In this article,a novel metaheuristic technique named Far and Near Optimization(FNO)is introduced,offeringversatile applications across various scientific domains for optimization tasks.The core concept behind FNO lie...In this article,a novel metaheuristic technique named Far and Near Optimization(FNO)is introduced,offeringversatile applications across various scientific domains for optimization tasks.The core concept behind FNO lies inintegrating global and local search methodologies to update the algorithm population within the problem-solvingspace based on moving each member to the farthest and nearest member to itself.The paper delineates the theoryof FNO,presenting a mathematical model in two phases:(i)exploration based on the simulation of the movementof a population member towards the farthest member from itself and(ii)exploitation based on simulating themovement of a population member towards the nearest member from itself.FNO’s efficacy in tackling optimizationchallenges is assessed through its handling of the CEC 2017 test suite across problem dimensions of 10,30,50,and 100,as well as to address CEC 2020.The optimization results underscore FNO’s adeptness in exploration,exploitation,and maintaining a balance between them throughout the search process to yield viable solutions.Comparative analysis against twelve established metaheuristic algorithms reveals FNO’s superior performance.Simulation findings indicate FNO’s outperformance of competitor algorithms,securing the top rank as the mosteffective optimizer across a majority of benchmark functions.Moreover,the outcomes derived by employing FNOon twenty-two constrained optimization challenges from the CEC 2011 test suite,alongside four engineering designdilemmas,showcase the effectiveness of the suggested method in tackling real-world scenarios.展开更多
Energy storage, such as lead acid batteries, is necessary for renewable energy sources’ autonomy because of their intermittent nature, which makes them more frequently used than traditional energy sources to reduce o...Energy storage, such as lead acid batteries, is necessary for renewable energy sources’ autonomy because of their intermittent nature, which makes them more frequently used than traditional energy sources to reduce operating costs. The battery storage system has to be monitored and managed to prevent serious problems such as battery overcharging, over-discharging, overheating, battery unbalancing, thermal runaway, and fire dangers. For voltage balancing between batteries in the pack throughout the charging period and the SOC estimate, a modified lossless switching mechanism is used in this research’s suggested battery management system. The OCV state of charge calculation, in the beginning, was used in conjunction with the coulomb counting approach to estimate the SOC. The results reveal that correlation factor K has an average value of 0.3 volts when VM ≥ 12 V and an average value of 0.825 when VM ≤ 12 V. The battery monitoring system revealed that voltage balancing was accomplished during the charging process in park one after 80 seconds with a SOC difference of 1.4% between Batteries 1 and 2. On the other hand, the system estimates the state of charge during the discharging process in two packs, with a maximum DOD of 10.8 V for all batteries. The project’s objectives were met since the BMS estimated SOC and achieved voltage balance.展开更多
Switched Reluctance Motors(SRMs),outfitted with rugged construction,good speed range,high torque density,and rare earth-free nature that outweigh induction motors(IM)and permanent magnet synchronous motor(PMSM),afford...Switched Reluctance Motors(SRMs),outfitted with rugged construction,good speed range,high torque density,and rare earth-free nature that outweigh induction motors(IM)and permanent magnet synchronous motor(PMSM),afford a broad range of applications in the domain of electric vehicles(EVs).Standard copper magnetic wire and low-carbon steel laminations are used to construct SRMs,which give them high efficiency in the range of 85-95%.Despite SRM's desirable features over traditional motor-speed drives,high torque ripples and radial distortions constrain their deployment in EVs.Precise rotor position is imperative for effective management of the speed and torque of SRMs.This paper provides an illustrative compendium on review of the torque-speed control and ripple mitigation techniques using design enhancements and control methods for SRM drives for EV applications.The various schemes were evaluated on their performance metricsoperational speed range,control complexity,practical realization,need for pre-stored parameters(look-up tables of current,inductance and torque profiles)and motor controller memory requirements.The findings provide valuable insights into balancing the gains and trade-offs associated with EV applications.Furthermore,they pinpoint opportunities for enhancement by analyzing the cost and technical aspects of different SRM controllers.展开更多
As organic thin film transistors(OTFTs)are set to play a crucial role in flexible and cost-effective electronic applica-tions,this paper investigates a high-mobility 6,13-bis(triisopropylsilylethynyl)pentacene(TIPS-pe...As organic thin film transistors(OTFTs)are set to play a crucial role in flexible and cost-effective electronic applica-tions,this paper investigates a high-mobility 6,13-bis(triisopropylsilylethynyl)pentacene(TIPS-pentacene)OTFT for use in flexi-ble electronics.The development of such high-mobility devices necessitates precise device modeling to support technology opti-misation and circuit design.The details of numerical simulation technique is discussed,in which,the electrical behavior of the device is well captured by fine tuning basic semiconductor equations.This technology computer-aided design(TCAD)has been validated with exprimental data.In addition,we have discussed about compact model fitting of the devices as well as parameter extraction procedure employed.This includes verification of Silvaco ATLAS finite element method(FEM)based results against experimental data gained from fabricated OTFT devices.Simulations for p-type TFT-based inverter are also per-formed to assess the performance of compact model in simple circuit simulation.展开更多
Heating,ventilation,and air conditioning(HVAC)systems contribute substantially to global energy consumption,while rejecting significant amounts of low-grade heat into the environment.This paper presents a nonintrusive...Heating,ventilation,and air conditioning(HVAC)systems contribute substantially to global energy consumption,while rejecting significant amounts of low-grade heat into the environment.This paper presents a nonintrusive spiral-coil heat exchanger designed to recover waste heat from the outdoor condenser of a split-type air conditioner.The system operates externally without altering the existing HVAC configuration,thereby rendering it suitable for retrofitting.Water was circulated as the working fluid at flow rates of 0.028–0.052 kg/s to assess thermal performance.Performance indicators,including the outlet water temperature,heat transfer rate,convective coefficient,and efficiency,were systematically evaluated.The system achieved a maximum outlet water temperature of 67℃and a peak thermal efficiency of 91.07%at the highest flow rate.The uncertainty analysis confirmed reliable measurements within±3.45%.The monthly energy savings were estimated at 178.35 kWh,accompanied by a reduction in CO_(2)emissions of up to 187.26 kg,yielding a short payback period of 1.06 years.These results demonstrate the feasibility of spiral-coil heat exchangers as cost-effective and eco-friendly alternatives to conventional electric water heaters.The proposed approach not only enhances the overall energy utilization but also contributes to energy conservation and climate mitigation objectives.展开更多
The usage of electric vehicles holds a crucial role in lowering the diminishing of the ozone layer because electric vehicles are not dependent on fossil fuels.With more research,evaluation,and its characteristics on e...The usage of electric vehicles holds a crucial role in lowering the diminishing of the ozone layer because electric vehicles are not dependent on fossil fuels.With more research,evaluation,and its characteristics on electric vehicles,the infrastructure of charging points,production of electric vehicles,and network modelling,this paper provides a comprehensive overview of electric vehicles,and hybrid vehicles,including an analysis of their market growth,as well as different types of optimization used in the current scenario.In developing countries like India,the biggest barrier is their unfulfilled facility over the charging.Without renewable energy sources,vehicle-to-grid technology facilitates the enhancement of additional power requirements.The mobility factor has been considered an important and special characteristic of electric vehicles.展开更多
