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Dynamic optimal network reconfiguration under photovoltaic generation and electric vehicle fleet load variability using self-adaptive butterfly optimization algorithm
Currently, electrical distribution networks (EDNs) have used modern technologies to operate and serve many types of consumers such as renewable energy, energy storage systems, electric vehicles, and demand response programs. Due to the variability and unpredictability of these technologies, all these technologies have brought various challenges to the operation and control of EDNs. In this case, in order to operate effectively, it is inevitable that effective power redistribution is required in the entire network. In this paper, a multi-objective based dynamic optimal network reconfiguration (DONR) problem is formulated using power loss and voltage deviation index considering the hourly variation of load, photovoltaic (PV) power, and electric vehicle (EV) fleet load in the network. This paper introduces recently introduced meta-heuristic butterfly optimization algorithm (BOA) and it's improve variant of self-adaptive method (SABOA) for solving the DONR problem. The simulation study of IEEE 33-bus EDN under different conditions has proved the effectiveness of DONR, and its adoptability for real-time applications. In addition, by comparing different performance indicators (such as mean, standard deviation, variance, and average calculation time) of 50 independently run simulations, the efficiency of SABOA can be evaluated compared with other heuristic methods (HMs). Comparative studies show that SABOA is better than PSO, TLBO, CSA and FPA in the frequent occurrence of global optimal values. 2021 Walter de Gruyter GmbH, Berlin/Boston 2021. -
Self-adaptive Butterfly Optimization for Simultaneous Optimal Integration of Electric Vehicle Fleets and Renewable Distribution Generation
Fuel prices and environmental concerns have prompted an increase in the use of electric vehicle (EV) technology in recent years. Charging stations (CSs) are a great way to support this shift to sustainability. This has increased the demand for EV charging on electrical distribution networks (EDNs). However, optimal EV charging stations along with renewable energy sources (RES) integration can maintain EDN performance. This paper proposes a novel hybrid approach based on self-adaptive butterfly optimization algorithm (SABOA) for optimal integration of EV CSs and RES problems under various EV load growth scenarios. A multi-objective function is created from distribution losses, GHG emissions, and VSI. The ideal locations for CSs and RES are found using SABOA while minimizing the proposed multi-objective function. The simulation results on IEEE 33-bus EDN validate the suggested technique's superiority in terms of global optima. This type of hybrid strategy is required for optimal real-time integration of EV CSs and RES, taking into account emerging high EV load penetrations. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Butterfly Optimization Algorithm-Based Optimal Sizing and Integration of Photovoltaic System in Multi-lateral Distribution Network for Interoperability
In this paper, a new and simple nature-inspired meta-heuristic search algorithm, namely butterfly optimization algorithm (BOA), is proposed for solving the optimal location and sizing of solar photovoltaic (SPV) system. An objective function for distribution loss minimization is formulated and minimized via optimally allocating the SPV system on themain feeder. At the first stage, the computational efficiency of BOA is compared with various other similar works and highlights its superiority in terms of global solution. In thesecond stage, the interoperability requirement of SPV system while determining the location and size of SPV system among multiple laterals in a distribution system is solved without compromises in radiality constraint. Various case studies on standard IEEE 33-bus system have shown the effectiveness of proposed concept of interline-photovoltaic (I-PV) system in improving the distribution system performance in terms of reduced losses and improved voltage profile via redistributing the feeder power flows effectively. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Caste, Cricket, and Community Fraternal Intersections in Blue Star
[No abstract available] -
Experimental Study on Warm Mix Asphalt Binders with a Focus on Rheological Performance
Warm mix asphalt (WMA) mixtures have been increasingly used in road construction due to their energy saving and environmental protect benefits. However, unsuitable additives were often adopted due to their variability and it often led to poorer quality of asphalt pavements. In this study, nine asphalt samples from two categories of warm mix additives, including six organic and three chemical additives, were prepared. The rotational viscosity test, temperature sweep test, linear amplitude sweep test (LAS), and bending beam rheometer (BBR) test were employed to comprehensively evaluate the effect of warm mix additives on the viscosity-reduction effect, high-temperature performance, fatigue resistance, and low-temperature performance of WMA, respectively. The results showed that the viscosity- reduction effect of organic additives was more significant compared to chemical additives. Besides, organic additives were generally favorable to the high-temperature and fatigue resistance of asphalt binders, but their effects on the low-temperature performance of asphalt binders were highly variable. Chemical additives had a limited effect on the high-temperature and fatigue resistance of asphalt binders. Meanwhile, the chemical additives have a marginally positive and stable impact on the low-temperature performance of asphalt binders. The findings provided a comprehensive basis for the selection and application of warm mix additives. The Author(s), under exclusive licence to Chinese Society of Pavement Engineering 2025. -
