Phytochemicals as weapons against drug resistance
ABC transporters; efflux pump modulation; Gram-negative bacteria nano-biotics; PDR; uro-pathogens
Phytochemicals are plant-based products with high medicinal value. These metabolites effectively target disease-causing microbes. Drug-resistant pathogens have developed mechanisms to sustain themselves even with inhibitors. Drug resistance has emerged as a global giant, causing all available treatment options to fail. The solution to this problem is in the phytochemicals of plants with antibacterial and drug resistance modulation properties. Phytochemicals might be able to get rid of efflux pumps, drug-modulating enzymes, resistance genes, quorum sensing, and biofilm, all of which cause pathogens to be resistant to drugs. Moreover, anti-obesogenic and cardioprotective properties are also observed in phytochemicals. Additionally, studies show the success of phytochemical-based nanoparticles in drug resistance regulation. This review emphasizes phytochemicals' different mechanisms of action and their derivatives in curbing drug-resistant pathogens and cancer cells. 2023 The Author(s)
Jan K.; Deshpande M.K.; Rebello S.J.; Rao K.G.; Pappuswamy M.; Chaudhary A.; Paari K.A.; Sebastian J.K.; Alagamuthu K.K.
Plant Science Today, Vol-10, pp. 212-219.
https://doi.org/10.14719/pst.2495 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175186750&doi=10.14719%2fpst.2495&partnerID=40&md5=fdf5d4a51e4c64d514a4bb9e18c77a79
Jan K., Department of Life Sciences, Christ (Deemed to be University), Bangalore, 560029, India; Deshpande M.K., Department of Life Sciences, Christ (Deemed to be University), Bangalore, 560029, India; Rebello S.J., Department of Life Sciences, Christ (Deemed to be University), Bangalore, 560029, India; Rao K.G., Department of Life Sciences, Christ (Deemed to be University), Bangalore, 560029, India; Pappuswamy M., Department of Life Sciences, Christ (Deemed to be University), Bangalore, 560029, India; Chaudhary A., Department of Life Sciences, Christ (Deemed to be University), Bangalore, 560029, India; Paari K.A., Department of Life Sciences, Christ (Deemed to be University), Bangalore, 560029, India; Sebastian J.K., Department of Life Sciences, Christ (Deemed to be University), Bangalore, 560029, India; Alagamuthu K.K., Selvamm Arts and Science College (Autonomous), Tamilnadu, Namakkal, India
Handwritten digit recognition using convolutional neural networks
Convolutional neural network; Deep neural network; Handwritten digit recognition; Multilayer perceptron; Optical character recognition; Recurrent neural network
Optical character recognition (OCR) systems have been used for extraction of text contained in scanned documents or images. This system consists of two steps: character detection and recognition. One classification algorithm is required for character recognition by their features. Character can be recognized using neural networks. The multilayer perceptron (MLP) provides acceptable recognition accuracy for character classification. Moreover, the convolutional neural network (CNN) and the recurrent neural network (RNN) are providing character recognition with high accuracy. MLP, RNN, and CNN may suffer from the large amount of computation in the training phase. MLP solves different types of problems with good accuracy but it takes huge amount of time due to its dense network connection. RNNs are suitable for sequence data, while CNNs are suitable for spatial data. In this chapter, a CNN is implemented for recognition of digits from MNIST database and a comparative study is established between MLP, RNN, and CNN. The CNN provides the higher accuracy for digit recognition and takes lowest amount of time for training the system with respect to MLP and RNN. The CNN gives better result with accuracy up to 98.92% as the MNIST digit dataset is used, which is spatial data. 2020 Walter de Gruyter GmbH, Berlin/Boston. All rights reserved.
Deep Learning: Research and Applications, pp. 51-68.
https://doi.org/10.1515/9783110670905-003 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092120901&doi=10.1515%2f9783110670905-003&partnerID=40&md5=83457da4acc4e1debb6ceca1e4dff978
Jana R., RCC Institute of Information Technology, Kolkata, West Bengal, India; Bhattacharyya S., CHRIST (Deemed to be University), Bangalore, Karnataka, India; Das S., Indian Statistical Institute, Kolkata, West Bengal, India
Epileptic Seizure Prediction from EEG Signals Using DenseNet
deep learning; DenseNet; EEG signals; epileptic seizure; scalp EEG; seizure prediction
Epilepsy is a disorder in which the normal electrical pattern in the brain is disrupted causing seizures or loss of consciousness. Seizure is harmful during various events like swimming or driving. The electroencephalogram (EEG) is the measurement of electrical activity received from the nerve cells of the cerebral cortex. Forthcoming seizures can be predicted from scalp EEG signal to improve the quality of life. The study proposes a method of automatic epileptic seizure prediction from raw EEG signal. The raw EEG signal is converted into EEG signal image for automatic extraction of features and classification of inter-ictal and pre-ictal state using Dense Convolutional Network (DenseNet). This classification process is carried out in a manner similar to the process followed by a medical practitioner without resorting to hand-crafted features. The public CHB-MIT EEG database is used for training, validation, and testing. An EEG signal for 1 second duration is taken as one sample. The accuracy for the classification of inter-ictal and pre-ictal state is achieved up to 94% by using 5-Fold cross validation. However, the accuracy is not up to the mark for the presence of common artifacts caused by eye-blinking and muscle activities during EEG recordings. Hence, a 30 seconds pool based technique is used for decision on correct state identification. The proposed pool based technique provides an average specificity of 95.87% and a false prediction rate of 0.0413/hour. It also provide average sensitivities of 100%, 97%, and 90% for the time slots 0 - 5 minutes, 5 - 10 minutes, and 10 - 15 minutes before the seizure event. 2019 IEEE.