Unmanned aerial vehicles(UAVs)technology is rapidly advancing,offering innovative solutions for various industries,including the critical task of oil and gas pipeline surveillance.However,the limited flight time of co...Unmanned aerial vehicles(UAVs)technology is rapidly advancing,offering innovative solutions for various industries,including the critical task of oil and gas pipeline surveillance.However,the limited flight time of conventional UAVs presents a significant challenge to comprehensive and continuous monitoring,which is crucial for maintaining the integrity of pipeline infrastructure.This review paper evaluates methods for extending UAV flight endurance,focusing on their potential application in pipeline inspection.Through an extensive literature review,this study identifies the latest advancements in UAV technology,evaluates their effectiveness,and highlights the existing gaps in achieving prolonged flight operations.Advanced techniques,including artificial intelligence(AI),machine learning(ML),and deep learning(DL),are reviewed for their roles in pipeline monitoring.Notably,DL algorithms like You Only Look Once(YOLO)are explored for autonomous flight in UAV-based inspections,real-time defect detection,such as cracks,corrosion,and leaks,enhancing reliability and accuracy.A vital aspect of this research is the proposed deployment of a hybrid drone design combining lighter-than-air(LTA)and heavier-than-air(HTA)principles,achieving a balance of endurance and maneuverability.LTA vehicles utilize buoyancy to reduce energy consumption,thereby extending flight durations.The paper details the methodology for designing LTA vehicles,presenting an analysis of design parameters that align with the requirements for effective pipeline surveillance.The ongoing work is currently at Technology Readiness Level(TRL)4,where key components have been validated in laboratory conditions,with fabrication and flight testing planned for the next phase.Initial design analysis indicates that LTA configurations could offer significant advantages in flight endurance compared to traditional UAV designs.These findings lay the groundwork for future fabrication and testing phases,which will be critical in validating and assessing the proposed approach’s real-world applicability.By outlining the technical complexities and proposing specialized techniques tailored for pipeline monitoring,this paper provides a foundational framework for advancing UAV capabilities in the oil and gas sector.Researchers and industry practitioners can use this roadmap to further develop UAV-enabled surveillance solutions,aiming to improve the reliability,efficiency,and safety of pipeline monitoring.展开更多
Millions of people throughout the world struggle with mental health disorders,but the widespread stigma associated with these issues often prevents them from seeking treatment.We propose a novel strategy that integrat...Millions of people throughout the world struggle with mental health disorders,but the widespread stigma associated with these issues often prevents them from seeking treatment.We propose a novel strategy that integrates Internet of Medical Things(IoMT),DAG-based hedera technology,and Artificial Intelligence(AI)to overcome these challenges.We also consider the costs of chronic diseases such as Parkinson’s and Alzheimer’s,which often require 24-hour care.Using smart monitoring tools coupled with AI algorithms that can detect early indicators of deterioration,our system aims to provide low-cost,continuous support.Since IoMT data is large in volume,we need a blockchain network with high transaction throughput without compromising the privacy of patient data.To address this concern,we propose to use Hedera technology to ensure the privacy,and security of personal mental health information,scalability and a faster transaction confirmation rate.Overall,this research paper outlines a holistic approach to mental health monitoring that respects privacy,promotes accessibility,and harnesses the potential of emerging technologies.By combining IoMT,Hedera,and AI,we offer a solution that helps break down the barriers preventing individuals from seeking mental well-being support.Furthermore,comparative analysis shows that our best-performing ML models achieve an accuracy of around 98%,which is more than 30%better than traditional models such as logistic regression。展开更多
Digital content such as games,extended reality(XR),and movies has been widely and easily distributed over wireless networks.As a result,unauthorized access,copyright infringement by third parties or eavesdroppers,and ...Digital content such as games,extended reality(XR),and movies has been widely and easily distributed over wireless networks.As a result,unauthorized access,copyright infringement by third parties or eavesdroppers,and cyberattacks over these networks have become pressing concerns.Therefore,protecting copyrighted content and preventing illegal distribution in wireless communications has garnered significant attention.The Intelligent Reflecting Surface(IRS)is regarded as a promising technology for future wireless and mobile networks due to its ability to reconfigure the radio propagation environment.This study investigates the security performance of an uplink Non-Orthogonal Multiple Access(NOMA)system integrated with an IRS and employing Fountain Codes(FCs).Specifically,two users send signals to the base station at separate distances.A relay receives the signal from the nearby user first and then relays it to the base station.The IRS receives the signal from the distant user and reflects it to the relay,which then sends the reflected signal to the base station.Furthermore,a malevolent eavesdropper intercepts both user and relay communications.We construct mathematical equations for Outage Probability(OP),throughput,diversity evaluation,and Interception Probability(IP),offering quantitative insights to assess system security and performance.Additionally,OP and IP are analyzed using a Deep Neural Network(DNN)model.A deeper comprehension of the security performance of the IRS-assisted NOMA systemin signal transmission is provided by Monte Carlo simulations,which are also carried out to confirm the theoretical conclusions.展开更多
The Tourism Industry plays a critical role in economic growth on the international scene but is equally responsible for contributing to greenhouse gas emissions and energy demand.The research analyzed the global trend...The Tourism Industry plays a critical role in economic growth on the international scene but is equally responsible for contributing to greenhouse gas emissions and energy demand.The research analyzed the global trends of energy consumption(EC)within this industry concerning environmental performance,limits,and prospects of sustained expan-sion.It includes 300 tourism-related businesses across different global economic regions.Key tourism factors include EC,greenhouse gas emissions,renewable energy(RE)use,tourism’s Gross Domestic Product(GDP)contribution,and energy efficiency.Statistical methods such as regression and panel data analysis assess the impact of tourism GDP,carbon emissions and RE.The regression analysis,including linear regression and panel data regression,to assess the influence of factors such as tourism GDP,carbon emissions,RE share,and energy efficiency improvements,providing a data-driven approach to understanding EC in tourism.The findings reveal regional differences,with developed regions consuming more energy per capita,while developing markets show progress in energy-efficient practices.The findings of the linear regression analysis show tourism GDP contribution(β=4,200).The outcomes of the panel data regression analysis show the t-statistic values of carbon emissions(t=5.64).The Difference-in-Differences analysis indicates that tourist GDP is greater in developed regions(β=4,100)compared to developing regions(β=3,500).Carbon emissions(β=4,800)are greater,although RE(β=4,200)and energy efficiency(β=2,500)increase more in developing nations.The research emphasizes expanding use of RE in tourism infrastructure,especially in ecotourism and green hotels.展开更多
In this study,ten wind turbines and fourteen solar photovoltaic(SPV)modules were employed to compare the potential of hydrogen production from wind and solar energy resources in the six geopolitical zones of Nigeria.T...In this study,ten wind turbines and fourteen solar photovoltaic(SPV)modules were employed to compare the potential of hydrogen production from wind and solar energy resources in the six geopolitical zones of Nigeria.The amount of hydrogen produced was considered as a technical parameter,cost of hydrogen production was considered as an economic index,and the amount of carbon(IV)oxide saved from the use of diesel fuel was considered as an environmental index.The results reveal that ENERCON E-40 turbine yields the highest capacity factor in Lagos,Jos,Sokoto,Bauchi and Enugu sites while FUHRLAENDER,GMBH yields the highest capacity factor in Delta.The mean annual hydrogen production from wind ranged from 2.05 tons/annum at site S6(Delta)to 17.33 tons/annum at site S3(Sokoto),and the mean annual hydrogen production from SPV ranged from 64.33 tons/annum at sites S1(Lagos)to 140.28 tons/annum at site S6(Delta).The cost of hydrogen production from wind was 6.3679 and 25.9007$/kg for sites S3 and S6,respectively,and the cost of hydrogen production from SPV was 5.6659 and 6.1206$/kg for sites S3 and S1,respectively.The amount of CO_(2) saved annually from wind-based hydrogen generation was 137,267 kg/year in site S6 and 504,180 kg/year in site S3,and was used to produce electricity via fuel cells.The amount of CO_(2) saved using hydrogen produced from SPV was 615,400 kg/year and 1,341,899 kg/year in sites S1 and S6,respectively.The results also revealed that 75.55%,88.93%,80.28%,80.54%,85.65%,98.53%more hydrogen could be produced from SPV for sites S1–S6,respectively,compared to the wind resources.This study serves as a source of reliable technical information to relevant government agencies,policy makers and investors in making informed decisions on optimal investment in the hydrogen economy of Nigeria.展开更多
