In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task schedul...In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task scheduling is crucial for efficiently handling IoT user requests,thereby improving system performance,cost,and energy consumption across nodes in cloud computing.With the large amount of data and user requests,achieving the optimal solution to the task scheduling problem is challenging,particularly in terms of cost and energy efficiency.In this paper,we develop novel strategies to save energy consumption across nodes in fog computing when users execute tasks through the least-cost paths.Task scheduling is developed using modified artificial ecosystem optimization(AEO),combined with negative swarm operators,Salp Swarm Algorithm(SSA),in order to competitively optimize their capabilities during the exploitation phase of the optimal search process.In addition,the proposed strategy,Enhancement Artificial Ecosystem Optimization Salp Swarm Algorithm(EAEOSSA),attempts to find the most suitable solution.The optimization that combines cost and energy for multi-objective task scheduling optimization problems.The backpack problem is also added to improve both cost and energy in the iFogSim implementation as well.A comparison was made between the proposed strategy and other strategies in terms of time,cost,energy,and productivity.Experimental results showed that the proposed strategy improved energy consumption,cost,and time over other algorithms.Simulation results demonstrate that the proposed algorithm increases the average cost,average energy consumption,and mean service time in most scenarios,with average reductions of up to 21.15%in cost and 25.8%in energy consumption.展开更多
Purpose:This study investigated the publication behaviour of 573 chief editors managing 432 Social Sciences journals in Turkey.Direct inquiries into editorial qualifications are rare,and this research aims to shed lig...Purpose:This study investigated the publication behaviour of 573 chief editors managing 432 Social Sciences journals in Turkey.Direct inquiries into editorial qualifications are rare,and this research aims to shed light on editors’scientific leadership capabilities.Design/methodology/approach:This study contrasts insider publication behaviour in national journals with international articles in journals indexed by the Web of Science(WOS)and Scopus.It argues that editors demonstrating a consistent ability to publish in competitive WOS and Scopus indexed journals signal high qualifications,while editors with persistent insider behaviour and strong local orientation signal low qualification.Scientific leadership capability is measured by first-authored publications.Correlation and various regression tests are conducted to identify significant determinants of publication behaviour.Findings:International publications are rare and concentrated on a few individuals,while insider publications are endemic and constitute nearly 40%of all national articles.Editors publish 3.2 insider papers and 8.1 national papers for every SSCI article.62%(58%)of the editors have no SSCI(Scopus)article,53%(63%)do not have a single lead-authored WOS(Scopus)article,and 89%publish at least one insider paper.Only a minority consistently publish in international journals;a fifth of the editors have three or more SSCI publications,and a quarter have three or more Scopus articles.Editors with foreign Ph.D.degrees are the most qualified and internationally oriented,whereas non-mobile editors are the most underqualified and underperform other editors by every measure.Illustrating the overall lack of qualification,nearly half of the professor editors and the majority of the WOS and Scopus indexed journal editors have no record of SSCI or Scopus publications.Research limitations:This research relies on local settings that encourage national publications at the expense of international journals.Findings should be evaluated in light of this setting and bearing in mind that narrow localities are more prone to peer favouritism.Practical implications:Incompetent and nepotistic editors pose an imminent threat to Turkish national literature.A lasting solution would likely include the dismissal and replacement of unqualified editors,as well as delisting and closure of dozens of journals that operate in questionable ways and serve little scientific purpose.Originality/value:To my knowledge,this is the first study to document the publication behaviour of national journal chief editors.展开更多
This study evaluates the predictive accuracy of traditional time series(TS)models versus machine learning(ML)methods in forecasting realized volatility across major cryptocurrencies—Bitcoin(BTC),Ethereum(ETH),Litecoi...This study evaluates the predictive accuracy of traditional time series(TS)models versus machine learning(ML)methods in forecasting realized volatility across major cryptocurrencies—Bitcoin(BTC),Ethereum(ETH),Litecoin(LTC),and Ripple(XRP).Employing high-frequency data,we analyze cross-cryptocurrency volatility dynamics through two complementary approaches:volatility forecasting and connectedness analysis.Our findings reveal three key insights:(i)TS models,particularly the heterogeneous autoregressive(HAR)model,exhibit superior predictive performance over their ML counterparts,with the long short-term memory(LSTM)model providing competitive yet inconsistent results due to overfitting and short-term volatility challenges;(ii)including lagged realized volatility of large-cap coins improves predictive accuracy for mid-cap coins,especially XRP,whereas forecasts for largecap coins remain stable,indicating more resilient volatility patterns;and(iii)volatility connectedness analysis reveals substantial spillover effects,particularly pronounced during market turmoil,with large-cap assets(BTC and ETH)acting as primary volatility transmitters and mid-cap assets(XRP and LTC)serving as volatility receivers.These results contribute to the understanding of volatility forecasting and risk management in cryptocurrency markets,offering implications for investors and policymakers in managing market risk and interdependencies in digital asset portfolios.展开更多
This study utilizes two complementary models,the Time-Varying Parameter Vector Autoregressive Diebold–Yilmaz(TVP-VAR-DY)and the Time-Varying Parameter Vector Autoregressive Barunik–Křehlik(TVP-VAR-BK),to investigate...This study utilizes two complementary models,the Time-Varying Parameter Vector Autoregressive Diebold–Yilmaz(TVP-VAR-DY)and the Time-Varying Parameter Vector Autoregressive Barunik–Křehlik(TVP-VAR-BK),to investigate the dynamic volatility transmission between exchange rates and stock returns in major commodity-exporting and-importing countries.The analysis focuses on periods of quantitative easing(QE)and quantitative tightening(QT)from March 15,2020 to December 30,2022.The countries examined are Canada and Australia(major commodity exporters)and the UK and Germany(major commodity importers).An essential contribution of this paper is new empirical insights into the dynamics of stock market returns and the transmission of volatility between these markets and exchange rates during the QE and QT periods.The results reveal that causality primarily flows from stock markets to exchange rates,especially during the QT period across all investment horizons.The Toronto Stock Exchange(TSX)emerges as the principal net driver among the markets under study.Furthermore,the Canadian exchange rate(USDCAD)and the Australian Stock Exchange(ASX)are the most significantly affected indices within the network across various investment horizons(excluding the long-term).These findings underscore the importance for investors and policymakers to consider the interplay between exchange rates and stock market returns,particularly in the context of the QE and QT periods,as well as other economic,political,and health-related events.Our findings are relevant to various stakeholders,including governments,traders,portfolio managers,and multinationals.展开更多
This study investigates the convergence hypothesis and stochastic dynamics of agricultural land use and ecological balance across 13 major agricultural countries from 1992 to 2022.The study's concentrated samples ...This study investigates the convergence hypothesis and stochastic dynamics of agricultural land use and ecological balance across 13 major agricultural countries from 1992 to 2022.The study's concentrated samples are Russia,the United States,the Netherlands,Brazil,Germany,China,France,Spain,Italy,Canada,Belgium,Indonesia,and India.The research uncovers notable variations in ecological balance by utilizing a comprehensive set of advanced panel unit root tests(Panel CIPS,CADF,Panel-LM,Panel-KPSS,and Bahmani-Oskooee et al.’s Panel KPSS Unit Root Test).The findings highlight significant improvements in Canada,contrasting with declines in the Netherlands,France,Germany,and the United States.The results indicate convergence in ecological balance among these countries,suggesting that agricultural practices are progressively aligning with sustainability objectives.The considered countries can determine and enact joint and collective policy actions addressing cropland sustainability.However,the univariate outcome also shows that the cropland ecological balance of Germany,China,France,Indonesia,and India does contain a unit root and stationary which means the presence of the constant-mean.The univariate actions from the mentioned governments will not promote persistent impact.Therefore,joint actions determined by the countries considered are recommended for the mentioned countries.However,the rest of the countries also enact local policies.The insights gained are critical for informing global sustainability strategies and aiding policymakers in developing effective measures to enhance agricultural practices and mitigate environmental impacts.This research provides a data-driven foundation for optimizing agricultural sustainability and supports international efforts to achieve long-term ecological stability.展开更多
