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
This study aims to determine the key and underlying Leadership and Top Management (LTM) factors that have a significant impact on sustaining the implementation of Total Quality Management (TQM) within the construction...This study aims to determine the key and underlying Leadership and Top Management (LTM) factors that have a significant impact on sustaining the implementation of Total Quality Management (TQM) within the construction industry in Ghana. The research methodology employed in this study was a quantitative technique. Questionnaires were distributed to 641 participants within construction industry in Ghana. Questionnaires retrieved for the analysis were 536. Three steps approached were used for the data analysis. These include Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM) analysis. After conducting the EFA and CFA, SEM was also used to analyze the construct validity. The SEM analysis helps to determine four key indicator variables for the leadership and top management construct. These include Leadership/Top Management approach to employees’ management, Leadership/Top Management understanding of TQM, Leadership/Top Management empowerment of employees to resolve quality issues, and Leadership/Top Management endorsement of TQM. All the four indicator variables were found to be good of fit and closely associated with the dependent variable. The study adds to the body of knowledge by using EFA, CFA and SEM techniques to establish key leadership and top management factors affecting TQM implementation in Ghana’s construction industry. The findings in general suggested that leadership and top Management factors identified have a direct positive impact on sustaining TQM implementation in the Ghanaian construction industry. Consequently, the leadership and top management factors identified in this study can help improve TQM in the Ghanaian construction industry.展开更多
With the rapid development of virtual reality technology,it has been widely used in the field of education.It can promote the development of learning transfer,which is an effective method for learners to learn effecti...With the rapid development of virtual reality technology,it has been widely used in the field of education.It can promote the development of learning transfer,which is an effective method for learners to learn effectively.Therefore,this paper describes how to use virtual reality technology to achieve learning transfer in order to achieve teaching goals and improve learning efficiency.展开更多
This study investigates the adoption of carbon footprint tracking apps(CFAs)among Thai millennials,a critical element in addressing climate change.CFAs have yet to gain significant traction among users despite offerin...This study investigates the adoption of carbon footprint tracking apps(CFAs)among Thai millennials,a critical element in addressing climate change.CFAs have yet to gain significant traction among users despite offering personalized missions.Employing an extended Technology Acceptance Model(TAM)framework,we examine factors influencing CFA adoption intentions based on a sample of 30 environmentally conscious Thai millennials.Our findings indicate that perceived ease of use and enjoyment are crucial drivers of CFA adoption.Trust significantly impacts perceived usefulness,while enjoyment influences perceived ease of use.The study underscores the importance of user experience(UX)and enjoyment in driving adoption,highlighting the need for intuitive interfaces and engaging features.This research provides comprehensive insights into CFA adoption in Thailand by integrating TAM with external trust and perceived enjoyment factors.These findings offer valuable guidance for app developers,policymakers,and marketers,emphasizing the critical role of user experience and fun in fostering widespread CFA adoption.We discuss implications for stakeholders and suggest directions for future research,including larger-scale studies and cross-cultural comparisons within Southeast Asia.This research contributes to SDG 13(Climate Action)and SDG 12(Responsible Consumption and Production).展开更多
Cyberbullying is a remarkable issue in the Arabic-speaking world,affecting children,organizations,and businesses.Various efforts have been made to combat this problem through proposed models using machine learning(ML)...Cyberbullying is a remarkable issue in the Arabic-speaking world,affecting children,organizations,and businesses.Various efforts have been made to combat this problem through proposed models using machine learning(ML)and deep learning(DL)approaches utilizing natural language processing(NLP)methods and by proposing relevant datasets.However,most of these endeavors focused predominantly on the English language,leaving a substantial gap in addressing Arabic cyberbullying.Given the complexities of the Arabic language,transfer learning techniques and transformers present a promising approach to enhance the detection and classification of abusive content by leveraging large and pretrained models that use a large dataset.Therefore,this study proposes a hybrid model using transformers trained on extensive Arabic datasets.It then fine-tunes the hybrid model on a newly curated Arabic cyberbullying dataset collected from social media platforms,in particular Twitter.Additionally,the following two hybrid transformer models are introduced:the first combines CAmelid Morphologically-aware pretrained Bidirectional Encoder Representations from Transformers(CAMeLBERT)with Arabic Generative Pre-trained Transformer 2(AraGPT2)and the second combines Arabic BERT(AraBERT)with Cross-lingual Language Model-RoBERTa(XLM-R).Two strategies,namely,feature fusion and ensemble voting,are employed to improve the model performance accuracy.Experimental results,measured through precision,recall,F1-score,accuracy,and AreaUnder the Curve-Receiver Operating Characteristic(AUC-ROC),demonstrate that the combined CAMeLBERT and AraGPT2 models using feature fusion outperformed traditional DL models,such as Long Short-Term Memory(LSTM)and Bidirectional Long Short-Term Memory(BiLSTM),as well as other independent Arabic-based transformer models.展开更多