2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019, pp. 604-609.
Institute of Electrical and Electronics Engineers Inc.
Jana R., RCC Institute of Information Technology, Dept. of Information Technology, Kolkata, India; Bhattacharyya S., Christ University, Dept. of Computer Science, Bangalore, India; Das S., Indian Statistical Institute, ECS Unit, Kolkata, India
Enhancing Dimensional Geometry Casting using Computer Modeling
CATIA; Copper tubes; DOE; Modeling; Sand casting
Sand casting method is used to produce many useful products for many applications. The aim of the study is to manufacture a product with excellent dimensional geometry is achieved in sand casting process at low cost. We would expect manuscripts to show how design and/or manufacturing problems have been solved using computer modeling, simulation and analysis. In this work, the important mechanical properties of hardness and surface roughness are investigated on Aluminum 6063 cast material with and without incorporating the copper tubes as a vent hole in sand casting process. Since copper has high thermal conductivity when compared to other metals, the heat transfer rate will be varying from existing system. The copper tubes have made different diameters of holes on outer surfaces with selective distance of intervals. The specific number of copper tubes with various diameters are designed by CATIA modeling software and analyzed with Taguchi Design of Experiment. Taguchi L9 orthogonal array is used proficiently in the optimal value of hardness and surface roughness. The results are revealed that the maximum hardness value of 104 BHN is attained for 10mm distance of holes made on copper tube with an angle of 90o degree. The minimum surface roughness of 2.11 micron is achieved for 20mm distance of holes made on copper tube with 45o of angle as a vent hole in sand casting process. 2024 E3S Web of Conferences
Janakiraman V.; Arulpandy P.; Al-Fatlawy R.R.; Ranganayaki S.; Singh R.
Janakiraman V., Department of Mechanical Engineering, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai, India; Arulpandy P., Department of Mathematics, Christ (Deemed to Be University), Karnataka, Bangalore, 560073, India; Al-Fatlawy R.R., The Islamic University, Najaf, Iraq; Ranganayaki S., Mathematics, Sri Ramakrishna Engineering College, Coimbatore, India; Singh R., Department of Electronics & Communication Engineering, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, 248007, India
Optimal Siting of Capacitors in Distribution Grids Considering Electric Vehicle Load Growth Using Improved Flower Pollination Algorithm
Capacitor banks allocation; Electric vehicle load; Improved flower pollination algorithm; Net-cost optimization; Radial distribution network; VAr compensation; Voltage stability index
The optimal VAr compensation using capacitor banks (CBs) in radial distribution networks (RDNs) is solved in this paper while taking the growth of the load from electric vehicles (EVs) into consideration. This is accomplished by adapting an improved variant of the flower pollination algorithm (IFPA) with an enhanced local search capability. The primary objective of determining the locations and sizes of CBs is to minimize the distribution losses in the operation and control of RDNs. Additionally, the effect of CBs is shown by the increased net savings, greater voltage stability, and improved voltage profile. A voltage stability index (VSI) was used in the optimization process to determine the predefined search space for CB locations, and a double-direction learning strategy (DLS) was then considered to optimize the locations and sizes while maintaining a balance between the exploration and exploitation phases. Three IEEE RDNs were used to simulate various EV load increase scenarios as well as typical loading situations. According to a comparison with the literature, the IPFA produced global optimum results, and the proposed CBs allocation approach demonstrated enhanced performance in RDNs under all scenarios of EV load growth. 2022, University of Kragujevac, Faculty of Science. All Rights Reserved.
Serbian Journal of Electrical Engineering, Vol-19, No. 3, pp. 329-349.
