Operations Management(OM)plays a critical role in managing oil spills from tankers,specifically in the Straits of Malacca(SOM),Malaysia.Ineffective decision-making and outdated operational practices have historically ...Operations Management(OM)plays a critical role in managing oil spills from tankers,specifically in the Straits of Malacca(SOM),Malaysia.Ineffective decision-making and outdated operational practices have historically led to significant environmental damage,endangering marine ecosystems and resources due to the activities of international shipping.Global experiences with tanker-related oil pollution disasters indicate that full recovery of spilled oil is rarely achieved,due to a combination of natural and physical constraints,a trend also observed in the SOM.Effective OM in this context requires a multidisciplinary approach involving experts in engineering,chemistry,biology,marine navigation,environmental economics,law,and the social sciences.These diverse inputs inform the development of comprehensive frameworks,including legal,institutional,and operational,which are integrated into the National Oil Spill Contingency Plan(NOSCP).The NOSCP outlines preparedness and response strategies,the strategic distribution of oil spill response equipment(OSRE),and the standard operating procedures(SOPs)to manage and sustain effective responses to future oil spill incidents in the SOM.It is a high time for the Malaysian authority to opt for a single agency to manage the major oil spill in the SOM for better operational efficiency and cost-effectiveness.展开更多
Traditional agricultural irrigation systems waste significant amounts of water and energy due to inefficient scheduling and the absence of real-time monitoring capabilities.This research developed a comprehensive IoT-...Traditional agricultural irrigation systems waste significant amounts of water and energy due to inefficient scheduling and the absence of real-time monitoring capabilities.This research developed a comprehensive IoT-based smart irrigation control systemto optimize water and energy management in agricultural greenhouses while enhancing crop productivity.The system employs a sophisticated four-layer Internet ofThings(IoT)architecture based on an ESP32 microcontroller,integrated with multiple environmental sensors,including soil moisture,temperature,humidity,and light intensity sensors,for comprehensive environmental monitoring.The system utilizes the Message Queuing Telemetry Transport(MQTT)communication protocol for reliable data transmission and incorporates a Random Forest machine learning algorithm for automated irrigation decision-making processes.The Random Forest model achieved exceptional performance with 99.3%overall accuracy,demonstrating high model reliability.Six operational modules were developed and implemented with three distinct control methods:manual operation,condition-based automatic control,and AI-driven intelligent control systems.A comprehensive one-month comparative analysis demonstrated remarkable improvements across multiple performance metrics:a 50%reduction in both water consumption(from 140 to 70 L/day)and energy usage(from 7.00 to 3.50 kWh/day),a substantial 130%increase in water use efficiency,and a significant 50%decrease in CO_(2) emissions.Furthermore,detailed factor importance analysis revealed soil moisture as the primary decision factor(38.6%),followed by temporal factors(20.3%)and light intensity(18.4%).The system demonstrates exceptional potential for annual energy conservation of 1277.5 kWh and CO_(2) emission reduction of 638.75 kg,contributing substantially to sustainable development goals and advancing smart agriculture technologies.展开更多
This study explores the influence of Green Logistics Management(GLM)on Sustainable Logistics Performance(SLP),emphasizing the pivotal role of Green Innovation(GI)in promoting sustainability and enhancing logistics eff...This study explores the influence of Green Logistics Management(GLM)on Sustainable Logistics Performance(SLP),emphasizing the pivotal role of Green Innovation(GI)in promoting sustainability and enhancing logistics efficiency(LE).As organizations increasingly seek to align operational efficiency with environmental goals,GLM has emerged as a strategic approach to achieving this balance.The research evaluates the impact of GLM on SLP,examines GI’s contribution to improving LE,and validates the relationship between green logistics practices and SLP.Survey-based data analysis employing reliable scales(AVE and Cronbach’s alpha>0.70)reveals that GI significantly advances LE.Firms demonstrate a strong commitment to sustainability,with high scores for eco-friendly packaging(5.35)and clean technologies(5.14).Despite this,variability in adoption rates highlights differences in implementation across organizations.The findings confirm that GLM positively influences SLP,underscoring the importance of integrating green practices into logistics operations.This study provides actionable insights for organizations and policymakers by addressing inconsistencies in green logistics practices and proposing strategies to enhance sustainability and operational efficiency.It presents a practical framework for improving environmental and business performance,offering valuable guidance for firms striving to achieve sustainable growth while meeting environmental objectives.The research contributes to advancing the logistics sector’s sustainability and innovation-driven performance.展开更多
The rapid digitalization of the energy sector has led to the deployment of large-scale smart metering systems that generate high-frequency time series data,creating new opportunities and challenges for energy anomaly ...The rapid digitalization of the energy sector has led to the deployment of large-scale smart metering systems that generate high-frequency time series data,creating new opportunities and challenges for energy anomaly detection.Accurate identification of anomalous patterns in building energy consumption is essential for optimizing operations,improving energy efficiency,and supporting grid reliability.This study investigates advanced feature engineering and machine learning modeling techniques for large-scale time series anomaly detection in building energy systems.Expanding upon previous benchmark frameworks,we introduce additional features such as oil price indices and solar cycle indicators,including sunset and sunrise times,to enhance the contextual understanding of consumption patterns.Our comparative modeling approach encompasses an extensive suite of algorithms,including KNeighborsUnif,KNeighborsDist,LightGBMXT,LightGBM,RandomForestMSE,CatBoost,ExtraTreesMSE,NeuralNetFastAI,XGBoost,NeuralNetTorch,and LightGBMLarge.Data preprocessing includes rigorous handling of missing values and normalization,while feature engineering focuses on temporal,environmental,and value-change attributes.The models are evaluated on a comprehensive dataset of smart meter readings,with performance assessed using metrics such as the Area Under the Receiver Operating Characteristic Curve(AUC-ROC).The results demonstrate that the integration of diverse exogenous variables and a hybrid ensemble of traditional tree-based and neural network models can significantly improve anomaly detection performance.This work provides new insights into the design of robust,scalable,and generalizable frameworks for energy anomaly detection in complex,real-world settings.展开更多
0 INTRODUCTION Throughout human history,three major energy transitions have occurred:from burning wood in primitive times to using coal in 18th Century,then to oil and gas in 20th Century,and to the renewable energy r...0 INTRODUCTION Throughout human history,three major energy transitions have occurred:from burning wood in primitive times to using coal in 18th Century,then to oil and gas in 20th Century,and to the renewable energy revolution in the 21st Century(Zou et al.,2023).The three transitions have three characteristics in common:shifted from nonrenewable to renewable energy,from“resource-centric”to a“technology-centric”,and from“high-carbon fossil”to“net-zero”.展开更多