Nowadays,wireless communication devices turn out to be transportable owing to the execution of the current technologies.The antenna is the most important component deployed for communication purposes.The antenna plays...Nowadays,wireless communication devices turn out to be transportable owing to the execution of the current technologies.The antenna is the most important component deployed for communication purposes.The antenna plays an imperative role in receiving and transmitting the signals for any sensor network.Among varied antennas,micro strip fractal antenna(MFA)significantly contributes to increasing antenna gain.This study employs a hybrid optimization method known as the elephant clan updated grey wolf algorithm to introduce an optimized MFA design.This method optimizes antenna characteristics,including directivity and gain.Here,the factors,including length,width,ground plane length,height,and feed offset-X and feed offset-Y,are taken into account to achieve the best performance of gain and directivity.Ultimately,the superiority of the suggested technique over state-of-the-art strategies is calculated for various metrics such as cost and gain.The adopted model converges to a minimal value of 0.2872.Further,the spider monkey optimization(SMO)model accomplishes the worst performance over all other existing models like elephant herding optimization(EHO),grey wolf optimization(GWO),lion algorithm(LA),support vector regressor(SVR),bacterial foraging-particle swarm optimization(BF-PSO)and shark smell optimization(SSO).Effective MFA design is obtained using the suggested strategy regarding various parameters.展开更多
Soil salinization is a prominent global environmental issue that considerably affects the sustainable development of agriculture worldwide.Maize,a key crop integral to the global agricultural economy,is especially sus...Soil salinization is a prominent global environmental issue that considerably affects the sustainable development of agriculture worldwide.Maize,a key crop integral to the global agricultural economy,is especially susceptible to the detrimental impacts of salt stress,which can impede its growth and development from the germination phase through to the seedling stage.Soil salinity tends to escalate due to improper irrigation methods,particularly in arid and semi-arid environments.Consequently,it is essential to evaluate potential genotypes and select those with high salt tolerance.In this study,39 popcorn kernel genotypes were examined under varying salinity levels(0,100,and 200 mM NaCl).Notable declines in seedling growth and significant differences in stress responses were recorded in relation to salinity levels.The application of 200 mM NaCl was found to severely hinder the growth of sensitive species such as maize,adversely impacting both the germination rate and speed.Even when germination occurred,subsequent seedling development was stunted.Therefore,it is advisable to utilize salinity concentrations below 200 mM in research focused on seedling development stages.The assessment of genotypes for their adaptability to saline conditions indicated that genotypes 4,33,12,28,18,21,25,37,16,and 31 exhibited high salt tolerance,while genotypes 1,17,35,and 36 were identified as susceptible.It is recommended that the more resilient genotypes be utilized in regions affected by salt stress or incorporated into breeding programs.展开更多
Bean(Phaseolus vulgaris)is a globally important legume crop valued for its nutritional content and adaptability.Establishing a robust root system during early growth is critical for optimal nutrient uptake,shoot devel...Bean(Phaseolus vulgaris)is a globally important legume crop valued for its nutritional content and adaptability.Establishing a robust root system during early growth is critical for optimal nutrient uptake,shoot development,and increased resistance to biotic stress.This study evaluated the effects of exogenous indole-3-butyric acid(IBA)on root and shoot development in two bean cultivars,Onceler-98 and Topcu,during the seedling stage.IBA was applied at four concentrations:0(control),50,100,and 150μM.Morphological parameters measured included root length(RL),root fresh weight(RFW),root dry weight(RDW),root nodule number(RNN),shoot length(SL),shoot fresh weight(SFW),and shoot dry weight(SDW).The experiment followed a randomized complete block design with four replications.Significant(p≤0.05)and highly significant(p≤0.01)differences were observed across treatments and cultivars.The results indicated that Onceler-98 generally responded more favorably to IBA application,with optimal growth performance observed at 100μM.In contrast,Topcu was less responsive to IBA overall,and high concentrations-particularly 150μM-tended to suppress nodule formation.展开更多
A recommender system is a tool designed to suggest relevant items to users based on their preferences and behaviors.Collaborative filtering,a popular technique within recommender systems,predicts user interests by ana...A recommender system is a tool designed to suggest relevant items to users based on their preferences and behaviors.Collaborative filtering,a popular technique within recommender systems,predicts user interests by analyzing patterns in interactions and similarities between users,leveraging past behavior data to make personalized recommendations.Despite its popularity,collaborative filtering faces notable challenges,and one of them is the issue of grey-sheep users who have unusual tastes in the system.Surprisingly,existing research has not extensively explored outlier detection techniques to address the grey-sheep problem.To fill this research gap,this study conducts a comprehensive comparison of 12 outlier detectionmethods(such as LOF,ABOD,HBOS,etc.)and introduces innovative user representations aimed at improving the identification of outliers within recommender systems.More specifically,we proposed and examined three types of user representations:1)the distribution statistics of user-user similarities,where similarities were calculated based on users’rating vectors;2)the distribution statistics of user-user similarities,but with similarities derived from users represented by latent factors;and 3)latent-factor vector representations.Our experiments on the Movie Lens and Yahoo!Movie datasets demonstrate that user representations based on latent-factor vectors consistently facilitate the identification of more grey-sheep users when applying outlier detection methods.展开更多
