Optimizing demand response and load balancing in smart EV charging networks using AI integrated blockchain framework.

The integration of Electric Vehicles (EVs) into power grids introduces several critical challenges, such as limited scalability, inefficiencies in real-time demand management, and significant data privacy and security vulnerabilities within centralized architectures. Furthermore, the increasing demand for decentralized systems necessitates robust solutions to handle the growing volume of EVs while ensuring grid stability and optimizing energy utilization. To address these challenges, this paper presents the Demand Response and Load Balancing using Artificial intelligence (DR-LB-AI) framework.

Enhancing darrieus wind turbine performance through varied plain flap configurations for the solar and wind tree.

Plain flaps (PFs) significantly increase camber, enhancing lift and aerodynamic performance when deployed. In Darrieus Vertical Axis Wind Turbines (VAWTs), which perform efficiently in low-speed, turbulent wind conditions, structural modifications like PFs can improve efficiency. This study explores plain flaps with 10-20-degree deflections at different chord lengths to enhance the NACA 2412 aerofoil's performance.

Rotor angle stability of a microgrid generator through polynomial approximation based on RFID data collection and deep learning.

The article proposes a novel approach to assess rotor angle stability in microgrids by enhancing the Modified Galerkin Method (MGM), which is based on the Polynomial Approximation, using real-time RFID data acquisition. Due to their reliance on assumptions, traditional rotor angle stability methodologies frequently fail in online transient stability testing. MGM successfully captures the dynamic behavior of microgrids by approximating state variables using a sequence of polynomials and coefficients.

A hybrid approach using support vector machine rule-based system: detecting cyber threats in internet of things.

While the proliferation of the Internet of Things (IoT) has revolutionized several industries, it has also created severe data security concerns. The security of these network devices and the dependability of IoT networks depend on efficient threat detection. Device heterogeneity, computing resource constraints, and the ever-changing nature of cyber threats are a few of the obstacles that make detecting cyber threats in IoT systems difficult.

Design and performance evaluation of a multi-load and multi-source DC-DC converter for efficient electric vehicle power systems.

This paper introduces the design and comprehensive performance evaluation of a novel Multi-Load and Multi-Source DC-DC converter tailored for electric vehicle (EV) power systems. The proposed converter integrates a primary battery power source with a secondary renewable energy source-specifically, solar energy-to enhance overall energy efficiency and reliability in EV applications. Unlike conventional multi-port converters that often suffer from cross-regulation issues and limited scalability, this converter ensures stable power distribution to various EV subsystems, including the motor, air conditioning unit, audio systems, and lighting.

Enhancing HVDC transmission line fault detection using disjoint bagging and bayesian optimization with artificial neural networks and scientometric insights.

DC grid fault protection techniques have previously faced challenges such as fixed thresholds, insensitivity to high-resistance faults, and dependency on specific threshold settings. These limitations can lead to elevated fault currents in the grid, particularly affecting multi-modular converters (MMCs) vulnerability to large fault current transients. This paper proposes a novel approach that combines the disjoint-based Bootstrap Aggregating (Bagging) technique and Bayesian optimization (BO) for fault detection in DC grids.

Design and implementation of a universal converter for microgrid applications using approximate dynamic programming and artificial neural networks.

This paper introduces a novel design for a universal DC-DC and DC-AC converter tailored for DC/AC microgrid applications using Approximate Dynamic Programming and Artificial Neural Networks (ADP-ANN). The proposed converter is engineered to operate efficiently with both low-power battery and single-phase AC supply, utilizing identical side terminals and switches for both chopper and inverter configurations. This innovation reduces component redundancy and enhances operational versatility.

Machine learning-based energy management and power forecasting in grid-connected microgrids with multiple distributed energy sources.

The growing integration of renewable energy sources into grid-connected microgrids has created new challenges in power generation forecasting and energy management. This paper explores the use of advanced machine learning algorithms, specifically Support Vector Regression (SVR), to enhance the efficiency and reliability of these systems. The proposed SVR algorithm leverages comprehensive historical energy production data, detailed weather patterns, and dynamic grid conditions to accurately forecast power generation.

