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
Since Serbia disposes with significant natural potentials, it could have been among the most attractive countries in Europe only if the historical circumstances had been different. Existing natural resources and possi...Since Serbia disposes with significant natural potentials, it could have been among the most attractive countries in Europe only if the historical circumstances had been different. Existing natural resources and possibilities for their improvement demand planned strategy to make them efficient. Tourism, as a significant instrument of development, can and should take an active part in the strategy of sustainable development. Special attention in the paper is paid to the development of Vrnjacka Banja Spa as it is one of the main tourist destinations in Serbia. The strengths, weaknesses, opportunities, and threats of a business venture （SWOT） analysis offers the basic elements for defining the strategic direction and program priorities, measures and targets for improving the overall development of spas in Serbia. To identify the SWOT factors, we used materials from a number of studies on Serbian tourism, surveys on tourists in Vrnjcka Banja Spa and analysis of the market and competition. The SWOT analysis proves that the priority in development of spas in Serbia is attractiveness of the destination which involves the spa-health tourism with great potential in complementary forms of tourism, such as sport-recreational, cultural, transit and hunting tourism. It means that the future strategy should be based on improving and involving modern spa products. It means improving and maximizing marketing so as to make the tourist destination attractive. It further implies maximum investment in modern tourist products.展开更多
Social media platforms provide new value for markets and research companies.This article explores the use of social media data to enhance customer value propositions.The case study involves a company that develops wea...Social media platforms provide new value for markets and research companies.This article explores the use of social media data to enhance customer value propositions.The case study involves a company that develops wearable Internet of Things(IoT)devices and services for stress management.Netnography and semantic annotation for recognizing and categorizing the context of tweets are conducted to gain a better understanding of users’stress management practices.The aim is to analyze the tweets about stress management practices and to identify the context from the tweets.Thereafter,we map the tweets on pleasure and arousal to elicit customer insights.We analyzed a case study of a marketing strategy on the Twitter platform.Participants in the marketing campaign shared photos and texts about their stress management practices.Machine learning techniques were used to evaluate and estimate the emotions and contexts of the tweets posted by the campaign participants.The computational semantic analysis of the tweets was compared to the text analysis of the tweets.The content analysis of only tweet images resulted in 96%accuracy in detecting tweet context,while that of the textual content of tweets yielded an accuracy of 91%.Semantic tagging by Ontotext was able to detect correct tweet context with an accuracy of 50%.展开更多
Behavioral operations management(BOM)is one of the new areas in operations management.In the past 12 years,the field has made huge progress and researchers have become interested in this new perspective to solving ope...Behavioral operations management(BOM)is one of the new areas in operations management.In the past 12 years,the field has made huge progress and researchers have become interested in this new perspective to solving operational problems.BOM is now one of the major subfields of operations management.In this paper,we examine and categorize areas of BOM based on the mainstream literature.Key areas include behavioral issues in new product development and project management,quality management,production management,inventory management,service operations,and forecasting.Studies in each area are divided into three subcategories,including OM context,individual attributes,heuristics,and biases,and individual differences.In OM context category,feedback and reward,training,work monitoring,teamwork and group decision making,goal setting,task assignment,and flexibility are among the main topics.In individual attributes,heuristics,and biases category,sunk cost effect and escalation of commitment,endowment effect,overprecision bias,planning fallacy,pullto-center effect,anchoring and insufficient adjustment,and misperceptions of feedback are mainly discussed.In individual differences,analytic thinking and system thinking are mainly studied.New areas for research are suggested in each related section and are summarized in future directions and conclusion sections.In contexts such as new product development,project management,and inventory management,a shift to finding solution to performance improvement is beneficial instead of focusing on heuristics and biases and considering them as a deficiency in human decision making.Regarding individual differences category,a shift toward attributes other than cognitive abilities,such as global processing,creative thinking,and design thinking are recommended.展开更多