Accurate measurement of anchor rod length is crucial for ensuring structural safety in tunnel engineering,yet conventional destructive techniques face limitations in efficiency and adaptability to complex underground ...Accurate measurement of anchor rod length is crucial for ensuring structural safety in tunnel engineering,yet conventional destructive techniques face limitations in efficiency and adaptability to complex underground environments.This study presents a novel wireless instrument based on the standing wave principle to enable remote,non-destructive length assessment.The system employs a master-slave architecture,where a handheld transmitter unit initiates measurements through robust 433 MHz wireless communication,optimized for signal penetration in obstructed spaces.The embedded measurement unit,integrated with anchor rods during installation,utilizes frequency-scanning technology to excite structural resonances.By analyzing standing wave characteristics,anchor length is derived from a calibrated frequency-length relationship.Power management adopts a standby-activation strategy to minimize energy consumption while maintaining operational readiness.Experimental validation confirms the system effectively measures anchor lengths with high precision and maintains reliable signal transmission through thick concrete barriers,demonstrating suitability for tunnel deployment.The non-destructive approach eliminates structural damage risks associated with traditional pull-out tests,while wireless operation enhances inspection efficiency in confined spaces.Thiswork establishes a paradigmfor embedded structural healthmonitoring in tunneling,offering significant improvements over existing methods in safety,cost-effectiveness,and scalability.The technology holds promise for broad applications in mining,underground infrastructure,and geotechnical engineering.展开更多
Background:Recent scholarly attention has increasingly focused on filial piety beliefs'impact on youth's psychological development.However,the mechanisms by which filial piety indirectly influences adolescent ...Background:Recent scholarly attention has increasingly focused on filial piety beliefs'impact on youth's psychological development.However,the mechanisms by which filial piety indirectly influences adolescent autonomy through depression and well-being remain underexplored.This study aimed to test a sequential mediation model among filial piety beliefs,depression,well-being,and autonomy in Taiwan region of China university students.Methods:A total of 566 Taiwan region of China undergraduate and graduate students,comprising 390 females and 176 males,and including 399 undergraduates and 167 graduate students,were recruited through convenience sampling.Data were collected via an online questionnaire.Validated instruments were employed,including the Filial Piety Scale(FPS),the Center for Epidemiological Studies Depression Scale(CES-D),the Chinese Well-being Inventory(CHI),and the Adolescent Autonomy Scale-Short Form(AAS-SF).Statistical analyses included group comparisons,correlation analyses,and structural equation modeling to examine the hypothesized relationships and mediation effects.Results:The results revealed that filial piety beliefs exerted a significant positive impact on adolescent autonomy,with depression and well-being serving as key mediators in this relationship.A sequential mediation effect was confirmed through structural equation modeling(β=0.052,95%CI[0.028,0.091]),with good model fit indices(x^(2)/df=4.25,RMSEA=0.076,CFI=0.968),supporting the hypothesized pathway from filial piety to autonomy via depression and well-being.In terms of demographic differences,male students showed significantly higher autonomy than females(p<0.001);students from single-parent families reported significantly higher depression levels than those from two-parent families(p<0.05);and graduate students exhibited significantly higher autonomy and well-being than undergraduates(p<0.05).Conclusions:These findings underscore not only the importance of filial piety beliefs for developing youth autonomy but also the critical role that mental health factors,such as depression and well-being,play in this process.The study concludes with a discussion of both theoretical implications and practical recommendations.These include strategies to foster reciprocal filial piety,strengthen parent-child relationships,and promote mental health.Additionally,the study outlines its limitations and proposes directions for future research.展开更多
The mechanical parameters and failure characteristics of sandstone under compressive-shear stress states provide crucial theoretical references for underground engineering construction.In this study,a series of varied...The mechanical parameters and failure characteristics of sandstone under compressive-shear stress states provide crucial theoretical references for underground engineering construction.In this study,a series of varied angle shear tests(VASTs)were designed using acoustic emission(AE)detection and digital image correlation technologies to evaluate the mechanical behaviors of typical red sandstone.AE signal parameters revealed differences in the number and intensity of microcracks within the sandstone,with a test angle(α)of 50°identified as a significant turning point for its failure properties.Whenα³50°,microcrack activity intensified,and the proportion of tensile cracks increased.Asαincreased,the number of fragments generated after failure decreased,fragment sizes became smaller,and the crack network simplified.Cracks extended from the two cut slits at the ends of the rock,gradually penetrating along the centerline towards the central location,as observed from the evolution of the strain concentration field.Both cohesion(c)and internal friction angle(ϕ)measured in VAST were lower than those measured under conventional triaxial compression.展开更多
Throughout this work,we explore the uniqueness properties of meromorphic functions concerning their interactions with complex differential-difference polynomial.Under the condition of finite order,we establish three d...Throughout this work,we explore the uniqueness properties of meromorphic functions concerning their interactions with complex differential-difference polynomial.Under the condition of finite order,we establish three distinct uniqueness results for a meromorphic function f associated with the differential-difference polynomial L_(η)^(n)f=Σ_(k=0)^(n)a_(k)f (z+k_(η))+a_(-1)f′.These results lead to a refined characterization of f (z)≡L_(η)^(n)f (z).Several illustrative examples are provided to demonstrate the sharpness and precision of the results obtained in this study.展开更多
