products, their cause is very different. A Mi-factory ("i" for induced) is predominant under low carbonate saturation in normal seawater; a Mc-factory ("c" for catalysed) is operative in higher-alkalinity waters. The latter conditions may not only occur in shallow seas restricted from open sea water but may also have occurred in the aftermath of catastrophic events (e.g. P/T boundary) or during the Precambrian, before the onset of metazoan calcifiers. Thus, adding the additional distinction between microbially induced and microbially catalysed precipitation would allow the application of Schlager's concept of benthic carbonate factories beyond the Phanerozoic and probably over the entire Earth history.

ENGINEERING ECONOMIC ANALYSIS OF A PROGRAM FOR ARTIFICIAL GROUNDWATER RECHARGE.

USGS Publications Warehouse

Reichard, Eric G.; Bredehoeft, John D.

1984-01-01

This study describes and demonstrates two alternate methods for evaluating the relative costs and benefits of artificial groundwater recharge using percolation ponds. The first analysis considers the benefits to be the reduction of pumping lifts and land subsidence; the second considers benefits as the alternative costs of a comparable surface delivery system. Example computations are carried out for an existing artificial recharge program in Santa Clara Valley in California. A computer groundwater model is used to estimate both the average long term and the drought period effects of artificial recharge in the study area. Results indicate that the costs of artificial recharge are considerably smaller than the alternative costs of an equivalent surface system. Refs.

Prediction of Human Intestinal Absorption of Compounds Using Artificial Intelligence Techniques.

PubMed

Kumar, Rajnish; Sharma, Anju; Siddiqui, Mohammed Haris; Tiwari, Rajesh Kumar

2017-01-01

Information about Pharmacokinetics of compounds is an essential component of drug design and development. Modeling the pharmacokinetic properties require identification of the factors effecting absorption, distribution, metabolism and excretion of compounds. There have been continuous attempts in the prediction of intestinal absorption of compounds using various Artificial intelligence methods in the effort to reduce the attrition rate of drug candidates entering to preclinical and clinical trials. Currently, there are large numbers of individual predictive models available for absorption using machine learning approaches. Six Artificial intelligence methods namely, Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis were used for prediction of absorption of compounds. Prediction accuracy of Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis for prediction of intestinal absorption of compounds was found to be 91.54%, 88.33%, 84.30%, 86.51%, 79.07% and 80.08% respectively. Comparative analysis of all the six prediction models suggested that Support vector machine with Radial basis function based kernel is comparatively better for binary classification of compounds using human intestinal absorption and may be useful at preliminary stages of drug design and development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Mathematical-Artificial Neural Network Hybrid Model to Predict Roll Force during Hot Rolling of Steel

NASA Astrophysics Data System (ADS)

Rath, S.; Sengupta, P. P.; Singh, A. P.; Marik, A. K.; Talukdar, P.

2013-07-01

Accurate prediction of roll force during hot strip rolling is essential for model based operation of hot strip mills. Traditionally, mathematical models based on theory of plastic deformation have been used for prediction of roll force. In the last decade, data driven models like artificial neural network have been tried for prediction of roll force. Pure mathematical models have accuracy limitations whereas data driven models have difficulty in convergence when applied to industrial conditions. Hybrid models by integrating the traditional mathematical formulations and data driven methods are being developed in different parts of world. This paper discusses the methodology of development of an innovative hybrid mathematical-artificial neural network model. In mathematical model, the most important factor influencing accuracy is flow stress of steel. Coefficients of standard flow stress equation, calculated by parameter estimation technique, have been used in the model. The hybrid model has been trained and validated with input and output data collected from finishing stands of Hot Strip Mill, Bokaro Steel Plant, India. It has been found that the model accuracy has been improved with use of hybrid model, over the traditional mathematical model.

Numerical modeling of solute transport in a sand tank physical model under varying hydraulic gradient and hydrological stresses

NASA Astrophysics Data System (ADS)

Atlabachew, Abunu; Shu, Longcang; Wu, Peipeng; Zhang, Yongjie; Xu, Yang

2018-03-01

This laboratory study improves the understanding of the impacts of horizontal hydraulic gradient, artificial recharge, and groundwater pumping on solute transport through aquifers. Nine experiments and numerical simulations were carried out using a sand tank. The variable-density groundwater flow and sodium chloride transport were simulated using the three-dimensional numerical model SEAWAT. Numerical modelling results successfully reproduced heads and concentrations observed in the sand tank. A higher horizontal hydraulic gradient enhanced the migration of sodium chloride, particularly in the groundwater flow direction. The application of constant artificial recharge increased the spread of the sodium chloride plume in both the longitudinal and lateral directions. In addition, groundwater pumping accelerated spreading of the sodium chloride plume towards the pumping well. Both higher hydraulic gradient and pumping rate generated oval-shaped plumes in the horizontal plane. However, the artificial recharge process produced stretched plumes. These effects of artificial recharge and groundwater pumping were greater under higher hydraulic gradient. The concentration breakthrough curves indicated that emerging solutions never attained the concentration of the originally injected solution. This is probably because of sorption of sodium chloride onto the silica sand and/or the exchange of sodium chloride between the mobile and immobile liquid domains. The fingering and protruding plume shapes in the numerical models constitute instability zones produced by buoyancy-driven flow. Overall, the results have substantiated the influences of hydraulic gradient, boundary condition, artificial recharge, pumping rate and density differences on solute transport through a homogeneous unconfined aquifer. The implications of these findings are important for managing liquid wastes.

Large memory capacity in chaotic artificial neural networks: a view of the anti-integrable limit.

PubMed

Lin, Wei; Chen, Guanrong

2009-08-01

In the literature, it was reported that the chaotic artificial neural network model with sinusoidal activation functions possesses a large memory capacity as well as a remarkable ability of retrieving the stored patterns, better than the conventional chaotic model with only monotonic activation functions such as sigmoidal functions. This paper, from the viewpoint of the anti-integrable limit, elucidates the mechanism inducing the superiority of the model with periodic activation functions that includes sinusoidal functions. Particularly, by virtue of the anti-integrable limit technique, this paper shows that any finite-dimensional neural network model with periodic activation functions and properly selected parameters has much more abundant chaotic dynamics that truly determine the model's memory capacity and pattern-retrieval ability. To some extent, this paper mathematically and numerically demonstrates that an appropriate choice of the activation functions and control scheme can lead to a large memory capacity and better pattern-retrieval ability of the artificial neural network models.

Modeling the long-term effect of winter cover crops on nitrate transport in artificially drained fields across the Midwest U.S.

USDA-ARS?s Scientific Manuscript database

A fall-planted cover crop is a management practice with multiple benefits including reducing nitrate losses from artificially drained fields. We used the Root Zone Water Quality Model (RZWQM) to simulate the impact of a cereal rye cover crop on reducing nitrate losses from drained fields across five...

The effects of self-selected light-dark cycles and social constraints on human sleep and circadian timing: a modeling approach.

PubMed

Skeldon, Anne C; Phillips, Andrew J K; Dijk, Derk-Jan

2017-03-27

Why do we go to sleep late and struggle to wake up on time? Historically, light-dark cycles were dictated by the solar day, but now humans can extend light exposure by switching on artificial lights. We use a mathematical model incorporating effects of light, circadian rhythmicity and sleep homeostasis to provide a quantitative theoretical framework to understand effects of modern patterns of light consumption on the human circadian system. The model shows that without artificial light humans wakeup at dawn. Artificial light delays circadian rhythmicity and preferred sleep timing and compromises synchronisation to the solar day when wake-times are not enforced. When wake-times are enforced by social constraints, such as work or school, artificial light induces a mismatch between sleep timing and circadian rhythmicity ('social jet-lag'). The model implies that developmental changes in sleep homeostasis and circadian amplitude make adolescents particularly sensitive to effects of light consumption. The model predicts that ameliorating social jet-lag is more effectively achieved by reducing evening light consumption than by delaying social constraints, particularly in individuals with slow circadian clocks or when imposed wake-times occur after sunrise. These theory-informed predictions may aid design of interventions to prevent and treat circadian rhythm-sleep disorders and social jet-lag.

Novel two-way artificial boundary condition for 2D vertical water wave propagation modelled with Radial-Basis-Function Collocation Method

NASA Astrophysics Data System (ADS)

Mueller, A.

2018-04-01

A new transparent artificial boundary condition for the two-dimensional (vertical) (2DV) free surface water wave propagation modelled using the meshless Radial-Basis-Function Collocation Method (RBFCM) as boundary-only solution is derived. The two-way artificial boundary condition (2wABC) works as pure incidence, pure radiation and as combined incidence/radiation BC. In this work the 2wABC is applied to harmonic linear water waves; its performance is tested against the analytical solution for wave propagation over horizontal sea bottom, standing and partially standing wave as well as wave interference of waves with different periods.

Artificial Immune Algorithm for Subtask Industrial Robot Scheduling in Cloud Manufacturing

NASA Astrophysics Data System (ADS)

Suma, T.; Murugesan, R.

2018-04-01

The current generation of manufacturing industry requires an intelligent scheduling model to achieve an effective utilization of distributed manufacturing resources, which motivated us to work on an Artificial Immune Algorithm for subtask robot scheduling in cloud manufacturing. This scheduling model enables a collaborative work between the industrial robots in different manufacturing centers. This paper discussed two optimizing objectives which includes minimizing the cost and load balance of industrial robots through scheduling. To solve these scheduling problems, we used the algorithm based on Artificial Immune system. The parameters are simulated with MATLAB and the results compared with the existing algorithms. The result shows better performance than existing.

An artificial muscle actuator for biomimetic underwater propulsors.

PubMed

Yim, Woosoon; Lee, Joonsoo; Kim, Kwang J

2007-06-01

In this paper, we introduce the analytical framework of the modeling dynamic characteristics of a soft artificial muscle actuator for aquatic propulsor applications. The artificial muscle used for this underwater application is an ionic polymer-metal composite (IPMC) which can generate bending motion in aquatic environments. The inputs of the model are the voltages applied to multiple IPMCs, and the output can be either the shape of the actuators or the thrust force generated from the interaction between dynamic actuator motions and surrounding water. In order to determine the relationship between the input voltages and the bending moments, the simplified RC model is used, and the mechanical beam theory is used for the bending motion of IPMC actuators. Also, the hydrodynamic forces exerted on an actuator as it moves relative to the surrounding medium or water are added to the equations of motion to study the effect of actuator bending on the thrust force generation. The proposed method can be used for modeling the general bending type artificial muscle actuator in a single or segmented form operating in the water. The segmented design has more flexibility in controlling the shape of the actuator when compared with the single form, especially in generating undulatory waves. Considering an inherent nature of large deformations in the IPMC actuator, a large deflection beam model has been developed and integrated with the electrical RC model and hydrodynamic forces to develop the state space model of the actuator system. The model was validated against existing experimental data.

Biomimicry of quorum sensing using bacterial lifecycle model.

PubMed

Niu, Ben; Wang, Hong; Duan, Qiqi; Li, Li

2013-01-01

Recent microbiologic studies have shown that quorum sensing mechanisms, which serve as one of the fundamental requirements for bacterial survival, exist widely in bacterial intra- and inter-species cell-cell communication. Many simulation models, inspired by the social behavior of natural organisms, are presented to provide new approaches for solving realistic optimization problems. Most of these simulation models follow population-based modelling approaches, where all the individuals are updated according to the same rules. Therefore, it is difficult to maintain the diversity of the population. In this paper, we present a computational model termed LCM-QS, which simulates the bacterial quorum-sensing (QS) mechanism using an individual-based modelling approach under the framework of Agent-Environment-Rule (AER) scheme, i.e. bacterial lifecycle model (LCM). LCM-QS model can be classified into three main sub-models: chemotaxis with QS sub-model, reproduction and elimination sub-model and migration sub-model. The proposed model is used to not only imitate the bacterial evolution process at the single-cell level, but also concentrate on the study of bacterial macroscopic behaviour. Comparative experiments under four different scenarios have been conducted in an artificial 3-D environment with nutrients and noxious distribution. Detailed study on bacterial chemotatic processes with quorum sensing and without quorum sensing are compared. By using quorum sensing mechanisms, artificial bacteria working together can find the nutrient concentration (or global optimum) quickly in the artificial environment. Biomimicry of quorum sensing mechanisms using the lifecycle model allows the artificial bacteria endowed with the communication abilities, which are essential to obtain more valuable information to guide their search cooperatively towards the preferred nutrient concentrations. It can also provide an inspiration for designing new swarm intelligence optimization algorithms, which can be used for solving the real-world problems.

Biomimicry of quorum sensing using bacterial lifecycle model

PubMed Central

2013-01-01

Background Recent microbiologic studies have shown that quorum sensing mechanisms, which serve as one of the fundamental requirements for bacterial survival, exist widely in bacterial intra- and inter-species cell-cell communication. Many simulation models, inspired by the social behavior of natural organisms, are presented to provide new approaches for solving realistic optimization problems. Most of these simulation models follow population-based modelling approaches, where all the individuals are updated according to the same rules. Therefore, it is difficult to maintain the diversity of the population. Results In this paper, we present a computational model termed LCM-QS, which simulates the bacterial quorum-sensing (QS) mechanism using an individual-based modelling approach under the framework of Agent-Environment-Rule (AER) scheme, i.e. bacterial lifecycle model (LCM). LCM-QS model can be classified into three main sub-models: chemotaxis with QS sub-model, reproduction and elimination sub-model and migration sub-model. The proposed model is used to not only imitate the bacterial evolution process at the single-cell level, but also concentrate on the study of bacterial macroscopic behaviour. Comparative experiments under four different scenarios have been conducted in an artificial 3-D environment with nutrients and noxious distribution. Detailed study on bacterial chemotatic processes with quorum sensing and without quorum sensing are compared. By using quorum sensing mechanisms, artificial bacteria working together can find the nutrient concentration (or global optimum) quickly in the artificial environment. Conclusions Biomimicry of quorum sensing mechanisms using the lifecycle model allows the artificial bacteria endowed with the communication abilities, which are essential to obtain more valuable information to guide their search cooperatively towards the preferred nutrient concentrations. It can also provide an inspiration for designing new swarm intelligence optimization algorithms, which can be used for solving the real-world problems. PMID:23815296

Artificial Intelligence Systems as Prognostic and Predictive Tools in Ovarian Cancer.

PubMed

Enshaei, A; Robson, C N; Edmondson, R J

2015-11-01

The ability to provide accurate prognostic and predictive information to patients is becoming increasingly important as clinicians enter an era of personalized medicine. For a disease as heterogeneous as epithelial ovarian cancer, conventional algorithms become too complex for routine clinical use. This study therefore investigated the potential for an artificial intelligence model to provide this information and compared it with conventional statistical approaches. The authors created a database comprising 668 cases of epithelial ovarian cancer during a 10-year period and collected data routinely available in a clinical environment. They also collected survival data for all the patients, then constructed an artificial intelligence model capable of comparing a variety of algorithms and classifiers alongside conventional statistical approaches such as logistic regression. The model was used to predict overall survival and demonstrated that an artificial neural network (ANN) algorithm was capable of predicting survival with high accuracy (93 %) and an area under the curve (AUC) of 0.74 and that this outperformed logistic regression. The model also was used to predict the outcome of surgery and again showed that ANN could predict outcome (complete/optimal cytoreduction vs. suboptimal cytoreduction) with 77 % accuracy and an AUC of 0.73. These data are encouraging and demonstrate that artificial intelligence systems may have a role in providing prognostic and predictive data for patients. The performance of these systems likely will improve with increasing data set size, and this needs further investigation.

Prediction of Metabolism of Drugs using Artificial Intelligence: How far have we reached?

PubMed

Kumar, Rajnish; Sharma, Anju; Siddiqui, Mohammed Haris; Tiwari, Rajesh Kumar

2016-01-01

Information about drug metabolism is an essential component of drug development. Modeling the drug metabolism requires identification of the involved enzymes, rate and extent of metabolism, the sites of metabolism etc. There has been continuous attempts in the prediction of metabolism of drugs using artificial intelligence in effort to reduce the attrition rate of drug candidates entering to preclinical and clinical trials. Currently, there are number of predictive models available for metabolism using Support vector machines, Artificial neural networks, Bayesian classifiers etc. There is an urgent need to review their progress so far and address the existing challenges in prediction of metabolism. In this attempt, we are presenting the currently available literature models and some of the critical issues regarding prediction of drug metabolism.

Magnetron Sputtered Pulsed Laser Deposition Scale Up

DTIC Science & Technology

2003-08-14

2:721-726 34 S. J. P. Laube and E. F. Stark, “ Artificial Intellegence in Process Control of Pulsed Laser Deposition”, Proceedings of...The model would be based on mathematical simulation of real process data, neural-networks, or other artificial intelligence methods based on in situ...Laube and E. F. Stark, Proc. Symp. Artificial Intel. Real Time Control, Valencia, Spain, 3-5 Oct. ,1994, p.159-163. International Federation of

Knowledge-Based Software Development Tools

DTIC Science & Technology

1993-09-01

GREEN, C., AND WESTFOLD, S. Knowledge-based programming self-applied. In Machine Intelligence 10, J. E. Hayes, D. Mitchie, and Y. Pao, Eds., Wiley...Technical Report KES.U.84.2, Kestrel Institute, April 1984. [181 KORF, R. E. Toward a model of representation changes. Artificial Intelligence 14, 1...Artificial Intelligence 27, 1 (February 1985), 43-96. Replinted in Readings in Artificial Intelligence and Software Engineering, C. Rich •ad R. Waters

Magnetoresistance in Permalloy Connected Brickwork Artificial Spin Ice

NASA Astrophysics Data System (ADS)

Park, Jungsik; Le, Brian; Chern, Gia-Wei; Watts, Justin; Leighton, Chris; Schiffer, Peter

Artificial spin ice refers to a two-dimensional array of elongated ferromagnetic elements in which frustrated lattice geometry induces novel magnetic behavior. Here we examine room-temperature magnetoresistance properties of connected permalloy (Ni81Fe19) brickwork artificial spin ice. Both the longitudinal and transverse magnetoresistance of the nanostructure demonstrate an angular sensitivity that has not been previously observed. The observed magnetoresistance behavior can be explained from micromagnetic modelling using an anisotropic magnetoresistance model (AMR). As part of this study, we find that the ground state of the connected brickwork artificial spin ice can be reproducibly created by a simple field sweep in a narrow range of angles, and manifests in the magnetotransport with a distinct signal. Supported by the US Department of Energy, Office of Basic Energy Sciences, Materials Sciences and Engineering Division under Grant Number DE-SC0010778. Work at the University of Minnesota was supported by the NSF MRSEC under award DMR-1420013, and DMR-1507048.

De Novo Design of Bioactive Small Molecules by Artificial Intelligence

PubMed Central

Merk, Daniel; Friedrich, Lukas; Grisoni, Francesca

2018-01-01

Abstract Generative artificial intelligence offers a fresh view on molecular design. We present the first‐time prospective application of a deep learning model for designing new druglike compounds with desired activities. For this purpose, we trained a recurrent neural network to capture the constitution of a large set of known bioactive compounds represented as SMILES strings. By transfer learning, this general model was fine‐tuned on recognizing retinoid X and peroxisome proliferator‐activated receptor agonists. We synthesized five top‐ranking compounds designed by the generative model. Four of the compounds revealed nanomolar to low‐micromolar receptor modulatory activity in cell‐based assays. Apparently, the computational model intrinsically captured relevant chemical and biological knowledge without the need for explicit rules. The results of this study advocate generative artificial intelligence for prospective de novo molecular design, and demonstrate the potential of these methods for future medicinal chemistry. PMID:29319225

Fabrication of Artificial Leaf to Develop Fluid Pump Driven by Surface Tension and Evaporation

NASA Astrophysics Data System (ADS)

Lee, Minki; Lim, Hosub; Lee, Jinkee

2017-11-01

Plants transport water from roots to leaves via xylem through transpiration, which is an evaporation process that occurs at the leaves. During transpiration, negative pressure can be generated by the porous structure of mesophyll cells in the leaves. Here, an artificial leaf mimicking structure using hydrogel, which has a nanoporous structure is fabricated. The cryogel method is used to develop a hierarchy structure on the nano- and microscale in the hydrogel media that is similar to the mesophyll cells and veins of a leaf, respectively. The theoretical model is analyzed to calculate the flow resistance in the artificial leaf, and compare the model with the experimental results. The experiment involves connecting a glass capillary tube at the bottom of the artificial leaf to observe the fluid velocity in the glass capillary tube generated by the negative pressure. The use of silicone oil as fluid instead of water to increase the flow resistance enables the measurement of negative pressure. The negative pressure of the artificial leaf is affected by several variables (e.g., pore size, wettability of the structure). Finally, by decreasing the pore size and increasing the wettability, the maximum negative pressure of the artificial leaf, -7.9 kPa is obtained.

Virtual Reality for Artificial Intelligence: human-centered simulation for social science.

PubMed

Cipresso, Pietro; Riva, Giuseppe

2015-01-01

There is a long last tradition in Artificial Intelligence as use of Robots endowing human peculiarities, from a cognitive and emotional point of view, and not only in shape. Today Artificial Intelligence is more oriented to several form of collective intelligence, also building robot simulators (hardware or software) to deeply understand collective behaviors in human beings and society as a whole. Modeling has also been crucial in the social sciences, to understand how complex systems can arise from simple rules. However, while engineers' simulations can be performed in the physical world using robots, for social scientist this is impossible. For decades, researchers tried to improve simulations by endowing artificial agents with simple and complex rules that emulated human behavior also by using artificial intelligence (AI). To include human beings and their real intelligence within artificial societies is now the big challenge. We present an hybrid (human-artificial) platform where experiments can be performed by simulated artificial worlds in the following manner: 1) agents' behaviors are regulated by the behaviors shown in Virtual Reality involving real human beings exposed to specific situations to simulate, and 2) technology transfers these rules into the artificial world. These form a closed-loop of real behaviors inserted into artificial agents, which can be used to study real society.

Real-space observation of magnetic excitations and avalanche behavior in artificial quasicrystal lattices

DOE PAGES

Brajuskovic, V.; Barrows, F.; Phatak, C.; ...

2016-10-03

Artificial spin ice lattices have emerged as model systems for studying magnetic frustration in recent years. Most work to date has looked at periodic artificial spin ice lattices. In this paper, we observe frustration effects in quasicrystal artificial spin ice lattices that lack translational symmetry and contain vertices with different numbers of interacting elements. We find that as the lattice state changes following demagnetizing and annealing, specific vertex motifs retain low-energy configurations, which excites other motifs into higher energy configurations. In addition, we find that unlike the magnetization reversal process for periodic artificial spin ice lattices, which occurs through 1Dmore » avalanches, quasicrystal lattices undergo reversal through a dendritic 2D avalanche mechanism.« less

Real-space observation of magnetic excitations and avalanche behavior in artificial quasicrystal lattices

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

Brajuskovic, V.; Barrows, F.; Phatak, C.

Artificial spin ice lattices have emerged as model systems for studying magnetic frustration in recent years. Most work to date has looked at periodic artificial spin ice lattices. In this paper, we observe frustration effects in quasicrystal artificial spin ice lattices that lack translational symmetry and contain vertices with different numbers of interacting elements. We find that as the lattice state changes following demagnetizing and annealing, specific vertex motifs retain low-energy configurations, which excites other motifs into higher energy configurations. In addition, we find that unlike the magnetization reversal process for periodic artificial spin ice lattices, which occurs through 1Dmore » avalanches, quasicrystal lattices undergo reversal through a dendritic 2D avalanche mechanism.« less

Magnetic Charge Organization and Screening in Thermalized Artificial Spin Ice

NASA Astrophysics Data System (ADS)

Gilbert, Ian

2014-03-01

Artificial spin ice is a material-by-design in which interacting single-domain ferromagnetic nanoislands are used to model Ising spins in frustrated spin systems. Artificial spin ice has proved a useful system in which to directly probe the physics of geometrical frustration, allowing us to better understand materials such as spin ice. Recently, several new experimental techniques have been developed that allow effective thermalization of artificial spin ice. Given the intense interest in magnetic monopole excitations in spin ice materials and artificial spin ice's success in modeling these materials, it should not come as a surprise that interesting monopole physics emerges here as well. The first experimental investigation of thermalized artificial square spin ice determined that the system's monopole-like excitations obeyed a Boltzmann distribution and also found evidence for monopole-antimonopole interactions. Further experiments have implicated these monopole excitations in the growth of ground state domains. Our recent study of artificial kagome spin ice, whose odd-coordinated vertices always possess a net magnetic charge, has revealed a theoretically-predicted magnetic charge ordering transition which has not been previously observed experimentally. We have also investigated the details of magnetic charge interactions in lattices of mixed coordination number. This work was done in collaboration with Sheng Zhang, Cristiano Nisoli, Gia-Wei Chern, Michael Erickson, Liam O'Brien, Chris Leighton, Paul Lammert, Vincent Crespi, and Peter Schiffer. This work was primarily funded by the US Department of Energy, Office of Basic Energy Sciences, Materials Science and Engineering Division, grant no. DE-SC0005313.

Curriculum Assessment Using Artificial Neural Network and Support Vector Machine Modeling Approaches: A Case Study. IR Applications. Volume 29

ERIC Educational Resources Information Center

Chen, Chau-Kuang

2010-01-01

Artificial Neural Network (ANN) and Support Vector Machine (SVM) approaches have been on the cutting edge of science and technology for pattern recognition and data classification. In the ANN model, classification accuracy can be achieved by using the feed-forward of inputs, back-propagation of errors, and the adjustment of connection weights. In…

Predictions on the Development Dimensions of Provincial Tourism Discipline Based on the Artificial Neural Network BP Model

ERIC Educational Resources Information Center

Yang, Yang; Hu, Jun; Lv, Yingchun; Zhang, Mu

2013-01-01

As the tourism industry has gradually become the strategic mainstay industry of the national economy, the scope of the tourism discipline has developed rigorously. This paper makes a predictive study on the development of the scope of Guangdong provincial tourism discipline based on the artificial neural network BP model in order to find out how…

Quality assurance paradigms for artificial intelligence in modelling and simulation

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

Oren, T.I.

1987-04-01

New classes of quality assurance concepts and techniques are required for the advanced knowledge-processing paradigms (such as artificial intelligence, expert systems, or knowledge-based systems) and the complex problems that only simulative systems can cope with. A systematization of quality assurance problems as well as examples are given to traditional and cognizant quality assurance techniques in traditional and cognizant modelling and simulation.

A New Framework for Adaptive Sampling and Analysis During Long-Term Monitoring and Remedial Action Management

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

Minsker, Barbara

2004-12-01

The Argonne team has gathered available data on monitoring wells and measured hydraulic heads from the Argonne 317/319 site and sent it to UIUC. Xiaodong Li, a research assistant supported by the project, has reviewed the data and has fit initial spatiotemporal statistical models to it. Another research assistant, Yonas Demissie, has completed generation of the artificial data that will be used for model development and testing. In order to generate the artificial data a detailed groundwater flow and contaminant transport model was developed based upon characteristics of the 317/319 site. The model covers a multi-year time horizon that includesmore » both before and after planting of the trees. As described in the proposal, the artificial data is created by adding ''measurement'' error to the ''true'' value from the numerical model. To date, only simple white noise error models have been considered. He is now reviewing the literature and beginning to develop a hierarchical modeling approach for the artificial data. Abhishek Singh, a third research assistant supported by the project, is implementing learning models for learning users preferences in an interactive genetic algorithm for solving the inverse problem. Meghna Babbar, the fourth research assistant supported by the project, has been improving the user interface for the interactive genetic algorithm and preparing a long-term monitoring design problem for testing the approach. Gayathri Gopalakrishnan, the last research assistant who is partially supported by the project, has collected substantial data from the 317/319 phytoremediation site at Argonne and has begun learning approaches for modeling these data.« less

Artificial Lipid Membranes: Past, Present, and Future

PubMed Central

Siontorou, Christina G.; Nikoleli, Georgia-Paraskevi; Nikolelis, Dimitrios P.

2017-01-01

The multifaceted role of biological membranes prompted early the development of artificial lipid-based models with a primary view of reconstituting the natural functions in vitro so as to study and exploit chemoreception for sensor engineering. Over the years, a fair amount of knowledge on the artificial lipid membranes, as both, suspended or supported lipid films and liposomes, has been disseminated and has helped to diversify and expand initial scopes. Artificial lipid membranes can be constructed by several methods, stabilized by various means, functionalized in a variety of ways, experimented upon intensively, and broadly utilized in sensor development, drug testing, drug discovery or as molecular tools and research probes for elucidating the mechanics and the mechanisms of biological membranes. This paper reviews the state-of-the-art, discusses the diversity of applications, and presents future perspectives. The newly-introduced field of artificial cells further broadens the applicability of artificial membranes in studying the evolution of life. PMID:28933723

The artificial neural network modelling of the piezoelectric actuator vibrations using laser displacement sensor

NASA Astrophysics Data System (ADS)

Paralı, Levent; Sarı, Ali; Kılıç, Ulaş; Şahin, Özge; Pěchoušek, Jiří

2017-09-01

We report an improvement of the artificial neural network (ANN) modelling of a piezoelectric actuator vibration based on the experimental data. The controlled vibrations of an actuator were obtained by utilizing the swept-sine signal excitation. The peak value in the displacement signal response was measured by a laser displacement sensor. The piezoelectric actuator was modelled in both linear and nonlinear operating range. A consistency from 90.3 up to 98.9% of ANN modelled output values and experimental ones was reached. The obtained results clearly demonstrate exact linear relationship between the ANN model and experimental values.

Artificial Intelligence Techniques for Predicting and Mapping Daily Pan Evaporation

NASA Astrophysics Data System (ADS)

Arunkumar, R.; Jothiprakash, V.; Sharma, Kirty

2017-09-01

In this study, Artificial Intelligence techniques such as Artificial Neural Network (ANN), Model Tree (MT) and Genetic Programming (GP) are used to develop daily pan evaporation time-series (TS) prediction and cause-effect (CE) mapping models. Ten years of observed daily meteorological data such as maximum temperature, minimum temperature, relative humidity, sunshine hours, dew point temperature and pan evaporation are used for developing the models. For each technique, several models are developed by changing the number of inputs and other model parameters. The performance of each model is evaluated using standard statistical measures such as Mean Square Error, Mean Absolute Error, Normalized Mean Square Error and correlation coefficient (R). The results showed that daily TS-GP (4) model predicted better with a correlation coefficient of 0.959 than other TS models. Among various CE models, CE-ANN (6-10-1) resulted better than MT and GP models with a correlation coefficient of 0.881. Because of the complex non-linear inter-relationship among various meteorological variables, CE mapping models could not achieve the performance of TS models. From this study, it was found that GP performs better for recognizing single pattern (time series modelling), whereas ANN is better for modelling multiple patterns (cause-effect modelling) in the data.

Artificial neural network model of the hybrid EGARCH volatility of the Taiwan stock index option prices

NASA Astrophysics Data System (ADS)

Tseng, Chih-Hsiung; Cheng, Sheng-Tzong; Wang, Yi-Hsien; Peng, Jin-Tang

2008-05-01

This investigation integrates a novel hybrid asymmetric volatility approach into an Artificial Neural Networks option-pricing model to upgrade the forecasting ability of the price of derivative securities. The use of the new hybrid asymmetric volatility method can simultaneously decrease the stochastic and nonlinearity of the error term sequence, and capture the asymmetric volatility. Therefore, analytical results of the ANNS option-pricing model reveal that Grey-EGARCH volatility provides greater predictability than other volatility approaches.

On DSS Implementation in the Dynamic Model of the Digital Oil field

NASA Astrophysics Data System (ADS)

Korovin, Iakov S.; Khisamutdinov, Maksim V.; Kalyaev, Anatoly I.

2018-02-01

Decision support systems (DSS), especially based on the artificial intelligence (AI) techniques are been widely applied in different domains nowadays. In the paper we depict an approach of implementing DSS in to Digital Oil Field (DOF) dynamic model structure in order to reduce the human factor influence, considering the automation of all production processes to be the DOF model clue element. As the basic tool of data handling we propose the hybrid application on artificial neural networks and evolutional algorithms.

An Artificial Particle Precipitation Technique Using HAARP-Generated VLF Waves

DTIC Science & Technology

2006-11-02

AFRL-VS-HA-TR-2007-1021 An Artificial Particle Precipitation Technique Using HAARP -Generated VLF Waves O o o r- Q M. J. Kosch T. Pedersen J...Artificial Particle Precipitation Technique Using HAARP Generated VLF Waves. 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 62101F...model. The frequency-time modulated VLF wave patterns have been successfully implemented at the HAARP ionospheric modification facility in Alaska

Comparison of Logistic Regression and Artificial Neural Network in Low Back Pain Prediction: Second National Health Survey

PubMed Central

Parsaeian, M; Mohammad, K; Mahmoudi, M; Zeraati, H

2012-01-01

Background: The purpose of this investigation was to compare empirically predictive ability of an artificial neural network with a logistic regression in prediction of low back pain. Methods: Data from the second national health survey were considered in this investigation. This data includes the information of low back pain and its associated risk factors among Iranian people aged 15 years and older. Artificial neural network and logistic regression models were developed using a set of 17294 data and they were validated in a test set of 17295 data. Hosmer and Lemeshow recommendation for model selection was used in fitting the logistic regression. A three-layer perceptron with 9 inputs, 3 hidden and 1 output neurons was employed. The efficiency of two models was compared by receiver operating characteristic analysis, root mean square and -2 Loglikelihood criteria. Results: The area under the ROC curve (SE), root mean square and -2Loglikelihood of the logistic regression was 0.752 (0.004), 0.3832 and 14769.2, respectively. The area under the ROC curve (SE), root mean square and -2Loglikelihood of the artificial neural network was 0.754 (0.004), 0.3770 and 14757.6, respectively. Conclusions: Based on these three criteria, artificial neural network would give better performance than logistic regression. Although, the difference is statistically significant, it does not seem to be clinically significant. PMID:23113198

Comparison of logistic regression and artificial neural network in low back pain prediction: second national health survey.

PubMed

Parsaeian, M; Mohammad, K; Mahmoudi, M; Zeraati, H

2012-01-01

The purpose of this investigation was to compare empirically predictive ability of an artificial neural network with a logistic regression in prediction of low back pain. Data from the second national health survey were considered in this investigation. This data includes the information of low back pain and its associated risk factors among Iranian people aged 15 years and older. Artificial neural network and logistic regression models were developed using a set of 17294 data and they were validated in a test set of 17295 data. Hosmer and Lemeshow recommendation for model selection was used in fitting the logistic regression. A three-layer perceptron with 9 inputs, 3 hidden and 1 output neurons was employed. The efficiency of two models was compared by receiver operating characteristic analysis, root mean square and -2 Loglikelihood criteria. The area under the ROC curve (SE), root mean square and -2Loglikelihood of the logistic regression was 0.752 (0.004), 0.3832 and 14769.2, respectively. The area under the ROC curve (SE), root mean square and -2Loglikelihood of the artificial neural network was 0.754 (0.004), 0.3770 and 14757.6, respectively. Based on these three criteria, artificial neural network would give better performance than logistic regression. Although, the difference is statistically significant, it does not seem to be clinically significant.

Optimization of DRASTIC method by supervised committee machine artificial intelligence to assess groundwater vulnerability for Maragheh-Bonab plain aquifer, Iran

NASA Astrophysics Data System (ADS)

Fijani, Elham; Nadiri, Ata Allah; Asghari Moghaddam, Asghar; Tsai, Frank T.-C.; Dixon, Barnali

2013-10-01

Contamination of wells with nitrate-N (NO3-N) poses various threats to human health. Contamination of groundwater is a complex process and full of uncertainty in regional scale. Development of an integrative vulnerability assessment methodology can be useful to effectively manage (including prioritization of limited resource allocation to monitor high risk areas) and protect this valuable freshwater source. This study introduces a supervised committee machine with artificial intelligence (SCMAI) model to improve the DRASTIC method for groundwater vulnerability assessment for the Maragheh-Bonab plain aquifer in Iran. Four different AI models are considered in the SCMAI model, whose input is the DRASTIC parameters. The SCMAI model improves the committee machine artificial intelligence (CMAI) model by replacing the linear combination in the CMAI with a nonlinear supervised ANN framework. To calibrate the AI models, NO3-N concentration data are divided in two datasets for the training and validation purposes. The target value of the AI models in the training step is the corrected vulnerability indices that relate to the first NO3-N concentration dataset. After model training, the AI models are verified by the second NO3-N concentration dataset. The results show that the four AI models are able to improve the DRASTIC method. Since the best AI model performance is not dominant, the SCMAI model is considered to combine the advantages of individual AI models to achieve the optimal performance. The SCMAI method re-predicts the groundwater vulnerability based on the different AI model prediction values. The results show that the SCMAI outperforms individual AI models and committee machine with artificial intelligence (CMAI) model. The SCMAI model ensures that no water well with high NO3-N levels would be classified as low risk and vice versa. The study concludes that the SCMAI model is an effective model to improve the DRASTIC model and provides a confident estimate of the pollution risk.

