bluuebunny/crossref_metadata_2025 huggingface dataset:
Shunt Active Power Filter with Three Level Inverter using Hysteresis Current Controllers
Authors: Raghutu Ramakrishna , Manoj Vasupalli , Kumar Yegireddy Narendra
Published: June 2024

This document introduces a Shunt Active Power Filter (SAPF) for three-phase three-wire systems, utilizing a Cascaded H Bridge Multilevel Inverter. The Hysteresis Current Control technique provides numerous benefits, including simplicity, minimal current tracking error, and immunity to load parameter variations. The research presented in this paper focuses on a three-level hysteresis band current controller for a three-level Cascaded H Bridge Inverter, specifically applied to SAPF. The proposed scheme effectively utilizes the available voltage states of the three-level inverter to confine the current error within predefined limits. The performance of this method is validated using Powersim (PSIM) software, which demonstrates the effective compensation capability of SAPF with three-level hysteresis band current controllers.
An Efficient Power Factor Maximization Model with Buk-Boost Converter in Power Systems
Authors: Vij Priya , Yadav Kamlesh Kumar
Published: June 2024

The problem of power management in power systems has been well studied. There exist number of approaches towards power handling which consider input voltage, residual voltage in different capacitors and uses different converters to maximize the output voltage. However, the methods suffer to achieve higher performance in voltage stabilization in power systems. To handle this issue, an efficient buck boost converter based power factor maximization model (BBC-PFM) is presented in this paper. The model fabricated with k number of buck boost converter in serial. According to the design, the method generates voltage ensemble where each has different voltage slots and values. A slot in voltage ensemble represents the residual voltage in specific converter. Accordingly, there will be number of ensembles will be generate based on the switching conditions. Generated voltage ensembles are used to perform circuit selection and based on that a small set of converters are triggered to discharge the voltage to support power system, where rest of them are triggered to get charged. The selection of converter is performed by computing Power Factor Maximization Support (PFMS) which is being measured based on the voltage in inductor, capacitor present in any ensemble with the input voltage. The proposed method improves the performance of power factor maximization with less voltage loss.
Artificial Intelligence Approaches for Predictive Power Consumption Modeling in Machining-Short Review
Authors: Singh Shweta , Singh Satendra , Pawar Rahul , Kulhar Kuldeep Singh
Published: June 2024

This article focuses on the crucial role of predictive modeling, particularly powered by artificial intelligence (AI), in optimizing power consumption in machining, a vital facet of modern manufacturing. Highlighting the growing significance of power utilization in machining operations due to economic, environmental, and equipment-related implications, the article underscores the importance of this area. It proceeds to discuss the contributions of predictive modelling , elucidating its capacity to predict and manage variability, optimize tool selection and cutting parameters, reduce downtime, enable energy-efficient scheduling, and enhance sustainability, all while reducing costs. AI, with its data-driven capabilities, is presented as a transformative force, providing real-time adaptability, predictive maintenance, and energy-efficient scheduling, aligning with sustainability and cost-efficiency goals. While acknowledging the current limitations of AI models, the article outlines future opportunities such as advanced machine learning, IoT integration, sensor monitoring, digital twins, hybrid models, industry standards, and the growing emphasis on explainable AI. These advancements are poised to shape a more sustainable, efficient, and data-informed future for the manufacturing industry.
Efficient Demand Centric Power Generation and Distribution Model for Improved Market Gain in Power Distribution Grids
Authors: Prakash Mahobia Ravi
Published: June 2024

The market gain of power grids is well studied and there exist various models to maximize the market gain of power distribution systems. However, the methods suffer to achieve higher performance in maximizing the market gain of power grids. Towards this, an efficient Demand Centric Power Generation Model (DCPGM) is presented in this article. The method maintains the traces of various power grids which have the capacity of grid in producing required voltage. The model focused on reducing the voltage loss and increasing the market gain of the power grids. To perform this, the method monitors the power demand at each cycle and maintains the cost of various power grids. According to the requirement and the cost of purchase, the method identifies set of grids and computes Market Gain Factor (MGF). Based on the MGF value, the method identifies set of grids and triggers them for power production. The rest of the grid units are triggered to silent mode. The proposed model improves the performance of power generation with higher market gain.
Optimization of Multi-Energy Systems for Efficient Power-to-X Conversion
Authors: Ramya G. , Jayalakshmi D. , Raghuwanshi Ashish , Jasim L. Hussien , Shah Sanjeev Kumar , Sherje Nitin P.
Published: June 2024