University of Kragujevac, Faculty of Science
https://doi.org/10.2298/SJEE2203329J https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153952898&doi=10.2298%2fSJEE2203329J&partnerID=40&md5=dad0e0e7b531bfc4d9adb9d71f4f0e33
Janamala V., Department of Electrical and Electronics Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India
Solar pv tree: Shade-free design and cost analysis considering Indian scenario
Rooftop solar PV system; Solar PV system; Solar PV tree; Techno-economic analysis
In this paper, the performance and the cost-effectiveness of a solar PV tree for supplying the energy demand of a flood lighting system at a basketball court in the School of Engineering and Technology, Christ (Deemed to be University) at Bangalore, India, are analyzed. Also, the energy demand of a flood lighting system for year 2017 is estimated (16 kWh/day), and the design of 4 individual trees of 1 kWp each is proposed, which saves around 40 sq.m area of land near to the basketball court. The experimental data was collected from June 1st, 2018 to May 31st, 2019, using a data acquisition system and processed to calculate the monthly cost of energy produced by each tree. In order to reduce the complexity in design and allow it to be shade-free, all the panels of a tree were oriented at the same azimuth angle. Based on technical and economical assessments with respect to rooftop systems, the solar PV tree presented reasonable results and could be a future adoptable technology for high population density areas, as well as for remote applications. Later, the adoptability of the proposed solar PV tree was simulated for 2 kWp, considering the climatic conditions of 2020, for different rural and urban locations of India. From the techno-economic-environmental analysis, it is highlighted that the annual energy yield is more with the solar PV tree model than with a land-mounted SPV system. The cost savings and greenhouse gas (GHG) reduction are also higher with the proposed oak tree-based solar PV tree in urban areas than in rural areas recommending it for practical applications. 2021, Walailak University. All rights reserved.
Walailak Journal of Science and Technology, Vol-18, No. 8
https://doi.org/10.48048/wjst.2021.8995 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105163776&doi=10.48048%2fwjst.2021.8995&partnerID=40&md5=4fa60071a7651fb26d1c1c9591c10a89
Janamala V., Department of Electrical and Electronics Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, 560 074, India
A new meta-heuristic pathfinder algorithm for solving optimal allocation of solar photovoltaic system in multi-lateral distribution system for improving resilience
Distributed generation; Interline-photovoltaic system; Meta-heuristic algorithms; Pathfinder algorithm; Power loss minimization; Radial distribution system
A new meta-heuristic Pathfinder Algorithm (PFA) is adopted in this paper for optimal allocation and simultaneous integration of a solar photovoltaic system among multi-laterals, called interline-photovoltaic (I-PV) system. At first, the performance of PFA is evaluated by solving the optimal allocation of distribution generation problem in IEEE 33- and 69-bus systems for loss minimization. The obtained results show that the performance of proposed PFA is superior to PSO, TLBO, CSA, and GOA and other approaches cited in literature. The comparison of different performance measures of 50 independent trail runs predominantly shows the effectiveness of PFA and its efficiency for global optima. Subsequently, PFA is implemented for determining the optimal I-PV configuration considering the resilience without compromising the various operational and radiality constraints. Different case studies are simulated and the impact of the I-PV system is analyzed in terms of voltage profile and voltage stability. The proposed optimal I-PV configuration resulted in loss reduction of 77.87% and 98.33% in IEEE 33- and 69-bus systems, respectively. Further, the reduced average voltage deviation index and increased voltage stability index result in an improved voltage profile and enhanced voltage stability margin in radial distribution systems and its suitability for practical applications. 2021, The Author(s).
All Open Access; Gold Open Access; Green Open Access
https://doi.org/10.1007/s42452-020-04044-8 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100769219&doi=10.1007%2fs42452-020-04044-8&partnerID=40&md5=102ced3d11be9d0e9d6ccfc19f23e4e8
Janamala V., Department of Electrical and Electronics Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bengaluru, 560 074, Karnataka, India
Congestion management approaches in deregulated power system an illustrative approach
Competitive electricity market; Congestion management; Deregulated power system
In deregulated power system with competitive electricity market environment, the provision of strategic bidding option to the market participants and its consequences are open up new challenging tasks to the system operator. The market economic efficiency is mainly dependent on transmission system support. The inability of transmission system support to drive market cleared schedule is known as congestion and which is not desirable. The remedial actions to relieve congestion in the transmission system known as congestion relief approaches and are differ in various markets around the world. The objective of this paper is to illustrate some of the technical and non-technical approaches using simple case studies. 2016 SERSC.
International Journal of Grid and Distributed Computing, Vol-9, No. 12, pp. 171-180.