A study of consumers' attitude towards online private label brands using the Tri - Component model
Online private label products seem to be a promising and profitable deal for the Indian online retailers. The purpose of this paper was to understand the consumers' attitude and buying behaviour towards online private label brands. For this purpose, we empirically tested a model comprising of variables such as cognitive, affective, behavioural, purchase intention, and actual buying behaviour. Data were gathered by using a schedule. A sample of 400 respondents was gathered, and the hypotheses were tested by performing structural equation modelling. The findings highlighted that the cognitive, affective, and behavioural factors of attitude influenced each other strongly as well as the purchase intention. In addition, the results obtained revealed that purchase intention led to the buying of online private label brands. It is expected that the findings of this study will enable the marketers of online private label brands to be more informed about the consumers' attitude formation process. Furthermore, it will help them to understand the areas related to private label brands, which need their attention. 1964-2018 Associated Management Consultants. -
Sentiment analysis on social media data using intelligent techniques
Social media gives a simple method of communication technology for people to share their opinion, attraction and feeling. The aim of the paper is to extract various sentiment behaviour and will be used to make a strategic decision and also aids to categorize sentiment and affections of people as clear, contradictory or neutral. The data was preprocessed with the help of noise removal for removing the noise. The research work applied various techniques. After the noise removal, the popular classification methods were applied to extract the sentiment. The data were classified with the help of Multi-layer Perceptron (MLP), Convolutional Neural Networks (CNN). These two classification results were checked against the others classified such as Support Vector Machine (SVM), Random Forest, Decision tree, Nae Bayes, etc., based on the sentiment classification from twitter data and consumer affairs website. The proposed work found that Multi-layer Perceptron and Convolutional Neural Networks performs better than another Machine Learning Classifier. International Research Publication House. -
An efficient load balancing in cloud computing using hybrid Harris hawks optimization and cuckoo search algorithm
Cloud computing has rapidly emerged as a burgeoning research field in recent times. However, despite this growth, a comprehensive examination of this domain reveals persistent issues in the application of cloud-based systems concerning workload distribution. The abundance of resources and virtual machines (VMs) within cloud computing underscores the importance of efficient task allocation as a critical process. Within the infrastructure as a service (IaaS) architecture, load balancing (LB) remains a pivotal but challenging task. The occurrence of overloaded or underloaded hosts/servers during cloud access is undesirable, as it leads to operational delays and system performance degradation. To address LB issues effectively, it is imperative to deploy a proficient access scheduling algorithm capable of distributing tasks across the available resources. A novel approach was introduced by combining the Harris hawks optimization and cuckoo search algorithm (HHO-CSA), with a specific focus on critical service level agreement (SLA) parameters, particularly deadlines, to uphold LB in a cloud environment. The primary objective of the hybrid HHO-CSA methodology is to provide task attributes, resource allocation, VMs prioritization, and quality of service (QoS) to clients within cloud computing applications. The outcome analysis reveals that the proposed hybrid HHO-CSA algorithm results in a resource utilization reduction of 52%, with an execution time of 529.84 ms and a makespan of 638.88 ms. These values outperform those of existing SLA-based LB algorithms. Effective task scheduling plays a pivotal role in ensuring the seamless execution of tasks within a cloud system, while LB significantly aligns with the SLAs available to users. Drawing insights from the existing literature, the suggested hybrid HHO-CSA method addresses the research gap by effectively mitigating the challenges. 2023, Accent Social and Welfare Society. All rights reserved. -
Inverse Hilbert Fractal-Metamaterial Rings for Microstrip Antennas and Wideband Applications
A Novel Metamaterial (MTM) property is obtained using a fractal pattern known as Inverse Hilbert. The Mu-negative(MNG) characteristics have been recovered by adopting NRW method. This MTM characteristic is studied for 2.45 GHz using FR4 epoxy as substrate. The dimension of the substrate is 30mm36mm 1.6mm. This fractal metamaterial structure can be amalgamated with an optimized Microstrip antenna (MSA) for improvement in antenna parameters and can be used for RF energy harvesting. 2022 IEEE. -