This paper reviews the work in the areas of optimization and efficiency enhancement of multi-energy systems (MES) for power-to-X conversion. The first study delves into the deployment of Power-to-Hydrogen (PtH2) within district-scale MES, emphasizing the role of PtH2 in achieving zero operational CO2 emissions, especially in systems with high renewable energy generation. The study also highlights the significance of heat pump efficiency, battery capital cost, and lifetime in influencing PtH2 implementation. The second investigation focuses on the integration of energy strategies for the transport and building sectors. It introduces a multi-objective optimization model that considers both sectors, aiming to minimize costs and life-cycle emissions. The findings suggest a potential transition from internal combustion engines to battery electric vehicles and a shift from gas boilers to heat pumps, leading to substantial emission reductions by 2050. Lastly, the third research explores the potential of power-to-gas (P2G) technology in enhancing the integration of renewable energy. By coordinating P2G with CO2-based electrothermal energy storage (ETES), the study demonstrates a significant improvement in the recovery efficiency of surplus wind power. Collectively, these studies underscore the importance of optimizing MES for sustainable and efficient energy conversion.
Sustainable Business Models for Photovoltaic (PV) Systems in Developing Countries
Authors: Latha B. , Sofia Liz Anita , Bansal Jyoti , Singh Devendra , Alkkhayat A.H. , Dhabliya Dharmesh
Published: June 2024

This paper reviews sustainable business models for Photovoltaic (PV) Systems in developing countries. Firstly, it examines the global adoption of energy business models, noting the shift from customer-owned to community-shared initiatives in developing nations and the prominence of the virtual net metering and store-on grid schemes. Secondly, it identifies barriers to distributed energy solutions, exploring the evolution of PV business models and the advantages of community-shared models over other alternatives. Lastly, it investigates the deployment of customersited PV systems in Germany, Japan, and the USA, highlighting distinctive business models that have catalysed PV adoption. The review offers insights for decision-makers and solar firms navigating the PV sector in developing countries.
Optimising Solar Energy: An Evaluation of IoT-Based Solar Panel Monitoring Systems
Authors: Rawat Vidhi , Athab Aya H. , Joshi S.K. , Jayasree S. , Dhabaliya Darmesh , Devika J.
Published: June 2024

The integration of Internet of Things (IoT) technologies within the realm of renewable energy, notably solar power, has paved the way for the creation of intelligent and decentralised systems for enhanced monitoring and fault detection. In this review, we delve into the use of IoTfacilitated remote IV tracing mechanisms to identify and address faults within urban solar power installations. Concentrating on desert environments, where solar power is a cornerstone for sustainable growth, we consider the challenges introduced by dust deposition and its repercussions on solar energy output. We further elaborate on the integral role of IoT in spearheading smart monitoring systems that assess the efficiency of solar panels in dust-affected scenarios. This paper also provides a detailed assessment of a specific IoT-integrated solar monitoring framework designed for expansive and distributed solar provisions in smart cities.
Solar Energy Forecasting: Perspectives of the State-Of-The-Art
Authors: Manikandan M. , Pathani Ashish , Dwivedi Akhilesh , Almusawi Muntather , P Allirani , Jeevitha D.
Published: June 2024

Solar energy is a promising renewable energy source, but its intermittent and variable nature poses significant challenges for accurate forecasting. Over the recent years, there has been a remarkable surge in research dedicated to improving the precision of solar energy forecasting models. This review article delves into the state-of-the-art in solar energy forecasting. Beginning with an exploration of the hurdles faced in forecasting solar radiation, we proceed to provide an extensive survey of various forecasting models that have been developed to tackle this complex problem. Factors influencing the accuracy of solar energy forecasts are discussed, and an insight into the future trends in solar energy forecasting is provided. Key areas of focus include machine learning techniques, artificial neural networks (ANNs), and support vector regression.
A Review of Smart Grid Management Systems Using Machine Learning Algorithms for Efficient Energy Distribution
Authors: E Sudha , Aggarwal A. Saurabh , Kalpana B. , Reddy M. Nirmala , Almusawi Muntather , Raja Kumar Jambi Ratna
Published: June 2024

The smart grid is an intelligent electricity network that uses digital technology to improve the efficiency, reliability, and sustainability of power delivery. Machine learning is a type of artificial intelligence that can be used to analyze data and learn from it. This makes it a valuable tool for the smart grid, as it can be used to solve a variety of problems, such as⸻forecasting energy demand, detecting, and preventing outages, optimizing power flows, managing distributed energy resources, ensuring grid security.In this article, we will review the use of machine learning in the smart grid. We will discuss the different machine learning algorithms that are being used, the challenges that need to be addressed, and the future of machine learning in the smart grid..
Adaptive Voltage Regulation Scheme for Improved Power Management in Metropolitan
Authors: Madhukar Haval Abhijeet , Chopra Taruna
Published: June 2024

The problem of voltage regulation in metropolitan cities has been well studied. There exist different models to handle this problem, which consider the available voltage and number of connections in the part of city as the key in voltage regulation. However, the methods suffer to achieve higher performance in voltage regulation. To handle this issue, an Adaptive Voltage Regulation Scheme (AVRS) is presented in this article. The proposed method considers the number of high load connections, number of low load connections, no of commercial connections, no of household connections and so on. Based on the connection details, the method categorizes the micro grids into two categories like commercial support grid and household support grid. Based on the category of the grid identified, the method computes the Adaptive Voltage Rate (AVR) for various grids, which represent the voltage to be regulated for the grid at particular time. The AVRS scheme performs voltage regulation according to the value of AVR. This process is iterated at different time stamp of a day. This will be analyzed in different times like business hours and normal hours. Based on the AVR measured, the method performs voltage regulation and produces higher performance.
Artificial Intelligence’s Role in Shaping Renewable Energy for Next-Generation Smart Cities
Authors: Singar Mahendra Kumar , Suneetha K. , Saraswat Nidhi , Shukla Gaurav
Published: June 2024