The Influence of the Shape of Model Hydrometeors on the Formation of Discharge between an Artificial-Thunderstorm Cell and the Ground

NASA Astrophysics Data System (ADS)

Temnikov, A. G.; Chernenskii, L. L.; Orlov, A. V.; Lysov, N. Yu.; Belova, O. S.; Gerastenok, T. K.; Zhuravkova, D. S.

2017-12-01

We have experimentally studied how arrays of model coarse hydrometeors influence the initiation and propagation of discharge between an artificial-thunderstorm cell of negative or positive polarity and the ground. It is established for the first time that the probability of initiation and stimulation of a channeled discharge between negatively or positively charged cloud and the ground significantly depends on the shape and size of coarse hydrometeors occurring near the thunderstorm cell boundaries. The obtained results can be used in developing methods for the artificial initiation of the cloud-ground type lightning of both polarities and targeted discharge of thunderstorm clouds.

The effects of self-selected light-dark cycles and social constraints on human sleep and circadian timing: a modeling approach

PubMed Central

Skeldon, Anne C.; Phillips, Andrew J. K.; Dijk, Derk-Jan

2017-01-01

Why do we go to sleep late and struggle to wake up on time? Historically, light-dark cycles were dictated by the solar day, but now humans can extend light exposure by switching on artificial lights. We use a mathematical model incorporating effects of light, circadian rhythmicity and sleep homeostasis to provide a quantitative theoretical framework to understand effects of modern patterns of light consumption on the human circadian system. The model shows that without artificial light humans wakeup at dawn. Artificial light delays circadian rhythmicity and preferred sleep timing and compromises synchronisation to the solar day when wake-times are not enforced. When wake-times are enforced by social constraints, such as work or school, artificial light induces a mismatch between sleep timing and circadian rhythmicity (‘social jet-lag’). The model implies that developmental changes in sleep homeostasis and circadian amplitude make adolescents particularly sensitive to effects of light consumption. The model predicts that ameliorating social jet-lag is more effectively achieved by reducing evening light consumption than by delaying social constraints, particularly in individuals with slow circadian clocks or when imposed wake-times occur after sunrise. These theory-informed predictions may aid design of interventions to prevent and treat circadian rhythm-sleep disorders and social jet-lag. PMID:28345624

Estimating tree bole volume using artificial neural network models for four species in Turkey.

PubMed

Ozçelik, Ramazan; Diamantopoulou, Maria J; Brooks, John R; Wiant, Harry V

2010-01-01

Tree bole volumes of 89 Scots pine (Pinus sylvestris L.), 96 Brutian pine (Pinus brutia Ten.), 107 Cilicica fir (Abies cilicica Carr.) and 67 Cedar of Lebanon (Cedrus libani A. Rich.) trees were estimated using Artificial Neural Network (ANN) models. Neural networks offer a number of advantages including the ability to implicitly detect complex nonlinear relationships between input and output variables, which is very helpful in tree volume modeling. Two different neural network architectures were used and produced the Back propagation (BPANN) and the Cascade Correlation (CCANN) Artificial Neural Network models. In addition, tree bole volume estimates were compared to other established tree bole volume estimation techniques including the centroid method, taper equations, and existing standard volume tables. An overview of the features of ANNs and traditional methods is presented and the advantages and limitations of each one of them are discussed. For validation purposes, actual volumes were determined by aggregating the volumes of measured short sections (average 1 meter) of the tree bole using Smalian's formula. The results reported in this research suggest that the selected cascade correlation artificial neural network (CCANN) models are reliable for estimating the tree bole volume of the four examined tree species since they gave unbiased results and were superior to almost all methods in terms of error (%) expressed as the mean of the percentage errors. 2009 Elsevier Ltd. All rights reserved.

Modeling and Prediction of Monthly Total Ozone Concentrations by Use of an Artificial Neural Network Based on Principal Component Analysis

NASA Astrophysics Data System (ADS)

Chattopadhyay, Surajit; Chattopadhyay, Goutami

2012-10-01

In the work discussed in this paper we considered total ozone time series over Kolkata (22°34'10.92″N, 88°22'10.92″E), an urban area in eastern India. Using cloud cover, average temperature, and rainfall as the predictors, we developed an artificial neural network, in the form of a multilayer perceptron with sigmoid non-linearity, for prediction of monthly total ozone concentrations from values of the predictors in previous months. We also estimated total ozone from values of the predictors in the same month. Before development of the neural network model we removed multicollinearity by means of principal component analysis. On the basis of the variables extracted by principal component analysis, we developed three artificial neural network models. By rigorous statistical assessment it was found that cloud cover and rainfall can act as good predictors for monthly total ozone when they are considered as the set of input variables for the neural network model constructed in the form of a multilayer perceptron. In general, the artificial neural network has good potential for predicting and estimating monthly total ozone on the basis of the meteorological predictors. It was further observed that during pre-monsoon and winter seasons, the proposed models perform better than during and after the monsoon.

An artificial pancreas provided a novel model of blood glucose level variability in beagles.

PubMed

Munekage, Masaya; Yatabe, Tomoaki; Kitagawa, Hiroyuki; Takezaki, Yuka; Tamura, Takahiko; Namikawa, Tsutomu; Hanazaki, Kazuhiro

2015-12-01

Although the effects on prognosis of blood glucose level variability have gained increasing attention, it is unclear whether blood glucose level variability itself or the manifestation of pathological conditions that worsen prognosis. Then, previous reports have not been published on variability models of perioperative blood glucose levels. The aim of this study is to establish a novel variability model of blood glucose concentration using an artificial pancreas. We maintained six healthy, male beagles. After anesthesia induction, a 20-G venous catheter was inserted in the right femoral vein and an artificial pancreas (STG-22, Nikkiso Co. Ltd., Tokyo, Japan) was connected for continuous blood glucose monitoring and glucose management. After achieving muscle relaxation, total pancreatectomy was performed. After 1 h of stabilization, automatic blood glucose control was initiated using the artificial pancreas. Blood glucose level varied for 8 h, alternating between the target blood glucose values of 170 and 70 mg/dL. Eight hours later, the experiment was concluded. Total pancreatectomy was performed for 62 ± 13 min. Blood glucose swings were achieved 9.8 ± 2.3 times. The average blood glucose level was 128.1 ± 5.1 mg/dL with an SD of 44.6 ± 3.9 mg/dL. The potassium levels after stabilization and at the end of the experiment were 3.5 ± 0.3 and 3.1 ± 0.5 mmol/L, respectively. In conclusion, the results of the present study demonstrated that an artificial pancreas contributed to the establishment of a novel variability model of blood glucose levels in beagles.

The Effect of Map Boundary on Estimates of Landscape Resistance to Animal Movement

PubMed Central

Koen, Erin L.; Garroway, Colin J.; Wilson, Paul J.; Bowman, Jeff

2010-01-01

Background Artificial boundaries on a map occur when the map extent does not cover the entire area of study; edges on the map do not exist on the ground. These artificial boundaries might bias the results of animal dispersal models by creating artificial barriers to movement for model organisms where there are no barriers for real organisms. Here, we characterize the effects of artificial boundaries on calculations of landscape resistance to movement using circuit theory. We then propose and test a solution to artificially inflated resistance values whereby we place a buffer around the artificial boundary as a substitute for the true, but unknown, habitat. Methodology/Principal Findings We randomly assigned landscape resistance values to map cells in the buffer in proportion to their occurrence in the known map area. We used circuit theory to estimate landscape resistance to organism movement and gene flow, and compared the output across several scenarios: a habitat-quality map with artificial boundaries and no buffer, a map with a buffer composed of randomized habitat quality data, and a map with a buffer composed of the true habitat quality data. We tested the sensitivity of the randomized buffer to the possibility that the composition of the real but unknown buffer is biased toward high or low quality. We found that artificial boundaries result in an overestimate of landscape resistance. Conclusions/Significance Artificial map boundaries overestimate resistance values. We recommend the use of a buffer composed of randomized habitat data as a solution to this problem. We found that resistance estimated using the randomized buffer did not differ from estimates using the real data, even when the composition of the real data was varied. Our results may be relevant to those interested in employing Circuitscape software in landscape connectivity and landscape genetics studies. PMID:20668690

Weight-elimination neural networks applied to coronary surgery mortality prediction.

PubMed

Ennett, Colleen M; Frize, Monique

2003-06-01

The objective was to assess the effectiveness of the weight-elimination cost function in improving classification performance of artificial neural networks (ANNs) and to observe how changing the a priori distribution of the training set affects network performance. Backpropagation feedforward ANNs with and without weight-elimination estimated mortality for coronary artery surgery patients. The ANNs were trained and tested on cases with 32 input variables describing the patient's medical history; the output variable was in-hospital mortality (mortality rates: training 3.7%, test 3.8%). Artificial training sets with mortality rates of 20%, 50%, and 80% were created to observe the impact of training with a higher-than-normal prevalence. When the results were averaged, weight-elimination networks achieved higher sensitivity rates than those without weight-elimination. Networks trained on higher-than-normal prevalence achieved higher sensitivity rates at the cost of lower specificity and correct classification. The weight-elimination cost function can improve the classification performance when the network is trained with a higher-than-normal prevalence. A network trained with a moderately high artificial mortality rate (artificial mortality rate of 20%) can improve the sensitivity of the model without significantly affecting other aspects of the model's performance. The ANN mortality model achieved comparable performance as additive and statistical models for coronary surgery mortality estimation in the literature.

Modeling of lighting behaviour of a hybrid lighting system in inner spaces of Building of Electrical Engineering

NASA Astrophysics Data System (ADS)

Amado, L.; Osma, G.; Villamizar, R.

2016-07-01

This paper presents the modelling of lighting behaviour of a hybrid lighting system - HLS in inner spaces for tropical climate. HLS aims to mitigate the problem of high electricity consumption used by artificial lighting in buildings. These systems integrate intelligently the daylight and artificial light through control strategies. However, selection of these strategies usually depends on expertise of designer and of available budget. In order to improve the selection process of the control strategies, this paper analyses the Electrical Engineering Building (EEB) case, initially modelling of lighting behaviour is established for the HLS of a classroom and an office. This allows estimating the illuminance level of the mixed lighting in the space, and energy consumption by artificial light according to different lighting control techniques, a control strategy based on occupancy and a combination of them. The model considers the concept of Daylight Factor (DF) for the estimating of daylight illuminance on the work plane for tropical climatic conditions. The validation of the model was carried out by comparing the measured and model-estimated indoor illuminances.

System identification of smart structures using a wavelet neuro-fuzzy model

NASA Astrophysics Data System (ADS)

Mitchell, Ryan; Kim, Yeesock; El-Korchi, Tahar

2012-11-01

This paper proposes a complex model of smart structures equipped with magnetorheological (MR) dampers. Nonlinear behavior of the structure-MR damper systems is represented by the use of a wavelet-based adaptive neuro-fuzzy inference system (WANFIS). The WANFIS is developed through the integration of wavelet transforms, artificial neural networks, and fuzzy logic theory. To evaluate the effectiveness of the WANFIS model, a three-story building employing an MR damper under a variety of natural hazards is investigated. An artificial earthquake is used for training the input-output mapping of the WANFIS model. The artificial earthquake is generated such that the characteristics of a variety of real recorded earthquakes are included. It is demonstrated that this new WANFIS approach is effective in modeling nonlinear behavior of the structure-MR damper system subjected to a variety of disturbances while resulting in shorter training times in comparison with an adaptive neuro-fuzzy inference system (ANFIS) model. Comparison with high fidelity data proves the viability of the proposed approach in a structural health monitoring setting, and it is validated using known earthquake signals such as El-Centro, Kobe, Northridge, and Hachinohe.

Application of the artificial bee colony algorithm for solving the set covering problem.

PubMed

Crawford, Broderick; Soto, Ricardo; Cuesta, Rodrigo; Paredes, Fernando

2014-01-01

The set covering problem is a formal model for many practical optimization problems. In the set covering problem the goal is to choose a subset of the columns of minimal cost that covers every row. Here, we present a novel application of the artificial bee colony algorithm to solve the non-unicost set covering problem. The artificial bee colony algorithm is a recent swarm metaheuristic technique based on the intelligent foraging behavior of honey bees. Experimental results show that our artificial bee colony algorithm is competitive in terms of solution quality with other recent metaheuristic approaches for the set covering problem.

Application of the Artificial Bee Colony Algorithm for Solving the Set Covering Problem

PubMed Central

Crawford, Broderick; Soto, Ricardo; Cuesta, Rodrigo; Paredes, Fernando

2014-01-01

The set covering problem is a formal model for many practical optimization problems. In the set covering problem the goal is to choose a subset of the columns of minimal cost that covers every row. Here, we present a novel application of the artificial bee colony algorithm to solve the non-unicost set covering problem. The artificial bee colony algorithm is a recent swarm metaheuristic technique based on the intelligent foraging behavior of honey bees. Experimental results show that our artificial bee colony algorithm is competitive in terms of solution quality with other recent metaheuristic approaches for the set covering problem. PMID:24883356

Thinking, Creativity, and Artificial Intelligence.

ERIC Educational Resources Information Center

DeSiano, Michael; DeSiano, Salvatore

This document provides an introduction to the relationship between the current knowledge of focused and creative thinking and artificial intelligence. A model for stages of focused and creative thinking gives: problem encounter/setting, preparation, concentration/incubation, clarification/generation and evaluation/judgment. While a computer can…

A Neural Relevance Model for Feature Extraction from Hyperspectral Images, and Its Application in the Wavelet Domain

DTIC Science & Technology

2006-08-01

Nikolas Avouris. Evaluation of classifiers for an uneven class distribution problem. Applied Artificial Intellegence , pages 1-24, 2006. Draft manuscript...data by a hybrid artificial neural network so we may evaluate the classification capabilities of the baseline GRLVQ and our improved GRLVQI. Chapter 4...performance of GRLVQ(I), we compare the results against a baseline classification of the 23-class problem with a hybrid artificial neural network (ANN

Artificial emotion triggered stochastic behavior transitions with motivational gain effects for multi-objective robot tasks

NASA Astrophysics Data System (ADS)

Dağlarli, Evren; Temeltaş, Hakan

2007-04-01

This paper presents artificial emotional system based autonomous robot control architecture. Hidden Markov model developed as mathematical background for stochastic emotional and behavior transitions. Motivation module of architecture considered as behavioral gain effect generator for achieving multi-objective robot tasks. According to emotional and behavioral state transition probabilities, artificial emotions determine sequences of behaviors. Also motivational gain effects of proposed architecture can be observed on the executing behaviors during simulation.

Experimental comparisons between McKibben type artificial muscles and straight fibers type artificial muscles

NASA Astrophysics Data System (ADS)

Nakamura, Taro

2007-01-01

This paper describes experimental comparison between a conventional McKibben type artificial muscle and a straight fibers type artificial muscle developed by the authors. A wearable device and a rehabilitation robot which assists a human muscle should have characteristics similar to those of human muscle. In addition, because the wearable device and the rehabilitation robot should be light, an actuator with a high power/weight ratio is needed. At present, the McKibben type is widely used as an artificial muscle, but in fact its physical model is highly nonlinear. Further, the heat and mechanical loss of this actuator are large because of the friction caused by the expansion and contraction of the sleeve. Therefore, the authors have developed an artificial muscle tube in which high strength glass fibers have been built into the tube made from natural latex rubber. As results, experimental results demonstrated that the developed artificial muscle is more effective regarding its fundamental characteristics than that of the McKibben type; the straight fibers types of artificial muscle have more contraction ratio and power, longer lifetime than the McKibben types. And it has almost same characteristics of human muscle for isotonic and isometric that evaluate it dynamically.

The Association Between Artificial Sweeteners and Obesity.

PubMed

Pearlman, Michelle; Obert, Jon; Casey, Lisa

2017-11-21

The purpose of this paper is to review the epidemiology of obesity and the evolution of artificial sweeteners; to examine the latest research on the effects of artificial sweeteners on the host microbiome, the gut-brain axis, glucose homeostasis, and energy consumption; and to discuss how all of these changes ultimately contribute to obesity. Although artificial sweeteners were developed as a sugar substitute to help reduce insulin resistance and obesity, data in both animal models and humans suggest that the effects of artificial sweeteners may contribute to metabolic syndrome and the obesity epidemic. Artificial sweeteners appear to change the host microbiome, lead to decreased satiety, and alter glucose homeostasis, and are associated with increased caloric consumption and weight gain. Artificial sweeteners are marketed as a healthy alternative to sugar and as a tool for weight loss. Data however suggests that the intended effects do not correlate with what is seen in clinical practice. Future research should focus on the newer plant-based sweeteners, incorporate extended study durations to determine the long-term effects of artificial sweetener consumption, and focus on changes in the microbiome, as that seems to be one of the main driving forces behind nutrient absorption and glucose metabolism.

Use of artificial intelligence to identify cardiovascular compromise in a model of hemorrhagic shock.

PubMed

Glass, Todd F; Knapp, Jason; Amburn, Philip; Clay, Bruce A; Kabrisky, Matt; Rogers, Steven K; Garcia, Victor F

2004-02-01

To determine whether a prototype artificial intelligence system can identify volume of hemorrhage in a porcine model of controlled hemorrhagic shock. Prospective in vivo animal model of hemorrhagic shock. Research foundation animal surgical suite; computer laboratories of collaborating industry partner. Nineteen, juvenile, 25- to 35-kg, male and female swine. Anesthetized animals were instrumented for arterial and systemic venous pressure monitoring and blood sampling, and a splenectomy was performed. Following a 1-hr stabilization period, animals were hemorrhaged in aliquots to 10, 20, 30, 35, 40, 45, and 50% of total blood volume with a 10-min recovery between each aliquot. Data were downloaded directly from a commercial monitoring system into a proprietary PC-based software package for analysis. Arterial and venous blood gas values, glucose, and cardiac output were collected at specified intervals. Electrocardiogram, electroencephalogram, mixed venous oxygen saturation, temperature (core and blood), mean arterial pressure, pulmonary artery pressure, central venous pressure, pulse oximetry, and end-tidal CO(2) were continuously monitored and downloaded. Seventeen of 19 animals (89%) died as a direct result of hemorrhage. Stored data streams were analyzed by the prototype artificial intelligence system. For this project, the artificial intelligence system identified and compared three electrocardiographic features (R-R interval, QRS amplitude, and R-S interval) from each of nine unknown samples of the QRS complex. We found that the artificial intelligence system, trained on only three electrocardiographic features, identified hemorrhage volume with an average accuracy of 91% (95% confidence interval, 84-96%). These experiments demonstrate that an artificial intelligence system, based solely on the analysis of QRS amplitude, R-R interval, and R-S interval of an electrocardiogram, is able to accurately identify hemorrhage volume in a porcine model of lethal hemorrhagic shock. We suggest that this technology may represent a noninvasive means of assessing the physiologic state during and immediately following hemorrhage. Point of care application of this technology may improve outcomes with earlier diagnosis and better titration of therapy of shock.

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.

Bias correction of temperature produced by the Community Climate System Model using Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Moghim, S.; Hsu, K.; Bras, R. L.

2013-12-01

General Circulation Models (GCMs) are used to predict circulation and energy transfers between the atmosphere and the land. It is known that these models produce biased results that will have impact on their uses. This work proposes a new method for bias correction: the equidistant cumulative distribution function-artificial neural network (EDCDFANN) procedure. The method uses artificial neural networks (ANNs) as a surrogate model to estimate bias-corrected temperature, given an identification of the system derived from GCM models output variables. A two-layer feed forward neural network is trained with observations during a historical period and then the adjusted network can be used to predict bias-corrected temperature for future periods. To capture the extreme values this method is combined with the equidistant CDF matching method (EDCDF, Li et al. 2010). The proposed method is tested with the Community Climate System Model (CCSM3) outputs using air and skin temperature, specific humidity, shortwave and longwave radiation as inputs to the ANN. This method decreases the mean square error and increases the spatial correlation between the modeled temperature and the observed one. The results indicate the EDCDFANN has potential to remove the biases of the model outputs.

Rate decline curves analysis of multiple-fractured horizontal wells in heterogeneous reservoirs

NASA Astrophysics Data System (ADS)

Wang, Jiahang; Wang, Xiaodong; Dong, Wenxiu

2017-10-01

In heterogeneous reservoir with multiple-fractured horizontal wells (MFHWs), due to the high density network of artificial hydraulic fractures, the fluid flow around fracture tips behaves like non-linear flow. Moreover, the production behaviors of different artificial hydraulic fractures are also different. A rigorous semi-analytical model for MFHWs in heterogeneous reservoirs is presented by combining source function with boundary element method. The model are first validated by both analytical model and simulation model. Then new Blasingame type curves are established. Finally, the effects of critical parameters on the rate decline characteristics of MFHWs are discussed. The results show that heterogeneity has significant influence on the rate decline characteristics of MFHWs; the parameters related to the MFHWs, such as fracture conductivity and length also can affect the rate characteristics of MFHWs. One novelty of this model is to consider the elliptical flow around artificial hydraulic fracture tips. Therefore, our model can be used to predict rate performance more accurately for MFHWs in heterogeneous reservoir. The other novelty is the ability to model the different production behavior at different fracture stages. Compared to numerical and analytic methods, this model can not only reduce extensive computing processing but also show high accuracy.

Comparison of Computational-Model and Experimental-Example Trained Neural Networks for Processing Speckled Fringe Patterns

NASA Technical Reports Server (NTRS)

Decker, A. J.; Fite, E. B.; Thorp, S. A.; Mehmed, O.

1998-01-01

The responses of artificial neural networks to experimental and model-generated inputs are compared for detection of damage in twisted fan blades using electronic holography. The training-set inputs, for this work, are experimentally generated characteristic patterns of the vibrating blades. The outputs are damage-flag indicators or second derivatives of the sensitivity-vector-projected displacement vectors from a finite element model. Artificial neural networks have been trained in the past with computational-model-generated training sets. This approach avoids the difficult inverse calculations traditionally used to compare interference fringes with the models. But the high modeling standards are hard to achieve, even with fan-blade finite-element models.

Comparison of Computational, Model and Experimental, Example Trained Neural Networks for Processing Speckled Fringe Patterns

NASA Technical Reports Server (NTRS)

Decker, A. J.; Fite, E. B.; Thorp, S. A.; Mehmed, O.

1998-01-01

The responses of artificial neural networks to experimental and model-generated inputs are compared for detection of damage in twisted fan blades using electronic holography. The training-set inputs, for this work, are experimentally generated characteristic patterns of the vibrating blades. The outputs are damage-flag indicators or second derivatives of the sensitivity-vector-projected displacement vectors from a finite element model. Artificial neural networks have been trained in the past with computational-model- generated training sets. This approach avoids the difficult inverse calculations traditionally used to compare interference fringes with the models. But the high modeling standards are hard to achieve, even with fan-blade finite-element models.

Biomechanical Effects of the Geometry of Ball-and-Socket Artificial Disc on Lumbar Spine: A Finite Element Study.

PubMed

Choi, Jisoo; Shin, Dong-Ah; Kim, Sohee

2017-03-15

A three-dimensional finite element model of intact lumbar spine was constructed and four surgical finite element models implanted with ball-and-socket artificial discs with four different radii of curvature were compared. To investigate biomechanical effects of the curvature of ball-and-socket artificial disc using finite element analysis. Total disc replacement (TDR) has been accepted as an alternative treatment because of its advantages over spinal fusion methods in degenerative disc disease. However, the influence of the curvature of artificial ball-and-socket discs has not been fully understood. Four surgical finite element models with different radii of curvature of ball-and-socket artificial discs were constructed. The range of motion (ROM) increased with decreasing radius of curvature in extension, flexion, and lateral bending, whereas it increased with increasing radius of curvature in axial torsion. The facet contact force was minimum with the largest radius of curvature in extension, flexion, and lateral bending, whereas it was maximum with the largest radius in axial torsion. It was also affected by the disc placement, more with posterior placement than anterior placement. The stress in L4 cancellous bone increased when the radius of curvature was too large or small. The geometry of ball-and-socket artificial disc significantly affects the ROM, facet contact force, and stress in the cancellous bone at the surgical level. The implication is that in performing TDR, the ball-and-socket design may not be ideal, as ROM and facet contact force are sensitive to the disc design, which may be exaggerated by the individual difference of anatomical geometry. N/A.

Evaluation of a Novel Artificial Tear in the Prevention and Treatment of Dry Eye in an Animal Model.

PubMed

She, Yujing; Li, Jinyang; Xiao, Bing; Lu, Huihui; Liu, Haixia; Simmons, Peter A; Vehige, Joseph G; Chen, Wei

2015-11-01

To evaluate effects of a novel multi-ingredient artificial tear formulation containing carboxymethylcellulose (CMC) and hyaluronic acid (HA) in a murine dry eye model. Dry eye was induced in mice (C57BL/6) using an intelligently controlled environmental system (ICES). CMC+HA (Optive Fusion™), CMC-only (Refresh Tears(®)), and HA-only (Hycosan(®)) artificial tears and control phosphate-buffered saline (PBS) were administered 4 times daily and compared with no treatment (n = 64 eyes per group). During regimen 1 (prevention regimen), mice were administered artificial tears or PBS for 14 days (starting day 0) while they were exposed to ICES, and assessed on days 0 and 14. During regimen 2 (treatment regimen), mice exposed to ICES for 14 days with no intervention were administered artificial tears or PBS for 14 days (starting day 14) while continuing exposure to ICES, and assessed on days 0, 14, and 28. Corneal fluorescein staining and conjunctival goblet cell density were measured. Artificial tear-treated mice had significantly better outcomes than control groups on corneal staining and goblet cell density (P < 0.01). Mice administered CMC+HA also showed significantly lower corneal fluorescein staining and higher goblet cell density, compared with CMC (P < 0.01) and HA (P < 0.05) in both regimens 1 and 2. The artificial tear formulation containing CMC and HA was effective in preventing and treating environmentally induced dry eye. Improvements observed for corneal fluorescein staining and conjunctival goblet cell retention suggest that this combination may be a viable treatment option for dry eye disease.

Handling a Small Dataset Problem in Prediction Model by employ Artificial Data Generation Approach: A Review

NASA Astrophysics Data System (ADS)

Lateh, Masitah Abdul; Kamilah Muda, Azah; Yusof, Zeratul Izzah Mohd; Azilah Muda, Noor; Sanusi Azmi, Mohd

2017-09-01

The emerging era of big data for past few years has led to large and complex data which needed faster and better decision making. However, the small dataset problems still arise in a certain area which causes analysis and decision are hard to make. In order to build a prediction model, a large sample is required as a training sample of the model. Small dataset is insufficient to produce an accurate prediction model. This paper will review an artificial data generation approach as one of the solution to solve the small dataset problem.

Use of an Artificial Neural Network to Construct a Model of Predicting Deep Fungal Infection in Lung Cancer Patients.

PubMed

Chen, Jian; Chen, Jie; Ding, Hong-Yan; Pan, Qin-Shi; Hong, Wan-Dong; Xu, Gang; Yu, Fang-You; Wang, Yu-Min

2015-01-01

The statistical methods to analyze and predict the related dangerous factors of deep fungal infection in lung cancer patients were several, such as logic regression analysis, meta-analysis, multivariate Cox proportional hazards model analysis, retrospective analysis, and so on, but the results are inconsistent. A total of 696 patients with lung cancer were enrolled. The factors were compared employing Student's t-test or the Mann-Whitney test or the Chi-square test and variables that were significantly related to the presence of deep fungal infection selected as candidates for input into the final artificial neural network analysis (ANN) model. The receiver operating characteristic (ROC) and area under curve (AUC) were used to evaluate the performance of the artificial neural network (ANN) model and logistic regression (LR) model. The prevalence of deep fungal infection from lung cancer in this entire study population was 32.04%(223/696), deep fungal infections occur in sputum specimens 44.05% (200/454). The ratio of candida albicans was 86.99% (194/223) in the total fungi. It was demonstrated that older (≥65 years), use of antibiotics, low serum albumin concentrations (≤37.18 g /L), radiotherapy, surgery, low hemoglobin hyperlipidemia (≤93.67 g /L), long time of hospitalization (≥14 days) were apt to deep fungal infection and the ANN model consisted of the seven factors. The AUC of ANN model (0.829±0.019) was higher than that of LR model (0.756±0.021). The artificial neural network model with variables consisting of age, use of antibiotics, serum albumin concentrations, received radiotherapy, received surgery, hemoglobin, time of hospitalization should be useful for predicting the deep fungal infection in lung cancer.

Artificial intelligence support for scientific model-building

NASA Technical Reports Server (NTRS)

Keller, Richard M.

1992-01-01

Scientific model-building can be a time-intensive and painstaking process, often involving the development of large and complex computer programs. Despite the effort involved, scientific models cannot easily be distributed and shared with other scientists. In general, implemented scientific models are complex, idiosyncratic, and difficult for anyone but the original scientific development team to understand. We believe that artificial intelligence techniques can facilitate both the model-building and model-sharing process. In this paper, we overview our effort to build a scientific modeling software tool that aids the scientist in developing and using models. This tool includes an interactive intelligent graphical interface, a high-level domain specific modeling language, a library of physics equations and experimental datasets, and a suite of data display facilities.

Generalized in vitro-in vivo relationship (IVIVR) model based on artificial neural networks

PubMed Central

Mendyk, Aleksander; Tuszyński, Paweł K; Polak, Sebastian; Jachowicz, Renata

2013-01-01

Background The aim of this study was to develop a generalized in vitro-in vivo relationship (IVIVR) model based on in vitro dissolution profiles together with quantitative and qualitative composition of dosage formulations as covariates. Such a model would be of substantial aid in the early stages of development of a pharmaceutical formulation, when no in vivo results are yet available and it is impossible to create a classical in vitro-in vivo correlation (IVIVC)/IVIVR. Methods Chemoinformatics software was used to compute the molecular descriptors of drug substances (ie, active pharmaceutical ingredients) and excipients. The data were collected from the literature. Artificial neural networks were used as the modeling tool. The training process was carried out using the 10-fold cross-validation technique. Results The database contained 93 formulations with 307 inputs initially, and was later limited to 28 in a course of sensitivity analysis. The four best models were introduced into the artificial neural network ensemble. Complete in vivo profiles were predicted accurately for 37.6% of the formulations. Conclusion It has been shown that artificial neural networks can be an effective predictive tool for constructing IVIVR in an integrated generalized model for various formulations. Because IVIVC/IVIVR is classically conducted for 2–4 formulations and with a single active pharmaceutical ingredient, the approach described here is unique in that it incorporates various active pharmaceutical ingredients and dosage forms into a single model. Thus, preliminary IVIVC/IVIVR can be available without in vivo data, which is impossible using current IVIVC/IVIVR procedures. PMID:23569360

[The Identification of the Origin of Chinese Wolfberry Based on Infrared Spectral Technology and the Artificial Neural Network].

PubMed

Li, Zhong; Liu, Ming-de; Ji, Shou-xiang

2016-03-01

The Fourier Transform Infrared Spectroscopy (FTIR) is established to find the geographic origins of Chinese wolfberry quickly. In the paper, the 45 samples of Chinese wolfberry from different places of Qinghai Province are to be surveyed by FTIR. The original data matrix of FTIR is pretreated with common preprocessing and wavelet transform. Compared with common windows shifting smoothing preprocessing, standard normal variation correction and multiplicative scatter correction, wavelet transform is an effective spectrum data preprocessing method. Before establishing model through the artificial neural networks, the spectra variables are compressed by means of the wavelet transformation so as to enhance the training speed of the artificial neural networks, and at the same time the related parameters of the artificial neural networks model are also discussed in detail. The survey shows even if the infrared spectroscopy data is compressed to 1/8 of its original data, the spectral information and analytical accuracy are not deteriorated. The compressed spectra variables are used for modeling parameters of the backpropagation artificial neural network (BP-ANN) model and the geographic origins of Chinese wolfberry are used for parameters of export. Three layers of neural network model are built to predict the 10 unknown samples by using the MATLAB neural network toolbox design error back propagation network. The number of hidden layer neurons is 5, and the number of output layer neuron is 1. The transfer function of hidden layer is tansig, while the transfer function of output layer is purelin. Network training function is trainl and the learning function of weights and thresholds is learngdm. net. trainParam. epochs=1 000, while net. trainParam. goal = 0.001. The recognition rate of 100% is to be achieved. It can be concluded that the method is quite suitable for the quick discrimination of producing areas of Chinese wolfberry. The infrared spectral analysis technology combined with the artificial neural networks is proved to be a reliable and new method for the identification of the original place of Traditional Chinese Medicine.

Self Improving Methods for Materials and Process Design

DTIC Science & Technology

1998-08-31

using inductive coupling techniques. The first phase of the work focuses on developing an artificial neural network learning for function approximation...developing an artificial neural network learning algorithm for time-series prediction. The third phase of the work focuses on model selection. We have

Probabilistic modelling to assess exposure to three artificial sweeteners of young Irish patients aged 1-3 years with PKU and CMPA.

PubMed

O'Sullivan, Aaron J; Pigat, Sandrine; O'Mahony, Cian; Gibney, Michael J; McKevitt, Aideen I

2016-11-01

The choice of suitable normal foods is limited for individuals with particular medical conditions, e.g., inborn errors of metabolism (phenylketonuria - PKU) or severe cow's milk protein allergy (CMPA). Patients may have dietary restrictions and exclusive or partial replacement of specific food groups with specially formulated products to meet particular nutrition requirements. Artificial sweeteners are used to improve the appearance and palatability of such food products to avoid food refusal and ensure dietary adherence. Young children have a higher risk of exceeding acceptable daily intakes for additives than adults due to higher food intakes kg -1 body weight. The Budget Method and EFSA's Food Additives Intake Model (FAIM) are not equipped to assess partial dietary replacement with special formulations as they are built on data from dietary surveys of consumers without special medical requirements impacting the diet. The aim of this study was to explore dietary exposure modelling as a means of estimating the intake of artificial sweeteners by young PKU and CMPA patients aged 1-3 years. An adapted validated probabilistic model (FACET) was used to assess patients' exposure to artificial sweeteners. Food consumption data were derived from the food consumption survey data of healthy young children in Ireland from the National Preschool and Nutrition Survey (NPNS, 2010-11). Specially formulated foods for special medical purposes were included in the exposure model to replace restricted foods. Inclusion was based on recommendations for adequate protein intake and dietary adherence data. Exposure assessment results indicated that young children with PKU and CMPA have higher relative average intakes of artificial sweeteners than healthy young children. The reliability and robustness of the model in the estimation of patient additive exposures was further investigated and provides the first exposure estimates for these special populations.

Validation of artificial neural network models for predicting biochemical markers associated with male infertility.

PubMed

Vickram, A S; Kamini, A Rao; Das, Raja; Pathy, M Ramesh; Parameswari, R; Archana, K; Sridharan, T B

2016-08-01

Seminal fluid is the secretion from many glands comprised of several organic and inorganic compounds including free amino acids, proteins, fructose, glucosidase, zinc, and other scavenging elements like Mg(2+), Ca(2+), K(+), and Na(+). Therefore, in the view of development of novel approaches and proper diagnosis to male infertility, overall understanding of the biochemical and molecular composition and its role in regulation of sperm quality is highly desirable. Perhaps this can be achieved through artificial intelligence. This study was aimed to elucidate and predict various biochemical markers present in human seminal plasma with three different neural network models. A total of 177 semen samples were collected for this research (both fertile and infertile samples) and immediately processed to prepare a semen analysis report, based on the protocol of the World Health Organization (WHO [2010]). The semen samples were then categorized into oligoasthenospermia (n=35), asthenospermia (n=35), azoospermia (n=22), normospermia (n=34), oligospermia (n=34), and control (n=17). The major biochemical parameters like total protein content, fructose, glucosidase, and zinc content were elucidated by standard protocols. All the biochemical markers were predicted by using three different artificial neural network (ANN) models with semen parameters as inputs. Of the three models, the back propagation neural network model (BPNN) yielded the best results with mean absolute error 0.025, -0.080, 0.166, and -0.057 for protein, fructose, glucosidase, and zinc, respectively. This suggests that BPNN can be used to predict biochemical parameters for the proper diagnosis of male infertility in assisted reproductive technology (ART) centres. AAS: absorption spectroscopy; AI: artificial intelligence; ANN: artificial neural networks; ART: assisted reproductive technology; BPNN: back propagation neural network model; DT: decision tress; MLP: multilayer perceptron; PESA: percutaneous epididymal sperm spiration; RBFN: radical basis function network; SRNN: simple recurrent neural network; SVM: support vector machines; TSE: testicular sperm extraction; WHO: World Health Organization.

Quantum neural networks: Current status and prospects for development

NASA Astrophysics Data System (ADS)

Altaisky, M. V.; Kaputkina, N. E.; Krylov, V. A.