Physiological signals such as electroencephalogram(EEG)signals are often corrupted by artifacts during the acquisition and processing.Some of these artifacts may deteriorate the essential properties of the signal that...Physiological signals such as electroencephalogram(EEG)signals are often corrupted by artifacts during the acquisition and processing.Some of these artifacts may deteriorate the essential properties of the signal that pertains to meaningful information.Most of these artifacts occur due to the involuntary movements or actions the human does during the acquisition process.So,it is recommended to eliminate these artifacts with signal processing approaches.This paper presents two mechanisms of classification and elimination of artifacts.In the first step,a customized deep network is employed to classify clean EEG signals and artifact-included signals.The classification is performed at the feature level,where common space pattern features are extracted with convolutional layers,and these features are later classified with a support vector machine classifier.In the second stage of the work,the artifact signals are decomposed with empirical mode decomposition,and they are then eliminated with the proposed adaptive thresholding mechanism where the threshold value changes for every intrinsic mode decomposition in the iterative mechanism.展开更多
Consecutive stresses,such as initial submergence during germination followed by water deficit during the seedling stage,pose significant challenges to direct-seeded rice cultivation.By Linkage disequilibrium analysis,...Consecutive stresses,such as initial submergence during germination followed by water deficit during the seedling stage,pose significant challenges to direct-seeded rice cultivation.By Linkage disequilibrium analysis,Sub1 and Dro1(Δbp:10 Mb),as well as Sub1 and TPP7(Δbp:6 Mb)were identified to exhibit long-range linkage disequilibrium(LRLD).Meta-QTL analysis further revealed that Sub1 and TPP7 co-segregated for tolerance to submergence at the germination and seedling stages.Based on this,we hypothesized that LRLD might influence plant responses to consecutive stresses.To test this hypothesis,we developed a structured recombinant inbred line population from a cross between Bhalum 2 and Nagina 22,with alleles(Sub1 and TPP7)in linkage equilibrium.Mendelian randomization analysis validated that the parental alleles,rather than the recombinant alleles of Sub1 and TPP7,significantly influenced 13 out of 41 traits under consecutive stress conditions.Additionally,16 minor additive effect QTLs were detected between the genomic regions,spanning Sub1 and TPP7 for various traits.A single allele difference between these genomic regions enhanced crown root number,root dry weight,and specific root area by 11.45%,15.69%,and 33.15%,respectively,under flooded germination conditions.Candidate gene analysis identified WAK79 and MRLK59 as regulators of stress responses during flooded germination,recovery,and subsequent water deficit conditions.These findings highlight the critical role of parental allele combinations and genomic regions between Sub1 and TPP7 in regulating the stress responses under consecutive stresses.Favourable haplotypes derived from these alleles can be utilized to improve stress resilience in direct-seeded rice.展开更多
Assessing the benefits and costs of digitalization in the energy industry is a complex issue.Traditional cost-benefit analysis(CBA)might encounter problems in addressing uncertainties,dynamic stakeholder interactions,...Assessing the benefits and costs of digitalization in the energy industry is a complex issue.Traditional cost-benefit analysis(CBA)might encounter problems in addressing uncertainties,dynamic stakeholder interactions,and feedback loops arising out of the evolving nature of digitalization.This paper introduces a methodological framework to help address the intricate inter connections between digital applications and business models in the energy industry.The proposed framework leverages system dynamics to achieve two primary objectives.It investigates how digitalization generally influences the value proposi-tion,value capture,and value creation dimensions of business models.It also quantifies the financial and social impacts of digitalization from a dynamic perspective.The proposed dynamic CBA allows for a more precise quantification of the benefits and costs,associated with evidence-based decision-making.Findings from an illustrative case study challenge the static assumptions of conventional methods.These methods often presume continuous operation,neglecting reinvestment and operational feedback loops,and resulting in negative net present values.Conversely,the outcomes of the proposed method indicate positive net present values when accounting for factors such as reinvestment rates and the will-ingness to invest in digitalization projects.The principles outlined in this paper can enable a more accu-rate assessment of digitalization projects,thus catalyzing the development of new CBA applications and guidelines for digitalization.展开更多
Feature selection(FS)plays a crucial role in medical imaging by reducing dimensionality,improving computational efficiency,and enhancing diagnostic accuracy.Traditional FS techniques,including filter,wrapper,and embed...Feature selection(FS)plays a crucial role in medical imaging by reducing dimensionality,improving computational efficiency,and enhancing diagnostic accuracy.Traditional FS techniques,including filter,wrapper,and embedded methods,have been widely used but often struggle with high-dimensional and heterogeneous medical imaging data.Deep learning-based FS methods,particularly Convolutional Neural Networks(CNNs)and autoencoders,have demonstrated superior performance but lack interpretability.Hybrid approaches that combine classical and deep learning techniques have emerged as a promising solution,offering improved accuracy and explainability.Furthermore,integratingmulti-modal imaging data(e.g.,MagneticResonance Imaging(MRI),ComputedTomography(CT),Positron Emission Tomography(PET),and Ultrasound(US))poses additional challenges in FS,necessitating advanced feature fusion strategies.Multi-modal feature fusion combines information fromdifferent imagingmodalities to improve diagnostic accuracy.Recently,quantum computing has gained attention as a revolutionary approach for FS,providing the potential to handle high-dimensional medical data more efficiently.This systematic literature review comprehensively examines classical,Deep Learning(DL),hybrid,and quantum-based FS techniques inmedical imaging.Key outcomes include a structured taxonomy of FS methods,a critical evaluation of their performance across modalities,and identification of core challenges such as computational burden,interpretability,and ethical considerations.Future research directions—such as explainable AI(XAI),federated learning,and quantum-enhanced FS—are also emphasized to bridge the current gaps.This review provides actionable insights for developing scalable,interpretable,and clinically applicable FS methods in the evolving landscape of medical imaging.