Science and Engineering Research Support Society
https://doi.org/10.14257/ijgdc.2016.9.12.15 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85027004136&doi=10.14257%2fijgdc.2016.9.12.15&partnerID=40&md5=cd9966313a7b69caeefbbd3d6851d382
Janamala V., Dept. of Electrical Engineering, Faculty of Engineering, Christ University, Bangaluru, 560074, India
Future search algorithm for optimal integration of distributed generation and electric vehicle fleets in radial distribution networks considering techno-environmental aspects
Distributed generation; Electric distribution networks; Electric vehicle fleet; Greenhouse gas emission minimization; Loss minimization; Voltage stability enhancement
In this paper, a new nature-inspire meta-heuristic algorithm called future search algorithm (FSA) is proposed for the first time to solve the simultaneous optimal allocation of distribution generation (DG) and electric vehicle (EV) fleets considering techno-environmental aspects in the operation and control of radial distribution networks (RDN). By imitating the human behavior in getting fruitful life, the FSA starts arbitrary search, discovers neighborhood best people in different nations and looks at worldwide best individuals to arrive at an ideal solution. A techno-environmental multi-objective function is formulated using real power loss, voltage stability index. The active and reactive power compensation limits and different operational constraints of RDN are considered while minimizing the proposed objective function. Post optimization, the impact of DGs on conventional energy sources is analyzed by evaluating their greenhouse gas emission. The effectiveness of the proposed methodology is presented using different case studies on Indian practical 106-bus agriculture feeder for DGs and 36-bus rural residential feeder for simultaneous allocation of DGs and EV fleets. Also, the superiority of FSA in terms of global optima, convergence characteristics is compared with various other recent heuristic algorithms. 2021, The Author(s).
Janamala V.; Kamal Kumar U.; Pandraju T.K.S.
https://doi.org/10.1007/s42452-021-04466-y https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102691069&doi=10.1007%2fs42452-021-04466-y&partnerID=40&md5=6c0b1dfa27b59b51f9aa542b7a9ad17f
Janamala V., Department of Electrical and Electronics Engineering, Faculty of Engineering, Christ (Deemed To Be University), Bangalore, 560 074, Karnataka, India; Kamal Kumar U., Department of Electrical and Electronics Engineering, Sree Vidyanikethan Engineering College, Tirupati, 517 102, Andhra Pradesh, India; Pandraju T.K.S., Department of Electrical and Electronics Engineering, Dhanekula Institute of Engineering and Technology, Vijayawada, 521 139, Andhra Pradesh, India
Voltage stability analyis of radial distribution systems by considering load models
backward/forward sweep load flow; distribution system; load models; voltage stability
Generally, the distribution systems have served for different types of loads like commercial, industrial, residential, agriculture and municipality etc. and diverse changes in consumption pattern occur at any part of the network at any time of the day. During light loading condition, the voltage profile can increase and vice versa for peak loading condition. Under these circumstances, it is worthwhile to understand the voltage stability for planning of any Volt/VAr controls. This paper has presented the voltage stability analysis of 12-bus and 85-bus standard radial distribution systems using line stability index. Different load models have been taken and under each model, the system performance as well as its stability discussed. The focal points are suitable for planning studies like Volt/VAr controls, optimal location of Distribution Generation (DG) or load shedding etc. 2018 IEEE.
Proceedings of the 2018 International Conference On Communication, Computing and Internet of Things, IC3IoT 2018, pp. 180-185.
https://doi.org/10.1109/IC3IoT.2018.8668125 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064162733&doi=10.1109%2fIC3IoT.2018.8668125&partnerID=40&md5=61c7e107abe0921f8ab87bd4a8b1c450
Janamala V., Dept. of Electrical and Electronics Engineering, Power Systems Engineering, Christ University, Bengaluru, 560074, India; Monica M.S., Dept. of Electrical and Electronics Engineering, Power Systems Engineering, Christ University, Bengaluru, 560074, India
Static voltage stability of reconfigurable radial distribution system considering voltage dependent load models
Network reconfiguration; Radial distribution system; Voltage stability analysis; Voltage-dependent load modeling
This paper presents the static voltage stability analysis of RDS. Initially the performance of RDS is evaluated using backward/forward load flow considering voltage-dependent load modeling. Later, the load flow solution is used for determining the static voltage stability of the system. The analysis is performed for different type of loads such as constant power, constant current, constant impedance, residential, industrial, commercial, agricultural and electric vehicle loads. The simulations are performed for standard and optimal reconfigured topology of standard IEEE 33-bus test system. The comparative study reveals the importance of load type and topology while assessing the static stability analysis of radial distribution systems. 2020, International Information and Engineering Technology Association.
Mathematical Modelling of Engineering Problems, Vol-7, No. 3, pp. 450-458.