This review article explores the perspective of artificial intelligence (AI) in shaping the future of smart cities, with a particular focus on the integration of renewable energy solutions. The smart city and AI perspective has gained significant attention in urban policy circles, yet many initiatives have faced challenges due to narrow, technology-centric approaches. In response, this paper advocates for a “green AI” approach that prioritises sustainability and equity alongside efficiency in urban development. Through a comprehensive analysis of AI and smart city literature, practices, trends, and applications, this review identifies critical research gaps, stressing the need for AI systems that promote sustainability, efficiency, and fairness in urban contexts. In light of pressing issues like climate change, overpopulation, and resource depletion, the urgency of AI-driven urban solutions is evident. The article examines Smart Cities in Spain and compares them to conventional cities, offering insights into strategic technological measures across areas such as economics, sustainability, transportation, governance, population management, and quality of life. It underscores AI’s potential to transform traditional cities into smart, sustainable urban hubs, contributing to the broader discourse on AI’s role in shaping the cities of tomorrow.
Performance Analysis for Control of A Unified Power Flow Controller (UPFC) Using MATLAB Simulink
Authors: Suresh Kumar A. , Deepak Karanam , Kumar Pakanati Hari , Reddy Gaddam Hemanth , Krishna Devamada Chinni , Devendra Badirla
Published: June 2024

The long transmission system is required to transfer generated power to distribution system. Hence, maximum power transfer devices with flexible operation are essential in transmission system. Thus, it is necessary to add Flexible AC Transmission System (FACTS) devices at the proper locations. Unified Power Flow Controller (UPFC) is the best FACTS transmission system device for ensuring adequate power flow between generating and distribution via transmission lines. The UPFC consists of two converters which likely to work as STATCOM and SSSC to make a proper power flow. In this paper modeling and analysis of UPFC is presented under platform of MATLAB/Simulink. Extensive responses are incorporated in results section with proper analysis under various operating conditions.
Non Linear Modelling and Control of Unified Power Flow Controller
Authors: Ravikumar N.V.A. , Manoj Vasupalli , Krishnam Naidu R.S.R.
Published: June 2024

Unified Power Flow Controller is modeled as a non linear system in order to apply a non linear control technique for the improvement of power flow in a Single Machine Infinite Bus system. The non linear system is linearized by Feedback Linearization technique .FBLC is able to linearize the non linear states of the system under all uncertainty conditions present. FBLC will also be track the references and control the converter operation. The non linear modelling of UPFC along with the FBLC control is done in MATLAB software. The UPFC along with FBLC control gives promising results on the stability of linearized system with guarantee that state information remains unaltered.
Line to Ground Faults Protection and Control method in Active Distribution Network using IEC 61850
Authors: Mathur Mala , Vinay Kumar S.B. , Gupta Rakhi , Alam Intekhab
Published: June 2024

Protection and control automation messages transfer between devices and the control center (CC) over IEC 61850 standard protocols in active distribution network (ADN) using Ethernet. Wireless communication network provides real-time monitoring and control of ADN. The proposed novel approach framework presentsline to ground (LG) faults protection and control method utilizing IEC 61850 standards at bay level between intelligent protection devices for fast and reliable protection. Moreover, this bay level peer to peer communication utilizes IEEE 802.11 wireless local area network (WLAN)to reduce the fault protection time and other messages transferring time instead of Ethernet communication by GOOSE multicast communication services. End to end time delay and throughput of data transmission between the coordination nodes are illustrated using NS2 simulation.
Improvement of Voltage Quality in a Distribution System by a Novel Simplified Unit Vector Theory
Authors: Abrol Monika , Pandeya Megha , Sharma Neeraj , Singh Kulhar Kuldeep
Published: June 2024

Nowadays, electrical engineers are increasingly concerned about power quality due to the rising load demand and the utilization of non-linear loads by end users. Power must be supplied continuously and with excellent quality. Voltage variation is a prevalent occurrence in power distribution systems due to the presence of reactance in AC systems and fluctuations in load demand. The dynamic voltage restorer is a compensation device that effectively resolves voltage problems within power distribution systems. The theory of synchronous reference frame is utilized for controlling the DVR components, requiring intricate computations due to the implementation of abc-dq transformation, phase-lock loops, and PI control. The following document introduces an innovative simplified unit vector theory designed to streamline the control of DVR, ultimately minimizing control complexity. The novel streamlined approach underwent testing in scenarios involving distribution systems experiencing sag, swell, or a combination of both. Simulation models were created and outcomes were achieved through the utilization of MATLAB/SIMULINK software.
Optimal Energy Storage scheduling of Microgrid by Using Ant colony optimization
Authors: Sharma Khushboo , Prabhu A. , Gupta Sheryl , Kulhar Kuldeep Singh
Published: June 2024

