Sardar Vallabhbhai National Institute of Technology Surat

We present the first public version (v0.2) of the open-source and community-developed Python package, Astropy. This package provides core astronomy-related functionality to the community, including support for domain-specific file formats such as flexible image transport system (FITS) files, Virtual Observatory (VO) tables, and common ASCII table formats, unit and physical quantity conversions, physical constants specific to astronomy, celestial coordinate and time transformations, world coordinate system (WCS) support, generalized containers for representing gridded as well as tabular data, and a framework for cosmological transformations and conversions. Significant functionality is under activedevelopment, such as a model fitting framework, VO client and server tools, and aperture and point spread function (PSF) photometry tools. The core development team is actively making additions and enhancements to the current code base, and we encourage anyone interested to participate in the development of future Astropy versions.

A simple yet powerful optimization algorithm is proposed in this paper for solving the constrained and unconstrained optimization problems. This algorithm is based on the concept that the solution obtained for a given problem should move towards the best solution and should avoid the worst solution. This algorithm requires only the common control parameters and does not require any algorithm-specific control parameters. The performance of the proposed algorithm is investigated by implementing it on 24 constrained benchmark functions having different characteristics given in Congress on Evolutionary Computation (CEC 2006) and the performance is compared with that of other well-known optimization algorithms. The results have proved the better effectiveness of the proposed algorithm. Furthermore, the statistical analysis of the experimental work has been carried out by conducting the Friedman's rank test and Holm-Sidak test. The proposed algorithm is found to secure first rank for the 'best' and 'mean' solutions in the Friedman's rank test for all the 24 constrained benchmark problems. In addition to solving the constrained benchmark problems, the algorithm is also investigated on 30 unconstrained benchmark problems taken from the literature and the performance of the algorithm is found better.

The performance of a photovoltaic (PV) array is affected by temperature, solar insolation, shading, and array configuration. Often, the PV arrays get shadowed, completely or partially, by the passing clouds, neighboring buildings and towers, trees, and utility and telephone poles. The situation is of particular interest in case of large PV installations such as those used in distributed power generation schemes. Under partially shaded conditions, the PV characteristics get more complex with multiple peaks. Yet, it is very important to understand and predict them in order to extract the maximum possible power. This paper presents a MATLAB-based modeling and simulation scheme suitable for studying the I-V and P-V characteristics of a PV array under a nonuniform insolation due to partial shading. It can also be used for developing and evaluating new maximum power point tracking techniques, especially for partially shaded conditions. The proposed models conveniently interface with the models of power electronic converters, which is a very useful feature. It can also be used as a tool to study the effects of shading patterns on PV panels having different configurations. It is observed that, for a given number of PV modules, the array configuration (how many modules in series and how many in parallel) significantly affects the maximum available power under partially shaded conditions. This is another aspect to which the developed tool can be applied. The model has been experimentally validated and the usefulness of this research is highlighted with the help of several illustrations. The MATLAB code of the developed model is freely available for download.

Current-voltage and power-voltage characteristics of large photovoltaic (PV) arrays under partially shaded conditions are characterized by multiple steps and peaks. This makes the tracking of the actual maximum power point (MPP) [global peak (GP)] a difficult task. In addition, most of the existing schemes are unable to extract maximum power from the PV array under these conditions. This paper proposes a novel algorithm to track the global power peak under partially shaded conditions. The formulation of the algorithm is based on several critical observations made out of an extensive study of the PV characteristics and the behavior of the global and local peaks under partially shaded conditions. The proposed algorithm works in conjunction with a DC-DC converter to track the GP. In order to accelerate the tracking speed, a feedforward control scheme for operating the DC-DC converter is also proposed, which uses the reference voltage information from the tracking algorithm to shift the operation toward the MPP. The tracking time with this controller is about one-tenth as compared to a conventional controller. All the observations and conclusions, including simulation and experimental results, are presented.

Iron is one of the most important elements in metabolic processes, being indispensable for all living systems and therefore it is extensively distributed in environmental and biological materials. However, both its deficiency and excess from the normal permissible limit can induce serious disorders. Therefore, several analytical techniques have been adopted for the detection of iron. Among the various techniques used for its detection, the method based on fluorescent sensors has received considerable interest in recent years because of its ability to provide online monitoring of very low concentrations without any pre-treatment of the sample together with the advantages of spatial and temporal resolution. In this article, efforts have been made to review the various molecular and supramolecular fluorescent sensors that have been developed for the selective detection of iron(III).