In recent years,Blockchain Technology has become a paradigm shift,providing Transparent,Secure,and Decentralized platforms for diverse applications,ranging from Cryptocurrency to supply chain management.Nevertheless,t...In recent years,Blockchain Technology has become a paradigm shift,providing Transparent,Secure,and Decentralized platforms for diverse applications,ranging from Cryptocurrency to supply chain management.Nevertheless,the optimization of blockchain networks remains a critical challenge due to persistent issues such as latency,scalability,and energy consumption.This study proposes an innovative approach to Blockchain network optimization,drawing inspiration from principles of biological evolution and natural selection through evolutionary algorithms.Specifically,we explore the application of genetic algorithms,particle swarm optimization,and related evolutionary techniques to enhance the performance of blockchain networks.The proposed methodologies aim to optimize consensus mechanisms,improve transaction throughput,and reduce resource consumption.Through extensive simulations and real-world experiments,our findings demonstrate significant improvements in network efficiency,scalability,and stability.This research offers a thorough analysis of existing optimization techniques,introduces novel strategies,and assesses their efficacy based on empirical outputs.展开更多
As circuit feature sizes approach the nanoscale,traditional Copper(Cu)interconnects face significant hurdles posed by rising resistance-capacitance(RC)delay,electromigration,and high power dissipation.These limitation...As circuit feature sizes approach the nanoscale,traditional Copper(Cu)interconnects face significant hurdles posed by rising resistance-capacitance(RC)delay,electromigration,and high power dissipation.These limitations impose constraints on the scalability and reliability of future semiconductor technologies.Our paper describes the new Vertical multilayer Aluminium Boron Nitride Nanoribbon(AlBN)interconnect structure,integrated with Density functional theory(DFT)using first-principles calculations.This study explores AlBN-based nanostructures with doping of 1Cu,2Cu,1Fe(Iron),and 2Fe for the application of Very Large Scale Integration(VLSI)interconnects.The AlBN structure utilized the advantages of vertical multilayer interconnects to both reduce the RC delay while enhancing signal integrity.Key parameters like Fermi energy,bandgap,binding energy,conduction channels,quantum resistance,and RC delay were analyzed.Through modeling and large-scale simulation,the structural,electronic,and stability attributes of the AlBN interconnects are analyzed,and the results illustrate considerable improvements in signal propagation against Cu interconnect structures.These findings confirm the tunable,high-performance nature of AlBN-2Fe,making it a promising candidate for future high-speed,low-power VLSI interconnect technologies.We demonstrated an advanced energy-efficient interconnect that can be easily scaled for future nanoscale VLSI circuit design and gives rise to a next generation of viable interconnect technology for high-capacity,high-speed,reliable semiconductor technology.展开更多
Background:Music has proven to be vital in enhancing resilience and promotingwell-being.Previously,the impact of music in sports environments was solely investigated,while this paper applies it to study environments,s...Background:Music has proven to be vital in enhancing resilience and promotingwell-being.Previously,the impact of music in sports environments was solely investigated,while this paper applies it to study environments,standing out as pioneering research.The study consists of a systematic development of a conceptual framework based on theories of Uses and Gratification Expectancy(UGE)and perceived motivation based on music elements.Their components are observed variables influencing students’psychological well-being(as the dependent variable).Resilience is examined as a mediator,influencing the relationships of both observed and dependent variables.The main purpose of this study is to highlight the positive effects of online music consumption on the psychological well-being of students.Methods:Semi-structured qualitative interviews were conducted with eighteen final year creative multimedia undergraduate students belonging to five central region Malaysian universities,especially on their UGE needs,and a similar concept survey instrument with two hundred participants.The interview data were analysed through thematic analysis,while the survey data through descriptive and Partial Least Squares Structural Equation Modeling(PLS-SEM).Results:The results highlight that students gain motivation from online music,which positively affects their psychological well-being(β=0.190,p=0.003,f^(2)=0.037),while resilience significantly affects this relationship(β=0.562,p<0.001,f^(2)=0.461).However,the results also predict a partial relationship between constructs based on UGE with psychological well-being,mediated by resilience,i.e.,AT-UGE(β=0.021,p=0.783,f^(2)=0.000),SIPI-UGE(β=0.228,p=0.004,f^(2)=0.044).Conclusion:The outcome of the study reflected practical,meaningful,and statistically significant results.The majority of the predictors,with the exception of one,i.e.,AT-UGE,displayed a clear positive relation of online music consumption on the Psychological Well-being of students.Future research will explore varying contextual factors impacting online music-related gratifications,motivations,and resilience,along with additional potential mediators and moderators.展开更多
Accurate,up to date,and quick information related to any disaster supports disaster management team/authorities to perform quick,easy,and cost-effective response to enhance rescue operations to alleviate the possible ...Accurate,up to date,and quick information related to any disaster supports disaster management team/authorities to perform quick,easy,and cost-effective response to enhance rescue operations to alleviate the possible loss of lives,financial risks,and properties.Due to damaged infrastructure in disaster-affected areas,social media is the only way to share/exchange real time information.Therefore,‘X’(formerly Twitter)has become a major platform for disseminating real-time information during disaster events or emergencies,i.e.,floods and earthquake.Rapid identification of actionable content is critical for effective humanitarian response;however,the brief and noisy nature of tweets makes automated classification challenging.To tackle this problem,this study proposes a hybrid classification framework that integrates term frequency–inverse document frequency(TF-IDF)features with graph convolutional networks(GCNs)to enhance disaster-related tweet analysis.The proposed model performs three classification tasks:identifying disaster-related tweets(achieving 94.47%accuracy),categorizing disaster types(earthquake,flood,and non-disaster)with 91.78%accuracy,and detecting aid requests such as food,donations,and medical assistance(94.64%accuracy).By combining the statistical strengths of TF-IDF with the relational learning capabilities of GCNs,the model attains high accuracy while maintaining computational efficiency and interpretability.The results demonstrate the framework’s strong potential for real-time disaster response,offering valuable insights to support emergency management systems and humanitarian decision-making.展开更多