2014-11-01

The idea of quantum artificial neural networks, first formulated in [34], unites the artificial neural network concept with the quantum computation paradigm. Quantum artificial neural networks were first systematically considered in the PhD thesis by T. Menneer (1998). Based on the works of Menneer and Narayanan [42, 43], Kouda, Matsui, and Nishimura [35, 36], Altaisky [2, 68], Zhou [67], and others, quantum-inspired learning algorithms for neural networks were developed, and are now used in various training programs and computer games [29, 30]. The first practically realizable scaled hardware-implemented model of the quantum artificial neural network is obtained by D-Wave Systems, Inc. [33]. It is a quantum Hopfield network implemented on the basis of superconducting quantum interference devices (SQUIDs). In this work we analyze possibilities and underlying principles of an alternative way to implement quantum neural networks on the basis of quantum dots. A possibility of using quantum neural network algorithms in automated control systems, associative memory devices, and in modeling biological and social networks is examined.

Towards artificial tissue models: past, present, and future of 3D bioprinting.

PubMed

Arslan-Yildiz, Ahu; El Assal, Rami; Chen, Pu; Guven, Sinan; Inci, Fatih; Demirci, Utkan

2016-03-01

Regenerative medicine and tissue engineering have seen unprecedented growth in the past decade, driving the field of artificial tissue models towards a revolution in future medicine. Major progress has been achieved through the development of innovative biomanufacturing strategies to pattern and assemble cells and extracellular matrix (ECM) in three-dimensions (3D) to create functional tissue constructs. Bioprinting has emerged as a promising 3D biomanufacturing technology, enabling precise control over spatial and temporal distribution of cells and ECM. Bioprinting technology can be used to engineer artificial tissues and organs by producing scaffolds with controlled spatial heterogeneity of physical properties, cellular composition, and ECM organization. This innovative approach is increasingly utilized in biomedicine, and has potential to create artificial functional constructs for drug screening and toxicology research, as well as tissue and organ transplantation. Herein, we review the recent advances in bioprinting technologies and discuss current markets, approaches, and biomedical applications. We also present current challenges and provide future directions for bioprinting research.

Nano-sized layered Mn oxides as promising and biomimetic water oxidizing catalysts for water splitting in artificial photosynthetic systems.

PubMed

Najafpour, Mohammad Mahdi; Heidari, Sima; Amini, Emad; Khatamian, Masoumeh; Carpentier, Robert; Allakhverdiev, Suleyman I

2014-04-05

One challenge in artificial photosynthetic systems is the development of artificial model compounds to oxidize water. The water-oxidizing complex of Photosystem II which is responsible for biological water oxidation contains a cluster of four Mn ions bridged by five oxygen atoms. Layered Mn oxides as efficient, stable, low cost, environmentally friendly and easy to use, synthesize, and manufacture compounds could be considered as functional and structural models for the site. Because of the related structure of these Mn oxides and the catalytic centre of the active site of the water oxidizing complex of Photosystem II, the study of layered Mn oxides may also help to understand more about the mechanism of water oxidation by the natural site. This review provides an overview of the current status of layered Mn oxides in artificial photosynthesis and discuss the sophisticated design strategies for Mn oxides as water oxidizing catalysts. Copyright © 2014 Elsevier B.V. All rights reserved.

Thermally induced magnetic relaxation in square artificial spin ice.

PubMed

Andersson, M S; Pappas, S D; Stopfel, H; Östman, E; Stein, A; Nordblad, P; Mathieu, R; Hjörvarsson, B; Kapaklis, V

2016-11-24

The properties of natural and artificial assemblies of interacting elements, ranging from Quarks to Galaxies, are at the heart of Physics. The collective response and dynamics of such assemblies are dictated by the intrinsic dynamical properties of the building blocks, the nature of their interactions and topological constraints. Here we report on the relaxation dynamics of the magnetization of artificial assemblies of mesoscopic spins. In our model nano-magnetic system - square artificial spin ice - we are able to control the geometrical arrangement and interaction strength between the magnetically interacting building blocks by means of nano-lithography. Using time resolved magnetometry we show that the relaxation process can be described using the Kohlrausch law and that the extracted temperature dependent relaxation times of the assemblies follow the Vogel-Fulcher law. The results provide insight into the relaxation dynamics of mesoscopic nano-magnetic model systems, with adjustable energy and time scales, and demonstrates that these can serve as an ideal playground for the studies of collective dynamics and relaxations.

Thermally induced magnetic relaxation in square artificial spin ice

NASA Astrophysics Data System (ADS)

Andersson, M. S.; Pappas, S. D.; Stopfel, H.; Östman, E.; Stein, A.; Nordblad, P.; Mathieu, R.; Hjörvarsson, B.; Kapaklis, V.

2016-11-01

The properties of natural and artificial assemblies of interacting elements, ranging from Quarks to Galaxies, are at the heart of Physics. The collective response and dynamics of such assemblies are dictated by the intrinsic dynamical properties of the building blocks, the nature of their interactions and topological constraints. Here we report on the relaxation dynamics of the magnetization of artificial assemblies of mesoscopic spins. In our model nano-magnetic system - square artificial spin ice - we are able to control the geometrical arrangement and interaction strength between the magnetically interacting building blocks by means of nano-lithography. Using time resolved magnetometry we show that the relaxation process can be described using the Kohlrausch law and that the extracted temperature dependent relaxation times of the assemblies follow the Vogel-Fulcher law. The results provide insight into the relaxation dynamics of mesoscopic nano-magnetic model systems, with adjustable energy and time scales, and demonstrates that these can serve as an ideal playground for the studies of collective dynamics and relaxations.

Artificial muscles made of chiral two-way shape memory polymer fibers

NASA Astrophysics Data System (ADS)

Yang, Qianxi; Fan, Jizhou; Li, Guoqiang

2016-10-01

In this work, we demonstrate the unusual improvement of the tensile actuation of hierarchically chiral structured artificial muscle made of two-way shape memory polymer (2W-SMP) fiber. Experimental results show that the chemically cross-linked poly(ethylene-co-vinyl acetate) 2W-SMP fibers possess an average negative coefficient of thermal expansion (NCTE) that is at least one order higher than that of the polyethylene fiber used previously. As expected, the increase in axial thermal contraction of the precursor fiber leads to an increase in the recovered torque ( 4.4 Nmm ) of the chiral fiber and eventually in the tensile actuation of the twisted-then-coiled artificial muscle ( 67.81 ±1.82 % ). A mechanical model based on Castigliano's second theorem is proposed, and the calculated result is consistent with the experimental result (64.17% tensile stroke). The model proves the significance of the NCTE and the recovered torque on tensile actuation of the artificial muscle and can be used as a guidance for the future design.

Approaches to 3D printing teeth from X-ray microtomography.

PubMed

Cresswell-Boyes, A J; Barber, A H; Mills, D; Tatla, A; Davis, G R

2018-06-28

Artificial teeth have several advantages in preclinical training. The aim of this study is to three-dimensionally (3D) print accurate artificial teeth using scans from X-ray microtomography (XMT). Extracted and artificial teeth were imaged at 90 kV and 40 kV, respectively, to create detailed high contrast scans. The dataset was visualised to produce internal and external meshes subsequently exported to 3D modelling software for modification before finally sending to a slicing program for printing. After appropriate parameter setting, the printer deposited material in specific locations layer by layer, to create a 3D physical model. Scans were manipulated to ensure a clean model was imported into the slicing software, where layer height replicated the high spatial resolution that was observed in the XMT scans. The model was then printed in two different materials (polylactic acid and thermoplastic elastomer). A multimaterial print was created to show the different physical characteristics between enamel and dentine. © 2018 The Authors Journal of Microscopy © 2018 Royal Microscopical Society.

De Novo Design of Bioactive Small Molecules by Artificial Intelligence.

PubMed

Merk, Daniel; Friedrich, Lukas; Grisoni, Francesca; Schneider, Gisbert

2018-01-01

Generative artificial intelligence offers a fresh view on molecular design. We present the first-time prospective application of a deep learning model for designing new druglike compounds with desired activities. For this purpose, we trained a recurrent neural network to capture the constitution of a large set of known bioactive compounds represented as SMILES strings. By transfer learning, this general model was fine-tuned on recognizing retinoid X and peroxisome proliferator-activated receptor agonists. We synthesized five top-ranking compounds designed by the generative model. Four of the compounds revealed nanomolar to low-micromolar receptor modulatory activity in cell-based assays. Apparently, the computational model intrinsically captured relevant chemical and biological knowledge without the need for explicit rules. The results of this study advocate generative artificial intelligence for prospective de novo molecular design, and demonstrate the potential of these methods for future medicinal chemistry. © 2018 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.

The application of data mining techniques to oral cancer prognosis.

PubMed

Tseng, Wan-Ting; Chiang, Wei-Fan; Liu, Shyun-Yeu; Roan, Jinsheng; Lin, Chun-Nan

2015-05-01

This study adopted an integrated procedure that combines the clustering and classification features of data mining technology to determine the differences between the symptoms shown in past cases where patients died from or survived oral cancer. Two data mining tools, namely decision tree and artificial neural network, were used to analyze the historical cases of oral cancer, and their performance was compared with that of logistic regression, the popular statistical analysis tool. Both decision tree and artificial neural network models showed superiority to the traditional statistical model. However, as to clinician, the trees created by the decision tree models are relatively easier to interpret compared to that of the artificial neural network models. Cluster analysis also discovers that those stage 4 patients whose also possess the following four characteristics are having an extremely low survival rate: pN is N2b, level of RLNM is level I-III, AJCC-T is T4, and cells mutate situation (G) is moderate.

Prediction of pork loin quality using online computer vision system and artificial intelligence model.

PubMed

Sun, Xin; Young, Jennifer; Liu, Jeng-Hung; Newman, David

2018-06-01

The objective of this project was to develop a computer vision system (CVS) for objective measurement of pork loin under industry speed requirement. Color images of pork loin samples were acquired using a CVS. Subjective color and marbling scores were determined according to the National Pork Board standards by a trained evaluator. Instrument color measurement and crude fat percentage were used as control measurements. Image features (18 color features; 1 marbling feature; 88 texture features) were extracted from whole pork loin color images. Artificial intelligence prediction model (support vector machine) was established for pork color and marbling quality grades. The results showed that CVS with support vector machine modeling reached the highest prediction accuracy of 92.5% for measured pork color score and 75.0% for measured pork marbling score. This research shows that the proposed artificial intelligence prediction model with CVS can provide an effective tool for predicting color and marbling in the pork industry at online speeds. Copyright © 2018 Elsevier Ltd. All rights reserved.

Numerical Simulation and Artificial Neural Network Modeling for Predicting Welding-Induced Distortion in Butt-Welded 304L Stainless Steel Plates

NASA Astrophysics Data System (ADS)

Narayanareddy, V. V.; Chandrasekhar, N.; Vasudevan, M.; Muthukumaran, S.; Vasantharaja, P.

2016-02-01

In the present study, artificial neural network modeling has been employed for predicting welding-induced angular distortions in autogenous butt-welded 304L stainless steel plates. The input data for the neural network have been obtained from a series of three-dimensional finite element simulations of TIG welding for a wide range of plate dimensions. Thermo-elasto-plastic analysis was carried out for 304L stainless steel plates during autogenous TIG welding employing double ellipsoidal heat source. The simulated thermal cycles were validated by measuring thermal cycles using thermocouples at predetermined positions, and the simulated distortion values were validated by measuring distortion using vertical height gauge for three cases. There was a good agreement between the model predictions and the measured values. Then, a multilayer feed-forward back propagation neural network has been developed using the numerically simulated data. Artificial neural network model developed in the present study predicted the angular distortion accurately.

Human Robotic Swarm Interaction Using an Artificial Physics Approach

DTIC Science & Technology

2014-12-01

calculates virtual forces that are summed and translated into velocity commands. The virtual forces are modeled after real physical forces such as...results from the physical experiments show that an artificial physics-based framework is an effective way to allow multiple agents to follow a human... modeled after real physical forces such as gravitational and Coulomb, forces but are not restricted to them, for example, the force magnitude may not be

Application of artificial intelligence (AI) concepts to the development of space flight parts approval model

NASA Technical Reports Server (NTRS)

Krishnan, G. S.

1997-01-01

A cost effective model which uses the artificial intelligence techniques in the selection and approval of parts is presented. The knowledge which is acquired from the specialists for different part types are represented in a knowledge base in the form of rules and objects. The parts information is stored separately in a data base and is isolated from the knowledge base. Validation, verification and performance issues are highlighted.

Incorporating GIS building data and census housing statistics for sub-block-level population estimation

USGS Publications Warehouse

Wu, S.-S.; Wang, L.; Qiu, X.

2008-01-01

This article presents a deterministic model for sub-block-level population estimation based on the total building volumes derived from geographic information system (GIS) building data and three census block-level housing statistics. To assess the model, we generated artificial blocks by aggregating census block areas and calculating the respective housing statistics. We then applied the model to estimate populations for sub-artificial-block areas and assessed the estimates with census populations of the areas. Our analyses indicate that the average percent error of population estimation for sub-artificial-block areas is comparable to those for sub-census-block areas of the same size relative to associated blocks. The smaller the sub-block-level areas, the higher the population estimation errors. For example, the average percent error for residential areas is approximately 0.11 percent for 100 percent block areas and 35 percent for 5 percent block areas.

Decade Review (1999-2009): Artificial Intelligence Techniques in Student Modeling

NASA Astrophysics Data System (ADS)

Drigas, Athanasios S.; Argyri, Katerina; Vrettaros, John

Artificial Intelligence applications in educational field are getting more and more popular during the last decade (1999-2009) and that is why much relevant research has been conducted. In this paper, we present the most interesting attempts to apply artificial intelligence methods such as fuzzy logic, neural networks, genetic programming and hybrid approaches such as neuro - fuzzy systems and genetic programming neural networks (GPNN) in student modeling. This latest research trend is a part of every Intelligent Tutoring System and aims at generating and updating a student model in order to modify learning content to fit individual needs or to provide reliable assessment and feedback to student's answers. In this paper, we make a brief presentation of methods used to point out their qualities and then we attempt a navigation to the most representative studies sought in the decade of our interest after classifying them according to the principal aim they attempted to serve.

Keratoprosthesis: preliminary results of an artificial corneal button as a full-thickness implant in the rabbit model.

PubMed

Hicks, C R; Chirila, T V; Dalton, P D; Clayton, A B; Vijayasekaran, S; Crawford, G J; Constable, I J

1996-08-01

To develop a prototype artificial cornea and evaluate it in the rabbit model. Hydrogel core-and-skirt keratoprostheses were made and were inserted as full-thickness implants covered with conjunctival flaps in the right eyes of eight rabbits. Peroperative complications related to inadequate mechanical strength led to failure in the early postoperative period in three animals, one was euthanased for an unrelated reason and the remaining four have been successful for up to 16 weeks' follow-up. Full-thickness implantation of an artificial cornea, analogous to penetrating keratoplasty, has been achieved in the rabbit model. Histological findings confirm that integration of the prosthesis with host tissue occurs. The main complications encountered in this preliminary series were related to inadequate strength of the sponge skirt of this prototype device. Work in our laboratories is now concentrated upon improving the mechanical qualities of the hydrogel skirt and on the enhancement of biointegration.

Stabilization of surface-immobilized enzymes using grafted polymers

NASA Astrophysics Data System (ADS)

Moskovitz, Yevgeny; Srebnik, Simcha

2004-03-01

Vast research efforts focus on improving the biocompatibility and biofunctionality of surfaces for artificial implants and organs. A relatively successful approach involves grafting of polymer (usually PEG) on the artificial surface, which significantly improves its biocompatibility. In addition, positioning enzymes on or in the vicinity of the surface can significantly enhance bioseparation processes. However, the catalytic activity of the anchored enzyme is often drastically impaired by the nonnatural environment, leading to loss of activity and denaturation. We study protein adsorption and stabilization by grafted polymers using a mean-field lattice model. The model protein is designed as a compact HP with a specific bulk conformation reproducing a catalytic cleft of natural enzymes. Using hydrophilic grafted polymers of tailored length and density, we show that the conformation as well as hydrophobic and active centers of the model enzyme can be restored. This research is inspired by the problem of biocompatibility and biofunctionality of surfaces for artificial implants and organs.

Microbenthic distribution of Proterozoic tidal flats: environmental and taphonomic considerations

NASA Technical Reports Server (NTRS)

Kah, L. C.; Knoll, A. H.

1996-01-01

Silicified carbonates of the late Mesoproterozoic to early Neoproterozoic Society Cliffs Formation, Baffin Island, contain distinctive microfabrics and microbenthic assemblages whose paleo-environmental distribution within the formation parallels the distribution of these elements through Proterozoic time. In the Society Cliffs Formation, restricted carbonates--including microdigitate stromatolites, laminated tufa, and tufted microbial mats--consist predominantly of synsedimentary cements; these facies and the cyanobacterial fossils they contain are common in Paleoproterozoic successions but rare in Neoproterozoic and younger rocks. Less restricted tidal-flat facies in the formation are composed of laminated microbialites dominated by micritic carbonate lithified early, yet demonstrably after compaction; these strata contain cyanobacteria that are characteristic in Neoproterozoic rocks. Within the formation, the facies-dependent distribution of microbial populations reflects both the style and timing of carbonate deposition because of the strong substrate specificity of benthic cyanobacteria. A reasonable conclusion is that secular changes in microbenthic assemblages through Proterozoic time reflect a decrease in the overall representation of rapidly lithified carbonate substrates in younger peritidal environments, as well as concomitant changes in the taphonomic window of silicification through which early life is observed.

Application of a PExSim for modeling a POLVAD artificial heart and the human circulatory system with left ventricle assistance

NASA Astrophysics Data System (ADS)

Siewnicka, Alicja; Fajdek, Bartlomiej; Janiszowski, Krzysztof

2010-01-01

This paper presents a model of the human circulatory system with the possible addition of a parallel assist device, which was developed for the purpose of artificial heart monitoring. Information about an identification experiment of an extracorporeal ventricle assist device POLVAD is included. The modelling methods applied and the corresponding functional blocks in a PExSim package are presented. The results of the simulation for physiological conditions, left ventricle failure and pathological conditions with parallel assistance are included.

The Hospital of the Future. Megatrends, Driving Forces, Barriers to Implementation, Overarching Perspectives, Major Trends into the Future, Implications for TATRC And Specific Recommendations for Action

DTIC Science & Technology

2008-10-01

Healthcare Systems Will Be Those That Work With Data/Info In New Ways • Artificial Intelligence Will Come to the Fore o Effectively Acquire...Education • Artificial Intelligence Will Assist in o History and Physical Examination o Imaging Selection via algorithms o Test Selection via algorithms...medical language into a simulation model based upon artificial intelligence , and • the content verification and validation of the cognitive

Functional approximation using artificial neural networks in structural mechanics

NASA Technical Reports Server (NTRS)

Alam, Javed; Berke, Laszlo

1993-01-01

The artificial neural networks (ANN) methodology is an outgrowth of research in artificial intelligence. In this study, the feed-forward network model that was proposed by Rumelhart, Hinton, and Williams was applied to the mapping of functions that are encountered in structural mechanics problems. Several different network configurations were chosen to train the available data for problems in materials characterization and structural analysis of plates and shells. By using the recall process, the accuracy of these trained networks was assessed.

Artificial Life in Quantum Technologies

NASA Astrophysics Data System (ADS)

Alvarez-Rodriguez, Unai; Sanz, Mikel; Lamata, Lucas; Solano, Enrique

2016-02-01

We develop a quantum information protocol that models the biological behaviours of individuals living in a natural selection scenario. The artificially engineered evolution of the quantum living units shows the fundamental features of life in a common environment, such as self-replication, mutation, interaction of individuals, and death. We propose how to mimic these bio-inspired features in a quantum-mechanical formalism, which allows for an experimental implementation achievable with current quantum platforms. This study paves the way for the realization of artificial life and embodied evolution with quantum technologies.

Artificial neural network in cosmic landscape

NASA Astrophysics Data System (ADS)

Liu, Junyu

2017-12-01

In this paper we propose that artificial neural network, the basis of machine learning, is useful to generate the inflationary landscape from a cosmological point of view. Traditional numerical simulations of a global cosmic landscape typically need an exponential complexity when the number of fields is large. However, a basic application of artificial neural network could solve the problem based on the universal approximation theorem of the multilayer perceptron. A toy model in inflation with multiple light fields is investigated numerically as an example of such an application.

Anatomy of the bovine ascending aorta and brachiocephalic artery found unfavorable for total artificial heart implant.

PubMed

Karimov, Jamshid H; Sunagawa, Gengo; Such, Kimberly A; Sale, Shiva; Golding, Leonard A R; Moazami, Nader; Fukamachi, Kiyotaka

2015-12-01

The biocompatibility assessment of the Cleveland Clinic continuous-flow total artificial heart is an important part of the device developmental program. Surgical and postoperative management are key factors in achieving optimal outcomes. However, the presence of vascular anatomical abnormalities in experimental animal models is often unpredictable and may worsen the expected outcomes. We report a technical impediment encountered during total artificial heart implantation complicated by unfavorable bovine anatomy of the ascending aorta and brachiocephalic arterial trunk.

Influence of Cleats-Surface Interaction on the Performance and Risk of Injury in Soccer: A Systematic Review

PubMed Central

Macedo, Rui; Montes, António Mesquita

2017-01-01

Objective To review the influence of cleats-surface interaction on the performance and risk of injury in soccer athletes. Design Systematic review. Data Sources Scopus, Web of science, PubMed, and B-on. Eligibility Criteria Full experimental and original papers, written in English that studied the influence of soccer cleats on sports performance and injury risk in artificial or natural grass. Results Twenty-three articles were included in this review: nine related to performance and fourteen to injury risk. On artificial grass, the soft ground model on dry and wet conditions and the turf model in wet conditions are related to worse performance. Compared to rounded studs, bladed ones improve performance during changes of directions in both natural and synthetic grass. Cleat models presenting better traction on the stance leg improve ball velocity while those presenting a homogeneous pressure across the foot promote better kicking accuracy. Bladed studs can be considered less secure by increasing plantar pressure on lateral border. The turf model decrease peak plantar pressure compared to other studded models. Conclusion The soft ground model provides lower performance especially on artificial grass, while the turf model provides a high protective effect in both fields. PMID:28684897

Influence of Cleats-Surface Interaction on the Performance and Risk of Injury in Soccer: A Systematic Review.

PubMed

Silva, Diogo C F; Santos, Rubim; Vilas-Boas, João Paulo; Macedo, Rui; Montes, António Mesquita; Sousa, Andreia S P

2017-01-01

To review the influence of cleats-surface interaction on the performance and risk of injury in soccer athletes. Systematic review. Scopus, Web of science, PubMed, and B-on. Full experimental and original papers, written in English that studied the influence of soccer cleats on sports performance and injury risk in artificial or natural grass. Twenty-three articles were included in this review: nine related to performance and fourteen to injury risk. On artificial grass, the soft ground model on dry and wet conditions and the turf model in wet conditions are related to worse performance. Compared to rounded studs, bladed ones improve performance during changes of directions in both natural and synthetic grass. Cleat models presenting better traction on the stance leg improve ball velocity while those presenting a homogeneous pressure across the foot promote better kicking accuracy. Bladed studs can be considered less secure by increasing plantar pressure on lateral border. The turf model decrease peak plantar pressure compared to other studded models. The soft ground model provides lower performance especially on artificial grass, while the turf model provides a high protective effect in both fields.

A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series

NASA Astrophysics Data System (ADS)

Wang, Wen-Chuan; Chau, Kwok-Wing; Cheng, Chun-Tian; Qiu, Lin

2009-08-01

SummaryDeveloping a hydrological forecasting model based on past records is crucial to effective hydropower reservoir management and scheduling. Traditionally, time series analysis and modeling is used for building mathematical models to generate hydrologic records in hydrology and water resources. Artificial intelligence (AI), as a branch of computer science, is capable of analyzing long-series and large-scale hydrological data. In recent years, it is one of front issues to apply AI technology to the hydrological forecasting modeling. In this paper, autoregressive moving-average (ARMA) models, artificial neural networks (ANNs) approaches, adaptive neural-based fuzzy inference system (ANFIS) techniques, genetic programming (GP) models and support vector machine (SVM) method are examined using the long-term observations of monthly river flow discharges. The four quantitative standard statistical performance evaluation measures, the coefficient of correlation ( R), Nash-Sutcliffe efficiency coefficient ( E), root mean squared error (RMSE), mean absolute percentage error (MAPE), are employed to evaluate the performances of various models developed. Two case study river sites are also provided to illustrate their respective performances. The results indicate that the best performance can be obtained by ANFIS, GP and SVM, in terms of different evaluation criteria during the training and validation phases.

Modeling of steam distillation mechanism during steam injection process using artificial intelligence.

PubMed

Daryasafar, Amin; Ahadi, Arash; Kharrat, Riyaz

2014-01-01

Steam distillation as one of the important mechanisms has a great role in oil recovery in thermal methods and so it is important to simulate this process experimentally and theoretically. In this work, the simulation of steam distillation is performed on sixteen sets of crude oil data found in the literature. Artificial intelligence (AI) tools such as artificial neural network (ANN) and also adaptive neurofuzzy interference system (ANFIS) are used in this study as effective methods to simulate the distillate recoveries of these sets of data. Thirteen sets of data were used to train the models and three sets were used to test the models. The developed models are highly compatible with respect to input oil properties and can predict the distillate yield with minimum entry. For showing the performance of the proposed models, simulation of steam distillation is also done using modified Peng-Robinson equation of state. Comparison between the calculated distillates by ANFIS and neural network models and also equation of state-based method indicates that the errors of the ANFIS model for training data and test data sets are lower than those of other methods.

Modeling of Steam Distillation Mechanism during Steam Injection Process Using Artificial Intelligence

PubMed Central

Ahadi, Arash; Kharrat, Riyaz

2014-01-01

Steam distillation as one of the important mechanisms has a great role in oil recovery in thermal methods and so it is important to simulate this process experimentally and theoretically. In this work, the simulation of steam distillation is performed on sixteen sets of crude oil data found in the literature. Artificial intelligence (AI) tools such as artificial neural network (ANN) and also adaptive neurofuzzy interference system (ANFIS) are used in this study as effective methods to simulate the distillate recoveries of these sets of data. Thirteen sets of data were used to train the models and three sets were used to test the models. The developed models are highly compatible with respect to input oil properties and can predict the distillate yield with minimum entry. For showing the performance of the proposed models, simulation of steam distillation is also done using modified Peng-Robinson equation of state. Comparison between the calculated distillates by ANFIS and neural network models and also equation of state-based method indicates that the errors of the ANFIS model for training data and test data sets are lower than those of other methods. PMID:24883365

Evolvable mathematical models: A new artificial Intelligence paradigm

NASA Astrophysics Data System (ADS)

Grouchy, Paul

We develop a novel Artificial Intelligence paradigm to generate autonomously artificial agents as mathematical models of behaviour. Agent/environment inputs are mapped to agent outputs via equation trees which are evolved in a manner similar to Symbolic Regression in Genetic Programming. Equations are comprised of only the four basic mathematical operators, addition, subtraction, multiplication and division, as well as input and output variables and constants. From these operations, equations can be constructed that approximate any analytic function. These Evolvable Mathematical Models (EMMs) are tested and compared to their Artificial Neural Network (ANN) counterparts on two benchmarking tasks: the double-pole balancing without velocity information benchmark and the challenging discrete Double-T Maze experiments with homing. The results from these experiments show that EMMs are capable of solving tasks typically solved by ANNs, and that they have the ability to produce agents that demonstrate learning behaviours. To further explore the capabilities of EMMs, as well as to investigate the evolutionary origins of communication, we develop NoiseWorld, an Artificial Life simulation in which interagent communication emerges and evolves from initially noncommunicating EMM-based agents. Agents develop the capability to transmit their x and y position information over a one-dimensional channel via a complex, dialogue-based communication scheme. These evolved communication schemes are analyzed and their evolutionary trajectories examined, yielding significant insight into the emergence and subsequent evolution of cooperative communication. Evolved agents from NoiseWorld are successfully transferred onto physical robots, demonstrating the transferability of EMM-based AIs from simulation into physical reality.

Novel solutions for an old disease: diagnosis of acute appendicitis with random forest, support vector machines, and artificial neural networks.

PubMed

Hsieh, Chung-Ho; Lu, Ruey-Hwa; Lee, Nai-Hsin; Chiu, Wen-Ta; Hsu, Min-Huei; Li, Yu-Chuan Jack

2011-01-01

Diagnosing acute appendicitis clinically is still difficult. We developed random forests, support vector machines, and artificial neural network models to diagnose acute appendicitis. Between January 2006 and December 2008, patients who had a consultation session with surgeons for suspected acute appendicitis were enrolled. Seventy-five percent of the data set was used to construct models including random forest, support vector machines, artificial neural networks, and logistic regression. Twenty-five percent of the data set was withheld to evaluate model performance. The area under the receiver operating characteristic curve (AUC) was used to evaluate performance, which was compared with that of the Alvarado score. Data from a total of 180 patients were collected, 135 used for training and 45 for testing. The mean age of patients was 39.4 years (range, 16-85). Final diagnosis revealed 115 patients with and 65 without appendicitis. The AUC of random forest, support vector machines, artificial neural networks, logistic regression, and Alvarado was 0.98, 0.96, 0.91, 0.87, and 0.77, respectively. The sensitivity, specificity, positive, and negative predictive values of random forest were 94%, 100%, 100%, and 87%, respectively. Random forest performed better than artificial neural networks, logistic regression, and Alvarado. We demonstrated that random forest can predict acute appendicitis with good accuracy and, deployed appropriately, can be an effective tool in clinical decision making. Copyright © 2011 Mosby, Inc. All rights reserved.

A comparative study of the antacid effect of raw spinach juice and spinach extract in an artificial stomach model.

PubMed

Panda, Vandana Sanjeev; Shinde, Priyanka Mangesh

2016-12-01

BackgroundSpinacia oleracea known as spinach is a green-leafy vegetable consumed by people across the globe. It is reported to possess potent medicinal properties by virtue of its numerous antioxidant phytoconstituents, together termed as the natural antioxidant mixture (NAO). The present study compares the antacid effect of raw spinach juice with an antioxidant-rich methanolic extract of spinach (NAOE) in an artificial stomach model. MethodsThe pH of NAOE at various concentrations (50, 100 and 200 mg/mL) and its neutralizing effect on artificial gastric acid was determined and compared with that of raw spinach juice, water, the active control sodium bicarbonate (SB) and a marketed antacid preparation ENO. A modified model of Vatier's artificial stomach was used to determine the duration of consistent neutralization of artificial gastric acid for the test compounds. The neutralizing capacity of test compounds was determined in vitro using the classical titration method of Fordtran. Results NAOE (50, 100 and 200 mg/mL), spinach juice, SB and ENO showed significantly better acid-neutralizing effect, consistent duration of neutralization and higher antacid capacity when compared with water. Highest antacid activity was demonstrated by ENO and SB followed by spinach juice and NAOE200. Spinach juice exhibited an effect comparable to NAOE (200 mg/mL). ConclusionsThus, it may be concluded that spinach displays significant antacid activity be it in the raw juice form or as an extract in methanol.

An Emotional ANN (EANN) approach to modeling rainfall-runoff process

NASA Astrophysics Data System (ADS)

Nourani, Vahid

2017-01-01

This paper presents the first hydrological implementation of Emotional Artificial Neural Network (EANN), as a new generation of Artificial Intelligence-based models for daily rainfall-runoff (r-r) modeling of the watersheds. Inspired by neurophysiological form of brain, in addition to conventional weights and bias, an EANN includes simulated emotional parameters aimed at improving the network learning process. EANN trained by a modified version of back-propagation (BP) algorithm was applied to single and multi-step-ahead runoff forecasting of two watersheds with two distinct climatic conditions. Also to evaluate the ability of EANN trained by smaller training data set, three data division strategies with different number of training samples were considered for the training purpose. The overall comparison of the obtained results of the r-r modeling indicates that the EANN could outperform the conventional feed forward neural network (FFNN) model up to 13% and 34% in terms of training and verification efficiency criteria, respectively. The superiority of EANN over classic ANN is due to its ability to recognize and distinguish dry (rainless days) and wet (rainy days) situations using hormonal parameters of the artificial emotional system.

Modeling and simulation of xylitol production in bioreactor by Debaryomyces nepalensis NCYC 3413 using unstructured and artificial neural network models.

PubMed

Pappu, J Sharon Mano; Gummadi, Sathyanarayana N

2016-11-01

This study examines the use of unstructured kinetic model and artificial neural networks as predictive tools for xylitol production by Debaryomyces nepalensis NCYC 3413 in bioreactor. An unstructured kinetic model was proposed in order to assess the influence of pH (4, 5 and 6), temperature (25°C, 30°C and 35°C) and volumetric oxygen transfer coefficient kLa (0.14h(-1), 0.28h(-1) and 0.56h(-1)) on growth and xylitol production. A feed-forward back-propagation artificial neural network (ANN) has been developed to investigate the effect of process condition on xylitol production. ANN configuration of 6-10-3 layers was selected and trained with 339 experimental data points from bioreactor studies. Results showed that simulation and prediction accuracy of ANN was apparently higher when compared to unstructured mechanistic model under varying operational conditions. ANN was found to be an efficient data-driven tool to predict the optimal harvest time in xylitol production. Copyright © 2016 Elsevier Ltd. All rights reserved.

Three-Dimensional Human iPSC-Derived Artificial Skeletal Muscles Model Muscular Dystrophies and Enable Multilineage Tissue Engineering.

PubMed

Maffioletti, Sara Martina; Sarcar, Shilpita; Henderson, Alexander B H; Mannhardt, Ingra; Pinton, Luca; Moyle, Louise Anne; Steele-Stallard, Heather; Cappellari, Ornella; Wells, Kim E; Ferrari, Giulia; Mitchell, Jamie S; Tyzack, Giulia E; Kotiadis, Vassilios N; Khedr, Moustafa; Ragazzi, Martina; Wang, Weixin; Duchen, Michael R; Patani, Rickie; Zammit, Peter S; Wells, Dominic J; Eschenhagen, Thomas; Tedesco, Francesco Saverio

2018-04-17

Generating human skeletal muscle models is instrumental for investigating muscle pathology and therapy. Here, we report the generation of three-dimensional (3D) artificial skeletal muscle tissue from human pluripotent stem cells, including induced pluripotent stem cells (iPSCs) from patients with Duchenne, limb-girdle, and congenital muscular dystrophies. 3D skeletal myogenic differentiation of pluripotent cells was induced within hydrogels under tension to provide myofiber alignment. Artificial muscles recapitulated characteristics of human skeletal muscle tissue and could be implanted into immunodeficient mice. Pathological cellular hallmarks of incurable forms of severe muscular dystrophy could be modeled with high fidelity using this 3D platform. Finally, we show generation of fully human iPSC-derived, complex, multilineage muscle models containing key isogenic cellular constituents of skeletal muscle, including vascular endothelial cells, pericytes, and motor neurons. These results lay the foundation for a human skeletal muscle organoid-like platform for disease modeling, regenerative medicine, and therapy development. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

ICCE/ICCAI 2000 Full & Short Papers (Artificial Intelligence in Education).

ERIC Educational Resources Information Center

2000

This document contains the full and short papers on artificial intelligence in education from ICCE/ICCAI 2000 (International Conference on Computers in Education/International Conference on Computer-Assisted Instruction) covering the following topics: a computational model for learners' motivation states in individualized tutoring system; a…

Braided artificial muscles: modeling and experimental validation

NASA Astrophysics Data System (ADS)

Dragan, Liliana; Cioban, Horia

2009-01-01

The paper presents a few graphical modalities for constructing the double helical braid, which is the basis for the braided artificial pneumatic muscles, by using specialized software applications. This represents the first stage in achieving the method of finite element analysis of this type of linear pneumatic actuator.

An Examination of Application of Artificial Neural Network in Cognitive Radios

NASA Astrophysics Data System (ADS)

Bello Salau, H.; Onwuka, E. N.; Aibinu, A. M.

2013-12-01

Recent advancement in software radio technology has led to the development of smart device known as cognitive radio. This type of radio fuses powerful techniques taken from artificial intelligence, game theory, wideband/multiple antenna techniques, information theory and statistical signal processing to create an outstanding dynamic behavior. This cognitive radio is utilized in achieving diverse set of applications such as spectrum sensing, radio parameter adaptation and signal classification. This paper contributes by reviewing different cognitive radio implementation that uses artificial intelligence such as the hidden markov models, metaheuristic algorithm and artificial neural networks (ANNs). Furthermore, different areas of application of ANNs and their performance metrics based approach are also examined.

Antioxidant responses of damiana (Turnera diffusa Willd) to exposure to artificial ultraviolet (UV) radiation in an in vitro model; part ii; UV-B radiation.

