Spatio-Temporal Information for Action Recognition in Thermal Video Using Deep Learning Model

Researchers can evaluate numerous information to ensure automated monitoring due to the widespread use of surveillance cameras in smart cities. For the monitoring of violence or abnormal behaviors in smart cities, schools, hospitals, residences, and other observational domains, an enhanced safety and security system is required to prevent any injuries that might result in ecological, economic and social losses. Automatic detection for prompt actions is vital and may help the respective departments effectively. Based on thermal imaging, several researchers have concentrated on object detection, tracking, and action identification. Few studies have simultaneously extracted spatial-temporal information from a thermal image and utilized it to recognize human actions. This research provides a novelty based on frame-level and spatial and temporal features which combines richer context temporal information to address the issue of poor efficiency and less accuracy in detecting abnormal/violent behavior in thermal monitoring devices. The model can locate (bounded box) video frame areas involving different human activities and recognize (classify) the actions. The dataset on human behavior includes videos captured with infrared cameras in both indoor and outdoor environments. The experimental results using the publicly available benchmark datasets reveal the proposed model\u27s efficiency. Our model achieves 98.5% and 94.85% accuracy on IITR Infrared Action Recognition (IITR-IAR) and Thermal Simulated Fall (TSF) datasets, respectively. In addition, the proposed method may be evaluated in more realistic conditions, such as zooming in and out etc

The Influence of GRA and TOPSIS for Assortment of Green Supply Chain Management Strategies in Cement Industry

The present paper aimed at proposing new strategies for evaluating the green supply chain management for enhancing the priority to environmental factors in cement manufacturing life cycle analysis, there by reducing the carbon foot prints. These strategies help in producing eco-friendly products there striking the balance between economy and environment. Initially green supply chain priorities are defined by using grey relational analysis (GRA). The priority weights obtained by GRA method is used to determine the weight for each indicator selected in the present study and then GRA is combined with TOPSIS methodology to obtain the priority for level-II measurement indicators used in the present study. These strategies will influence the decision making priorities during cement manufacturing. Keywords - Green Supply Chain Management (GSCM), strategy prioritization, Grey Relational Analysis (GRA), TOPSIS, Life Cycle Analysis (LCA

Identifying collateral and synthetic lethal vulnerabilities within the DNA-damage response.

BackgroundA pair of genes is defined as synthetically lethal if defects on both cause the death of the cell but a defect in only one of the two is compatible with cell viability. Ideally, if A and B are two synthetic lethal genes, inhibiting B should kill cancer cells with a defect on A, and should have no effects on normal cells. Thus, synthetic lethality can be exploited for highly selective cancer therapies, which need to exploit differences between normal and cancer cells.ResultsIn this paper, we present a new method for predicting synthetic lethal (SL) gene pairs. As neighbouring genes in the genome have highly correlated profiles of copy number variations (CNAs), our method clusters proximal genes with a similar CNA profile, then predicts mutually exclusive group pairs, and finally identifies the SL gene pairs within each group pairs. For mutual-exclusion testing we use a graph-based method which takes into account the mutation frequencies of different subjects and genes. We use two different methods for selecting the pair of SL genes; the first is based on the gene essentiality measured in various conditions by means of the "Gene Activity Ranking Profile" GARP score; the second leverages the annotations of gene to biological pathways.ConclusionsThis method is unique among current SL prediction approaches, it reduces false-positive SL predictions compared to previous methods, and it allows establishing explicit collateral lethality relationship of gene pairs within mutually exclusive group pairs

Multi-Channel Scheduling with Optimal Spectrum Channel Hole Filling (MCS-OSHF) for Cognitive Radio Wireless Networks

In this study, a contemporary method of scheduling algorithm has been proposed for working on scheduling of varying size data-frames transmission in CR based wireless networks. The objective of the proposed model is to achieve maximum throughput, and also reduction of loss of dataframes in the transmission. Some of the key elements that are considered in the development of the model are optimal bandwidth and idle channel availability. Using the three level hierarchical approach, the scheduling strategy is constructed. The optimal idle channel allocation, allocation with considerable transmission intervals allocation and optimal multiple channels models are considered at respective levels in the hierarchy in the proposed algorithm. The proposed model while tested under simulated environment in comparison to the other two bench marking models, the outcome depicts that the process is more efficient and supports in improving the overall process of scheduling of data-frames as per the desired objectives of the model

Formulation and Evaluation of Pulsatile Drug Delivery System of Zafirlukast

 In the current scenario of pharmaceutical research much attention has been focused on patients health in terms of therapeutic efficacy and economical standards (price factor).The formulation design consist of core tablets designed by direct compression method. Core tablets were coated with an naturally occurring swelling agent (carbopol & Karaya gum). Evaluation studies were performed for prepared pulsatile tablets hardness. In in vitro release profile of 6 hours study in first 5 hours it shows minimum drug release and at the end of six hours rapid and transient release was observed. Stability studies proved that coating of tablets seems to decrease the effect of temperature and moisture on degradation of Zafirlukast. The pulsatile release has been achieved from tablet over a 7-8 hour period. &nbsp