The emergence of smart grids in India is propelled by an intricate interaction of market dynamics,regulatory structures,and stakeholder obligations.This study analyzes the primary factors that are driving the widespre...The emergence of smart grids in India is propelled by an intricate interaction of market dynamics,regulatory structures,and stakeholder obligations.This study analyzes the primary factors that are driving the widespread use of smart grid technologies and outlines the specific roles and obligations of different stakeholders,such as government entities,utility companies,technology suppliers,and consumers.Government activities and regulations are crucial in facilitating the implementation of smart grid technology by offering financial incentives,regulatory assistance,and strategic guidance.Utility firms have the responsibility of implementing and integrating smart grid infrastructure,with an emphasis on improving the dependability of the grid,minimizing losses in transmission and distribution,and integrating renewable energy sources.Technology companies offer the essential hardware and software solutions,which stimulate creativity and enhance efficiency.Consumers actively engage in the energy ecosystem by participating in demand response,implementing energy saving measures,and adopting distributed energy resources like solar panels and electric vehicles.This study examines the difficulties and possibilities in India’s smart grid industry,highlighting the importance of cooperation among stakeholders to build a strong,effective,and environmentally friendly energy future.展开更多
The Kosovo National Park is an area rich in exceptional natural values,including mountainous landscapes,rivers,green forests,and a rich biodiversity.This park offers opportunities for scenic views and is an ideal dest...The Kosovo National Park is an area rich in exceptional natural values,including mountainous landscapes,rivers,green forests,and a rich biodiversity.This park offers opportunities for scenic views and is an ideal destination for those who wish to explore untouched nature.The region is rich in rare flora and fauna,which are important for the preservation of local ecosystems and the global environment.The main objective of this paper is to examine the opportunities and challenges of ecotourism development in the Kosovo National Park,focusing on balancing biodiversity conservation with economic benefits for the local community and sustainable tourism development.The methodology used in this paper is multidimensional,combining analysis of existing literature,similar case studies,and interviews with experts in the field and local stakeholders.This process allows for an in-depth understanding of the opportunities and challenges that ecotourism development presents.To collect reliable data,interviews were conducted with local authorities and environmental experts,and management policies and strategies of similar parks were analyzed.The data were analyzed through qualitative and quantitative methods.The research results show that the development of ecotourism in the Kosovo National Park can have a positive impact on nature conservation and can contribute to the economic development of the region.However,this development requires careful management and a balance between biodiversity conservation and economic benefits.Ecotourism activities should be guided by sustainable practices that respect the environment and provide long-term benefits.展开更多
This study aims to assess the potential of natural tourism in Kosovo,especially in the Dukagjin region as a case study.The main objective is to identify and analyze this region’s tourism potential to understand its i...This study aims to assess the potential of natural tourism in Kosovo,especially in the Dukagjin region as a case study.The main objective is to identify and analyze this region’s tourism potential to understand its impact and importance.180 respondents from the Dukagjin region participated in this study,and the quantitative method was used as a methodology.The measurement instrument consisted of 30 closed questions,which aimed to collect detailed information on the potential of natural tourism in this area.The study results showed that mountain,cultural,winter,healing,and rural tourism are especially important for developing the Dukagjin region.Furthermore,the analysis shows that 30%of the tourism potential is explained by rural tourism,underlining its importance in the local economy.Finally,the study’s findings are discussed,emphasizing their practical value for promoting and developing sustainable tourism in the region.展开更多
The study aims to identify and analyze the perceptions and expectations of tourists regarding tourism development in the Sharr Mountains Region.In the Sharr Mountains Region,sustainable tourism is another opportunity ...The study aims to identify and analyze the perceptions and expectations of tourists regarding tourism development in the Sharr Mountains Region.In the Sharr Mountains Region,sustainable tourism is another opportunity for economic growth,cultural integrity,and environmental conservation.Public investments are vital in enhancing this investment as they help to develop infrastructure,promote businesses,and even promote environment-friendly activities.The research seeks to understand the relationship between public investments and investments in sustainable tourism in the Sharr Mountains region,focusing on the direct and indirect relational aspects of the economy,conservation,and the people.Key questions,address their expectations from public investment,infrastructure