789 research outputs found

    Tiger Monitoring in Bhutan Using Non-invasive Genetic Tools

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    Large carnivores are one of the most threatened group of animals in the world. They suffer from prey depletion, persecution by humans, and habitat loss and fragmentation which are extensively driven by anthropogenic activities. One such species is the tiger Panthera tigris. Tigers are found in thirteen countries in Asia and are protected across the range; however, tiger numbers have declined as an after effect of habitat loss, prey depletion and poaching. Human-induced changes have reduced the tiger\u27s historical range to about 7% in which a little more than 3900 tigers are found. Most of these individuals currently exist in small and highly structured populations. Obtaining reliable estimates of population size and density and a solid understanding of the connectivity between populations are critical to understanding crucial aspects of effective tiger conservation. Bhutan, with a vast expanse of contiguous pristine forest cover, abundant prey, and active conservation policies, form a very critical part of tiger conservation in South Asia. However, due to limited funds, monitoring is erratic. Camera traps are a sought-after tool for monitoring tiger population and density in Bhutan, but costs have been a limiting factor. Therefore, we evaluated non-invasive genetic sampling (NGS) as an effective alternative to camera trapping for monitoring tigers in Bhutan. We carried out systematic camera trap and scat surveys in Royal Manas National Park in Southern Bhutan in 2018 and compared density, variability, and costs between the two methods. The densities were estimated under a spatially-explicit capture-recapture framework, and camera trap and NGS produced a density of 2.38 tigers/100 km2(95% CI 1.11-4.02) and 3.6 tigers/100km2(95% CI 1.06-12.23) respectively. Density and other parameters were estimated more precisely using camera traps, but the field and equipment cost was high as compared to single-session genetic sampling. When controlled for sampling effort, NGS performed better. There is also no information regarding population connectivity and gene flow in tigers within Bhutan. We genotyped 24 individuals using thirteen microsatellite loci and found that Bhutanese tigers overall have a high genetic variation (He=0.75). Individual-based and multivariate analyses indicated three genetic clusters within the sampled individuals; however, the overall genetic differentiation was low (FST=0.44). Our results suggest that Bhutanese tigers can be a source of genetic variation in the region and could play a crucial role in the long-term persistence of the species. We strongly recommend a transboundary and landscape-level conservation approach using common genetic data sets to understand tiger dispersal, threats, and other factors influencing dispersal events

    ESTIMATION OF GREENHOUSE GAS AND ODOUR EMISSIONS FROM COLD REGION MUNICIPAL BIOLOGICAL NUTRIENT REMOVAL WASTEWATER TREATMENT PROCESSES

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    Rising human populations and ever-increasing demand for potable water result in increased municipal wastewater production. The collection, treatment, and management of municipal wastewaters include energy-intensive processes leading to the generation and emission of greenhouse, potentially toxic, and odorous gases. The main goal of this thesis was to advance knowledge of greenhouse gas (including carbon dioxide, CO2; methane, CH4; and nitrous oxide, N2O) and smelly compound (including ammonia, NH3; and hydrogen sulphide, H2S) emissions from typical municipal wastewater treatment plants (MWTPs) to accurately describe their emission rate estimates (EREs) using operating parameters. This research included laboratory and field assessments of greenhouse gas (GHG) and odour emissions in conjunction with monitored operating parameters. Laboratory-scale reactors simulating open-to-air treatment processes including primary and secondary clarifiers and anaerobic, anoxic, and aerobic reactors, were used to monitor gas EREs using wastewater samples taken from the analogous MWTP processes in winter and summer seasons. The Saskatoon Wastewater Treatment plan (SWTP) is a state-of-the-art biological nutrient removal (BNR) type MWTP and a Class IV treatment facility in Canada which was selected as a case study given its highly variable seasonal temperatures from −40 °C to 30 °C and its geographic location near the University of Saskatchewan. The experimental results were then used to develop a variety of novel machine learning models describing gas EREs with further optimization of operating parameters using genetic algorithm (GA). Studied machine learning models were artificial data generation algorithms (including generative adversarial network, GAN) and data-driven models (including artificial neural network, ANN; adaptive network-based fuzzy inference systems, ANFIS; and linear/non-linear regression models). To my knowledge, this is the first application of GAN used for MWTP modelling purposes. Results indicated that anaerobic digestion EREs averagely reached 4,443 kg CH4/d, 9,145 kg CO2/d, and 59.7 kg H2S/d. In contrast, GHG and odour ERE variabilities given ambient temperature changes were more noticeable for open-to-air treatment processes such that the winter EREs were 45,129 kg CO2/d, 21.9 kg CH4/d, 3.20 kg N2O/d, and insignificant for H2S and NH3. The higher temperature for the summer samples resulted in increased EREs for CH4, N2O, and H2S EREs of 33.0 kg CH4/d, 3.87 kg N2O/d, and 2.29 kg H2S/d, respectively, and still insignificant NH3 emissions. However, the CO2 EREs were reduced to 37,794 kg CO2/d, and interestingly, NH3 emissions were still negligible. Overall, the aerobic reactor was the dominant source of GHG emissions for both seasons, and changes in the aerobic reactor aeration rates (in reactor) and BNR treatment configurations (from site) further impacted the EREs. The integration of field monitoring data with data-driven models showed that the ANN, ANFIS, and regression models provided reasonable EREs using: (1) volatile fatty acids, total/fixed/volatile solids, pH, and inflow rate for anaerobic digestion biogas generations; and (2) hydraulic retention time, temperature, total organic carbon, dissolved oxygen, phosphate, and nitrogen concentrations for aerobic GHG emissions. However, when both model accuracy and uncertainty were considered there appears to be a compromise between these parameters with no model having simultaneously both high accuracy and low uncertainty. Additionally, and interestingly, virtual data augmentation using GAN was found to be a valuable resource in supplementation of limited data for improved modelling outcomes. GA was also coupled with the data-driven models to determine optimal operating parameters resulting in either GHG emission maximization given biogas could be beneficial for energy generation or GHG emission minimization given the aerobic reactor is an open-to-air process that can impact nearby residential neighbourhood air quality. The current study provides a hybrid methodology of mathematical modelling and experiments that can be used to accurately estimate and optimize the GHG and odour EREs from other MWTPs in Canada and worldwide