PubMed

Soriano-Melgar, Lluvia de Abril Alexandra; Alcaraz-Meléndez, Lilia; Méndez-Rodríguez, Lía C; Puente, María Esther; Rivera-Cabrera, Fernando; Zenteno-Savín, Tania

2014-05-01

Ultraviolet type B (UV-B) radiation effects on medicinal plants have been recently investigated in the context of climate change, but the modifications generated by UV-B radiation might be used to increase the content of antioxidants, including phenolic compounds. To generate information on the effect of exposure to artificial UV-B radiation at different highdoses in the antioxidant content of damiana plants in an in vitro model. Damiana plantlets (tissue cultures in Murashige- Skoog medium) were irradiated with artificial UV-B at 3 different doses (1) 0.5 ± 0.1 mW cm-2 (high) for 2 h daily, (2) 1 ± 0,1 mW cm-2 (severe) for 2 h daily, or (3) 1 ± 0.1 mW cm-2 for 4 h daily during 3 weeks. The concentration of photosynthetic pigments (chlorophylls a and b, carotenoids), vitamins (C and E) and total phenolic compounds, the enzymatic activity of superoxide dismutase (SOD, EC 1.15.1.1) and total peroxidases (POX, EC 1.11.1), as well as total antioxidant capacity and lipid peroxidation levels were quantified to assess the effect of high artificial UV-B radiation in the antioxidant content of in vitro damiana plants. Severe and high doses of artificial UV-B radiation modified the antioxidant content by increasing the content of vitamin C and decreased the phenolic compound content, as well as modified the oxidative damage of damiana plants in an in vitro model. UV-B radiation modified the antioxidant content in damiana plants in an in vitro model, depending on the intensity and duration of the exposure. Copyright AULA MEDICA EDICIONES 2014. Published by AULA MEDICA. All rights reserved.

Modelling fuel cell performance using artificial intelligence

NASA Astrophysics Data System (ADS)

Ogaji, S. O. T.; Singh, R.; Pilidis, P.; Diacakis, M.

Over the last few years, fuel cell technology has been increasing promisingly its share in the generation of stationary power. Numerous pilot projects are operating worldwide, continuously increasing the amount of operating hours either as stand-alone devices or as part of gas turbine combined cycles. An essential tool for the adequate and dynamic analysis of such systems is a software model that enables the user to assess a large number of alternative options in the least possible time. On the other hand, the sphere of application of artificial neural networks has widened covering such endeavours of life such as medicine, finance and unsurprisingly engineering (diagnostics of faults in machines). Artificial neural networks have been described as diagrammatic representation of a mathematical equation that receives values (inputs) and gives out results (outputs). Artificial neural networks systems have the capacity to recognise and associate patterns and because of their inherent design features, they can be applied to linear and non-linear problem domains. In this paper, the performance of the fuel cell is modelled using artificial neural networks. The inputs to the network are variables that are critical to the performance of the fuel cell while the outputs are the result of changes in any one or all of the fuel cell design variables, on its performance. Critical parameters for the cell include the geometrical configuration as well as the operating conditions. For the neural network, various network design parameters such as the network size, training algorithm, activation functions and their causes on the effectiveness of the performance modelling are discussed. Results from the analysis as well as the limitations of the approach are presented and discussed.

Longitudinal modelling of the exposure of young UK patients with PKU to acesulfame K and sucralose.

PubMed

O'Sullivan, Aaron J; Pigat, Sandrine; O'Mahony, Cian; Gibney, Michael J; McKevitt, Aideen I

2017-11-01

Artificial sweeteners are used in protein substitutes intended for the dietary management of inborn errors of metabolism (phenylketonuria, PKU) to improve the variety of medical foods available to patients and ensure dietary adherence to the prescribed course of dietary management. These patients can be exposed to artificial sweeteners from the combination of free and prescribed foods. Young children have a higher risk of exceeding acceptable daily intakes (ADI) for additives than adults, due to higher food intakes per kg body weight. Young patients with PKU aged 1-3 years can be exposed to higher levels of artificial sweeteners from these dual sources than normal healthy children and are at a higher risk of exceeding the ADI. Standard intake assessment methods are not adequate to assess the additive exposure of young patients with PKU. The aim of this study was to estimate the combination effect on the intake of artificial sweeteners and the impact of the introduction of new provisions for an artificial sweetener (sucralose, E955) on exposure of PKU patients using a validated probabilistic model. Food consumption data were derived from the food consumption survey data of healthy young children in the United Kingdom from the National Diet and Nutrition Survey (NDNS, 1992-2012). Specially formulated protein substitutes as foods for special medical purposes (FSMPs) were included in the exposure model to replace restricted foods. Inclusion of these protein substitutes is based on recommendations to ensure adequate protein intake in these patients. Exposure assessment results indicated the availability of sucralose for use in FSMPs for PKU leads to changes in intakes in young patients. These data further support the viability of probabilistic modelling as a means to estimate food additive exposure in patients consuming medical nutrition products.

Geometric Bioinspired Networks for Recognition of 2-D and 3-D Low-Level Structures and Transformations.

PubMed

Bayro-Corrochano, Eduardo; Vazquez-Santacruz, Eduardo; Moya-Sanchez, Eduardo; Castillo-Munis, Efrain

2016-10-01

This paper presents the design of radial basis function geometric bioinspired networks and their applications. Until now, the design of neural networks has been inspired by the biological models of neural networks but mostly using vector calculus and linear algebra. However, these designs have never shown the role of geometric computing. The question is how biological neural networks handle complex geometric representations involving Lie group operations like rotations. Even though the actual artificial neural networks are biologically inspired, they are just models which cannot reproduce a plausible biological process. Until now researchers have not shown how, using these models, one can incorporate them into the processing of geometric computing. Here, for the first time in the artificial neural networks domain, we address this issue by designing a kind of geometric RBF using the geometric algebra framework. As a result, using our artificial networks, we show how geometric computing can be carried out by the artificial neural networks. Such geometric neural networks have a great potential in robot vision. This is the most important aspect of this contribution to propose artificial geometric neural networks for challenging tasks in perception and action. In our experimental analysis, we show the applicability of our geometric designs, and present interesting experiments using 2-D data of real images and 3-D screw axis data. In general, our models should be used to process different types of inputs, such as visual cues, touch (texture, elasticity, temperature), taste, and sound. One important task of a perception-action system is to fuse a variety of cues coming from the environment and relate them via a sensor-motor manifold with motor modules to carry out diverse reasoned actions.

Modeling of nitrate concentration in groundwater using artificial intelligence approach--a case study of Gaza coastal aquifer.

PubMed

Alagha, Jawad S; Said, Md Azlin Md; Mogheir, Yunes

2014-01-01

Nitrate concentration in groundwater is influenced by complex and interrelated variables, leading to great difficulty during the modeling process. The objectives of this study are (1) to evaluate the performance of two artificial intelligence (AI) techniques, namely artificial neural networks and support vector machine, in modeling groundwater nitrate concentration using scant input data, as well as (2) to assess the effect of data clustering as a pre-modeling technique on the developed models' performance. The AI models were developed using data from 22 municipal wells of the Gaza coastal aquifer in Palestine from 2000 to 2010. Results indicated high simulation performance, with the correlation coefficient and the mean average percentage error of the best model reaching 0.996 and 7 %, respectively. The variables that strongly influenced groundwater nitrate concentration were previous nitrate concentration, groundwater recharge, and on-ground nitrogen load of each land use land cover category in the well's vicinity. The results also demonstrated the merit of performing clustering of input data prior to the application of AI models. With their high performance and simplicity, the developed AI models can be effectively utilized to assess the effects of future management scenarios on groundwater nitrate concentration, leading to more reasonable groundwater resources management and decision-making.

HF Propagation Effects Caused by an Artificial Plasma Cloud in the Ionosphere

NASA Astrophysics Data System (ADS)

Joshi, D. R.; Groves, K. M.; McNeil, W. J.; Caton, R. G.; Parris, R. T.; Pedersen, T. R.; Cannon, P. S.; Angling, M. J.; Jackson-Booth, N. K.

2014-12-01

In a campaign carried out by the NASA sounding rocket team, the Air Force Research Laboratory (AFRL) launched two sounding rockets in the Kwajalein Atoll, Marshall Islands, in May 2013 known as the Metal Oxide Space Cloud (MOSC) experiment to study the interactions of artificial ionization and the background plasma and measure the effects on high frequency (HF) radio wave propagation. The rockets released samarium metal vapor in the lower F-region of the ionosphere that ionized forming a plasma cloud that persisted for tens of minutes to hours in the post-sunset period. Data from the experiments has been analyzed to understand the impacts of the artificial ionization on HF radio wave propagation. Swept frequency HF links transiting the artificial ionization region were employed to produce oblique ionograms that clearly showed the effects of the samarium cloud. Ray tracing has been used to successfully model the effects of the ionized cloud. Comparisons between observations and modeled results will be presented, including model output using the International Reference Ionosphere (IRI), the Parameterized Ionospheric Model (PIM) and PIM constrained by electron density profiles measured with the ALTAIR radar at Kwajalein. Observations and modeling confirm that the cloud acted as a divergent lens refracting energy away from direct propagation paths and scattering energy at large angles relative to the initial propagation direction. The results confirm that even small amounts of ionized material injected in the upper atmosphere can result in significant changes to the natural propagation environment.

Perception of Sexual Orientation from Facial Structure: A Study with Artificial Face Models.

PubMed

González-Álvarez, Julio

2017-07-01

Research has shown that lay people can perceive sexual orientation better than chance from face stimuli. However, the relation between facial structure and sexual orientation has been scarcely examined. Recently, an extensive morphometric study on a large sample of Canadian people (Skorska, Geniole, Vrysen, McCormick, & Bogaert, 2015) identified three (in men) and four (in women) facial features as unique multivariate predictors of sexual orientation in each sex group. The present study tested the perceptual validity of these facial traits with two experiments based on realistic artificial 3D face models created by manipulating the key parameters and presented to Spanish participants. Experiment 1 included 200 White and Black face models of both sexes. The results showed an overall accuracy (0.74) clearly above chance in a binary hetero/homosexual judgment task and significant differences depending on the race and sex of the face models. Experiment 2 produced five versions of 24 artificial faces of both sexes varying the key parameters in equal steps, and participants had to rate on a 1-7 scale how likely they thought that the depicted person had a homosexual sexual orientation. Rating scores displayed an almost perfect linear regression as a function of the parameter steps. In summary, both experiments demonstrated the perceptual validity of the seven multivariate predictors identified by Skorska et al. and open up new avenues for further research on this issue with artificial face models.

Understanding Surface Adhesion in Nature: A Peeling Model.

PubMed

Gu, Zhen; Li, Siheng; Zhang, Feilong; Wang, Shutao

2016-07-01

Nature often exhibits various interesting and unique adhesive surfaces. The attempt to understand the natural adhesion phenomena can continuously guide the design of artificial adhesive surfaces by proposing simplified models of surface adhesion. Among those models, a peeling model can often effectively reflect the adhesive property between two surfaces during their attachment and detachment processes. In the context, this review summarizes the recent advances about the peeling model in understanding unique adhesive properties on natural and artificial surfaces. It mainly includes four parts: a brief introduction to natural surface adhesion, the theoretical basis and progress of the peeling model, application of the peeling model, and finally, conclusions. It is believed that this review is helpful to various fields, such as surface engineering, biomedicine, microelectronics, and so on.

Comparing the plant diversity between artificial forest and nature growth forest in a giant panda habitat.

PubMed

Kang, Dongwei; Wang, Xiaorong; Li, Shuang; Li, Junqing

2017-06-15

Artificial restoration is an important way to restore forests, but little is known about its effect on the habitat restoration of the giant panda. In the present study, we investigated the characteristics of artificial forest in the Wanglang Nature Reserve to determine whether through succession it has formed a suitable habitat for the giant panda. We compared artificial forest characteristics with those of natural habitat used by the giant panda. We found that the dominant tree species in artificial forest differed from those in the natural habitat. The artificial forest had lower plant species richness and diversity in the tree and shrub layers than did the latter, and its community structure was characterized by smaller tree and bamboo sizes, and fewer and lower bamboo clumps, but more trees and larger shrub sizes. The typical community collocation of artificial forest was a "Picea asperata + no-bamboo" model, which differs starkly from the giant panda's natural habitat. After several years of restoration, the artificial forest has failed to become a suitable habitat for the giant panda. Therefore, a simple way of planting individual trees cannot restore giant panda habitat; instead, habitat restoration should be based on the habitat requirements of the giant panda.

Human Artificial Chromosomes with Alpha Satellite-Based De Novo Centromeres Show Increased Frequency of Nondisjunction and Anaphase Lag

PubMed Central

Rudd, M. Katharine; Mays, Robert W.; Schwartz, Stuart; Willard, Huntington F.

2003-01-01

Human artificial chromosomes have been used to model requirements for human chromosome segregation and to explore the nature of sequences competent for centromere function. Normal human centromeres require specialized chromatin that consists of alpha satellite DNA complexed with epigenetically modified histones and centromere-specific proteins. While several types of alpha satellite DNA have been used to assemble de novo centromeres in artificial chromosome assays, the extent to which they fully recapitulate normal centromere function has not been explored. Here, we have used two kinds of alpha satellite DNA, DXZ1 (from the X chromosome) and D17Z1 (from chromosome 17), to generate human artificial chromosomes. Although artificial chromosomes are mitotically stable over many months in culture, when we examined their segregation in individual cell divisions using an anaphase assay, artificial chromosomes exhibited more segregation errors than natural human chromosomes (P < 0.001). Naturally occurring, but abnormal small ring chromosomes derived from chromosome 17 and the X chromosome also missegregate more than normal chromosomes, implicating overall chromosome size and/or structure in the fidelity of chromosome segregation. As different artificial chromosomes missegregate over a fivefold range, the data suggest that variable centromeric DNA content and/or epigenetic assembly can influence the mitotic behavior of artificial chromosomes. PMID:14560014

Human artificial chromosomes with alpha satellite-based de novo centromeres show increased frequency of nondisjunction and anaphase lag.

PubMed

Rudd, M Katharine; Mays, Robert W; Schwartz, Stuart; Willard, Huntington F

2003-11-01

Human artificial chromosomes have been used to model requirements for human chromosome segregation and to explore the nature of sequences competent for centromere function. Normal human centromeres require specialized chromatin that consists of alpha satellite DNA complexed with epigenetically modified histones and centromere-specific proteins. While several types of alpha satellite DNA have been used to assemble de novo centromeres in artificial chromosome assays, the extent to which they fully recapitulate normal centromere function has not been explored. Here, we have used two kinds of alpha satellite DNA, DXZ1 (from the X chromosome) and D17Z1 (from chromosome 17), to generate human artificial chromosomes. Although artificial chromosomes are mitotically stable over many months in culture, when we examined their segregation in individual cell divisions using an anaphase assay, artificial chromosomes exhibited more segregation errors than natural human chromosomes (P < 0.001). Naturally occurring, but abnormal small ring chromosomes derived from chromosome 17 and the X chromosome also missegregate more than normal chromosomes, implicating overall chromosome size and/or structure in the fidelity of chromosome segregation. As different artificial chromosomes missegregate over a fivefold range, the data suggest that variable centromeric DNA content and/or epigenetic assembly can influence the mitotic behavior of artificial chromosomes.

Artificial intelligence: a new approach for prescription and monitoring of hemodialysis therapy.

PubMed

Akl, A I; Sobh, M A; Enab, Y M; Tattersall, J

2001-12-01

The effect of dialysis on patients is conventionally predicted using a formal mathematical model. This approach requires many assumptions of the processes involved, and validation of these may be difficult. The validity of dialysis urea modeling using a formal mathematical model has been challenged. Artificial intelligence using neural networks (NNs) has been used to solve complex problems without needing a mathematical model or an understanding of the mechanisms involved. In this study, we applied an NN model to study and predict concentrations of urea during a hemodialysis session. We measured blood concentrations of urea, patient weight, and total urea removal by direct dialysate quantification (DDQ) at 30-minute intervals during the session (in 15 chronic hemodialysis patients). The NN model was trained to recognize the evolution of measured urea concentrations and was subsequently able to predict hemodialysis session time needed to reach a target solute removal index (SRI) in patients not previously studied by the NN model (in another 15 chronic hemodialysis patients). Comparing results of the NN model with the DDQ model, the prediction error was 10.9%, with a not significant difference between predicted total urea nitrogen (UN) removal and measured UN removal by DDQ. NN model predictions of time showed a not significant difference with actual intervals needed to reach the same SRI level at the same patient conditions, except for the prediction of SRI at the first 30-minute interval, which showed a significant difference (P = 0.001). This indicates the sensitivity of the NN model to what is called patient clearance time; the prediction error was 8.3%. From our results, we conclude that artificial intelligence applications in urea kinetics can give an idea of intradialysis profiling according to individual clinical needs. In theory, this approach can be extended easily to other solutes, making the NN model a step forward to achieving artificial-intelligent dialysis control.

Weighted social networks for a large scale artificial society

NASA Astrophysics Data System (ADS)

Fan, Zong Chen; Duan, Wei; Zhang, Peng; Qiu, Xiao Gang

2016-12-01

The method of artificial society has provided a powerful way to study and explain how individual behaviors at micro level give rise to the emergence of global social phenomenon. It also creates the need for an appropriate representation of social structure which usually has a significant influence on human behaviors. It has been widely acknowledged that social networks are the main paradigm to describe social structure and reflect social relationships within a population. To generate social networks for a population of interest, considering physical distance and social distance among people, we propose a generation model of social networks for a large-scale artificial society based on human choice behavior theory under the principle of random utility maximization. As a premise, we first build an artificial society through constructing a synthetic population with a series of attributes in line with the statistical (census) data for Beijing. Then the generation model is applied to assign social relationships to each individual in the synthetic population. Compared with previous empirical findings, the results show that our model can reproduce the general characteristics of social networks, such as high clustering coefficient, significant community structure and small-world property. Our model can also be extended to a larger social micro-simulation as an input initial. It will facilitate to research and predict some social phenomenon or issues, for example, epidemic transition and rumor spreading.

Analysis of Artificial Neural Network in Erosion Modeling: A Case Study of Serang Watershed

NASA Astrophysics Data System (ADS)

Arif, N.; Danoedoro, P.; Hartono

2017-12-01

Erosion modeling is an important measuring tool for both land users and decision makers to evaluate land cultivation and thus it is necessary to have a model to represent the actual reality. Erosion models are a complex model because of uncertainty data with different sources and processing procedures. Artificial neural networks can be relied on for complex and non-linear data processing such as erosion data. The main difficulty in artificial neural network training is the determination of the value of each network input parameters, i.e. hidden layer, momentum, learning rate, momentum, and RMS. This study tested the capability of artificial neural network application in the prediction of erosion risk with some input parameters through multiple simulations to get good classification results. The model was implemented in Serang Watershed, Kulonprogo, Yogyakarta which is one of the critical potential watersheds in Indonesia. The simulation results showed the number of iterations that gave a significant effect on the accuracy compared to other parameters. A small number of iterations can produce good accuracy if the combination of other parameters was right. In this case, one hidden layer was sufficient to produce good accuracy. The highest training accuracy achieved in this study was 99.32%, occurred in ANN 14 simulation with combination of network input parameters of 1 HL; LR 0.01; M 0.5; RMS 0.0001, and the number of iterations of 15000. The ANN training accuracy was not influenced by the number of channels, namely input dataset (erosion factors) as well as data dimensions, rather it was determined by changes in network parameters.

Using Artificial Neural Networks in Educational Research: Some Comparisons with Linear Statistical Models.

ERIC Educational Resources Information Center

Everson, Howard T.; And Others

This paper explores the feasibility of neural computing methods such as artificial neural networks (ANNs) and abductory induction mechanisms (AIM) for use in educational measurement. ANNs and AIMS methods are contrasted with more traditional statistical techniques, such as multiple regression and discriminant function analyses, for making…

Reconstructing missing daily precipitation data using regression trees and artificial neural networks

USDA-ARS?s Scientific Manuscript database

Incomplete meteorological data has been a problem in environmental modeling studies. The objective of this work was to develop a technique to reconstruct missing daily precipitation data in the central part of Chesapeake Bay Watershed using regression trees (RT) and artificial neural networks (ANN)....

Reconstructing missing daily precipitation data using regression trees and artificial neural networks

USDA-ARS?s Scientific Manuscript database

Missing meteorological data have to be estimated for agricultural and environmental modeling. The objective of this work was to develop a technique to reconstruct the missing daily precipitation data in the central part of the Chesapeake Bay Watershed using regression trees (RT) and artificial neura...

Applications of Artificial Intelligence in Education--A Personal View.

ERIC Educational Resources Information Center

Richer, Mark H.

1985-01-01

Discusses: how artificial intelligence (AI) can advance education; if the future of software lies in AI; the roots of intelligent computer-assisted instruction; protocol analysis; reactive environments; LOGO programming language; student modeling and coaching; and knowledge-based instructional programs. Numerous examples of AI programs are cited.…

Predicting the Emplacement of Improvised Explosive Devices: An Innovative Solution

ERIC Educational Resources Information Center

Lerner, Warren D.

2013-01-01

In this quantitative correlational study, simulated data were employed to examine artificial-intelligence techniques or, more specifically, artificial neural networks, as they relate to the location prediction of improvised explosive devices (IEDs). An ANN model was developed to predict IED placement, based upon terrain features and objects…

Phase-locking behavior in a high-frequency gymnotiform weakly electric fish, Adontosternarchus.

PubMed

Kawasaki, Masashi; Leonard, John

2017-02-01

An apteronotid weakly electric fish, Adontosternarchus, emits high-frequency electric organ discharges (700-1500 Hz) which are stable in frequency if no other fish or artificial signals are present. When encountered with an artificial signal of higher frequency than the fish's discharge, the fish raised its discharge frequency and eventually matched its own frequency to that of the artificial signal. At this moment, phase locking was observed, where the timing of the fish's discharge was precisely stabilized at a particular phase of the artificial signal over a long period of time (up to minutes) with microsecond precision. Analyses of the phase-locking behaviors revealed that the phase values of the artificial stimulus at which the fish stabilizes the phase of its own discharge (called lock-in phases) have three populations between -180° and +180°. During the frequency rise and the phase-locking behavior, the electrosensory system is exposed to the mixture of feedback signals from its electric organ discharges and the artificial signal. Since the signal mixture modulates in both amplitude and phase, we explored whether amplitude or phase information participated in driving the phase-locking behavior, using a numerical model. The model which incorporates only amplitude information well predicted the three populations of lock-in phases. When phase information was removed from the electrosensory stimulus, phase-locking behavior was still observed. These results suggest that phase-locking behavior of Adontosternarchus requires amplitude information but not phase information available in the electrosensory stimulus.

A validated finite element model of a soft artificial muscle motor

NASA Astrophysics Data System (ADS)

Tse, Tony Chun H.; O'Brien, Benjamin; McKay, Thomas; Anderson, Iain A.

2011-04-01

The Biomimetics Laboratory has developed a soft artificial muscle motor based on Dielectric Elastomers. The motor, 'Flexidrive', is light-weight and has low system complexity. It works by gripping and turning a shaft with a soft gear, like we would with our fingers. The motor's performance depends on many factors, such as actuation waveform, electrode patterning, geometries and contact tribology between the shaft and gear. We have developed a finite element model (FEM) of the motor as a study and design tool. Contact interaction was integrated with previous material and electromechanical coupling models in ABAQUS. The model was experimentally validated through a shape and blocked force analysis.

A model of pathways to artificial superintelligence catastrophe for risk and decision analysis

NASA Astrophysics Data System (ADS)

Barrett, Anthony M.; Baum, Seth D.

2017-03-01

An artificial superintelligence (ASI) is an artificial intelligence that is significantly more intelligent than humans in all respects. Whilst ASI does not currently exist, some scholars propose that it could be created sometime in the future, and furthermore that its creation could cause a severe global catastrophe, possibly even resulting in human extinction. Given the high stakes, it is important to analyze ASI risk and factor the risk into decisions related to ASI research and development. This paper presents a graphical model of major pathways to ASI catastrophe, focusing on ASI created via recursive self-improvement. The model uses the established risk and decision analysis modelling paradigms of fault trees and influence diagrams in order to depict combinations of events and conditions that could lead to AI catastrophe, as well as intervention options that could decrease risks. The events and conditions include select aspects of the ASI itself as well as the human process of ASI research, development and management. Model structure is derived from published literature on ASI risk. The model offers a foundation for rigorous quantitative evaluation and decision-making on the long-term risk of ASI catastrophe.

Structure-function relationship of skeletal muscle provides inspiration for design of new artificial muscle

NASA Astrophysics Data System (ADS)

Gao, Yingxin; Zhang, Chi

2015-03-01

A variety of actuator technologies have been developed to mimic biological skeletal muscle that generates force in a controlled manner. Force generation process of skeletal muscle involves complicated biophysical and biochemical mechanisms; therefore, it is impossible to replace biological muscle. In biological skeletal muscle tissue, the force generation of a muscle depends not only on the force generation capacity of the muscle fiber, but also on many other important factors, including muscle fiber type, motor unit recruitment, architecture, structure and morphology of skeletal muscle, all of which have significant impact on the force generation of the whole muscle or force transmission from muscle fibers to the tendon. Such factors have often been overlooked, but can be incorporated in artificial muscle design, especially with the discovery of new smart materials and the development of innovative fabrication and manufacturing technologies. A better understanding of the physiology and structure-function relationship of skeletal muscle will therefore benefit the artificial muscle design. In this paper, factors that affect muscle force generation are reviewed. Mathematical models used to model the structure-function relationship of skeletal muscle are reviewed and discussed. We hope the review will provide inspiration for the design of a new generation of artificial muscle by incorporating the structure-function relationship of skeletal muscle into the design of artificial muscle.

Modeling artificial graphene in Si/SiGe hetrostructures

NASA Astrophysics Data System (ADS)

Maurer, Leon; Gamble, John King; Moussa, Jonathan; Tracy, Lisa; Huang, Shih-Hsien; Chuang, Yen; Li, Jiun-Yun; Liu, Chih-Wen; Lu, Tzu-Ming

Artificial graphene is a synthetic material made using a nanostructure with identical 2D potential wells arranged in a honeycomb lattice. Unlike normal graphene, the properties of artificial graphene can be controlled by changing the nanostructure geometry and adjusting applied voltages. We perform a theoretical study of artificial graphene formed from a 2D electron gas (2DEG) in Si/SiGe and Ge/SiGe heterostructures by a metal honeycomb gate and a global top gate. While many models of artificial graphene assume a simple form for the potential landscape in the 2DEG, we instead calculate the potential landscape for actual devices with a range of bias voltages and geometries. This allows us to find the resulting bandstructure and calculate transport parameters, which we compare directly to experimental results. Sandia is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the US Department of Energy's National Nuclear Security Administration under Contract No. DE-AC04-94AL85000. This work was funded by the Laboratory Directed Research and Development Program. The work at NTU was supported by the Ministry of Science and Technology (103-2622-E-002-031 and 103-2112-M- 002-002-MY3).

Effects of Artificial Ligaments with Different Porous Structures on the Migration of BMSCs

PubMed Central

Wang, Chun-Hui; Hou, Wei; Yan, Ming; Guo, Zhong-shang; Wu, Qi; Bi, Long; Han, Yi-Sheng

2015-01-01

Polyethylene terephthalate- (PET-) based artificial ligaments (PET-ALs) are commonly used in anterior cruciate ligament (ACL) reconstruction surgery. The effects of different porous structures on the migration of bone marrow mesenchymal stem cells (BMSCs) on artificial ligaments and the underlying mechanisms are unclear. In this study, a cell migration model was utilized to observe the migration of BMSCs on PET-ALs with different porous structures. A rabbit extra-articular graft-to-bone healing model was applied to investigate the in vivo effects of four types of PET-ALs, and a mechanical test and histological observation were performed at 4 weeks and 12 weeks. The BMSC migration area of the 5A group was significantly larger than that of the other three groups. The migration of BMSCs in the 5A group was abolished by blocking the RhoA/ROCK signaling pathway with Y27632. The in vivo study demonstrated that implantation of 5A significantly improved osseointegration. Our study explicitly demonstrates that the migration ability of BMSCs can be regulated by varying the porous structures of the artificial ligaments and suggests that this regulation is related to the RhoA/ROCK signaling pathway. Artificial ligaments prepared using a proper knitting method and line density may exhibit improved biocompatibility and clinical performance. PMID:26106429

Several necessary conditions for the evolution of complex forms of life in an artificial environment.

PubMed

Suzuki, Hideaki; Ono, Naoaki; Yuta, Kikuo

2003-01-01

In order for an artificial life (Alife) system to evolve complex creatures, an artificial environment prepared by a designer has to satisfy several conditions. To clarify this requirement, we first assume that an artificial environment implemented in the computational medium is composed of an information space in which elementary symbols move around and react with each other according to human-prepared elementary rules. As fundamental properties of these factors (space, symbols, transportation, and reaction), we present ten criteria from a comparison with the biochemical reaction space in the real world. Then, in the latter half of the article, we take several computational Alife systems one by one, and assess them in terms of the proposed criteria. The assessment can be used not only for improving previous Alife systems but also for devising new Alife models in which complex forms of artificial creatures can be expected to evolve.

Artificial fluid properties for large-eddy simulation of compressible turbulent mixing

NASA Astrophysics Data System (ADS)

Cook, Andrew W.

2007-05-01

An alternative methodology is described for large-eddy simulation (LES) of flows involving shocks, turbulence, and mixing. In lieu of filtering the governing equations, it is postulated that the large-scale behavior of a LES fluid, i.e., a fluid with artificial properties, will be similar to that of a real fluid, provided the artificial properties obey certain constraints. The artificial properties consist of modifications to the shear viscosity, bulk viscosity, thermal conductivity, and species diffusivity of a fluid. The modified transport coefficients are designed to damp out high wavenumber modes, close to the resolution limit, without corrupting lower modes. Requisite behavior of the artificial properties is discussed and results are shown for a variety of test problems, each designed to exercise different aspects of the models. When combined with a tenth-order compact scheme, the overall method exhibits excellent resolution characteristics for turbulent mixing, while capturing shocks and material interfaces in a crisp fashion.

[Research progress in human artificial chromosomes(HACs) and the potentials in application].

PubMed

Zuo, Guo-Wei; Lü, Feng-Lin

2005-11-01

Since the first report of the establishment of human artificial chromosome(HAC) was published in 1997, several types of HAC have been created by different strategies. Compared to other artificial chromosomes, such as yeast artificial chromosome (YAC) and bacterial artificial chromosome(BAC), HAC exists in a cell independently, in other words, HAC does not integrated into the cellular genome, and can undergo normal mitosis and meiosis from generation to generation in vitro and in vivo. Recent results proved that HAC, as a DNA carrier, is able to host a large fragment of DNA or mini-chromosome, thus it could be a very important tool in the study of human gene expression and regulation, human chromosome function and minimum functional elements and animal models for human diseases. In the near future, HAC can also be used in gene therapy for human genetic diseases.

Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction.

PubMed

Gao, Xiang-Ming; Yang, Shi-Feng; Pan, San-Bo

2017-01-01

Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.

Spindle Thermal Error Optimization Modeling of a Five-axis Machine Tool

NASA Astrophysics Data System (ADS)

Guo, Qianjian; Fan, Shuo; Xu, Rufeng; Cheng, Xiang; Zhao, Guoyong; Yang, Jianguo

2017-05-01

Aiming at the problem of low machining accuracy and uncontrollable thermal errors of NC machine tools, spindle thermal error measurement, modeling and compensation of a two turntable five-axis machine tool are researched. Measurement experiment of heat sources and thermal errors are carried out, and GRA(grey relational analysis) method is introduced into the selection of temperature variables used for thermal error modeling. In order to analyze the influence of different heat sources on spindle thermal errors, an ANN (artificial neural network) model is presented, and ABC(artificial bee colony) algorithm is introduced to train the link weights of ANN, a new ABC-NN(Artificial bee colony-based neural network) modeling method is proposed and used in the prediction of spindle thermal errors. In order to test the prediction performance of ABC-NN model, an experiment system is developed, the prediction results of LSR (least squares regression), ANN and ABC-NN are compared with the measurement results of spindle thermal errors. Experiment results show that the prediction accuracy of ABC-NN model is higher than LSR and ANN, and the residual error is smaller than 3 μm, the new modeling method is feasible. The proposed research provides instruction to compensate thermal errors and improve machining accuracy of NC machine tools.

Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction

PubMed Central

2017-01-01

Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization. PMID:28912803

Artificial intelligence based model for optimization of COD removal efficiency of an up-flow anaerobic sludge blanket reactor in the saline wastewater treatment.

PubMed

Picos-Benítez, Alain R; López-Hincapié, Juan D; Chávez-Ramírez, Abraham U; Rodríguez-García, Adrián

2017-03-01

The complex non-linear behavior presented in the biological treatment of wastewater requires an accurate model to predict the system performance. This study evaluates the effectiveness of an artificial intelligence (AI) model, based on the combination of artificial neural networks (ANNs) and genetic algorithms (GAs), to find the optimum performance of an up-flow anaerobic sludge blanket reactor (UASB) for saline wastewater treatment. Chemical oxygen demand (COD) removal was predicted using conductivity, organic loading rate (OLR) and temperature as input variables. The ANN model was built from experimental data and performance was assessed through the maximum mean absolute percentage error (= 9.226%) computed from the measured and model predicted values of the COD. Accordingly, the ANN model was used as a fitness function in a GA to find the best operational condition. In the worst case scenario (low energy requirements, high OLR usage and high salinity) this model guaranteed COD removal efficiency values above 70%. This result is consistent and was validated experimentally, confirming that this ANN-GA model can be used as a tool to achieve the best performance of a UASB reactor with the minimum requirement of energy for saline wastewater treatment.

Modeling the cost-effectiveness of insect rearing on artificial diets: A test with a tephritid fly used in the sterile insect technique.

PubMed

Pascacio-Villafán, Carlos; Birke, Andrea; Williams, Trevor; Aluja, Martín

2017-01-01

We modeled the cost-effectiveness of rearing Anastrepha ludens, a major fruit fly pest currently mass reared for sterilization and release in pest control programs implementing the sterile insect technique (SIT). An optimization model was generated by combining response surface models of artificial diet cost savings with models of A. ludens pupation, pupal weight, larval development time and adult emergence as a function of mixtures of yeast, a costly ingredient, with corn flour and corncob fractions in the diet. Our model revealed several yeast-reduced mixtures that could be used to prepare diets that were considerably cheaper than a standard diet used for mass rearing. Models predicted a similar production of insects (pupation and adult emergence), with statistically similar pupal weights and larval development times between yeast-reduced diets and the standard mass rearing diet formulation. Annual savings from using the modified diets could be up to 5.9% of the annual cost of yeast, corn flour and corncob fractions used in the standard diet, representing a potential saving of US $27.45 per ton of diet (US $47,496 in the case of the mean annual production of 1,730.29 tons of artificial diet in the Moscafrut mass rearing facility at Metapa, Chiapas, Mexico). Implementation of the yeast-reduced diet on an experimental scale at mass rearing facilities is still required to confirm the suitability of new mixtures of artificial diet for rearing A. ludens for use in SIT. This should include the examination of critical quality control parameters of flies such as adult flight ability, starvation resistance and male sexual competitiveness across various generations. The method used here could be useful for improving the cost-effectiveness of invertebrate or vertebrate mass rearing diets worldwide.

Modeling the cost-effectiveness of insect rearing on artificial diets: A test with a tephritid fly used in the sterile insect technique

PubMed Central

Birke, Andrea; Williams, Trevor; Aluja, Martín

2017-01-01

We modeled the cost-effectiveness of rearing Anastrepha ludens, a major fruit fly pest currently mass reared for sterilization and release in pest control programs implementing the sterile insect technique (SIT). An optimization model was generated by combining response surface models of artificial diet cost savings with models of A. ludens pupation, pupal weight, larval development time and adult emergence as a function of mixtures of yeast, a costly ingredient, with corn flour and corncob fractions in the diet. Our model revealed several yeast-reduced mixtures that could be used to prepare diets that were considerably cheaper than a standard diet used for mass rearing. Models predicted a similar production of insects (pupation and adult emergence), with statistically similar pupal weights and larval development times between yeast-reduced diets and the standard mass rearing diet formulation. Annual savings from using the modified diets could be up to 5.9% of the annual cost of yeast, corn flour and corncob fractions used in the standard diet, representing a potential saving of US $27.45 per ton of diet (US $47,496 in the case of the mean annual production of 1,730.29 tons of artificial diet in the Moscafrut mass rearing facility at Metapa, Chiapas, Mexico). Implementation of the yeast-reduced diet on an experimental scale at mass rearing facilities is still required to confirm the suitability of new mixtures of artificial diet for rearing A. ludens for use in SIT. This should include the examination of critical quality control parameters of flies such as adult flight ability, starvation resistance and male sexual competitiveness across various generations. The method used here could be useful for improving the cost-effectiveness of invertebrate or vertebrate mass rearing diets worldwide. PMID:28257496

Artificial topography changes the growth strategy of Spartina alterniflora, case study with wave exposure as a comparison.

