Advanced Systems Research Articles

Energy‐efficient hybrid beamforming for millimeter‐wave‐based massive multiple‐input multiple‐output system

SummaryThe advanced wireless communication system requires abridged energy consumption, enhanced data rate, and good signal coverage. The massive MIMO technology for 5G systems has been developed to accommodate several users simultaneously with superior throughput. The claim for high data rate wireless communication services is expanding quickly as time goes. Thus, the key difficulty is that as the number of users grows, the number of phase shifters grows as well, causing the system to consume more power; as a result, the system's energy efficiency decreases. Hybrid beamforming has recently emerged as an attractive technique for millimeter‐wave (mmWave) communication systems. The analog beamformer in the RF domain and digital beamformer in the baseband are coupled through a minimal number of RF chains in hybrid beamforming architecture. Hybrid beamforming utilizes fewer RF (radio frequency) chains than the total number of antennas to have a lower energy consumption design. The hybrid beamforming for a mmWave‐based massive MIMO system through different phase shifter selection mechanisms is proposed to achieve the highest energy efficiency for mmWave communications systems. The fully connected with phase shifter selection, sub‐connected with phase shifter selection (SPSS), and fully connected and sub‐connected with phase shifter selection with halved and doubled switches are considered for this research. The simulation results show the SPSS with halved switch outperforms on energy efficiency.

Intelligent library management system

A library is a collection of sources of information and similar other resources. The emergence of Intelligent Library Management Systems (ILMS) is a key trend in library automation. In libraries, the management of the book is a very complicated and time-consuming process. A Library includes books, question papers, newspaper journals, and previous year’s project details, etc. The integration of emerging technologies beyond RFID in academic libraries. The introduction lays the groundwork for exploring innovative solutions such as AI, machine learning, and data analytics, aiming to optimize library services and meet evolving user needs. This paper mainly deals with the description and evolution of library systems and their disadvantages over centuries in different parts of the world. The source of information in an Institution is a Library. Here Librarian plays a very important role in managing the Library functions such as adding new students, book data, date of issuing and returning the book, and entering all the relevant details of the books, etc. For this, the student has to wait for his /her turn as the Librarian enters data student by student. Thus, it is a very time-consuming process and costly because of high manpower requirements. An innovative Intelligent Library Management System (ILMS) utilizing AI, GSM, and RFID. Their system aims to streamline library operations and improve user experiences through advanced technology integration. This research showcases the potential of ILMS to modernize library services and meet the demands of the digital era. This paper mainly focuses on the basic library operation and some added features like viewing total books, viewing available books, updating information, searching books, and a facility to request and return books and some alert systems. This paper examines the amalgamation of AI, GSM, and RFID for advanced library management systems. Results demonstrate enhanced efficiency and user experience, showcasing the efficacy of this integrated approach. Our project introduces novel advancements in library management, exemplified by its successful deployment in a real college library. This implementation underscores the worth of our work, offering tangible improvements in efficiency and effectiveness.

Attapulgite-supported sulfidated nano Zero-Valent Iron activated persulfate advanced oxidation technology for degradation of polyethylene microplastics: Optimal design, change of particle size and degradation mechanisms

Microplastics (MPs), as a novel pollutant, have raised concerns about their potentialtoxicity and the wide-ranging ecological risks they may pose. In this study, a novel attapulgate-supported sulfidized nanoscale zero-valent iron (S-nZVI@ATP) catalyst was prepared for the activation of persulfate. We investigated the degradation efficiency and mechanism of polyethylene microplastics (PE) using the S-nZVI@ATP activated persulfate advanced oxidation process. Various factors, such as temperature,pH, catalyst dosage, and persulfate concentration, were evaluated for their influence on the oxidative reaction in the system to determine the optimal degradation conditions. The average particle size of PE decreased from 64.2±0.86μm to 28.1±0.91μm after 24 hours of oxidative degradation. The reaction progressed, TOC in the solution showed an initial increase, followed by a decrease. Some PE was oxidized into small molecules substances such as alkanes, alkenes, ketones, alcohols, and carboxylic acids. S-nZVI@ATP continuously supplied Fe2+ for persulfate activation, generating strong oxidants SO4•-, •OH, and 1O2, among which SO4•- played a dominant role in oxidation system. The heterogeneous advanced oxidation system provided in this study offers an effective reference for PE degradation and holds great promise as a method for degrading MPs in water.