Evaluating firms’financial performance is important for survival in the competitive environment arising from technological advancements and gaining a competitive advantage.This study aims to assess the financial perf...Evaluating firms’financial performance is important for survival in the competitive environment arising from technological advancements and gaining a competitive advantage.This study aims to assess the financial performances of firms traded on the Istanbul Stock Exchange 100 Index(Borsa Istanbul(BIST)100 Index)between 2018 and 2022 using an integrated multi-criteria decision-making(MCDM)method.This is the first study to use a new combined approach proposed based on the indifference thresholdbased attribute ratio analysis(ITARA)and cost estimation,benchmarking,and risk assessment(COBRA)methods,which have not been applied to corporate performance assessment.The ITARA method is used to find the weights of the criteria,and the ranking of the firms in terms of financial performance is obtained using the COBRA method.The results show that the firms with the highest financial performance are ISMEN,ALGYO,PGSUS,KOZAA,and IPEKE.The companies with the lowest financial performance are AKFGY,BAGFS,MGROS,KOZAL,and GOZDE.The results of this ranking provide important information for firms to recognize their position and for investors who want to invest in firms in the BIST 100 Index.Additionally,sensitivity analyses are performed to assess the impact of changes in the criteria on the ranking of the firms and to validate the results of the proposed method.A comparative analysis is made with different MCDM methods like the technique for order preference by similarity to ideal solution(TOPSIS),and combined compromise solution(CoCoSo)methods and Spearman’s rank correlation test results are presented.This approach leads to the conclusion that the proposed approach can be a useful and effective tool for assessing the financial performance of firms.展开更多
Global climate change has created substantial difficulties in the areas of sustainability,development,and environmental conservation due to the widespread dependence on fossil fuels for energy production.Nevertheless,...Global climate change has created substantial difficulties in the areas of sustainability,development,and environmental conservation due to the widespread dependence on fossil fuels for energy production.Nevertheless,the promotion of renewable energy programs has the potential to significantly expedite endeavors aimed at tackling climate change.Thus,it is essential to conduct a thorough analysis that considers the financial aspects to fully understand the main hurdles that are preventing the advancement of renewable energy initiatives.Italy is a leading country in the worldwide deployment of renewable energy.The objective of this research is to assess the impact of financial growth,economic progress,and energy expenses on Italy’s adoption of renewable energy sources.By employing the Auto-Regressive Distributed Lag(ARDL)technique,we analyzed annual data spanning from1990 to 2022.Findings revealed that a 1%increase in financial and economic development would boost renewable energy consumption in the long run by 0.29%and 0.48%,respectively.Instead,a 1%increase in energy prices might reduce consumption of renewable energy by 0.05%in the long run.This study’s primary significance lies in furnishing actionable strategies for Italy to augment green finance for renewable energy,fostering sustained social and economic progress.Moreover,the analytical insights gleaned from this research offer valuable insights for energy-importing nations worldwide.展开更多
Climate change and environmental degradation threaten the world and global economic conditions.As one of countries’most important economic components,the financial sector might be an effective tool for reducing and e...Climate change and environmental degradation threaten the world and global economic conditions.As one of countries’most important economic components,the financial sector might be an effective tool for reducing and even reversing environmental degradation.The financial sector can affect sustainability through its lending and investment practices.The sector can play a role in financing sustainable projects and businesses,helping reduce CO_(2) emissions.By aligning its financial objectives with environmental protection,the financial sector can support the transition to a more sustainable future by helping reduce environmental degradation’s negative impacts.This paper examines the domestic financial sector’s impact on CO_(2) emissions in the United States over the 1990:Q1–2022:Q3 period.In this research,the nexus between the domestic financial sector(total debt securities,loans,liabilities,and total financial assets)and Carbon dioxide emissions in the U.S.is investigated by Morlet wavelet analysis.Rest of the world:sector discrepancy transactions,rest of the world:debt securities and loans,gross domestic product,and the square of the gross domestic product,are control variables in the estimated models.Partial wavelet coherency analyses prove that the financial sector reduces CO_(2) emissions at the 5–8-year frequency band during different subsample periods.The financial sector’s instruments can be effective in struggling with climate change.展开更多
An individual's mental health influences their capacity to think effectively,feel emotionally stable,and perform daily activities.As mental health concerns become more prevalent worldwide,new awareness and diagnos...An individual's mental health influences their capacity to think effectively,feel emotionally stable,and perform daily activities.As mental health concerns become more prevalent worldwide,new awareness and diagnostic and treatment tactics are needed.Digital tools and technology are helping solve these problems by providing scalable,tailored solutions for large populations.This detailed review examines mental health‐promoting internet tools.Smartphone applications,web‐based therapy systems,wearable tech,artificial intelligence‐powered resources,and virtual reality(VR)technologies were evaluated for efficacy and side effects.PubMed,PsycINFO,Scopus,IEEE Xplore,and Google Scholar were carefully searched.Search terms included“digital mental health tools,”“online therapy,”and“AI in mental health.”Randomized controlled trials,cohort studies,cross‐sectional studies,systematic reviews,and meta‐analyses of digital technology and mental health were included from among the literature published after 2010.Cognitive behavioral therapy methods,mood monitoring,and mindfulness exercises are among the numerous features of smartphone applications that have been demonstrated to mitigate symptoms of anxiety,depression,and tension.Online therapy platforms let marginalized individuals obtain therapy remotely.Wearable technology may detect heart rate,blood pressure,and sleep length,which may reveal mental health difficulties.Chatbots employ machine learning algorithms and natural language processing to deliver customized support and show promise for quick intervention.Exposure therapy for anxiety and trauma is increasingly using virtual reality environments.Although digital mental health therapies face challenges in relation to data privacy,limited long‐term efficacy,and technological inequality,digital technologies are modernizing mental healthcare.By offering inexpensive and effective alternatives to traditional therapies,digital technologies may help healthcare systems meet the growing demand for mental health services and overall well‐being.展开更多
Accurately modeling heavy-tailed data is critical across applied sciences,particularly in finance,medicine,and actuarial analysis.This work presents the heavy-tailed power XLindley distribution(HTPXLD),a unique heavy-...Accurately modeling heavy-tailed data is critical across applied sciences,particularly in finance,medicine,and actuarial analysis.This work presents the heavy-tailed power XLindley distribution(HTPXLD),a unique heavy-tailed distribution.Adding one more parameter to the power XLindley distribution improves this new distribution,especially when modeling leptokurtic lifetime data.The suggested density provides greater flexibility with asymmetric forms and different degrees of peakedness.Its statistical features,like the quantile function,moments,extropy measures,incomplete moments,stochastic ordering,and stress-strength parameters,are explored.We further investigate its use in actuarial science through the computation of pertinent metrics,such as value-at-risk,tail value-at-risk,tail variance,and tail variance premium.To obtain the point and interval parameter estimates,we use the maximum likelihood estimation approach.We do many simulation tests to evaluate the performance of our proposed estimator.Metrics like bias,relative bias,mean squared error,root mean squared error,average interval length,and coverage probability will be used in these tests to assess the estimator’s performance.To illustrate the practical value of our proposed model,we apply it to analyze three real-world datasets.We then compare its performance to established competing models,highlighting its advantages.展开更多