International Information and Engineering Technology Association
https://doi.org/10.18280/mmep.070316 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091873413&doi=10.18280%2fmmep.070316&partnerID=40&md5=b4f8f13025a458b9a40302605fb959bb
Janamala V., Dept. of Electrical and Electronics Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, Karnataka, 560074, India; Pandraju T.K.S., Dept. of Electrical and Electronics Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, Karnataka, 560074, India, Dept. of Electrical and Electronics Engineering, Dhanekula Institute of Engineering and Technology, Vijayawada, Andhra Pradesh, 521139, India
Optimal Switching Operations of Soft Open Points in Active Distribution Network for Handling Variable Penetration of Photovoltaic and Electric Vehicles Using Artificial Rabbits Optimization
Active distribution network; Artificial rabbits optimization; Photovoltaic generation; Plug-in-electric vehicle fleets; Reliability indices; Soft open points
Global warming, rising fuel prices, and limited conventional fuel supplies are driving the use of renewable energy, battery energy storage, and electric vehicles, transforming traditional electrical distribution networks into active distribution networks. Stochastic technologies can present operational and control challenges, especially for radially configured active distribution networks. In this scenario, strengthening the existing active distribution networks is necessary. This study optimally integrates soft open points for dynamic network reconfiguration to handle uncertainty in active distribution networks. The location, size, and reconfiguration of the soft open points were obtained for the hourly load profile, which included electric vehicle fleet load penetration and PV distributed generation. The proposed multi-objective function uses active power loss, voltage profile, and reliability indices. The proposed multivariable optimization problem was solved using artificial rabbits optimization. The simulations were performed on a modified IEEE 33-bus radial distribution system. The computational efficiency of artificial rabbits optimization is competitive with other prominent algorithms. The proposed approach of optimal soft open points and dynamic network reconfiguration is utilized to cope with uncertainty and run the present active distribution networks with better technical and reliability characteristics. 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
Janamala V.; Radha Rani K.; Sobha Rani P.; Venkateswarlu A.N.; Inkollu S.R.
Process Integration and Optimization for Sustainability, Vol-7, No. 45689, pp. 419-437.
https://doi.org/10.1007/s41660-022-00304-9 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144969240&doi=10.1007%2fs41660-022-00304-9&partnerID=40&md5=fbd06c6c25cdce8b48d5c0708ab8c3cd
Janamala V., Department of Electrical and Electronics Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Karnataka, Bangalore, 560074, India; Radha Rani K., Department of Electrical and Electronics Engineering, R.V.R & J.C. College of Engineering, Chowdavaram, Andhra Pradesh, Guntur, India; Sobha Rani P., Department of Electrical and Electronics Engineering, Lakireddy Bali Reddy College of Engineering (Autonomous), Jawaharlal Nehru Technological University, Kakinada (JNTUK), East Godavari, Andhra Pradesh, Kakinada, 533003, India; Venkateswarlu A.N., Department of Electrical and Electronics Engineering, Vignans Lara Institute of Technology and Science, Andhra Pradesh, Guntur, 522213, India; Inkollu S.R., Department of Electrical and Electronics Engineering, Dhanekula Institute of Engineering & Technology, Andhra Pradesh, Vijayawada, 521139, India
Realization of Green 5G Cellular Network Role in Medical Applications: Use of ChatGPT-AI
5G cellular networks; ChatGPT; Green technologies; Medical research
Wireless communication in medical applications improves patient monitoring, care coordination, early disease detection, and patient empowerment. It improves healthcare and patient outcomes. The design and configuration of a solar-powered emergency battery backup system for 5G telecommunication base stations, including medical applications, may vary depending on local climate, power requirements, and resources. In this connection, uninterrupted power supply to the base stations become crucial. The author utilizes the ChatGPT-AI features and prepared this comprehensive letter for realizing the role of sustainable practices towards climatic changes. 2023, The Author(s) under exclusive licence to Biomedical Engineering Society.
Annals of Biomedical Engineering, Vol-51, No. 11, pp. 2337-2339.
All Open Access; Bronze Open Access; Green Open Access
ISSN: 906964; PubMed ID: 37261590; CODEN: ABMEC
https://doi.org/10.1007/s10439-023-03257-3 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85160822125&doi=10.1007%2fs10439-023-03257-3&partnerID=40&md5=f6fce6dbf9bb9f6dfde2d525f948c2dc
Janamala V., Department of Electrical and Electronics Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Karnataka, Bangalore, 560074, India; Ram I.S., Department of Electrical and Electronics Engineering, Dhanekula Institute of Engineering & Technology, Andhra Pradesh, Vijayawada, 521139, India; Daram S.B., Department of Electrical and Electronics Engineering, Mohan Babu University (Erst while Sree Vidyanikethan Engineering College), Andhra Pradesh, Tirupati, 517102, India
Optimal allocation of solar photovoltaic distributed generation in electrical distribution networks using Archimedes optimization algorithm
Archimedes optimization algorithm; Composite load modelling; Distributed generation; Electrical distribution network; Solar photovoltaic system
This paper proposes to resolve optimal solar photovoltaic (SPV) system locations and sizes in electrical distribution networks using a novel Archimedes optimization algorithm (AOA) inspired by physical principles in order to minimize network dependence and greenhouse gas (GHG) emissions to the greatest extent possible. Loss sensitivity factors are used to predefine the search space for sites, and AOA is used to identify the optimal locations and sizes of SPV systems for reducing grid dependence and GHG emissions from conventional power plants. Experiments with composite agriculture loads on a practical Indian 22-bus agricultural feeder, a 28-bus rural feeder and an IEEE 85-bus feeder demonstrated the critical nature of optimally distributed SPV systems for minimizing grid reliance and reducing GHG emissions from conventional energy sources. Additionally, the voltage profile of the network has been enhanced, resulting in significant reductions in distribution losses. The results of AOA were compared to those of several other nature-inspired heuristic algorithms previously published in the literature, and it was observed that AOA outperformed them in terms of convergence and redundancy when solving complex, non-linear and multivariable optimization problems. The Author(s) 2022.