Numerical Study of Pulse Detonation Engine with One-step Overall Reaction Model

This paper presents an insight for the study of transient, compressible, intermittent pulsed detonation engine with one-step overall reaction model to reduce the computational complexity in detonation simulations. Investigations are done on flow field conditions developing inside the tube with the usage of irreversible one-step chemical reactions for detonations. In the present simulations 1-D and 2-D axisymmetric tubes are considered for the investigation. The flow conditions inside the detonation tube are estimated as a function of time and distance. Studies are also performed with different grid sizes which influence the prediction of Von-Neumann spike, CJ Pressure and detonation velocity. The simulation result from the single-cycle reaction model agrees well with the previous published literature of multi-step reaction models. The present studies shows that one-step overall reaction model is sufficient to predict the flow properties with reasonable accuracy. Finally, the results from the present study were compared and validated using NASA CEA.Defence Science Journal, Vol. 65, No. 4, July 2015, pp. 265-271, DOI: http://dx.doi.org/10.14429/dsj.65.873

Synthesis of Hydrogen Getter Zr1-xCox (x=0-1) Alloy Films by Magnetron Co-Sputtering

The Zr1-xCox(x=0, 0.25, 0.53, 0.63, 1) thin films were deposited on quartz substrate using magnetron co-sputtering of Zirconium and Cobalt targets in confocal geometry. A constant pulsed direct current (PDC) on Zirconium and radio frequency (RF) of various powers on Cobalt target were applied to vary the concentration of Co in the Zr1-xCox film. The film composition was quantified using EDX measurements. The hydrogen storage capacity of these films was studied using an in-house developed hydrogen adsorption setup, in which the electrical resistivity of the film was monitored as a function of hydrogen partial pressure and temperature. The films' surface morphology and crystal structure before and after hydrogenation were characterized using atomic force microscopy and grazing incidence X-ray diffraction techniques using synchrotron radiation, respectively. An increase in the particle size after hydrogenation was observed for all the films. An increase in resistivity was also observed due to the absorption of hydrogen in all the compositions. The near stoichiometric film Zr0.47Co0.53 showed the highest hydrogen absorption level at 200 oC at all partial pressures. However, a decrease in the response at temperatures higher than 200 oC was observed in the film containing a Co concentration. The mechanism for the increase in resistivity of the film on hydrogenation is explained. Keywords: ZrCo alloy, hydrogen getter, magnetron co-sputtering, four-probe resistivity, thin film

MCL-CAw: A refinement of MCL for detecting yeast complexes from weighted PPI networks by incorporating core-attachment structure

Abstract Background The reconstruction of protein complexes from the physical interactome of organisms serves as a building block towards understanding the higher level organization of the cell. Over the past few years, several independent high-throughput experiments have helped to catalogue enormous amount of physical protein interaction data from organisms such as yeast. However, these individual datasets show lack of correlation with each other and also contain substantial number of false positives (noise). Over these years, several affinity scoring schemes have also been devised to improve the qualities of these datasets. Therefore, the challenge now is to detect meaningful as well as novel complexes from protein interaction (PPI) networks derived by combining datasets from multiple sources and by making use of these affinity scoring schemes. In the attempt towards tackling this challenge, the Markov Clustering algorithm (MCL) has proved to be a popular and reasonably successful method, mainly due to its scalability, robustness, and ability to work on scored (weighted) networks. However, MCL produces many noisy clusters, which either do not match known complexes or have additional proteins that reduce the accuracies of correctly predicted complexes. Results Inspired by recent experimental observations by Gavin and colleagues on the modularity structure in yeast complexes and the distinctive properties of "core" and "attachment" proteins, we develop a core-attachment based refinement method coupled to MCL for reconstruction of yeast complexes from scored (weighted) PPI networks. We combine physical interactions from two recent "pull-down" experiments to generate an unscored PPI network. We then score this network using available affinity scoring schemes to generate multiple scored PPI networks. The evaluation of our method (called MCL-CAw) on these networks shows that: (i) MCL-CAw derives larger number of yeast complexes and with better accuracies than MCL, particularly in the presence of natural noise; (ii) Affinity scoring can effectively reduce the impact of noise on MCL-CAw and thereby improve the quality (precision and recall) of its predicted complexes; (iii) MCL-CAw responds well to most available scoring schemes. We discuss several instances where MCL-CAw was successful in deriving meaningful complexes, and where it missed a few proteins or whole complexes due to affinity scoring of the networks. We compare MCL-CAw with several recent complex detection algorithms on unscored and scored networks, and assess the relative performance of the algorithms on these networks. Further, we study the impact of augmenting physical datasets with computationally inferred interactions for complex detection. Finally, we analyse the essentiality of proteins within predicted complexes to understand a possible correlation between protein essentiality and their ability to form complexes. Conclusions We demonstrate that core-attachment based refinement in MCL-CAw improves the predictions of MCL on yeast PPI networks. We show that affinity scoring improves the performance of MCL-CAw.http://deepblue.lib.umich.edu/bitstream/2027.42/78256/1/1471-2105-11-504.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/2/1471-2105-11-504-S1.PDFhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/3/1471-2105-11-504-S2.ZIPhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/4/1471-2105-11-504.pdfPeer Reviewe