Reconfiguration,as well as optimal utilization of distributed generation sources and capacitor banks,are highly effective methods for reducing losses and improving the voltage profile,or in other words,the power quali...Reconfiguration,as well as optimal utilization of distributed generation sources and capacitor banks,are highly effective methods for reducing losses and improving the voltage profile,or in other words,the power quality in the power distribution system.Researchers have considered the use of distributed generation resources in recent years.There are numerous advantages to utilizing these resources,the most significant of which are the reduction of network losses and enhancement of voltage stability.Non-dominated Sorting Genetic Algorithm II(NSGA-II),Multi-Objective Particle Swarm Optimization(MOPSO),and Intersect Mutation Differential Evolution(IMDE)algorithms are used in this paper to perform optimal reconfiguration,simultaneous location,and capacity determination of distributed generation resources and capacitor banks.Three scenarios were used to replicate the studies.The reconfiguration of the switches,as well as the location and determination of the capacitor bank’s optimal capacity,were investigated in this scenario.However,in the third scenario,reconfiguration,and determining the location and capacity of the Distributed Generation(DG)resources and capacitor banks have been carried out simultaneously.Finally,the simulation results of these three algorithms are compared.The results indicate that the proposed NSGAII algorithm outperformed the other two multi-objective algorithms and was capable of maintaining smaller objective functions in all scenarios.Specifically,the energy losses were reduced from 211 to 51.35 kW(a 75.66%reduction),119.13 kW(a 43.54%reduction),and 23.13 kW(an 89.04%reduction),while the voltage stability index(VSI)decreased from 6.96 to 2.105,1.239,and 1.257,respectively,demonstrating significant improvement in the voltage profile.展开更多
The Dhofar region of Oman,renowned for its unique monsoon-influenced climate and substantial agricultural potential,faces significant challenges in achieving sustainable agricultural practices that balance productivit...The Dhofar region of Oman,renowned for its unique monsoon-influenced climate and substantial agricultural potential,faces significant challenges in achieving sustainable agricultural practices that balance productivity with environmental conservation.This review critically explores a range of sustainable agricultural methods currently im-plemented in the region,including organic farming,water conservation techniques such as drip irrigation and rainwater harvesting,agroforestry systems,crop rotation,and soil conservation measures like terracing and composting.These strategies aim to mitigate pressing environmental concerns such as water scarcity,soil erosion,and land degradation while enhancing crop yield and farm profitability.The review further examines the economic implications of these practices, evaluating their cost-effectiveness, potential for long-term returns, and influence on the growing market demandfor organic and eco-friendly products. Despite their benefits, the broader adoption of these sustainable approaches ishindered by several challenges, including limited access to advanced technologies, inadequate financial resources, lackof technical knowledge, and minimal awareness among local farmers. The article also assesses the role of governmentalpolicies, subsidies, and extension services in promoting the adoption of sustainable agriculture in Dhofar. Finally, it offersstrategic recommendations for future research, policy development, and capacity-building initiatives. This reviewemphasizes the urgent need for continued investment in sustainable solutions to ensure long-term agricultural resilienceand environmental sustainability in the region.展开更多
Membrane fouling is a persistent challenge in membrane-based technologies,significantly impacting efficiency,operational costs,and system lifespan in applications like water treatment,desalination,and industrial proce...Membrane fouling is a persistent challenge in membrane-based technologies,significantly impacting efficiency,operational costs,and system lifespan in applications like water treatment,desalination,and industrial processing.Foul-ing,caused by the accumulation of particulates,organic compounds,and microorganisms,leads to reduced permeability,increased energy demands,and frequent maintenance.Traditional fouling control approaches,relying on empirical models and reactive strategies,often fail to address these issues efficiently.In this context,artificial intelligence(AI)and machine learning(ML)have emerged as innovative tools offering predictive and proactive solutions for fouling man-agement.By utilizing historical and real-time data,AI/ML techniques such as artificial neural networks,support vector machines,and ensemble models enable accurate prediction of fouling onset,identification of fouling mechanisms,and optimization of control measures.This review provides a detailed examination of the integration of AI/ML in membrane fouling prediction and mitigation,discussing advanced algorithms,the role of sensor-based monitoring,and the importance of robust datasets in enhancing predictive accuracy.Case studies highlighting successful AI/ML applications across various membrane processes are presented,demonstrating their transformative potential in improving system performance.Emerging trends,such as hybrid modeling and IoT-enabled smart systems,are explored,alongside a criti-cal analysis of research gaps and opportunities.This review emphasizes AI/ML as a cornerstone for sustainable,cost-effective membrane operations.展开更多
Automotive radar has emerged as a critical component in Advanced Driver Assistance Systems(ADAS)and autonomous driving,enabling robust environmental perception through precise range-Doppler and angular measurements.It...Automotive radar has emerged as a critical component in Advanced Driver Assistance Systems(ADAS)and autonomous driving,enabling robust environmental perception through precise range-Doppler and angular measurements.It plays a pivotal role in enhancing road safety by supporting accurate detection and localization of surrounding objects.However,real-world deployment of automotive radar faces significant challenges,including mutual interference among radar units and dense clutter due to multiple dynamic targets,which demand advanced signal processing solutions beyond conventional methodologies.This paper presents a comprehensive review of traditional signal processing techniques and recent advancements specifically designed to address contemporary operational challenges in automotive radar.Emphasis is placed on direction-of-arrival(DoA)estimation algorithms such as Bartlett beamforming,Minimum Variance Distortionless Response(MVDR),Multiple Signal Classification(MUSIC),and Estimation of Signal Parameters via Rotational Invariance Techniques(ESPRIT).Among these,ESPRIT offers superior resolution for multi-target scenarios with reduced computational complexity compared to MUSIC,making it particularly advantageous for real-time applications.Furthermore,the study evaluates state-of-the-art tracking algorithms,including the Kalman Filter(KF),Extended KF(EKF),Unscented KF,and Bayesian filter.EKF is especially suitable for radar systems due to its capability to linearize nonlinear measurement models.The integration of machine learning approaches for target detection and classification is also discussed,highlighting the trade-off between the simplicity of implementation in K-Nearest Neighbors(KNN)and the enhanced accuracy provided by Support Vector Machines(SVM).A brief overview of benchmark radar datasets,performance metrics,and relevant standards is included to support future research.The paper concludes by outlining ongoing challenges and identifying promising research directions in automotive radar signal processing,particularly in the context of increasingly complex traffic scenarios and autonomous navigation systems.展开更多
This study systematically reviews the applications of generative artificial intelligence(GAI)in breast cancer research,focusing on its role in diagnosis and therapeutic development.While GAI has gained significant att...This study systematically reviews the applications of generative artificial intelligence(GAI)in breast cancer research,focusing on its role in diagnosis and therapeutic development.While GAI has gained significant attention across various domains,its utility in breast cancer research has yet to be comprehensively reviewed.This study aims to fill that gap by synthesizing existing research into a unified document.A comprehensive search was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)guidelines,resulting in the retrieval of 3827 articles,of which 31 were deemed eligible for analysis.The included studies were categorized based on key criteria,such as application types,geographical distribution,contributing organizations,leading journals,publishers,and temporal trends.Keyword co-occurrence mapping and subject profiling further highlighted the major research themes in this field.The findings reveal that GAI models have been applied to improve breast cancer diagnosis,treatment planning,and outcome predictions.Geographical and network analyses showed that most contributions come from a few leading institutions,with limited global collaboration.The review also identifies key challenges in implementing GAI in clinical practice,such as data availability,ethical concerns,and model validation.Despite these challenges,the study highlights GAI’s potential to enhance breast cancer research,particularly in generating synthetic data,improving diagnostic accuracy,and personalizing treatment approaches.This review serves as a valuable resource for researchers and stakeholders,providing insights into current research trends,major contributors,and collaborative networks in GAI-based breast cancer studies.By offering a holistic overview,it aims to support future research directions and encourage broader adoption of GAI technologies in healthcare.Additionally,the study emphasizes the importance of overcoming implementation barriers to fully realizeGAI’s potential in transforming breast cancer management.展开更多