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
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This study aims to determine the key and underlying Leadership and Top Management (LTM) factors that have a significant impact on sustaining the implementation of Total Quality Management (TQM) within the construction industry in Ghana. The research methodology employed in this study was a quantitative technique. Questionnaires were distributed to 641 participants within construction industry in Ghana. Questionnaires retrieved for the analysis were 536. Three steps approached were used for the data analysis. These include Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM) analysis. After conducting the EFA and CFA, SEM was also used to analyze the construct validity. The SEM analysis helps to determine four key indicator variables for the leadership and top management construct. These include Leadership/Top Management approach to employees’ management, Leadership/Top Management understanding of TQM, Leadership/Top Management empowerment of employees to resolve quality issues, and Leadership/Top Management endorsement of TQM. All the four indicator variables were found to be good of fit and closely associated with the dependent variable. The study adds to the body of knowledge by using EFA, CFA and SEM techniques to establish key leadership and top management factors affecting TQM implementation in Ghana’s construction industry. The findings in general suggested that leadership and top Management factors identified have a direct positive impact on sustaining TQM implementation in the Ghanaian construction industry. Consequently, the leadership and top management factors identified in this study can help improve TQM in the Ghanaian construction industry.
文摘With the rapid development of virtual reality technology,it has been widely used in the field of education.It can promote the development of learning transfer,which is an effective method for learners to learn effectively.Therefore,this paper describes how to use virtual reality technology to achieve learning transfer in order to achieve teaching goals and improve learning efficiency.
文摘This study investigates the adoption of carbon footprint tracking apps(CFAs)among Thai millennials,a critical element in addressing climate change.CFAs have yet to gain significant traction among users despite offering personalized missions.Employing an extended Technology Acceptance Model(TAM)framework,we examine factors influencing CFA adoption intentions based on a sample of 30 environmentally conscious Thai millennials.Our findings indicate that perceived ease of use and enjoyment are crucial drivers of CFA adoption.Trust significantly impacts perceived usefulness,while enjoyment influences perceived ease of use.The study underscores the importance of user experience(UX)and enjoyment in driving adoption,highlighting the need for intuitive interfaces and engaging features.This research provides comprehensive insights into CFA adoption in Thailand by integrating TAM with external trust and perceived enjoyment factors.These findings offer valuable guidance for app developers,policymakers,and marketers,emphasizing the critical role of user experience and fun in fostering widespread CFA adoption.We discuss implications for stakeholders and suggest directions for future research,including larger-scale studies and cross-cultural comparisons within Southeast Asia.This research contributes to SDG 13(Climate Action)and SDG 12(Responsible Consumption and Production).
基金funded by the Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia,through the project number“NBU-FFR-2025-1197-01”.
文摘Cyberbullying is a remarkable issue in the Arabic-speaking world,affecting children,organizations,and businesses.Various efforts have been made to combat this problem through proposed models using machine learning(ML)and deep learning(DL)approaches utilizing natural language processing(NLP)methods and by proposing relevant datasets.However,most of these endeavors focused predominantly on the English language,leaving a substantial gap in addressing Arabic cyberbullying.Given the complexities of the Arabic language,transfer learning techniques and transformers present a promising approach to enhance the detection and classification of abusive content by leveraging large and pretrained models that use a large dataset.Therefore,this study proposes a hybrid model using transformers trained on extensive Arabic datasets.It then fine-tunes the hybrid model on a newly curated Arabic cyberbullying dataset collected from social media platforms,in particular Twitter.Additionally,the following two hybrid transformer models are introduced:the first combines CAmelid Morphologically-aware pretrained Bidirectional Encoder Representations from Transformers(CAMeLBERT)with Arabic Generative Pre-trained Transformer 2(AraGPT2)and the second combines Arabic BERT(AraBERT)with Cross-lingual Language Model-RoBERTa(XLM-R).Two strategies,namely,feature fusion and ensemble voting,are employed to improve the model performance accuracy.Experimental results,measured through precision,recall,F1-score,accuracy,and AreaUnder the Curve-Receiver Operating Characteristic(AUC-ROC),demonstrate that the combined CAMeLBERT and AraGPT2 models using feature fusion outperformed traditional DL models,such as Long Short-Term Memory(LSTM)and Bidirectional Long Short-Term Memory(BiLSTM),as well as other independent Arabic-based transformer models.
