Optimal Placement and Sizing of Renewable Distributed Generators for Power Loss Reduction in Microgrid using Swarm Intelligence and Bio-inspired Algorithms

To responsibly fulfill the world\u27s expanding electrical energy needs, renewable energy sources are now essential. Future energy policies must include these sources—like solar and wind energy—because they lower carbon emissions and save the environment. The optimal location and sizing of renewable distributed generators (OLSRDG) in the microgrid are determined in this study by applying one of the universal bio-inspired techniques and one of the swarms’ algorithms. With lower power losses, an improved voltage profile, increased dependability, and stability, the goal is to improve energy efficiency and lessen reliance on the main grid while also enhancing the grid\u27s overall performance and stability. The acquired results are promising and show the efficacy and resilience of the suggested technique in solving OLSRDG problems compared to recently published results. The results showed that the optimization process led to loss reduction, with the percentage of power loss reduction ranging from 45.387% to 73.89% using the PSO. While the percentage of loss reduction using the BAT ranged from 51.78% to 71.57%

Suitability Assessment of Organic Carbon Additives in the Carburization of Low Carbon Steel (AISI 1020) for Engineering Applications

The quest to enhance the mechanical properties of low-carbon steel (LCS) has stimulated the exploration of diverse carburization techniques, with growing attention on organic additives derived from agricultural wastes as sustainable alternatives to conventional materials. This study investigated sheanut shell (SNS) and eggshell (ES) ash as eco-friendly carburizing agents for AISI 1020 steel to improve its performance for engineering applications. The objective was to evaluate their potential in enhancing hardness, strength, impact resistance, and microstructural properties of LCS. Experimental analysis compared carburized and un-carburized (UC) samples, focusing on hardness, tensile strength, impact energy, and microstructural features. The findings showed that carburization significantly increased hardness, with carburized LCS reaching 513 HB compared to 398 HB for UC LCS. However, UC LCS exhibited higher yield strength (221.3 N/mm²) and ultimate tensile strength (241.1 N/mm²), whereas carburized LCS absorbed more fracture energy (63.72 J), reflecting a trade-off between hardness and tensile strength. Microstructural examination revealed improved surface morphology, metallurgical bonding, and higher pearlite concentration due to carbon diffusion, while energy dispersive spectroscopy confirmed elevated carbon content in carburized samples. Structural analysis further identified both crystalline and amorphous carbon phases. The study concludes that SNS and ES ash are effective sustainable carburizing additives capable of enhancing surface properties of LCS, making the material suitable for high-strength and wear-resistant applications. It recommends the wider adoption of these agro-waste additives in industrial carburization processes to reduce reliance on costly conventional materials while promoting sustainable engineering practices

Spare Parts and Material Management in Electric Vehicle Maintenance: A Multidisciplinary Review

Maintenance and management of spare parts for electric vehicles requires a specific approach due to high-voltage components, technological complexity, and limited availability of specialized parts. Key challenges include optimizing inventory, monitoring the life of battery systems, ensuring compatibility of parts across different vehicle models, and organizing efficient procurement, storage, and distribution. Of particular importance are safety protocols when working with high voltages, the use of specialized technical and protective materials, and environmentally friendly waste management and battery recycling. Digital solutions, including ERP and CMMS systems, IoT sensors, remote diagnostics, and predictive analytics based on artificial intelligence, enable better maintenance planning, cost reduction, and increased vehicle reliability. The paper provides an analysis of the technical, safety, environmental, and economic aspects of electric vehicle maintenance, with an emphasis on the digitalization and optimization of logistics processes. Based on the analysis, recommendations are proposed for improving the maintenance system, including the integration of advanced technologies, process standardization, and strengthening the education of service personnel. In addition, the paper identifies key research questions and outlines directions for future research—particularly in areas such as the digital integration of spare parts logistics, the environmental impact of material uses and disposal, and the role of artificial intelligence in predictive maintenance strategies. The findings suggest that the greatest improvement potential lies in combining predictive maintenance, sustainable practices, and operational cost reduction, thereby contributing to the long-term reliability and competitiveness of electric mobility

A Comparative Case Study of Two Pedagogical Approaches in Web Development Education: From a Traditional Java Environment to a Modern Ruby on Rails Ecosystem