With an increase in internet-connected devices and a dependency on online services,the threat of Distributed Denial of Service(DDoS)attacks has become a significant concern in cybersecurity.The proposed system follows...With an increase in internet-connected devices and a dependency on online services,the threat of Distributed Denial of Service(DDoS)attacks has become a significant concern in cybersecurity.The proposed system follows a multi-step process,beginning with the collection of datasets from different edge devices and network nodes.To verify its effectiveness,experiments were conducted using the CICDoS2017,NSL-KDD,and CICIDS benchmark datasets alongside other existing models.Recursive feature elimination(RFE)with random forest is used to select features from the CICDDoS2019 dataset,on which a BiLSTM model is trained on local nodes.Local models are trained until convergence or stability criteria are met while simultaneously sharing the updates globally for collaborative learning.A centralised server evaluates real-time traffic using the global BiLSTM model,which triggers alerts for potential DDoS attacks.Furthermore,blockchain technology is employed to secure model updates and to provide an immutable audit trail,thereby ensuring trust and accountability among network nodes.This research introduces a novel decentralized method called Federated Random Forest Bidirectional Long Short-Term Memory(FRF-BiLSTM)for detecting DDoS attacks,utilizing the advanced Bidirectional Long Short-Term Memory Networks(BiLSTMs)to analyze sequences in both forward and backward directions.The outcome shows the proposed model achieves a mean accuracy of 97.1%with an average training delay of 88.7 s and testing delay of 21.4 s.The model demonstrates scalability and the best detection performance in large-scale attack scenarios.展开更多
Recently,the Internet of Things(IoT)technology has been utilized in a wide range of services and applications which significantly transforms digital ecosystems through seamless interconnectivity between various smart ...Recently,the Internet of Things(IoT)technology has been utilized in a wide range of services and applications which significantly transforms digital ecosystems through seamless interconnectivity between various smart devices.Furthermore,the IoT plays a key role in multiple domains,including industrial automation,smart homes,and intelligent transportation systems.However,an increasing number of connected devices presents significant challenges related to efficient resource allocation and system responsiveness.To address these issue,this research proposes a Modified Walrus Optimization Algorithm(MWaOA)for effective resource management in smart IoT systems.In the proposed MWaOA,a crowding process is incorporated to maintain diversity and avoid premature convergence thereby enhancing the global search capability.During resource allocation,the MWaOA prevents early convergence,which aids in achieving a better balance between the exploration and exploitation phases during optimization.Empirical evaluations show that the MWaOA reduces energy consumption by approximately 4% to 34%and minimizes the response time by 6% to 33% across different service arrival rates.Compared to traditional optimization algorithms,MWaOA reduces energy consumption by 5% to 30%and minimizes the response time by 4% to 28% across different simulation epochs.The proposed MWaOA provides adaptive and robust resource allocation,thereby minimizing transmission cost while considering network constraints and real-time performance parameters.展开更多
The growing use of Portable Document Format(PDF)files across various sectors such as education,government,and business has inadvertently turned them into a major target for cyberattacks.Cybercriminals take advantage o...The growing use of Portable Document Format(PDF)files across various sectors such as education,government,and business has inadvertently turned them into a major target for cyberattacks.Cybercriminals take advantage of the inherent flexibility and layered structure ofPDFs to inject malicious content,often employing advanced obfuscation techniques to evade detection by traditional signature-based security systems.These conventional methods are no longer adequate,especially against sophisticated threats like zero-day exploits and polymorphic malware.In response to these challenges,this study introduces a machine learning-based detection framework specifically designed to combat such threats.Central to the proposed solution is a stacked ensemble learning model that combines the strengths of four high-performing classifiers:Random Forest(RF),Extreme Gradient Boosting(XGB),LightGBM(LGBM),and CatBoost(CB).These models operate in parallel as base learners,each capturing different aspects of the data.Their outputs are then refined by a Gradient Boosting Classifier(GBC),which serves as a meta-learner to enhance prediction accuracy.To ensure the model remains both efficient and effective,Principal Component Analysis(PCA)is applied to reduce feature dimensionality while preserving critical information necessary for malware classification.The model is trained and validated using the CIC-Evasive PDFMalware2022 dataset,which includes a wide range of both malicious and benign PDF samples.The results demonstrate that the framework achieves impressive performance,with 97.10% accuracy and a 97.39% F1-score,surpassing several existing techniques.To enhance trust and interpretability,the system incorporates Local Interpretable Model-agnostic Explanations(LIME),which provides user-friendly insights into the rationale behind each prediction.This research emphasizes how the integration of ensemble learning,feature reduction,and explainable AI can lead to a practical and scalable solution for detecting complex PDF-based threats.The proposed framework lays the foundation for the next generation of intelligent,resilient cybersecurity systems that can address ever-evolving attack strategies.展开更多
Numerous sectors,such as education,the IT sector,and corporate organizations,transitioned to virtual meetings after the COVID-19 crisis.Organizations now seek to assess participants’fatigue levels in online meetings ...Numerous sectors,such as education,the IT sector,and corporate organizations,transitioned to virtual meetings after the COVID-19 crisis.Organizations now seek to assess participants’fatigue levels in online meetings to remain competitive.Instructors cannot effectively monitor every individual in a virtual environment,which raises significant concerns about participant fatigue.Our proposed system monitors fatigue,identifying attentive and drowsy individuals throughout the online session.We leverage Dlib’s pre-trained facial landmark detector and focus on the eye landmarks only,offering a more detailed analysis for predicting eye opening and closing of the eyes,rather than focusing on the entire face.We introduce an Eye Polygon Area(EPA)formula,which computes eye activity from Dlib eye landmarks by measuring the polygonal area of the eye opening.Unlike the Eye Aspect Ratio(EAR),which relies on a single distance ratio,EPA adapts to different eye shapes(round,narrow,or wide),providing a more reliable measure for fatigue detection.The VMFD system issues a warning if a participant remains in a fatigued condition for 36 consecutive frames.The proposed technology is tested under multiple scenarios,including low-to high-lighting conditions(50-1400 lux)and both with and without glasses.This study builds an OpenCV application in Python,evaluated using the iBUG 300-W dataset,achieving 97.5%accuracy in detecting active participants.We compare VMFD with conventional methods relying on the EAR and show that the EPA technique performs significantly better.展开更多