文摘Nowadays,wireless communication devices turn out to be transportable owing to the execution of the current technologies.The antenna is the most important component deployed for communication purposes.The antenna plays an imperative role in receiving and transmitting the signals for any sensor network.Among varied antennas,micro strip fractal antenna(MFA)significantly contributes to increasing antenna gain.This study employs a hybrid optimization method known as the elephant clan updated grey wolf algorithm to introduce an optimized MFA design.This method optimizes antenna characteristics,including directivity and gain.Here,the factors,including length,width,ground plane length,height,and feed offset-X and feed offset-Y,are taken into account to achieve the best performance of gain and directivity.Ultimately,the superiority of the suggested technique over state-of-the-art strategies is calculated for various metrics such as cost and gain.The adopted model converges to a minimal value of 0.2872.Further,the spider monkey optimization(SMO)model accomplishes the worst performance over all other existing models like elephant herding optimization(EHO),grey wolf optimization(GWO),lion algorithm(LA),support vector regressor(SVR),bacterial foraging-particle swarm optimization(BF-PSO)and shark smell optimization(SSO).Effective MFA design is obtained using the suggested strategy regarding various parameters.
文摘Soil salinization is a prominent global environmental issue that considerably affects the sustainable development of agriculture worldwide.Maize,a key crop integral to the global agricultural economy,is especially susceptible to the detrimental impacts of salt stress,which can impede its growth and development from the germination phase through to the seedling stage.Soil salinity tends to escalate due to improper irrigation methods,particularly in arid and semi-arid environments.Consequently,it is essential to evaluate potential genotypes and select those with high salt tolerance.In this study,39 popcorn kernel genotypes were examined under varying salinity levels(0,100,and 200 mM NaCl).Notable declines in seedling growth and significant differences in stress responses were recorded in relation to salinity levels.The application of 200 mM NaCl was found to severely hinder the growth of sensitive species such as maize,adversely impacting both the germination rate and speed.Even when germination occurred,subsequent seedling development was stunted.Therefore,it is advisable to utilize salinity concentrations below 200 mM in research focused on seedling development stages.The assessment of genotypes for their adaptability to saline conditions indicated that genotypes 4,33,12,28,18,21,25,37,16,and 31 exhibited high salt tolerance,while genotypes 1,17,35,and 36 were identified as susceptible.It is recommended that the more resilient genotypes be utilized in regions affected by salt stress or incorporated into breeding programs.
文摘Bean(Phaseolus vulgaris)is a globally important legume crop valued for its nutritional content and adaptability.Establishing a robust root system during early growth is critical for optimal nutrient uptake,shoot development,and increased resistance to biotic stress.This study evaluated the effects of exogenous indole-3-butyric acid(IBA)on root and shoot development in two bean cultivars,Onceler-98 and Topcu,during the seedling stage.IBA was applied at four concentrations:0(control),50,100,and 150μM.Morphological parameters measured included root length(RL),root fresh weight(RFW),root dry weight(RDW),root nodule number(RNN),shoot length(SL),shoot fresh weight(SFW),and shoot dry weight(SDW).The experiment followed a randomized complete block design with four replications.Significant(p≤0.05)and highly significant(p≤0.01)differences were observed across treatments and cultivars.The results indicated that Onceler-98 generally responded more favorably to IBA application,with optimal growth performance observed at 100μM.In contrast,Topcu was less responsive to IBA overall,and high concentrations-particularly 150μM-tended to suppress nodule formation.
文摘A recommender system is a tool designed to suggest relevant items to users based on their preferences and behaviors.Collaborative filtering,a popular technique within recommender systems,predicts user interests by analyzing patterns in interactions and similarities between users,leveraging past behavior data to make personalized recommendations.Despite its popularity,collaborative filtering faces notable challenges,and one of them is the issue of grey-sheep users who have unusual tastes in the system.Surprisingly,existing research has not extensively explored outlier detection techniques to address the grey-sheep problem.To fill this research gap,this study conducts a comprehensive comparison of 12 outlier detectionmethods(such as LOF,ABOD,HBOS,etc.)and introduces innovative user representations aimed at improving the identification of outliers within recommender systems.More specifically,we proposed and examined three types of user representations:1)the distribution statistics of user-user similarities,where similarities were calculated based on users’rating vectors;2)the distribution statistics of user-user similarities,but with similarities derived from users represented by latent factors;and 3)latent-factor vector representations.Our experiments on the Movie Lens and Yahoo!Movie datasets demonstrate that user representations based on latent-factor vectors consistently facilitate the identification of more grey-sheep users when applying outlier detection methods.
基金supported in part by the Natural Science Foundation of Gansu Province(Nos.20JR10RA614,22YF7GA182,22JR11RA042,22JR5RA1006,24CXGA024)the National Natural Science Foundation of China under Grant 61804071.
