Rajat Kumar Pal

Bio: Rajat Kumar Pal is an academic researcher from University of Calcutta. The author has contributed to research in topics: Routing (electronic design automation) & Biochip. The author has an hindex of 14, co-authored 197 publications receiving 866 citations. Previous affiliations of Rajat Kumar Pal include Islamic Azad University & Medi-Caps Institute of Technology & Management.

Engineered silybin nanoparticles educe efficient control in experimental diabetes.

Abstract: Silybin, is one imminent therapeutic for drug induced hepatotoxicity, human prostrate adenocarcinoma and other degenerative organ diseases. Recent evidences suggest that silybin influences gluconeogenesis pathways favorably and is beneficial in the treatment of type 1 and type 2 diabetes. The compound however is constrained due to solubility (0.4 mg/mL) and bioavailabilty limitations. Appropriate nanoparticle design for silybin in biocompatible polymers was thus proposed as a probable solution for therapeutic inadequacy. New surface engineered biopolymeric nanoparticles with high silybin encapsulation efficiency of 92.11% and zeta potential of +21 mV were designed. Both the pure compound and the nanoparticles were evaluated in vivo for the first time in experimental diabetic conditions. Animal health recovered substantially and the blood glucose levels came down to near normal values after 28 days treatment schedule with the engineered nanoparticles. Restoration from hyperglycemic damage condition was traced to serum insulin regeneration. Serum insulin recovered from the streptozotocin induced pancreatic damage levels of 0.17±0.01 µg/lit to 0.57±0.11 µg/lit after nanoparticle treatment. Significant reduction in glycated hemoglobin level, and restoration of liver glycogen content were some of the other interesting observations. Engineered silybin nanoparticle assisted recovery in diabetic conditions was reasoned due to improved silybin dissolution, passive transport in nanoscale, and restoration of antioxidant status.

Characterization of citrate capped gold nanoparticle-quercetin complex: Experimental and quantum chemical approach

Abstract: Quercetin and several other bioflavonoids possess antioxidant property. These biomolecules can reduce the diabetic complications, but metabolize very easily in the body. Nanoparticle-mediated delivery of a flavonoid may further increase its efficacy. Gold nanoparticle is used by different groups as vehicle for drug delivery, as it is least toxic to human body. Prior to search for the enhanced efficacy, the gold nanoparticle-flavonoid complex should be prepared and well characterized. In this article, we report the interaction of gold nanoparticle with quercetin. The interaction is confirmed by different biophysical techniques, such as Scanning Electron Microscope (SEM), Circular Dichroism (CD), Fourier-Transform InfraRed (FT-IR) spectroscopy and Thermal Gravimetric Analysis (TGA) and cross checked by quantum chemical calculations. These studies indicate that gold clusters are covered by citrate groups, which are hydrogen bonded to the quercetin molecules in the complex. We have also provided evidences how capping is important in stabilizing the gold nanoparticle and further enhances its interaction with other molecules, such as drugs. Our finding also suggests that gold nanoparticle-quercetin complex can pass through the membranes of human red blood cells.

An efficient genetic algorithm for multi-objective solid travelling salesman problem under fuzziness

Abstract: In this paper, we have presented a multi-objective solid travelling salesman problem (TSP) in a fuzzy environment. The attraction of the solid TSP is that a traveller visits all the cities in his tour using multiple conveyance facilities. Here we consider cost and time as two objectives of the solid TSP. The objective of the study is to find a complete tour such that both the total cost and the time are minimized. We consider travelling costs and times for one city to another using different conveyances are different and fuzzy in nature. Since cost and time are considered as fuzzy in nature, so the total cost and the time for a particular tour are also fuzzy in nature. To find out Pareto-optimal solution of fuzzy objectives we use fuzzy possibility and necessity measure approach. A multi-objective genetic algorithm with cyclic crossover, two-point mutation, and refining operation is used to solve the TSP problem. In this paper a multi-objective genetic algorithm has been modified by introducing the refining operator. Finally, experimental results are given to illustrate the proposed approach; experimental results obtained are also highly encouraging.