Energy storage device such as battery is playing very important aspect in microgrid since it can handle the bidirectional power flow to stabilize the power quality issues. Renewable energy sources based microgrids are becoming popular now a day because of those arte main networks which are playing key role in reduction of global warming as well as main power sources for many areas in world wide. The PV and wind based power generation systems are wildly used sources in microgrids. Unfortunately, these sources are depends on whether condition and cannot have a stable output to supply quality power to consumers. Therefore an storage device is must to incorporate in such kind of microgrids. Hence it is very important to have an optimization of scheduling a day-ahead energy storage system in case of renewable energy sources based microgrid power supply system. Ant colony optimization is one of the best optimization techniquesto get feasible solution for complexity networks such as microgrids. The data demonstrate the utilization of ant colony optimization technique in order to minimize microgrid expenses under a time-of-use tariff system. The findings also indicate the influence of the microgrid’s self-reliance and the storage system’s capability on the energy expenses of the microgrid.
Hybrid Intelligent Optimization Techniques for Grid Integration with Renewable Systems: Review
Authors: Kumar Gautam Anupam , Kant Pareek Ravi , Jessie Rani R Hannah , Solanki Vipin
Published: June 2024

Renewable energy sources are essential in fulfilling the increasing need for electricity. Researchers are actively exploring eco-friendly alternative energy sources and technologies, particularly in the form of micro grids or gridintegrated systems. A hybrid renewable energy system with battery storage in a small-scale industry was optimized using a blend of traditional and cutting-edge models, employing mixed integer linear programming techniques, as demonstrated in a recent study. The proposed optimization algorithm offers improved accuracy and reduced computational burdens. The model considers the intrinsic stochastic nature of hybrid energy systems and integrates fluctuations in load forecasting. By employing an intelligent computational optimization algorithm, the study focuses on optimizing the PV-Wind, Diesel, and battery storage hybrid system. The findings of this research shed light on the impact of load variations on component sizing in small-scale industries. This review holds significant value for researchers who aim to tackle the intricacies of algorithm analysis and power system design in order to drive future enhancements.
Enhancement of Power Quality in Grid-Connected Distribution Systems Employing AI-Based Controllers
Authors: Pandu Ranga Reddy G. , Harshitha G. , Lakshmi Priya P. , Roshini J.V.N.S. , Harshiya M.
Published: June 2024

The main concerns with electrical distribution networks are the degradation of power quality. The main culprit behind this problem is non-linear technology, specifically switching devices and power electronics converters. Power supply efficiency, system efficacy, power factor loss, etc. Are all adversely impacted by non- linearity. It is necessary to compensate for the reactive power since it has increased as a result of the power factor drop and does not contribute to the transmission of energy. In this project, Artificial Intelligence based controllers have been proposed for improving Power Quality in Grid connected Distribution System. This is used to reduce the harmonics of current and other correlated power quality issues caused by reactive power. With the aid of the MATLAB/ SIMULINK toolkit, these control approaches are to be simulated. In this paper, we examine whether ANN or ANFIS is the most appropriate method.
A Time Orient Power Regulation Model for Improved Power Distribution in Smart Cities Using Power Grids
Authors: Chandrawanshi Ekta
Published: June 2024

The power distribution problem in smart cities is well analyzed. From the analysis, there are number of methods been recommended by various researchers to handle this problem. The most approaches consider the number of connections, devices, number of high tension connection available in the city or area of the city towards regulating the power source. However, these methods suffer to achieve higher performance in distribution and energy utilization. To handle this issue, an Time Orient Power Regulation Model (TOPRM) is presented in this article. The method maintains the records of various region of the city about the organizations, commercial buildings, residential buildings, household connections, devices and others. Using these information, the method estimates Time orient energy utilization factor (TEUF) for various regions based on average utilization of electricity at different time stamp. Based on these factors, the method identifies the most claiming region and poor claiming region for various time stamp to perform regulation. According to the value of TEUF, the method regulates the electric power for various sectors of the city. The proposed model improve the performance of power utilization and power distribution.
Grid-Supportive Inverter Technologies for Power Quality Improvement
Authors: Mirjalol Ashurov , alkkhayat A.H. , Thangam Ms. , Chandramauli Awadhesh , K Divya , Dhabliya Dharmesh
Published: June 2024

