Determinants of research productivity

Earlier researchers like Turkeli, suggested that ‘the factors which determine the productivity of scientists are admittedly complex and perhaps not amenable to real scientific analysis′. The present investigation was designed with the sole purpose of confronting such a complex problem. Nearly 200 variables influencing research productivity were collected through relevant literature, analysis of biographies of great scientists, and discussion with eminent scientists. Finally, through a critical examination, 80 variables were selected for the use of Q-sort technique. The sample for the study consisted of a cross section of scientists ranging from Fellows of Indian National Science Academy to young agricultural scientists. Mailed questionnaires and personal interview methods were used for collecting data. Out of a total of 912 respondents, reply was obtained from 325. On the basis of Q-sorted data, 26 variables were selected for further analysis and they were subjected to principal component factor analysis. The results indicated eleven factors affecting research productivity of scientists. They were: persistence, resource adequacy, access to literature, initiative, intelligence, creativity, learning capability, stimulative leadership, concern for advancement, external orientation, and professional commitment.     

Snowpack response in the Assiniboine-Red River basin associated with projected global warming of 1.0 °C to 3.0 °C

This study assesses snow response in the Assiniboine-Red River basin, located in the Lake Winnipeg watershed, due to anthropogenic climate change. We use a process-based distributed snow model driven by an ensemble of eight statistically downscaled global climate models (GCMs) to project future changes under policy-relevant global mean temperature (GMT) increases of 1.0 °C to 3.0 °C above the pre-industrial period. Results indicate that basin scale seasonal warmings generally exceed the GMT increases, with greater warming in winter months. The majority of GCMs project wetter winters and springs, and drier summers, while autumn could become either drier or wetter. An analysis of snow water equivalent (SWE) responses under GMT changes reveal higher correlations of snow cover duration (SCD), snowmelt rate, maximum SWE (SWE_max) and timing of SWE_max with winter and spring temperatures compared to precipitation, implying that these variables are predominantly temperature controlled. Consequently, under the GMT increases from 1.0 °C to 3.0 °C, the basin will experience successively shorter SCD, slower snowmelt, smaller monthly SWE and SWE_max, earlier SWE_max, and a transition from snow-dominated to rain-snow hybrid regime. Further, while the winter precipitation increases for some GCMs compensate the temperature-driven changes in SWE, the increases for most GCMs occur as rainfall, thus limiting the positive contribution to snow storage. Overall, this study provides a detailed diagnosis of the snow regime changes under the policy-relevant GMT changes, and a basis for further investigations on water quantity and quality changes.     

COVIDScreen: explainable deep learning framework for differential diagnosis of COVID-19 using chest X-rays

Neural Computing and Applications - COVID-19 has emerged as a global crisis with unprecedented socio-economic challenges, jeopardizing our lives and livelihoods for years to come. The...     

Estimation of elastic constant of rocks using an ANFIS approach

The engineering properties of the rocks have the most vital role in planning of rock excavation and construction for optimum utilization of earth resources with greater safety and least damage to surroundings. The design and construction of structure is influenced by physico-mechanical properties of rock mass. Young's modulus provides insight about the magnitude and characteristic of the rock mass deformation due to change in stress field. The determination of the Young's modulus in laboratory is very time consuming and costly. Therefore, basic rock properties like point load, density and water absorption have been used to predict the Young's modulus. Point load, density and water absorption can be easily determined in field as well as laboratory and are pertinent properties to characterize a rock mass. The artificial neural network (ANN), fuzzy inference system (FIS) and neuro fuzzy are promising techniques which have proven to be very reliable in recent years. In, present study, neuro fuzzy system is applied to predict the rock Young's modulus to overcome the limitation of ANN and fuzzy logic. Total 85 dataset were used for training the network and 10 dataset for testing and validation of network rules. The network performance indices correlation coefficient, mean absolute percentage error (MAPE), root mean square error (RMSE), and variance account for (VAF) are found to be 0.6643, 7.583, 6.799, and 91.95 respectively, which endow with high performance of predictive neuro-fuzzy system to make use for prediction of complex rock parameter.     

Structural Studies of TeO2–SeO2–Na2O Glass System

Glass Physics and Chemistry - The growth of nanocrystalization in TeO2–SeO2–Na2O glasses is achieved by the conventional heat treatment method. The influence of Na2O concentration on...     