Music recommendation systems are essential due to the vast amount of music available on streaming platforms,which can overwhelm users trying to find new tracks that match their preferences.These systems analyze users...Music recommendation systems are essential due to the vast amount of music available on streaming platforms,which can overwhelm users trying to find new tracks that match their preferences.These systems analyze users’emotional responses,listening habits,and personal preferences to provide personalized suggestions.A significant challenge they face is the“cold start”problem,where new users have no past interactions to guide recommendations.To improve user experience,these systems aimto effectively recommendmusic even to such users by considering their listening behavior and music popularity.This paper introduces a novel music recommendation system that combines order clustering and a convolutional neural network,utilizing user comments and rankings as input.Initially,the system organizes users into clusters based on semantic similarity,followed by the utilization of their rating similarities as input for the convolutional neural network.This network then predicts ratings for unreviewed music by users.Additionally,the system analyses user music listening behaviour and music popularity.Music popularity can help to address cold start users as well.Finally,the proposed method recommends unreviewed music based on predicted high rankings and popularity,taking into account each user’s music listening habits.The proposed method combines predicted high rankings and popularity by first selecting popular unreviewedmusic that themodel predicts to have the highest ratings for each user.Among these,the most popular tracks are prioritized,defined by metrics such as frequency of listening across users.The number of recommended tracks is aligned with each user’s typical listening rate.The experimental findings demonstrate that the new method outperformed other classification techniques and prior recommendation systems,yielding a mean absolute error(MAE)rate and rootmean square error(RMSE)rate of approximately 0.0017,a hit rate of 82.45%,an average normalized discounted cumulative gain(nDCG)of 82.3%,and a prediction accuracy of new ratings at 99.388%.展开更多
This study introduces the type-I heavy-tailed Burr XII(TIHTBXII)distribution,a highly flexible and robust statistical model designed to address the limitations of conventional distributions in analyzing data character...This study introduces the type-I heavy-tailed Burr XII(TIHTBXII)distribution,a highly flexible and robust statistical model designed to address the limitations of conventional distributions in analyzing data characterized by skewness,heavy tails,and diverse hazard behaviors.We meticulously develop the TIHTBXII’s mathematical foundations,including its probability density function(PDF),cumulative distribution function(CDF),and essential statistical properties,crucial for theoretical understanding and practical application.A comprehensive Monte Carlo simulation evaluates four parameter estimation methods:maximum likelihood(MLE),maximum product spacing(MPS),least squares(LS),and weighted least squares(WLS).The simulation results consistently show that as sample sizes increase,the Bias and RMSE of all estimators decrease,with WLS and LS often demonstrating superior and more stable performance.Beyond theoretical development,we present a practical application of the TIHTBXII distribution in constructing a group acceptance sampling plan(GASP)for truncated life tests.This application highlights how the TIHTBXII model can optimize quality control decisions by minimizing the average sample number(ASN)while effectively managing consumer and producer risks.Empirical validation using real-world datasets,including“Active Repair Duration,”“Groundwater Contaminant Measurements,”and“Dominica COVID-19 Mortality,”further demonstrates the TIHTBXII’s superior fit compared to existing models.Our findings confirm the TIHTBXII distribution as a powerful and reliable alternative for accurately modeling complex data in fields such as reliability engineering and quality assessment,leading to more informed and robust decision-making.展开更多
Offshore logistics operations must continuously balance safety,fuel efficiency,and emissions reduction while navigating under uncertain and highly variable sea states.To address this challenge,we present anα-cut inte...Offshore logistics operations must continuously balance safety,fuel efficiency,and emissions reduction while navigating under uncertain and highly variable sea states.To address this challenge,we present anα-cut interval framework in which environmental uncertainties,specifically wave height and wind speed,are modeled as fuzzy numbers.Their correspondingα-level intervals are systematically propagated through a discrete vessel dynamics model,focusing on surge and heave responses.This procedure generates families of nested motion envelopes that tighten monotonically with increasingα,thereby producing deterministic yet progressively refined safety bounds without relying on full probabilistic distributions.A case study off the Karnataka coast is used to demonstrate the approach for a 20 km offshore supply voyage.Route planning constrained byα-envelopes ensures adherence to vessel structural and stability limits while enabling optimized transit speed.Comparative evaluation indicates that,relative to standard interval analysis,α-cut propagation substantially reduces over-conservatism,while against Monte Carlo-based envelopes it achieves similar coverage with significantly lower computational effort.Sensitivity analyses further quantify the influence ofα-grid resolution,membership-function design,and hydrodynamic coupling coefficients on envelope width,fuel use,and emissions.In the tested scenario,higherαlevels allow up to~15%reduction in worst-case energy consumption and nearly 10%reduction in CO_(2)emissions,all while preserving safety margins.Overall,the proposed framework is transparent,computationally efficient,and easily integrable into digital-twin-enabled operational workflows,providing a practical and sustainable decision-support tool for adaptive offshore logistics planning.展开更多
The rapid transformation of Arctic maritime routes,driven by diminishing sea ice and shifting geopolitical conditions,presents both opportunities and challenges for global shipping.This study develops an integrated op...The rapid transformation of Arctic maritime routes,driven by diminishing sea ice and shifting geopolitical conditions,presents both opportunities and challenges for global shipping.This study develops an integrated optimization framework for sustainable Arctic marine logistics,grounded in Agile Supply Chain Theory(ASCT),to address cost efficiency,environmental sustainability,and operational robustness under climate and policy uncertainty.A Mixed‐Integer Linear Programming(MILP)model was employed to optimize vessel routing across Arctic corridors,incorporating Energy Efficiency Operational Indicator(EEOI)and Carbon Intensity Indicator(CII)metrics directly into the objective function.Scenario analyses tested performance under varying climate conditions and policy constraints.The model was parameterized using vessel operational data from Arctic shipping logs,environmental datasets from ESA CryoSat‐2 and NSIDC,port accessibility records from Arctic port authorities,and economic data from Clarksons and the World Bank,ensuring realistic and replicable inputs for the analysis.Results demonstrate that ASCT‐based optimized routes achieved an average 14.8%reduction in operating costs,12.3%reduction in CO₂emissions,and an 11.6%improvement in EEOI,with the majority of voyages improving by at least one CII grade.Robustness analysis showed that optimized routes maintained up to 14.7 percentage points higher feasibility under severe ice scenarios and reduced cost volatility by 20–28%under carbon tax regimes.These findings confirm the value of embedding agility and resilience principles into Arctic shipping,aligning operational efficiency with International Maritime Organization(IMO)decarbonization objectives.The study extends ASCT into extreme maritime contexts,offering a replicable model for sustainable route planning in high‐risk logistics sectors.展开更多