Clean Energy, Vol-6, No. 2, pp. 1036-1052.
https://doi.org/10.1093/ce/zkac010 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126752288&doi=10.1093%2fce%2fzkac010&partnerID=40&md5=907609d2e828ab9a03859695cead9b45
Janamala V., Department of Electrical and Electronics Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, 560 074, India; Rani K.R., Department of Electrical & Electronics Engineering, R.V.R. & J.C. College of Engineering, Andhra Pradesh, Guntur, 522 019, India
Coyote optimization algorithm for optimal allocation of interline Photovoltaic battery storage system in islanded electrical distribution network considering EV load penetration
Battery energy storage system; Coyote optimization algorithm; Electric vehicle load; Islanding operation; Multi-objective optimization; Solar photovoltaic system
In current times, there is a need to do power system planning to endure situations of any kind. An islanding operation is one such unavoidable situation that may be required in many cases for both technical and economic reasons. First and foremost, this paper focuses on the determination of the best allotment of Interline-Photovoltaic (I-PV) system as per Electric Vehicle (EV) load penetration in the network. With different operational constraints, a multi-objective optimization using real power loss and voltage deviation index is formulated and solved using the Coyote Optimization Algorithm (COA).The paper highlights the computational efficiency of COA with Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO), in addition to various literary works, and the results suggest the superiority of COA by its global optima. The required battery energy storage system (BESS) capacity for supplying an islanded network's entire load demand for a day is determined in the second stage. The simulations were carried out on the IEEE 33-bus electrical distribution network (EDN) contemplating different levels of EV load penetration. The proposed methodology results have proved that the required energy is provided by optimal I-PV-BESS backup for a daylong islanding operation and its adaptability for practical situations. 2021 Elsevier Ltd
https://doi.org/10.1016/j.est.2021.102981 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111473997&doi=10.1016%2fj.est.2021.102981&partnerID=40&md5=5aec6b1cd3fbdb6c2e926cab51d4e59b
Janamala V., Dept. of Electrical and Electronics Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, 560 074, Karnataka, India; Sreenivasulu Reddy D., Dept. of Electrical and Electronics Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, 560 074, Karnataka, India
Experimental data-driven machine learning approach for predicting workability in sustainable concrete using green material replacements
Compaction Factor Equivalent (CFE); Concrete workability; Ensemble learning; Experimental data; Gradient Boosting; Green material replacement; Machine learning; Predictive modelling; Random Forest; Sustainable concrete; Vee Bee Time Equivalent (VBTE); XGBoost
Concrete workability is a critical factor governing the placement, compaction, and durability of fresh concrete, yet it remains less explored in data-driven studies compared to hardened properties. This study presents an experimentally validated machine learning framework for predicting fresh concrete workability, namely Compaction Factor Equivalent (CFE) and Vee Bee Time Equivalent (VBTE), using a newly generated laboratory dataset comprising 300 concrete mixes. The dataset was developed through controlled experiments by systematically varying the waterbinder ratio (W/B), aggregatebinder ratio (A/B), type of green material, and replacement percentage, with fly ash and ground granulated blast-furnace slag (GGBS) used as partial cement replacements to promote sustainability, aligning strategies with SDG 9 (Industry, Innovation, and Infrastructure) and SDG 12 (Responsible Consumption and Production). To capture the nonlinear relationships between mix design parameters and workability indicators, three ensemble learning modelsRandom Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGBoost)were developed and evaluated. Model performance was assessed using standard statistical metrics, including R, RMSE, MAE, and MAPE. The results indicate that boosting-based models outperform baseline approaches, with XGBoost achieving the highest prediction accuracy for both CFE and VBTE. By shifting the focus from hardened properties to fresh-state performance, this study addresses a critical research gap and demonstrates that ensemble machine learning models, when combined with experimentally generated datasets, can significantly reduce experimental workload while supporting intelligent and sustainable concrete mix design for practical engineering applications. 2026 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC license. http://creativecommons.org/licenses/by-nc/4.0/
Janamala, Varaprasad; M, Beulah; Chaparala, Aparna; Daram, Suresh Babu
Next Materials;Volume;12;Issue;;Article No.;102234;
