Successor features based multi-agent RL for event-based decentralized MDPs

Decentralized MDPs (Dec-MDPs) provide a rigorous framework for collaborative multi-agent sequential decisionmaking under uncertainty. However, their computational complexity limits the practical impact. To address this, we focus on a class of Dec-MDPs consisting of independent collaborating agents that are tied together through a global reward function that depends upon their entire histories of states and actions to accomplish joint tasks. To overcome scalability barrier, our main contributions are: (a) We propose a new actor-critic based Reinforcement Learning (RL) approach for event-based Dec-MDPs using successor features (SF) which is a value function representation that decouples the dynamics of the environment from the rewards; (b) We then present Dec-ESR (Decentralized Event based Successor Representation) which generalizes learning for event-based Dec-MDPs using SF within an end-to-end deep RL framework; (c) We also show that Dec-ESR allows useful transfer of information on related but different tasks, hence bootstraps the learning for faster convergence on new tasks; (d) For validation purposes, we test our approach on a large multi-agent coverage problem which models schedule coordination of agents in a real urban subway network and achieves better quality solutions than previous best approaches

EVALUATION AND MODELLING OF GROUND WATER QUALITY DATA OF ALLAHABAD CITY BY ENVIRONMETRIC METHODS

Water is an essential resource for all the organisms, plants and animals including human beings. It is the backbone for the agricultural and industrial sectors and all the small business units. Increase in human population and economic activities have tremendously increased the demand for large-scale suppliers of freshwater for various competing end users. The quality evaluation of water is represented in terms of physical, chemical and biological parameters. A particular problem in the case of water quality monitoring is the complexity associated with analyzing a large number of measured variables. The data sets contain rich information about the behaviour of the water resources. Multivariate statistical approaches allow deriving hidden information from the data sets about the possible influences of the environment on water quality. Classification, modelling and interpretation of monitored data are the most important steps in the assessment of water quality. The application of different multivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA) and factor analysis (FA) help to identify important components or factors accounting for most of the variances of a system. In the present study water samples were analyzed for various physicochemical analyses by different methods following the standards of APHA, BIS and WHO and were subjected to further statistical analysis viz. the cluster analysis to understand the similarity and differences among the various sampling stations.  Three clusters were found. Cluster 1 was marked with 3 sampling locations 1, 3 & 5; Cluster-2 was marked with sampling location-2 and cluster-3 was marked with sampling location-4. Principal component analysis/factor analysis is a pattern reorganization technique which is used to assess the correlation between the observations in terms of different factors which are not observable. Observations correlated either positively or negatively, are likely to be affected by the same factors while the observations which are not correlated are influenced by different factors. In our study, three factors explained 99.827% of variances. F1 marked  51.619% of total variances, high positive strong loading with TSS, TS, Temp, TDS, phosphate and moderate with electrical conductivity with loading values of 0.986, 0.970, 0.792, 0.744, 0.695,  0.701, respectively. Factor 2 marked 27.236% of the total variance with moderate positive loading with total alkalinity & temp. with loading values 0.723 & 0.606 respectively. It also explained the moderate negative loading with conductivity, TDS, and chloride with loading values -0.698, -0.690, -0.582. Factor F 3 marked 20.972 % of the variances with positive loading with pH, chloride, and phosphate with strong loading of pH 0.872 and moderate positive loading with chloride and phosphate with loading values 0.721, and 0.569 respectively.&nbsp

Homotopy perturbation method to space–time fractional solidification in a finite slab

AbstractA mathematical model describing the space and time fractional solidification of fluid initially at its freezing temperature contained in a finite slab under the constant wall temperature is presented. The approximate analytical solution of this problem is obtained by the homotopy perturbation method. The results thus obtained are compared with exact solution of integer order (β=1,α=2) and are good agreement. The problem has been studied in detail by considering different order time and space fractional derivatives. The temperature distribution and the moving interface position for different fractional order space and time derivatives are shown graphically. The model and the solution are the generalization of the previous works and include them as special cases

Correlates of microalbuminuria in hypertensive patients of a tertiary care teaching hospital of Central India

