Jadavpur University

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

Microbial activity on the microstructure of bacteria modified mortar

Abstract: Microbial modified mortar or concrete has become an important area of research for high-performance construction materials. This study investigates the effects of incorporating a facultative anaerobic hot spring bacterium on the microstructure of a cement–sand mortar. Environmental scanning electron microscopic (ESEM) views and image analysis (IA) of the bacteria modified mortar (thin-section) showed significant textural differences with respect to the control (without bacteria) samples. X-ray diffraction (XRD) study confirmed the formation of new phases of silicates (Gehlenite) within the matrix of such mortar material, which causes an improvement in the strength of the material. Electron probe microstructure analysis (EPMA) suggested that the bacterial treatment promoted uniform distribution of silicate phases and increased the calcium/silicon ratio within CSH gel of the matrices. The bacterium is found to leach a novel protein, which is capable of isolating silica from its source. The addition of such isolated protein, instead of the bacteria, into mortar also improves the strength of mortar.

Soil organic carbon pools and productivity relationships for a 34 year old rice–wheat–jute agroecosystem under different fertilizer treatments

Abstract: Soil organic carbon (SOC) pools are important in maintaining soil productivity and influencing the CO2 loading into the atmosphere. An attempt is made here to investigate into the dynamics of pools of SOC viz., total organic carbon (C tot), oxidisable organic carbon (C oc) and its four different fractions such as very labile (C frac 1), labile (C frac 2), less labile (C frac 3) and non-labile (C frac 4), microbial biomass carbon (C mic), mineralizable carbon (C min), and particulate organic carbon (C p) in relation to crop productivity using a 34 year old rice (Oryza sativa L)–wheat (Triticum aestivum L)–jute (Corchorus olitorius L) cropping system with different management strategies (no fertilization, only N, NP, NPK and NPK + FYM) in the hot humid, subtropics of India. A fallow treatment was also included to compare the impact of cultivation vis-a-vis no cultivation. Cultivation over the years caused a net decrease, while balanced fertilization with NPK maintained the SOC pools at par with the fallow. Only 22% of the C applied as FYM was stabilized into SOC, while the rest got lost. Of the analysed pools, C frac 1, C mic, C p and C min were influenced most by the treatments imposed. Most of the labile pools were significantly correlated with each other and with the yield and sustainable yield index (SYI) of the studied system. Of them, C frac1, C min, C mic and C p explained higher per cent variability in the SYI and yield of the crops. Results suggest that because of low cost and ease of estimation and also for upkeeping environmental conditions, C frac1 may be used as a good indicator for assessment of soil as to its crop productivity.

Self-adaptive differential evolution with multi-trajectory search for large-scale optimization

Abstract: In this paper, self-adaptive differential evolution (DE) is enhanced by incorporating the JADE mutation strategy and hybridized with modified multi-trajectory search (MMTS) algorithm (SaDE-MMTS) to solve large-scale continuous optimization problems. The JADE mutation strategy, the “DE/current-to-pbest” which is a variation of the classic “DE/current-to-best”, is used for generating mutant vectors. After the mutation phase, the binomial (uniform) crossover, the exponential crossover as well as no crossover option are used to generate each pair of target and trial vectors. By utilizing the self-adaptation in SaDE, both trial vector generation strategies and their associated control parameter values are gradually self-adapted by learning from their previous experiences in generating promising solutions. Consequently, suitable offspring generation strategy along with associated parameter settings will be determined adaptively to match different phases of the search process. MMTS is applied frequently to refine several diversely distributed solutions at different search stages satisfying both the global and the local search requirement. The initialization of step sizes is also defined by a self-adaption during every MMTS step. The success rates of both SaDE and the MMTS are determined and compared; consequently, future function evaluations for both search algorithms are assigned proportionally to their recent past performance. The proposed SaDE-MMTS is employed to solve the 19 numerical optimization problems in special issue of soft computing on scalability of evolutionary algorithms for large-scale continuous optimization problems and competitive results are presented.

Design of Non-Uniform Circular Antenna Arrays Using a Modified Invasive Weed Optimization Algorithm

Abstract: An ecologically inspired optimization algorithm, called invasive weed optimization (IWO), is presented for the design of non-uniform, planar, and circular antenna arrays that can achieve minimum side lobe levels for a specific first null beamwidth while avoiding the mutual coupling effects simultaneously. IWO recently emerged as a derivative-free real parameter optimizer that mimics the ecological behavior of colonizing weeds. For the present application, classical IWO has been modified by introducing a more explorative routine of changing the standard deviation of the seed population (equivalent to mutation step-size in evolutionary algorithms) of the algorithm. Simulation results over three significant instances of the circular array design problem have been presented to illustrate the effectiveness of the modified IWO algorithm. The design results obtained with modified IWO have been shown to comfortably beat those obtained with other state-of-the-art metaheuristics like genetic algorithm (GA), particle swarm optimization (PSO), original IWO and differential evolution (DE) in a statistically meaningful way.