文摘Accurate measurement of anchor rod length is crucial for ensuring structural safety in tunnel engineering,yet conventional destructive techniques face limitations in efficiency and adaptability to complex underground environments.This study presents a novel wireless instrument based on the standing wave principle to enable remote,non-destructive length assessment.The system employs a master-slave architecture,where a handheld transmitter unit initiates measurements through robust 433 MHz wireless communication,optimized for signal penetration in obstructed spaces.The embedded measurement unit,integrated with anchor rods during installation,utilizes frequency-scanning technology to excite structural resonances.By analyzing standing wave characteristics,anchor length is derived from a calibrated frequency-length relationship.Power management adopts a standby-activation strategy to minimize energy consumption while maintaining operational readiness.Experimental validation confirms the system effectively measures anchor lengths with high precision and maintains reliable signal transmission through thick concrete barriers,demonstrating suitability for tunnel deployment.The non-destructive approach eliminates structural damage risks associated with traditional pull-out tests,while wireless operation enhances inspection efficiency in confined spaces.Thiswork establishes a paradigmfor embedded structural healthmonitoring in tunneling,offering significant improvements over existing methods in safety,cost-effectiveness,and scalability.The technology holds promise for broad applications in mining,underground infrastructure,and geotechnical engineering.
文摘Background:Recent scholarly attention has increasingly focused on filial piety beliefs'impact on youth's psychological development.However,the mechanisms by which filial piety indirectly influences adolescent autonomy through depression and well-being remain underexplored.This study aimed to test a sequential mediation model among filial piety beliefs,depression,well-being,and autonomy in Taiwan region of China university students.Methods:A total of 566 Taiwan region of China undergraduate and graduate students,comprising 390 females and 176 males,and including 399 undergraduates and 167 graduate students,were recruited through convenience sampling.Data were collected via an online questionnaire.Validated instruments were employed,including the Filial Piety Scale(FPS),the Center for Epidemiological Studies Depression Scale(CES-D),the Chinese Well-being Inventory(CHI),and the Adolescent Autonomy Scale-Short Form(AAS-SF).Statistical analyses included group comparisons,correlation analyses,and structural equation modeling to examine the hypothesized relationships and mediation effects.Results:The results revealed that filial piety beliefs exerted a significant positive impact on adolescent autonomy,with depression and well-being serving as key mediators in this relationship.A sequential mediation effect was confirmed through structural equation modeling(β=0.052,95%CI[0.028,0.091]),with good model fit indices(x^(2)/df=4.25,RMSEA=0.076,CFI=0.968),supporting the hypothesized pathway from filial piety to autonomy via depression and well-being.In terms of demographic differences,male students showed significantly higher autonomy than females(p<0.001);students from single-parent families reported significantly higher depression levels than those from two-parent families(p<0.05);and graduate students exhibited significantly higher autonomy and well-being than undergraduates(p<0.05).Conclusions:These findings underscore not only the importance of filial piety beliefs for developing youth autonomy but also the critical role that mental health factors,such as depression and well-being,play in this process.The study concludes with a discussion of both theoretical implications and practical recommendations.These include strategies to foster reciprocal filial piety,strengthen parent-child relationships,and promote mental health.Additionally,the study outlines its limitations and proposes directions for future research.
基金Project(52374150)supported by the National Natural Science Foundation of ChinaProject(2021RC3007)supported by the Science and Technology Innovation Program of Hunan Province,China。
文摘The mechanical parameters and failure characteristics of sandstone under compressive-shear stress states provide crucial theoretical references for underground engineering construction.In this study,a series of varied angle shear tests(VASTs)were designed using acoustic emission(AE)detection and digital image correlation technologies to evaluate the mechanical behaviors of typical red sandstone.AE signal parameters revealed differences in the number and intensity of microcracks within the sandstone,with a test angle(α)of 50°identified as a significant turning point for its failure properties.Whenα³50°,microcrack activity intensified,and the proportion of tensile cracks increased.Asαincreased,the number of fragments generated after failure decreased,fragment sizes became smaller,and the crack network simplified.Cracks extended from the two cut slits at the ends of the rock,gradually penetrating along the centerline towards the central location,as observed from the evolution of the strain concentration field.Both cohesion(c)and internal friction angle(ϕ)measured in VAST were lower than those measured under conventional triaxial compression.
基金Supported by the National Natural Science Foundation of China (Grant No.12161074)the Talent Introduction Research Foundation of Suqian University (Grant No.106-CK00042/028)+1 种基金Suqian Sci&Tech Program (Grant No.M202206)Sponsored by Qing Lan Project of Jiangsu Province and Suqian Talent Xiongying Plan of Suqian。
文摘Throughout this work,we explore the uniqueness properties of meromorphic functions concerning their interactions with complex differential-difference polynomial.Under the condition of finite order,we establish three distinct uniqueness results for a meromorphic function f associated with the differential-difference polynomial L_(η)^(n)f=Σ_(k=0)^(n)a_(k)f (z+k_(η))+a_(-1)f′.These results lead to a refined characterization of f (z)≡L_(η)^(n)f (z).Several illustrative examples are provided to demonstrate the sharpness and precision of the results obtained in this study.