The convergence of Artificial Intelligence(AI)and the Internet of Things(IoT)has enabled Artificial Intelligence of Things(AIoT)systems that support intelligent and responsive smart societies,but it also introduces ma...The convergence of Artificial Intelligence(AI)and the Internet of Things(IoT)has enabled Artificial Intelligence of Things(AIoT)systems that support intelligent and responsive smart societies,but it also introduces major security and privacy concerns across domains such as healthcare,transportation,and smart cities.This Systemic Literature Review(SLR)addresses three research questions:identifying major threats and challenges in AIoT ecosystems,reviewing state-of-the-art security and privacy techniques,and evaluating their effectiveness.An SLR covering the period from 2020 to 2025 was conducted using major academic digital libraries,including IEEE Xplore,ACM Digital Library,ScienceDirect,SpringerLink,and Wiley Online Library,with a focus on security-and privacy-enhancing techniques such as blockchain,federated learning,and edge AI.The SLR identifies key challenges including data privacy leakage,authentication,cloud dependency,and attack surface expansion,and finds that emerging techniques,while promising,often involve trade-offs related to latency,scalability,and compliance.The study highlights future directions including lightweight cryptography,standardization,and explainable AI to support secure and trustworthy AIoT-enabled smart societies.展开更多
The contemporary smart cities,smart homes,smart buildings,and smart health care systems are the results of the explosive growth of Internet of Things(IoT)devices and deep learning.Yet the centralized training paradigm...The contemporary smart cities,smart homes,smart buildings,and smart health care systems are the results of the explosive growth of Internet of Things(IoT)devices and deep learning.Yet the centralized training paradigms have fundamental issues in data privacy,regulatory compliance,and ownership silo alongside the scaled limitations of the real-life application.The concept of Federated Deep Learning(FDL)is a privacy-by-design method that will enable the distributed training of machine learning models among distributed clients without sharing raw data and is suitable in heterogeneous urban settings.It is an overview of the privacy-preserving developments in FDL as of 2018-2025 with a narrow scope on its usage in smart cities(traffic prediction,environmental monitoring,energy grids),smart homes/buildings/IoT(non-intrusive load monitoring,HVAC optimization,anomaly detection)and the healthcare application(medical imaging,Electronic Health Records(EHR)analysis,remote monitoring).It gives coherent taxonomy,domain pipelines,comparative analyses of privacy mechanisms(differential privacy,secure aggregation,Homomorphic Encryption(HE),Trusted Execution Environments(TEEs),blockchain enhanced and hybrids),system structures,security/robustness defense,deployment/Machine Learning Operation(MLOps)issues,and the longstanding challenges(non-IID heterogeneity,communication efficiency,fairness,and sustainability).Some of the contributions made are structured comparisons of privacy threats,practical design advice on urban areas,recognition of open problems,and a research roadmap into the future up to 2035.The paper brings out the transformational worth of FDL in building credible,scalable,and sustainable intelligent urban ecosystems and the need to do further interdisciplinary research in standardization,real-world testbeds,and ethical governance.展开更多
The rise in noise and air pollution poses severe risks to human health and the environment.Industrial and vehicular emissions release harmful pollutants such as CO_(2),SO_(2),CO,CH_(4),and noise,leading to significant...The rise in noise and air pollution poses severe risks to human health and the environment.Industrial and vehicular emissions release harmful pollutants such as CO_(2),SO_(2),CO,CH_(4),and noise,leading to significant environmental degradation.Monitoring and analyzing pollutant concentrations in real-time is crucial for mitigating these risks.However,existing systems often lack the capacity to monitor both indoor and outdoor environments effectively.This study presents a low-cost,Io'T-based pollution detection system that integrates gas sensors(MQ-135and M(Q-4),a noise sensor(LM393),and a humidity sensor(DHT-22),all connected to a Node MCU(ESP8266)microcontroller.The system leverages cloud-based storage and real-time analytics to monitor harmful gas levels and sound pollution.Sensor data is processed using decision tree algorithms for classification,enabling threshold-based detection with environmental context.A Progressive Web Application(PWA)interface provides tusers with accessible,cross-platform visualizations.Experimental validation demonstrated the system’s ability to detect pollutant concentration variations across both indoor and outdoor settings,with real-time alerts triggered when thresholds were exceeded.The collected data showed consistent classification of normal,warning,and critical states for methane,CO_(2),temperature,humidity,and noise levels.These results confirm the system's reliability in dynamic environmental conditions.The proposed framework offers ascalable,energy-efficient,and user-friendly solution for pollution detectionand public awareness.Future enhancements will focus on extending the sensor suite,improving machine learning accuracy,and integrating meteorological data for predictive pollution modeling.展开更多
An understanding of the influence of contractor monitoring on performance of road infrastructural projects in Uganda provided an impetus for this study. The objectives of the study were to: assess the relationship bet...An understanding of the influence of contractor monitoring on performance of road infrastructural projects in Uganda provided an impetus for this study. The objectives of the study were to: assess the relationship between contractors monitoring and performance of national road infrastructure projects and the relationship between contractor monitoring components and performance of national road infrastructure projects in Uganda. Purposive sampling was employed in selecting the procurement professionals, engineers and simple random sampling was adopted in selecting private consultants, members of parliament and respondents from the civil society organizations. Data for this study were collected using a closed ended questionnaire and interviews. Some of the major finding from this study include: weak procurement rules which lead to awarding road projects to incompetent contractors;contractor monitoring being handled by unqualified, incompetent and inexperienced professionals;lack of contractors and contract supervisors appraisal system;delay of contractors payments which affects timelines in services delivery;lack of a strong internal project monitoring and evaluation mechanism at the Uganda National Roads Agency (UNRA). The research therefore recommends the establishment of an Independent Public Infrastructure Development and Monitoring Unit by government and adoption of systems that appraise both contractors and contract supervisors with clear disciplinary actions for unsatisfactory performance by the UNRA.展开更多
This paper reported the effect of oolong tea processing procedure of turn-over on quality of the Jinmudan Oolong tea,including taste components and volatile compounds.The content of the water extractable solids was gr...This paper reported the effect of oolong tea processing procedure of turn-over on quality of the Jinmudan Oolong tea,including taste components and volatile compounds.The content of the water extractable solids was gradually increased,but the content of amino acid decreased and then increased,and the content of the soluble sugar and tea polyphenols increased after the first turn-over processing.The major volatiles of the three tested Jinmudan Oolong tea samples were nerolidolcistrans,α-farnesene,palmitic acid,indole and 9,12,15-octadecatrienoicacid and methyl ester.The sensory evaluation results showed that an appropriate increase in the number of turn-over was helpful to quality of the Jinmudan Oolong tea.展开更多