https://doi.org/10.1016/j.nxmate.2026.102234 https://www.scopus.com/pages/publications/105038237896?origin=resultslist
Janamala V., Department of Electrical and Electronics Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, 560074, India; M B., Department of Civil Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, 560074, India; Chaparala A., Department of Computer Science and Engineering, R.V.R. & J.C. College of Engineering, Andhra Pradesh, Guntur, 522019, India; Daram S.B., Department of Electrical and Electronics Engineering, Mohan Babu University, Andhra Pradesh, Tirupati, 517102, India
Optimal Reactive Power Compensation in Indian Urban Electrical Distribution Networks Using Hybrid Starfish Optimization Algorithm
Capacitor banks; Distribution static synchronous compensators; Loss reduction; Radial distribution networks; Reactive power compensation; Starfish optimization algorithm; Voltage stability index
This paper presents an efficient hybrid optimization approach for optimal reactive power compensation (ORPC) problem in electrical distribution networks (EDNs) using a Hybrid Starfish Optimization Algorithm (HSFOA). A Voltage Stability Index (VSI) is integrated to identify critical buses and narrow the search space, improving solution quality and convergence efficiency. The proposed method determines the optimal locations and sizes of capacitor banks (CBs) and Distribution static synchronous compensators (DSTATCOMs) to minimize real power losses and enhance voltage stability. The effectiveness of the HSFOA is evaluated first on the IEEE 33-bus benchmark system. The results demonstrate that the proposed approach provides superior improvements compared to conventional techniques. Later, the approach is implemented on 106-bus and 85-bus real-time Indian urban distribution networks. For the 106-bus system, losses decrease from 644.768 kW (base case) to 495.273 kW with CBs and to 487.933 kW with DSTATCOMs, corresponding to 23.25% and 24.32% reductions. In the 85-bus system, real power losses are reduced by 34.56% with CBs and 34.44% with DSTATCOMs, while the VSI improves by 15.05% and 20.70%, respectively. Similar improvements were recorded for the IEEE 33-bus system. Overall, the findings confirm that HSFOA offers a robust and effective solution for optimal reactive power planning and enhanced operational performance in modern EDNs. This article is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. https://creativecommons.org/licenses/by-sa/4.0/
Janamala, Varaprasad; Nimmagadda, Chaitanya; Gamini, Gayatri; Thalluru, Suneetha
International Journal of Intelligent Engineering and Systems;Volume;19;Issue;1;pp.869-879
https://doi.org/10.22266/ijies2026.0131.52 https://www.scopus.com/pages/publications/105027201331?origin=resultslist
Janamala V., Christ University, Karnataka, Bangalore, 560074, India; Nimmagadda C., R.V.R. & J.C. College of Engineering, Andhra Pradesh, Guntur, 522019, India; Gamini G., Prasad V. Potluri Siddhartha Institute of Technology, Andhra Pradesh, Vijayawada, 520007, India; Thalluru S., R.V.R. & J.C. College of Engineering, Andhra Pradesh, Guntur, 522019, India
Transformative impact of electrical engineering on society, education, academia, and industry: a brief review
Education 4.0; Electrical engineering; Industry 4.0; Interdependency; Societal evolution; Society 5.0; Technological progress; University systems
The transforming power of electrical engineering (EE) on societal evolution, educational paradigms, university systems, and industrial revolutions is comprehensively reviewed in this study. This study emphasizes the critical contribution of EE in promoting technological development, improving learning approaches, and allowing sustainable industrial practices by methodically analyzing the co-evolution of these domains from Society 1.0 to 5.0, Education 1.0 to 4.0, University 1.0 to 4.0, and Industry 1.0 to 4.0. Unlike conventional stories that credit EE alone for development, this assessment critically examines the multidisciplinary character of progress and acknowledges the contributions of computer science, computer engineering, and artificial intelligence (AI) in forming the digital world. Focusing on fundamental technologies, including power systems, semiconductor devices, renewable energy integration, and automation, which have been the backbone of recent AI-driven advancements, this study offers a crucial contribution. This study clarifies EEs special contribution of EE in the global technological revolution by separating its basic contributions from those resulting from its junction with computing disciplines. Furthermore underlined in this paper are EEs contributions to smart infrastructure development, sustainable energy solutions, and society resilience. Presenting an evidence-based evaluation, this paper provides an insightful analysis for academics, teachers, and legislators, thus supporting EEs basic enabler of multidisciplinary technical and societal advancement. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.
Janamala, Varaprasad; Rani, P. Sobha; Rani, K. Radha; Swarnasri, K.