文摘Physiological signals such as electroencephalogram(EEG)signals are often corrupted by artifacts during the acquisition and processing.Some of these artifacts may deteriorate the essential properties of the signal that pertains to meaningful information.Most of these artifacts occur due to the involuntary movements or actions the human does during the acquisition process.So,it is recommended to eliminate these artifacts with signal processing approaches.This paper presents two mechanisms of classification and elimination of artifacts.In the first step,a customized deep network is employed to classify clean EEG signals and artifact-included signals.The classification is performed at the feature level,where common space pattern features are extracted with convolutional layers,and these features are later classified with a support vector machine classifier.In the second stage of the work,the artifact signals are decomposed with empirical mode decomposition,and they are then eliminated with the proposed adaptive thresholding mechanism where the threshold value changes for every intrinsic mode decomposition in the iterative mechanism.
基金supported by the Director General,Indian Council of Agricultural Research(ICAR),New Delhithe Director,ICAR-National Rice Research Institute,Cuttack.
文摘Consecutive stresses,such as initial submergence during germination followed by water deficit during the seedling stage,pose significant challenges to direct-seeded rice cultivation.By Linkage disequilibrium analysis,Sub1 and Dro1(Δbp:10 Mb),as well as Sub1 and TPP7(Δbp:6 Mb)were identified to exhibit long-range linkage disequilibrium(LRLD).Meta-QTL analysis further revealed that Sub1 and TPP7 co-segregated for tolerance to submergence at the germination and seedling stages.Based on this,we hypothesized that LRLD might influence plant responses to consecutive stresses.To test this hypothesis,we developed a structured recombinant inbred line population from a cross between Bhalum 2 and Nagina 22,with alleles(Sub1 and TPP7)in linkage equilibrium.Mendelian randomization analysis validated that the parental alleles,rather than the recombinant alleles of Sub1 and TPP7,significantly influenced 13 out of 41 traits under consecutive stress conditions.Additionally,16 minor additive effect QTLs were detected between the genomic regions,spanning Sub1 and TPP7 for various traits.A single allele difference between these genomic regions enhanced crown root number,root dry weight,and specific root area by 11.45%,15.69%,and 33.15%,respectively,under flooded germination conditions.Candidate gene analysis identified WAK79 and MRLK59 as regulators of stress responses during flooded germination,recovery,and subsequent water deficit conditions.These findings highlight the critical role of parental allele combinations and genomic regions between Sub1 and TPP7 in regulating the stress responses under consecutive stresses.Favourable haplotypes derived from these alleles can be utilized to improve stress resilience in direct-seeded rice.
基金conducted as part of the project Innovative Tools for Cyber-Physical Energy Systems(InnoCyPES)received funding from the European Union’s Horizon 2020 research and innovation pro-gram under the Marie Skłodowska-Curie(956433).
文摘Assessing the benefits and costs of digitalization in the energy industry is a complex issue.Traditional cost-benefit analysis(CBA)might encounter problems in addressing uncertainties,dynamic stakeholder interactions,and feedback loops arising out of the evolving nature of digitalization.This paper introduces a methodological framework to help address the intricate inter connections between digital applications and business models in the energy industry.The proposed framework leverages system dynamics to achieve two primary objectives.It investigates how digitalization generally influences the value proposi-tion,value capture,and value creation dimensions of business models.It also quantifies the financial and social impacts of digitalization from a dynamic perspective.The proposed dynamic CBA allows for a more precise quantification of the benefits and costs,associated with evidence-based decision-making.Findings from an illustrative case study challenge the static assumptions of conventional methods.These methods often presume continuous operation,neglecting reinvestment and operational feedback loops,and resulting in negative net present values.Conversely,the outcomes of the proposed method indicate positive net present values when accounting for factors such as reinvestment rates and the will-ingness to invest in digitalization projects.The principles outlined in this paper can enable a more accu-rate assessment of digitalization projects,thus catalyzing the development of new CBA applications and guidelines for digitalization.
文摘Feature selection(FS)plays a crucial role in medical imaging by reducing dimensionality,improving computational efficiency,and enhancing diagnostic accuracy.Traditional FS techniques,including filter,wrapper,and embedded methods,have been widely used but often struggle with high-dimensional and heterogeneous medical imaging data.Deep learning-based FS methods,particularly Convolutional Neural Networks(CNNs)and autoencoders,have demonstrated superior performance but lack interpretability.Hybrid approaches that combine classical and deep learning techniques have emerged as a promising solution,offering improved accuracy and explainability.Furthermore,integratingmulti-modal imaging data(e.g.,MagneticResonance Imaging(MRI),ComputedTomography(CT),Positron Emission Tomography(PET),and Ultrasound(US))poses additional challenges in FS,necessitating advanced feature fusion strategies.Multi-modal feature fusion combines information fromdifferent imagingmodalities to improve diagnostic accuracy.Recently,quantum computing has gained attention as a revolutionary approach for FS,providing the potential to handle high-dimensional medical data more efficiently.This systematic literature review comprehensively examines classical,Deep Learning(DL),hybrid,and quantum-based FS techniques inmedical imaging.Key outcomes include a structured taxonomy of FS methods,a critical evaluation of their performance across modalities,and identification of core challenges such as computational burden,interpretability,and ethical considerations.Future research directions—such as explainable AI(XAI),federated learning,and quantum-enhanced FS—are also emphasized to bridge the current gaps.This review provides actionable insights for developing scalable,interpretable,and clinically applicable FS methods in the evolving landscape of medical imaging.
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.