Bonding quality at the interface of solid propellant grains is crucial for the reliability and safety of solid rocket motors.Although bonding reliability is influenced by numerous factors,the lack of quantitative char...Bonding quality at the interface of solid propellant grains is crucial for the reliability and safety of solid rocket motors.Although bonding reliability is influenced by numerous factors,the lack of quantitative characterization of interface debonding mechanisms and the challenge of identifying key factors have made precise control of process variables difficult,resulting in unpredictable failure risks.This paper presents an improved fuzzy failure probability evaluation method that combines fuzzy fault tree analysis with expert knowledge,transforming process data into fuzzy failure probability to accurately assess debonding probabilities.The predictive model is constructed through a general regression neural network and optimized using the particle swarm optimization algorithm.Sensitivity analysis is conducted to identify key decision variables,including normal force,grain rotation speed,and adhesive weight,which are verified experimentally.Compared with classical models,the maximum error margin of the constructed reliability prediction model is only 0.02%,and it has high stability.The experimental results indicate that the main factors affecting debonding are processing roughness and coating uniformity.Controlling the key decision variable as the median resulted in a maximum increase of 200.7%in bonding strength.The feasibility of the improved method has been verified,confirming that identifying key decision variables has the ability to improve bonding reliability.The proposed method simplifies the evaluation of propellant interface bonding reliability under complex conditions by quantifying the relationship between process parameters and failure risk,enabling targeted management of key decision variables.展开更多
Offshore support operations must balance safety and sustainability under highly variable sea conditions.Deterministic motion analyses can underestimate extreme vessel responses,leading to insufficient operational limi...Offshore support operations must balance safety and sustainability under highly variable sea conditions.Deterministic motion analyses can underestimate extreme vessel responses,leading to insufficient operational limits and increased environmental impact.We develop a fuzzy‐enhanced multi‐body dynamics framework in which key inputs significant wave height,peak period,added mass,and radiation damping are represented as fuzzy numbers.Anα-cut decomposition yields interval bounds at each confidence level,and a fourth-order Runge-Kutta scheme integrates the six-degree-of-freedom equations of motion for both lower and upper“vertex”systems.A case study off the Karnataka coast applies both full 6-DoF and single-DOF heave approximations to demonstrate methodology.The heave response envelopes under calm(nominalα=1:0.73 m;full range atα=0:0.64–1.64 m)and severe(nominal 1.58 m;range 1.32–2.36 m)sea states reveal potential underestimations of 124%and 49%,respectively,when using only nominal values.By selecting an operationalα-level(e.g.,α^(*)=0.35 to cap heave≤1.8 m),decision-makers can balance risk tolerance and conservatism.Sensitivity analysis identifies significant wave height as the dominant uncertainty driver.Computational trade-offs and adaptiveα-sampling strategies are discussed.This work provides a self-contained,uncertainty-aware tool for deriving operational envelopes that improve risk-informed planning and enable fuel-efficiency optimization.By embedding fuzzy uncertainty quantification into vessel dynamics,the methodology supports safer,more sustainable marine operations and can be extended to real-time sensor fusion,multi-vessel interactions,and frequency-dependent hydrodynamics.展开更多
This study addresses the critical need for decarbonization in offshore marine logistics by developing an integrated modeling framework to support low-emission operations across complex,multi-echelon vessel networks.It...This study addresses the critical need for decarbonization in offshore marine logistics by developing an integrated modeling framework to support low-emission operations across complex,multi-echelon vessel networks.It focuses on port-to-platform supply chains serving offshore wind farms,oil rigs,and floating logistics hubs.A hybrid analytical approach was adopted,combining Mixed-Integer Linear Programming(MILP)for optimizing emission-minimizing routing,Discrete-Event Simulation(DES)to evaluate offshore scheduling performance under variability,and a Multi-Criteria Decision Analysis(MCDA)model using AHP-TOPSIS to rank alternative marine fuel types.Monte Carlo simulation was also employed to assess cost and delivery fluctuations across uncertain operational scenarios.Data inputs were compiled from real-world offshore fleet specifications,port emissions records,and marine fuel technology benchmarks.MILP-based network flow optimization reduced CO₂emissions by 22%while maintaining service reliability across all demand points.DES simulations revealed congestion-driven scheduling delays during peak vessel utilization.MCDA analysis ranked bio-LNG and hydrogen propulsion systems as optimal choices based on emission,cost,and availability trade-offs.Hypothesis testing confirmed significant relationships between fuel type,network structure,and emission performance.The study demonstrates how multi-echelon logistics planning,integrated with emissions-based modeling,can facilitate environmentally responsible marine supply chain design.The framework offers practical guidance for offshore fleet managers,port authorities,and policy regulators aiming to align operational efficiency with decarbonization objectives under IMO and EU directives.展开更多
As offshore wind infrastructure becomes more important to global efforts to reduce carbon emissions,it is becoming more important to connect lifecycle cost management with circular economy(CE)principles.When looking a...As offshore wind infrastructure becomes more important to global efforts to reduce carbon emissions,it is becoming more important to connect lifecycle cost management with circular economy(CE)principles.When looking at the long-term costs of infrastructure,traditional lifecycle cost models often fail to account for residual value recovery,material circularity,or environmental externalities.This study creates a unified analytical framework that adds CE strategies to lifecycle cost modelling for offshore wind systems,such as turbines,substructures,moorings,and floating platforms.The method uses multi-objective optimization and system dynamics simulation along with net present value(NPV)modelling,material flow analysis,and carbonadjusted cost accounting.We modelled project-level datasets over 25 years to look at the trade-offs between economic and environmental factors in both linear and circular lifecycle scenarios.We use Python,MATLAB,and OpenLCA to look at key metrics like the Material Circularity Indicator(MCI),estimates of residual value,and internalized carbon costs.The results show that circular infrastructure strategies greatly lower lifecycle costs while also increasing material recovery and carbon efficiency.Scenario simulations showed that CE-based configurations could cut costs by up to 18%and emissions over the life of the product by 22%.Regression and sensitivity analyses showed that MCI,CAPEX,and circular design strategies are good at predicting residual value and long-term economic performance.This study adds a new,evidence-based model for making decisions about infrastructure that takes into account financial,environmental,and material circularity.展开更多
Vessel motions in offshore operations are heavily influenced by uncertain wave loads and hydrodynamic parameters.Yet,traditional deterministic or probabilistic models often fail to capture epistemic ambiguity when dat...Vessel motions in offshore operations are heavily influenced by uncertain wave loads and hydrodynamic parameters.Yet,traditional deterministic or probabilistic models often fail to capture epistemic ambiguity when data are scarce.We introduce a fuzzy–set framework usingα-cut interval analysis to represent imprecise wave heights,periods,added mass,damping,and stiffness as fuzzy numbers.These are incorporated into the multi-body equations of motion and solved via a fuzzy Runge–Kutta scheme across nestedα-levels.A simulation architecture iterates overα-cuts and time-steps to produce interval bounds on heavy responses.A case study off the Karnataka coast,with realistic sea-state data for moderate and severe scenarios,yields heave-amplitude envelopes whose widths quantify response uncertainty.At mid-confidence(α=0.5),moderate seas produce amplitudes of 8.30–9.65 m(±15%),while severe seas yield 7.15–8.90 m(±22%).Envelope narrowing asα→1 confirms that increased parameter confidence reduces prediction spread,and bias analysis against crisp baselines highlights the impact of imprecision on mean responses.This non-probabilistic approach provides interpretable,worst-and best-case motion bounds without requiring large datasets,offering marine engineers robust safety margins and guidance for targeted data collection and real-time uncertainty updating.展开更多
In the present article, He＇s fractional derivative, the ansatz method, the （ C / G）-expansion method, and the exp-function method are used to construct the exact solutions of nonlinear space-time fractional Kadomts...In the present article, He＇s fractional derivative, the ansatz method, the （ C / G）-expansion method, and the exp-function method are used to construct the exact solutions of nonlinear space-time fractional Kadomtsev-Petviashvili- Benjamin-Bona Mahony （KP-BBM）. As a result, different types of exact solutions are obtained. Also we have examined the relation between the solutions obtained from the different methods. These methods are an efficient mathematical tool for solving fractional differential equations （FDEs） and it can be applied to other nonlinear FDEs.展开更多
基金supported and funded by theDeanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2503).