PubMed

Hong, Hualong; Dai, Minyue; Lu, Haoliang; Liu, Jingchun; Zhang, Jie; Chen, Chaoqi; Xia, Kang; Yan, Chongling

2017-11-17

This paper reports findings about the growth of Spartina alterniflora (Loisel.) near an engineered coastal protection defences to discover the potential influences on vegetation growth from the artificial topography. Impacts of the artificial topography on the sediment element composition were detected by comparing the fixed effects caused by artificial topography and wave exposure using linear mixed models. Surficial sediments under the impacts of artificial topography contain elevated levels of biogenic elements and heavy metals, including C (and organic carbon), N, S, Al, Fe, Mn, Cu, Zn, As, Cd, Cr, Ni, and Pb. The results showed that element enrichment caused by artificial topography reduced the vegetation sexual reproduction. Contrary to the potential inhibition caused by direct wave exposure, which was due to the biomass accumulation limit, the inhibition caused by artificial topography was related to the transition of growth strategy. The contents of Cu, Mn, N, Ni, S and As in the sediments were critical in considering the relationship between the change in the sediment element composition and the alteration in the plant growth. Our study emphasizes the importance of rethinking the impacts of coastal development projects, especially regarding the heterogeneity of sediment element composition and its ecological consequences.

Science of the science, drug discovery and artificial neural networks.

PubMed

Patel, Jigneshkumar

2013-03-01

Drug discovery process many times encounters complex problems, which may be difficult to solve by human intelligence. Artificial Neural Networks (ANNs) are one of the Artificial Intelligence (AI) technologies used for solving such complex problems. ANNs are widely used for primary virtual screening of compounds, quantitative structure activity relationship studies, receptor modeling, formulation development, pharmacokinetics and in all other processes involving complex mathematical modeling. Despite having such advanced technologies and enough understanding of biological systems, drug discovery is still a lengthy, expensive, difficult and inefficient process with low rate of new successful therapeutic discovery. In this paper, author has discussed the drug discovery science and ANN from very basic angle, which may be helpful to understand the application of ANN for drug discovery to improve efficiency.

Artificial and Bayesian Neural Networks

PubMed

Korhani Kangi, Azam; Bahrampour, Abbas

2018-02-26

Introduction and purpose: In recent years the use of neural networks without any premises for investigation of prognosis in analyzing survival data has increased. Artificial neural networks (ANN) use small processors with a continuous network to solve problems inspired by the human brain. Bayesian neural networks (BNN) constitute a neural-based approach to modeling and non-linearization of complex issues using special algorithms and statistical methods. Gastric cancer incidence is the first and third ranking for men and women in Iran, respectively. The aim of the present study was to assess the value of an artificial neural network and a Bayesian neural network for modeling and predicting of probability of gastric cancer patient death. Materials and Methods: In this study, we used information on 339 patients aged from 20 to 90 years old with positive gastric cancer, referred to Afzalipoor and Shahid Bahonar Hospitals in Kerman City from 2001 to 2015. The three layers perceptron neural network (ANN) and the Bayesian neural network (BNN) were used for predicting the probability of mortality using the available data. To investigate differences between the models, sensitivity, specificity, accuracy and the area under receiver operating characteristic curves (AUROCs) were generated. Results: In this study, the sensitivity and specificity of the artificial neural network and Bayesian neural network models were 0.882, 0.903 and 0.954, 0.909, respectively. Prediction accuracy and the area under curve ROC for the two models were 0.891, 0.944 and 0.935, 0.961. The age at diagnosis of gastric cancer was most important for predicting survival, followed by tumor grade, morphology, gender, smoking history, opium consumption, receiving chemotherapy, presence of metastasis, tumor stage, receiving radiotherapy, and being resident in a village. Conclusion: The findings of the present study indicated that the Bayesian neural network is preferable to an artificial neural network for predicting survival of gastric cancer patients in Iran. Creative Commons Attribution License

Association between nighttime artificial light pollution and sea turtle nest density along Florida coast: A geospatial study using VIIRS remote sensing data.

PubMed

Hu, Zhiyong; Hu, Hongda; Huang, Yuxia

2018-08-01

Artificial lighting at night has becoming a new type of pollution posing an important anthropogenic environmental pressure on organisms. The objective of this research was to examine the potential association between nighttime artificial light pollution and nest densities of the three main sea turtle species along Florida beaches, including green turtles, loggerheads, and leatherbacks. Sea turtle survey data was obtained from the "Florida Statewide Nesting Beach Survey program". We used the new generation of satellite sensor "Visible Infrared Imaging Radiometer Suite (VIIRS)" (version 1 D/N Band) nighttime annual average radiance composite image data. We defined light pollution as artificial light brightness greater than 10% of the natural sky brightness above 45° of elevation (>1.14 × 10 -11 Wm -2 sr -1 ). We fitted a generalized linear model (GLM), a GLM with eigenvectors spatial filtering (GLM-ESF), and a generalized estimating equations (GEE) approach for each species to examine the potential correlation of nest density with light pollution. Our models are robust and reliable in terms of the ability to deal with data distribution and spatial autocorrelation (SA) issues violating model assumptions. All three models found that nest density is significantly negatively correlated with light pollution for each sea turtle species: the higher light pollution, the lower nest density. The two spatially extended models (GLM-ESF and GEE) show that light pollution influences nest density in a descending order from green turtles, to loggerheads, and then to leatherbacks. The research findings have an implication for sea turtle conservation policy and ordinance making. Near-coastal lights-out ordinances and other approaches to shield lights can protect sea turtles and their nests. The VIIRS DNB light data, having significant improvements over comparable data by its predecessor, the DMSP-OLS, shows promise for continued and improved research about ecological effects of artificial light pollution. Copyright © 2018 Elsevier Ltd. All rights reserved.

Probabilistic machine learning and artificial intelligence.

PubMed

Ghahramani, Zoubin

2015-05-28

How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.

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.

Probabilistic machine learning and artificial intelligence

NASA Astrophysics Data System (ADS)

Ghahramani, Zoubin

2015-05-01

How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.

Long-term culture of bovine nucleus pulposus explants in a native environment.

PubMed

van Dijk, Bart G M; Potier, Esther; Ito, Keita

2013-04-01

Chronic low back pain is a disease with tremendous financial and social implications, and it is often caused by intervertebral disc degeneration. Regenerative therapies for disc repair are promising treatments, but they need to be tested in physiological models. To develop a physiological in vitro explant model that incorporates the native environment of the intervertebral disc, for example, hypoxia, low glucose, and high tissue osmolarity. Bovine nucleus pulposus (NP) explants were cultured for 42 days in conditions mimicking the native physiological environment. Two different approaches were used to balance the swelling pressure of the NP: raised medium osmolarity or an artificial annulus. Bovine NP explants were either cultured in media with osmolarity balanced at isotonic and hypertonic levels compared with the native tissue or cultured inside a fiber jacket used as an artificial annulus. Oxygen and glucose levels were set at either standard (21% O2 and 4.5 g/L glucose) or physiological (5% O2 and 1 g/L glucose) levels. Samples were analyzed at Day 0, 3, and 42 for tissue composition (water, sulfated glycosaminoglycans, DNA, and hydroxyproline contents and fixed charge density), tissue histology, cell viability, and cellular behavior with messenger RNA (mRNA) expression. Both the hypertonic culture and the artificial annulus approach maintained the tissue matrix composition for 42 days. At Day 3, mRNA expressions of aggrecan, collagen Type I, and collagen Type II in both hypertonic and artificial annulus cultures were not different from Day 0; however, at Day 42, the artificial annulus preserved the mRNA expression closer to Day 0. Gene expressions of matrix metalloprotease 13, tissue inhibitor of matrix metalloprotease 1, and tissue inhibitor of matrix metalloprotease 2 were downregulated under physiological O2 and glucose levels, whereas the other parameters analyzed were not affected. Although the hypertonic culture and the artificial annulus approach are both promising models to test regenerative therapies, the artificial annulus was better able to maintain a cellular behavior closer to the native tissue in longer term cultures. Copyright © 2013 Elsevier Inc. All rights reserved.

The 1988 Goddard Conference on Space Applications of Artificial Intelligence

NASA Technical Reports Server (NTRS)

Rash, James (Editor); Hughes, Peter (Editor)

1988-01-01

This publication comprises the papers presented at the 1988 Goddard Conference on Space Applications of Artificial Intelligence held at the NASA/Goddard Space Flight Center, Greenbelt, Maryland on May 24, 1988. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The papers in these proceedings fall into the following areas: mission operations support, planning and scheduling; fault isolation/diagnosis; image processing and machine vision; data management; modeling and simulation; and development tools/methodologies.

[Methods of artificial intelligence: a new trend in pharmacy].

PubMed

Dohnal, V; Kuca, K; Jun, D

2005-07-01

Artificial neural networks (ANN) and genetic algorithms are one group of methods called artificial intelligence. The application of ANN on pharmaceutical data can lead to an understanding of the inner structure of data and a possibility to build a model (adaptation). In addition, for certain cases it is possible to extract rules from data. The adapted ANN is prepared for the prediction of properties of compounds which were not used in the adaptation phase. The applications of ANN have great potential in pharmaceutical industry and in the interpretation of analytical, pharmacokinetic or toxicological data.

Artificial Life in Quantum Technologies

PubMed Central

Alvarez-Rodriguez, Unai; Sanz, Mikel; Lamata, Lucas; Solano, Enrique

2016-01-01

We develop a quantum information protocol that models the biological behaviours of individuals living in a natural selection scenario. The artificially engineered evolution of the quantum living units shows the fundamental features of life in a common environment, such as self-replication, mutation, interaction of individuals, and death. We propose how to mimic these bio-inspired features in a quantum-mechanical formalism, which allows for an experimental implementation achievable with current quantum platforms. This study paves the way for the realization of artificial life and embodied evolution with quantum technologies. PMID:26853918

Modeling the Learner in Computer-Assisted Instruction

ERIC Educational Resources Information Center

Fletcher, J. D.

1975-01-01

This paper briefly reviews relevant work in four areas: 1) quantitative models of memory; 2) regression models of performance; 3) automation models of performance; and 4) artificial intelligence. (Author/HB)

Application of Artificial Neural Networks in the Heart Electrical Axis Position Conclusion Modeling

NASA Astrophysics Data System (ADS)

Bakanovskaya, L. N.

2016-08-01

The article touches upon building of a heart electrical axis position conclusion model using an artificial neural network. The input signals of the neural network are the values of deflections Q, R and S; and the output signal is the value of the heart electrical axis position. Training of the network is carried out by the error propagation method. The test results allow concluding that the created neural network makes a conclusion with a high degree of accuracy.

Artificial Intelligence for VHSIC Systems Design (AIVD) User Reference Manual

DTIC Science & Technology

1988-12-01

The goal of this program was to develop prototype tools which would use artificial intelligence techniques to extend the Architecture Design and Assessment (ADAS) software capabilities. These techniques were applied in a number of ways to increase the productivity of ADAS users. AIM will reduce the amount of time spent on tedious, negative, and error-prone steps. It will also provide f documentation that will assist users in varifying that the models they build are correct Finally, AIVD will help make ADAS models more reusable.

The Fundamental Structure and the Reproduction of Spiral Wave in a Two-Dimensional Excitable Lattice.

PubMed

Qian, Yu; Zhang, Zhaoyang

2016-01-01

In this paper we have systematically investigated the fundamental structure and the reproduction of spiral wave in a two-dimensional excitable lattice. A periodically rotating spiral wave is introduced as the model to reproduce spiral wave artificially. Interestingly, by using the dominant phase-advanced driving analysis method, the fundamental structure containing the loop structure and the wave propagation paths has been revealed, which can expose the periodically rotating orbit of spiral tip and the charity of spiral wave clearly. Furthermore, the fundamental structure is utilized as the core for artificial spiral wave. Additionally, the appropriate parameter region, in which the artificial spiral wave can be reproduced, is studied. Finally, we discuss the robustness of artificial spiral wave to defects.

A comparative study of the antacid effect of some commonly consumed foods for hyperacidity in an artificial stomach model.

PubMed

Panda, Vandana; Shinde, Priyanka; Deora, Jyoti; Gupta, Pankaj

2017-10-01

The incorporation of certain alkalinizing vegetables, fruits, milk and its products in the diet has been known to alleviate hyperacidity. These foods help to restore the natural gastric balance and function, curb acid reflux, aid digestion, reduce the burning sensation due to hyperacidity and soothe the inflamed mucosa of the stomach. The present study evaluates and compares the antacid effect of broccoli, kale, radish, cucumber, lemon juice, cold milk and curd in an artificial stomach model. The pH of the test samples and their neutralizing effect on artificial gastric acid was determined and compared with that of water, the active control sodium bicarbonate and a marketed antacid preparation ENO. A modified model of Vatier's artificial stomach was used to determine the duration of consistent neutralization of artificial gastric acid by the test samples. The neutralizing capacity of the test samples was determined in vitro using the classical titration method of Fordtran. All test samples except lemon showed significantly higher (p<0.05 for cucumber and p<0.001 for the rest) acid neutralizing effect than water. All test samples also exhibited a significantly (p<0.001) higher duration of consistent neutralization and higher antacid capacity than water. Highest antacid activity was demonstrated by cold milk and broccoli which was comparable with ENO and sodium bicarbonate. It may be concluded that the natural food ingredients used in this study exhibited significant antacid activity, justifying their use as essential dietary components to counter hyperacidity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Fluid-driven origami-inspired artificial muscles.

PubMed

Li, Shuguang; Vogt, Daniel M; Rus, Daniela; Wood, Robert J

2017-12-12

Artificial muscles hold promise for safe and powerful actuation for myriad common machines and robots. However, the design, fabrication, and implementation of artificial muscles are often limited by their material costs, operating principle, scalability, and single-degree-of-freedom contractile actuation motions. Here we propose an architecture for fluid-driven origami-inspired artificial muscles. This concept requires only a compressible skeleton, a flexible skin, and a fluid medium. A mechanical model is developed to explain the interaction of the three components. A fabrication method is introduced to rapidly manufacture low-cost artificial muscles using various materials and at multiple scales. The artificial muscles can be programed to achieve multiaxial motions including contraction, bending, and torsion. These motions can be aggregated into systems with multiple degrees of freedom, which are able to produce controllable motions at different rates. Our artificial muscles can be driven by fluids at negative pressures (relative to ambient). This feature makes actuation safer than most other fluidic artificial muscles that operate with positive pressures. Experiments reveal that these muscles can contract over 90% of their initial lengths, generate stresses of ∼600 kPa, and produce peak power densities over 2 kW/kg-all equal to, or in excess of, natural muscle. This architecture for artificial muscles opens the door to rapid design and low-cost fabrication of actuation systems for numerous applications at multiple scales, ranging from miniature medical devices to wearable robotic exoskeletons to large deployable structures for space exploration. Copyright © 2017 the Author(s). Published by PNAS.

Artificial sweeteners and metabolic dysregulation: Lessons learned from agriculture and the laboratory.

PubMed

Shearer, Jane; Swithers, Susan E

2016-06-01

Escalating rates of obesity and public health messages to reduce excessive sugar intake have fuelled the consumption of artificial sweeteners in a wide range of products from breakfast cereals to snack foods and beverages. Artificial sweeteners impart a sweet taste without the associated energy and have been widely recommended by medical professionals since they are considered safe. However, associations observed in long-term prospective studies raise the concern that regular consumption of artificial sweeteners might actually contribute to development of metabolic derangements that lead to obesity, type 2 diabetes and cardiovascular disease. Obtaining mechanistic data on artificial sweetener use in humans in relation to metabolic dysfunction is difficult due to the long time frames over which dietary factors might exert their effects on health and the large number of confounding variables that need to be considered. Thus, mechanistic data from animal models can be highly useful because they permit greater experimental control. Results from animal studies in both the agricultural sector and the laboratory indicate that artificial sweeteners may not only promote food intake and weight gain but can also induce metabolic alterations in a wide range of animal species. As a result, simple substitution of artificial sweeteners for sugars in humans may not produce the intended consequences. Instead consumption of artificial sweeteners might contribute to increases in risks for obesity or its attendant negative health outcomes. As a result, it is critical that the impacts of artificial sweeteners on health and disease continue to be more thoroughly evaluated in humans.

Fluid-driven origami-inspired artificial muscles

PubMed Central

Li, Shuguang; Vogt, Daniel M.; Rus, Daniela; Wood, Robert J.

2017-01-01

Artificial muscles hold promise for safe and powerful actuation for myriad common machines and robots. However, the design, fabrication, and implementation of artificial muscles are often limited by their material costs, operating principle, scalability, and single-degree-of-freedom contractile actuation motions. Here we propose an architecture for fluid-driven origami-inspired artificial muscles. This concept requires only a compressible skeleton, a flexible skin, and a fluid medium. A mechanical model is developed to explain the interaction of the three components. A fabrication method is introduced to rapidly manufacture low-cost artificial muscles using various materials and at multiple scales. The artificial muscles can be programed to achieve multiaxial motions including contraction, bending, and torsion. These motions can be aggregated into systems with multiple degrees of freedom, which are able to produce controllable motions at different rates. Our artificial muscles can be driven by fluids at negative pressures (relative to ambient). This feature makes actuation safer than most other fluidic artificial muscles that operate with positive pressures. Experiments reveal that these muscles can contract over 90% of their initial lengths, generate stresses of ∼600 kPa, and produce peak power densities over 2 kW/kg—all equal to, or in excess of, natural muscle. This architecture for artificial muscles opens the door to rapid design and low-cost fabrication of actuation systems for numerous applications at multiple scales, ranging from miniature medical devices to wearable robotic exoskeletons to large deployable structures for space exploration. PMID:29180416

Fluid-driven origami-inspired artificial muscles

NASA Astrophysics Data System (ADS)

Li, Shuguang; Vogt, Daniel M.; Rus, Daniela; Wood, Robert J.

2017-12-01

Artificial muscles hold promise for safe and powerful actuation for myriad common machines and robots. However, the design, fabrication, and implementation of artificial muscles are often limited by their material costs, operating principle, scalability, and single-degree-of-freedom contractile actuation motions. Here we propose an architecture for fluid-driven origami-inspired artificial muscles. This concept requires only a compressible skeleton, a flexible skin, and a fluid medium. A mechanical model is developed to explain the interaction of the three components. A fabrication method is introduced to rapidly manufacture low-cost artificial muscles using various materials and at multiple scales. The artificial muscles can be programed to achieve multiaxial motions including contraction, bending, and torsion. These motions can be aggregated into systems with multiple degrees of freedom, which are able to produce controllable motions at different rates. Our artificial muscles can be driven by fluids at negative pressures (relative to ambient). This feature makes actuation safer than most other fluidic artificial muscles that operate with positive pressures. Experiments reveal that these muscles can contract over 90% of their initial lengths, generate stresses of ˜600 kPa, and produce peak power densities over 2 kW/kg—all equal to, or in excess of, natural muscle. This architecture for artificial muscles opens the door to rapid design and low-cost fabrication of actuation systems for numerous applications at multiple scales, ranging from miniature medical devices to wearable robotic exoskeletons to large deployable structures for space exploration.

All Together Now: Concurrent Learning of Multiple Structures in an Artificial Language

ERIC Educational Resources Information Center

Romberg, Alexa R.; Saffran, Jenny R.

2013-01-01

Natural languages contain many layers of sequential structure, from the distribution of phonemes within words to the distribution of phrases within utterances. However, most research modeling language acquisition using artificial languages has focused on only one type of distributional structure at a time. In two experiments, we investigated adult…

Artificial Intelligence: Themes in the Second Decade. Memo Number 67.

ERIC Educational Resources Information Center

Feigenbaum, Edward A.

The text of an invited address on artificial intelligence (AI) research over the 1963-1968 period is presented. A survey of recent studies on the computer simulation of intellective processes emphasizes developments in heuristic programing, problem-solving and closely related learning models. Progress and problems in these areas are indicated by…

[Artificial neural networks for decision making in urologic oncology].

PubMed

Remzi, M; Djavan, B

2007-06-01

This chapter presents a detailed introduction regarding Artificial Neural Networks (ANNs) and their contribution to modern Urologic Oncology. It includes a description of ANNs methodology and points out the differences between Artifical Intelligence and traditional statistic models in terms of usefulness for patients and clinicians, and its advantages over current statistical analysis.

Artificial Intelligence, Computational Thinking, and Mathematics Education

ERIC Educational Resources Information Center

Gadanidis, George

2017-01-01

Purpose: The purpose of this paper is to examine the intersection of artificial intelligence (AI), computational thinking (CT), and mathematics education (ME) for young students (K-8). Specifically, it focuses on three key elements that are common to AI, CT and ME: agency, modeling of phenomena and abstracting concepts beyond specific instances.…

Modeling Career Counselor Decisions with Artificial Neural Networks: Predictions of Fit across a Comprehensive Occupational Map.

ERIC Educational Resources Information Center

Carson, Andrew D.; Bizot, Elizabeth B.; Hendershot, Peggy E.; Barton, Margaret G.; Garvin, Mary K.; Kraemer, Barbara

1999-01-01

Career recommendations were made based on aptitude scores of 335 high school freshmen. Artificial neural networks were used to map recommendations to 12 occupational clusters. Overall accuracy of neural networks (.80) approached that of discriminant function analysis (.84). The two methods had different strengths and weaknesses. (SK)

An Artificial Intelligence Approach to the Symbolic Factorization of Multivariable Polynomials. Technical Report No. CS74019-R.

ERIC Educational Resources Information Center

Claybrook, Billy G.

A new heuristic factorization scheme uses learning to improve the efficiency of determining the symbolic factorization of multivariable polynomials with interger coefficients and an arbitrary number of variables and terms. The factorization scheme makes extensive use of artificial intelligence techniques (e.g., model-building, learning, and…

An Application of Fuzzy Analytic Hierarchy Process (FAHP) for Evaluating Students' Project

ERIC Educational Resources Information Center

Çebi, Ayça; Karal, Hasan

2017-01-01

In recent years, artificial intelligence applications for understanding the human thinking process and transferring it to virtual environments come into prominence. The fuzzy logic which paves the way for modeling human behaviors and expressing even vague concepts mathematically, and is also regarded as an artificial intelligence technique has…

Prediction of the Wall Factor of Arbitrary Particle Settling through Various Fluid Media in a Cylindrical Tube Using Artificial Intelligence

PubMed Central

Li, Mingzhong; Xue, Jianquan; Li, Yanchao; Tang, Shukai

2014-01-01

Considering the influence of particle shape and the rheological properties of fluid, two artificial intelligence methods (Artificial Neural Network and Support Vector Machine) were used to predict the wall factor which is widely introduced to deduce the net hydrodynamic drag force of confining boundaries on settling particles. 513 data points were culled from the experimental data of previous studies, which were divided into training set and test set. Particles with various shapes were divided into three kinds: sphere, cylinder, and rectangular prism; feature parameters of each kind of particle were extracted; prediction models of sphere and cylinder using artificial neural network were established. Due to the little number of rectangular prism sample, support vector machine was used to predict the wall factor, which is more suitable for addressing the problem of small samples. The characteristic dimension was presented to describe the shape and size of the diverse particles and a comprehensive prediction model of particles with arbitrary shapes was established to cover all types of conditions. Comparisons were conducted between the predicted values and the experimental results. PMID:24772024

Prediction of the wall factor of arbitrary particle settling through various fluid media in a cylindrical tube using artificial intelligence.

PubMed

Li, Mingzhong; Zhang, Guodong; Xue, Jianquan; Li, Yanchao; Tang, Shukai

2014-01-01

Considering the influence of particle shape and the rheological properties of fluid, two artificial intelligence methods (Artificial Neural Network and Support Vector Machine) were used to predict the wall factor which is widely introduced to deduce the net hydrodynamic drag force of confining boundaries on settling particles. 513 data points were culled from the experimental data of previous studies, which were divided into training set and test set. Particles with various shapes were divided into three kinds: sphere, cylinder, and rectangular prism; feature parameters of each kind of particle were extracted; prediction models of sphere and cylinder using artificial neural network were established. Due to the little number of rectangular prism sample, support vector machine was used to predict the wall factor, which is more suitable for addressing the problem of small samples. The characteristic dimension was presented to describe the shape and size of the diverse particles and a comprehensive prediction model of particles with arbitrary shapes was established to cover all types of conditions. Comparisons were conducted between the predicted values and the experimental results.

Optimization of groundwater artificial recharge systems using a genetic algorithm: a case study in Beijing, China

NASA Astrophysics Data System (ADS)

Hao, Qichen; Shao, Jingli; Cui, Yali; Zhang, Qiulan; Huang, Linxian

2018-05-01

An optimization approach is used for the operation of groundwater artificial recharge systems in an alluvial fan in Beijing, China. The optimization model incorporates a transient groundwater flow model, which allows for simulation of the groundwater response to artificial recharge. The facilities' operation with regard to recharge rates is formulated as a nonlinear programming problem to maximize the volume of surface water recharged into the aquifers under specific constraints. This optimization problem is solved by the parallel genetic algorithm (PGA) based on OpenMP, which could substantially reduce the computation time. To solve the PGA with constraints, the multiplicative penalty method is applied. In addition, the facilities' locations are implicitly determined on the basis of the results of the recharge-rate optimizations. Two scenarios are optimized and the optimal results indicate that the amount of water recharged into the aquifers will increase without exceeding the upper limits of the groundwater levels. Optimal operation of this artificial recharge system can also contribute to the more effective recovery of the groundwater storage capacity.

Thermally induced magnetic relaxation in square artificial spin ice

DOE PAGES

Andersson, M. S.; Pappas, S. D.; Stopfel, H.; ...

2016-11-24

The properties of natural and artificial assemblies of interacting elements, ranging from Quarks to Galaxies, are at the heart of Physics. The collective response and dynamics of such assemblies are dictated by the intrinsic dynamical properties of the building blocks, the nature of their interactions and topological constraints. Here in this paper, we report on the relaxation dynamics of the magnetization of artificial assemblies of mesoscopic spins. In our model nano-magnetic system $-$ square artificial spin ice $-$ we are able to control the geometrical arrangement and interaction strength between the magnetically interacting building blocks by means of nano-lithography. Usingmore » time resolved magnetometry we show that the relaxation process can be described using the Kohlrausch law and that the extracted temperature dependent relaxation times of the assemblies follow the Vogel-Fulcher law. The results provide insight into the relaxation dynamics of mesoscopic nano-magnetic model systems, with adjustable energy and time scales, and demonstrates that these can serve as an ideal playground for the studies of collective dynamics and relaxations.« less

An analysis of artificial viscosity effects on reacting flows using a spectral multi-domain technique

NASA Technical Reports Server (NTRS)

Macaraeg, M. G.; Streett, C. L.; Hussaini, M. Y.

1987-01-01

Standard techniques used to model chemically-reacting flows require an artificial viscosity for stability in the presence of strong shocks. The resulting shock is smeared over at least three computational cells, so that the thickness of the shock is dictated by the structure of the overall mesh and not the shock physics. A gas passing through a strong shock is thrown into a nonequilibrium state and subsequently relaxes down over some finite distance to an equilibrium end state. The artificial smearing of the shock envelops this relaxation zone which causes the chemical kinetics of the flow to be altered. A method is presented which can investigate these issues by following the chemical kinetics and flow kinetics of a gas passing through a fully resolved shock wave at hypersonic Mach numbers. A nonequilibrium chemistry model for air is incorporated into a spectral multidomain Navier-Stokes solution method. Since no artificial viscosity is needed for stability of the multidomain technique, the precise effect of this artifice on the chemical kinetics and relevant flow features can be determined.

Thermally induced magnetic relaxation in square artificial spin ice

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

Andersson, M. S.; Pappas, S. D.; Stopfel, H.

The properties of natural and artificial assemblies of interacting elements, ranging from Quarks to Galaxies, are at the heart of Physics. The collective response and dynamics of such assemblies are dictated by the intrinsic dynamical properties of the building blocks, the nature of their interactions and topological constraints. Here in this paper, we report on the relaxation dynamics of the magnetization of artificial assemblies of mesoscopic spins. In our model nano-magnetic system $-$ square artificial spin ice $-$ we are able to control the geometrical arrangement and interaction strength between the magnetically interacting building blocks by means of nano-lithography. Usingmore » time resolved magnetometry we show that the relaxation process can be described using the Kohlrausch law and that the extracted temperature dependent relaxation times of the assemblies follow the Vogel-Fulcher law. The results provide insight into the relaxation dynamics of mesoscopic nano-magnetic model systems, with adjustable energy and time scales, and demonstrates that these can serve as an ideal playground for the studies of collective dynamics and relaxations.« less

Understanding Surface Adhesion in Nature: A Peeling Model

PubMed Central

Gu, Zhen; Li, Siheng; Zhang, Feilong

2016-01-01

Nature often exhibits various interesting and unique adhesive surfaces. The attempt to understand the natural adhesion phenomena can continuously guide the design of artificial adhesive surfaces by proposing simplified models of surface adhesion. Among those models, a peeling model can often effectively reflect the adhesive property between two surfaces during their attachment and detachment processes. In the context, this review summarizes the recent advances about the peeling model in understanding unique adhesive properties on natural and artificial surfaces. It mainly includes four parts: a brief introduction to natural surface adhesion, the theoretical basis and progress of the peeling model, application of the peeling model, and finally, conclusions. It is believed that this review is helpful to various fields, such as surface engineering, biomedicine, microelectronics, and so on. PMID:27812476

Modelling the effect of structural QSAR parameters on skin penetration using genetic programming

NASA Astrophysics Data System (ADS)

Chung, K. K.; Do, D. Q.

2010-09-01

In order to model relationships between chemical structures and biological effects in quantitative structure-activity relationship (QSAR) data, an alternative technique of artificial intelligence computing—genetic programming (GP)—was investigated and compared to the traditional method—statistical. GP, with the primary advantage of generating mathematical equations, was employed to model QSAR data and to define the most important molecular descriptions in QSAR data. The models predicted by GP agreed with the statistical results, and the most predictive models of GP were significantly improved when compared to the statistical models using ANOVA. Recently, artificial intelligence techniques have been applied widely to analyse QSAR data. With the capability of generating mathematical equations, GP can be considered as an effective and efficient method for modelling QSAR data.

Artificial Neural Network Approach in Laboratory Test Reporting:  Learning Algorithms.

PubMed

Demirci, Ferhat; Akan, Pinar; Kume, Tuncay; Sisman, Ali Riza; Erbayraktar, Zubeyde; Sevinc, Suleyman

2016-08-01

In the field of laboratory medicine, minimizing errors and establishing standardization is only possible by predefined processes. The aim of this study was to build an experimental decision algorithm model open to improvement that would efficiently and rapidly evaluate the results of biochemical tests with critical values by evaluating multiple factors concurrently. The experimental model was built by Weka software (Weka, Waikato, New Zealand) based on the artificial neural network method. Data were received from Dokuz Eylül University Central Laboratory. "Training sets" were developed for our experimental model to teach the evaluation criteria. After training the system, "test sets" developed for different conditions were used to statistically assess the validity of the model. After developing the decision algorithm with three iterations of training, no result was verified that was refused by the laboratory specialist. The sensitivity of the model was 91% and specificity was 100%. The estimated κ score was 0.950. This is the first study based on an artificial neural network to build an experimental assessment and decision algorithm model. By integrating our trained algorithm model into a laboratory information system, it may be possible to reduce employees' workload without compromising patient safety. © American Society for Clinical Pathology, 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Incorporating geographical factors with artificial neural networks to predict reference values of erythrocyte sedimentation rate

PubMed Central

2013-01-01

Background The measurement of the Erythrocyte Sedimentation Rate (ESR) value is a standard procedure performed during a typical blood test. In order to formulate a unified standard of establishing reference ESR values, this paper presents a novel prediction model in which local normal ESR values and corresponding geographical factors are used to predict reference ESR values using multi-layer feed-forward artificial neural networks (ANN). Methods and findings Local normal ESR values were obtained from hospital data, while geographical factors that include altitude, sunshine hours, relative humidity, temperature and precipitation were obtained from the National Geographical Data Information Centre in China. The results show that predicted values are statistically in agreement with measured values. Model results exhibit significant agreement between training data and test data. Consequently, the model is used to predict the unseen local reference ESR values. Conclusions Reference ESR values can be established with geographical factors by using artificial intelligence techniques. ANN is an effective method for simulating and predicting reference ESR values because of its ability to model nonlinear and complex relationships. PMID:23497145

Evaluation of the probability of arrester failure in a high-voltage transmission line using a Q learning artificial neural network model

NASA Astrophysics Data System (ADS)

Ekonomou, L.; Karampelas, P.; Vita, V.; Chatzarakis, G. E.

2011-04-01

One of the most popular methods of protecting high voltage transmission lines against lightning strikes and internal overvoltages is the use of arresters. The installation of arresters in high voltage transmission lines can prevent or even reduce the lines' failure rate. Several studies based on simulation tools have been presented in order to estimate the critical currents that exceed the arresters' rated energy stress and to specify the arresters' installation interval. In this work artificial intelligence, and more specifically a Q-learning artificial neural network (ANN) model, is addressed for evaluating the arresters' failure probability. The aims of the paper are to describe in detail the developed Q-learning ANN model and to compare the results obtained by its application in operating 150 kV Greek transmission lines with those produced using a simulation tool. The satisfactory and accurate results of the proposed ANN model can make it a valuable tool for designers of electrical power systems seeking more effective lightning protection, reducing operational costs and better continuity of service.

Time-lapse gravity data for monitoring and modeling artificial recharge through a thick unsaturated zone

USGS Publications Warehouse

Kennedy, Jeffrey R.; Ferre, Ty P.A.; Creutzfeldt, Benjamin

2016-01-01

Groundwater-level measurements in monitoring wells or piezometers are the most common, and often the only, hydrologic measurements made at artificial recharge facilities. Measurements of gravity change over time provide an additional source of information about changes in groundwater storage, infiltration, and for model calibration. We demonstrate that for an artificial recharge facility with a deep groundwater table, gravity data are more sensitive to movement of water through the unsaturated zone than are groundwater levels. Groundwater levels have a delayed response to infiltration, change in a similar manner at many potential monitoring locations, and are heavily influenced by high-frequency noise induced by pumping; in contrast, gravity changes start immediately at the onset of infiltration and are sensitive to water in the unsaturated zone. Continuous gravity data can determine infiltration rate, and the estimate is only minimally affected by uncertainty in water-content change. Gravity data are also useful for constraining parameters in a coupled groundwater-unsaturated zone model (Modflow-NWT model with the Unsaturated Zone Flow (UZF) package).

Incorporating geographical factors with artificial neural networks to predict reference values of erythrocyte sedimentation rate.

PubMed

Yang, Qingsheng; Mwenda, Kevin M; Ge, Miao

2013-03-12

The measurement of the Erythrocyte Sedimentation Rate (ESR) value is a standard procedure performed during a typical blood test. In order to formulate a unified standard of establishing reference ESR values, this paper presents a novel prediction model in which local normal ESR values and corresponding geographical factors are used to predict reference ESR values using multi-layer feed-forward artificial neural networks (ANN). Local normal ESR values were obtained from hospital data, while geographical factors that include altitude, sunshine hours, relative humidity, temperature and precipitation were obtained from the National Geographical Data Information Centre in China.The results show that predicted values are statistically in agreement with measured values. Model results exhibit significant agreement between training data and test data. Consequently, the model is used to predict the unseen local reference ESR values. Reference ESR values can be established with geographical factors by using artificial intelligence techniques. ANN is an effective method for simulating and predicting reference ESR values because of its ability to model nonlinear and complex relationships.

Time-lapse gravity data for monitoring and modeling artificial recharge through a thick unsaturated zone

NASA Astrophysics Data System (ADS)

Kennedy, Jeffrey; Ferré, Ty P. A.; Creutzfeldt, Benjamin

2016-09-01

Groundwater-level measurements in monitoring wells or piezometers are the most common, and often the only, hydrologic measurements made at artificial recharge facilities. Measurements of gravity change over time provide an additional source of information about changes in groundwater storage, infiltration, and for model calibration. We demonstrate that for an artificial recharge facility with a deep groundwater table, gravity data are more sensitive to movement of water through the unsaturated zone than are groundwater levels. Groundwater levels have a delayed response to infiltration, change in a similar manner at many potential monitoring locations, and are heavily influenced by high-frequency noise induced by pumping; in contrast, gravity changes start immediately at the onset of infiltration and are sensitive to water in the unsaturated zone. Continuous gravity data can determine infiltration rate, and the estimate is only minimally affected by uncertainty in water-content change. Gravity data are also useful for constraining parameters in a coupled groundwater-unsaturated zone model (Modflow-NWT model with the Unsaturated Zone Flow (UZF) package).