Background: Although the prevalence of hypertension is high in India, the relationship between micro-albuminuria and target organ damage in hypertension is not well studied. Hence this study aims to study the prevalence of micro-albuminuria in patients of hypertension and its correlation with other cardiovascular risk factors.Methods: This cross-sectional study was done in 112 essential hypertension non-diabetic patients presented at a tertiary care hospital of Madhya Pradesh, India who fulfilled inclusion criteria and exclusion criteria during a calendar year. The diagnosis of essential hypertension was made by the study physician after complete medical history, physical examination and routine biochemical analysis of blood and urine. The data was analysed using SPSS version 20 and Mann Whitney U and Chi-square test was used for quantitative and qualitative data respectively.Results: The total number of patients having micro-albuminuria was 26 and the prevalence came out to be 23.21%. The mean age of micro-albuminuric patients was less compared to non-microalbuminuric patients (p<0.05). The systolic, diastolic blood pressure and cholesterol levels were found to be higher but was statistically insignificant whereas body mass index (BMI) and duration of disease was statistically  higher (p<0.05) amongst the cases having micro-albumin in their urine.Conclusions: The prevalence of micro-albuminuria increases with the increase in duration, stages /severity of hypertension. Micro-albuminuria may be considered as a marker of adverse cardiovascular risk profile such as LVH and hyperlipidemia. High BMI, smoking and advanced stages of retinopathy are also the risk factors of micro-albuminuria.

Integrated Approach for Bioethanol and Paper Production using Populus deltoides Wood Biomass: An Experimental Study

Lignocellulosic materials contain two major sugar macromolecules, cellulose and hemicellulose, and polyphenolic lignin. During pulping, lignin and hemicellulose are broken down into smaller molecules such as organic acids, and removed in the black liquor, leaving cellulose fibers for papermaking. Lignocellulose consists of approximately 28–35% hemicelluloses, which are lost during the pulping process in black liquor and are an important source of sugars that can be used to produce bioethanol as a liquid fuel. The hemicellulosic sugars from the Populus deltoides (poplar) lignocellulosic biomass were partially extracted keeping in mind that it does not affect the properties of paper beyond acceptable limits, further converting these extracted sugars by fermentation to bioethanol, followed by pulping the residual biomass and papermaking and determining pulping and papermaking properties. With the increasing demand for lignocellulosic biomass by various industries, an integrated biorefinery approach for maximum utilization of its chemical components with minimum degradation is necessary in the future. The maximum bioethanol yield was found to be 3.58 g/L. On manufactured paper sheets, the mechanical properties tensile index and tear index of pre-extracted biomass were observed as 19.23 Nm/g and 3.5 mNm2/g and slightly lower against the control 21.34 Nm/g and 4.0 mNm2/g. The main objective of the present study is to recover reducing sugars before the pulping process for bioethanol production and to further utilize the remaining residue for papermaking without disturbing its fiber integrity

Effectiveness of LRB in Curved Bridge Isolation: A Numerical Study

Lead Rubber Bearings (LRBs) represent one of the most widely employed devices for the seismic protection of structures. However, the effectiveness of the same in the case of curved bridges has not been judged well because of the complexity involved in curved bridges, especially in controlling torsional moments. This study investigates the performance of an LRB-isolated horizontally curved continuous bridge under various seismic loadings. The effectiveness of LRBs on the bridge response control was determined by considering various aspects, such as the changes in ground motion characteristics, multidirectional effects, the degree of seismic motion, and the variation of incident angles. Three recorded ground motions were considered in this study, representing historical earthquakes with near-field, far-field, and forward directivity effects. The effectiveness of the bidirectional behavior considering the interaction effect of the bearing and pier was also studied. The finite element method was adopted. A sensitivity study of the bridge response related to the bearing design parameters was carried out for the considered ground motions. The importance of non-linearity and critical design parameters of LRBs were assessed. It was found that LRBs resulted in a significant increase in deck displacement for Turkey ground motion, which might be due to the forward directivity effect. The bi-directional effect is crucial for the curved bridge as it enhances the displacement significantly compared to uni-directional motion

Ekspresija i pročišćavanje glavnoga proteina (OmpH) bakterije Pasteurella multocida P52 proizvedenoga u bakteriji Escherichia coli.