文摘In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task scheduling is crucial for efficiently handling IoT user requests,thereby improving system performance,cost,and energy consumption across nodes in cloud computing.With the large amount of data and user requests,achieving the optimal solution to the task scheduling problem is challenging,particularly in terms of cost and energy efficiency.In this paper,we develop novel strategies to save energy consumption across nodes in fog computing when users execute tasks through the least-cost paths.Task scheduling is developed using modified artificial ecosystem optimization(AEO),combined with negative swarm operators,Salp Swarm Algorithm(SSA),in order to competitively optimize their capabilities during the exploitation phase of the optimal search process.In addition,the proposed strategy,Enhancement Artificial Ecosystem Optimization Salp Swarm Algorithm(EAEOSSA),attempts to find the most suitable solution.The optimization that combines cost and energy for multi-objective task scheduling optimization problems.The backpack problem is also added to improve both cost and energy in the iFogSim implementation as well.A comparison was made between the proposed strategy and other strategies in terms of time,cost,energy,and productivity.Experimental results showed that the proposed strategy improved energy consumption,cost,and time over other algorithms.Simulation results demonstrate that the proposed algorithm increases the average cost,average energy consumption,and mean service time in most scenarios,with average reductions of up to 21.15%in cost and 25.8%in energy consumption.
文摘Purpose:This study investigated the publication behaviour of 573 chief editors managing 432 Social Sciences journals in Turkey.Direct inquiries into editorial qualifications are rare,and this research aims to shed light on editors’scientific leadership capabilities.Design/methodology/approach:This study contrasts insider publication behaviour in national journals with international articles in journals indexed by the Web of Science(WOS)and Scopus.It argues that editors demonstrating a consistent ability to publish in competitive WOS and Scopus indexed journals signal high qualifications,while editors with persistent insider behaviour and strong local orientation signal low qualification.Scientific leadership capability is measured by first-authored publications.Correlation and various regression tests are conducted to identify significant determinants of publication behaviour.Findings:International publications are rare and concentrated on a few individuals,while insider publications are endemic and constitute nearly 40%of all national articles.Editors publish 3.2 insider papers and 8.1 national papers for every SSCI article.62%(58%)of the editors have no SSCI(Scopus)article,53%(63%)do not have a single lead-authored WOS(Scopus)article,and 89%publish at least one insider paper.Only a minority consistently publish in international journals;a fifth of the editors have three or more SSCI publications,and a quarter have three or more Scopus articles.Editors with foreign Ph.D.degrees are the most qualified and internationally oriented,whereas non-mobile editors are the most underqualified and underperform other editors by every measure.Illustrating the overall lack of qualification,nearly half of the professor editors and the majority of the WOS and Scopus indexed journal editors have no record of SSCI or Scopus publications.Research limitations:This research relies on local settings that encourage national publications at the expense of international journals.Findings should be evaluated in light of this setting and bearing in mind that narrow localities are more prone to peer favouritism.Practical implications:Incompetent and nepotistic editors pose an imminent threat to Turkish national literature.A lasting solution would likely include the dismissal and replacement of unqualified editors,as well as delisting and closure of dozens of journals that operate in questionable ways and serve little scientific purpose.Originality/value:To my knowledge,this is the first study to document the publication behaviour of national journal chief editors.
文摘This study evaluates the predictive accuracy of traditional time series(TS)models versus machine learning(ML)methods in forecasting realized volatility across major cryptocurrencies—Bitcoin(BTC),Ethereum(ETH),Litecoin(LTC),and Ripple(XRP).Employing high-frequency data,we analyze cross-cryptocurrency volatility dynamics through two complementary approaches:volatility forecasting and connectedness analysis.Our findings reveal three key insights:(i)TS models,particularly the heterogeneous autoregressive(HAR)model,exhibit superior predictive performance over their ML counterparts,with the long short-term memory(LSTM)model providing competitive yet inconsistent results due to overfitting and short-term volatility challenges;(ii)including lagged realized volatility of large-cap coins improves predictive accuracy for mid-cap coins,especially XRP,whereas forecasts for largecap coins remain stable,indicating more resilient volatility patterns;and(iii)volatility connectedness analysis reveals substantial spillover effects,particularly pronounced during market turmoil,with large-cap assets(BTC and ETH)acting as primary volatility transmitters and mid-cap assets(XRP and LTC)serving as volatility receivers.These results contribute to the understanding of volatility forecasting and risk management in cryptocurrency markets,offering implications for investors and policymakers in managing market risk and interdependencies in digital asset portfolios.
文摘This study utilizes two complementary models,the Time-Varying Parameter Vector Autoregressive Diebold–Yilmaz(TVP-VAR-DY)and the Time-Varying Parameter Vector Autoregressive Barunik–Křehlik(TVP-VAR-BK),to investigate the dynamic volatility transmission between exchange rates and stock returns in major commodity-exporting and-importing countries.The analysis focuses on periods of quantitative easing(QE)and quantitative tightening(QT)from March 15,2020 to December 30,2022.The countries examined are Canada and Australia(major commodity exporters)and the UK and Germany(major commodity importers).An essential contribution of this paper is new empirical insights into the dynamics of stock market returns and the transmission of volatility between these markets and exchange rates during the QE and QT periods.The results reveal that causality primarily flows from stock markets to exchange rates,especially during the QT period across all investment horizons.The Toronto Stock Exchange(TSX)emerges as the principal net driver among the markets under study.Furthermore,the Canadian exchange rate(USDCAD)and the Australian Stock Exchange(ASX)are the most significantly affected indices within the network across various investment horizons(excluding the long-term).These findings underscore the importance for investors and policymakers to consider the interplay between exchange rates and stock market returns,particularly in the context of the QE and QT periods,as well as other economic,political,and health-related events.Our findings are relevant to various stakeholders,including governments,traders,portfolio managers,and multinationals.