    Analysis of ultra-sensitive fluorescence experiments

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    This work primarily investigates use of the neural network(NN) method to analyze spectral data collected in single molecule detection(SMD) and identification (SMI) ex-periments.. The 2-layer neural networks, with sigmoid as the activation function, are constructed and trained on a set of simulated data using back-propagation and the 6- learning rule. The trained networks are then used for identification of photon bursts in subsequent simulations. Results show that the NN method yields better identification of individual photon bursts than the traditional maximum likelihood estimation (MLE), particularly in cases where the fluorophores have disparate fluorescence quantum effi-ciencies, absorption cross-sections, or photodegradation efficiencies. In addition, this work reports several improvements over the prior version of the Monte Carlo simulation program. The improved version considers the fluorescence prob-ability as the convolution of the pure exponential decay function characterized by the fluorescence lifetime and the instrument impulse response function in the experiment. The setting of the time window is then implemented by monitoring the variation of signal and noise. A number of problems have been investigated by using the improved version. In particular, the effects of the number and widths of the bins within the time window on the precision of identification of molecules are studied. The results from the improved version of the simulation show that only a small number of bins (4-8) are required to achieve approximately 90% correct predictions with the NN method. Bin widths chosen in accordance with the intuitive algorithm, or equal bin widths, generally give better predictions. Experimental improvements are also reported in this work. In particular, the transit time of BODIBY-TR(D-6116) dye molecules in an SMD experiment was improved to less than 200 μ, and a circuit is implemented to accomplish fast and continuous data collection to be used in future single molecule identification experiments

    Socio-Cognitive and Affective Computing

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    Social cognition focuses on how people process, store, and apply information about other people and social situations. It focuses on the role that cognitive processes play in social interactions. On the other hand, the term cognitive computing is generally used to refer to new hardware and/or software that mimics the functioning of the human brain and helps to improve human decision-making. In this sense, it is a type of computing with the goal of discovering more accurate models of how the human brain/mind senses, reasons, and responds to stimuli. Socio-Cognitive Computing should be understood as a set of theoretical interdisciplinary frameworks, methodologies, methods and hardware/software tools to model how the human brain mediates social interactions. In addition, Affective Computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects, a fundamental aspect of socio-cognitive neuroscience. It is an interdisciplinary field spanning computer science, electrical engineering, psychology, and cognitive science. Physiological Computing is a category of technology in which electrophysiological data recorded directly from human activity are used to interface with a computing device. This technology becomes even more relevant when computing can be integrated pervasively in everyday life environments. Thus, Socio-Cognitive and Affective Computing systems should be able to adapt their behavior according to the Physiological Computing paradigm. This book integrates proposals from researchers who use signals from the brain and/or body to infer people's intentions and psychological state in smart computing systems. The design of this kind of systems combines knowledge and methods of ubiquitous and pervasive computing, as well as physiological data measurement and processing, with those of socio-cognitive and affective computing

    Searches for continuous gravitational waves : sensitivity estimation and deep learning as a novel search method