Progression of Metamaterial for Microstrip Antenna Applications: A Review
This article provides an overview of the evolution of metamaterials (MTM) and all the aspects related to metamaterial development for antenna applications. It will be a useful collection of information for antenna researchers working in metamaterials applications. It gives an insight into the various metamaterial structures utilized along with miniature antenna designs. Different types of design parameters studied by the previous researchers are showcased to understand better perception of the metamaterial usage. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Successful turnarounds: the role of appropriate entrepreneurial strategies
Purpose The purpose of this paper is to report on a research study aimed at comparing the causes of organisational decline and turnaround strategies involved in cases of successful and unsuccessful turnarounds, with a view to identifying the differences, if any, between the two groups, which in turn is expected to provide useful information to academics, practitioners and policy makers. Design/methodology/approach Since turnaround is a business phenomenon of general interest, their stories are often published in business periodicals, which are a rich source of data on them. In order to tap this data source, the present paper employed a method of content analysis for the proposed investigation on the cause of organisational decline and turnaround strategies used. In order to quantify the data, a three-point scale was developed, where the presence of a cause/strategy is rated as 3, its ambivalence as 2 and its absence as 1, whose validity was assessed through the inter-rater agreement indices. The data thus generated are amenable to statistical analyses, using which the more commonly prevalent causes of organisational decline and the strategies commonly employed for turnaround by the successful and unsuccessful companies are identified. Findings The findings of the present study have generated a few useful insights. First, the primary causes for organisational decline are the internal weaknesses of the organisation; in fact the external changes can adversely affect the organisation only if it is internally weak. Second, organisational decline caused by multiple factors (which is usually the case) can be managed effectively by adopting a variety of strategies; hence a single-pronged strategy is often found to be ineffective. Third, the more successful turnarounds had a diverse portfolio of strategies including those of institution-building, often employed in a phased manner, consistent with the stage theories of turnaround. Research limitations/implications The limitations of this research arise mainly from the generation of data from published sources and the consequent biases, which can be managed, to a large extent, by using multiple sources for the same case for reducing the publishers biases as well as by having multiple raters for identifying the researchers biases, if any. Originality/value The study has highlighted the need for addressing the internal causes of organisational decline on a priority-basis rather than blaming the external factors, besides pointing to the need for adopting a variety of strategies for dealing with the diversity of causes affecting the organisations health, particularly the need for institutionalising the changes. These findings can be of help especially to turnaround managers and policy-makers in dealing with organisational decline and thus contribute to the creation and enhancement of economic value. 2015, Emerald Group Publishing Limited. -
A Scoping Review on Integration of Electroencephalogram Neurofeedback Training for Alcohol Use Disorder: Clinical and Neurocognitive Outcomes
Background. The conventional treatment for alcohol use disorder (AUD) consists of dual treatment encompassing pharmacotherapy and psychotherapy. Nonetheless, the impact of these treatments on clinical and neurocognitive outcomes is only low to medium efficacy. Research studies substantiate the integration of electroencephalogram neurofeedback training (EEG-NFT) as an add-on tool with significant improvements in clinical and neurocognitive outcomes. Methods. A scoping review of the existing literature on EEG-NFT and AUD, which are open access, including review papers and empirical studies in the English language, and with human subjects are deemed worthy of the scope of this study. The keywords electroencephalogram neurofeedback training, alcohol use disorder, stress, neurocognition, and relapse were used. The primary sources of the literature search were Science Direct, Scopus, PubMed, and Google Scholar. A total of 35 articles have been included in the scoping review. Studies from the last 15 years were considered for the same. Results. This review revealed that EEG-NFT is a promising tool with significant improvements in stress levels, cognitive deficits, and relapse rates for individuals with AUD when used in integration with conventional treatments. Conclusion. Chronic alcohol use affects cognitive functions, escalates relapse rate, and increases stress experienced by the individual. The present study highlights the significance of NFT as a potent add-on treatment modality to improve clinical and cognitive outcomes, thereby facilitating abstinence and reducing relapse rates in individuals with AUD. Copyright: 2023. -