文摘This study investigates the convergence hypothesis and stochastic dynamics of agricultural land use and ecological balance across 13 major agricultural countries from 1992 to 2022.The study's concentrated samples are Russia,the United States,the Netherlands,Brazil,Germany,China,France,Spain,Italy,Canada,Belgium,Indonesia,and India.The research uncovers notable variations in ecological balance by utilizing a comprehensive set of advanced panel unit root tests(Panel CIPS,CADF,Panel-LM,Panel-KPSS,and Bahmani-Oskooee et al.’s Panel KPSS Unit Root Test).The findings highlight significant improvements in Canada,contrasting with declines in the Netherlands,France,Germany,and the United States.The results indicate convergence in ecological balance among these countries,suggesting that agricultural practices are progressively aligning with sustainability objectives.The considered countries can determine and enact joint and collective policy actions addressing cropland sustainability.However,the univariate outcome also shows that the cropland ecological balance of Germany,China,France,Indonesia,and India does contain a unit root and stationary which means the presence of the constant-mean.The univariate actions from the mentioned governments will not promote persistent impact.Therefore,joint actions determined by the countries considered are recommended for the mentioned countries.However,the rest of the countries also enact local policies.The insights gained are critical for informing global sustainability strategies and aiding policymakers in developing effective measures to enhance agricultural practices and mitigate environmental impacts.This research provides a data-driven foundation for optimizing agricultural sustainability and supports international efforts to achieve long-term ecological stability.
文摘Evaluating firms’financial performance is important for survival in the competitive environment arising from technological advancements and gaining a competitive advantage.This study aims to assess the financial performances of firms traded on the Istanbul Stock Exchange 100 Index(Borsa Istanbul(BIST)100 Index)between 2018 and 2022 using an integrated multi-criteria decision-making(MCDM)method.This is the first study to use a new combined approach proposed based on the indifference thresholdbased attribute ratio analysis(ITARA)and cost estimation,benchmarking,and risk assessment(COBRA)methods,which have not been applied to corporate performance assessment.The ITARA method is used to find the weights of the criteria,and the ranking of the firms in terms of financial performance is obtained using the COBRA method.The results show that the firms with the highest financial performance are ISMEN,ALGYO,PGSUS,KOZAA,and IPEKE.The companies with the lowest financial performance are AKFGY,BAGFS,MGROS,KOZAL,and GOZDE.The results of this ranking provide important information for firms to recognize their position and for investors who want to invest in firms in the BIST 100 Index.Additionally,sensitivity analyses are performed to assess the impact of changes in the criteria on the ranking of the firms and to validate the results of the proposed method.A comparative analysis is made with different MCDM methods like the technique for order preference by similarity to ideal solution(TOPSIS),and combined compromise solution(CoCoSo)methods and Spearman’s rank correlation test results are presented.This approach leads to the conclusion that the proposed approach can be a useful and effective tool for assessing the financial performance of firms.
文摘Global climate change has created substantial difficulties in the areas of sustainability,development,and environmental conservation due to the widespread dependence on fossil fuels for energy production.Nevertheless,the promotion of renewable energy programs has the potential to significantly expedite endeavors aimed at tackling climate change.Thus,it is essential to conduct a thorough analysis that considers the financial aspects to fully understand the main hurdles that are preventing the advancement of renewable energy initiatives.Italy is a leading country in the worldwide deployment of renewable energy.The objective of this research is to assess the impact of financial growth,economic progress,and energy expenses on Italy’s adoption of renewable energy sources.By employing the Auto-Regressive Distributed Lag(ARDL)technique,we analyzed annual data spanning from1990 to 2022.Findings revealed that a 1%increase in financial and economic development would boost renewable energy consumption in the long run by 0.29%and 0.48%,respectively.Instead,a 1%increase in energy prices might reduce consumption of renewable energy by 0.05%in the long run.This study’s primary significance lies in furnishing actionable strategies for Italy to augment green finance for renewable energy,fostering sustained social and economic progress.Moreover,the analytical insights gleaned from this research offer valuable insights for energy-importing nations worldwide.
文摘Climate change and environmental degradation threaten the world and global economic conditions.As one of countries’most important economic components,the financial sector might be an effective tool for reducing and even reversing environmental degradation.The financial sector can affect sustainability through its lending and investment practices.The sector can play a role in financing sustainable projects and businesses,helping reduce CO_(2) emissions.By aligning its financial objectives with environmental protection,the financial sector can support the transition to a more sustainable future by helping reduce environmental degradation’s negative impacts.This paper examines the domestic financial sector’s impact on CO_(2) emissions in the United States over the 1990:Q1–2022:Q3 period.In this research,the nexus between the domestic financial sector(total debt securities,loans,liabilities,and total financial assets)and Carbon dioxide emissions in the U.S.is investigated by Morlet wavelet analysis.Rest of the world:sector discrepancy transactions,rest of the world:debt securities and loans,gross domestic product,and the square of the gross domestic product,are control variables in the estimated models.Partial wavelet coherency analyses prove that the financial sector reduces CO_(2) emissions at the 5–8-year frequency band during different subsample periods.The financial sector’s instruments can be effective in struggling with climate change.
文摘An individual's mental health influences their capacity to think effectively,feel emotionally stable,and perform daily activities.As mental health concerns become more prevalent worldwide,new awareness and diagnostic and treatment tactics are needed.Digital tools and technology are helping solve these problems by providing scalable,tailored solutions for large populations.This detailed review examines mental health‐promoting internet tools.Smartphone applications,web‐based therapy systems,wearable tech,artificial intelligence‐powered resources,and virtual reality(VR)technologies were evaluated for efficacy and side effects.PubMed,PsycINFO,Scopus,IEEE Xplore,and Google Scholar were carefully searched.Search terms included“digital mental health tools,”“online therapy,”and“AI in mental health.”Randomized controlled trials,cohort studies,cross‐sectional studies,systematic reviews,and meta‐analyses of digital technology and mental health were included from among the literature published after 2010.Cognitive behavioral therapy methods,mood monitoring,and mindfulness exercises are among the numerous features of smartphone applications that have been demonstrated to mitigate symptoms of anxiety,depression,and tension.Online therapy platforms let marginalized individuals obtain therapy remotely.Wearable technology may detect heart rate,blood pressure,and sleep length,which may reveal mental health difficulties.Chatbots employ machine learning algorithms and natural language processing to deliver customized support and show promise for quick intervention.Exposure therapy for anxiety and trauma is increasingly using virtual reality environments.Although digital mental health therapies face challenges in relation to data privacy,limited long‐term efficacy,and technological inequality,digital technologies are modernizing mental healthcare.By offering inexpensive and effective alternatives to traditional therapies,digital technologies may help healthcare systems meet the growing demand for mental health services and overall well‐being.
基金supported by researchers Supporting Project Number(RSPD2025R548)King Saud University,Riyadh,Saudi Arabia.