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    The first direct detections of gravitational waves from merging black holes and neutron stars started the era of gravitational-wave astronomy. Since then, observing merging compact objects has become routine. Other exciting sources still remain undetected. Rapidly-rotating neutron stars are predicted to emit weak, long-lasting quasi-monochromatic waves called continuous gravitational waves (CWs). In the current detector generation, advanced LIGO and Virgo, various noise sources create far more signal output than a potential CW signal. CW data analysis tries to overcome the weakness of the signals by integrating over long stretches of data. Analyzing large amounts of data usually corresponds to large computing cost. For that reason, CW searches for signals from unknown neutron stars are limited in their sensitivity by computational cost. This thesis is concerned with estimating and improving the sensitivity of continuous gravita- tional wave searches. The first main research work presented in this thesis is a new sensitivity estimator that can swiftly and accurately predict the sensitivity of a CW search before it is started. This makes optimizing the search algorithms and therefore improving the sensitivity easier. The accuracy of the estimator is studied by applying it to many different CW searches. The work is expanded with an extensive sensitivity review of past CW searches by calculating their sensitivity depth. The second main part of this thesis is the development of a new CW search method based on deep neural networks (DNNs). DNNs are extremely fast once trained and therefore might present an interesting possibility of circumventing the computational limitations and creating a more sensitive CW search. In this thesis such a DNN CW search is developed first as a single-detector search for signals from all over the sky and then expanded to a multi-detector all-sky search and to directed multi-detector searches for signals from a single position in the sky. The DNNs’ performance is compared to coherent matched-filtering searches in terms of detection probability at fixed false-alarm level first on idealized Gaussian noise and then on realistic LIGO detector noise. This thesis finds that the DNNs show a lot of potential: For short timespans of about one day the networks only lose a few percent in sensitivity depth compared to coherent matched- filtering. For longer timespans the networks’ performance gradually deteriorates making further research necessary. As an outlook to future research, this thesis proposes the combination of short-timespan network outputs, similar to semi-coherent matched-filtering, as a DNN search method over longer timespans.Die ersten direkten Detektionen von Gravitationswellen von verschmelzenden Schwarzen Löchern und Neutronensternen haben die Ära der Gravitationswellenastronomie eingeläutet. Seitdem ist die Beobachtung von verschmelzenden kompakten Objekten zur Routine geworden. Andere interessante Quellen von Gravitationswellen sind jedoch noch unentdeckt. Schnell rotierende Neutronensterne können schwache, langanhaltende quasi-monochromatische Wellen aussenden, genannt Kontinuierliche Gravitationswellen (engl.: continuous waves CWs). In der aktuellen Detektorgeneration, advanced LIGO und Virgo, wird mehr Detektoroutput durch diverse Rauschquellen erzeugt als durch potenzielle CW Signale. Die Datenanalyse für CWs versucht die Schwäche der Signale zu überwinden, indem die Daten über lange Zeitspan- nen integriert werden. Große Mengen von Daten zu analysieren ist jedoch für gewöhnlich mit großen Ansprüchen an die Rechenleistung verbunden. Deshalb sind Suchen nach CWs von un- bekannten Neutronensternen in ihrer Empfindlichkeit limitiert durch die begrenzt vorhandene Rechenleistung. Diese Doktorarbeit beschäftigt sich mit dem Abschätzen und Verbessern der Empfindlichkeit von Suchen nach Kontinuierlichen Gravitationswellen. Das erste Hauptforschungsergebnis dieser Arbeit ist ein neuartiger Abschätzer, der die Empfindlichkeit einer CW-Suche schnell und genau vorhersagen kann bevor die Suche gestartet wird. Dies vereinfacht die Verbesserung der Suchal- gorithmen und kann deshalb zu empfindlicheren Suchen führen. Die Genauigkeit des Abschätzers wird anhand von vielen verschiedenen CW-Suchen untersucht. Die Untersuchung wird ergänzt durch eine ausführliche Studie der Empfindlichkeit von vergangenen CW-Suchen. Dazu wird deren Empfindlichkeit in die gemeinsame Größe der Empfindlichkeitstiefe (engl.: sensitivity depth) umgerechnet. Das zweite Hauptforschungsergebnis dieser Dissertation ist eine neuartige CW-Suchmethode mit Hilfe von tiefen neuronalen Netzwerken (engl.: deep neural networks, DNNs). Fertig trainierte DNNs können extrem schnell angewendet werden und stellen deshalb eine interes- sante Art und Weise dar, wie möglicherweise mit der Limitierung durch fehlende Rechenleistung umgegangen und eine empfindlichere Suche konstruiert werden kann. In dieser Arbeit wird eine solche DNN nutzende CW-Suchmethode präsentiert: zuerst als Suche mit Daten von einem einzigen Detektor nach Signalen vom gesamten Himmel und dann erweitert zu Suchen mit Daten von mehreren Detektoren nach Signalen vom gesamten Himmel oder nach Signalen von einer speziellen Himmelsposition. Die Leistungsfähigkeit der DNNs wird dabei verglichen mit kohärenten Optimalfiltermethoden im Hinblick auf ihre Detektionswahrscheinlichkeit bei festem Fehlalarmniveau. Diese Arbeit zeigt das diesbezüglich große Potential von DNNs: Bei der Analyse von kurzen Zeitspannen von etwa einem Tag verliert das Netwerk nur wenige Prozent in Empfindlichkeitstiefe gegenüber kohärenten Optimalfiltermethoden. Für längere Zeitspannen nimmt die Leistungsfähigkeit der Netzwerke im Vergleich jedoch nach und nach ab. An dieser Stelle wird deshalb weitere Forschungsarbeit benötigt um die Leistungsfähigkeit der DNNs zu verbessern. Ein Ansatz, der in dieser Arbeit für zukünftige Forschung vorgeschlagen wird, ist die Kombination von Ergebnissen, die Netzwerke auf kurzen Zeitspannen erreicht haben, als Ergebnis für längere Zeitspannen zu nutzen. Dieser Ansatz ist ähnlich zum semi-kohärenten Optimalfilter, der in klassischen CW-Suchen benutzt wird