QSRR using evolved artificial neural network for 52 common pharmaceuticals and drugs of abuse in hair from UPLC-TOF-MS.

PubMed

Noorizadeh, Hadi; Farmany, Abbas; Narimani, Hojat; Noorizadeh, Mehrab

2013-05-01

A quantitative structure-retention relationship (QSRR) study based on an artificial neural network (ANN) was carried out for the prediction of the ultra-performance liquid chromatography-Time-of-Flight mass spectrometry (UPLC-TOF-MS) retention time (RT) of a set of 52 pharmaceuticals and drugs of abuse in hair. The genetic algorithm was used as a variable selection tool. A partial least squares (PLS) method was used to select the best descriptors which were used as input neurons in neural network model. For choosing the best predictive model from among comparable models, square correlation coefficient R(2) for the whole set calculated based on leave-group-out predicted values of the training set and model-derived predicted values for the test set compounds is suggested to be a good criterion. Finally, to improve the results, structure-retention relationships were followed by a non-linear approach using artificial neural networks and consequently better results were obtained. This also demonstrates the advantages of ANN. Copyright © 2011 John Wiley & Sons, Ltd.

Optimality in Microwave-Assisted Drying of Aloe Vera ( Aloe barbadensis Miller) Gel using Response Surface Methodology and Artificial Neural Network Modeling

NASA Astrophysics Data System (ADS)

Das, Chandan; Das, Arijit; Kumar Golder, Animes

2016-10-01

The present work illustrates the Microwave-Assisted Drying (MWAD) characteristic of aloe vera gel combined with process optimization and artificial neural network modeling. The influence of microwave power (160-480 W), gel quantity (4-8 g) and drying time (1-9 min) on the moisture ratio was investigated. The drying of aloe gel exhibited typical diffusion-controlled characteristics with a predominant interaction between input power and drying time. Falling rate period was observed for the entire MWAD of aloe gel. Face-centered Central Composite Design (FCCD) developed a regression model to evaluate their effects on moisture ratio. The optimal MWAD conditions were established as microwave power of 227.9 W, sample amount of 4.47 g and 5.78 min drying time corresponding to the moisture ratio of 0.15. A computer-stimulated Artificial Neural Network (ANN) model was generated for mapping between process variables and the desired response. `Levenberg-Marquardt Back Propagation' algorithm with 3-5-1 architect gave the best prediction, and it showed a clear superiority over FCCD.

Prediction of octanol-water partition coefficients of organic compounds by multiple linear regression, partial least squares, and artificial neural network.

PubMed

Golmohammadi, Hassan

2009-11-30

A quantitative structure-property relationship (QSPR) study was performed to develop models those relate the structure of 141 organic compounds to their octanol-water partition coefficients (log P(o/w)). A genetic algorithm was applied as a variable selection tool. Modeling of log P(o/w) of these compounds as a function of theoretically derived descriptors was established by multiple linear regression (MLR), partial least squares (PLS), and artificial neural network (ANN). The best selected descriptors that appear in the models are: atomic charge weighted partial positively charged surface area (PPSA-3), fractional atomic charge weighted partial positive surface area (FPSA-3), minimum atomic partial charge (Qmin), molecular volume (MV), total dipole moment of molecule (mu), maximum antibonding contribution of a molecule orbital in the molecule (MAC), and maximum free valency of a C atom in the molecule (MFV). The result obtained showed the ability of developed artificial neural network to prediction of partition coefficients of organic compounds. Also, the results revealed the superiority of ANN over the MLR and PLS models. Copyright 2009 Wiley Periodicals, Inc.

Artificial Surfaces in Phyllosphere Microbiology.

PubMed

Doan, Hung K; Leveau, Johan H J

2015-08-01

The study of microorganisms that reside on plant leaf surfaces, or phyllosphere microbiology, greatly benefits from the availability of artificial surfaces that mimic in one or more ways the complexity of foliage as a microbial habitat. These leaf surface proxies range from very simple, such as nutrient agars that can reveal the metabolic versatility or antagonistic properties of leaf-associated microorganisms, to the very complex, such as silicon-based casts that replicate leaf surface topography down to nanometer resolution. In this review, we summarize the various uses of artificial surfaces in experimental phyllosphere microbiology and discuss how these have advanced our understanding of the biology of leaf-associated microorganisms and the habitat they live in. We also provide an outlook into future uses of artificial leaf surfaces, foretelling a greater role for microfluidics to introduce biological and chemical gradients into artificial leaf environments, stressing the importance of artificial surfaces to generate quantitative data that support computational models of microbial life on real leaves, and rethinking the leaf surface ('phyllosphere') as a habitat that features two intimately connected but very different compartments, i.e., the leaf surface landscape ('phylloplane') and the leaf surface waterscape ('phyllotelma').

Replacement of long segments of the esophagus with a collagen-silicone composite tube.

PubMed

Takimoto, Y; Nakamura, T; Teramachi, M; Kiyotani, T; Shimizu, Y

1995-01-01

Artificial esophagi designed thus far can be classified into three types in terms of the materials used: natural, artificial, and composite. In conventional models, even when artificial esophagi were made of ideal materials with high tissue affinity, they remained in the tissue as a foreign body, and therefore were not free of the complications caused by implanted material. The authors have designed a new type of artificial esophagus composed of a Silicone tube covered with nonantigenic collagen. The novel feature of this artificial esophagus is that the prosthesis does not remain in the implanted site, but is replaced by regenerated host tissue. Using this artificial esophagus, the authors have already succeeded in replacing a 5 cm gap in the esophagus. In this study, replacement of longer portions of the esophagus was assessed in seven dogs using a 10 cm long artificial esophagus. Stenosis did not occur in five of the seven dogs and, consequently, these dogs survived by oral feeding alone for more than 6 months without dry weight loss. The other two animals died of anesthetic accidents at the time of stent removal 6 weeks after surgery. In both cases, the internal surface of the neoesophagus was covered with a polylayer of squamous epithelium. Regenerated esophagi had normal esophageal glands and immature muscle tissue. It is therefore concluded that this new artificial esophagus is also applicable for replacement of long segments of esophagus.

A surgical simulator for peeling the inner limiting membrane during wet conditions.

PubMed

Omata, Seiji; Someya, Yusei; Adachi, Shyn'ya; Masuda, Taisuke; Hayakawa, Takeshi; Harada, Kanako; Mitsuishi, Mamoru; Totsuka, Kiyohito; Araki, Fumiyuki; Takao, Muneyuki; Aihara, Makoto; Arai, Fumihito

2018-01-01

The present study was performed to establish a novel ocular surgery simulator for training in peeling of the inner limited membrane (ILM). This simulator included a next-generation artificial ILM with mechanical properties similar to the natural ILM that could be peeled underwater in the same manner as in actual surgery. An artificial eye consisting of a fundus and eyeball parts was fabricated. The artificial eye was installed in the eye surgery simulator. The fundus part was mounted in the eyeball, which consisted of an artificial sclera, retina, and ILM. To measure the thickness of the fabricated ILM on the artificial retina, we calculated the distance of the step height as the thickness of the artificial ILM. Two experienced ophthalmologists then assessed the fabricated ILM by sensory evaluation. The minimum thickness of the artificial ILM was 1.9 ± 0.3 μm (n = 3). We were able to perform the peeling task with the ILM in water. Based on the sensory evaluation, an ILM with a minimum thickness and 1000 degrees of polymerization was suitable for training. We installed the eye model on an ocular surgery simulator, which allowed for the performance of a sequence of operations similar to ILM peeling. In conclusion, we developed a novel ocular surgery simulator for ILM peeling. The artificial ILM was peeled underwater in the same manner as in an actual operation.

Proactive learning for artificial cognitive systems

NASA Astrophysics Data System (ADS)

Lee, Soo-Young

2010-04-01

The Artificial Cognitive Systems (ACS) will be developed for human-like functions such as vision, auditory, inference, and behavior. Especially, computational models and artificial HW/SW systems will be devised for Proactive Learning (PL) and Self-Identity (SI). The PL model provides bilateral interactions between robot and unknown environment (people, other robots, cyberspace). For the situation awareness in unknown environment it is required to receive audiovisual signals and to accumulate knowledge. If the knowledge is not enough, the PL should improve by itself though internet and others. For human-oriented decision making it is also required for the robot to have self-identify and emotion. Finally, the developed models and system will be mounted on a robot for the human-robot co-existing society. The developed ACS will be tested against the new Turing Test for the situation awareness. The Test problems will consist of several video clips, and the performance of the ACSs will be compared against those of human with several levels of cognitive ability.

Cloud Model-Based Artificial Immune Network for Complex Optimization Problem

PubMed Central

Wang, Mingan; Li, Jianming; Guo, Dongliang

2017-01-01

This paper proposes an artificial immune network based on cloud model (AINet-CM) for complex function optimization problems. Three key immune operators—cloning, mutation, and suppression—are redesigned with the help of the cloud model. To be specific, an increasing half cloud-based cloning operator is used to adjust the dynamic clone multipliers of antibodies, an asymmetrical cloud-based mutation operator is used to control the adaptive evolution of antibodies, and a normal similarity cloud-based suppressor is used to keep the diversity of the antibody population. To quicken the searching convergence, a dynamic searching step length strategy is adopted. For comparative study, a series of numerical simulations are arranged between AINet-CM and the other three artificial immune systems, that is, opt-aiNet, IA-AIS, and AAIS-2S. Furthermore, two industrial applications—finite impulse response (FIR) filter design and proportional-integral-differential (PID) controller tuning—are investigated and the results demonstrate the potential searching capability and practical value of the proposed AINet-CM algorithm. PMID:28630620

Kinetics and equilibrium models for the sorption of tributyltin to nZnO, activated carbon and nZnO/activated carbon composite in artificial seawater.

PubMed

Ayanda, Olushola S; Fatoki, Olalekan S; Adekola, Folahan A; Ximba, Bhekumusa J

2013-07-15

The removal of tributyltin (TBT) from artificial seawater using nZnO, activated carbon and nZnO/activated carbon composite was systematically studied. The equilibrium and kinetics of adsorption were investigated in a batch adsorption system. Equilibrium adsorption data were analyzed using Langmuir, Freundlich, Temkin and Dubinin-Radushkevich (D-R) isotherm models. Pseudo first- and second-order, Elovich, fractional power and intraparticle diffusion models were applied to test the kinetic data. Thermodynamic parameters such as ΔG°, ΔS° and ΔH° were also calculated to understand the mechanisms of adsorption. Optimal conditions for the adsorption of TBT from artificial seawater were then applied to TBT removal from natural seawater. A higher removal efficiency of TBT (>99%) was obtained for the nZnO/activated carbon composite material and for activated carbon but not for nZnO. Copyright © 2013 Elsevier Ltd. All rights reserved.

Cloud Model-Based Artificial Immune Network for Complex Optimization Problem.

PubMed

Wang, Mingan; Feng, Shuo; Li, Jianming; Li, Zhonghua; Xue, Yu; Guo, Dongliang

2017-01-01

This paper proposes an artificial immune network based on cloud model (AINet-CM) for complex function optimization problems. Three key immune operators-cloning, mutation, and suppression-are redesigned with the help of the cloud model. To be specific, an increasing half cloud-based cloning operator is used to adjust the dynamic clone multipliers of antibodies, an asymmetrical cloud-based mutation operator is used to control the adaptive evolution of antibodies, and a normal similarity cloud-based suppressor is used to keep the diversity of the antibody population. To quicken the searching convergence, a dynamic searching step length strategy is adopted. For comparative study, a series of numerical simulations are arranged between AINet-CM and the other three artificial immune systems, that is, opt-aiNet, IA-AIS, and AAIS-2S. Furthermore, two industrial applications-finite impulse response (FIR) filter design and proportional-integral-differential (PID) controller tuning-are investigated and the results demonstrate the potential searching capability and practical value of the proposed AINet-CM algorithm.

Emerging trends in geospatial artificial intelligence (geoAI): potential applications for environmental epidemiology.

PubMed

VoPham, Trang; Hart, Jaime E; Laden, Francine; Chiang, Yao-Yi

2018-04-17

Geospatial artificial intelligence (geoAI) is an emerging scientific discipline that combines innovations in spatial science, artificial intelligence methods in machine learning (e.g., deep learning), data mining, and high-performance computing to extract knowledge from spatial big data. In environmental epidemiology, exposure modeling is a commonly used approach to conduct exposure assessment to determine the distribution of exposures in study populations. geoAI technologies provide important advantages for exposure modeling in environmental epidemiology, including the ability to incorporate large amounts of big spatial and temporal data in a variety of formats; computational efficiency; flexibility in algorithms and workflows to accommodate relevant characteristics of spatial (environmental) processes including spatial nonstationarity; and scalability to model other environmental exposures across different geographic areas. The objectives of this commentary are to provide an overview of key concepts surrounding the evolving and interdisciplinary field of geoAI including spatial data science, machine learning, deep learning, and data mining; recent geoAI applications in research; and potential future directions for geoAI in environmental epidemiology.

Evaluation of articulation simulation system using artificial maxillectomy models.

PubMed

Elbashti, M E; Hattori, M; Sumita, Y I; Taniguchi, H

2015-09-01

Acoustic evaluation is valuable for guiding the treatment of maxillofacial defects and determining the effectiveness of rehabilitation with an obturator prosthesis. Model simulations are important in terms of pre-surgical planning and pre- and post-operative speech function. This study aimed to evaluate the acoustic characteristics of voice generated by an articulation simulation system using a vocal tract model with or without artificial maxillectomy defects. More specifically, we aimed to establish a speech simulation system for maxillectomy defect models that both surgeons and maxillofacial prosthodontists can use in guiding treatment planning. Artificially simulated maxillectomy defects were prepared according to Aramany's classification (Classes I-VI) in a three-dimensional vocal tract plaster model of a subject uttering the vowel /a/. Formant and nasalance acoustic data were analysed using Computerized Speech Lab and the Nasometer, respectively. Formants and nasalance of simulated /a/ sounds were successfully detected and analysed. Values of Formants 1 and 2 for the non-defect model were 675.43 and 976.64 Hz, respectively. Median values of Formants 1 and 2 for the defect models were 634.36 and 1026.84 Hz, respectively. Nasalance was 11% in the non-defect model, whereas median nasalance was 28% in the defect models. The results suggest that an articulation simulation system can be used to help surgeons and maxillofacial prosthodontists to plan post-surgical defects that will be facilitate maxillofacial rehabilitation. © 2015 John Wiley & Sons Ltd.

Training Spiking Neural Models Using Artificial Bee Colony

PubMed Central

Vazquez, Roberto A.; Garro, Beatriz A.

2015-01-01

Spiking neurons are models designed to simulate, in a realistic manner, the behavior of biological neurons. Recently, it has been proven that this type of neurons can be applied to solve pattern recognition problems with great efficiency. However, the lack of learning strategies for training these models do not allow to use them in several pattern recognition problems. On the other hand, several bioinspired algorithms have been proposed in the last years for solving a broad range of optimization problems, including those related to the field of artificial neural networks (ANNs). Artificial bee colony (ABC) is a novel algorithm based on the behavior of bees in the task of exploring their environment to find a food source. In this paper, we describe how the ABC algorithm can be used as a learning strategy to train a spiking neuron aiming to solve pattern recognition problems. Finally, the proposed approach is tested on several pattern recognition problems. It is important to remark that to realize the powerfulness of this type of model only one neuron will be used. In addition, we analyze how the performance of these models is improved using this kind of learning strategy. PMID:25709644

Modeling of bromate formation by ozonation of surface waters in drinking water treatment.

PubMed

Legube, Bernard; Parinet, Bernard; Gelinet, Karine; Berne, Florence; Croue, Jean-Philippe

2004-04-01

The main objective of this paper is to try to develop statistically and chemically rational models for bromate formation by ozonation of clarified surface waters. The results presented here show that bromate formation by ozonation of natural waters in drinking water treatment is directly proportional to the "Ct" value ("Ctau" in this study). Moreover, this proportionality strongly depends on many parameters: increasing of pH, temperature and bromide level leading to an increase of bromate formation; ammonia and dissolved organic carbon concentrations causing a reverse effect. Taking into account limitation of theoretical modeling, we proposed to predict bromate formation by stochastic simulations (multi-linear regression and artificial neural networks methods) from 40 experiments (BrO(3)(-) vs. "Ctau") carried out with three sand filtered waters sampled on three different waterworks. With seven selected variables we used a simple architecture of neural networks, optimized by "neural connection" of SPSS Inc./Recognition Inc. The bromate modeling by artificial neural networks gives better result than multi-linear regression. The artificial neural networks model allowed us classifying variables by decreasing order of influence (for the studied cases in our variables scale): "Ctau", [N-NH(4)(+)], [Br(-)], pH, temperature, DOC, alkalinity.

Reduced-Order Modeling for Flutter/LCO Using Recurrent Artificial Neural Network

NASA Technical Reports Server (NTRS)

Yao, Weigang; Liou, Meng-Sing

2012-01-01

The present study demonstrates the efficacy of a recurrent artificial neural network to provide a high fidelity time-dependent nonlinear reduced-order model (ROM) for flutter/limit-cycle oscillation (LCO) modeling. An artificial neural network is a relatively straightforward nonlinear method for modeling an input-output relationship from a set of known data, for which we use the radial basis function (RBF) with its parameters determined through a training process. The resulting RBF neural network, however, is only static and is not yet adequate for an application to problems of dynamic nature. The recurrent neural network method [1] is applied to construct a reduced order model resulting from a series of high-fidelity time-dependent data of aero-elastic simulations. Once the RBF neural network ROM is constructed properly, an accurate approximate solution can be obtained at a fraction of the cost of a full-order computation. The method derived during the study has been validated for predicting nonlinear aerodynamic forces in transonic flow and is capable of accurate flutter/LCO simulations. The obtained results indicate that the present recurrent RBF neural network is accurate and efficient for nonlinear aero-elastic system analysis

A simplified model of all-sky artificial sky glow derived from VIIRS Day/Night band data

NASA Astrophysics Data System (ADS)

Duriscoe, Dan M.; Anderson, Sharolyn J.; Luginbuhl, Christian B.; Baugh, Kimberly E.

2018-07-01

We present a simplified method using geographic analysis tools to predict the average artificial luminance over the hemisphere of the night sky, expressed as a ratio to the natural condition. The VIIRS Day/Night Band upward radiance data from the Suomi NPP orbiting satellite was used for input to the model. The method is based upon a relation between sky glow brightness and the distance from the observer to the source of upward radiance. This relationship was developed using a Garstang radiative transfer model with Day/Night Band data as input, then refined and calibrated with ground-based all-sky V-band photometric data taken under cloudless and low atmospheric aerosol conditions. An excellent correlation was found between observed sky quality and the predicted values from the remotely sensed data. Thematic maps of large regions of the earth showing predicted artificial V-band sky brightness may be quickly generated with modest computing resources. We have found a fast and accurate method based on previous work to model all-sky quality. We provide limitations to this method. The proposed model meets requirements needed by decision makers and land managers of an easy to interpret and understand metric of sky quality.

[Study on artificial neural network combined with multispectral remote sensing imagery for forest site evaluation].

PubMed

Gong, Yin-Xi; He, Cheng; Yan, Fei; Feng, Zhong-Ke; Cao, Meng-Lei; Gao, Yuan; Miao, Jie; Zhao, Jin-Long

2013-10-01

Multispectral remote sensing data containing rich site information are not fully used by the classic site quality evaluation system, as it merely adopts artificial ground survey data. In order to establish a more effective site quality evaluation system, a neural network model which combined remote sensing spectra factors with site factors and site index relations was established and used to study the sublot site quality evaluation in the Wangyedian Forest Farm in Inner Mongolia Province, Chifeng City. Based on the improved back propagation artificial neural network (BPANN), this model combined multispectral remote sensing data with sublot survey data, and took larch as example, Through training data set sensitivity analysis weak or irrelevant factor was excluded, the size of neural network was simplified, and the efficiency of network training was improved. This optimal site index prediction model had an accuracy up to 95.36%, which was 9.83% higher than that of the neural network model based on classic sublot survey data, and this shows that using multi-spectral remote sensing and small class survey data to determine the status of larch index prediction model has the highest predictive accuracy. The results fully indicate the effectiveness and superiority of this method.

Application of principal component regression and artificial neural network in FT-NIR soluble solids content determination of intact pear fruit

NASA Astrophysics Data System (ADS)

Ying, Yibin; Liu, Yande; Fu, Xiaping; Lu, Huishan

2005-11-01

The artificial neural networks (ANNs) have been used successfully in applications such as pattern recognition, image processing, automation and control. However, majority of today's applications of ANNs is back-propagate feed-forward ANN (BP-ANN). In this paper, back-propagation artificial neural networks (BP-ANN) were applied for modeling soluble solid content (SSC) of intact pear from their Fourier transform near infrared (FT-NIR) spectra. One hundred and sixty-four pear samples were used to build the calibration models and evaluate the models predictive ability. The results are compared to the classical calibration approaches, i.e. principal component regression (PCR), partial least squares (PLS) and non-linear PLS (NPLS). The effects of the optimal methods of training parameters on the prediction model were also investigated. BP-ANN combine with principle component regression (PCR) resulted always better than the classical PCR, PLS and Weight-PLS methods, from the point of view of the predictive ability. Based on the results, it can be concluded that FT-NIR spectroscopy and BP-ANN models can be properly employed for rapid and nondestructive determination of fruit internal quality.

Vitamin D and ferritin correlation with chronic neck pain using standard statistics and a novel artificial neural network prediction model.

PubMed

Eloqayli, Haytham; Al-Yousef, Ali; Jaradat, Raid

2018-02-15

Despite the high prevalence of chronic neck pain, there is limited consensus about the primary etiology, risk factors, diagnostic criteria and therapeutic outcome. Here, we aimed to determine if Ferritin and Vitamin D are modifiable risk factors with chronic neck pain using slandered statistics and artificial intelligence neural network (ANN). Fifty-four patients with chronic neck pain treated between February 2016 and August 2016 in King Abdullah University Hospital and 54 patients age matched controls undergoing outpatient or minor procedures were enrolled. Patients and control demographic parameters, height, weight and single measurement of serum vitamin D, Vitamin B12, ferritin, calcium, phosphorus, zinc were obtained. An ANN prediction model was developed. The statistical analysis reveals that patients with chronic neck pain have significantly lower serum Vitamin D and Ferritin (p-value <.05). 90% of patients with chronic neck pain were females. Multilayer Feed Forward Neural Network with Back Propagation(MFFNN) prediction model were developed and designed based on vitamin D and ferritin as input variables and CNP as output. The ANN model output results show that, 92 out of 108 samples were correctly classified with 85% classification accuracy. Although Iron and vitamin D deficiency cannot be isolated as the sole risk factors of chronic neck pain, they should be considered as two modifiable risk. The high prevalence of chronic neck pain, hypovitaminosis D and low ferritin amongst women is of concern. Bioinformatics predictions with artificial neural network can be of future benefit in classification and prediction models for chronic neck pain. We hope this initial work will encourage a future larger cohort study addressing vitamin D and iron correction as modifiable factors and the application of artificial intelligence models in clinical practice.

Modelling Potential Consequences of Different Geo-Engineering Treatments for the Baltic Sea Ecosystem

NASA Astrophysics Data System (ADS)

Schrum, C.; Daewel, U.

2017-12-01

From 1950 onwards, the Baltic Sea ecosystem suffered increasingly from eutrophication. The most obvious reason for the eutrophication is the huge amount of nutrients (nitrogen and phosphorus) reaching the Baltic Sea from human activities. However, although nutrient loads have been decreasing since 1980, the hypoxic areas have not decreased accordingly. Thus, geo-engineering projects were discussed and evaluated to artificially ventilate the Baltic Sea deep water and suppress nutrient release from the sediments. Here, we aim at understanding the consequences of proposed geo-engineering projects in the Baltic Sea using long-term scenario modelling. For that purpose, we utilize a 3d coupled ecosystem model ECOSMO E2E, a novel NPZD-Fish model approach that resolves hydrodynamics, biogeochemical cycling and lower and higher trophic level dynamics. We performed scenario modelling that consider proposed geo-engineering projects such as artificial ventilation of Baltic Sea deep waters and phosphorus binding in sediments with polyaluminium chlorides. The model indicates that deep-water ventilation indeed suppresses phosphorus release in the first 1-4 years of treatment. Thereafter macrobenthos repopulates the formerly anoxic bottom regions and nutrients are increasingly recycled in the food web. Consequently, overall system productivity and fish biomass increases and toxic algae blooms decrease. However, deep-water ventilation has no long-lasting effect on the ecosystem: soon after completion of the ventilation process, the system turns back into its original state. Artificial phosphorus binding in sediments in contrast decreases overall ecosystem productivity through permanent removal of phosphorus. As expected it decreases bacterial production and toxic algae blooms, but it also decreases fish production substantially. Contrastingly to deep water ventilation, artificial phosphorus binding show a long-lasting effect over decades after termination of the treatment.

Artificial pigs in space: using artificial intelligence and artificial life techniques to design animal housing.

PubMed

Stricklin, W R; de Bourcier, P; Zhou, J Z; Gonyou, H W

1998-10-01

Computer simulations have been used by us since the early 1970s to gain an understanding of the spacing and movement patterns of confined animals. The work has progressed from the early stages, in which we used randomly positioned points, to current investigations of animats (computer-simulated animals), which show low levels of learning via artificial neural networks. We have determined that 1) pens of equal floor area but of different shape result in different spatial and movement patterns for randomly positioned and moving animats; 2) when group size increases under constant density, freedom of movement approaches an asymptote at approximately six animats; 3) matching the number of animats with the number of corners results in optimal freedom of movement for small groups of animats; and 4) perimeter positioning occurs in groups of animats that maximize their distance to first- and second-nearest neighbors. Recently, we developed animats that move, compete for social dominance, and are motivated to obtain resources (food, resting sites, etc.). We are currently developing an animat that learns its behavior from the spatial and movement data collected on live pigs. The animat model is then used to pretest pen designs, followed by new pig spatial data fed into the animat model, resulting in a new pen design to be tested, and the steps are repeated. We believe that methodologies from artificial-life and artificial intelligence can contribute to the understanding of basic animal behavior principles, as well as to the solving of problems in production agriculture in areas such as animal housing design.

A Novel Higher Order Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Xu, Shuxiang

2010-05-01

In this paper a new Higher Order Neural Network (HONN) model is introduced and applied in several data mining tasks. Data Mining extracts hidden patterns and valuable information from large databases. A hyperbolic tangent function is used as the neuron activation function for the new HONN model. Experiments are conducted to demonstrate the advantages and disadvantages of the new HONN model, when compared with several conventional Artificial Neural Network (ANN) models: Feedforward ANN with the sigmoid activation function; Feedforward ANN with the hyperbolic tangent activation function; and Radial Basis Function (RBF) ANN with the Gaussian activation function. The experimental results seem to suggest that the new HONN holds higher generalization capability as well as abilities in handling missing data.

Virtual personal assistance

NASA Astrophysics Data System (ADS)

Aditya, K.; Biswadeep, G.; Kedar, S.; Sundar, S.

2017-11-01

Human computer communication has growing demand recent days. The new generation of autonomous technology aspires to give computer interfaces emotional states that relate and consider user as well as system environment considerations. In the existing computational model is based an artificial intelligent and externally by multi-modal expression augmented with semi human characteristics. But the main problem with is multi-model expression is that the hardware control given to the Artificial Intelligence (AI) is very limited. So, in our project we are trying to give the Artificial Intelligence (AI) more control on the hardware. There are two main parts such as Speech to Text (STT) and Text to Speech (TTS) engines are used accomplish the requirement. In this work, we are using a raspberry pi 3, a speaker and a mic as hardware and for the programing part, we are using python scripting.

A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms

PubMed Central

Meiring, Gys Albertus Marthinus; Myburgh, Hermanus Carel

2015-01-01

In this paper the various driving style analysis solutions are investigated. An in-depth investigation is performed to identify the relevant machine learning and artificial intelligence algorithms utilised in current driver behaviour and driving style analysis systems. This review therefore serves as a trove of information, and will inform the specialist and the student regarding the current state of the art in driver style analysis systems, the application of these systems and the underlying artificial intelligence algorithms applied to these applications. The aim of the investigation is to evaluate the possibilities for unique driver identification utilizing the approaches identified in other driver behaviour studies. It was found that Fuzzy Logic inference systems, Hidden Markov Models and Support Vector Machines consist of promising capabilities to address unique driver identification algorithms if model complexity can be reduced. PMID:26690164

A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms.

PubMed

Meiring, Gys Albertus Marthinus; Myburgh, Hermanus Carel

2015-12-04

In this paper the various driving style analysis solutions are investigated. An in-depth investigation is performed to identify the relevant machine learning and artificial intelligence algorithms utilised in current driver behaviour and driving style analysis systems. This review therefore serves as a trove of information, and will inform the specialist and the student regarding the current state of the art in driver style analysis systems, the application of these systems and the underlying artificial intelligence algorithms applied to these applications. The aim of the investigation is to evaluate the possibilities for unique driver identification utilizing the approaches identified in other driver behaviour studies. It was found that Fuzzy Logic inference systems, Hidden Markov Models and Support Vector Machines consist of promising capabilities to address unique driver identification algorithms if model complexity can be reduced.

Predictive control of intersegmental tarsal movements in an insect.

PubMed

Costalago-Meruelo, Alicia; Simpson, David M; Veres, Sandor M; Newland, Philip L

2017-08-01

In many animals intersegmental reflexes are important for postural and movement control but are still poorly undesrtood. Mathematical methods can be used to model the responses to stimulation, and thus go beyond a simple description of responses to specific inputs. Here we analyse an intersegmental reflex of the foot (tarsus) of the locust hind leg, which raises the tarsus when the tibia is flexed and depresses it when the tibia is extended. A novel method is described to measure and quantify the intersegmental responses of the tarsus to a stimulus to the femoro-tibial chordotonal organ. An Artificial Neural Network, the Time Delay Neural Network, was applied to understand the properties and dynamics of the reflex responses. The aim of this study was twofold: first to develop an accurate method to record and analyse the movement of an appendage and second, to apply methods to model the responses using Artificial Neural Networks. The results show that Artificial Neural Networks provide accurate predictions of tarsal movement when trained with an average reflex response to Gaussian White Noise stimulation compared to linear models. Furthermore, the Artificial Neural Network model can predict the individual responses of each animal and responses to others inputs such as a sinusoid. A detailed understanding of such a reflex response could be included in the design of orthoses or functional electrical stimulation treatments to improve walking in patients with neurological disorders as well as the bio/inspired design of robots.

Predicting Air Permeability of Handloom Fabrics: A Comparative Analysis of Regression and Artificial Neural Network Models

NASA Astrophysics Data System (ADS)

Mitra, Ashis; Majumdar, Prabal Kumar; Bannerjee, Debamalya

2013-03-01

This paper presents a comparative analysis of two modeling methodologies for the prediction of air permeability of plain woven handloom cotton fabrics. Four basic fabric constructional parameters namely ends per inch, picks per inch, warp count and weft count have been used as inputs for artificial neural network (ANN) and regression models. Out of the four regression models tried, interaction model showed very good prediction performance with a meager mean absolute error of 2.017 %. However, ANN models demonstrated superiority over the regression models both in terms of correlation coefficient and mean absolute error. The ANN model with 10 nodes in the single hidden layer showed very good correlation coefficient of 0.982 and 0.929 and mean absolute error of only 0.923 and 2.043 % for training and testing data respectively.

Experimental model of atelectasis in newborn piglets.

PubMed

Comaru, Talitha; Fiori, Humberto Holmer; Fiori, Renato Machado; Padoim, Priscila; Stivanin, Jaqueline Basso; da Silva, Vinicius Duval

2014-01-01

There are few studies using animal models in chest physical therapy. However, there are no models to assess these effects in newborns. This study aimed to develop a model of obstructive atelectasis induced by artificial mucus injection in the lungs of newborn piglets, for the study of neonatal physiotherapy. Thirteen newborn piglets received artificial mucus injection via the endotracheal tube. X-rays and blood gas analysis confirmed the atelectasis. The model showed consistent results between oxygenation parameters and radiological findings. Ten (76.9%) of the 13 piglets responded to the intervention. This did not significantly differ from the expected percentage of 50% by the binomial test (95% CI 46.2-95%, P = .09). Our model of atelectasis in newborn piglets is both feasible and appropriate to evaluate the impact of physical therapies on atelectasis in newborns.

Implementation of the ANNs ensembles in macro-BIM cost estimates of buildings' floor structural frames

NASA Astrophysics Data System (ADS)

Juszczyk, Michał

2018-04-01

This paper reports some results of the studies on the use of artificial intelligence tools for the purposes of cost estimation based on building information models. A problem of the cost estimates based on the building information models on a macro level supported by the ensembles of artificial neural networks is concisely discussed. In the course of the research a regression model has been built for the purposes of cost estimation of buildings' floor structural frames, as higher level elements. Building information models are supposed to serve as a repository of data used for the purposes of cost estimation. The core of the model is the ensemble of neural networks. The developed model allows the prediction of cost estimates with satisfactory accuracy.

Critical comparison of several order-book models for stock-market fluctuations

NASA Astrophysics Data System (ADS)

Slanina, F.

2008-01-01

Far-from-equilibrium models of interacting particles in one dimension are used as a basis for modelling the stock-market fluctuations. Particle types and their positions are interpreted as buy and sel orders placed on a price axis in the order book. We revisit some modifications of well-known models, starting with the Bak-Paczuski-Shubik model. We look at the four decades old Stigler model and investigate its variants. One of them is the simplified version of the Genoa artificial market. The list of studied models is completed by the models of Maslov and Daniels et al. Generically, in all cases we compare the return distribution, absolute return autocorrelation and the value of the Hurst exponent. It turns out that none of the models reproduces satisfactorily all the empirical data, but the most promising candidates for further development are the Genoa artificial market and the Maslov model with moderate order evaporation.

Ion Permeability of Artificial Membranes Evaluated by Diffusion Potential and Electrical Resistance Measurements

ERIC Educational Resources Information Center

Shlyonsky, Vadim

2013-01-01

In the present article, a novel model of artificial membranes that provides efficient assistance in teaching the origins of diffusion potentials is proposed. These membranes are made of polycarbonate filters fixed to 12-mm plastic rings and then saturated with a mixture of creosol and "n"-decane. The electrical resistance and potential…

Data mining: sophisticated forms of managed care modeling through artificial intelligence.

PubMed

Borok, L S

1997-01-01

Data mining is a recent development in computer science that combines artificial intelligence algorithms and relational databases to discover patterns automatically, without the use of traditional statistical methods. Work with data mining tools in health care is in a developmental stage that holds great promise, given the combination of demographic and diagnostic information.

Factors Influencing Sensitivity to Lexical Tone in an Artificial Language: Implications for Second Language Learning

ERIC Educational Resources Information Center

Caldwell-Harris, Catherine L.; Lancaster, Alia; Ladd, D. Robert; Dediu, Dan; Christiansen, Morten H.

2015-01-01

This study examined whether musical training, ethnicity, and experience with a natural tone language influenced sensitivity to tone while listening to an artificial tone language. The language was designed with three tones, modeled after level-tone African languages. Participants listened to a 15-min random concatenation of six 3-syllable words.…

Predicting Final GPA of Graduate School Students: Comparing Artificial Neural Networking and Simultaneous Multiple Regression

ERIC Educational Resources Information Center

Anderson, Joan L.

2006-01-01

Data from graduate student applications at a large Western university were used to determine which factors were the best predictors of success in graduate school, as defined by cumulative graduate grade point average. Two statistical models were employed and compared: artificial neural networking and simultaneous multiple regression. Both models…

Applications of artificial intelligence systems in the analysis of epidemiological data.

PubMed

Flouris, Andreas D; Duffy, Jack

2006-01-01

A brief review of the germane literature suggests that the use of artificial intelligence (AI) statistical algorithms in epidemiology has been limited. We discuss the advantages and disadvantages of using AI systems in large-scale sets of epidemiological data to extract inherent, formerly unidentified, and potentially valuable patterns that human-driven deductive models may miss.