Computer Vision-Based Position Estimation for an Autonomous Underwater Vehicle

Autonomous Underwater Vehicles (AUVs) are currently one of the most intensively developing branches of marine technology. Their widespread use and versatility allow them to perform tasks that, until recently, required human resources. One problem in AUVs is inadequate navigation, which results in inaccurate positioning. Weaknesses in electronic equipment lead to errors in determining a vehicle’s position during underwater missions, requiring periodic reduction of accumulated errors through the use of radio navigation systems (e.g., GNSS). However, these signals may be unavailable or deliberately distorted. Therefore, in this paper, we propose a new computer vision-based method for estimating the position of an AUV. Our method uses computer vision and deep learning techniques to generate the surroundings of the vehicle during temporary surfacing at the point where it is currently located. The next step is to compare this with the shoreline representation on the map, which is generated for a set of points that are in a specific vicinity of a point determined by dead reckoning. This method is primarily intended for low-cost vehicles without advanced navigation systems. Our results suggest that the proposed solution reduces the error in vehicle positioning to 30–60 m and can be used in incomplete shoreline representations. Further research will focus on the use of the proposed method in fully autonomous navigation systems.

Artificial intelligence and IoT driven technologies for environmental pollution monitoring and management

Detecting hazardous substances in the environment is crucial for protecting human wellbeing and ecosystems. As technology continues to advance, artificial intelligence (AI) has emerged as a promising tool for creating sensors that can effectively detect and analyze these hazardous substances. The increasing advancements in information technology have led to a growing interest in utilizing this technology for environmental pollution detection. AI-driven sensor systems, AI and Internet of Things (IoT) can be efficiently used for environmental monitoring, such as those for detecting air pollutants, water contaminants, and soil toxins. With the increasing concerns about the detrimental impact of legacy and emerging hazardous substances on ecosystems and human health, it is necessary to develop advanced monitoring systems that can efficiently detect, analyze, and respond to potential risks. Therefore, this review aims to explore recent advancements in using AI, sensors and IOTs for environmental pollution monitoring, taking into account the complexities of predicting and tracking pollution changes due to the dynamic nature of the environment. Integrating machine learning (ML) methods has the potential to revolutionize environmental science, but it also poses challenges. Important considerations include balancing model performance and interpretability, understanding ML model requirements, selecting appropriate models, and addressing concerns related to data sharing. Through examining these issues, this study seeks to highlight the latest trends in leveraging AI and IOT for environmental pollution monitoring.

Analyze and evaluate Apple’s investment potential in the current financial market

This research is centered on Apple, Inc., a prominent American-based multinational corporation renowned for its preeminent role in the design, manufacture, and distribution of advanced electronic devices alongside a diverse array of internet services. Established in 1976 by Steve Jobs and Steve Wozniak (Alex, 2015), Apple has risen to become the world’s largest company, boasting a market capitalization of $3 trillion and a listing on NASDAQ while occupying a significant position within the S&P 500 index (Bob, 2023). Apple’s influence on computing and mobile technology is unparalleled, exemplified by the widespread adoption of its macOS and iOS operating systems in personal computers and mobile devices, contributing to the global popularity of Apple products. Apple is a frontrunner in the computer sector, known for its advanced systems and hardware. MacOS is one of the three major computer operating systems, alongside Microsoft and Linux.