Electrical Engineering;Volume;107;Issue;8;pp.10879-10897
Springer Science and Business Media Deutschland GmbH
All Open Access; Hybrid Gold Open Access
https://doi.org/10.1007/s00202-025-03066-y https://www.scopus.com/pages/publications/105002081097?origin=resultslist
Janamala V., Department of Electrical and Electronics Engineering, CHRIST (Deemed to be University), School of Engineering and Technology, Mysore Road, Kanmanike, Karnataka, Bangalore, 560074, India; Rani P.S., Department of Electrical and Electronics Engineering, Lakireddy Bali Reddy College of Engineering (Autonomous), Jawaharlal Nehru Technological University, Kakinada (JNTUK), Kakinada, 533003, India; Rani K.R., Department of Electrical and Electronics Engineering, R.V.R. & J.C. College of Engineering, Guntur, 522019, India; Swarnasri K., Department of Electrical and Electronics Engineering, R.V.R. & J.C. College of Engineering, Guntur, 522019, India
Response of ChatGPT for Humanoid Robots Role in Improving Healthcare and Patient Outcomes
ChatGPT; Humanoid robotics; Medical field; Patient outcomes
Humanoid robotics is characterized by constant developments, which are supported by several research facilities across the world. Humanoid robots are used in many different industries. In this setting, this letter, written by people, makes use of ChatGPT answers to examine how humanoid robots might be used in the medical industry, particularly in light of the COVID-19 pandemic and in future. Although humanoid robots can help with certain jobs, it is important to recognize the indispensable importance of human healthcare professionals who have knowledge, empathy, and the capacity for critical judgment. Although humanoid robots can complement healthcare initiatives, they shouldnt be viewed as a full-fledged replacement for human care. 2023, The Author(s) under exclusive licence to Biomedical Engineering Society.
Janamla V.; Daram S.B.; Rajesh P.; Kumari C.H.N.
Annals of Biomedical Engineering, Vol-51, No. 11, pp. 2359-2361.
ISSN: 906964; PubMed ID: 37270457; CODEN: ABMEC
Janamla V., Department of Electrical and Electronics Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Karnataka, Bangalore, 560074, India; Daram S.B., Department of Electrical and Electronics Engineering, Mohan Babu University (Erst while Sree Vidyanikethan Engineering College), Andhra Pradesh, Tirupati, 517102, India; Rajesh P., Department of Electrical and Electronics Engineering, SVERIS College of Engineering, Pamdharpur, Maharashtra, Solarpur, 413304, India; Kumari C.H.N., Department of Electrical and Electronics Engineering, Guru Nanak Institute of Technology, Telangana, Ibrahimpatnam, 501506, India
Perception of Anticipatory Psychological Contract: A Study among Post Graduate Students
Psychological Contract is the the unwritten contract between employer and employee, representing mutual expectations, beliefs, and obligations. The concept is popular among the HR managers as it is based on managing employment relationships. In order to best align the requirements of the organization and employee, the Human Resource manager has to be cognizant of this unwritten ???contract???. Like all relationships, it is important to shape and understand it starting from recruitment, through talent management and finally the employee exit. High volume campus recruitment still forms a key strategy among companies in growing markets, segments. Hence the understanding of this employment relationship begins with prospective employees who are the final year students. This study talks about the employment beliefs, & future employment relationships of final year students as an ???anticipatory psychological contract??? and includes promises that the future employee wants to make to their employer, along with the obligations they expect in return. Additionally, the long-term Career expectations of students, in conjunction with their Work values was also explored, to understand their interrelationship with the anticipatory psychological contract. This interrelationship has gained importance in organizational psychology, organizational behavior, and HRM where there is an opportunity for practical application. In this study, the respondents' expectations were measured through the collection of inputs on pre-employment beliefs from final year postgraduate students soon to be part of their respective industry???s workforce. The independent variables identified from the review of literature were career strategy and work values. As part of the study, these variables were tested for correlation, and multiple regression was done to understand the impact of career strategy and work values on anticipatory psychological contract. The results proved that a significant relationship exists between the dependent variable (anticipatory psychological contract ) and independent variables( career strategy and work values). Managing psychological contract is an advantage for employees and employers whereby, both can understand the expectations of the other to prevent breach or violation of the contract. Maintaining a positive contract ensures job satisfaction, commitment and talent retention. This study is significant as the expectations during the pre-employment stage affect the psychological contract even after organizational entry. Understanding contributions to the organization and what employees receive in return are also about managing the psychological contract. This study is also aimed at employers in redesigning their recruitment & talent management strategies. The outcomes of strong HRM strategy based on psychological contract are high employee motivation, productivity, and controlled attrition rates. The study also explores the formation and transition of psychological contract from university to workplace. Lastly, the study aims in exposing the expectations of the younger generation, future workforce. The absence of a longitudinal study is a limitation which would provide the longer term view of the concept.