This paper offers a comprehensive review of grid-supportive inverter technologies aimed at enhancing power quality. One area of exploration presents a two-stage three-phase three-wire solar photovoltaic (SPV) system. This system utilises a boost converter for optimal power point tracking and a three-leg voltage source converter to channel the harnessed SPV energy. Distinctively, this approach provides harmonics elimination, grid current balancing, and compensation for the nonactive portion of load currents, going beyond traditional solar inverter functionalities. An innovative adjustable step adaptive neuron-based control method is employed to gauge the genuine power reflecting segment of the load current, ensuring swift dynamic adaptability to environmental shifts. Another focal point addresses the complexities introduced by the rising integration of microgrid systems within the AC distribution framework. Highlighting the imperative of maintaining voltage and frequency, the narrative accentuates the pivotal role of energy storage systems (ESS) for the proficient operation of sensitive loads. A grid adaptive power management strategy (GA-PMS) is proposed, orchestrating the current references for renewable energy sources, ESS, and microgridconnected converters. This strategy guarantees uninterrupted microgrid functionality, priority-driven load shedding, and the maintenance of power quality standards at the local bus. Both areas significantly contribute to the evolution of grid-supportive inverter technologies and their potential in power quality betterment.
A multi objective feeder optimization model for Improved Power Stability Performance in Grid Networks
Authors: Pillai Anu G.
Published: June 2024

The problem of power grid performance management has been well studied. There exist numerous techniques towards this problem which consider limited parameters like energy of feeders, production of grids and so on. However, they suffer to achieve higher performance of grid networks. To handle this problem, an efficient Multi-Objective Feeder Optimization Model (MFOM) is presented in this article. The method focused on improving the performance of feeders by stimulating the usage of electricity available with the feeders. To perform this, the method monitors the feeder status about their average residual energy, average power production and average power drain. Using these factors, the method triggers the specific set of feeders for the power requirement by computing Multi-Objective Feeder Power Quality (MFPQ). According to the MFPQ value, the model identifies set of feeders for the current cycle to support the power requirement of any electrical system. The proposed MFOM model improves the performance of power utilization and power stability.
Integrating Hydrogen Energy Storage into Urban Mobility Solutions
Authors: Suresh Ashwin Raj , Venkatasubramanian R. , Hussien Jasim L. , Santhoshkumar M.P. , Aggarwal Saurabh , Kale Mangesh
Published: June 2024

The transition to sustainable urban mobility solutions is imperative in the face of escalating environmental and health challenges. This paper reviews the work in the areas of decarbonizing power systems, urban mobility and transportation modes, and the integration of renewable energy in smart cities. The integration of renewable energy sources, such as wind and solar power, into power systems is pivotal for decarbonization. However, their intermittent nature poses challenges for high-level integration. Energy storage emerges as a crucial solution, with technologies like fuel cell vehicles, water electrolysis, and flow batteries playing significant roles. Urban mobility, dominated by internal combustion engines, has led to environmental degradation and health issues. Transitioning to cleaner transportation modes, integrating electric vehicle stations with renewable energy, and ensuring uninterrupted power supply are essential steps forward. Furthermore, the potential of smart cities harnessing non-dispatchable photovoltaic and wind power is vast. Yet, the challenge of large-scale energy storage remains. The concept of using renewable energy to produce hydrogen, which can then stabilize the grid, offers a promising avenue. This review underscores the importance of integrating hydrogen energy storage to revolutionize urban mobility solutions.
Real Time Machine Learning Based Voltage Regulation Model for Smart Agriculture
Authors: Madhukar Haval Abhijeet , Mishra Akanksha
Published: June 2024

The problem of voltage regulation in smart agriculture unit is well studied. There exists number of approaches to support voltage regulation in agriculture sector. However, the methods consider only the number of motors share the voltage as the key in regulating the artificial voltage to the agriculture unit. The methods suffer to achieve higher performance in smart agriculture. To solve this issue, an efficient Machine Learning Based Voltage Regulation Model (MLVRM) is presented in this paper. The method maintains the agriculture trace and uses them to compute mean voltage utilization (MVU) at various duty cycles. With the information like no of smart motors connected, average voltage utilization of motors, and other features, the method computes MVU value. The method trains the neural network with the features extracted. The network is designed with number of intermediate layers where each layer neuron computes the value of MVU according to the features available. The output layer neurons produces number of MVU value. Based on the MVU values obtained, the method computes Optimal Regulation Voltage (ORV) for the current input voltage according to the required voltage for the smart motor connected. The proposed model improves the performance of voltage regulation and smart agriculture.
Utilizing Artificial Intelligence and IoT for Enhanced Renewable Energy Management in Smart Cities
Authors: J Bhuvana , Kant Gautam Chandra , Yadav Dhananjay Kumar , Sharma Neeraj
Published: June 2024

This paper reviews the use of Artificial Intelligence (AI) and the Internet of Things (IoT) in better managing renewable energy in smart cities. With a growing focus on sustainable living globally, many cities are updating their infrastructure to be more eco-friendly. This includes improving waste management, increasing renewable energy production, and using modern technology for better energy systems. A key idea discussed is the Smart Green Energy (IoT-SGE) system for smart cities, which uses IoT to help finely control energy use through constant monitoring and secure communication. By using AI, this system can learn and improve over time, making energy management more efficient. The paper also looks at examples from successful smart cities around the world, like London and Singapore, highlighting how good policies can help overcome challenges in energy, transport, and building sectors. It emphasises the importance of working together with local government and other stakeholders to bring about better use of clean energy, making cities smarter and more sustainable. Through reviewing a wide range of studies, this paper aims to provide a clear path forward for developing smarter cities with better renewable energy management.
Harnessing Microbial Communities for Advanced Bioenergy Production
Authors: Alawadi Ahmed Hussein , Alawadi Ahmed Hussien Radie , M Jeyalaxmi , Sivasubramanian S. , G Swathi , Raja Kumar Jambi Ratna
Published: June 2024