Autonomous Vessel Design for Efficient Marine Debris Collection: A MATLAB Simulink and Arduino-Based Approach

The growing threat that marine pollution poses to human health and biodiversity requires innovative responses. This study aims to create autonomous, unmanned vessels for collecting marine debris to prevent the negative effects of marine litter, especially plastics and industrial waste. The study uses MATLAB Simulink for dynamic modeling, simulating how the ship interacts with different debris and maritime settings. The effectiveness of advanced control systems that combine feedforward and feedback controls is evaluated regarding debris collection and pathfinding. The methodology of this study involves the design and simulation of the vessel’s hydrodynamic behavior, control systems, and debris interaction mechanisms. A scaled-down prototype has been built, integrating elements like a double-propeller hull, Arduino-driven control mechanisms, and a debris-gathering system. The prototype’s efficiency in gathering debris and mobility is demonstrated by real-world testing conducted in controlled situations. According to the results, the integration of modern control systems has been found to greatly improve the vessel’s debris collection efficiency. How the prototype functions in different situations demonstrates its ability to serve as a scalable solution for marine pollution. The result of this study emphasizes the possibility of automated, unmanned marine debris collection vessels as an environmentally friendly way to reduce marine pollution. The effective prototype testing, modeling, and simulation bring up new avenues for research and development in this area, which benefits the preservation of marine ecosystems.

Developing a rapid analytical method for simultaneous determination of apigenin and gallic acid: validation and application in a nanoliposomal formulation

Objective Apigenin and gallic acid are natural compounds that are useful as antioxidant, anti-inflammatory and anticancer agents, especially when used together in combination. Therefore, the development and validation of a simultaneous method of analysis for both compounds in pure form and when encapsulated in an advanced delivery system such as liposomes would be useful. Methods Analysis was performed using C18 column under isocratic conditions. The mobile phase was acetonitrile: water containing 0.2% orthophosphoric acid at a ratio of 67:33, flow rate 1 ml/min, and detection wavelength 334 nm for apigenin and 271 nm for gallic acid. Results The assay method was linear at the concentration range (5–600 µg/mL) with R2 of 1 for both drugs. The method was also shown to be precise and robust with RSD less than 2% with LOD (0.12, 0.1 µg/mL) and LOQ (4.14, 3.58 µg/mL) for apigenin and gallic acid respectively. The method was also applicable for the determination of the entrapment efficiency of both drugs when co-loaded in a nanoliposomal formulation. Conclusion The described HPLC method was shown to be suitable, sensitive, and reproducible for the simultaneous identification and quantification of apigenin and gallic acid. The analytical results were accurate and precise, with good recovery, low limit of detection, and the chromatographic assay was accomplished in less than 3 min, suggesting the suitability of the method for routine analysis of both drugs in pharmaceutical formulations.

Kaolin supported synergistic effects in g-C3N4/V2O5 nanocomposite systems for advanced energy storage applications

In order to investigate the synergistic potential of a new nanocomposite for improved energy storage applications, this work combines graphitic carbon nitride (g-C3N4), vanadium pentoxide (V2O5) and kaolin. Kaolin functions as a structural matrix, offering stability and support for the integration of g-C3N4 and V2O5 nanoparticles. It is well-known for its wide availability and thermal characteristics. A variety of analytical methods, such as electrochemical analysis, scanning electron microscopy and X-ray diffraction, are used to characterise the synthesised nanocomposite. The specific capacitance and cycling stability of the nanocomposite's electrochemical performance are rigorously assessed. Key issues in efficiency, stability and cost-effectiveness are addressed by an optimised material for advanced energy storage systems, which is the result of the synergistic effects coming from the unique features of each component. With a superior cyclic stability and capacitance retention of 77.7 % even after 2000 cycles, the composite material exhibits a higher specific capacitance value of 415 Fg−1 at 5 mVs−1. This work is a major step towards the creation of novel nanocomposites for high-performing, environmentally friendly energy storage systems.