文摘Accurately modeling heavy-tailed data is critical across applied sciences,particularly in finance,medicine,and actuarial analysis.This work presents the heavy-tailed power XLindley distribution(HTPXLD),a unique heavy-tailed distribution.Adding one more parameter to the power XLindley distribution improves this new distribution,especially when modeling leptokurtic lifetime data.The suggested density provides greater flexibility with asymmetric forms and different degrees of peakedness.Its statistical features,like the quantile function,moments,extropy measures,incomplete moments,stochastic ordering,and stress-strength parameters,are explored.We further investigate its use in actuarial science through the computation of pertinent metrics,such as value-at-risk,tail value-at-risk,tail variance,and tail variance premium.To obtain the point and interval parameter estimates,we use the maximum likelihood estimation approach.We do many simulation tests to evaluate the performance of our proposed estimator.Metrics like bias,relative bias,mean squared error,root mean squared error,average interval length,and coverage probability will be used in these tests to assess the estimator’s performance.To illustrate the practical value of our proposed model,we apply it to analyze three real-world datasets.We then compare its performance to established competing models,highlighting its advantages.
基金funded by the National Nature Sciences Foundation of China with Grant No.42250410321。
文摘Music recommendation systems are essential due to the vast amount of music available on streaming platforms,which can overwhelm users trying to find new tracks that match their preferences.These systems analyze users’emotional responses,listening habits,and personal preferences to provide personalized suggestions.A significant challenge they face is the“cold start”problem,where new users have no past interactions to guide recommendations.To improve user experience,these systems aimto effectively recommendmusic even to such users by considering their listening behavior and music popularity.This paper introduces a novel music recommendation system that combines order clustering and a convolutional neural network,utilizing user comments and rankings as input.Initially,the system organizes users into clusters based on semantic similarity,followed by the utilization of their rating similarities as input for the convolutional neural network.This network then predicts ratings for unreviewed music by users.Additionally,the system analyses user music listening behaviour and music popularity.Music popularity can help to address cold start users as well.Finally,the proposed method recommends unreviewed music based on predicted high rankings and popularity,taking into account each user’s music listening habits.The proposed method combines predicted high rankings and popularity by first selecting popular unreviewedmusic that themodel predicts to have the highest ratings for each user.Among these,the most popular tracks are prioritized,defined by metrics such as frequency of listening across users.The number of recommended tracks is aligned with each user’s typical listening rate.The experimental findings demonstrate that the new method outperformed other classification techniques and prior recommendation systems,yielding a mean absolute error(MAE)rate and rootmean square error(RMSE)rate of approximately 0.0017,a hit rate of 82.45%,an average normalized discounted cumulative gain(nDCG)of 82.3%,and a prediction accuracy of new ratings at 99.388%.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(Grant Number IMSIU-DDRSP2501).
文摘This study introduces the type-I heavy-tailed Burr XII(TIHTBXII)distribution,a highly flexible and robust statistical model designed to address the limitations of conventional distributions in analyzing data characterized by skewness,heavy tails,and diverse hazard behaviors.We meticulously develop the TIHTBXII’s mathematical foundations,including its probability density function(PDF),cumulative distribution function(CDF),and essential statistical properties,crucial for theoretical understanding and practical application.A comprehensive Monte Carlo simulation evaluates four parameter estimation methods:maximum likelihood(MLE),maximum product spacing(MPS),least squares(LS),and weighted least squares(WLS).The simulation results consistently show that as sample sizes increase,the Bias and RMSE of all estimators decrease,with WLS and LS often demonstrating superior and more stable performance.Beyond theoretical development,we present a practical application of the TIHTBXII distribution in constructing a group acceptance sampling plan(GASP)for truncated life tests.This application highlights how the TIHTBXII model can optimize quality control decisions by minimizing the average sample number(ASN)while effectively managing consumer and producer risks.Empirical validation using real-world datasets,including“Active Repair Duration,”“Groundwater Contaminant Measurements,”and“Dominica COVID-19 Mortality,”further demonstrates the TIHTBXII’s superior fit compared to existing models.Our findings confirm the TIHTBXII distribution as a powerful and reliable alternative for accurately modeling complex data in fields such as reliability engineering and quality assessment,leading to more informed and robust decision-making.
文摘Offshore logistics operations must continuously balance safety,fuel efficiency,and emissions reduction while navigating under uncertain and highly variable sea states.To address this challenge,we present anα-cut interval framework in which environmental uncertainties,specifically wave height and wind speed,are modeled as fuzzy numbers.Their correspondingα-level intervals are systematically propagated through a discrete vessel dynamics model,focusing on surge and heave responses.This procedure generates families of nested motion envelopes that tighten monotonically with increasingα,thereby producing deterministic yet progressively refined safety bounds without relying on full probabilistic distributions.A case study off the Karnataka coast is used to demonstrate the approach for a 20 km offshore supply voyage.Route planning constrained byα-envelopes ensures adherence to vessel structural and stability limits while enabling optimized transit speed.Comparative evaluation indicates that,relative to standard interval analysis,α-cut propagation substantially reduces over-conservatism,while against Monte Carlo-based envelopes it achieves similar coverage with significantly lower computational effort.Sensitivity analyses further quantify the influence ofα-grid resolution,membership-function design,and hydrodynamic coupling coefficients on envelope width,fuel use,and emissions.In the tested scenario,higherαlevels allow up to~15%reduction in worst-case energy consumption and nearly 10%reduction in CO_(2)emissions,all while preserving safety margins.Overall,the proposed framework is transparent,computationally efficient,and easily integrable into digital-twin-enabled operational workflows,providing a practical and sustainable decision-support tool for adaptive offshore logistics planning.
文摘The rapid transformation of Arctic maritime routes,driven by diminishing sea ice and shifting geopolitical conditions,presents both opportunities and challenges for global shipping.This study develops an integrated optimization framework for sustainable Arctic marine logistics,grounded in Agile Supply Chain Theory(ASCT),to address cost efficiency,environmental sustainability,and operational robustness under climate and policy uncertainty.A Mixed‐Integer Linear Programming(MILP)model was employed to optimize vessel routing across Arctic corridors,incorporating Energy Efficiency Operational Indicator(EEOI)and Carbon Intensity Indicator(CII)metrics directly into the objective function.Scenario analyses tested performance under varying climate conditions and policy constraints.The model was parameterized using vessel operational data from Arctic shipping logs,environmental datasets from ESA CryoSat‐2 and NSIDC,port accessibility records from Arctic port authorities,and economic data from Clarksons and the World Bank,ensuring realistic and replicable inputs for the analysis.Results demonstrate that ASCT‐based optimized routes achieved an average 14.8%reduction in operating costs,12.3%reduction in CO₂emissions,and an 11.6%improvement in EEOI,with the majority of voyages improving by at least one CII grade.Robustness analysis showed that optimized routes maintained up to 14.7 percentage points higher feasibility under severe ice scenarios and reduced cost volatility by 20–28%under carbon tax regimes.These findings confirm the value of embedding agility and resilience principles into Arctic shipping,aligning operational efficiency with International Maritime Organization(IMO)decarbonization objectives.The study extends ASCT into extreme maritime contexts,offering a replicable model for sustainable route planning in high‐risk logistics sectors.