The Sine and Wormhole Energy Whale Optimization Algorithm(SWEWOA)represents an advanced solution method for resolving Optimal Power Flow(OPF)problems in power systems equipped with Flexible AC Transmission System(FACT...The Sine and Wormhole Energy Whale Optimization Algorithm(SWEWOA)represents an advanced solution method for resolving Optimal Power Flow(OPF)problems in power systems equipped with Flexible AC Transmission System(FACTS)devices which include Thyristor-Controlled Series Compensator(TCSC),Thyristor-Controlled Phase Shifter(TCPS),and Static Var Compensator(SVC).SWEWOA expands Whale Optimization Algorithm(WOA)through the integration of sine and wormhole energy features thus improving exploration and exploitation capabilities for efficient convergence in complex non-linear OPF problems.A performance evaluation of SWEWOA takes place on the IEEE-30 bus test system through static and dynamic loading scenarios where it demonstrates better results than five contemporary algorithms:Adaptive Chaotic WOA(ACWOA),WOA,Chaotic WOA(CWOA),Sine Cosine Algorithm Differential Evolution(SCADE),and Hybrid Grey Wolf Optimization(HGWO).The research shows that SWEWOA delivers superior generation cost reduction than other algorithms by reaching a minimum of 0.9%better performance.SWEWOA demonstrates superior power loss performance by achieving(P_(loss,min))at the lowest level compared to all other tested algorithms which leads to better system energy efficiency.The dynamic loading performance of SWEWOA leads to a 4.38%reduction in gross costs which proves its capability to handle different operating conditions.The algorithm achieves top performance in Friedman Rank Test(FRT)assessments through multiple performance metrics which verifies its consistent reliability and strong stability during changing power demands.The repeated simulations show that SWEWOA generates mean costs(C_(gen,min))and mean power loss values(P_(loss,min))with small deviations which indicate its capability to maintain cost-effective solutions in each simulation run.SWEWOA demonstrates great potential as an advanced optimization solution for power system operations through the results presented in this study.展开更多
The management of agricultural wastes is essential for resource conservation and environmental sustainability.Due to escalating worries regarding plastic pollution and the surging expenses linked to petroleum-based pl...The management of agricultural wastes is essential for resource conservation and environmental sustainability.Due to escalating worries regarding plastic pollution and the surging expenses linked to petroleum-based plastics,there has been a notable transition towards the creation of biodegradable alternatives sourced from natural materials.Biofibres and bioplastics,especially those derived from agricultural waste,have garnered significant attention for their prospective uses in food packaging,biomedical sciences,and sustainable manufacturing.This study examines the viability of employing banana peel as a natural and environmentally sustainable raw material for the production of biodegradable bioplastic sheets.Due to its abundant polysaccharides and lignocellulosic fibers,banana peel presents advantageous structural and mechanical characteristics for bioplastic manufacturing.Experimental findings demonstrate that bioplastic derived from banana peels has enhanced biodegradability and environmental compatibility relative to traditional synthetic plastics,positioning it as a feasible alternative to mitigate the worldwide plastic waste epidemic.An optimal formulation was constructed using Design Expert software,comprising 55.38 g of banana peel,27.63 g of fish scales,and 20 g of chitosan powder.This formulation improves the film’s tensile strength,flexibility,and degradation rate,ensuring its efficacy in industrial applications including food packaging and molding.The study’s results highlight the promise of bioplastics made from banana peels as an economical and sustainable alternative,decreasing dependence on petroleum-based plastics and alleviating environmental pollution.展开更多
The results of mass appraisal in many countries are used as a basis for calculating the amount of real estate tax,therefore,regardless of the methods used to calculate it,the resulting value should be as close as poss...The results of mass appraisal in many countries are used as a basis for calculating the amount of real estate tax,therefore,regardless of the methods used to calculate it,the resulting value should be as close as possible to the market value of the real estate to maintain a balance of interests between the state and the rights holders.In practice,this condition is not always met,since,firstly,the quality of market data is often very low,and secondly,some markets are characterized by low activity,which is expressed in a deficit of information on asking prices.The aim of the work is ecological valuation of land use:how regression-based mass appraisal can inform ecological conservation,land degradation,and sustainable land management.Four multiple regression models were constructed for AI generated map of land plots for recreational use in St.Petersburg(Russia)with different volumes of market information(32,30,20 and 15 units of market information with four price-forming factors).During the analysis of the quality of the models,it was revealed that the best result is shown by the model built on the maximum sample size,then the model based on 15 analogs,which proves that a larger number of analog objects does not always allow us to achieve better results,since the more analog objects there are.展开更多
Artificial intelligence(AI)is transforming the tourism industry and affecting on natural ecology,making it more environmentally friendly,efficient and personalized.In 2025,AI technologies are being actively implemente...Artificial intelligence(AI)is transforming the tourism industry and affecting on natural ecology,making it more environmentally friendly,efficient and personalized.In 2025,AI technologies are being actively implemented to reduce the carbon footprint,optimize resources,and improve the travel experience.Here are the key applications of AI in environmentally sustainable smart tourism:AI in smart tourism is not just a technological trend,but a necessity for the sustainable development of the industry.Paper analyses personalized and green travel experience and smart tourism.AI-based applications(Google ARCore)allow tourists to get information about attractions without paper booklets.Virtual tours reduce the need for physical travel by reducing the carbon footprint.Platforms offer routes with minimal impact on nature(for example,hiking trails instead of car tours).Tourists can offset their carbon footprint through AI tools by financing tree planting.The introduction of AI solutions allows combining economic benefits with environmental responsibility,creating a future where travel becomes safer for the planet.Paper confirms idea about sustainable tourism development in developing countries and focus on premium ecotourism.Instead of mass tourism,AI helps promote unique destinations(safaris,diving,ethnographic tours),which increases income with less environmental damage.Smart cities with AI-driven transport and energy-saving solutions make tourism more sustainable.展开更多
文摘In this article,a novel metaheuristic technique named Far and Near Optimization(FNO)is introduced,offeringversatile applications across various scientific domains for optimization tasks.The core concept behind FNO lies inintegrating global and local search methodologies to update the algorithm population within the problem-solvingspace based on moving each member to the farthest and nearest member to itself.The paper delineates the theoryof FNO,presenting a mathematical model in two phases:(i)exploration based on the simulation of the movementof a population member towards the farthest member from itself and(ii)exploitation based on simulating themovement of a population member towards the nearest member from itself.FNO’s efficacy in tackling optimizationchallenges is assessed through its handling of the CEC 2017 test suite across problem dimensions of 10,30,50,and 100,as well as to address CEC 2020.The optimization results underscore FNO’s adeptness in exploration,exploitation,and maintaining a balance between them throughout the search process to yield viable solutions.Comparative analysis against twelve established metaheuristic algorithms reveals FNO’s superior performance.Simulation findings indicate FNO’s outperformance of competitor algorithms,securing the top rank as the mosteffective optimizer across a majority of benchmark functions.Moreover,the outcomes derived by employing FNOon twenty-two constrained optimization challenges from the CEC 2011 test suite,alongside four engineering designdilemmas,showcase the effectiveness of the suggested method in tackling real-world scenarios.