The electricity situation in Nigeria can be described as epileptic with no sign in view of improvement. This epileptic power situation affects the manufacturing, service and residential sectors of the economy which in...The electricity situation in Nigeria can be described as epileptic with no sign in view of improvement. This epileptic power situation affects the manufacturing, service and residential sectors of the economy which in turn affects the country’s economic growth. Even with the recent reforms in the power sector, more than half of the country’s population still lack access to electricity. The epileptic condition of the power sector can be attributed to the inadequate and inefficient power plants, poor transmission and distribution facilities, and outdated metering system used by electricity consumers. This paper attempts to present the way forward for the Nigerian poor electricity situation by reviewing the power sector as a whole and the renewable energy potentials. We identified the problems in the national grid and then proposed a smart grid model for the Nigerian power sector which will include renewable energy source. We believe that the content of this review paper will solve the poor epileptic condition of the power sector in Nigeria and also enable the proper integration of smart grid technology into the national grid.展开更多
文摘Operations Management(OM)plays a critical role in managing oil spills from tankers,specifically in the Straits of Malacca(SOM),Malaysia.Ineffective decision-making and outdated operational practices have historically led to significant environmental damage,endangering marine ecosystems and resources due to the activities of international shipping.Global experiences with tanker-related oil pollution disasters indicate that full recovery of spilled oil is rarely achieved,due to a combination of natural and physical constraints,a trend also observed in the SOM.Effective OM in this context requires a multidisciplinary approach involving experts in engineering,chemistry,biology,marine navigation,environmental economics,law,and the social sciences.These diverse inputs inform the development of comprehensive frameworks,including legal,institutional,and operational,which are integrated into the National Oil Spill Contingency Plan(NOSCP).The NOSCP outlines preparedness and response strategies,the strategic distribution of oil spill response equipment(OSRE),and the standard operating procedures(SOPs)to manage and sustain effective responses to future oil spill incidents in the SOM.It is a high time for the Malaysian authority to opt for a single agency to manage the major oil spill in the SOM for better operational efficiency and cost-effectiveness.
文摘Traditional agricultural irrigation systems waste significant amounts of water and energy due to inefficient scheduling and the absence of real-time monitoring capabilities.This research developed a comprehensive IoT-based smart irrigation control systemto optimize water and energy management in agricultural greenhouses while enhancing crop productivity.The system employs a sophisticated four-layer Internet ofThings(IoT)architecture based on an ESP32 microcontroller,integrated with multiple environmental sensors,including soil moisture,temperature,humidity,and light intensity sensors,for comprehensive environmental monitoring.The system utilizes the Message Queuing Telemetry Transport(MQTT)communication protocol for reliable data transmission and incorporates a Random Forest machine learning algorithm for automated irrigation decision-making processes.The Random Forest model achieved exceptional performance with 99.3%overall accuracy,demonstrating high model reliability.Six operational modules were developed and implemented with three distinct control methods:manual operation,condition-based automatic control,and AI-driven intelligent control systems.A comprehensive one-month comparative analysis demonstrated remarkable improvements across multiple performance metrics:a 50%reduction in both water consumption(from 140 to 70 L/day)and energy usage(from 7.00 to 3.50 kWh/day),a substantial 130%increase in water use efficiency,and a significant 50%decrease in CO_(2) emissions.Furthermore,detailed factor importance analysis revealed soil moisture as the primary decision factor(38.6%),followed by temporal factors(20.3%)and light intensity(18.4%).The system demonstrates exceptional potential for annual energy conservation of 1277.5 kWh and CO_(2) emission reduction of 638.75 kg,contributing substantially to sustainable development goals and advancing smart agriculture technologies.
文摘This study explores the influence of Green Logistics Management(GLM)on Sustainable Logistics Performance(SLP),emphasizing the pivotal role of Green Innovation(GI)in promoting sustainability and enhancing logistics efficiency(LE).As organizations increasingly seek to align operational efficiency with environmental goals,GLM has emerged as a strategic approach to achieving this balance.The research evaluates the impact of GLM on SLP,examines GI’s contribution to improving LE,and validates the relationship between green logistics practices and SLP.Survey-based data analysis employing reliable scales(AVE and Cronbach’s alpha>0.70)reveals that GI significantly advances LE.Firms demonstrate a strong commitment to sustainability,with high scores for eco-friendly packaging(5.35)and clean technologies(5.14).Despite this,variability in adoption rates highlights differences in implementation across organizations.The findings confirm that GLM positively influences SLP,underscoring the importance of integrating green practices into logistics operations.This study provides actionable insights for organizations and policymakers by addressing inconsistencies in green logistics practices and proposing strategies to enhance sustainability and operational efficiency.It presents a practical framework for improving environmental and business performance,offering valuable guidance for firms striving to achieve sustainable growth while meeting environmental objectives.The research contributes to advancing the logistics sector’s sustainability and innovation-driven performance.
文摘The rapid digitalization of the energy sector has led to the deployment of large-scale smart metering systems that generate high-frequency time series data,creating new opportunities and challenges for energy anomaly detection.Accurate identification of anomalous patterns in building energy consumption is essential for optimizing operations,improving energy efficiency,and supporting grid reliability.This study investigates advanced feature engineering and machine learning modeling techniques for large-scale time series anomaly detection in building energy systems.Expanding upon previous benchmark frameworks,we introduce additional features such as oil price indices and solar cycle indicators,including sunset and sunrise times,to enhance the contextual understanding of consumption patterns.Our comparative modeling approach encompasses an extensive suite of algorithms,including KNeighborsUnif,KNeighborsDist,LightGBMXT,LightGBM,RandomForestMSE,CatBoost,ExtraTreesMSE,NeuralNetFastAI,XGBoost,NeuralNetTorch,and LightGBMLarge.Data preprocessing includes rigorous handling of missing values and normalization,while feature engineering focuses on temporal,environmental,and value-change attributes.The models are evaluated on a comprehensive dataset of smart meter readings,with performance assessed using metrics such as the Area Under the Receiver Operating Characteristic Curve(AUC-ROC).The results demonstrate that the integration of diverse exogenous variables and a hybrid ensemble of traditional tree-based and neural network models can significantly improve anomaly detection performance.This work provides new insights into the design of robust,scalable,and generalizable frameworks for energy anomaly detection in complex,real-world settings.