Microbial communities, with their diverse metabolic capabilities and intricate interactions, have emerged as a promising frontier in the quest for sustainable bioenergy solutions. These microorganisms, through their unique metabolic pathways, offer the potential to convert organic matter into usable energy forms, presenting an eco-friendly alternative to conventional energy sources. The recent surge in biotechnological research has unveiled a plethora of insights into the mechanisms employed by these microbial communities, particularly in the context of bioenergy production. This review amalgamates findings from studiescontributing a distinct perspective on the role and potential of microorganisms in energy production; from the intricate roles of cellular structures in energy processes to the regulatory mechanisms of immune cells and the innovative prospects of electricity-producing bacterial communities, the studies collectively paint a comprehensive picture of the current state of microbial bioenergy research. By synthesising these findings, this review not only underscores the contributions of these studies but also charts a roadmap for future research, highlighting areas ripe for exploration and innovation.
Characterization of Bioethanol Extracted from Napier Grass Using the Fermentation Process
Authors: Ramesh B.T. , Sayyad Javed , Bongale Arunkumar , Ramesh R.S.
Published: June 2024

Due to the diminishing supply of fossil fuels, biodiesel is gaining popularity as a feasible fuel. It is produced through the fermentation of Napier liquid with the help of bakery yeast. The pH test, viscosity analysis, multi-cylinder, and other tests yielded favourable results when compared to traditional fuels such as diesel. In the Viscosity analysis, we compare the pure and blended fuel with regular diesel, it was discovered that pure ethanol has reduced dynamic viscosity, which improves when blended with diesel. For the multi-cylinder engine performance test, adding 10 % ethanol to gasoline results in a bit of improvement in efficiency over regular diesel. The efficiency is greater than the previous one when it reaches 20 %. As a significance, we blended ethanol up to 20 % with normal diesel to obtain optimum results. Because complete combustion is achieved due to oxygen contained in ethanoldiesel blends, the exhaust gas temperature of the blends is found to be lower, and Nitrogen emission is higher compared to diesel.
High Speed and Less Area Efficient Montgomery Modular Multiplication for VLSI Applications
Authors: Sreenivasa Murthy K.E. , Vishnu Kumar P. , Shamsheer Daula S.M.
Published: June 2024

A high speed and area efficient Montgomery modular multiplication algorithm is implemented. In the proposed multiplier high speed is achieved by using the carry save adder (CSA) to reduce the carry propagation at the addition operation stage due to this delay is reduced and the input and outputs are propagated in the binary format. In modular multiplication these carry save adders are used for the format conversion leads less area consumption and the critical path is also reduced. In the proposed multiplier the number of addition and multiplication stages is reduced at the each stage of computation with the use of parallelism. The proposed multiplier is designed in the Xilinx ISE tool with the use of Verilog HDL programming language. Tentative results shows that the proposed Montgomery modular multiplication achieves better performance with respect to area and delay while comparing with the previous Montgomery modular multiplication.
Euler is an innovator of F =ma, Newton’s second law gives F = KdV; F =ma may be obtained from Newton’s law by logically modifying it
Authors: Sharma Ajay
Published: June 2024

There are two distinct forms of Newton’s second law of motion (1686) i.e. original or Principia’s form of (change in motion is proportional to impressed force, F = KdV) and textbook form (rate of change of momentum is proportional to impressed force, F =ma). Newton neither gave acceleration nor F =ma, it is mentioned by IOP England, publications of the American Institute of Physics, etc. Truesdell has inconsistently pointed out in 1960 that Euler had given F =ma in 1752, but the truth is that Euler had given F=2ma in the said paper. Euler had also given various equations such as F =ma/n, F =2ma, F =ma/2g, F =ma etc.; but these are ignored by Truesdell. The exceptionally useful equation F =ma was given by Euler in 1775, and then succeeding scientists inconsistently tried to show that F =ma follows from the original form of the law. Consequently, some arbitrary assumptions are made, original form, F =KdV; and the fact that Euler gave F =ma are not mentioned in the standard textbooks. For comparison, Newton’s first law and third law (Reaction =-Action) are the same in the Principia and textbooks. In the existing literature, F =ma is obtained from Principia’s definition of NSLM, by replacing ‘change in motion’ equal to ‘rate of change of momentum’, but motion is not ascribed to any units and dimensions. If the original definition of Newton’s law is changed in a postulatory way i.e. ‘change in motion’ is replaced by ‘rate of change in momentum’ and ‘proportionality’ by ‘equality’; then F=ma is obtained from a modified equation without any arbitrary assumption. In 1893, Rouse Ball randomly altered Newton’s second law as a change in momentum [per unit time] is always proportional to the impressed force.
A Systematic Review on Network Intrusion Detection System based on machine learning and deep learning approach
Authors: Immastephy Anto Jenisha A. , Punitha K.
Published: June 2024