文摘Energy storage, such as lead acid batteries, is necessary for renewable energy sources’ autonomy because of their intermittent nature, which makes them more frequently used than traditional energy sources to reduce operating costs. The battery storage system has to be monitored and managed to prevent serious problems such as battery overcharging, over-discharging, overheating, battery unbalancing, thermal runaway, and fire dangers. For voltage balancing between batteries in the pack throughout the charging period and the SOC estimate, a modified lossless switching mechanism is used in this research’s suggested battery management system. The OCV state of charge calculation, in the beginning, was used in conjunction with the coulomb counting approach to estimate the SOC. The results reveal that correlation factor K has an average value of 0.3 volts when VM ≥ 12 V and an average value of 0.825 when VM ≤ 12 V. The battery monitoring system revealed that voltage balancing was accomplished during the charging process in park one after 80 seconds with a SOC difference of 1.4% between Batteries 1 and 2. On the other hand, the system estimates the state of charge during the discharging process in two packs, with a maximum DOD of 10.8 V for all batteries. The project’s objectives were met since the BMS estimated SOC and achieved voltage balance.
基金supported in part by the Universitat Politècnica de València under grant PAID-10-21supported through AMRITA Seed Grant(Proposal ID:ASG2022188)。
文摘Switched Reluctance Motors(SRMs),outfitted with rugged construction,good speed range,high torque density,and rare earth-free nature that outweigh induction motors(IM)and permanent magnet synchronous motor(PMSM),afford a broad range of applications in the domain of electric vehicles(EVs).Standard copper magnetic wire and low-carbon steel laminations are used to construct SRMs,which give them high efficiency in the range of 85-95%.Despite SRM's desirable features over traditional motor-speed drives,high torque ripples and radial distortions constrain their deployment in EVs.Precise rotor position is imperative for effective management of the speed and torque of SRMs.This paper provides an illustrative compendium on review of the torque-speed control and ripple mitigation techniques using design enhancements and control methods for SRM drives for EV applications.The various schemes were evaluated on their performance metricsoperational speed range,control complexity,practical realization,need for pre-stored parameters(look-up tables of current,inductance and torque profiles)and motor controller memory requirements.The findings provide valuable insights into balancing the gains and trade-offs associated with EV applications.Furthermore,they pinpoint opportunities for enhancement by analyzing the cost and technical aspects of different SRM controllers.
基金The DST government of India is appreciated by the researchers for giving them the early career research grant under the project ECR/2017/000179。
文摘As organic thin film transistors(OTFTs)are set to play a crucial role in flexible and cost-effective electronic applica-tions,this paper investigates a high-mobility 6,13-bis(triisopropylsilylethynyl)pentacene(TIPS-pentacene)OTFT for use in flexi-ble electronics.The development of such high-mobility devices necessitates precise device modeling to support technology opti-misation and circuit design.The details of numerical simulation technique is discussed,in which,the electrical behavior of the device is well captured by fine tuning basic semiconductor equations.This technology computer-aided design(TCAD)has been validated with exprimental data.In addition,we have discussed about compact model fitting of the devices as well as parameter extraction procedure employed.This includes verification of Silvaco ATLAS finite element method(FEM)based results against experimental data gained from fabricated OTFT devices.Simulations for p-type TFT-based inverter are also per-formed to assess the performance of compact model in simple circuit simulation.
文摘Heating,ventilation,and air conditioning(HVAC)systems contribute substantially to global energy consumption,while rejecting significant amounts of low-grade heat into the environment.This paper presents a nonintrusive spiral-coil heat exchanger designed to recover waste heat from the outdoor condenser of a split-type air conditioner.The system operates externally without altering the existing HVAC configuration,thereby rendering it suitable for retrofitting.Water was circulated as the working fluid at flow rates of 0.028–0.052 kg/s to assess thermal performance.Performance indicators,including the outlet water temperature,heat transfer rate,convective coefficient,and efficiency,were systematically evaluated.The system achieved a maximum outlet water temperature of 67℃and a peak thermal efficiency of 91.07%at the highest flow rate.The uncertainty analysis confirmed reliable measurements within±3.45%.The monthly energy savings were estimated at 178.35 kWh,accompanied by a reduction in CO_(2)emissions of up to 187.26 kg,yielding a short payback period of 1.06 years.These results demonstrate the feasibility of spiral-coil heat exchangers as cost-effective and eco-friendly alternatives to conventional electric water heaters.The proposed approach not only enhances the overall energy utilization but also contributes to energy conservation and climate mitigation objectives.
文摘The usage of electric vehicles holds a crucial role in lowering the diminishing of the ozone layer because electric vehicles are not dependent on fossil fuels.With more research,evaluation,and its characteristics on electric vehicles,the infrastructure of charging points,production of electric vehicles,and network modelling,this paper provides a comprehensive overview of electric vehicles,and hybrid vehicles,including an analysis of their market growth,as well as different types of optimization used in the current scenario.In developing countries like India,the biggest barrier is their unfulfilled facility over the charging.Without renewable energy sources,vehicle-to-grid technology facilitates the enhancement of additional power requirements.The mobility factor has been considered an important and special characteristic of electric vehicles.
基金supported by the Yayasan Universiti Teknologi PETRONAS(YUTP)under Cost Center 015LC0-485.