The Fundamental Structure and the Reproduction of Spiral Wave in a Two-Dimensional Excitable Lattice

PubMed Central

Qian, Yu; Zhang, Zhaoyang

2016-01-01

In this paper we have systematically investigated the fundamental structure and the reproduction of spiral wave in a two-dimensional excitable lattice. A periodically rotating spiral wave is introduced as the model to reproduce spiral wave artificially. Interestingly, by using the dominant phase-advanced driving analysis method, the fundamental structure containing the loop structure and the wave propagation paths has been revealed, which can expose the periodically rotating orbit of spiral tip and the charity of spiral wave clearly. Furthermore, the fundamental structure is utilized as the core for artificial spiral wave. Additionally, the appropriate parameter region, in which the artificial spiral wave can be reproduced, is studied. Finally, we discuss the robustness of artificial spiral wave to defects. PMID:26900841

Imaging dipole flow sources using an artificial lateral-line system made of biomimetic hair flow sensors

PubMed Central

Dagamseh, Ahmad; Wiegerink, Remco; Lammerink, Theo; Krijnen, Gijs

2013-01-01

In Nature, fish have the ability to localize prey, school, navigate, etc., using the lateral-line organ. Artificial hair flow sensors arranged in a linear array shape (inspired by the lateral-line system (LSS) in fish) have been applied to measure airflow patterns at the sensor positions. Here, we take advantage of both biomimetic artificial hair-based flow sensors arranged as LSS and beamforming techniques to demonstrate dipole-source localization in air. Modelling and measurement results show the artificial lateral-line ability to image the position of dipole sources accurately with estimation error of less than 0.14 times the array length. This opens up possibilities for flow-based, near-field environment mapping that can be beneficial to, for example, biologists and robot guidance applications. PMID:23594816

Application of Artificial Neural Network to Predict the use of Runway at Juanda International Airport

NASA Astrophysics Data System (ADS)

Putra, J. C. P.; Safrilah

2017-06-01

Artificial neural network approaches are useful to solve many complicated problems. It solves a number of problems in various areas such as engineering, medicine, business, manufacturing, etc. This paper presents an application of artificial neural network to predict a runway capacity at Juanda International Airport. An artificial neural network model of backpropagation and multi-layer perceptron is adopted to this research to learning process of runway capacity at Juanda International Airport. The results indicate that the training data is successfully recognizing the certain pattern of runway use at Juanda International Airport. Whereas, testing data indicate vice versa. Finally, it can be concluded that the approach of uniformity data and network architecture is the critical part to determine the accuracy of prediction results.

Modeling the effect of terraces on land degradation in tropical upland agricultural area

NASA Astrophysics Data System (ADS)

Christanto, N.; Shrestha, D. P.; Jetten, V. G.; Setiawan, A.

2012-04-01

Java, the most populated Island in Indonesia, in the pas view decades suffer land degradation do to extreme weather, population pressure and landuse/cover change. The study area, Serayu sub-catchment, as part of Serayu catchment is one of the representative example of Indonesia region facing land use change and land degradation problem. The study attempted to simulate the effect of terraces on land degradation (Soil erosion and landslide hazard) in Serayu sub-catchment using deterministic modeling by means of PCRaster® simulation. The effect of the terraces on tropical upland agricultural area is less studied. This paper will discuss about the effect of terraces on land degradation assessment. Detail Dem is extremely difficult to obtain in developing country like Indonesia. Therefore, an artificial DEM which give an impression of the terraces was built. Topographical maps, Ikonos Image and average of height distribution based on field measurement were used to build the artificial DEM. The result is used in STARWARS model as an input. In combine with Erosion model and PROBSTAB, soil erosion and landslide hazard were quantified. The models were run in two different environment based on the: 1) normal DEM 2.) Artificial DEM (with terraces impression). The result is compared. The result shows that the models run in an artificial DEM give a significant increase on the probability of failure by 20.5%. In the other hand, the erosion rate has fall by 11.32% as compared to the normal DEM. The result of hydrological sensitivity analysis shows that soil depth was the most sensitive parameter. For the slope stability modeling, the most sensitive parameter was slope followed by friction angle and cohesion. The erosion modeling, the model was sensitive to the vegetation cover, soil erodibility followed by BD and KSat. Model validations were applied to assess the accuracy of the models. However, the results of dynamic modeling are ideal for land degradation assessment. Dynamic modeling software such as PC Raster® which is open source and free are reliable alternative to other commercial software

Temperature based daily incoming solar radiation modeling based on gene expression programming, neuro-fuzzy and neural network computing techniques.

NASA Astrophysics Data System (ADS)

Landeras, G.; López, J. J.; Kisi, O.; Shiri, J.

2012-04-01

The correct observation/estimation of surface incoming solar radiation (RS) is very important for many agricultural, meteorological and hydrological related applications. While most weather stations are provided with sensors for air temperature detection, the presence of sensors necessary for the detection of solar radiation is not so habitual and the data quality provided by them is sometimes poor. In these cases it is necessary to estimate this variable. Temperature based modeling procedures are reported in this study for estimating daily incoming solar radiation by using Gene Expression Programming (GEP) for the first time, and other artificial intelligence models such as Artificial Neural Networks (ANNs), and Adaptive Neuro-Fuzzy Inference System (ANFIS). Traditional temperature based solar radiation equations were also included in this study and compared with artificial intelligence based approaches. Root mean square error (RMSE), mean absolute error (MAE) RMSE-based skill score (SSRMSE), MAE-based skill score (SSMAE) and r2 criterion of Nash and Sutcliffe criteria were used to assess the models' performances. An ANN (a four-input multilayer perceptron with ten neurons in the hidden layer) presented the best performance among the studied models (2.93 MJ m-2 d-1 of RMSE). A four-input ANFIS model revealed as an interesting alternative to ANNs (3.14 MJ m-2 d-1 of RMSE). Very limited number of studies has been done on estimation of solar radiation based on ANFIS, and the present one demonstrated the ability of ANFIS to model solar radiation based on temperatures and extraterrestrial radiation. By the way this study demonstrated, for the first time, the ability of GEP models to model solar radiation based on daily atmospheric variables. Despite the accuracy of GEP models was slightly lower than the ANFIS and ANN models the genetic programming models (i.e., GEP) are superior to other artificial intelligence models in giving a simple explicit equation for the phenomenon which shows the relationship between the input and output parameters. This study provided new alternatives for solar radiation estimation based on temperatures.

Reliability analysis of C-130 turboprop engine components using artificial neural network

NASA Astrophysics Data System (ADS)

Qattan, Nizar A.

In this study, we predict the failure rate of Lockheed C-130 Engine Turbine. More than thirty years of local operational field data were used for failure rate prediction and validation. The Weibull regression model and the Artificial Neural Network model including (feed-forward back-propagation, radial basis neural network, and multilayer perceptron neural network model); will be utilized to perform this study. For this purpose, the thesis will be divided into five major parts. First part deals with Weibull regression model to predict the turbine general failure rate, and the rate of failures that require overhaul maintenance. The second part will cover the Artificial Neural Network (ANN) model utilizing the feed-forward back-propagation algorithm as a learning rule. The MATLAB package will be used in order to build and design a code to simulate the given data, the inputs to the neural network are the independent variables, the output is the general failure rate of the turbine, and the failures which required overhaul maintenance. In the third part we predict the general failure rate of the turbine and the failures which require overhaul maintenance, using radial basis neural network model on MATLAB tool box. In the fourth part we compare the predictions of the feed-forward back-propagation model, with that of Weibull regression model, and radial basis neural network model. The results show that the failure rate predicted by the feed-forward back-propagation artificial neural network model is closer in agreement with radial basis neural network model compared with the actual field-data, than the failure rate predicted by the Weibull model. By the end of the study, we forecast the general failure rate of the Lockheed C-130 Engine Turbine, the failures which required overhaul maintenance and six categorical failures using multilayer perceptron neural network (MLP) model on DTREG commercial software. The results also give an insight into the reliability of the engine turbine under actual operating conditions, which can be used by aircraft operators for assessing system and component failures and customizing the maintenance programs recommended by the manufacturer.

Investigation of rat exploratory behavior via evolving artificial neural networks.

PubMed

Costa, Ariadne de Andrade; Tinós, Renato

2016-09-01

Neuroevolution comprises the use of evolutionary computation to define the architecture and/or to train artificial neural networks (ANNs). This strategy has been employed to investigate the behavior of rats in the elevated plus-maze, which is a widely used tool for studying anxiety in mice and rats. Here we propose a neuroevolutionary model, in which both the weights and the architecture of artificial neural networks (our virtual rats) are evolved by a genetic algorithm. This model is an improvement of a previous model that involves the evolution of just the weights of the ANN by the genetic algorithm. In order to compare both models, we analyzed traditional measures of anxiety behavior, like the time spent and the number of entries in both open and closed arms of the maze. When compared to real rat data, our findings suggest that the results from the model introduced here are statistically better than those from other models in the literature. In this way, the neuroevolution of architecture is clearly important for the development of the virtual rats. Moreover, this technique allowed the comprehension of the importance of different sensory units and different number of hidden neurons (performing as memory) in the ANNs (virtual rats). Copyright © 2016 Elsevier B.V. All rights reserved.

Integrating an artificial intelligence approach with k-means clustering to model groundwater salinity: the case of Gaza coastal aquifer (Palestine)

NASA Astrophysics Data System (ADS)

Alagha, Jawad S.; Seyam, Mohammed; Md Said, Md Azlin; Mogheir, Yunes

2017-12-01

Artificial intelligence (AI) techniques have increasingly become efficient alternative modeling tools in the water resources field, particularly when the modeled process is influenced by complex and interrelated variables. In this study, two AI techniques—artificial neural networks (ANNs) and support vector machine (SVM)—were employed to achieve deeper understanding of the salinization process (represented by chloride concentration) in complex coastal aquifers influenced by various salinity sources. Both models were trained using 11 years of groundwater quality data from 22 municipal wells in Khan Younis Governorate, Gaza, Palestine. Both techniques showed satisfactory prediction performance, where the mean absolute percentage error (MAPE) and correlation coefficient ( R) for the test data set were, respectively, about 4.5 and 99.8% for the ANNs model, and 4.6 and 99.7% for SVM model. The performances of the developed models were further noticeably improved through preprocessing the wells data set using a k-means clustering method, then conducting AI techniques separately for each cluster. The developed models with clustered data were associated with higher performance, easiness and simplicity. They can be employed as an analytical tool to investigate the influence of input variables on coastal aquifer salinity, which is of great importance for understanding salinization processes, leading to more effective water-resources-related planning and decision making.

Artificial neural networks modelling the prednisolone nanoprecipitation in microfluidic reactors.

PubMed

Ali, Hany S M; Blagden, Nicholas; York, Peter; Amani, Amir; Brook, Toni

2009-06-28

This study employs artificial neural networks (ANNs) to create a model to identify relationships between variables affecting drug nanoprecipitation using microfluidic reactors. The input variables examined were saturation levels of prednisolone, solvent and antisolvent flow rates, microreactor inlet angles and internal diameters, while particle size was the single output. ANNs software was used to analyse a set of data obtained by random selection of the variables. The developed model was then assessed using a separate set of validation data and provided good agreement with the observed results. The antisolvent flow rate was found to have the dominant role on determining final particle size.

Artificial bee colony algorithm for constrained possibilistic portfolio optimization problem

NASA Astrophysics Data System (ADS)

Chen, Wei

2015-07-01

In this paper, we discuss the portfolio optimization problem with real-world constraints under the assumption that the returns of risky assets are fuzzy numbers. A new possibilistic mean-semiabsolute deviation model is proposed, in which transaction costs, cardinality and quantity constraints are considered. Due to such constraints the proposed model becomes a mixed integer nonlinear programming problem and traditional optimization methods fail to find the optimal solution efficiently. Thus, a modified artificial bee colony (MABC) algorithm is developed to solve the corresponding optimization problem. Finally, a numerical example is given to illustrate the effectiveness of the proposed model and the corresponding algorithm.

Phrase-level speech simulation with an airway modulation model of speech production

PubMed Central

Story, Brad H.

2012-01-01

Artificial talkers and speech synthesis systems have long been used as a means of understanding both speech production and speech perception. The development of an airway modulation model is described that simulates the time-varying changes of the glottis and vocal tract, as well as acoustic wave propagation, during speech production. The result is a type of artificial talker that can be used to study various aspects of how sound is generated by humans and how that sound is perceived by a listener. The primary components of the model are introduced and simulation of words and phrases are demonstrated. PMID:23503742

Intelligent Evaluation Method of Tank Bottom Corrosion Status Based on Improved BP Artificial Neural Network

NASA Astrophysics Data System (ADS)

Qiu, Feng; Dai, Guang; Zhang, Ying

According to the acoustic emission information and the appearance inspection information of tank bottom online testing, the external factors associated with tank bottom corrosion status are confirmed. Applying artificial neural network intelligent evaluation method, three tank bottom corrosion status evaluation models based on appearance inspection information, acoustic emission information, and online testing information are established. Comparing with the result of acoustic emission online testing through the evaluation of test sample, the accuracy of the evaluation model based on online testing information is 94 %. The evaluation model can evaluate tank bottom corrosion accurately and realize acoustic emission online testing intelligent evaluation of tank bottom.

National Systems of Innovation and Technological Differentiation:. a Multi-Country Model

NASA Astrophysics Data System (ADS)

Ribeiro, Leonardo C.; Ruiz, Ricardo M.; Albuquerque, Eduardo M.; Bernardes, Américo T.

Science and technology have a fundamental role in the economic development. Although this statement is generally well accepted, the internal mechanisms which are responsible for these interactions are not clear. In the last decade, dealing with this problem, many models have been proposed. In this paper, we introduce a model that creates an artificial world economy that is a network of countries. Each country has its own national system of innovation and the interactions between countries are given by functions that connect the competitiveness of their prices and their technological capabilities. Starting from different configurations, the artificial world economy self-organizes itself and creates a hierarchies of countries.

1988 Goddard Conference on Space Applications of Artificial Intelligence, Greenbelt, MD, May 24, 1988, Proceedings

NASA Technical Reports Server (NTRS)

Rash, James L. (Editor)

1988-01-01

This publication comprises the papers presented at the 1988 Goddard Conference on Space Applications of Artificial Intelligence held at the NASA/Goddard Space Flight Center, Greenbelt, Maryland on May 24, 1988. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The papers in these proceedings fall into the following areas: mission operations support, planning and scheduling; fault isolation/diagnosis; image processing and machine vision; data management; modeling and simulation; and development tools methodologies.

Determination of the real structure of artificial and natural opals on the basis of three-dimensional reconstructions of reciprocal space

NASA Astrophysics Data System (ADS)

Eliseev, A. A.; Gorozhankin, D. F.; Napolskii, K. S.; Petukhov, A. V.; Sapoletova, N. A.; Vasilieva, A. V.; Grigoryeva, N. A.; Mistonov, A. A.; Byelov, D. V.; Bouwman, W. G.; Kvashnina, K. O.; Chernyshov, D. Yu.; Bosak, A. A.; Grigoriev, S. V.

2009-10-01

The distribution of the scattering intensity in the reciprocal space for natural and artificial opals has been reconstructed from a set of small-angle X-ray diffraction patterns. The resulting three-dimensional intensity maps are used to analyze the defect structure of opals. The structure of artificial opals can be satisfactorily described in the Wilson probability model with the prevalence of layers in the fcc environment. The diffraction patterns observed for a natural opal confirm the presence of sufficiently long unequally occupied fcc domains.

The Toulmin Argument Model in Artificial Intelligence

NASA Astrophysics Data System (ADS)

Verheij, Bart

In 1958, Toulmin published The Uses of Argument. Although this anti-formalistic monograph initially received mixed reviews (see section 2 of [20] for Toulmin’s own recounting of the reception of his book), it has become a classical text on argumentation, and the number of references to the book (when writing these words1 —by a nice numerological coincidence—1958) continues to grow (see [7] and the special issue of Argumentation 2005; Vol. 19, No. 3). Also the field of Artificial Intelligence has discovered Toulmin’s work. Especially four of Toulmin’s themes have found follow-up in Artificial Intelligence.

Biologically inspired technologies using artificial muscles

NASA Technical Reports Server (NTRS)

Bar-Cohen, Yoseph

2005-01-01

One of the newest fields of biomimetics is the electroactive polymers (EAP) that are also known as artificial muscles. To take advantage of these materials, efforts are made worldwide to establish a strong infrastructure addressing the need for comprehensive analytical modeling of their response mechanism and develop effective processing and characterization techniques. The field is still in its emerging state and robust materials are still not readily available however in recent years significant progress has been made and commercial products have already started to appear. This paper covers the current state of- the-art and challenges to making artificial muscles and their potential biomimetic applications.

Scaffold-free trachea regeneration by tissue engineering with bio-3D printing.

PubMed

Taniguchi, Daisuke; Matsumoto, Keitaro; Tsuchiya, Tomoshi; Machino, Ryusuke; Takeoka, Yosuke; Elgalad, Abdelmotagaly; Gunge, Kiyofumi; Takagi, Katsunori; Taura, Yasuaki; Hatachi, Go; Matsuo, Naoto; Yamasaki, Naoya; Nakayama, Koichi; Nagayasu, Takeshi

2018-05-01

Currently, most of the artificial airway organs still require scaffolds; however, such scaffolds exhibit several limitations. Alternatively, the use of an autologous artificial trachea without foreign materials and immunosuppressants may solve these issues and constitute a preferred tool. The rationale of this study was to develop a new scaffold-free approach for an artificial trachea using bio-3D printing technology. Here, we assessed the circumferential tracheal replacement using scaffold-free trachea-like grafts generated from isolated cells in an inbred animal model. Chondrocytes and mesenchymal stem cells were isolated from F344 rats. Rat lung microvessel endothelial cells were purchased. Our bio-3D printer generates spheroids consisting of several types of cells to create 3D structures. The bio-3D-printed artificial trachea from spheroids was matured in a bioreactor and transplanted into F344 rats as a tracheal graft under general anaesthesia. The mechanical strength of the artificial trachea was measured, and histological and immunohistochemical examinations were performed. Tracheal transplantation was performed in 9 rats, which were followed up postoperatively for 23 days. The average tensile strength of artificial tracheas before transplantation was 526.3 ± 125.7 mN. The bio-3D-printed scaffold-free artificial trachea had sufficient strength to transplant into the trachea with silicone stents that were used to prevent collapse of the artificial trachea and to support the graft until sufficient blood supply was obtained. Chondrogenesis and vasculogenesis were observed histologically. The scaffold-free isogenic artificial tracheas produced by a bio-3D printer could be utilized as tracheal grafts in rats.

Conceptual model of a logical system processor of selection to electrical filters for correction of harmonics in low voltage lines

NASA Astrophysics Data System (ADS)

Lastre, Arlys; Torriente, Ives; Méndez, Erik F.; Cordovés, Alexis

2017-06-01

In the present investigation, the authors propose a conceptual model for the analysis and the decision making of the corrective models to use in the mitigation of the harmonic distortion. The authors considered the setting of conventional models, and such adaptive models like the filters incorporation to networks neuronal artificial (RNA's) for the mitigating effect. In addition to the present work is a showing of the experimental model that learns by means of a flowchart denoting the need to use artificial intelligence skills for the exposition of the proposed model. The other aspect considered and analyzed are the adaptability and usage of the same, considering a local reference of the laws and lineaments of energy quality that demands the Department of Electricity and Energy Renewable (MEER) of Equator.

Artificial equilibrium points for a generalized sail in the elliptic restricted three-body problem

NASA Astrophysics Data System (ADS)

Aliasi, Generoso; Mengali, Giovanni; Quarta, Alessandro A.

2012-10-01

Different types of propulsion systems with continuous and purely radial thrust, whose modulus depends on the distance from a massive body, may be conveniently described within a single mathematical model by means of the concept of generalized sail. This paper discusses the existence and stability of artificial equilibrium points maintained by a generalized sail within an elliptic restricted three-body problem. Similar to the classical case in the absence of thrust, a generalized sail guarantees the existence of equilibrium points belonging only to the orbital plane of the two primaries. The geometrical loci of existing artificial equilibrium points are shown to coincide with those obtained for the circular three body problem when a non-uniformly rotating and pulsating coordinate system is chosen to describe the spacecraft motion. However, the generalized sail has to provide a periodically variable acceleration to maintain a given artificial equilibrium point. A linear stability analysis of the artificial equilibrium points is provided by means of the Floquet theory.

CRITICAL ANALYSIS OF ARTIFICIAL TEETH FOR ENDODONTIC TEACHING

PubMed Central

Nassri, Maria Renata Giazzi; Carlik, Jaime; da Silva, Camila Roberta Nepomuceno; Okagawa, Renata Elisa; Lin, Suzy

2008-01-01

The purpose of this study was to evaluate the use of artificial teeth for endodontic teaching. A questionnaire was prepared and submitted to 18 professors of Endodontics from different Brazilian universities to evaluate the following features of five cloudy resin artificial teeth: internal and external anatomy; coronal chambers regarding their size, shape and canal path; root canal regarding their size, shape and position; fulfillment of the pulp chamber and root canals by considering the texture, quantity, color, and ease of handling; resin hardness and visualization of the radiographic image. The results presented favorable opinions, in terms of internal and external anatomy, coronal pulp chambers and root canal and handling and radiographic imaging. The contents of the pulp space and hardness of the teeth were considered satisfactory. The average grade assigned to the artificial tooth quality was 8.4, in a 0-10 scale. In conclusion, the artificial teeth have potential to replace the natural teeth in endodontic teaching; however, improvements are still necessary to reach a better quality model. PMID:19089288

The dilemma of the symbols: analogies between philosophy, biology and artificial life.

PubMed

Spadaro, Salvatore

2013-01-01

This article analyzes some analogies going from Artificial Life questions about the symbol-matter connection to Artificial Intelligence questions about symbol-grounding. It focuses on the notion of the interpretability of syntax and how the symbols are integrated in a unity ("binding problem"). Utilizing the DNA code as a model, this paper discusses how syntactic features could be defined as high-grade characteristics of the non syntactic relations in a material-dynamic structure, by using an emergentist approach. This topic furnishes the ground for a confutation of J. Searle's statement that syntax is observer-relative, as he wrote in his book "Mind: A Brief Introduction". Moreover the evolving discussion also modifies the classic symbol-processing doctrine in the mind which Searle attacks as a strong AL argument, that life could be implemented in a computational mode. Lastly, this paper furnishes a new way of support for the autonomous systems thesis in Artificial Life and Artificial Intelligence, using, inter alia, the "adaptive resonance theory" (ART).

Functional tooth restoration by next-generation bio-hybrid implant as a bio-hybrid artificial organ replacement therapy

PubMed Central

Oshima, Masamitsu; Inoue, Kaoru; Nakajima, Kei; Tachikawa, Tetsuhiko; Yamazaki, Hiromichi; Isobe, Tomohide; Sugawara, Ayaka; Ogawa, Miho; Tanaka, Chie; Saito, Masahiro; Kasugai, Shohei; Takano-Yamamoto, Teruko; Inoue, Takashi; Tezuka, Katsunari; Kuboki, Takuo; Yamaguchi, Akira; Tsuji, Takashi

2014-01-01

Bio-hybrid artificial organs are an attractive concept to restore organ function through precise biological cooperation with surrounding tissues in vivo. However, in bio-hybrid artificial organs, an artificial organ with fibrous connective tissues, including muscles, tendons and ligaments, has not been developed. Here, we have enveloped with embryonic dental follicle tissue around a HA-coated dental implant, and transplanted into the lower first molar region of a murine tooth-loss model. We successfully developed a novel fibrous connected tooth implant using a HA-coated dental implant and dental follicle stem cells as a bio-hybrid organ. This bio-hybrid implant restored physiological functions, including bone remodelling, regeneration of severe bone-defect and responsiveness to noxious stimuli, through regeneration with periodontal tissues, such as periodontal ligament and cementum. Thus, this study represents the potential for a next-generation bio-hybrid implant for tooth loss as a future bio-hybrid artificial organ replacement therapy. PMID:25116435

Application of artificial neural network to search for gravitational-wave signals associated with short gamma-ray bursts

NASA Astrophysics Data System (ADS)

Kim, Kyungmin; Harry, Ian W.; Hodge, Kari A.; Kim, Young-Min; Lee, Chang-Hwan; Lee, Hyun Kyu; Oh, John J.; Oh, Sang Hoon; Son, Edwin J.

2015-12-01

We apply a machine learning algorithm, the artificial neural network, to the search for gravitational-wave signals associated with short gamma-ray bursts (GRBs). The multi-dimensional samples consisting of data corresponding to the statistical and physical quantities from the coherent search pipeline are fed into the artificial neural network to distinguish simulated gravitational-wave signals from background noise artifacts. Our result shows that the data classification efficiency at a fixed false alarm probability (FAP) is improved by the artificial neural network in comparison to the conventional detection statistic. Specifically, the distance at 50% detection probability at a fixed false positive rate is increased about 8%-14% for the considered waveform models. We also evaluate a few seconds of the gravitational-wave data segment using the trained networks and obtain the FAP. We suggest that the artificial neural network can be a complementary method to the conventional detection statistic for identifying gravitational-wave signals related to the short GRBs.

Critical analysis of artificial teeth for endodontic teaching.

PubMed

Nassri, Maria Renata Giazzi; Carlik, Jaime; da Silva, Camila Roberta Nepomuceno; Okagawa, Renata Elisa; Lin, Suzy

2008-01-01

The purpose of this study was to evaluate the use of artificial teeth for endodontic teaching. A questionnaire was prepared and submitted to 18 professors of Endodontics from different Brazilian universities to evaluate the following features of five cloudy resin artificial teeth: internal and external anatomy; coronal chambers regarding their size, shape and canal path; root canal regarding their size, shape and position; fulfillment of the pulp chamber and root canals by considering the texture, quantity, color, and ease of handling; resin hardness and visualization of the radiographic image. The results presented favorable opinions, in terms of internal and external anatomy, coronal pulp chambers and root canal and handling and radiographic imaging. The contents of the pulp space and hardness of the teeth were considered satisfactory. The average grade assigned to the artificial tooth quality was 8.4, in a 0-10 scale. In conclusion, the artificial teeth have potential to replace the natural teeth in endodontic teaching; however, improvements are still necessary to reach a better quality model.

Development of a transient, lumped hydrologic model for geomorphologic units in a geomorphology based rainfall-runoff modelling framework

NASA Astrophysics Data System (ADS)

Vannametee, E.; Karssenberg, D.; Hendriks, M. R.; de Jong, S. M.; Bierkens, M. F. P.

2010-05-01

We propose a modelling framework for distributed hydrological modelling of 103-105 km2 catchments by discretizing the catchment in geomorphologic units. Each of these units is modelled using a lumped model representative for the processes in the unit. Here, we focus on the development and parameterization of this lumped model as a component of our framework. The development of the lumped model requires rainfall-runoff data for an extensive set of geomorphological units. Because such large observational data sets do not exist, we create artificial data. With a high-resolution, physically-based, rainfall-runoff model, we create artificial rainfall events and resulting hydrographs for an extensive set of different geomorphological units. This data set is used to identify the lumped model of geomorphologic units. The advantage of this approach is that it results in a lumped model with a physical basis, with representative parameters that can be derived from point-scale measurable physical parameters. The approach starts with the development of the high-resolution rainfall-runoff model that generates an artificial discharge dataset from rainfall inputs as a surrogate of a real-world dataset. The model is run for approximately 105 scenarios that describe different characteristics of rainfall, properties of the geomorphologic units (i.e. slope gradient, unit length and regolith properties), antecedent moisture conditions and flow patterns. For each scenario-run, the results of the high-resolution model (i.e. runoff and state variables) at selected simulation time steps are stored in a database. The second step is to develop the lumped model of a geomorphological unit. This forward model consists of a set of simple equations that calculate Hortonian runoff and state variables of the geomorphologic unit over time. The lumped model contains only three parameters: a ponding factor, a linear reservoir parameter, and a lag time. The model is capable of giving an appropriate representation of the transient rainfall-runoff relations that exist in the artificial data set generated with the high-resolution model. The third step is to find the values of empirical parameters in the lumped forward model using the artificial dataset. For each scenario of the high-resolution model run, a set of lumped model parameters is determined with a fitting method using the corresponding time series of state variables and outputs retrieved from the database. Thus, the parameters in the lumped model can be estimated by using the artificial data set. The fourth step is to develop an approach to assign lumped model parameters based upon the properties of the geomorphological unit. This is done by finding relationships between the measurable physical properties of geomorphologic units (i.e. slope gradient, unit length, and regolith properties) and the lumped forward model parameters using multiple regression techniques. In this way, a set of lumped forward model parameters can be estimated as a function of morphology and physical properties of the geomorphologic units. The lumped forward model can then be applied to different geomorphologic units. Finally, the performance of the lumped forward model is evaluated; the outputs of the lumped forward model are compared with the results of the high-resolution model. Our results show that the lumped forward model gives the best estimates of total discharge volumes and peak discharges when rain intensities are not significantly larger than the infiltration capacities of the units and when the units are small with a flat gradient. Hydrograph shapes are fairly well reproduced for most cases except for flat and elongated units with large runoff volumes. The results of this study provide a first step towards developing low-dimensional models for large ungauged basins.

Modelling the role of national system of innovation in economical differentiation

NASA Astrophysics Data System (ADS)

Ruiz, Ricardo M.; Albuquerque, Eduardo; Ribeiro, Leonardo C.; Bernardes, Américo T.

2005-07-01

Nowadays it is well accepted that science and technology has a fundamental role in the economic development (GNP per capita) of any country. Aiming to study this role, we introduce a model that creates an artificial world economy that is a network of countries. Each country has its own national system of innovation (represented by a technological parameter). The interactions among the countries are given by functions that connect their prices, demands and incomes. Starting from random values, the artificial world economy self-organize itself and create hierarchies of countries.

Fish attraction to artificial reefs not always harmful: a simulation study.

PubMed

Smith, James A; Lowry, Michael B; Suthers, Iain M

2015-10-01

The debate on whether artificial reefs produce new fish or simply attract existing fish biomass continues due to the difficulty in distinguishing these processes, and there remains considerable doubt as to whether artificial reefs are a harmful form of habitat modification. The harm typically associated with attraction is that fish will be easier to harvest due to the existing biomass aggregating at a newly deployed reef. This outcome of fish attraction has not progressed past an anecdotal form, however, and is always perceived as a harmful process. We present a numerical model that simulates the effect that a redistributed fish biomass, due to an artificial reef, has on fishing catch per unit effort (CPUE). This model can be used to identify the scenarios (in terms of reef, fish, and harvest characteristics) that pose the most risk of exploitation due to fish attraction. The properties of this model were compared to the long-standing predictions by Bohnsack (1989) on the factors that increase the risk or the harm of attraction. Simulations revealed that attraction is not always harmful because it does not always increase maximum fish density. Rather, attraction sometimes disperses existing fish biomass making them harder to catch. Some attraction can be ideal, with CPUE lowest when attraction leads to an equal distribution of biomass between natural and artificial reefs. Simulations also showed that the outcomes from attraction depend on the characteristics of the target fish species, such that transient or pelagic species are often at more risk of harmful attraction than resident species. Our findings generally agree with Bohnsack's predictions, although we recommend distinguishing "mobility" and "fidelity" when identifying species most at risk from attraction, as these traits had great influence on patterns of harvest of attracted fish biomass.

Using artificial intelligence to predict the risk for posterior capsule opacification after phacoemulsification.

PubMed

Mohammadi, Seyed-Farzad; Sabbaghi, Mostafa; Z-Mehrjardi, Hadi; Hashemi, Hassan; Alizadeh, Somayeh; Majdi, Mercede; Taee, Farough

2012-03-01

To apply artificial intelligence models to predict the occurrence of posterior capsule opacification (PCO) after phacoemulsification. Farabi Eye Hospital, Tehran, Iran. Clinical-based cross-sectional study. The posterior capsule status of eyes operated on for age-related cataract and the need for laser capsulotomy were determined. After a literature review, data polishing, and expert consultation, 10 input variables were selected. The QUEST algorithm was used to develop a decision tree. Three back-propagation artificial neural networks were constructed with 4, 20, and 40 neurons in 2 hidden layers and trained with the same transfer functions (log-sigmoid and linear transfer) and training protocol with randomly selected eyes. They were then tested on the remaining eyes and the networks compared for their performance. Performance indices were used to compare resultant models with the results of logistic regression analysis. The models were trained using 282 randomly selected eyes and then tested using 70 eyes. Laser capsulotomy for clinically significant PCO was indicated or had been performed 2 years postoperatively in 40 eyes. A sample decision tree was produced with accuracy of 50% (likelihood ratio 0.8). The best artificial neural network, which showed 87% accuracy and a positive likelihood ratio of 8, was achieved with 40 neurons. The area under the receiver-operating-characteristic curve was 0.71. In comparison, logistic regression reached accuracy of 80%; however, the likelihood ratio was not measurable because the sensitivity was zero. A prototype artificial neural network was developed that predicted posterior capsule status (requiring capsulotomy) with reasonable accuracy. No author has a financial or proprietary interest in any material or method mentioned. Copyright © 2012 ASCRS and ESCRS. Published by Elsevier Inc. All rights reserved.

A semi-automated tool for reducing the creation of false closed depressions from a filled LIDAR-derived digital elevation model

USGS Publications Warehouse

Waller, John S.; Doctor, Daniel H.; Terziotti, Silvia

2015-01-01

Closed depressions on the land surface can be identified by ‘filling’ a digital elevation model (DEM) and subtracting the filled model from the original DEM. However, automated methods suffer from artificial ‘dams’ where surface streams cross under bridges and through culverts. Removal of these false depressions from an elevation model is difficult due to the lack of bridge and culvert inventories; thus, another method is needed to breach these artificial dams. Here, we present a semi-automated workflow and toolbox to remove falsely detected closed depressions created by artificial dams in a DEM. The approach finds the intersections between transportation routes (e.g., roads) and streams, and then lowers the elevation surface across the roads to stream level allowing flow to be routed under the road. Once the surface is corrected to match the approximate location of the National Hydrologic Dataset stream lines, the procedure is repeated with sequentially smaller flow accumulation thresholds in order to generate stream lines with less contributing area within the watershed. Through multiple iterations, artificial depressions that may arise due to ephemeral flow paths can also be removed. Preliminary results reveal that this new technique provides significant improvements for flow routing across a DEM and minimizes artifacts within the elevation surface. Slight changes in the stream flow lines generally improve the quality of flow routes; however some artificial dams may persist. Problematic areas include extensive road ditches, particularly along divided highways, and where surface flow crosses beneath road intersections. Limitations do exist, and the results partially depend on the quality of data being input. Of 166 manually identified culverts from a previous study by Doctor and Young in 2013, 125 are within 25 m of culverts identified by this tool. After three iterations, 1,735 culverts were identified and cataloged. The result is a reconditioned elevation dataset, which retains the karst topography for further analysis, and a culvert catalog.

A text-based data mining and toxicity prediction modeling system for a clinical decision support in radiation oncology: A preliminary study

NASA Astrophysics Data System (ADS)

Kim, Kwang Hyeon; Lee, Suk; Shim, Jang Bo; Chang, Kyung Hwan; Yang, Dae Sik; Yoon, Won Sup; Park, Young Je; Kim, Chul Yong; Cao, Yuan Jie

2017-08-01

The aim of this study is an integrated research for text-based data mining and toxicity prediction modeling system for clinical decision support system based on big data in radiation oncology as a preliminary research. The structured and unstructured data were prepared by treatment plans and the unstructured data were extracted by dose-volume data image pattern recognition of prostate cancer for research articles crawling through the internet. We modeled an artificial neural network to build a predictor model system for toxicity prediction of organs at risk. We used a text-based data mining approach to build the artificial neural network model for bladder and rectum complication predictions. The pattern recognition method was used to mine the unstructured toxicity data for dose-volume at the detection accuracy of 97.9%. The confusion matrix and training model of the neural network were achieved with 50 modeled plans (n = 50) for validation. The toxicity level was analyzed and the risk factors for 25% bladder, 50% bladder, 20% rectum, and 50% rectum were calculated by the artificial neural network algorithm. As a result, 32 plans could cause complication but 18 plans were designed as non-complication among 50 modeled plans. We integrated data mining and a toxicity modeling method for toxicity prediction using prostate cancer cases. It is shown that a preprocessing analysis using text-based data mining and prediction modeling can be expanded to personalized patient treatment decision support based on big data.

Spatiotemporal groundwater level modeling using hybrid artificial intelligence-meshless method

NASA Astrophysics Data System (ADS)

Nourani, Vahid; Mousavi, Shahram

2016-05-01

Uncertainties of the field parameters, noise of the observed data and unknown boundary conditions are the main factors involved in the groundwater level (GL) time series which limit the modeling and simulation of GL. This paper presents a hybrid artificial intelligence-meshless model for spatiotemporal GL modeling. In this way firstly time series of GL observed in different piezometers were de-noised using threshold-based wavelet method and the impact of de-noised and noisy data was compared in temporal GL modeling by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). In the second step, both ANN and ANFIS models were calibrated and verified using GL data of each piezometer, rainfall and runoff considering various input scenarios to predict the GL at one month ahead. In the final step, the simulated GLs in the second step of modeling were considered as interior conditions for the multiquadric radial basis function (RBF) based solve of governing partial differential equation of groundwater flow to estimate GL at any desired point within the plain where there is not any observation. In order to evaluate and compare the GL pattern at different time scales, the cross-wavelet coherence was also applied to GL time series of piezometers. The results showed that the threshold-based wavelet de-noising approach can enhance the performance of the modeling up to 13.4%. Also it was found that the accuracy of ANFIS-RBF model is more reliable than ANN-RBF model in both calibration and validation steps.