A Quasi-Experimental Study on the Effectiveness of Integrated Electroencephalogram Neurofeedback Training and Group Psychotherapy for Harmful Alcohol Use: Neurocognitive and Clinical Outcomes
Introduction. This study investigates the efficacy of integrating electroencephalogram (EEG) neurofeedback training and group psychotherapy for individuals with harmful alcohol use (AUDIT-10 scores 1013). Methods. Seventy-six participants were purposively sampled and divided into treatment (EEG neurofeedback training and group psychotherapy) and control groups. Baseline assessments measured alcohol consumption (AUDIT-10), stress (perceived stress scale [PSS]), neurocognition (NIMHANS neuropsychological battery), craving (PACS), and visual analog scale. The treatment group underwent 20 sessions of EEG neurofeedback (Peniston-Kulkosky and Scott-Kaiser modification protocols) and four sessions of group psychotherapy (motivational interviewing [MI], psychoeducation). Result/Discussion. A repeated measures ANOVA showed significant improvement in postcondition scores for the treatment group compared to controls, who exhibited deterioration over time. The study provides evidence supporting the efficacy of integrated EEG neurofeedback training and group psychotherapy in mitigating harmful alcohol use progression. Conclusion. By addressing stress, cognition, and cravings, this intervention offers crucial support to individuals with problematic drinking. 2024. Panicker et al. -
An Empirical Analysis of Turnaround and its Benefits to Stakeholders
International Journal of Applied Management Research, Vol-6 (1(3), pp. 470-473. ISSN-0974-8709 -
Design and Development of Novel Nut-shaped SRR unit cell and analysis of DNG property
This paper presents design and development of a novel Nut-shaped Metamaterial Split Ring Resonator as a unit cell. Metamaterials with negative permittivity and permeability play a crucial role in improving the antenna performance. The unit cell comprises of two Split Ring Resonator with a thin transmission line connecting them. This structure measures a size 12 mm x 12 mm on the FR4 substrate of 16 mm x 16 mm x 1.6 mm. The shape of the unit cell developed in this work has been evolved using nature-inspired fractal geometries on a FR4 substrate. The simulation in the frequency range 2-4 GHz and the retrieval of negative medium properties have been carried out using HFSS 18.2 and MATLAB respectively. The fabrication and experimental verification have also been performed. Using the transmission S#$ and reflection S$$ properties the double negative medium properties have been studied using both the Nicholson-Ross-Weir as well as the Transmission-Reflection methods. Comparison of simulation and experimental results of both the methods exhibit the existence of negative medium properties of the unit cell within the selected frequency range. The proposed unit cell exhibits the Effective medium ratio value of 8.16 and 7.45 at 3.06 GHz and 3.35 GHz. This type of DNG SRR unit cell is recommended for Electromagnetic Energy harvesting applications. 2025, Advanced Electromagnetics. All rights reserved. -
Correlation of temperature, velocity and perforation location in a flat unglazed transpired solar collector (Utc) due to air flow
An unglazed transpired solar collector is a system that can leverage the abundant solar energy for various purposes. The solar collector is available in flat or corrugated form and is seen to be installed as an exterior layer of building facades. The cladding thus made absorbs radiation from the sun and heats up air being sucked by fan and flowing through perforations. In this paper, the focus has been to understand the correlation of plate temperature, exit temperature, the velocity distribution in the chamber and perforation location when air flows past a flat unglazed transpired solar collector (UTC). The establishment of correlations was carried out in the dataset of flow variables obtained after solving the problem using Navier-Stokes (NS) equations along with the standard k-? turbulence model and shear stress transport (SST) k-? model. An attempt has also been made to compute Pearsons correlation coefficient of any two flow variables to understand their strong and weak correlations. A linear regression analysis has been done to predict the response variables against the response obtained in CFD solver by using an open source software Rstudio . A strong correlation among cavity vertical velocity, perforation location and temperature has been established. From the study, it is noted that the location of a perforation has a strong correlation with the cavity vertical velocity and a weak correlation exists with temperature and plate temperature. 2020, Pushpa Publishing House. All rights reserved. -
Numerical simulation of flow over a flat unglazed transpired solar collector (UTC) and its performance prediction