Empowering distribution system operators: A review of distributed energy resource forecasting techniques.

Effective management of Distributed Energy Resources (DERs) and optimization of grid operations are crucial responsibilities of Distribution System Operators (DSOs). Hence, this comprehensive critical review aims to analyze the current state of DER forecasting practices for DSOs and their implications for achieving the SDG goals. These goals underscore the significance of clean and accessible energy, advancements in infrastructure, sustainable urban development, climate change mitigation, and collaborative partnerships.

A high-efficiency poly-input boost DC-DC converter for energy storage and electric vehicle applications.

This research paper introduces an avant-garde poly-input DC-DC converter (PIDC) meticulously engineered for cutting-edge energy storage and electric vehicle (EV) applications. The pioneering converter synergizes two primary power sources-solar energy and fuel cells-with an auxiliary backup source, an energy storage device battery (ESDB). The PIDC showcases a remarkable enhancement in conversion efficiency, achieving up to 96% compared to the conventional 85-90% efficiency of traditional converters.

Bayesian-optimized LSTM-DWT approach for reliable fault detection in MMC-based HVDC systems.

As Europe integrates more renewable energy resources, notably offshore wind power, into its super meshed grid, the demand for reliable long-distance High Voltage Direct Current (HVDC) transmission systems has surged. This paper addresses the intricacies of HVDC systems built upon Modular Multi-Level Converters (MMCs), especially concerning the rapid rise of DC fault currents. We propose a novel fault identification and classification for DC transmission lines only by employing Long Short-Term Memory (LSTM) networks integrated with Discrete Wavelet Transform (DWT) for feature extraction.

A techno-economic perspective on efficient hybrid renewable energy solutions in Douala, Cameroon's grid-connected systems.

Cameroon is currently grappling with a significant energy crisis, which is adversely affecting its economy due to cost, reliability, and availability constraints within the power infrastructure. While electrochemical storage presents a potential remedy, its implementation faces hurdles like high costs and technical limitations. Conversely, generator-based systems, although a viable alternative, bring their own set of issues such as noise pollution and demanding maintenance requirements.

State-of-the-art review on energy sharing and trading of resilient multi microgrids.

Independently run single microgrids (MGs) encounter difficulties with inadequate self-consumption of local renewable energy and frequent power exchange with the grid. Combining numerous MGs to form a multi-microgrid (MMG) is a viable approach to enhance smart distribution networks' operational and financial performance. However, the correlation and coordination of intermittent power generation within each MG network pose many techno-economic challenges for energy sharing and trading.

Hybrid genetic algorithm-simulated annealing based electric vehicle charging station placement for optimizing distribution network resilience.

Rapid placement of electric vehicle charging stations (EVCSs) is essential for the transportation industry in response to the growing electric vehicle (EV) fleet. The widespread usage of EVs is an essential strategy for reducing greenhouse gas emissions from traditional vehicles. The focus of this study is the challenge of smoothly integrating Plug-in EV Charging Stations (PEVCS) into distribution networks, especially when distributed photovoltaic (PV) systems are involved.

Sustainable power management in light electric vehicles with hybrid energy storage and machine learning control.

This paper presents a cutting-edge Sustainable Power Management System for Light Electric Vehicles (LEVs) using a Hybrid Energy Storage Solution (HESS) integrated with Machine Learning (ML)-enhanced control. The system's central feature is its ability to harness renewable energy sources, such as Photovoltaic (PV) panels and supercapacitors, which overcome traditional battery-dependent constraints. The proposed control algorithm orchestrates power sharing among the battery, supercapacitor, and PV sources, optimizing the utilization of available renewable energy and ensuring stringent voltage regulation of the DC bus.

A novel strategy towards efficient and reliable electric vehicle charging for the realisation of a true sustainable transportation landscape.