Investigating the improvement of the quality of industrial effluents for reuse with added processes: coagulation, flocculation, multi-layer filter and UV

Reuse of wastewater is one of the ways to develop water resources. In addition to the need for drinking water, many industries also need high-quality water in the production line. Therefore, the purpose of the present study is to investigate the advanced treatment of the wastewater treatment plant of Morche Khort industrial town using the processes of coagulation, flocculation with aeration, multi-layer filter, and disinfection by ultraviolet radiation to increase the quality of wastewater and reuse it in industries. In this study, to investigate the effect of coagulation and flocculation units along with aeration, filtration, and disinfection by ultraviolet rays (UV), on the quality of the secondary effluent from the wastewater treatment plant of Morche Khort industrial town, they were operated on a pilot scale. Polyaluminum chloride (PAC) was used as a coagulant. Layering of three layers of sand filter, from bottom to top including granulated silica at a height of 10 cm, sand at a height of 20 cm, and activated carbon at a height of 70 cm was used. The input and output sampling points of each unit were considered. By repeating twice in five stages of flow rates of 1, 2, 4, 6, and 8 (L/min), the samples were collected to determine COD, TSS, TDS, turbidity, pH, hardness, total coliform, and fecal coliform. Jar test results showed that Alum coagulant works almost the same as PAC in removing turbidity, but the efficiency of removing organic substances by PAC coagulant is higher than that of Alum at lower doses. The results of this study showed that the efficiency of the coagulation and flocculation process in removing turbidity, COD, TSS, TDS, and fat was 56.88%, 46.66%, 38%, 23.19%, and 91.43% respectively. In the current study, the results of the wastewater entering the sand filter showed that the percentage of removal efficiency with a loading rate of 1 (L/min) was turbidity, TSS, COD, TDS, and fat was 16. 93%, 56.84%, 50%, 5.67%, 33.44% respectively. In the UV disinfection unit, the removal efficiency percentage with a loading rate of 1 (L/min) for COD, TSS, turbidity, hardness, total coliform, and fecal coliform is 16%, 3.45%, 3.58%, 5.21%, 99.88%, and 98.37% respectively. Coagulation and flocculation system—multi-layer filter and disinfection can remove chemical–physical and microbial parameters to an acceptable level for using water in advanced purification systems and also for irrigation.

A low-cost wireless extension for object detection and data logging for educational robotics using the ESP-NOW protocol.

In recent years, inexpensive and easy to use robotics platforms have been incorporated into middle school, high school, and college educational curricula and competitions all over the world. Students have access to advanced microprocessors and sensor systems that engage, educate, and encourage their creativity. In this study, the capabilities of the widely available VEX Robotics System are extended using the wireless ESP-NOW protocol to allow for real-time data logging and to extend the computational capabilities of the system. Specifically, this study presents an open source system that interfaces a VEX V5 microprocessor, an OpenMV camera, and a computer. Images from OpenMV are sent to a computer where object detection algorithms can be run and instructions sent to the VEX V5 microprocessor while system data and sensor readings are sent from the VEX V5 microprocessor to the computer. System performance was evaluated as a function of distance between transmitter and receiver, data packet round trip timing, and object detection using YoloV8. Three sample applications are detailed including the evaluation of a vision-based object sorting machine, a drivetrain trajectory analysis, and a proportional-integral-derivative (PID) control algorithm tuning experiment. It was concluded that the system is well suited for real time object detection tasks and could play an important role in improving robotics education.

Evaluation of Weibull parameters for wind energy analysis in the eastern region of the Kingdom of Morocco

This document delves into evaluating wind power potential within Morocco’s Oriental region, encompassing an extensive study of 23 locations over 43 years. The analysis was conducted using the advanced MERRA2 data reanalysis system coupled with MATLAB software. Our comprehensive study aims to map the wind energy capabilities across these sites. We employed eight distinct algorithms to adapt the Weibull distribution for the wind speed data. Additionally, the research includes an analysis of the wind rose and assesses the Capacity Factor ([Formula: see text]) to determine the most efficient periods for wind energy production. Our findings highlight that sites S4, S7, and S11 create an ideal geographic formation for wind farm placement. Within this formation, site S8, boasting a ([Formula: see text]) of 36.97%, emerges as a critical location, especially when paired with the EWT DW54 500 wind turbine model. This investigation opens new avenues for advancing wind energy in the region.