An improved genetic algorithm based approach to solve constrained knapsack problem in fuzzy environment

Abstract: We have proposed an improved genetic algorithm (GA) to solve constrained knapsack problem.A discount constant has been considered for knapsack problem.The genetic algorithm has been modified by introducing refining and repairing operations.We use fuzzy formulation to solve knapsack problem. In this paper, we have proposed an improved genetic algorithm (GA) to solve constrained knapsack problem in fuzzy environment. Some of the objects among all the objects are associated with a discount. If at least a predetermined quantity of the object(s) (those are associated with a discount) is selected, then an amount (in $) is considered as discount. The aim of the model is to maximize the total profit of the loaded/selected objects with obtaining minimum discount price (predetermined). For the imprecise model, profit and weight (for each of the objects) have been considered as fuzzy number. This problem has been solved using two types of fuzzy systems, one is credibility measure and another is graded mean integration approach. We have presented an improved GA to solve the problem. The genetic algorithm has been improved by introducing 'refining' and 'repairing' operations. Computational experiments with different randomly generated data sets are given in experiment section. Some sensitivity analysis have also been made and presented in experiment section.

A genetic ant colony optimization based algorithm for solid multiple travelling salesmen problem in fuzzy rough environment

Abstract: In this paper, a genetic-ant colony optimization algorithm has been presented to solve a solid multiple Travelling Salesmen Problem (mTSP) in fuzzy rough environment. In solid mTSP, a set of nodes (locations/cities) are given, and each of the cities must be visited exactly once by the salesmen such that all of them start and finish at a depot using different conveyance facility. A solid mTSP is an extension of mTSP where the travellers use different conveyance facilities for travelling from one city to another. To solve an mTSP, a hybrid algorithm has been developed based on the concept of two algorithms, namely genetic algorithm (GA) and ant colony optimization (ACO) based algorithm. Each salesman selects his/her route using ACO and the routes of different salesmen (to construct a complete solution) are controlled by the GA. Here, a set of simple ACO characteristics have further been modified by incorporating a special feature namely ‘refinement’. In this paper, we have utilized cyclic crossover and two-point’s mutation in the proposed algorithm to solve the problem. The travelling cost is considered as imprecise in nature (fuzzy-rough) and is reduced to its approximate crisp using fuzzy-rough expectation. Computational results with different data sets are presented and some sensitivity analysis has also been made.

Triangulating a simple polygon in linear time

Abstract: We give a deterministic algorithm for triangulating a simple polygon in linear time. The basic strategy is to build a coarse approximation of a triangulation in a bottom-up phase and then use the information computed along the way to refine the triangulation in a top-down phase. The main tools used are the polygon-cutting theorem, which provides us with a balancing scheme, and the planar separator theorem, whose role is essential in the discovery of new diagonals. Only elementary data structures are required by the algorithm. In particular, no dynamic search trees, of our algorithm.

Applications of nonlinear fiber optics

Abstract: * The only book describing applications of nonlinear fiber optics * Two new chapters on the latest developments: highly nonlinear fibers and quantum applications* Coverage of biomedical applications* Problems provided at the end of each chapterThe development of new highly nonlinear fibers - referred to as microstructured fibers, holey fibers and photonic crystal fibers - is the next generation technology for all-optical signal processing and biomedical applications. This new edition has been thoroughly updated to incorporate these key technology developments.The book presents sound coverage of the fundamentals of lightwave technology, along with material on pulse compression techniques and rare-earth-doped fiber amplifiers and lasers. The extensively revised chapters include information on fiber-optic communication systems and the ultrafast signal processing techniques that make use of nonlinear phenomena in optical fibers.New material focuses on the applications of highly nonlinear fibers in areas ranging from wavelength laser tuning and nonlinear spectroscopy to biomedical imaging and frequency metrology. Technologies such as quantum cryptography, quantum computing, and quantum communications are also covered in a new chapter.This book will be an ideal reference for: RD scientists involved with research on fiber amplifiers and lasers; graduate students and researchers working in the fields of optical communications and quantum information. * The only book on how to develop nonlinear fiber optic applications* Two new chapters on the latest developments; Highly Nonlinear Fibers and Quantum Applications* Coverage of biomedical applications

Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm

Abstract: During the past decade, solving complex optimization problems with metaheuristic algorithms has received considerable attention among practitioners and researchers. Hence, many metaheuristic algorithms have been developed over the last years. Many of these algorithms are inspired by various phenomena of nature. In this paper, a new population based algorithm, the Lion Optimization Algorithm (LOA), is introduced. Special lifestyle of lions and their cooperation characteristics has been the basic motivation for development of this optimization algorithm. Some benchmark problems are selected from the literature, and the solution of the proposed algorithm has been compared with those of some well-known and newest meta-heuristics for these problems. The obtained results confirm the high performance of the proposed algorithm in comparison to the other algorithms used in this paper.