Metal ions or clusters that have been bonded with organic linkers to create one- or more-dimensional structures are referred to as metal-organic frameworks (MOFs). Reticular synthesis also forms MOFs with properly designated components that can result in crystals with high porosities and great chemical and thermal stability. Due to the wider surface area, huge pore size, crystalline nature, and tunability, numerous MOFs have been shown to be potential candidates in various fields like gas storage and delivery, energy storage, catalysis, and chemical/biosensing. This study provides a quick overview of the current MOF synthesis techniques in order to familiarize newcomers in the chemical sciences field with the fast-growing MOF research. Beginning with the classification and nomenclature of MOFs, synthesis approaches of MOFs have been demonstrated. We also emphasize the potential applications of MOFs in numerous fields such as gas storage, drug delivery, rechargeable batteries, supercapacitors, and separation membranes. Lastly, the future scope is discussed along with prospective opportunities for the synthesis and application of nano-MOFs, which will help promote their uses in multidisciplinary research.

Drug delivery technology has a wide spectrum, which is continuously being upgraded at a stupendous speed. Different fabricated nanoparticles and drugs possessing low solubility and poor pharmacokinetic profiles are the two major substances extensively delivered to target sites. Among the colloidal carriers, nanolipid dispersions (liposomes, deformable liposomes, virosomes, ethosomes, and solid lipid nanoparticles) are ideal delivery systems with the advantages of biodegradation and nontoxicity. Among them, nano-structured lipid carriers and solid lipid nanoparticles (SLNs) are dominant, which can be modified to exhibit various advantages, compared to liposomes and polymeric nanoparticles. Nano-structured lipid carriers and SLNs are non-biotoxic since they are biodegradable. Besides, they are highly stable. Their (nano-structured lipid carriers and SLNs) morphology, structural characteristics, ingredients used for preparation, techniques for their production, and characterization using various methods are discussed in this review. Also, although nano-structured lipid carriers and SLNs are based on lipids and surfactants, the effect of these two matrixes to build excipients is also discussed together with their pharmacological significance with novel theranostic approaches, stability and storage.

Nature inspired population based algorithms is a research field which simulates different natural phenomena to solve a wide range of problems. Researchers have proposed several algorithms considering different natural phenomena. Teaching-Learning-based optimization (TLBO) is one of the recently proposed population based algorithms which simulates the teaching-learning process of the class room. This algorithm does not require any algorithm-specific control parameters. In this paper, elitism concept is introduced in the TLBO algorithm and its effect on the performance of the algorithm is investigated. The effects of common controlling parameters such as the population size and the number of generations on the performance of the algorithm are also investigated. The proposed algorithm is tested on 35 constrained benchmark functions with different characteristics and the performance of the algorithm is compared with that of other well known optimization algorithms. The proposed algorithm can be applied to various optimization problems of the industrial environment.

Image fusion is the process of combining high spatial resolution panchromatic (PAN) image and rich multispectral (MS) image into a single image. The fused single image obtained is known to be spatially and spectrally enhanced compared to the raw input images. In recent years, many image fusion techniques such as principal component analysis, intensity hue saturation, brovey transforms and multi-scale transforms, etc., have been proposed to fuse the PAN and MS images effectively. However, it is important to assess the quality of the fused image before using it for various applications of remote sensing. In order to evaluate the quality of the fused image, many researchers have proposed different quality metrics in terms of both qualitative and quantitative analyses. Qualitative analysis determines the performance of the fused image by visual comparison between the fused image and raw input images. On the other hand, quantitative analysis determines the performance of the fused image by two variants such as with reference image and without reference image. When the reference image is available, the performance of fused image is evaluated using the metrics such as root mean square error, mean bias, mutual information, etc. When the reference image is not available the performance of fused image is evaluated using the metrics such as standard deviation, entropy, etc. The paper reviews the various quality metrics available in the literature, for assessing the quality of fused image.

In recent years, the progress of efficient green chemistry approaches for the fabrication of commercially viable noble metallic nanoparticles has become a major focus of researchers. The present review has focused on the various plant-mediated nanoparticle fabrication approaches, with brief discussions on the categories of various plant-mediated synthesis approaches and mechanistic aspects of plant-mediated nanoparticle synthesis. The review also focused on the commercial applications of plant-mediated noble metal nanoparticles. Significant remarks on the limitations of plant-mediated fabrication approaches with prospective future direction are also examined.