Sensor-enhanced wearables and automated analytics for injury prevention in sports

The integration of sensor technology and automated analytics in wearable devices marks a significant advancement in sports science, aiming to proactively prevent injuries and optimize athletic performance. This paper delves into the application of advanced sensor systems and automated data analysis tools in the realm of athletics. These wearables, equipped with a multitude of sensors, continuously monitor an athlete's physiological and biomechanical data, including heart rate, muscle activity, and movement dynamics. The automated processing of this data through machine learning algorithms enables real-time insights and predictive analytics, offering a novel approach to injury prevention. By analyzing the intricate patterns of an athlete's performance metrics, these sensor-augmented wearables facilitate a data-driven strategy for reducing risks of soft-tissue and heat-related injuries. This technology empowers coaches and athletes with actionable insights, fostering a safer and more efficient training environment. Our exploration showcases the transformative impact of sensor-enhanced wearables and automated analytics in elevating the standards of injury prevention and performance optimization in sports.

Modulating the Microenvironment of Coumarin Dyes within Multihierarchical Supraparticles: En Route toward Versatile Luminescent Dual‐Threshold Temperature Indicators with Ratiometric Readout

AbstractTemperature indicators trace (un)desired thermal impacts across their thermal history upon readout at every point of interest via defined irreversible signal changes. Ratiometric luminescent temperature indicators are attractive due to their fast, sensitive, contactless, spatially‐resolved, and self‐referenced readout. However, they have a limited working range as commonly only one specific temperature‐induced physicochemical effect is exploited. Herein, dual‐threshold temperature indicator supraparticles (SPs) are introduced that expand the working range of individual luminescent probes and enhance the extractable information on the thermal history. These SPs derive their functionality from the synergistic interplay of three spectrally distinguishable luminescent species assembled in one multihierarchical, micrometer‐sized particle of hybrid organic‐inorganic nature. This engineered nanostructure enables the stepwise modulation of the physicochemical microenvironment of two incorporated coumarin species at programmable threshold temperatures, resulting in distinct changes in their emission. As these changes are based on dye‐specific mechanisms that are induced in consecutive temperature regimes, the SPs achieve a scarcely reported broad working range (60–200 °C) with temperature‐dependent response times of minutes to seconds. From a broader perspective, the conceptualized SPs pioneer in how multiple individual luminescent probes with different excitation‐emission properties and indicator mechanisms are synergistically united in one advanced indicator system by precise material engineering.

Advancements in Vaccine Strategies for Chronic Liver Disease Patients: Navigating Post-COVID Challenges and Opportunities.

This review addresses the vital role of vaccinations in managing patients with chronic liver disease (CLD), especially in the context of the post-COVID-19 landscape. The pandemic has highlighted the unique vulnerabilities of CLD patients, including those awaiting liver transplantation and post-transplant individuals, who face heightened risks of infection due to compromised immune responses. Recent advancements in vaccine technology, such as mRNA platforms, novel adjuvants, and advanced delivery systems, have significantly accelerated vaccine development, enhancing both speed and efficacy. Moreover, the emergence of personalized vaccines, tailored to everyone's unique immunological profile, presents new opportunities, particularly for those with chronic conditions. This review synthesizes the current state of evidence regarding vaccine recommendations for CLD patients, focusing on their response to vaccinations and proposing effective strategies to protect this vulnerable group from vaccine-preventable diseases. It also explores the challenges in implementing these strategies and considers the impact of emerging vaccine delivery systems on improving outcomes for CLD patients. The paper aims to provide nuanced guidance on vaccination in the rapidly evolving healthcare landscape, addressing both technological innovations and comprehensive patient care strategies.