基金supported by the National Natural Science Foundation of China“Quantitative characterization of lacustrine shale oil mobility based on nano-scale oil-rock interactions”(No.42172180)Science and Technology Research Project for the China National Petroleum Corporation“Source-reservoir characteristics and sweet spot evaluation for terrestrial shale oil in China”(No.2021DJ1802)。
文摘0 INTRODUCTION Throughout human history,three major energy transitions have occurred:from burning wood in primitive times to using coal in 18th Century,then to oil and gas in 20th Century,and to the renewable energy revolution in the 21st Century(Zou et al.,2023).The three transitions have three characteristics in common:shifted from nonrenewable to renewable energy,from“resource-centric”to a“technology-centric”,and from“high-carbon fossil”to“net-zero”.
文摘In recent years,Blockchain Technology has become a paradigm shift,providing Transparent,Secure,and Decentralized platforms for diverse applications,ranging from Cryptocurrency to supply chain management.Nevertheless,the optimization of blockchain networks remains a critical challenge due to persistent issues such as latency,scalability,and energy consumption.This study proposes an innovative approach to Blockchain network optimization,drawing inspiration from principles of biological evolution and natural selection through evolutionary algorithms.Specifically,we explore the application of genetic algorithms,particle swarm optimization,and related evolutionary techniques to enhance the performance of blockchain networks.The proposed methodologies aim to optimize consensus mechanisms,improve transaction throughput,and reduce resource consumption.Through extensive simulations and real-world experiments,our findings demonstrate significant improvements in network efficiency,scalability,and stability.This research offers a thorough analysis of existing optimization techniques,introduces novel strategies,and assesses their efficacy based on empirical outputs.
文摘As circuit feature sizes approach the nanoscale,traditional Copper(Cu)interconnects face significant hurdles posed by rising resistance-capacitance(RC)delay,electromigration,and high power dissipation.These limitations impose constraints on the scalability and reliability of future semiconductor technologies.Our paper describes the new Vertical multilayer Aluminium Boron Nitride Nanoribbon(AlBN)interconnect structure,integrated with Density functional theory(DFT)using first-principles calculations.This study explores AlBN-based nanostructures with doping of 1Cu,2Cu,1Fe(Iron),and 2Fe for the application of Very Large Scale Integration(VLSI)interconnects.The AlBN structure utilized the advantages of vertical multilayer interconnects to both reduce the RC delay while enhancing signal integrity.Key parameters like Fermi energy,bandgap,binding energy,conduction channels,quantum resistance,and RC delay were analyzed.Through modeling and large-scale simulation,the structural,electronic,and stability attributes of the AlBN interconnects are analyzed,and the results illustrate considerable improvements in signal propagation against Cu interconnect structures.These findings confirm the tunable,high-performance nature of AlBN-2Fe,making it a promising candidate for future high-speed,low-power VLSI interconnect technologies.We demonstrated an advanced energy-efficient interconnect that can be easily scaled for future nanoscale VLSI circuit design and gives rise to a next generation of viable interconnect technology for high-capacity,high-speed,reliable semiconductor technology.
基金funded by Malaysian Ministry of Higher Education(MOHE)under the Fundamental Research Grant Scheme(FRGS/1/2023/SSI07/MMU/02/3)which is awarded to the Multimedia University.The project is led by the second author.
文摘Background:Music has proven to be vital in enhancing resilience and promotingwell-being.Previously,the impact of music in sports environments was solely investigated,while this paper applies it to study environments,standing out as pioneering research.The study consists of a systematic development of a conceptual framework based on theories of Uses and Gratification Expectancy(UGE)and perceived motivation based on music elements.Their components are observed variables influencing students’psychological well-being(as the dependent variable).Resilience is examined as a mediator,influencing the relationships of both observed and dependent variables.The main purpose of this study is to highlight the positive effects of online music consumption on the psychological well-being of students.Methods:Semi-structured qualitative interviews were conducted with eighteen final year creative multimedia undergraduate students belonging to five central region Malaysian universities,especially on their UGE needs,and a similar concept survey instrument with two hundred participants.The interview data were analysed through thematic analysis,while the survey data through descriptive and Partial Least Squares Structural Equation Modeling(PLS-SEM).Results:The results highlight that students gain motivation from online music,which positively affects their psychological well-being(β=0.190,p=0.003,f^(2)=0.037),while resilience significantly affects this relationship(β=0.562,p<0.001,f^(2)=0.461).However,the results also predict a partial relationship between constructs based on UGE with psychological well-being,mediated by resilience,i.e.,AT-UGE(β=0.021,p=0.783,f^(2)=0.000),SIPI-UGE(β=0.228,p=0.004,f^(2)=0.044).Conclusion:The outcome of the study reflected practical,meaningful,and statistically significant results.The majority of the predictors,with the exception of one,i.e.,AT-UGE,displayed a clear positive relation of online music consumption on the Psychological Well-being of students.Future research will explore varying contextual factors impacting online music-related gratifications,motivations,and resilience,along with additional potential mediators and moderators.
文摘Accurate,up to date,and quick information related to any disaster supports disaster management team/authorities to perform quick,easy,and cost-effective response to enhance rescue operations to alleviate the possible loss of lives,financial risks,and properties.Due to damaged infrastructure in disaster-affected areas,social media is the only way to share/exchange real time information.Therefore,‘X’(formerly Twitter)has become a major platform for disseminating real-time information during disaster events or emergencies,i.e.,floods and earthquake.Rapid identification of actionable content is critical for effective humanitarian response;however,the brief and noisy nature of tweets makes automated classification challenging.To tackle this problem,this study proposes a hybrid classification framework that integrates term frequency–inverse document frequency(TF-IDF)features with graph convolutional networks(GCNs)to enhance disaster-related tweet analysis.The proposed model performs three classification tasks:identifying disaster-related tweets(achieving 94.47%accuracy),categorizing disaster types(earthquake,flood,and non-disaster)with 91.78%accuracy,and detecting aid requests such as food,donations,and medical assistance(94.64%accuracy).By combining the statistical strengths of TF-IDF with the relational learning capabilities of GCNs,the model attains high accuracy while maintaining computational efficiency and interpretability.The results demonstrate the framework’s strong potential for real-time disaster response,offering valuable insights to support emergency management systems and humanitarian decision-making.