This study presents a comparative case study of the evolution of a software development course at Kindai University. We analyze two distinct pedagogical ecosystems: a traditional course based on Java Servlet/JSP with a local integrated development environment (IDE), and its subsequent iteration, a modern course employing the Ruby on Rails framework (a Web Application Framework, or WAF), Git for version control, a cloud-based IDE, and Platform as a Service (PaaS) for deployment. This study was not a controlled experiment isolating the effects of a WAF but rather an exploratory analysis of how a shift in the entire toolchain impacted student outcomes and perceptions. Quantitative analyses of student projects over three years for each course revealed that the modern Ruby-based ecosystem resulted in applications with approximately 50% more screens and screen transitions, despite requiring approximately 40% less source code. Furthermore, student surveys indicated significantly higher comprehension and interest in the modern courses. However, the number of data models and user stories remained consistent, suggesting that upstream design thinking was less affected by the technology stack. These findings suggest that adopting a modern, integrated development ecosystem can foster a more productive and engaging learning experience. We conclude by discussing the implications of these findings for curriculum design, emphasizing the value of incorporating contemporary, industry-aligned toolchains into software engineering education, while acknowledging that the observed benefits stem from the synergistic effect of multiple technologies rather than from a single component

Direct Torque Control of Dual Three Phase Induction Motor fed by Direct Power Control Rectifier using Fuzzy Logic Speed Controller

This paper presents an advanced Direct Torque Control (DTC) approach for a Dual Three-Phase Induction Motor (DTPIM) powered by a Direct Power Control (DPC) rectifier. Traditional control methods, such as Proportional-Integral-Derivative (PID) controllers, often face performance issues when motor system parameters vary or exhibit non-linearity. To tackle these challenges, we propose a fuzzy logic-based speed controller for DTC, which enhances adaptability to system dynamics without necessitating a precise mathematical model. The fuzzy logic controller (FLC) is particularly effective in regulating speed under varying load conditions, improving robustness, and minimizing torque ripple. Furthermore, the DPC rectifier enhances power quality by reducing harmonic distortions, maintaining a stable DC link voltage, and improving the power factor. Simulation results obtained using MATLAB/Simulink software demonstrate that the combined DTC-DPC approach with fuzzy logic control delivers a superior dynamic response with minimal overshoot. This framework offers a promising solution for high-performance industrial applications that require precise torque control and stability under fluctuating loads while also supporting sustainable energy practices through improved power efficiency

Uncertainty Quantification and Sensitivity Analysis of Concrete Structure Using Multi-Linear Regression Technique

The dynamic analysis of structures with uncertain parameters presents an attractive field of structural health monitoring in many cases of technological interest. In the dynamic analysis of hydraulic structures, such as existing dams, modeling assumptions, resulting inaccuracies, and changes in seismic loading are typically the main sources of uncertainties. Many hydraulic structures of concrete can be subjected to seismic loads. However, it is necessary to take haphazard or random phenomena as crucial considerations when assessing the security of these structures or planning new ones. This paper shows computational analysis for the characterization of the behavior of a concrete gravity dam under seismic loads, which are considered sources of uncertainties. The multi-linear regression methodology was performed and applied to evaluate the dynamic response of the considered structure. Numerous nonlinear time history analyses based on Latin Hypercube Sampling were realized to investigate the effect of uncertain parameters on the dynamic response. These analyses were applied to two types of seismic actions, the near and far earthquakes, which act on a concrete gravity dam. Then, a sensitivity analysis was used for each random variable to quantify its risk and clarify its influence on the dynamic behavior of the dam. Results divulge that for near-fault cases, major variables affecting the global sensitivity across all limit states are the Young’s modulus of soil and concrete. On the other hand, for far-fault cases, the important variables influencing the global sensitivity index include the compressive strength of concrete, Young’s modulus of soil, and cohesion

A Survey on Blockchain-driven Music Industry: Trends, Gaps, and Future Directions

The music industry is a vast field that has different stakeholders, such as artists, publishers, promoters, etc., for the creation, distribution, promotion, and monetization of music. Blockchains can help the music industry maintain the legal and ethical aspects of music creation, distribution, and incentivization while also preventing frauds owing to their intrinsic security attributes. We oversee various blockchain-driven music industry systems, where we comprehend 11 functions of blockchain-driven music industry perception and inspect them comprehensively towards music industry- and blockchain-linked attributions. We lumped a precursory sample of 89 resources by selecting the reports for filtering benchmarks looked up from E-libraries by applying a descriptive and persistent narrative synthesis-driven quality analysis methodology to identify trends, gaps, strengths, and weaknesses. Founded on the overview, in the blockchain-driven music industry, blockchain can pave the path for blockchain-based musical platforms (D1), decentralized music apps (D2), author attribution, monetization, and royalty payments (D3), preventing ticketing frauds (D4), music recommendation (D5), piracy prevention (D6), digital rights management systems (D7), music supply chain automation (D8), metadata optimization and tracking (D9), disintermediation (D10), and licensing (D11). Comprehensive inspection exposes that in the blockchain-driven music industry, 28.2% draw upon digital rights management (D7), 79.4% draw upon traditional blockchain, and 12.9% draw upon PoS/PoW consensus, drawing the hypothesis that there exists a trend toward reducing third-party reliance and improving revenue transparency and rights for artists. Another hypothesis is that there are gaps such as lack of practical implementation, lack of experimental validation under quantum attacks, and lack of focus for music ticketing fraud prevention, music recommendation, music supply chain automation, and metadata optimization and tracking. At last, we announce the capabilities and adversities to the perception of the blockchain-driven music industry and then contribute propositions to impede them along with future directions to cater to the gaps identified