improvements,involvement of local authorities and the community,as well as concerns regarding the potential negative impact of tourism on the natural environment and local culture.The methodology of this study includes in-depth research through the quantitative method.Based on the results of data analysis,recommendations include improving tourism infrastructure,increasing awareness of environmental impact,and developing environmental education programs for tourists.Among the proposed actions is the improvement of cooperation between local authorities,the local community,and the private sector for the sustainable development of tourism in the Sharr Mountains Region.Finally,this study provides an essential contribution to the understanding of the expectations,evaluations,and impacts of tourists in this region,defining the key points of sustainable tourism development.展开更多
Breast cancer’s heterogeneous progression demands innovative tools for accurate prediction.We present a hybrid framework that integrates machine learning(ML)and fractional-order dynamics to predict tumor growth acros...Breast cancer’s heterogeneous progression demands innovative tools for accurate prediction.We present a hybrid framework that integrates machine learning(ML)and fractional-order dynamics to predict tumor growth across diagnostic and temporal scales.On the Wisconsin Diagnostic Breast Cancer dataset,seven ML algorithms were evaluated,with deep neural networks(DNNs)achieving the highest accuracy(97.72%).Key morphological features(area,radius,texture,and concavity)were identified as top malignancy predictors,aligning with clinical intuition.Beyond static classification,we developed a fractional-order dynamical model using Caputo derivatives to capture memory-driven tumor progression.The model revealed clinically interpretable patterns:lower fractional orders correlated with prolonged aggressive growth,while higher orders indicated rapid stabilization,mimicking indolent subtypes.Theoretical analyses were rigorously proven,and numerical simulations closely fit clinical data.The framework’s clinical utility is demonstrated through an interactive graphics user interface(GUI)that integrates real-time risk assessment with growth trajectory simulations.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
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
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
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
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 Human–Robot Interaction(HRI),generating robot trajectories that accurately reflect user intentions while ensuring physical realism remains challenging,especially in unstructured environments.In this study,we devel...In Human–Robot Interaction(HRI),generating robot trajectories that accurately reflect user intentions while ensuring physical realism remains challenging,especially in unstructured environments.In this study,we develop a multimodal framework that integrates symbolic task reasoning with continuous trajectory generation.The approach employs transformer models and adversarial training to map high-level intent to robotic motion.Information from multiple data sources,such as voice traits,hand and body keypoints,visual observations,and recorded paths,is integrated simultaneously.These signals are mapped into a shared representation that supports interpretable reasoning while enabling smooth and realistic motion generation.Based on this design,two different learning strategies are investigated.In the first step,grammar-constrained Linear Temporal Logic(LTL)expressions are created from multimodal human inputs.These expressions are subsequently decoded into robot trajectories.The second method generates trajectories directly from symbolic intent and linguistic data,bypassing an intermediate logical representation.Transformer encoders combine multiple types of information,and autoregressive transformer decoders generate motion sequences.Adding smoothness and speed limits during training increases the likelihood of physical feasibility.To improve the realism and stability of the generated trajectories during training,an adversarial discriminator is also included to guide them toward the distribution of actual robot motion.Tests on the NATSGLD dataset indicate that the complete system exhibits stable training behaviour and performance.In normalised coordinates,the logic-based pipeline has an Average Displacement Error(ADE)of 0.040 and a Final Displacement Error(FDE)of 0.036.The adversarial generator makes substantially more progress,reducing ADE to 0.021 and FDE to 0.018.Visual examination confirms that the generated trajectories closely align with observed motion patterns while preserving smooth temporal dynamics.展开更多