    Use of MOS Gas Sensors with Temperature Modulation-Specified Detection Point for Potential Identification of Soil Status Using Electronic-Nose Principle

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    13301甲第4410号博士(学術)金沢大学博士論文本文Full 以下に掲載:Sensors & Transducers 186(3) pp.93-103 2015. IFSA Publishing, S. L. 共著者:Arief Sudarmaji, Akio Kitagaw

    Characterization of CAXCK31, a Bacterial Calcium/Proton Antiporter

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    To better understand a class of transporters known as Calcium/Cation Antiporters (CaCAs), the bacterial calcium/proton antiporter CAXCK31 was purified and characterized. New methods were developed for its heterologous overexpression and purification. These methods help to define stress responses to toxic membrane overproduction in E. coli and may be broadly applicable to studies of membrane proteins. The results from a variety of biochemical and biophysical experiments demonstrated that CAXCK31 exists as a dimer in the membrane and can be purified in the dimeric state. The methods used include chemical cross-linking, FRET, and SEC-MALS. In addition, various transport properties of CAXCK31, including substrate selectivity, pH dependence, and transport rates, have been characterized for the first time

    Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

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    Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words

    Improvements of X-band and Q-band EPR /ENDOR spectrometers Studies of ferritin iron nitrosyl and copper cis, cis-1,3,5-triaminocyclohexane chloride complexes, and the application of Q -band EPR to the dating of fossil teeth

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    Several modifications were made on a Varian Q-band EPR/ENDOR spectrometer, including the installation of a microwave amplifier in the E110 bridge, design and assembly of a new cavity coupler, addition of frequency counting capability, as well as other useful minor changes. The performance of the spectrometer was improved in signal-to-noise ratio, convenience of tuning and operation and accuracy of g-value and hyperfine coupling measurements. The signal-to-noise ratio was increased by a factor of six. Improvements were also achieved with our X-band EPR/ENDOR spectrometer by design and assembly of a new printed ENDOR coil for use in a TE104 rectangular cavity. The Dewar and sample holder were changed to accommodate EPR tubes of 5 mm o.d. The ENDOR signal-to-noise ratio of a sucrose standard sample was increased by a factor of three by these changes. 1H ENDOR studies of ferritin iron nitrosyl complexes and other model complexes were carried out with the new ENDOR system. The proton ENDOR signals of ferritin iron nitrosyl complexes were first observed in this laboratory. Molecular modeling calculations and the equations derived for the iron nitrosyl complexes enable one to undertake a complete ENDOR data analysis. The ENDOR studies suggested that the local structure of the iron site in the ferritin iron nitrosyl complex was identical to that of a model complex of penicillamine with the iron atom coordinated to a sulphur atom of a cysteine residue, a nitrogen atom in the protein backbone and two nitric oxide radicals. EPR/ENDOR studies of copper cis,cis-1,3,5-triaminocyclohexane have shown the complexes to have a rhombic magnetic symmetry in powders, but axial symmetry in aqueous solution. When the complex was prepared in methanol, it retained its molecular configuration as in the crystal. However, when prepared in aqueous solution, two water molecules might replace one or two of the chloride ions in the equatorial plane of the complex. The sample in aqueous solution had covalent in-plane sigma bonding. The out-plane pi bonds and the in-plane pi bond were ionic for the aqueous sample. Finally, Q-band EPR studies of fossil tooth enamel demonstrated that X-band EPR could be used for routine dating of fossil teeth samples by slightly over modulating the overlapping signals of the dating and interfering radical centers. The interfering peak in some of the fossil tooth enamel samples appears to arise from a slight structural deformation of the radical center in hydroxyapatite. The age of the measured fossil teeth sample was determined to be about 1400 years old
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