An unglazed transpired solar collector is a system that can leverage the abundant solar energy for various purposes. The solar collector is available in flat or corrugated form and is seen to be installed as an exterior layer of building facades. The cladding thus made absorbs radiation from the sun and heats up air being sucked by fan and flowing through perforations. In this research the focus has been to understand the correlation of plate temperature, exit temperature, the velocity distribution in the chamber and perforation location when air flows past an unglazed transpired solar collector (UTC). The establishment of correlations was carried out in the dataset of flow variables obtained after solving the problem using Navier-Stokes (NS) equations along with standard two-equation (k-?) turbulence models and Shear Stress Transport (SST ) k-? models for turbulent flow. The same problem was also solved using NS equation using laminar model. An attempt has also been made to compute Pearsons correlation coefficient of any two variables to understand their strong and weak correlations. A linear regression analysis was done through an open source software Rstudio for a dataset produced during the computational modeling using a commercial CFD solver, Ansys Fluent. At the end a Monte Carlo simulation has been done to predict the likelihood of using the flat UTC for drying as well as to understand the dependency of system efficiency on plate exit temperature, suction velocity and freestream temperature. BEIESP. -
Do institutional quality and capital account openness affect capital flow? Evidence from Asian bond markets
Capital inflow into local bond markets helps countries with infrastructure financing, funding fiscal deficit, enhancing bond market liquidity, and diversifying investment portfolios globally. This study aims to assess the impact of institutional quality and capital account openness on capital inflow into Asian local bond markets for the period 20022023. For reflecting Asian bond markets, seven countries, namely, China, Malaysia, South Korea, Japan, the Philippines, Indonesia, and Thailand, have been considered. The rule of law, regulatory quality, control of corruption, voice & accountability, political stability, and government effectiveness indices are the various proxies considered in this study to measure the different aspects of institutional quality. Further, the Chinn-Ito index is employed to measure capital account openness. Fixed effect, random effect, and pooled data ordinary least squares are employed as different forms of panel data estimation methods in this study. Moreover, Breusch-Pagan LM and Hausman tests are performed to select the most efficient estimation method. This study reveals that the rule of law, regulatory quality, and control of corruption have a positive influence on capital inflow at a 5% significance level and political stability at a 1% significance level. In contrast, capital account openness has a negative impact at a 1% significance level. However, neither voice & accountability nor government effectiveness have a significant influence over capital inflow. These findings suggest improving the rule of law and regulatory quality, creating policies for political stability, stringent acts against corruption, and controlling capital account openness to encourage capital inflow into local bond markets. Swarupa Ranjan Panigrahi, Suresha B., Krishna T. A., Latha Ramesh, Nijumon K. John, 2025. -
The impact of unconventional US monetary policy shock on emerging bond markets: A comprehensive assessment of global transmission channels
Extensive research has been conducted on the global effects of the US unconventional monetary policy shock on capital flows in emerging markets. However, there is limited empirical evidence on the transmission channels of capital flows in emerging bond markets. This study examines it by analysing capital flows across 45 emerging bond markets from 2009 to 2023. Contemporaneous shock transmission is examined using the contemporaneous impact matrix, and dynamic shock transmission is assessed using the impulse response function of the structural vector autoregression (SVAR) model. All variables in this study are standardised to account for differences in scale within the model. The pairwise correlation coeffcient matrix indicates that multicollinearity is not a concern for parameter estimates in this model. The ADF-Fisher Chi-square unit root test result reveals that all variables are stationary in this model. The contemporaneous coeffcient matrix results indicate that changes in the US term spread serve as the contemporaneous transmission channel through which US Treasury bond purchase and US MBS purchase shocks positively affect capital flows in emerging bond markets. The impulse response function indicates that changes in the global financial cycle serve as a dynamic transmission channel through which US MBS purchase shocks affect capital flows in emerging bond markets. Moreover, changes in the US mortgage spread serve as the dynamic transmission channel through which US Treasury bond purchases and US MBS purchases affect capital flows in emerging bond markets. Swarupa Ranjan Panigrahi, Suresha B., Sudhansu Sekhar Nanda, Biplab Kumar Biswal, 2025. -
Plano framework for graph indexing - A statistical analysis
Graph Mining is becoming one of the most dominant fields of research. There are plenty methods to index, re-index and to search the features throughout the index but still from the literature study there is no specific frame work which can sum up all three so that indexing and updating the index with new feature can be done in consistent intervals according to the arrival of new features. PLANO is the frame work which has the latest algorithms to look into the data and index. In this paper, Time and Memory efficiency of the proposed algorithms in the PLANO framework is tested statistically and compared with the existing algorithms memory and time usage. Research India Publications.