Biomechanical measurements of stiffness and strength for five types of whole human and artificial humeri.

PubMed

Aziz, Mina S R; Nicayenzi, Bruce; Crookshank, Meghan C; Bougherara, Habiba; Schemitsch, Emil H; Zdero, Radovan

2014-05-01

The human humerus is the third largest longbone and experiences 2-3% of all fractures. Yet, almost no data exist on its intact biomechanical properties, thus preventing researchers from obtaining a full understanding of humerus behavior during injury and after being repaired with fracture plates and nails. The aim of this experimental study was to compare the biomechanical stiffness and strength of "gold standard" fresh-frozen humeri to a variety of humerus models. A series of five types of intact whole humeri were obtained: human fresh-frozen (n = 19); human embalmed (n = 18); human dried (n = 15); artificial "normal" (n = 12); and artificial "osteoporotic" (n = 12). Humeri were tested under "real world" clinical loading modes for shear stiffness, torsional stiffness, cantilever bending stiffness, and cantilever bending strength. After removing geometric effects, fresh-frozen results were 585.8 ± 181.5 N/mm2 (normalized shear stiffness); 3.1 ± 1.1 N/(mm2 deg) (normalized torsional stiffness); 850.8 ± 347.9 N/mm2 (normalized cantilever stiffness); and 8.3 ± 2.7 N/mm2 (normalized cantilever strength). Compared to fresh-frozen values, statistical equivalence (p ≥ 0.05) was obtained for all four test modes (embalmed humeri), 1 of 4 test modes (dried humeri), 1 of 4 test modes (artificial "normal" humeri), and 1 of 4 test modes (artificial "osteoporotic" humeri). Age and bone mineral density versus experimental results had Pearson linear correlations ranging from R = -0.57 to 0.80. About 77% of human humeri failed via a transverse or oblique distal shaft fracture, whilst 88% of artificial humeri failed with a mixed transverse + oblique fracture. To date, this is the most comprehensive study on the biomechanics of intact human and artificial humeri and can assist researchers to choose an alternate humerus model that can substitute for fresh-frozen humeri.

Artificial Intelligence for the Artificial Kidney: Pointers to the Future of a Personalized Hemodialysis Therapy.

PubMed

Hueso, Miguel; Vellido, Alfredo; Montero, Nuria; Barbieri, Carlo; Ramos, Rosa; Angoso, Manuel; Cruzado, Josep Maria; Jonsson, Anders

2018-02-01

Current dialysis devices are not able to react when unexpected changes occur during dialysis treatment or to learn about experience for therapy personalization. Furthermore, great efforts are dedicated to develop miniaturized artificial kidneys to achieve a continuous and personalized dialysis therapy, in order to improve the patient's quality of life. These innovative dialysis devices will require a real-time monitoring of equipment alarms, dialysis parameters, and patient-related data to ensure patient safety and to allow instantaneous changes of the dialysis prescription for the assessment of their adequacy. The analysis and evaluation of the resulting large-scale data sets enters the realm of "big data" and will require real-time predictive models. These may come from the fields of machine learning and computational intelligence, both included in artificial intelligence, a branch of engineering involved with the creation of devices that simulate intelligent behavior. The incorporation of artificial intelligence should provide a fully new approach to data analysis, enabling future advances in personalized dialysis therapies. With the purpose to learn about the present and potential future impact on medicine from experts in artificial intelligence and machine learning, a scientific meeting was organized in the Hospital Universitari Bellvitge (L'Hospitalet, Barcelona). As an outcome of that meeting, the aim of this review is to investigate artificial intel ligence experiences on dialysis, with a focus on potential barriers, challenges, and prospects for future applications of these technologies. Artificial intelligence research on dialysis is still in an early stage, and the main challenge relies on interpretability and/or comprehensibility of data models when applied to decision making. Artificial neural networks and medical decision support systems have been used to make predictions about anemia, total body water, or intradialysis hypotension and are promising approaches for the prescription and monitoring of hemodialysis therapy. Current dialysis machines are continuously improving due to innovative technological developments, but patient safety is still a key challenge. Real-time monitoring systems, coupled with automatic instantaneous biofeedback, will allow changing dialysis prescriptions continuously. The integration of vital sign monitoring with dialysis parameters will produce large data sets that will require the use of data analysis techniques, possibly from the area of machine learning, in order to make better decisions and increase the safety of patients.

[Simulation study on the effects of climate change on aboveground biomass of plantation in southern China: Taking Moshao forest farm in Huitong Ecological Station as an example].

PubMed

Dai, Er Fu; Zhou, Heng; Wu, Zhuo; Wang, Xiao-Fan; Xi, Wei Min; Zhu, Jian Jia

2016-10-01

Global climate warming has significant effect on territorial ecosystem, especially on forest ecosystem. The increase in temperature and radiative forcing will significantly alter the structure and function of forest ecosystem. The southern plantation is an important part of forests in China, its response to climate change is getting more and more intense. In order to explore the responses of southern plantation to climate change under future climate scenarios and to reduce the losses that might be caused by climate change, we used climatic estimated data under three new emission scenarios, representative concentration pathways (RCPs) scenarios (RCP2.6 scenario, RCP4.5 scenario, and RCP8.5 scenario). We used the spatially dynamic forest landscape model LANDIS-2, coupled with a forest ecosystem process model PnET-2, to simulate the impact of climate change on aboveground net primary production (ANPP), species' establishment probability (SEP) and aboveground biomass of Moshao forest farm in Huitong Ecological Station, which located in Hunan Province during the period of 2014-2094. The results showed that there were obvious differences in SEP and ANPP among different forest types under changing climate. The degrees of response of SEP to climate change for different forest types were shown as: under RCP2.6 and RCP4.5, artificial coniferous forest>natural broadleaved forest>artificial broadleaved forest. Under RCP8.5, natural broadleaved forest>artificial broadleaved forest>artificial coniferous forest. The degrees of response of ANPP to climate change for different forest types were shown as: under RCP2.6, artificial broadleaved forest> natural broadleaved forest>artificial coniferous forest. Under RCP4.5 and RCP8.5, natural broadleaved forest>artificial broadleaved forest>artificial coniferous forest. The aboveground biomass of the artificial coniferous forest would decline at about 2050, but the natural broadleaved forest and artificial broadleaved forest showed a rising trend in general. During the period of 2014-2094, the total aboveground biomass under RCP2.6, RCP4.5 and RCP8.5 scenarios increased by 68.2%, 79.3% and 72.6%, respectively. The total aboveground biomass under various climatic scenarios sort as: RCP4.5>RCP8.5>RCP2.6. We thought that an appropriate temperature might be beneficial to the biomass accumulation in this study area. However, overextended temperature might hinder the sustainable development of forest production and ecological function.

Forecasting SPEI and SPI Drought Indices Using the Integrated Artificial Neural Networks

PubMed Central

Maca, Petr; Pech, Pavel

2016-01-01

The presented paper compares forecast of drought indices based on two different models of artificial neural networks. The first model is based on feedforward multilayer perceptron, sANN, and the second one is the integrated neural network model, hANN. The analyzed drought indices are the standardized precipitation index (SPI) and the standardized precipitation evaporation index (SPEI) and were derived for the period of 1948–2002 on two US catchments. The meteorological and hydrological data were obtained from MOPEX experiment. The training of both neural network models was made by the adaptive version of differential evolution, JADE. The comparison of models was based on six model performance measures. The results of drought indices forecast, explained by the values of four model performance indices, show that the integrated neural network model was superior to the feedforward multilayer perceptron with one hidden layer of neurons. PMID:26880875

Past, present and prospect of an Artificial Intelligence (AI) based model for sediment transport prediction

NASA Astrophysics Data System (ADS)

Afan, Haitham Abdulmohsin; El-shafie, Ahmed; Mohtar, Wan Hanna Melini Wan; Yaseen, Zaher Mundher

2016-10-01

An accurate model for sediment prediction is a priority for all hydrological researchers. Many conventional methods have shown an inability to achieve an accurate prediction of suspended sediment. These methods are unable to understand the behaviour of sediment transport in rivers due to the complexity, noise, non-stationarity, and dynamism of the sediment pattern. In the past two decades, Artificial Intelligence (AI) and computational approaches have become a remarkable tool for developing an accurate model. These approaches are considered a powerful tool for solving any non-linear model, as they can deal easily with a large number of data and sophisticated models. This paper is a review of all AI approaches that have been applied in sediment modelling. The current research focuses on the development of AI application in sediment transport. In addition, the review identifies major challenges and opportunities for prospective research. Throughout the literature, complementary models superior to classical modelling.

Modeling river total bed material load discharge using artificial intelligence approaches (based on conceptual inputs)

NASA Astrophysics Data System (ADS)

Roushangar, Kiyoumars; Mehrabani, Fatemeh Vojoudi; Shiri, Jalal

2014-06-01

This study presents Artificial Intelligence (AI)-based modeling of total bed material load through developing the accuracy level of the predictions of traditional models. Gene expression programming (GEP) and adaptive neuro-fuzzy inference system (ANFIS)-based models were developed and validated for estimations. Sediment data from Qotur River (Northwestern Iran) were used for developing and validation of the applied techniques. In order to assess the applied techniques in relation to traditional models, stream power-based and shear stress-based physical models were also applied in the studied case. The obtained results reveal that developed AI-based models using minimum number of dominant factors, give more accurate results than the other applied models. Nonetheless, it was revealed that k-fold test is a practical but high-cost technique for complete scanning of applied data and avoiding the over-fitting.

Forecasting SPEI and SPI Drought Indices Using the Integrated Artificial Neural Networks.

PubMed

Maca, Petr; Pech, Pavel

2016-01-01

The presented paper compares forecast of drought indices based on two different models of artificial neural networks. The first model is based on feedforward multilayer perceptron, sANN, and the second one is the integrated neural network model, hANN. The analyzed drought indices are the standardized precipitation index (SPI) and the standardized precipitation evaporation index (SPEI) and were derived for the period of 1948-2002 on two US catchments. The meteorological and hydrological data were obtained from MOPEX experiment. The training of both neural network models was made by the adaptive version of differential evolution, JADE. The comparison of models was based on six model performance measures. The results of drought indices forecast, explained by the values of four model performance indices, show that the integrated neural network model was superior to the feedforward multilayer perceptron with one hidden layer of neurons.

Artificial Intelligence Software Engineering (AISE) model

NASA Technical Reports Server (NTRS)

Kiss, Peter A.

1990-01-01

The American Institute of Aeronautics and Astronautics has initiated a committee on standards for Artificial Intelligence. Presented are the initial efforts of one of the working groups of that committee. A candidate model is presented for the development life cycle of knowledge based systems (KBSs). The intent is for the model to be used by the aerospace community and eventually be evolved into a standard. The model is rooted in the evolutionary model, borrows from the spiral model, and is embedded in the standard Waterfall model for software development. Its intent is to satisfy the development of both stand-alone and embedded KBSs. The phases of the life cycle are shown and detailed as are the review points that constitute the key milestones throughout the development process. The applicability and strengths of the model are discussed along with areas needing further development and refinement by the aerospace community.

Quantitative structure-activity relationship study of P2X7 receptor inhibitors using combination of principal component analysis and artificial intelligence methods.

PubMed

Ahmadi, Mehdi; Shahlaei, Mohsen

2015-01-01

P2X7 antagonist activity for a set of 49 molecules of the P2X7 receptor antagonists, derivatives of purine, was modeled with the aid of chemometric and artificial intelligence techniques. The activity of these compounds was estimated by means of combination of principal component analysis (PCA), as a well-known data reduction method, genetic algorithm (GA), as a variable selection technique, and artificial neural network (ANN), as a non-linear modeling method. First, a linear regression, combined with PCA, (principal component regression) was operated to model the structure-activity relationships, and afterwards a combination of PCA and ANN algorithm was employed to accurately predict the biological activity of the P2X7 antagonist. PCA preserves as much of the information as possible contained in the original data set. Seven most important PC's to the studied activity were selected as the inputs of ANN box by an efficient variable selection method, GA. The best computational neural network model was a fully-connected, feed-forward model with 7-7-1 architecture. The developed ANN model was fully evaluated by different validation techniques, including internal and external validation, and chemical applicability domain. All validations showed that the constructed quantitative structure-activity relationship model suggested is robust and satisfactory.

Quantitative structure–activity relationship study of P2X7 receptor inhibitors using combination of principal component analysis and artificial intelligence methods

PubMed Central

Ahmadi, Mehdi; Shahlaei, Mohsen

2015-01-01

P2X7 antagonist activity for a set of 49 molecules of the P2X7 receptor antagonists, derivatives of purine, was modeled with the aid of chemometric and artificial intelligence techniques. The activity of these compounds was estimated by means of combination of principal component analysis (PCA), as a well-known data reduction method, genetic algorithm (GA), as a variable selection technique, and artificial neural network (ANN), as a non-linear modeling method. First, a linear regression, combined with PCA, (principal component regression) was operated to model the structure–activity relationships, and afterwards a combination of PCA and ANN algorithm was employed to accurately predict the biological activity of the P2X7 antagonist. PCA preserves as much of the information as possible contained in the original data set. Seven most important PC's to the studied activity were selected as the inputs of ANN box by an efficient variable selection method, GA. The best computational neural network model was a fully-connected, feed-forward model with 7−7−1 architecture. The developed ANN model was fully evaluated by different validation techniques, including internal and external validation, and chemical applicability domain. All validations showed that the constructed quantitative structure–activity relationship model suggested is robust and satisfactory. PMID:26600858

Multi-time-step ahead daily and hourly intermittent reservoir inflow prediction by artificial intelligent techniques using lumped and distributed data

NASA Astrophysics Data System (ADS)

Jothiprakash, V.; Magar, R. B.

2012-07-01

SummaryIn this study, artificial intelligent (AI) techniques such as artificial neural network (ANN), Adaptive neuro-fuzzy inference system (ANFIS) and Linear genetic programming (LGP) are used to predict daily and hourly multi-time-step ahead intermittent reservoir inflow. To illustrate the applicability of AI techniques, intermittent Koyna river watershed in Maharashtra, India is chosen as a case study. Based on the observed daily and hourly rainfall and reservoir inflow various types of time-series, cause-effect and combined models are developed with lumped and distributed input data. Further, the model performance was evaluated using various performance criteria. From the results, it is found that the performances of LGP models are found to be superior to ANN and ANFIS models especially in predicting the peak inflows for both daily and hourly time-step. A detailed comparison of the overall performance indicated that the combined input model (combination of rainfall and inflow) performed better in both lumped and distributed input data modelling. It was observed that the lumped input data models performed slightly better because; apart from reducing the noise in the data, the better techniques and their training approach, appropriate selection of network architecture, required inputs, and also training-testing ratios of the data set. The slight poor performance of distributed data is due to large variations and lesser number of observed values.

The research of laryngeal joints to reconstruction and modeling.

PubMed

Zhang, Yi; Shi, Tingchun

2014-01-01

Larynx has a complex structure with joints and multiple functions. In order to study the artificial larynx and artificial auricle scaffold, a three-dimensional digital model of laryngeal joint is established in this paper using MIMICS with its biomechanical properties analyzed and calculated by using the finite element method. This model is based on the CT scanned images of 281 layers with an interlamellar spacing of 1.25 mm. The obtained data are denoised, segmented and smoothed before being loaded into MIMICS. By further optimizations, an accurate and complete 3D model can be obtained. Subsequently, a 3D FEM of the normal larynx joint is performed which allows observations from any dimensions and angles. Compared with natural laryngeal joint, this model has good geometric similarity and mechanically similar throat voicing functions.

A comprehensive overview of the applications of artificial life.

PubMed

Kim, Kyung-Joong; Cho, Sung-Bae

2006-01-01

We review the applications of artificial life (ALife), the creation of synthetic life on computers to study, simulate, and understand living systems. The definition and features of ALife are shown by application studies. ALife application fields treated include robot control, robot manufacturing, practical robots, computer graphics, natural phenomenon modeling, entertainment, games, music, economics, Internet, information processing, industrial design, simulation software, electronics, security, data mining, and telecommunications. In order to show the status of ALife application research, this review primarily features a survey of about 180 ALife application articles rather than a selected representation of a few articles. Evolutionary computation is the most popular method for designing such applications, but recently swarm intelligence, artificial immune network, and agent-based modeling have also produced results. Applications were initially restricted to the robotics and computer graphics, but presently, many different applications in engineering areas are of interest.

Novel amelogenin-releasing hydrogel for remineralization of enamel artificial caries

PubMed Central

Fan, Yuwei; Wen, Zezhang T; Liao, Sumei; Lallier, Thomas; Hagan, Joseph L; Twomley, Jefferson T; Zhang, Jian-Feng; Sun, Zhi; Xu, Xiaoming

2013-01-01

Recently, the use of recombinant full-length amelogenin protein in combination with fluoride has shown promising results in the formation of densely packed enamel-like structures. In this study, amelogenin (rP172)-releasing hydrogels containing calcium, phosphate, and fluoride were investigated for remineralization efficacy using in vitro early enamel caries models. The hydrogels were applied to artificial caries lesions on extracted human third molars, and the remineralization efficacy was tested in different models: static gel remineralization in the presence of artificial saliva, pH cyclic treatment at pH 5.4 acetic buffer and pH 7.3 gel remineralization, and treatment with multispecies oral biofilms grown in a continuous flowing constant-depth film fermenter. The surface microhardness of remineralized enamel increased significantly when amelogenin was released from hydrogel. No cytotoxicity was observed when periodontal ligament cells were cultured with the mineralized hydrogels. PMID:23338820

Artificial Intelligence Procedures for Tree Taper Estimation within a Complex Vegetation Mosaic in Brazil

PubMed Central

Nunes, Matheus Henrique

2016-01-01

Tree stem form in native tropical forests is very irregular, posing a challenge to establishing taper equations that can accurately predict the diameter at any height along the stem and subsequently merchantable volume. Artificial intelligence approaches can be useful techniques in minimizing estimation errors within complex variations of vegetation. We evaluated the performance of Random Forest® regression tree and Artificial Neural Network procedures in modelling stem taper. Diameters and volume outside bark were compared to a traditional taper-based equation across a tropical Brazilian savanna, a seasonal semi-deciduous forest and a rainforest. Neural network models were found to be more accurate than the traditional taper equation. Random forest showed trends in the residuals from the diameter prediction and provided the least precise and accurate estimations for all forest types. This study provides insights into the superiority of a neural network, which provided advantages regarding the handling of local effects. PMID:27187074

Topologically Nontrivial Magnon Bands in Artificial Square Spin Ices with Dzyaloshinskii-Moriya Interaction [Topologically Non-Trivial Magnon Bands in Artificial Square Spin Ices Subject to Dzyaloshinskii-Moriya Interaction

DOE PAGES

Iacocca, Ezio; Heinonen, Olle

2017-09-20

Systems that exhibit topologically protected edge states are interesting both from a fundamental point of view as well as for potential applications, the latter because of the absence of backscattering and robustness to perturbations. It is desirable to be able to control and manipulate such edge states. Here, we demonstrate using a semi-analytical model that artificial square ices can incorporate both features: an interfacial Dzyaloshinksii-Moriya gives rise to topologically non-trivial magnon bands, and the equilibrium state of the spin ice is reconfigurable with different states having different magnon dispersions and topology. Micromagnetic simulations are used to determine the magnetization equilibriummore » states and to validate the semi-analytical model. Lastly, our results are amenable to experimental verification via, e.g., lithographic patterning and micro-focused Brillouin light scattering.« less

Clonal Selection Based Artificial Immune System for Generalized Pattern Recognition

NASA Technical Reports Server (NTRS)

Huntsberger, Terry

2011-01-01

The last two decades has seen a rapid increase in the application of AIS (Artificial Immune Systems) modeled after the human immune system to a wide range of areas including network intrusion detection, job shop scheduling, classification, pattern recognition, and robot control. JPL (Jet Propulsion Laboratory) has developed an integrated pattern recognition/classification system called AISLE (Artificial Immune System for Learning and Exploration) based on biologically inspired models of B-cell dynamics in the immune system. When used for unsupervised or supervised classification, the method scales linearly with the number of dimensions, has performance that is relatively independent of the total size of the dataset, and has been shown to perform as well as traditional clustering methods. When used for pattern recognition, the method efficiently isolates the appropriate matches in the data set. The paper presents the underlying structure of AISLE and the results from a number of experimental studies.

Artificial Intelligence Procedures for Tree Taper Estimation within a Complex Vegetation Mosaic in Brazil.

PubMed

Nunes, Matheus Henrique; Görgens, Eric Bastos

2016-01-01

Tree stem form in native tropical forests is very irregular, posing a challenge to establishing taper equations that can accurately predict the diameter at any height along the stem and subsequently merchantable volume. Artificial intelligence approaches can be useful techniques in minimizing estimation errors within complex variations of vegetation. We evaluated the performance of Random Forest® regression tree and Artificial Neural Network procedures in modelling stem taper. Diameters and volume outside bark were compared to a traditional taper-based equation across a tropical Brazilian savanna, a seasonal semi-deciduous forest and a rainforest. Neural network models were found to be more accurate than the traditional taper equation. Random forest showed trends in the residuals from the diameter prediction and provided the least precise and accurate estimations for all forest types. This study provides insights into the superiority of a neural network, which provided advantages regarding the handling of local effects.

Molecular biology of mycoplasmas: from the minimum cell concept to the artificial cell.

PubMed

Cordova, Caio M M; Hoeltgebaum, Daniela L; Machado, Laís D P N; Santos, Larissa Dos

2016-01-01

Mycoplasmas are a large group of bacteria, sorted into different genera in the Mollicutes class, whose main characteristic in common, besides the small genome, is the absence of cell wall. They are considered cellular and molecular biology study models. We present an updated review of the molecular biology of these model microorganisms and the development of replicative vectors for the transformation of mycoplasmas. Synthetic biology studies inspired by these pioneering works became possible and won the attention of the mainstream media. For the first time, an artificial genome was synthesized (a minimal genome produced from consensus sequences obtained from mycoplasmas). For the first time, a functional artificial cell has been constructed by introducing a genome completely synthesized within a cell envelope of a mycoplasma obtained by transformation techniques. Therefore, this article offers an updated insight to the state of the art of these peculiar organisms' molecular biology.

Topologically Nontrivial Magnon Bands in Artificial Square Spin Ices with Dzyaloshinskii-Moriya Interaction [Topologically Non-Trivial Magnon Bands in Artificial Square Spin Ices Subject to Dzyaloshinskii-Moriya Interaction

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

Iacocca, Ezio; Heinonen, Olle

Systems that exhibit topologically protected edge states are interesting both from a fundamental point of view as well as for potential applications, the latter because of the absence of backscattering and robustness to perturbations. It is desirable to be able to control and manipulate such edge states. Here, we demonstrate using a semi-analytical model that artificial square ices can incorporate both features: an interfacial Dzyaloshinksii-Moriya gives rise to topologically non-trivial magnon bands, and the equilibrium state of the spin ice is reconfigurable with different states having different magnon dispersions and topology. Micromagnetic simulations are used to determine the magnetization equilibriummore » states and to validate the semi-analytical model. Lastly, our results are amenable to experimental verification via, e.g., lithographic patterning and micro-focused Brillouin light scattering.« less

The Artificial Hamiltonian, First Integrals, and Closed-Form Solutions of Dynamical Systems for Epidemics

NASA Astrophysics Data System (ADS)

Naz, Rehana; Naeem, Imran

2018-03-01

The non-standard Hamiltonian system, also referred to as a partial Hamiltonian system in the literature, of the form {\\dot q^i} = {partial H}/{partial {p_i}},\\dot p^i = - {partial H}/{partial {q_i}} + {Γ ^i}(t,{q^i},{p_i}) appears widely in economics, physics, mechanics, and other fields. The non-standard (partial) Hamiltonian systems arise from physical Hamiltonian structures as well as from artificial Hamiltonian structures. We introduce the term `artificial Hamiltonian' for the Hamiltonian of a model having no physical structure. We provide here explicitly the notion of an artificial Hamiltonian for dynamical systems of ordinary differential equations (ODEs). Also, we show that every system of second-order ODEs can be expressed as a non-standard (partial) Hamiltonian system of first-order ODEs by introducing an artificial Hamiltonian. This notion of an artificial Hamiltonian gives a new way to solve dynamical systems of first-order ODEs and systems of second-order ODEs that can be expressed as a non-standard (partial) Hamiltonian system by using the known techniques applicable to the non-standard Hamiltonian systems. We employ the proposed notion to solve dynamical systems of first-order ODEs arising in epidemics.

Artificial immune system algorithm in VLSI circuit configuration

NASA Astrophysics Data System (ADS)

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

2017-08-01

In artificial intelligence, the artificial immune system is a robust bio-inspired heuristic method, extensively used in solving many constraint optimization problems, anomaly detection, and pattern recognition. This paper discusses the implementation and performance of artificial immune system (AIS) algorithm integrated with Hopfield neural networks for VLSI circuit configuration based on 3-Satisfiability problems. Specifically, we emphasized on the clonal selection technique in our binary artificial immune system algorithm. We restrict our logic construction to 3-Satisfiability (3-SAT) clauses in order to outfit with the transistor configuration in VLSI circuit. The core impetus of this research is to find an ideal hybrid model to assist in the VLSI circuit configuration. In this paper, we compared the artificial immune system (AIS) algorithm (HNN-3SATAIS) with the brute force algorithm incorporated with Hopfield neural network (HNN-3SATBF). Microsoft Visual C++ 2013 was used as a platform for training, simulating and validating the performances of the proposed network. The results depict that the HNN-3SATAIS outperformed HNN-3SATBF in terms of circuit accuracy and CPU time. Thus, HNN-3SATAIS can be used to detect an early error in the VLSI circuit design.

Spin Solid versus Magnetic Charge Ordered State in Artificial Honeycomb Lattice of Connected Elements

PubMed Central

Glavic, Artur; Summers, Brock; Dahal, Ashutosh; Kline, Joseph; Van Herck, Walter; Sukhov, Alexander; Ernst, Arthur

2018-01-01

Abstract The nature of magnetic correlation at low temperature in two‐dimensional artificial magnetic honeycomb lattice is a strongly debated issue. While theoretical researches suggest that the system will develop a novel zero entropy spin solid state as T → 0 K, a confirmation to this effect in artificial honeycomb lattice of connected elements is lacking. This study reports on the investigation of magnetic correlation in newly designed artificial permalloy honeycomb lattice of ultrasmall elements, with a typical length of ≈12 nm, using neutron scattering measurements and temperature‐dependent micromagnetic simulations. Numerical modeling of the polarized neutron reflectometry data elucidates the temperature‐dependent evolution of spin correlation in this system. As temperature reduces to ≈7 K, the system tends to develop novel spin solid state, manifested by the alternating distribution of magnetic vortex loops of opposite chiralities. Experimental results are complemented by temperature‐dependent micromagnetic simulations that confirm the dominance of spin solid state over local magnetic charge ordered state in the artificial honeycomb lattice with connected elements. These results enable a direct investigation of novel spin solid correlation in the connected honeycomb geometry of 2D artificial structure. PMID:29721429

The potential of artificial aging for modelling of natural aging processes of ballpoint ink.

PubMed

Weyermann, Céline; Spengler, Bernhard

2008-08-25

Artificial aging has been used to reproduce natural aging processes in an accelerated pace. Questioned documents were exposed to light or high temperature in a well-defined manner in order to simulate an increased age. This may be used to study the aging processes or to date documents by reproducing their aging curve. Ink was studied especially because it is deposited on the paper when a document, such as a contract, is produced. Once on the paper, aging processes start through degradation of dyes, solvents drying and resins polymerisation. Modelling of dye's and solvent's aging was attempted. These processes, however, follow complex pathways, influenced by many factors which can be classified as three major groups: ink composition, paper type and storage conditions. The influence of these factors is such that different aging states can be obtained for an identical point in time. Storage conditions in particular are difficult to simulate, as they are dependent on environmental conditions (e.g. intensity and dose of light, temperature, air flow, humidity) and cannot be controlled in the natural aging of questioned documents. The problem therefore lies more in the variety of different conditions a questioned document might be exposed to during its natural aging, rather than in the simulation of such conditions in the laboratory. Nevertheless, a precise modelling of natural aging curves based on artificial aging curves is obtained when performed on the same paper and ink. A standard model for aging processes of ink on paper is therefore presented that is based on a fit of aging curves to a power law of solvent concentrations as a function of time. A mathematical transformation of artificial aging curves into modelled natural aging curves results in excellent overlap with data from real natural aging processes.

Learning and evolution in bacterial taxis: an operational amplifier circuit modeling the computational dynamics of the prokaryotic 'two component system' protein network.

PubMed

Di Paola, Vieri; Marijuán, Pedro C; Lahoz-Beltra, Rafael

2004-01-01

Adaptive behavior in unicellular organisms (i.e., bacteria) depends on highly organized networks of proteins governing purposefully the myriad of molecular processes occurring within the cellular system. For instance, bacteria are able to explore the environment within which they develop by utilizing the motility of their flagellar system as well as a sophisticated biochemical navigation system that samples the environmental conditions surrounding the cell, searching for nutrients or moving away from toxic substances or dangerous physical conditions. In this paper we discuss how proteins of the intervening signal transduction network could be modeled as artificial neurons, simulating the dynamical aspects of the bacterial taxis. The model is based on the assumption that, in some important aspects, proteins can be considered as processing elements or McCulloch-Pitts artificial neurons that transfer and process information from the bacterium's membrane surface to the flagellar motor. This simulation of bacterial taxis has been carried out on a hardware realization of a McCulloch-Pitts artificial neuron using an operational amplifier. Based on the behavior of the operational amplifier we produce a model of the interaction between CheY and FliM, elements of the prokaryotic two component system controlling chemotaxis, as well as a simulation of learning and evolution processes in bacterial taxis. On the one side, our simulation results indicate that, computationally, these protein 'switches' are similar to McCulloch-Pitts artificial neurons, suggesting a bridge between evolution and learning in dynamical systems at cellular and molecular levels and the evolutive hardware approach. On the other side, important protein 'tactilizing' properties are not tapped by the model, and this suggests further complexity steps to explore in the approach to biological molecular computing.

Characterising the fate of nitrogenous waste from the sea-cage aquaculture of spiny lobsters using numerical modelling.

PubMed

Lee, Soxi; Hartstein, Neil D; Jeffs, Andrew

2015-06-01

Although the aquaculture of spiny lobsters has been expanding since the 1970s, very little is known about the potential environmental impacts on water quality of this activity. This study quantified the production of dissolved inorganic nitrogen (DIN) from Australasian red spiny lobsters, Jasus edwardsii, in the laboratory, and these data were then used in a numerical model to predict the dispersal pattern of DIN from a hypothetical commercial spiny lobster farm for a coastal site where such a farm would typically be located. Modelling scenarios were set up with combinations of two different stocking densities (3 and 5 kg m(-3)), two different diets (mussels and moist artificial diet) and three different feed conversion ratios (FCR = 3, 5 and 28). DIN excretion rate from unfed lobsters in the laboratory on average was 1.10 ± 0.12 μg N g(-1) h(-1) while feeding lobsters on mussels and artificial diet increased DIN excretion significantly by around eightfold and twofold, respectively. Ammonia was consistently the dominant contributor to measured DIN output from lobsters. Modelling results indicated that the mean elevated DIN from a hypothetical farm where the lobsters were fed with mussels ranged from 7 up to 20 μg N L(-1) with increasing stocking density and FCR and was 30-150 % higher than the mean elevated DIN resulting from lobsters fed with artificial diet. Overall, the results indicated that DIN output from the hypothetical spiny lobster sea-cage farming is unlikely to be problematic using the FCR, stocking density, and the number of cages modelled at the coastal site in this study. Furthermore, feeding lobsters with artificial diet can help maintain a lower DIN output than seafood, such as mussels or trash fish.

Representing Learning With Graphical Models

NASA Technical Reports Server (NTRS)

Buntine, Wray L.; Lum, Henry, Jr. (Technical Monitor)

1994-01-01

Probabilistic graphical models are being used widely in artificial intelligence, for instance, in diagnosis and expert systems, as a unified qualitative and quantitative framework for representing and reasoning with probabilities and independencies. Their development and use spans several fields including artificial intelligence, decision theory and statistics, and provides an important bridge between these communities. This paper shows by way of example that these models can be extended to machine learning, neural networks and knowledge discovery by representing the notion of a sample on the graphical model. Not only does this allow a flexible variety of learning problems to be represented, it also provides the means for representing the goal of learning and opens the way for the automatic development of learning algorithms from specifications.

Lake Sturgeon, Lake Whitefish, and Walleye egg deposition patterns with response to fish spawning substrate restoration in the St. Clair–Detroit River system

USGS Publications Warehouse

Fischer, Jason L.; Pritt, Jeremy J.; Roseman, Edward; Prichard, Carson G.; Craig, Jaquelyn M.; Kennedy, Gregory W.; Manny, Bruce A.

2018-01-01

Egg deposition and use of restored spawning substrates by lithophilic fishes (e.g., Lake Sturgeon Acipenser fulvescens, Lake Whitefish Coregonus clupeaformis, and Walleye Sander vitreus) were assessed throughout the St. Clair–Detroit River system from 2005 to 2016. Bayesian models were used to quantify egg abundance and presence/absence relative to site-specific variables (e.g., depth, velocity, and artificial spawning reef presence) and temperature to evaluate fish use of restored artificial spawning reefs and assess patterns in egg deposition. Lake Whitefish and Walleye egg abundance, probability of detection, and probability of occupancy were assessed with detection-adjusted methods; Lake Sturgeon egg abundance and probability of occurrence were assessed using delta-lognormal methods. The models indicated that the probability of Walleye eggs occupying a site increased with water velocity and that the rate of increase decreased with depth, whereas Lake Whitefish egg occupancy was not correlated with any of the attributes considered. Egg deposition by Lake Whitefish and Walleyes was greater at sites with high water velocities and was lower over artificial spawning reefs. Lake Sturgeon eggs were collected least frequently but were more likely to be collected over artificial spawning reefs and in greater abundances than elsewhere. Detection-adjusted egg abundances were not greater over artificial spawning reefs, indicating that these projects may not directly benefit spawning Walleyes and Lake Whitefish. However, 98% of the Lake Sturgeon eggs observed were collected over artificial spawning reefs, supporting the hypothesis that the reefs provided spawning sites for Lake Sturgeon and could mitigate historic losses of Lake Sturgeon spawning habitat.

Technical Operations (TOPS) IV Task Order 0003: Responsive Interface for Transport Tuning (RITT)

DTIC Science & Technology

2016-05-29

on the further development of artificial hair sensors (AHS) featuring a responsive carbon nanotube (CNT) array to serve as a piezoresistive element...under separate cover, as AFRL Interim Report AFRL-RX-WP-TR-2016-0071 dated 30 October 2015. 15. SUBJECT TERMS artificial hair sensor, carbon... Hair Sensors: Fabrication and Model Parameterization .......................... 4 3.1.1 Introduction

Reflections on the relationship between artificial intelligence and operations research

NASA Technical Reports Server (NTRS)

Fox, Mark S.

1989-01-01

Historically, part of Artificial Intelligence's (AI's) roots lie in Operations Research (OR). How AI has extended the problem solving paradigm developed in OR is explored. In particular, by examining how scheduling problems are solved using OR and AI, it is demonstrated that AI extends OR's model of problem solving through the opportunistic use of knowledge, problem reformulation and learning.

Development of the first sphingomyelin biomimetic stationary phase for immobilized artificial membrane (IAM) chromatography.

PubMed

Verzele, Dieter; Lynen, Frédéric; De Vrieze, Mike; Wright, Adrian G; Hanna-Brown, Melissa; Sandra, Pat

2012-01-28

A prototype sphingomyelin stationary phase for Immobilized Artificial Membrane (IAM) chromatography was synthesized by an ultra-short, solid-phase inspired methodology, in which an oxidative release monitoring strategy played a vital role. Evaluated in a proof-of-concept model for blood-brain barrier passage, partial least squares regression demonstrated its potential as an in vitro prediction tool.

A Short Version of SIS (Support Intensity Scale): The Utility of the Application of Artificial Adaptive Systems

ERIC Educational Resources Information Center

Gomiero, Tiziano; Croce, Luigi; Grossi, Enzo; Luc, De Vreese; Buscema, Massimo; Mantesso, Ulrico; De Bastiani, Elisa

2011-01-01

The aim of this paper is to present a shortened version of the SIS (support intensity scale) obtained by the application of mathematical models and instruments, adopting special algorithms based on the most recent developments in artificial adaptive systems. All the variables of SIS applied to 1,052 subjects with ID (intellectual disabilities)…