基金supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(NRF-2025S1A5A2A01005171)by the BK21 programat Chungbuk National University(2025).
文摘With an increase in internet-connected devices and a dependency on online services,the threat of Distributed Denial of Service(DDoS)attacks has become a significant concern in cybersecurity.The proposed system follows a multi-step process,beginning with the collection of datasets from different edge devices and network nodes.To verify its effectiveness,experiments were conducted using the CICDoS2017,NSL-KDD,and CICIDS benchmark datasets alongside other existing models.Recursive feature elimination(RFE)with random forest is used to select features from the CICDDoS2019 dataset,on which a BiLSTM model is trained on local nodes.Local models are trained until convergence or stability criteria are met while simultaneously sharing the updates globally for collaborative learning.A centralised server evaluates real-time traffic using the global BiLSTM model,which triggers alerts for potential DDoS attacks.Furthermore,blockchain technology is employed to secure model updates and to provide an immutable audit trail,thereby ensuring trust and accountability among network nodes.This research introduces a novel decentralized method called Federated Random Forest Bidirectional Long Short-Term Memory(FRF-BiLSTM)for detecting DDoS attacks,utilizing the advanced Bidirectional Long Short-Term Memory Networks(BiLSTMs)to analyze sequences in both forward and backward directions.The outcome shows the proposed model achieves a mean accuracy of 97.1%with an average training delay of 88.7 s and testing delay of 21.4 s.The model demonstrates scalability and the best detection performance in large-scale attack scenarios.
文摘Recently,the Internet of Things(IoT)technology has been utilized in a wide range of services and applications which significantly transforms digital ecosystems through seamless interconnectivity between various smart devices.Furthermore,the IoT plays a key role in multiple domains,including industrial automation,smart homes,and intelligent transportation systems.However,an increasing number of connected devices presents significant challenges related to efficient resource allocation and system responsiveness.To address these issue,this research proposes a Modified Walrus Optimization Algorithm(MWaOA)for effective resource management in smart IoT systems.In the proposed MWaOA,a crowding process is incorporated to maintain diversity and avoid premature convergence thereby enhancing the global search capability.During resource allocation,the MWaOA prevents early convergence,which aids in achieving a better balance between the exploration and exploitation phases during optimization.Empirical evaluations show that the MWaOA reduces energy consumption by approximately 4% to 34%and minimizes the response time by 6% to 33% across different service arrival rates.Compared to traditional optimization algorithms,MWaOA reduces energy consumption by 5% to 30%and minimizes the response time by 4% to 28% across different simulation epochs.The proposed MWaOA provides adaptive and robust resource allocation,thereby minimizing transmission cost while considering network constraints and real-time performance parameters.
文摘The growing use of Portable Document Format(PDF)files across various sectors such as education,government,and business has inadvertently turned them into a major target for cyberattacks.Cybercriminals take advantage of the inherent flexibility and layered structure ofPDFs to inject malicious content,often employing advanced obfuscation techniques to evade detection by traditional signature-based security systems.These conventional methods are no longer adequate,especially against sophisticated threats like zero-day exploits and polymorphic malware.In response to these challenges,this study introduces a machine learning-based detection framework specifically designed to combat such threats.Central to the proposed solution is a stacked ensemble learning model that combines the strengths of four high-performing classifiers:Random Forest(RF),Extreme Gradient Boosting(XGB),LightGBM(LGBM),and CatBoost(CB).These models operate in parallel as base learners,each capturing different aspects of the data.Their outputs are then refined by a Gradient Boosting Classifier(GBC),which serves as a meta-learner to enhance prediction accuracy.To ensure the model remains both efficient and effective,Principal Component Analysis(PCA)is applied to reduce feature dimensionality while preserving critical information necessary for malware classification.The model is trained and validated using the CIC-Evasive PDFMalware2022 dataset,which includes a wide range of both malicious and benign PDF samples.The results demonstrate that the framework achieves impressive performance,with 97.10% accuracy and a 97.39% F1-score,surpassing several existing techniques.To enhance trust and interpretability,the system incorporates Local Interpretable Model-agnostic Explanations(LIME),which provides user-friendly insights into the rationale behind each prediction.This research emphasizes how the integration of ensemble learning,feature reduction,and explainable AI can lead to a practical and scalable solution for detecting complex PDF-based threats.The proposed framework lays the foundation for the next generation of intelligent,resilient cybersecurity systems that can address ever-evolving attack strategies.
文摘Numerous sectors,such as education,the IT sector,and corporate organizations,transitioned to virtual meetings after the COVID-19 crisis.Organizations now seek to assess participants’fatigue levels in online meetings to remain competitive.Instructors cannot effectively monitor every individual in a virtual environment,which raises significant concerns about participant fatigue.Our proposed system monitors fatigue,identifying attentive and drowsy individuals throughout the online session.We leverage Dlib’s pre-trained facial landmark detector and focus on the eye landmarks only,offering a more detailed analysis for predicting eye opening and closing of the eyes,rather than focusing on the entire face.We introduce an Eye Polygon Area(EPA)formula,which computes eye activity from Dlib eye landmarks by measuring the polygonal area of the eye opening.Unlike the Eye Aspect Ratio(EAR),which relies on a single distance ratio,EPA adapts to different eye shapes(round,narrow,or wide),providing a more reliable measure for fatigue detection.The VMFD system issues a warning if a participant remains in a fatigued condition for 36 consecutive frames.The proposed technology is tested under multiple scenarios,including low-to high-lighting conditions(50-1400 lux)and both with and without glasses.This study builds an OpenCV application in Python,evaluated using the iBUG 300-W dataset,achieving 97.5%accuracy in detecting active participants.We compare VMFD with conventional methods relying on the EAR and show that the EPA technique performs significantly better.