Effects of F− substitution on crystal structure, band characteristics and microwave dielectric properties of Li3Mg2NbO6 ceramics for high frequency applications

The structure–performance mechanism is essential to achieve comprehensive characteristics for advanced communication system. In the present work, F− ions were adopted to fabricated Li3Mg2NbO6-x/2Fx ceramics at a nitrogen atmosphere to modify dielectric characteristics. Correlation between bond characteristics, crystal structure and enhanced microwave dielectric characteristics was investigated through vibration analysis, P–V-L theory, Rietveld refinement, and First-principle calculations. Raman analysis and Rietveld refinement show that F− substitution led to a single phase. First-principle calculations indicates a high energy gap of 3.676 eV and p orbit contributed to Fermi energy. F− substitution led to the increase of Nb O bond ionicity and lattice energy, accounting for the variation in Q×f value and dielectric constant. The τf value was related to the expansion coefficient of the Li–O bond. Remarkably, the samples (x = 0.08) exhibited comprehensive dielectric characteristics with εr = 15.9, Q×f = 143,000 GHz and near-zero τf = −2.1 ppm/°C, proving an effective strategy for collaborative regulation. Based on structure simulation, 5G filter with ultra-low loss and high-suppression was designed through introducing transmission zeros from cross coupling. More specifically, a dielectric filter exhibited an interpolation loss of 0.2 dB at 3.3–3.6 GHz and high attenuations of 15 dB for 20 MHz outside the passband, verifying the feasibility for 5G applications.

In which rounds were the most rotations of key players made, and how did this affect physical activity? Analysis of the eight best teams of the 2018 FIFA world cup Russia

BackgroundTeam management, especially player selection, rotation, and availability, are critical issues when dealing with the high demands of modern training and gameplay. As such, research continuously seeks ways to improve these actions or implement new ideas to gain a competitive advantage through the rotation of players in the starting line-up. The current study aimed to examine the rounds of the 2018 FIFA World Cup Russia in which the most rotations of key players were made and how this affected physical activity.MethodsThe sample consisted of 110 players from the top eight teams in the 2018 World Cup Russia who played entire matches for up to 90 min in seven consecutive games. All players were divided into key players (KPs, n = 58) and non-key players (NKPs, n = 52). The analysis used data collected by an advanced motion analysis system known as STATS®, with physical activity variables analyzed, including total distance covered (TDC), distance covered with high intensity over 20 km/h (HIR), and the number of sprints undertaken. In statistical analysis, differences between categories and consecutive matches were calculated using the Kruskal-Wallis H test, and if a significant effect size was found, a multiple comparisons p values test was performed.ResultsThe best teams at the 2018 FIFA World Cup Russia used the most KP rotations with NKPs in the third match of the group stage. In addition, this was even more visible among more successful teams than less successful teams. The rotation strategy among the best eight teams allowed them to maintain the physical activity of KPs and NKPs in all rounds of the tournament.ConclusionsCoaches and coaching staff should incorporate squad rotation that includes a large group of players in their team management to improve their success. Team management expertise in player rotation during matches played over congested schedules at top tournaments maintains high levels of physical activity indicators (TDC, HIR, and sprints).

An advanced accident avoidance system based on imbalance-control ensemble and deep learning fusion design

The concept of endorsing AI in embedded systems is growing in all sectors including the development of Accident Avoidance Systems. Although real-time road crash prediction is vital for enhancing road user safety, there has been limited focus on the analysis of real-time crash events within ensemble and deep learning fused systems. The main aim of this paper is to design an advanced Accident Avoidance System established on a deep learning and ensemble fusion strategy in order to acquire more performant crash predictions. As such, four highly optimized models for crash prediction have been designed based on the popular ensemble techniques: CatBoost, AdaBoost and Bagging and the deep learning CNN. Additionally, four categories of features, including driver inputs, vehicle kinematics, driver states and weather conditions, were measured during the execution of various driving tasks performed on a driving simulator. Moreover, given the infrequent nature of crash events, an imbalance-control procedure was adopted using the SMOTE and ADASYN techniques. The highest performances results have been acquired using CatBoost along with ADASYN on almost all the adopted metrics during the different weather conditions, and more than 50% of all crashes have occurred in rainy weather conditions, whereas 31% have been exhibited in fog patterns. The sensitivity analysis results indicate that the fusing all the acquired features has the highest impact on the prediction performance. To our knowledge, there has been a limited interest, if not at all, at adopting a fused ensemble deep learning system examining the real-time impact of the adopted features’ combinations on the prediction of road crashes while taking into account class imbalance. The findings provide new insights into crash prediction and emphasize the relevance of the explanatory features which can be endorsed in designing efficient Accident Avoidance Systems.