Teaching–Learning-Based Optimization (TLBO) algorithms simulate the teaching–learning phenomenon of a classroom to solve multi-dimensional, linear and nonlinear problems with appreciable efficiency. In this paper, the basic TLBO algorithm is improved to enhance its exploration and exploitation capacities by introducing the concept of number of teachers, adaptive teaching factor, tutorial training and self motivated learning. Performance of the improved TLBO algorithm is assessed by implementing it on a range of standard unconstrained benchmark functions having different characteristics. The results of optimization obtained using the improved TLBO algorithm are validated by comparing them with those obtained using the basic TLBO and other optimization algorithms available in the literature.

Purpose Fused deposition modelling (FDM) is the most economical additive manufacturing technique. The purpose of this paper is to describe a detailed review of this technique. Total 211 research papers published during the past 26 years, that is, from the year 1994 to 2019 are critically reviewed. Based on the literature review, research gaps are identified and the scope for future work is discussed. Design/methodology/approach Literature review in the domain of FDM is categorized into five sections – (i) process parameter optimization, (ii) environmental factors affecting the quality of printed parts, (iii) post-production finishing techniques to improve quality of parts, (iv) numerical simulation of process and (iv) recent advances in FDM. Summary of major research work in FDM is presented in tabular form. Findings Based on literature review, research gaps are identified and scope of future work in FDM along with roadmap is discussed. Research limitations/implications In the present paper, literature related to chemical, electric and magnetic properties of FDM parts made up of various filament feedstock materials is not reviewed. Originality/value This is a comprehensive literature review in the domain of FDM focused on identifying the direction for future work to enhance the acceptability of FDM printed parts in industries.

This paper introduces Internet of Things (IoTs), which offers capabilities to identify and connect worldwide physical objects into a unified system. As a part of IoTs, serious concerns are raised over access of personal information pertaining to device and individual privacy. This survey summarizes the security threats and privacy concerns of IoT..

An efficient optimization algorithm called teaching–learning-based optimization (TLBO) is proposed in this article to solve continuous unconstrained and constrained optimization problems. The proposed method is based on the effect of the influence of a teacher on the output of learners in a class. The basic philosophy of the method is explained in detail. The algorithm is tested on 25 different unconstrained benchmark functions and 35 constrained benchmark functions with different characteristics. For the constrained benchmark functions, TLBO is tested with different constraint handling techniques such as superiority of feasible solutions, self-adaptive penalty, ϵ-constraint, stochastic ranking and ensemble of constraints. The performance of the TLBO algorithm is compared with that of other optimization algorithms and the results show the better performance of the proposed algorithm.

This paper provides a comprehensive analysis of decarbonising cement and concrete production, addressing strategies, technologies, policy considerations, case studies, economic implications, challenges and future recommendations. The cement and concrete industry are major contributors to carbon emissions and environmental degradation, making decarbonisation crucial for sustainable development. The paper explores various strategies, including alternative clinker technologies, carbon capture and storage, improved energy efficiency, low-carbon cements and circular economy approaches. Additionally, it examines technologies such as supplementary cementitious materials, carbonation, low-carbon concrete mixes, recycling and novel manufacturing processes. The importance of policy interventions, collaboration and standards and certifications is emphasised. Case studies and best practices highlight successful decarbonisation initiatives, while economic implications and market opportunities are considered. The paper also identifies challenges, including technological limitations, financing constraints, resistance to change and the need for awareness and education. Finally, future recommendations focus on pathways for deep decarbonisation, policy measures, research priorities and fostering collaboration. This review serves as a valuable resource for researchers, policymakers and industry professionals striving to achieve sustainable and low-carbon cement and concrete production.

Antimonene: a 2D graphene-like material made of antimony atoms.

Self-assembly of amphiphilic block copolymers display a multiplicity of nanoscale periodic patterns proposed as a dominant tool for the 'bottom-up' fabrication of nanomaterials with different levels of ordering. The present review article focuses on the recent updates to the self-association of amphiphilic block copolymers in aqueous media into varied core-shell morphologies. We briefly describe the block copolymers, their types, microdomain formation in bulk and micellization in selective solvents. We also discuss the characteristic features of block copolymers nanoaggregates viz., polymer micelles (PMs) and polymersomes. Amphiphilic block copolymers (with a variety of hydrophobic blocks and hydrophilic blocks; often polyethylene oxide) self-assemble in water to micelles/niosomes similar to conventional nonionic surfactants with high drug loading capacity. Double hydrophilic block copolymers (DHBCs) made of neutral block-neutral block or neutral block-charged block can transform one block to become hydrophobic under the influence of a stimulus (physical/chemical/biological), and thus induced amphiphilicity and display self-assembly are discussed. Different kinds of polymer micelles (viz. shell and core-cross-linked, core-shell-corona, schizophrenic, crew cut, Janus) are presented in detail. Updates on polymerization-induced self-assembly (PISA) and crystallization-driven self-assembly (CDSA) are also provided. Polyion complexes (PICs) and polyion complex micelles (PICMs) are discussed. Applications of these block copolymeric micelles and polymersomes as nanocarriers in drug delivery systems are described.