Artificial Intelligence System Approach for Optimization of Drilling Parameters of Glass-Carbon Fiber/Polymer Composites

Silicon - In recent times, the study on machining characteristics of combined (hybrid) fiber polymer composites has drawn a remarkable research attention because of its emerging industrial...     

Determination of Binder Decomposition Kinetics for Specifying Heating Parameters in Binder Burnout Cycles

The decomposition kinetics of poly(vinyl butyral) binder from barium titanate multilayer ceramic capacitors with platinum metal electrodes were analyzed by thermogravimetric analysis as a function of the heating rate. The activation energy and pre-exponential factor for the decomposition kinetics were determined from two types of integral equations, from the Redhead method, and from the variation in heating rate method. The accuracy of the kinetic parameters determined from these methods was then evaluated for describing the observed rate of binder decomposition. Although the individual models yielded very different kinetic parameters, all were capable of describing the experimental data within ±15% accuracy. The kinetic parameters were then used in a coupled transport and kinetic model for describing the buildup of pressure within the ceramic green body as a function of the heating cycle. A methodology based on calculus of variations was also developed to predict the minimum duration for the binder burnout cycle.     

Thermal degradation of cellulose and its phosphorylated products in air and nitrogen

The thermal degradation of cellulose and its phosphorylated products (phosphates, diethylphosphate, and diphenylphosphate) were studied in air and nitrogen by differential thermal analysis and dynamic thermogravimetry from ambient temperature to 750°C. From the resulting data various thermodynamic parameters were obtained following the methods of Broido and Freeman and Carroll. The values of E_a for decomposition for phosphorylated cellulose were found to be in the range 55–138 kJ mol^?1 in air and 85–152 kJ mol^?1 in nitrogen and depended upon the percent of phosphorus contents in the samples. The mass spectrum of cellobiose phosphate indicated the absence of the molecular ion, indicating that the compound was thermally unstable. The IR spectra of the pyrolysis residues of cellulose phosphate gave indication of formation of a compound having C?O and P?O groups. A fire retardancy mechanism for the thermal degradation of cellulose phosphate has been proposed.     

Improvement of constructive heuristics using variable neighbourhood descent for scheduling a flow shop with sequence dependent setup time

This paper deals with the problem of scheduling a flow shop operating in a sequence dependent setup time environment. The objective is to determine the sequence that minimises the makespan. Two efficient neighbourhood search-based heuristics have been developed and tested using 960 problems, and the results obtained reveal their usefulness. The algorithms make use of two existing constructive heuristics. A neighbourhood search known as variable neighbourhood descent is used to improve the two constructive heuristics. Experimentation is carried out on the 96 groups of problems with 10 problem instances in each group. Performance analysis is carried out using the relative performance improvement of each heuristic. The analysis shows a consistently better performance of the neighbourhood-based improvement heuristics. A paired comparison test is used for validating the superiority of the proposed heuristics. The statistical analysis reveals that the performance of the neighbourhood-based heuristics is very much dependent on the initial constructive heuristics used.     

Short-term prediction of traffic flow using a binary neural network

This paper introduces a binary neural network-based prediction algorithm incorporating both spatial and temporal characteristics into the prediction process. The algorithm is used to predict short-term traffic flow by combining information from multiple traffic sensors (spatial lag) and time series prediction (temporal lag). It extends previously developed Advanced Uncertain Reasoning Architecture (AURA) k-nearest neighbour (k-NN) techniques. Our task was to produce a fast and accurate traffic flow predictor. The AURA k-NN predictor is comparable to other machine learning techniques with respect to recall accuracy but is able to train and predict rapidly. We incorporated consistency evaluations to determine whether the AURA k-NN has an ideal algorithmic configuration or an ideal data configuration or whether the settings needed to be varied for each data set. The results agree with previous research in that settings must be bespoke for each data set. This configuration process requires rapid and scalable learning to allow the predictor to be set-up for new data. The fast processing abilities of the AURA k-NN ensure this combinatorial optimisation will be computationally feasible for real-world applications. We intend to use the predictor to proactively manage traffic by predicting traffic volumes to anticipate traffic network problems.