文摘Unmanned aerial vehicles(UAVs)technology is rapidly advancing,offering innovative solutions for various industries,including the critical task of oil and gas pipeline surveillance.However,the limited flight time of conventional UAVs presents a significant challenge to comprehensive and continuous monitoring,which is crucial for maintaining the integrity of pipeline infrastructure.This review paper evaluates methods for extending UAV flight endurance,focusing on their potential application in pipeline inspection.Through an extensive literature review,this study identifies the latest advancements in UAV technology,evaluates their effectiveness,and highlights the existing gaps in achieving prolonged flight operations.Advanced techniques,including artificial intelligence(AI),machine learning(ML),and deep learning(DL),are reviewed for their roles in pipeline monitoring.Notably,DL algorithms like You Only Look Once(YOLO)are explored for autonomous flight in UAV-based inspections,real-time defect detection,such as cracks,corrosion,and leaks,enhancing reliability and accuracy.A vital aspect of this research is the proposed deployment of a hybrid drone design combining lighter-than-air(LTA)and heavier-than-air(HTA)principles,achieving a balance of endurance and maneuverability.LTA vehicles utilize buoyancy to reduce energy consumption,thereby extending flight durations.The paper details the methodology for designing LTA vehicles,presenting an analysis of design parameters that align with the requirements for effective pipeline surveillance.The ongoing work is currently at Technology Readiness Level(TRL)4,where key components have been validated in laboratory conditions,with fabrication and flight testing planned for the next phase.Initial design analysis indicates that LTA configurations could offer significant advantages in flight endurance compared to traditional UAV designs.These findings lay the groundwork for future fabrication and testing phases,which will be critical in validating and assessing the proposed approach’s real-world applicability.By outlining the technical complexities and proposing specialized techniques tailored for pipeline monitoring,this paper provides a foundational framework for advancing UAV capabilities in the oil and gas sector.Researchers and industry practitioners can use this roadmap to further develop UAV-enabled surveillance solutions,aiming to improve the reliability,efficiency,and safety of pipeline monitoring.
基金supported by CHANAKYA Fellowship Program of TIH Foundation for IoT&IoE(TIH-IoT)received by Dr.Vinay Chamola under Project Grant File CFP/2022/027.
文摘Millions of people throughout the world struggle with mental health disorders,but the widespread stigma associated with these issues often prevents them from seeking treatment.We propose a novel strategy that integrates Internet of Medical Things(IoMT),DAG-based hedera technology,and Artificial Intelligence(AI)to overcome these challenges.We also consider the costs of chronic diseases such as Parkinson’s and Alzheimer’s,which often require 24-hour care.Using smart monitoring tools coupled with AI algorithms that can detect early indicators of deterioration,our system aims to provide low-cost,continuous support.Since IoMT data is large in volume,we need a blockchain network with high transaction throughput without compromising the privacy of patient data.To address this concern,we propose to use Hedera technology to ensure the privacy,and security of personal mental health information,scalability and a faster transaction confirmation rate.Overall,this research paper outlines a holistic approach to mental health monitoring that respects privacy,promotes accessibility,and harnesses the potential of emerging technologies.By combining IoMT,Hedera,and AI,we offer a solution that helps break down the barriers preventing individuals from seeking mental well-being support.Furthermore,comparative analysis shows that our best-performing ML models achieve an accuracy of around 98%,which is more than 30%better than traditional models such as logistic regression。
基金supported in part by Vietnam National Foundation for Science and Technology Development(NAFOSTED)under Grant 102.04-2021.57in part by Culture,Sports and Tourism R&D Program through the Korea Creative Content Agency grant funded by the Ministry of Culture,Sports and Tourism in 2024(Project Name:Global Talent Training Program for Copyright Management Technology in Game Contents,Project Number:RS-2024-00396709,Contribution Rate:100%).
文摘Digital content such as games,extended reality(XR),and movies has been widely and easily distributed over wireless networks.As a result,unauthorized access,copyright infringement by third parties or eavesdroppers,and cyberattacks over these networks have become pressing concerns.Therefore,protecting copyrighted content and preventing illegal distribution in wireless communications has garnered significant attention.The Intelligent Reflecting Surface(IRS)is regarded as a promising technology for future wireless and mobile networks due to its ability to reconfigure the radio propagation environment.This study investigates the security performance of an uplink Non-Orthogonal Multiple Access(NOMA)system integrated with an IRS and employing Fountain Codes(FCs).Specifically,two users send signals to the base station at separate distances.A relay receives the signal from the nearby user first and then relays it to the base station.The IRS receives the signal from the distant user and reflects it to the relay,which then sends the reflected signal to the base station.Furthermore,a malevolent eavesdropper intercepts both user and relay communications.We construct mathematical equations for Outage Probability(OP),throughput,diversity evaluation,and Interception Probability(IP),offering quantitative insights to assess system security and performance.Additionally,OP and IP are analyzed using a Deep Neural Network(DNN)model.A deeper comprehension of the security performance of the IRS-assisted NOMA systemin signal transmission is provided by Monte Carlo simulations,which are also carried out to confirm the theoretical conclusions.
文摘The Tourism Industry plays a critical role in economic growth on the international scene but is equally responsible for contributing to greenhouse gas emissions and energy demand.The research analyzed the global trends of energy consumption(EC)within this industry concerning environmental performance,limits,and prospects of sustained expan-sion.It includes 300 tourism-related businesses across different global economic regions.Key tourism factors include EC,greenhouse gas emissions,renewable energy(RE)use,tourism’s Gross Domestic Product(GDP)contribution,and energy efficiency.Statistical methods such as regression and panel data analysis assess the impact of tourism GDP,carbon emissions and RE.The regression analysis,including linear regression and panel data regression,to assess the influence of factors such as tourism GDP,carbon emissions,RE share,and energy efficiency improvements,providing a data-driven approach to understanding EC in tourism.The findings reveal regional differences,with developed regions consuming more energy per capita,while developing markets show progress in energy-efficient practices.The findings of the linear regression analysis show tourism GDP contribution(β=4,200).The outcomes of the panel data regression analysis show the t-statistic values of carbon emissions(t=5.64).The Difference-in-Differences analysis indicates that tourist GDP is greater in developed regions(β=4,100)compared to developing regions(β=3,500).Carbon emissions(β=4,800)are greater,although RE(β=4,200)and energy efficiency(β=2,500)increase more in developing nations.The research emphasizes expanding use of RE in tourism infrastructure,especially in ecotourism and green hotels.