文摘The convergence of Artificial Intelligence(AI)and the Internet of Things(IoT)has enabled Artificial Intelligence of Things(AIoT)systems that support intelligent and responsive smart societies,but it also introduces major security and privacy concerns across domains such as healthcare,transportation,and smart cities.This Systemic Literature Review(SLR)addresses three research questions:identifying major threats and challenges in AIoT ecosystems,reviewing state-of-the-art security and privacy techniques,and evaluating their effectiveness.An SLR covering the period from 2020 to 2025 was conducted using major academic digital libraries,including IEEE Xplore,ACM Digital Library,ScienceDirect,SpringerLink,and Wiley Online Library,with a focus on security-and privacy-enhancing techniques such as blockchain,federated learning,and edge AI.The SLR identifies key challenges including data privacy leakage,authentication,cloud dependency,and attack surface expansion,and finds that emerging techniques,while promising,often involve trade-offs related to latency,scalability,and compliance.The study highlights future directions including lightweight cryptography,standardization,and explainable AI to support secure and trustworthy AIoT-enabled smart societies.
文摘The contemporary smart cities,smart homes,smart buildings,and smart health care systems are the results of the explosive growth of Internet of Things(IoT)devices and deep learning.Yet the centralized training paradigms have fundamental issues in data privacy,regulatory compliance,and ownership silo alongside the scaled limitations of the real-life application.The concept of Federated Deep Learning(FDL)is a privacy-by-design method that will enable the distributed training of machine learning models among distributed clients without sharing raw data and is suitable in heterogeneous urban settings.It is an overview of the privacy-preserving developments in FDL as of 2018-2025 with a narrow scope on its usage in smart cities(traffic prediction,environmental monitoring,energy grids),smart homes/buildings/IoT(non-intrusive load monitoring,HVAC optimization,anomaly detection)and the healthcare application(medical imaging,Electronic Health Records(EHR)analysis,remote monitoring).It gives coherent taxonomy,domain pipelines,comparative analyses of privacy mechanisms(differential privacy,secure aggregation,Homomorphic Encryption(HE),Trusted Execution Environments(TEEs),blockchain enhanced and hybrids),system structures,security/robustness defense,deployment/Machine Learning Operation(MLOps)issues,and the longstanding challenges(non-IID heterogeneity,communication efficiency,fairness,and sustainability).Some of the contributions made are structured comparisons of privacy threats,practical design advice on urban areas,recognition of open problems,and a research roadmap into the future up to 2035.The paper brings out the transformational worth of FDL in building credible,scalable,and sustainable intelligent urban ecosystems and the need to do further interdisciplinary research in standardization,real-world testbeds,and ethical governance.
文摘The rise in noise and air pollution poses severe risks to human health and the environment.Industrial and vehicular emissions release harmful pollutants such as CO_(2),SO_(2),CO,CH_(4),and noise,leading to significant environmental degradation.Monitoring and analyzing pollutant concentrations in real-time is crucial for mitigating these risks.However,existing systems often lack the capacity to monitor both indoor and outdoor environments effectively.This study presents a low-cost,Io'T-based pollution detection system that integrates gas sensors(MQ-135and M(Q-4),a noise sensor(LM393),and a humidity sensor(DHT-22),all connected to a Node MCU(ESP8266)microcontroller.The system leverages cloud-based storage and real-time analytics to monitor harmful gas levels and sound pollution.Sensor data is processed using decision tree algorithms for classification,enabling threshold-based detection with environmental context.A Progressive Web Application(PWA)interface provides tusers with accessible,cross-platform visualizations.Experimental validation demonstrated the system’s ability to detect pollutant concentration variations across both indoor and outdoor settings,with real-time alerts triggered when thresholds were exceeded.The collected data showed consistent classification of normal,warning,and critical states for methane,CO_(2),temperature,humidity,and noise levels.These results confirm the system's reliability in dynamic environmental conditions.The proposed framework offers ascalable,energy-efficient,and user-friendly solution for pollution detectionand public awareness.Future enhancements will focus on extending the sensor suite,improving machine learning accuracy,and integrating meteorological data for predictive pollution modeling.
文摘An understanding of the influence of contractor monitoring on performance of road infrastructural projects in Uganda provided an impetus for this study. The objectives of the study were to: assess the relationship between contractors monitoring and performance of national road infrastructure projects and the relationship between contractor monitoring components and performance of national road infrastructure projects in Uganda. Purposive sampling was employed in selecting the procurement professionals, engineers and simple random sampling was adopted in selecting private consultants, members of parliament and respondents from the civil society organizations. Data for this study were collected using a closed ended questionnaire and interviews. Some of the major finding from this study include: weak procurement rules which lead to awarding road projects to incompetent contractors;contractor monitoring being handled by unqualified, incompetent and inexperienced professionals;lack of contractors and contract supervisors appraisal system;delay of contractors payments which affects timelines in services delivery;lack of a strong internal project monitoring and evaluation mechanism at the Uganda National Roads Agency (UNRA). The research therefore recommends the establishment of an Independent Public Infrastructure Development and Monitoring Unit by government and adoption of systems that appraise both contractors and contract supervisors with clear disciplinary actions for unsatisfactory performance by the UNRA.
基金supported in part by the rural science and technology innovation fund project of technology division from Ningbo city science and technology bureau (No.201001C8002011201002C1011003) for financial support
文摘This paper reported the effect of oolong tea processing procedure of turn-over on quality of the Jinmudan Oolong tea,including taste components and volatile compounds.The content of the water extractable solids was gradually increased,but the content of amino acid decreased and then increased,and the content of the soluble sugar and tea polyphenols increased after the first turn-over processing.The major volatiles of the three tested Jinmudan Oolong tea samples were nerolidolcistrans,α-farnesene,palmitic acid,indole and 9,12,15-octadecatrienoicacid and methyl ester.The sensory evaluation results showed that an appropriate increase in the number of turn-over was helpful to quality of the Jinmudan Oolong tea.
文摘The electricity situation in Nigeria can be described as epileptic with no sign in view of improvement. This epileptic power situation affects the manufacturing, service and residential sectors of the economy which in turn affects the country’s economic growth. Even with the recent reforms in the power sector, more than half of the country’s population still lack access to electricity. The epileptic condition of the power sector can be attributed to the inadequate and inefficient power plants, poor transmission and distribution facilities, and outdated metering system used by electricity consumers. This paper attempts to present the way forward for the Nigerian poor electricity situation by reviewing the power sector as a whole and the renewable energy potentials. We identified the problems in the national grid and then proposed a smart grid model for the Nigerian power sector which will include renewable energy source. We believe that the content of this review paper will solve the poor epileptic condition of the power sector in Nigeria and also enable the proper integration of smart grid technology into the national grid.