Stress is a common part of everyday life that most people have to deal with on various occasions. However, having long-term stress, or a high degree of stress, will hinder our safety and disrupt our normal lives. Detecting mental stress earlier can prevent many health problems associated with stress. When a person gets stressed, there are notable shifts in various bio-signals like thermal, electrical, impedance, acoustic, optical, etc., by using such bio-signals stress levels can be identified. This paper proposes different machine learning and deep learning techniques for stress detection on individuals using multimodal dataset recorded from wearable physiological and motion sensors, which can prevent a person from various stress-related health problems. Data of sensor modalities like three-axis acceleration (ACC), electrocardiogram (ECG), blood volume pulse (BVP), body temperature (TEMP), respiration (RESP), electromyogram (EMG) and electrodermal activity (EDA) are for three physiological conditions - amusement, neutral and stress states, are taken from WESAD dataset. The accuracies for three-class (amusement vs. baseline vs. stress) and binary (stress vs. non-stress) classifications were evaluated and compared by using machine learning techniques like K-Nearest Neighbour, Linear Discriminant Analysis, Random Forest, Decision Tree, AdaBoost and Kernel Support Vector Machine. Besides, simple feed forward deep learning artificial neural network is introduced for these three-class and binary classifications. During the study, by using machine learning techniques, accuracies of up to 81.65% and 93.20% are achieved for three-class and binary classification problems respectively, and by using deep learning, the achieved accuracy is up to 84.32% and 95.21% respectively.

Purpose The purpose of this paper to study the tensile strength of the fused deposition modelling (FDM) printed PLA part. In recent times, FDM has been evolving from rapid prototyping to rapid manufacturing where parts fabricated by FDM process can be directly used for application. However, application of FDM fabricated part is significantly affected by poor and anisotropic mechanical properties. Mechanical properties of FDM part can be improved by proper selection of process parameters. Design/methodology/approach In the present study, three process parameter, namely, raster angle, layer height and raster width, have been selected to study their effect on tensile properties. Parts are fabricated as per ASTM D638 Type I standard. Findings It has been observed that the highest tensile strength obtained at 0° raster angle. Lower value of layer height is observed to be good for higher tensile strength because of higher bonding area between the layers. At higher value of raster width, tensile strength is improved up to certain extent after which presence of void reduces the tensile strength. Originality/value In the present investigation, layer height and raster width have been also varied along with raster angle to study their effect on the tensile strength of FDM printed PLA part.

In privacy preserving data mining, the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:mi>l</mml:mi></mml:mrow></mml:math>-diversity and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:math>-anonymity models are the most widely used for preserving the sensitive private information of an individual. Out of these two, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M4"><mml:mrow><mml:mi>l</mml:mi></mml:mrow></mml:math>-diversity model gives better privacy and lesser information loss as compared to the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M5"><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:math>-anonymity model. In addition, we observe that numerous clustering algorithms have been proposed in data mining, namely, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M6"><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:math>-means, PSO, ACO, and BFO. Amongst them, the BFO algorithm is more stable and faster as compared to all others except <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M7"><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:math>-means. However, BFO algorithm suffers from poor convergence behavior as compared to other optimization algorithms. We also observed that the current literature lacks any approaches that apply BFO with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M8"><mml:mrow><mml:mi>l</mml:mi></mml:mrow></mml:math>-diversity model to realize privacy preservation in data mining. Motivated by this observation, we propose here an approach that uses fractional calculus (FC) in the chemotaxis step of the BFO algorithm. The FC is used to boost the computational performance of the algorithm. We also evaluate our proposed FC-BFO and BFO algorithms empirically, focusing on information loss and execution time as vital metrics. The experimental evaluation shows that our proposed FC-BFO algorithm derives an optimal cluster as compared to the original BFO algorithm and existing clustering algorithms.