基金supported by Abiola Ajimobi Technical UniversityUniversity of Ibadan
文摘In this study,ten wind turbines and fourteen solar photovoltaic(SPV)modules were employed to compare the potential of hydrogen production from wind and solar energy resources in the six geopolitical zones of Nigeria.The amount of hydrogen produced was considered as a technical parameter,cost of hydrogen production was considered as an economic index,and the amount of carbon(IV)oxide saved from the use of diesel fuel was considered as an environmental index.The results reveal that ENERCON E-40 turbine yields the highest capacity factor in Lagos,Jos,Sokoto,Bauchi and Enugu sites while FUHRLAENDER,GMBH yields the highest capacity factor in Delta.The mean annual hydrogen production from wind ranged from 2.05 tons/annum at site S6(Delta)to 17.33 tons/annum at site S3(Sokoto),and the mean annual hydrogen production from SPV ranged from 64.33 tons/annum at sites S1(Lagos)to 140.28 tons/annum at site S6(Delta).The cost of hydrogen production from wind was 6.3679 and 25.9007$/kg for sites S3 and S6,respectively,and the cost of hydrogen production from SPV was 5.6659 and 6.1206$/kg for sites S3 and S1,respectively.The amount of CO_(2) saved annually from wind-based hydrogen generation was 137,267 kg/year in site S6 and 504,180 kg/year in site S3,and was used to produce electricity via fuel cells.The amount of CO_(2) saved using hydrogen produced from SPV was 615,400 kg/year and 1,341,899 kg/year in sites S1 and S6,respectively.The results also revealed that 75.55%,88.93%,80.28%,80.54%,85.65%,98.53%more hydrogen could be produced from SPV for sites S1–S6,respectively,compared to the wind resources.This study serves as a source of reliable technical information to relevant government agencies,policy makers and investors in making informed decisions on optimal investment in the hydrogen economy of Nigeria.
文摘Reconfiguration,as well as optimal utilization of distributed generation sources and capacitor banks,are highly effective methods for reducing losses and improving the voltage profile,or in other words,the power quality in the power distribution system.Researchers have considered the use of distributed generation resources in recent years.There are numerous advantages to utilizing these resources,the most significant of which are the reduction of network losses and enhancement of voltage stability.Non-dominated Sorting Genetic Algorithm II(NSGA-II),Multi-Objective Particle Swarm Optimization(MOPSO),and Intersect Mutation Differential Evolution(IMDE)algorithms are used in this paper to perform optimal reconfiguration,simultaneous location,and capacity determination of distributed generation resources and capacitor banks.Three scenarios were used to replicate the studies.The reconfiguration of the switches,as well as the location and determination of the capacitor bank’s optimal capacity,were investigated in this scenario.However,in the third scenario,reconfiguration,and determining the location and capacity of the Distributed Generation(DG)resources and capacitor banks have been carried out simultaneously.Finally,the simulation results of these three algorithms are compared.The results indicate that the proposed NSGAII algorithm outperformed the other two multi-objective algorithms and was capable of maintaining smaller objective functions in all scenarios.Specifically,the energy losses were reduced from 211 to 51.35 kW(a 75.66%reduction),119.13 kW(a 43.54%reduction),and 23.13 kW(an 89.04%reduction),while the voltage stability index(VSI)decreased from 6.96 to 2.105,1.239,and 1.257,respectively,demonstrating significant improvement in the voltage profile.
文摘The Dhofar region of Oman,renowned for its unique monsoon-influenced climate and substantial agricultural potential,faces significant challenges in achieving sustainable agricultural practices that balance productivity with environmental conservation.This review critically explores a range of sustainable agricultural methods currently im-plemented in the region,including organic farming,water conservation techniques such as drip irrigation and rainwater harvesting,agroforestry systems,crop rotation,and soil conservation measures like terracing and composting.These strategies aim to mitigate pressing environmental concerns such as water scarcity,soil erosion,and land degradation while enhancing crop yield and farm profitability.The review further examines the economic implications of these practices, evaluating their cost-effectiveness, potential for long-term returns, and influence on the growing market demandfor organic and eco-friendly products. Despite their benefits, the broader adoption of these sustainable approaches ishindered by several challenges, including limited access to advanced technologies, inadequate financial resources, lackof technical knowledge, and minimal awareness among local farmers. The article also assesses the role of governmentalpolicies, subsidies, and extension services in promoting the adoption of sustainable agriculture in Dhofar. Finally, it offersstrategic recommendations for future research, policy development, and capacity-building initiatives. This reviewemphasizes the urgent need for continued investment in sustainable solutions to ensure long-term agricultural resilienceand environmental sustainability in the region.
文摘Membrane fouling is a persistent challenge in membrane-based technologies,significantly impacting efficiency,operational costs,and system lifespan in applications like water treatment,desalination,and industrial processing.Foul-ing,caused by the accumulation of particulates,organic compounds,and microorganisms,leads to reduced permeability,increased energy demands,and frequent maintenance.Traditional fouling control approaches,relying on empirical models and reactive strategies,often fail to address these issues efficiently.In this context,artificial intelligence(AI)and machine learning(ML)have emerged as innovative tools offering predictive and proactive solutions for fouling man-agement.By utilizing historical and real-time data,AI/ML techniques such as artificial neural networks,support vector machines,and ensemble models enable accurate prediction of fouling onset,identification of fouling mechanisms,and optimization of control measures.This review provides a detailed examination of the integration of AI/ML in membrane fouling prediction and mitigation,discussing advanced algorithms,the role of sensor-based monitoring,and the importance of robust datasets in enhancing predictive accuracy.Case studies highlighting successful AI/ML applications across various membrane processes are presented,demonstrating their transformative potential in improving system performance.Emerging trends,such as hybrid modeling and IoT-enabled smart systems,are explored,alongside a criti-cal analysis of research gaps and opportunities.This review emphasizes AI/ML as a cornerstone for sustainable,cost-effective membrane operations.
基金supported in part by the National Science and Technology Council,Taiwan:NSTC 113-2410-H-030-077-MY2.
文摘Automotive radar has emerged as a critical component in Advanced Driver Assistance Systems(ADAS)and autonomous driving,enabling robust environmental perception through precise range-Doppler and angular measurements.It plays a pivotal role in enhancing road safety by supporting accurate detection and localization of surrounding objects.However,real-world deployment of automotive radar faces significant challenges,including mutual interference among radar units and dense clutter due to multiple dynamic targets,which demand advanced signal processing solutions beyond conventional methodologies.This paper presents a comprehensive review of traditional signal processing techniques and recent advancements specifically designed to address contemporary operational challenges in automotive radar.Emphasis is placed on direction-of-arrival(DoA)estimation algorithms such as Bartlett beamforming,Minimum Variance Distortionless Response(MVDR),Multiple Signal Classification(MUSIC),and Estimation of Signal Parameters via Rotational Invariance Techniques(ESPRIT).Among these,ESPRIT offers superior resolution for multi-target scenarios with reduced computational complexity compared to MUSIC,making it particularly advantageous for real-time applications.Furthermore,the study evaluates state-of-the-art tracking algorithms,including the Kalman Filter(KF),Extended KF(EKF),Unscented KF,and Bayesian filter.EKF is especially suitable for radar systems due to its capability to linearize nonlinear measurement models.The integration of machine learning approaches for target detection and classification is also discussed,highlighting the trade-off between the simplicity of implementation in K-Nearest Neighbors(KNN)and the enhanced accuracy provided by Support Vector Machines(SVM).A brief overview of benchmark radar datasets,performance metrics,and relevant standards is included to support future research.The paper concludes by outlining ongoing challenges and identifying promising research directions in automotive radar signal processing,particularly in the context of increasingly complex traffic scenarios and autonomous navigation systems.
基金financial support from the Fundamental Research Grant Scheme(FRGS)under grant number:FRGS/1/2024/ICT02/TARUMT/02/1from the Ministry of Higher Education Malaysiafunded in part by the internal grant from the Tunku Abdul Rahman University of Management and Technology(TAR UMT)with grant number:UC/I/G2024-00129.