Optimizing Vertical-Axis Wind Turbine Performance and Determining Optimal Velocity and Deflector Configuration Based on Wind Speed

In the realm of wind energy harnessing, Vertical-axis Wind Turbines (VAWTs) have emerged as promising alternatives. However, they face persistent challenges due to their low efficiency and poor self-starting properties. This study addresses these limitations and aims to unlock the full potential of VAWTs by conducting a comprehensive investigation. The focus of this research lies in determining the optimal operating velocities to enhance the performance of Savonius-type VAWTs, leveraging advanced deflectors. The study delves into an in-depth analysis of the optimal deflector distances and angles for the turbine, taking into account different geographical areas with varying wind velocities. To achieve this, a Savonius-type VAWT equipped with advanced wind deflectors was meticulously designed and tested. Utilizing SOLIDWORKS software, sophisticated turbine models were crafted, which were then subjected to rigorous analysis using ANSYS Fluent, Computational Fluid Dynamics (CFD) software. The research methodology involved conducting simulations across a range of wind velocities, spanning from 1 ms-1 to 8 ms-1, and changing the deflector distance from 100 cm to 150 cm from the centre of the turbine, and the deflector angle from 12 to 45 degrees. During the simulations, critical indicators of turbine performance, including moment coefficient, moment, force coefficient, and force acting on the blade, were closely examined. The study uncovered significant variations in the power output of the Savonius-type vertical-axis wind turbine (VAWT) fitted with an advanced deflector system, especially at lower wind speeds. Notably, the study revealed that at different wind velocities, the optimum deflector configuration changed, with the highest energy-generating capacity observed at 1 m/s for the wind turbine equipped with advanced deflectors. Outperforming the turbine lacking a deflector system, this inventive method demonstrates the capacity to notably boost energy generation. This novel method underscores the capacity for substantial improvements in energy production. Additionally, the research offers valuable insights into customized deflector distance and angle combinations, optimizing turbine performance for specific wind velocity ranges. By strategically positioning the deflectors, higher energy output can be achieved, making the VAWT system more adaptable and efficient across diverse wind conditions
 
 Keywords: Vertical-axis wind turbine, Wind deflectors, Computational Fluid Dynamics, SOLIDWORKS, ANSYS Fluent

Attribute reductions based on δ-fusion condition entropy and harmonic similarity degree in interval-valued decision systems

This paper defines an improved similarity degree based on inclusion degree as well as advanced information system based on interval coverage and credibility, and thus an attribute reduction framework embodying 4×2 = 8 reduct algorithms is systematically constructed for application and optimization in interval-valued decision systems. Firstly, a harmonic similarity degree is constructed by introducing interval inclusion degree and harmonic average mechanism, which has better semantic interpretation and robustness. Secondly, interval credibility degree and coverage degree are defined for information fusion, and they are combined to propose a δ-fusion condition entropy. The improved condition entropy achieves the information reinforcement and integrity by dual quantization fusion of credibility and coverage, and it obtains measure development from granularity monotonicity to non-monotonicity. In addition, information and joint entropies are also constructed to obtain system equations. Furthermore, 8 reduct algorithms are designed by using attribute significance for heuristic searches. Finally, data experiments show that our five novel reduct algorithms are superior to the three contrast algorithms on classification performance, which also further verify the effectiveness of proposed similarity degree, information measures and attribute reductions.