This paper proposes an innovative approach for improving the charging efficiency of electric vehicles (EVs) by combining photovoltaic (PV) systems with AC-DC Power Factor Correction (PFC). The proposed approach employs bi-directional power flow management within the PFC system, allowing for enhanced resource utilization and EV battery capacity under a variety of environmental circumstances. A modified Lyapunov-based robust model reference adaptive controller (M-LRMRAC) is developed to provide real-time Maximum Power Point Tracking (MPPT) for the PV array.

Adsorption-biodegradation coupled remediation process for the efficient removal of a textile dye through chemically functionalized sugarcane bagasse.

Textile dye effluents have many deleterious effects; therefore, it is essential to remove before releasing into waterbodies. This study developed a two-step process for decolorization of textile dye using sugarcane bagasse (SCB). The first step of the process involved functionalization of SCB with alginic acid and applying as packing material in column and assessing its performance for adsorptive removal of Drimarene red.

Safety of Aflibercept in Metastatic Colorectal Cancer: A Literature Review and Expert Perspective on Clinical and Real-World Data.

Background: Metastatic colorectal cancer (mCRC) represents a substantial health burden globally and an increasing challenge in Asian countries. Treatment options include chemotherapy plus a vascular endothelial growth factor (VEGF) inhibitor (such as bevacizumab, aflibercept or ramucirumab), or anti-epidermal growth factor receptor (EGFR) therapies. Aflibercept, a recombinant fusion protein, has been approved for treatment of mCRC in combination with FOLFIRI for patients whose disease progresses during or after treatment with an oxaliplatin-containing regimen, based on its efficacy and tolerability profile in clinical trials.

Chemical Modification and Performance Evaluation of an Agro-Waste for Column Mode Removal of Textile Dyes.

The performance of a newly designed column bed device packed with chemically modified agro-waste (sugarcane bagasse) is evaluated for efficient removal of two textile dyes, Optilan yellow and Lanasyn brown and textile industry dye effluent. The parameters used for performance evaluation are removal efficiency (%), adsorption capacity (), and breakthrough ( / ). The column exhibits >90% removal of both the dyes and >80% removal of textile industry dye effluent.

Chitosan nanoencapsulated exogenous trypsin biomimics zymogen-like enzyme in fish gastrointestinal tract.

Exogenous proteolytic enzyme supplementation is required in certain disease conditions in humans and animals and due to compelling reasons on use of more plant protein ingredients and profitability in animal feed industry. However, limitations on their utility in diet are imposed by their pH specificity, thermolabile nature, inhibition due to a variety of factors and the possibility of intestinal damage. For enhancing the efficacy and safety of exogenous trypsin, an efficient chitosan (0.

Haplotype-dependent differential activation of the human IL-10 gene promoter in macrophages and trophoblasts: implications for placental IL-10 deficiency and pregnancy complications.

Problem: polymorphic changes in the IL-10 gene promoter have been identified that lead to altered IL-10 production. We hypothesized that because of these genotypic changes, the IL-10 promoter might be expressed in a cell type-specific manner and may respond differentially to inflammatory triggers.

Method Of Study: we created reporter gene promoter constructs containing GCC, ACC, and ATA haplotypes using DNA from patients harboring polymorphic changes at -1082 (G→A), -819 (C→T), and -592 (C→A) sites in the IL-10 promoter.

TSPY gene copy number as a potential new risk factor for male infertility.

The human TSPY (testis-specific protein, Y-linked) gene family (30-60 copies) is situated in the MSY (male-specific) region of the Y chromosome. Testis-specific expression indicates that the gene plays a role in spermatogenesis. Refined quantitative fluorescence PCR (polymerase chain reaction) was applied to evaluate the relative number of TSPY copies compared with AMELY/X (amelogenin gene, Y-linked) genes in 84 stratified infertile men and in 40 controls.

Pyknodysostosis: visceral manifestations and simian crease.

Pyknodysostosis is a rare autosomal recessive osteosclerosing skeletal disorder caused by mutations in the CTSK gene situated at 1q21 that codes for cathepsin K - a lysosomal cysteine protease. Mutations in this gene affect the metabolism of skeletal system. This causes problems in bone resorption and remodelling and craniofacial abnormalities.