文摘This study systematically reviews the applications of generative artificial intelligence(GAI)in breast cancer research,focusing on its role in diagnosis and therapeutic development.While GAI has gained significant attention across various domains,its utility in breast cancer research has yet to be comprehensively reviewed.This study aims to fill that gap by synthesizing existing research into a unified document.A comprehensive search was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)guidelines,resulting in the retrieval of 3827 articles,of which 31 were deemed eligible for analysis.The included studies were categorized based on key criteria,such as application types,geographical distribution,contributing organizations,leading journals,publishers,and temporal trends.Keyword co-occurrence mapping and subject profiling further highlighted the major research themes in this field.The findings reveal that GAI models have been applied to improve breast cancer diagnosis,treatment planning,and outcome predictions.Geographical and network analyses showed that most contributions come from a few leading institutions,with limited global collaboration.The review also identifies key challenges in implementing GAI in clinical practice,such as data availability,ethical concerns,and model validation.Despite these challenges,the study highlights GAI’s potential to enhance breast cancer research,particularly in generating synthetic data,improving diagnostic accuracy,and personalizing treatment approaches.This review serves as a valuable resource for researchers and stakeholders,providing insights into current research trends,major contributors,and collaborative networks in GAI-based breast cancer studies.By offering a holistic overview,it aims to support future research directions and encourage broader adoption of GAI technologies in healthcare.Additionally,the study emphasizes the importance of overcoming implementation barriers to fully realizeGAI’s potential in transforming breast cancer management.
文摘The Sine and Wormhole Energy Whale Optimization Algorithm(SWEWOA)represents an advanced solution method for resolving Optimal Power Flow(OPF)problems in power systems equipped with Flexible AC Transmission System(FACTS)devices which include Thyristor-Controlled Series Compensator(TCSC),Thyristor-Controlled Phase Shifter(TCPS),and Static Var Compensator(SVC).SWEWOA expands Whale Optimization Algorithm(WOA)through the integration of sine and wormhole energy features thus improving exploration and exploitation capabilities for efficient convergence in complex non-linear OPF problems.A performance evaluation of SWEWOA takes place on the IEEE-30 bus test system through static and dynamic loading scenarios where it demonstrates better results than five contemporary algorithms:Adaptive Chaotic WOA(ACWOA),WOA,Chaotic WOA(CWOA),Sine Cosine Algorithm Differential Evolution(SCADE),and Hybrid Grey Wolf Optimization(HGWO).The research shows that SWEWOA delivers superior generation cost reduction than other algorithms by reaching a minimum of 0.9%better performance.SWEWOA demonstrates superior power loss performance by achieving(P_(loss,min))at the lowest level compared to all other tested algorithms which leads to better system energy efficiency.The dynamic loading performance of SWEWOA leads to a 4.38%reduction in gross costs which proves its capability to handle different operating conditions.The algorithm achieves top performance in Friedman Rank Test(FRT)assessments through multiple performance metrics which verifies its consistent reliability and strong stability during changing power demands.The repeated simulations show that SWEWOA generates mean costs(C_(gen,min))and mean power loss values(P_(loss,min))with small deviations which indicate its capability to maintain cost-effective solutions in each simulation run.SWEWOA demonstrates great potential as an advanced optimization solution for power system operations through the results presented in this study.
文摘The management of agricultural wastes is essential for resource conservation and environmental sustainability.Due to escalating worries regarding plastic pollution and the surging expenses linked to petroleum-based plastics,there has been a notable transition towards the creation of biodegradable alternatives sourced from natural materials.Biofibres and bioplastics,especially those derived from agricultural waste,have garnered significant attention for their prospective uses in food packaging,biomedical sciences,and sustainable manufacturing.This study examines the viability of employing banana peel as a natural and environmentally sustainable raw material for the production of biodegradable bioplastic sheets.Due to its abundant polysaccharides and lignocellulosic fibers,banana peel presents advantageous structural and mechanical characteristics for bioplastic manufacturing.Experimental findings demonstrate that bioplastic derived from banana peels has enhanced biodegradability and environmental compatibility relative to traditional synthetic plastics,positioning it as a feasible alternative to mitigate the worldwide plastic waste epidemic.An optimal formulation was constructed using Design Expert software,comprising 55.38 g of banana peel,27.63 g of fish scales,and 20 g of chitosan powder.This formulation improves the film’s tensile strength,flexibility,and degradation rate,ensuring its efficacy in industrial applications including food packaging and molding.The study’s results highlight the promise of bioplastics made from banana peels as an economical and sustainable alternative,decreasing dependence on petroleum-based plastics and alleviating environmental pollution.
基金financed as part of the project“Development of a methodology for instrumental base formation for analysis and modeling of the spatial socio-economic development of systems based on internal reserves in the context of digitalization”(FSEG-2023-0008)funded by the Russian Science Foundation(Agreement 23-41-10001,https://doi.org/https://rscf.ru/project/23-41-10001/).
文摘The results of mass appraisal in many countries are used as a basis for calculating the amount of real estate tax,therefore,regardless of the methods used to calculate it,the resulting value should be as close as possible to the market value of the real estate to maintain a balance of interests between the state and the rights holders.In practice,this condition is not always met,since,firstly,the quality of market data is often very low,and secondly,some markets are characterized by low activity,which is expressed in a deficit of information on asking prices.The aim of the work is ecological valuation of land use:how regression-based mass appraisal can inform ecological conservation,land degradation,and sustainable land management.Four multiple regression models were constructed for AI generated map of land plots for recreational use in St.Petersburg(Russia)with different volumes of market information(32,30,20 and 15 units of market information with four price-forming factors).During the analysis of the quality of the models,it was revealed that the best result is shown by the model built on the maximum sample size,then the model based on 15 analogs,which proves that a larger number of analog objects does not always allow us to achieve better results,since the more analog objects there are.
基金financed as part of the project“Development of a methodology for instrumental base formation for analysis and modeling of the spatial socio-economic development of systems based on internal reserves in the context of digitalization”(FSEG-2023-0008).
文摘Artificial intelligence(AI)is transforming the tourism industry and affecting on natural ecology,making it more environmentally friendly,efficient and personalized.In 2025,AI technologies are being actively implemented to reduce the carbon footprint,optimize resources,and improve the travel experience.Here are the key applications of AI in environmentally sustainable smart tourism:AI in smart tourism is not just a technological trend,but a necessity for the sustainable development of the industry.Paper analyses personalized and green travel experience and smart tourism.AI-based applications(Google ARCore)allow tourists to get information about attractions without paper booklets.Virtual tours reduce the need for physical travel by reducing the carbon footprint.Platforms offer routes with minimal impact on nature(for example,hiking trails instead of car tours).Tourists can offset their carbon footprint through AI tools by financing tree planting.The introduction of AI solutions allows combining economic benefits with environmental responsibility,creating a future where travel becomes safer for the planet.Paper confirms idea about sustainable tourism development in developing countries and focus on premium ecotourism.Instead of mass tourism,AI helps promote unique destinations(safaris,diving,ethnographic tours),which increases income with less environmental damage.Smart cities with AI-driven transport and energy-saving solutions make tourism more sustainable.