GFEA: Leader Election Algorithm for Choosing a GroupDecision Support System Facilitator

Group decision support systems (GDSSs) are computer-assisted collaborative work software that facilitates group meetings asynchronously and from different locations. Even so, collaborative work in GDSS demands coordination provided by a single controlling entity known as the GDSS facilitator. However, the problem of electing a GDSS Facilitator hasn’t been treated enough in the literature, and it is often neglected. Despite that, the large number of responsibilities assigned to the facilitator makes his role crucial to the effectiveness of the group meeting. Thus, the authors focused on finding an appropriate approach for electing the facilitator. The similarities between the problematics of electing a GDSS facilitator and a distributed system leader led the authors to consider applying a distributed election algorithm for electing a GDSS facilitator. Nonetheless, current algorithms only consider computer criteria and lack a formal weighting method. Consequently, we proposed a new distributed election algorithm called GFEA (GDSS Facilitator Election Algorithm) that is designed to choose a facilitator within a GDSS. This algorithm selects a facilitator among a set of decision-makers based on multiple election criteria weighted using an objective weighting method called MEREC. A backup leader is reserved to replace the leader if he fails, and a new tie-breaking mechanism is proposed. Moreover, the initiator failure is handled. By adopting distributed system leader election principles, GFEA provides a robust solution for a decisive GDSS challenge

Identification of Plasma Proteins Associated with Alzheimer\u27s Disease Using Feature Selection Techniques and Machine Learning Algorithms

Alzheimer’s disease (AD) is a chronic, progressive neurodegenerative disorder that typically affects elderly individuals. Detecting Alzheimer’s using plasma proteins is a critical step toward improving treatment results for this disease. This study aims to use computational algorithms to explore the relationship between plasma proteins and AD progression by identifying a panel of plasma proteins that can serve as biomarkers for tracking and diagnosing AD. We applied two feature selection methods, Sequential Backward Feature Selection (SBFS) and Analysis of Variance (ANOVA) to extract significant proteins from a dataset of 146  proteins. The data was collected from the plasma of 566 individuals, comprising both Alzheimer’s patients and healthy controls. The SBFS technique generated all possible combinations of protein groups from the 146 proteins, which were then trained and tested using five machine learning models: Decision Tree, Random Forest, Extremely Randomized Trees, Extreme Gradient Boosting, and Adaptive Boosting. Subsequently, ANOVA was applied to refine and reduce the selected panel size. Finally, we used XGBoost and AdaBoost models to validate the final panel. The findings introduce a plasma protein panel consisting of A2Macro, BNP, BTC, PPP, and PYY proteins for diagnosing AD. This panel achieved a sensitivity of 88.88%, a specificity of 66.66%, and an AUC of 0.85. These results demonstrate that plasma protein biomarkers can facilitate timely interventions, potentially slowing disease progression and improving patient outcomes. This non-invasive and affordable diagnostic method has the potential to make Alzheimer’s screening accessible to a broader population

Odour, Colour and Turbidity Removal from Selected Industrial Wastewater Using Electrocoagulation Process

Electrocoagulation (EC) is an efficient electrochemical method for treating water using electric charges to destabilize and coagulate pollutants. In this study, a bipolar electrocoagulation reactor with aluminum electrodes was used to treat selected industrial wastewater. Key parameters, including odor, turbidity, Color, and other physicochemical parameters, were analyzed to evaluate the performance of the reactor. Focusing on textile wastewater and two types of cassava wastewater, fufu and starch, this study assessed Odor and turbidity removal using aluminum electrodes. Textile wastewater was used to examine Color removal. The operational parameters—voltage (10V–40V), temperature (30°C–38°C), and operating time (15 minutes to 1 hour)—were systematically varied to optimize the performance. The reactor significantly improved turbidity and color removal, with moderate odor reduction.  This study highlights the strong capability of the EC process in reducing turbidity using aluminum electrodes, despite challenges in Odor reduction. The electrocoagulation process effectively removed color, BOD, COD, TSS, and TDS from the wastewater. Voltage adjustments and electrolysis time are critical in optimizing pollutant removal and aligning with regulatory standards for industrial wastewater discharge. Despite its overall effectiveness, the challenges of achieving complete odor and BOD removal highlight areas for further research. This study highlights the potential of EC as a sustainable solution for industrial wastewater treatment