Jadavpur University

About: Jadavpur University is a education organization based out in Kolkata, India. It is known for research contribution in the topics: Population & Fuzzy logic. The organization has 10856 authors who have published 27678 publications receiving 422069 citations. The organization is also known as: JU & Jadabpur University.

Organic Amendments Influence Soil Organic Carbon Pools and Rice–Wheat Productivity

Abstract: Soil organic C (SOC) pools under long-term management practices provide information on C sequestration pathways, soil quality maintenance, and crop productivity. Farmyard manure (FYM), paddy straw (PS), and green manure (GM) along with inorganic fertilizers were used in a 19-yr-old rice (Oryza sativa L.)-wheat (Triticum aestivum L.) cropping system in subtropical India to evaluate their impact on SOC stock, its different pools-total organic C (C tot ); oxidizable organic C (C oc ) and its four fractions of very labile (C frac1 ), labile (C frac2 ), less labile (C frac3 ), and nonlabile C (C frac4 ); microbial biomass C (C mic ); and mineralizable C (C min ). Cropping with only N-P-K fertilization just maintained SOC content, while N-P-K plus organics increased SOC by 24.3% over the control, their relative efficacy being FYM > PS > GM. A minimum of 3.56 Mg C ha -1 yr -1 was required to be added as organic amendments to compensate for SOC loss from cropping. The passive (C frac3 + C frac4 ) pool and C min constituted about 39 and 11.5%, respectively, of C tot . Organics contributed toward the passive pool in the order FYM > PS > GM. Most of the pools were significantly (P = 0.005) correlated with each other. Yield and sustainable yield index were strongly related with C frac1 , C oc , C mic , and C min . Results suggest C frac1 as a useful indicator for assessing soil health, and balanced fertilization with FYM as suitable management for sustaining crop productivity of the rice-wheat system.

Fractional order fuzzy control of hybrid power system with renewable generation using chaotic PSO.

Abstract: This paper investigates the operation of a hybrid power system through a novel fuzzy control scheme. The hybrid power system employs various autonomous generation systems like wind turbine, solar photovoltaic, diesel engine, fuel-cell, aqua electrolyzer etc. Other energy storage devices like the battery, flywheel and ultra-capacitor are also present in the network. A novel fractional order (FO) fuzzy control scheme is employed and its parameters are tuned with a particle swarm optimization (PSO) algorithm augmented with two chaotic maps for achieving an improved performance. This FO fuzzy controller shows better performance over the classical PID, and the integer order fuzzy PID controller in both linear and nonlinear operating regimes. The FO fuzzy controller also shows stronger robustness properties against system parameter variation and rate constraint nonlinearity, than that with the other controller structures. The robustness is a highly desirable property in such a scenario since many components of the hybrid power system may be switched on/off or may run at lower/higher power output, at different time instants.

A Multi-Organ Nucleus Segmentation Challenge

Abstract: Generalized nucleus segmentation techniques can contribute greatly to reducing the time to develop and validate visual biomarkers for new digital pathology datasets. We summarize the results of MoNuSeg 2018 Challenge whose objective was to develop generalizable nuclei segmentation techniques in digital pathology. The challenge was an official satellite event of the MICCAI 2018 conference in which 32 teams with more than 80 participants from geographically diverse institutes participated. Contestants were given a training set with 30 images from seven organs with annotations of 21,623 individual nuclei. A test dataset with 14 images taken from seven organs, including two organs that did not appear in the training set was released without annotations. Entries were evaluated based on average aggregated Jaccard index (AJI) on the test set to prioritize accurate instance segmentation as opposed to mere semantic segmentation. More than half the teams that completed the challenge outperformed a previous baseline. Among the trends observed that contributed to increased accuracy were the use of color normalization as well as heavy data augmentation. Additionally, fully convolutional networks inspired by variants of U-Net, FCN, and Mask-RCNN were popularly used, typically based on ResNet or VGG base architectures. Watershed segmentation on predicted semantic segmentation maps was a popular post-processing strategy. Several of the top techniques compared favorably to an individual human annotator and can be used with confidence for nuclear morphometrics.

Exploring quantitative structure–activity relationship studies of antioxidant phenolic compounds obtained from traditional Chinese medicinal plants

Abstract: In the present work, quantitative structure–activity relationship (QSAR) models have been built for a wide variety of antioxidant phenolic compounds obtained from traditional Chinese medicinal plants, with their Trolox equivalent antioxidant capacity measured using 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical and 2,2′-azinobis-(3-ethylbenzothiazoline-6-sulphonic acid) radical (ABTS√+) assay methods. Non-linear models obtained using genetic partial least-squares technique were acceptable both in terms of internal and external predictivity. Validation of developed models using metrics and randomisation technique yielded results indicating the predictivity and robustness, respectively, of the developed models. The models signify that the presence of ketonic oxygen within the molecular structure favours their antioxidant activity. In addition, the number of hydroxyl groups, extent of branching, degree of methoxylation and the number of methyl and methylene substituents also dictate the antioxidant activity of...