Today’s security attacks on computer networks are becoming more complex and severe, which has prompted security researchers to use a variety of machine learning techniques to safeguard the information and reputation of their clients. Detecting network infiltration has long been a difficult task. Machine learning advancements have raised the way for improving intrusion detection systems (IDS). This development has led to intrusion detection’s integration into network security. Using supervised machine learning techniques, intrusion detection has attained great detection accuracy. However, it is unlikely that a machine learning (ML) classifier will be able to correctly identify all attacks, particularly obscure ones.An approach based on deep learning is presented for more precise intrusion detection. This review article presents an extensive survey and classification of deep learning-based intrusion detection techniques with an emphasis on these approaches. The main background ideas about the IDS architecture and several machine and deep learning approaches are initially presented. Then, it categorizes these schemes based on the many types of methodologies each one employs. It explains how accurate intrusion detection is achieved through the use of machine and deep learning networks. The researched IDS frameworks are then fully analysed, with final thoughts and suggested directions for the future underlined.
Apport de la surveillance continue de la glycémie dans le suivi du diabète sucré chez le chien et le chat
Authors: Fabrès Virginie
Published: June 2024

La courbe de glycémie représente l’outil le plus informatif pour évaluer l’efficacité de l’insulinothérapie chez le patient diabétique. Les systèmes de surveillance continue de la glycémie révolutionnent actuellement le suivi du patient diabétique en permettant une évaluation détaillée des fluctuations du glucose sans qu’aucun prélèvement sanguin ne soit nécessaire. Le système de surveillance continue FreeStyle Libre® (Abbott) a été validé chez le chien et le chat. En générant des courbes sur 24 heures pendant plusieurs jours, il permet au clinicien de prendre des décisions thérapeutiques plus éclairées.
Nährstoffsupplementierung bei Osteoporose: Herausforderungen und
Chancen der aktuellen Evidenzlage
Authors: Genest Franca , Genest Alexander , Rak Dominik , Seefried Lothar
Published: June 2024

Die Untersuchung des Stellenwerts von Supplementen und Mangelvermeidung im Rahmen
der Ernährung bei Osteoporose rückt zunehmend in den Interessenmittelpunkt.
Gleichzeitig basiert die Wissenschaft zu diesem Thema überwiegend auf
Observationsstudien während die genaue Erforschung von Kausalzusammenhängen und
Dosis-Wirkungsbeziehungen eine Herausforderung bleibt. Insbesondere die
Datenlage zur Wertigkeit einer Proteinsupplementation, sowie der gezielten
Zufuhr von Folsäure, Vitamin B6 und B12, Kalium und grünem Tee für die
Knochengesundheit ist heterogen. Während die Daten zur Proteinsupplementation
grundsätzlich positive Effekte annehmen lassen, bleiben die optimale Menge und
die Proteinzusammensetzung bzw. Quellen sowie auch potenzielle Nebenwirkungen
Gegenstand der Diskussion. Die Rolle von Folsäure, Vitamin B6 und B12 ist gerade
im Kontext des Homocystein-Stoffwechsel vielschichtig und legt einen umsichtigen
Umgang mit dahingehenden Empfehlungen nahe. Die Untersuchungen zum Einfluss von
Kalium auf den Knochenstoffwechsel sind vielversprechend, jedoch variieren
Dosierungen und Formen in Studien erheblich. Auch zum grünem Tee zeigen einige
Studien positive Auswirkungen auf den Knochenstoffwechsel, aber die genauen
Mechanismen und optimalen Mengen bleiben auch hier unklar. Insgesamt zeigt die
verfügbare Evidenz bei umsichtigem Einsatz der genannten Supplemente keine
unmittelbaren Risiken. Im Hinblick auf einen konkreten Nutzen der letztlich eine
gezielte Empfehlung rechtfertigen würde besteht für alle genannten Supplemente
jedoch eine unzureichende Datenlage und weiterhin Forschungsbedarf um
ausreichend präzise und standardisierte Empfehlungen zu deren Einsatz für die
Knochengesundheit ableiten zu können.
Clinical Implications and Genetic Basis of Sleep Deprivation in Children
Authors: Martínez-Torres Laura , Diaz-Orjuela Natalia , Morales María Montaño , Jiménez Sara Poveda , Amaya Paula Pinilla , Contreras Mariana Vargas , Figueroa Sneyder Quitiaquez , Naranjo Juan Ramírez , Uribe Omar Rodriguez , Camacho-Cruz Jhon , Gutierrez-Castañeda Luz , Sanchez Daniela Rodriguez , Restrepo-Gualteros Sonia
Published: June 2024