Porin H (OmpH) is the major outer membrane protein in the envelope of Pasteurella multocida. The gene ompH, encoding major outer membrane protein was amplified by PCR excluding the region coding for signal peptide and cloned in the pQE32 prokaryotic expression vector. The recombinant OmpH was expressed as a fusion protein with 6-His tag at N-terminal in E. coli M15 cells transformed with recombinant plasmid pQE32-ompH. The expressed protein was purified from E. coli and characterized by SDS-PAGE and western blot analysis. The fusion recombinant protein eluted had a molecular mass of about 37 kDa. The expressed recombinant protein was confirmed with western blot analysis using RGS-His antibody and anti-P. multocida serum raised against whole cell lysate.Porin H (OmpH) je glavni protein stanične stijenke bakterije Pasteurella multocida. Gen ompH, koji kodira njegovu tvorbu, isključujući područje za tvorbu signalnog peptida, bio je umnožen lančanom reakcijom polimerazom i kloniran u prokariotskom vektoru pQE32. Rekombinantni OmpH bio je izražen kao fuzijski protein sa 6-His tag na N-kraju u stanicama E. coli M15 transformiranima rekombinantnim plazmidom pQE32- ompH. Proizveden protein bio je pročišćen iz E. coli i identificiran SDS-PAGE-om i western blotom. Izdvojeni fuzijski rekombinantni protein imao je molekularnu masu oko 37 kDa. Identitet proizvedenog rekombinantnog proteina bio je povrđen western blot analizom uporabom protutijela za RGS-His i antiseruma za lizat cjelovite stanice P. multocida

Artificial Intelligence-Based Machine and Deep Learning Techniques That Use Brain Waves to Detect Depression

Electroencephalogram (EEG) lsignal-based lemotion lrecognition lhas lattracted lwide linterests in lrecent lyears land lhas lbeen lbroadly ladopted in lmedical, laffective lcomputing, land lother lrelevant lfields. Depression has lbecome la lleading lmental ldisorder lworldwide. Evidence lhas lshown lthat lsubjects lwith ldepression lexhibit ldifferent lspatial lresponses in lneurophysiologic lsignals lfrom lthe lhealthy lcontrols lwhen lthey lare lexposed lto lpositive land lnegative. Depression isla common lreason lfor an increase in lsuicide lcases lworldwide. EEG lplays an important lrole in lE-healthcare lsystems, lespecially in lthe lmental lhealthcare larea, lwhere lconstant land lunobtrusive lmonitoring lis ldesirable. EEG lsignals lcan lreflect lactivities lof lthe lhuman lbrain land lrepresent different lemotional lstates. Mental lstress lhas lbecome la lsocial lissue land lcould lbecome la lcause lof lfunctional ldisability lduring lroutine lwork. This lResearch presents ldeep llearning ltechnique lfor ldetecting ldepression lusing lEEG. The lalgorithm lfirst lextracts lfeatures lfrom lEEG lsignals land lclassifies lemotions lusing lmachine land ldeep llearning ltechniques, in lwhich ldifferent lparts lof la ltrial lare lused lto ltrain lthe lproposed lmodel land lassess lits limpact lon lemotion lrecognition lresults. The simulation is performed lusing lthe lPython lspyder lsoftware. The lprecision lof lthe lproposed lwork lis l99% lwhile in lthe lprevious lwork lit lis l91.00%. lSimilarly lthe lother lparameters llike lRecall land lF_Measure lis l94% land l97% lby lthe lproposed lwork land l88.00% land l89.00% lby lthe lprevious lwork. The loverall laccuracy lachieved lby lthe lproposed lwork lis l96.48% lwhile lprevious lit lis lachieved l91.00%. The error rate of proposed technique is l3.52% lwhile l9.008% in existing lwork. Therefore, lit lis clear lfrom lthe lsimulation lresults; lthe lproposed lwork lis lachieved significant lbetter lresults lthan lexisting lwork

Selection of Response Reduction Factor Considering Resilience Aspect

The selection of an adequate response reduction factor (R) in the seismic design of a reinforced concrete building is critical to the building’s seismic response. To construct a robust structure, the R factor should be chosen based on the building’s resilience performance. Since no background was provided for the selection of R factors, the study focuses on the right selection of R factors in relation to the building’s functionality, performance level, and resilience. In this study, a high-rise building with multiple R factors (R = 3, 4, 5, and 6) is developed. Five potential recovery paths (RP-1 to RP-5) that matched the realistic scenario were used to estimate the building’s functionality. The building was subjected to uni and bi-directional loadings, and two design levels, Design Basic Earthquake (DBE) and Maximum Considered Earthquake were used to monitor the building’s response. According to the findings, a decrease in the lateral design force with the highest R results in a high ductility requirement and a substantial loss of resilience. The maximum R factor can be recommended under uni-directional loading up to 6, in which the building’s resilience is almost 50%, whereas under bi-directional loading and taking the recommended R factor decreased from 6 to 4