基金supported in part by the Equipment Development Pre-research Project funded by Equipment Development Department,PRC under Grant No.50923010501Fundamental Research Program of Shenyang Institute of Automation(SIA),Chinese Academy of Sciencess under Grant No.355060201。
文摘Bonding quality at the interface of solid propellant grains is crucial for the reliability and safety of solid rocket motors.Although bonding reliability is influenced by numerous factors,the lack of quantitative characterization of interface debonding mechanisms and the challenge of identifying key factors have made precise control of process variables difficult,resulting in unpredictable failure risks.This paper presents an improved fuzzy failure probability evaluation method that combines fuzzy fault tree analysis with expert knowledge,transforming process data into fuzzy failure probability to accurately assess debonding probabilities.The predictive model is constructed through a general regression neural network and optimized using the particle swarm optimization algorithm.Sensitivity analysis is conducted to identify key decision variables,including normal force,grain rotation speed,and adhesive weight,which are verified experimentally.Compared with classical models,the maximum error margin of the constructed reliability prediction model is only 0.02%,and it has high stability.The experimental results indicate that the main factors affecting debonding are processing roughness and coating uniformity.Controlling the key decision variable as the median resulted in a maximum increase of 200.7%in bonding strength.The feasibility of the improved method has been verified,confirming that identifying key decision variables has the ability to improve bonding reliability.The proposed method simplifies the evaluation of propellant interface bonding reliability under complex conditions by quantifying the relationship between process parameters and failure risk,enabling targeted management of key decision variables.
文摘Offshore support operations must balance safety and sustainability under highly variable sea conditions.Deterministic motion analyses can underestimate extreme vessel responses,leading to insufficient operational limits and increased environmental impact.We develop a fuzzy‐enhanced multi‐body dynamics framework in which key inputs significant wave height,peak period,added mass,and radiation damping are represented as fuzzy numbers.Anα-cut decomposition yields interval bounds at each confidence level,and a fourth-order Runge-Kutta scheme integrates the six-degree-of-freedom equations of motion for both lower and upper“vertex”systems.A case study off the Karnataka coast applies both full 6-DoF and single-DOF heave approximations to demonstrate methodology.The heave response envelopes under calm(nominalα=1:0.73 m;full range atα=0:0.64–1.64 m)and severe(nominal 1.58 m;range 1.32–2.36 m)sea states reveal potential underestimations of 124%and 49%,respectively,when using only nominal values.By selecting an operationalα-level(e.g.,α^(*)=0.35 to cap heave≤1.8 m),decision-makers can balance risk tolerance and conservatism.Sensitivity analysis identifies significant wave height as the dominant uncertainty driver.Computational trade-offs and adaptiveα-sampling strategies are discussed.This work provides a self-contained,uncertainty-aware tool for deriving operational envelopes that improve risk-informed planning and enable fuel-efficiency optimization.By embedding fuzzy uncertainty quantification into vessel dynamics,the methodology supports safer,more sustainable marine operations and can be extended to real-time sensor fusion,multi-vessel interactions,and frequency-dependent hydrodynamics.
文摘This study addresses the critical need for decarbonization in offshore marine logistics by developing an integrated modeling framework to support low-emission operations across complex,multi-echelon vessel networks.It focuses on port-to-platform supply chains serving offshore wind farms,oil rigs,and floating logistics hubs.A hybrid analytical approach was adopted,combining Mixed-Integer Linear Programming(MILP)for optimizing emission-minimizing routing,Discrete-Event Simulation(DES)to evaluate offshore scheduling performance under variability,and a Multi-Criteria Decision Analysis(MCDA)model using AHP-TOPSIS to rank alternative marine fuel types.Monte Carlo simulation was also employed to assess cost and delivery fluctuations across uncertain operational scenarios.Data inputs were compiled from real-world offshore fleet specifications,port emissions records,and marine fuel technology benchmarks.MILP-based network flow optimization reduced CO₂emissions by 22%while maintaining service reliability across all demand points.DES simulations revealed congestion-driven scheduling delays during peak vessel utilization.MCDA analysis ranked bio-LNG and hydrogen propulsion systems as optimal choices based on emission,cost,and availability trade-offs.Hypothesis testing confirmed significant relationships between fuel type,network structure,and emission performance.The study demonstrates how multi-echelon logistics planning,integrated with emissions-based modeling,can facilitate environmentally responsible marine supply chain design.The framework offers practical guidance for offshore fleet managers,port authorities,and policy regulators aiming to align operational efficiency with decarbonization objectives under IMO and EU directives.
文摘As offshore wind infrastructure becomes more important to global efforts to reduce carbon emissions,it is becoming more important to connect lifecycle cost management with circular economy(CE)principles.When looking at the long-term costs of infrastructure,traditional lifecycle cost models often fail to account for residual value recovery,material circularity,or environmental externalities.This study creates a unified analytical framework that adds CE strategies to lifecycle cost modelling for offshore wind systems,such as turbines,substructures,moorings,and floating platforms.The method uses multi-objective optimization and system dynamics simulation along with net present value(NPV)modelling,material flow analysis,and carbonadjusted cost accounting.We modelled project-level datasets over 25 years to look at the trade-offs between economic and environmental factors in both linear and circular lifecycle scenarios.We use Python,MATLAB,and OpenLCA to look at key metrics like the Material Circularity Indicator(MCI),estimates of residual value,and internalized carbon costs.The results show that circular infrastructure strategies greatly lower lifecycle costs while also increasing material recovery and carbon efficiency.Scenario simulations showed that CE-based configurations could cut costs by up to 18%and emissions over the life of the product by 22%.Regression and sensitivity analyses showed that MCI,CAPEX,and circular design strategies are good at predicting residual value and long-term economic performance.This study adds a new,evidence-based model for making decisions about infrastructure that takes into account financial,environmental,and material circularity.
文摘Vessel motions in offshore operations are heavily influenced by uncertain wave loads and hydrodynamic parameters.Yet,traditional deterministic or probabilistic models often fail to capture epistemic ambiguity when data are scarce.We introduce a fuzzy–set framework usingα-cut interval analysis to represent imprecise wave heights,periods,added mass,damping,and stiffness as fuzzy numbers.These are incorporated into the multi-body equations of motion and solved via a fuzzy Runge–Kutta scheme across nestedα-levels.A simulation architecture iterates overα-cuts and time-steps to produce interval bounds on heavy responses.A case study off the Karnataka coast,with realistic sea-state data for moderate and severe scenarios,yields heave-amplitude envelopes whose widths quantify response uncertainty.At mid-confidence(α=0.5),moderate seas produce amplitudes of 8.30–9.65 m(±15%),while severe seas yield 7.15–8.90 m(±22%).Envelope narrowing asα→1 confirms that increased parameter confidence reduces prediction spread,and bias analysis against crisp baselines highlights the impact of imprecision on mean responses.This non-probabilistic approach provides interpretable,worst-and best-case motion bounds without requiring large datasets,offering marine engineers robust safety margins and guidance for targeted data collection and real-time uncertainty updating.
文摘In the present article, He＇s fractional derivative, the ansatz method, the （ C / G）-expansion method, and the exp-function method are used to construct the exact solutions of nonlinear space-time fractional Kadomtsev-Petviashvili- Benjamin-Bona Mahony （KP-BBM）. As a result, different types of exact solutions are obtained. Also we have examined the relation between the solutions obtained from the different methods. These methods are an efficient mathematical tool for solving fractional differential equations （FDEs） and it can be applied to other nonlinear FDEs.