Sleep is a complex biological and physiological process that allows the body to rest in addition to playing an important role in proper homeostasis in different body systems such as immune, metabolic, cardiovascular, neurological, and hormonal. It is important to preserve the quality of sleep, for adequate vitality, since the alterations that occur in any of the phases of sleep have repercussions on several systems of an organism, whether they are short or long term, such as the negative effect of sleep deprivation on the hormonal and metabolic regulation of various pathophysiological processes that will contribute to the development of obesity in pediatric patients. It has been found that sleep-related problems are common in children, being a frequent reason for medical consultations. In addition to the aforementioned, there may also be alterations at the level of the cortex, which is associated with the nonregulation of emotions in preadolescent and adolescent pediatric patients. Finally, sleep could depend on polymorphisms that become risk alleles for having short-term sleep; likewise, there are genes that have a greater expression at the time of rest, which allows a relationship to be made with diseases developed in the face of sleep depletion. This article describes the clinical implications in pediatric patients as a consequence of sleep deprivation and its genetic bases.
Sequential Treatment with Modified Atkins Diet and Low Glycemic Index Treatment for Drug-Resistant Epilepsy in Children
Authors: Mulyan Aparna , Kaushik Jaya Shankar , Dabla Surekha
Published: June 2024

Objectives The present study was designed to study the efficacy of sequential dietary therapy with a modified Atkins diet (mAD) followed by low glycemic index treatment (LGIT) in treating drug-resistant epilepsy in children.

Methods This interventional study was conducted from February 2021 to February 2022 among children aged 6 months to 5 years who had failed to respond to more than two conventional and correctly chosen antiseizure medications. The primary endpoint was the proportion of good responders, that is, children with more than 50% seizure reduction. Secondary outcome measures were the proportion of children with seizure freedom, > 90% seizure reduction, and the nature of parent-reported adverse events.

Results A total of 45 children were recruited for the study, with 6 children being lost to follow-up at 12 weeks. At 12 weeks, 30 of 39 (76.9%) children were good responders with more than 50% seizure reduction. Of these 30 children, 11 (24.4%) had more than 90% seizure reduction, with 9 (20%) achieving complete spasm freedom. Constipation was the most common side effect of the diet among the enrolled subjects.

Conclusion Clinicians can consider sequential dietary therapy with a mAD in the first month followed by LGIT in the next 2 months for treating children who could not tolerate mAD beyond 1 month.
Languishing: Experiences of emerging adults in outpatient mental health care one year into the COVID-19 pandemic
Authors: Chitpin Jeremy , Wammes Michael , Ross James , Pillai Aiswarya , Demy Jazzmin , Carter Chloe , Tremblay Paul , Vingilis Evelyn , Osuch Elizabeth
Published: June 2024

This qualitative study was conducted to assess the responses of emerging adults with pre-existing mood and anxiety disorders to the third wave of the COVID-19 pandemic. Patients from the First Episode Mood and Anxiety Program in London, Ontario, Canada, which treats individuals aged 16–29 with mood and anxiety disorders, were invited between April 16th – 21st, 2021 to complete a survey on their current emotional states, activities and coping. Responses were analyzed using thematic analysis. A thematic analysis identified the theme of “Languishing,” among responses comprised of 3 organizing subthemes: “Dominance of Negative Emotion,” “Waiting and Stagnating,” and “Loss of Opportunity.” This study suggests that emerging adults with pre-existing mental illness languished as the pandemic and associated restrictions persisted. Emphasis on “Coping through Intentional Action,” a separate theme identified among those coping well, may be protective for this group.
The influence of age at first regular digital device access on digital reading performance: the mediating effect of cognitive flexibility
Authors: Hu Jie , Hu Jingdan
Published: June 2024

Younger generations are receiving their first regular digital device access at progressively earlier ages than previous generations on average, whereas disparities in their early digital device access are still being observed globally. The inequality of first regular digital device access may have profound long-term effects on one’s information and communication technology (ICT) skills, but no evidence has suggested how unequal first regular digital device access impacts one’s digital reading, which enables one to process and comprehend information in this digitized world. Thus, the current study investigated the relationship between the age at which children first regularly access digital devices and their digital reading performance at age 15 while considering the potential mediating effect of cognitive flexibility. A multilevel mediation model was proposed and tested using data from a cross-national sample of 156,277 15-year-old students from 18 OECD countries from the Programme for International Student Assessment (PISA). The results suggested that first regular digital device access after age 9 exerted a negative influence on digital reading scores in countries that generally had high levels of early digital access, and after age 13 in countries that had lower levels of early digital access. Additionally, cognitive flexibility mediated the association between the age at first regularly accessing digital devices and digital reading performance. Starting using digital devices regularly only after age 6, more evident at ages 9 to 12, was associated with lower levels of cognitive flexibility, which in turn led to worse digital reading performance. These findings demonstrated the importance of early first regular access to digital devices before school age (age 6) and highlighted the need for the cultivation of cognitive flexibility in family and school uses of digital devices.