Lassa Fever(LF)is a viral hemorrhagic illness transmitted via rodents and is endemic in West Africa,causing thousands of deaths annually.This study develops a dynamic model of Lassa virus transmission,capturing the pr...Lassa Fever(LF)is a viral hemorrhagic illness transmitted via rodents and is endemic in West Africa,causing thousands of deaths annually.This study develops a dynamic model of Lassa virus transmission,capturing the progression of the disease through susceptible,exposed,infected,and recovered populations.The focus is on simulating this model using the fractional Caputo derivative,allowing both qualitative and quantitative analyses of boundedness,positivity,and solution uniqueness.Fixed-point theory and Lipschitz conditions are employed to confirm the existence and uniqueness of solutions,while Lyapunov functions establish the global stability of both disease-free and endemic equilibria.The study further examines the role of the Caputo operator by solving the generalized power-law kernel via a two-step Lagrange polynomial method.This approach offers practical advantages in handling additional data points in integral forms,though Newton polynomial-based schemes are generally more accurate and can outperform Lagrange-based Adams-Bashforth methods.Graphical simulations validate the proposed numerical approach for different fractional orders(ν)and illustrate the influence of model parameters on disease dynamics.Results indicate that increasing the fractional order accelerates the decline of Lassa fever in both human and rodent populations.Moreover,fractional-order modeling provides more nuanced insights than traditional integer-order models,suggesting potential improvements for medical intervention strategies.The study demonstrates that carefully chosen fractional orders can optimize convergence and enhance the predictive capacity of Lassa fever models,offering a promising direction for future research in epidemiological modeling.展开更多
The life cycle phase of fossil fuel extraction is mainly considered in the life cycle assessment(LCA)when evaluating the energy production processes.It is then only one of many unit processes,which contribute to the b...The life cycle phase of fossil fuel extraction is mainly considered in the life cycle assessment(LCA)when evaluating the energy production processes.It is then only one of many unit processes,which contribute to the blurring of mining-relevant results.There are few items in the literature focusing exclusively on the lignite mining phase and analysing the specific mining conditions and associated environmental impacts.The article focuses on the LCA of lignite mining processes on the basis of data coming from a Polish mine.The technology for opencast lignite mining is noted for its high production efficiency,high level of recovery and lower risk as regards the safety of workers when compared with underground mining systems.However,the need to remove large amounts of overburden to uncover the deposit contributes to a much greater degradation of the landscape.Analysing the results obtained,several key(hot spot)elements of the lignite mining operations were distinguished for modelling the environmental impact,i.e.:calorific value,the amount of electricity consumption,the manner in which waste and overburden are managed.As a result there is a high sensitivity of the final indicator to changes in these impacts.展开更多
The principal component analysis (PCA) is a kind of algorithms in biometrics. It is a statistics technical and used orthogonal transformation to convert a set of observations of possibly correlated variables into a se...The principal component analysis (PCA) is a kind of algorithms in biometrics. It is a statistics technical and used orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables. PCA also is a tool to reduce multidimensional data to lower dimensions while retaining most of the information. It covers standard deviation, covariance, and eigenvectors. This background knowledge is meant to make the PCA section very straightforward, but can be skipped if the concepts are already familiar.展开更多
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Since Serbia disposes with significant natural potentials, it could have been among the most attractive countries in Europe only if the historical circumstances had been different. Existing natural resources and possibilities for their improvement demand planned strategy to make them efficient. Tourism, as a significant instrument of development, can and should take an active part in the strategy of sustainable development. Special attention in the paper is paid to the development of Vrnjacka Banja Spa as it is one of the main tourist destinations in Serbia. The strengths, weaknesses, opportunities, and threats of a business venture （SWOT） analysis offers the basic elements for defining the strategic direction and program priorities, measures and targets for improving the overall development of spas in Serbia. To identify the SWOT factors, we used materials from a number of studies on Serbian tourism, surveys on tourists in Vrnjcka Banja Spa and analysis of the market and competition. The SWOT analysis proves that the priority in development of spas in Serbia is attractiveness of the destination which involves the spa-health tourism with great potential in complementary forms of tourism, such as sport-recreational, cultural, transit and hunting tourism. It means that the future strategy should be based on improving and involving modern spa products. It means improving and maximizing marketing so as to make the tourist destination attractive. It further implies maximum investment in modern tourist products.
基金This work was supported by Taif University Researchers Supporting Project number(TURSP-2020/292),Taif University,Taif,Saudi Arabia.This research was funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the fast-track Research Funding Program.
文摘Social media platforms provide new value for markets and research companies.This article explores the use of social media data to enhance customer value propositions.The case study involves a company that develops wearable Internet of Things(IoT)devices and services for stress management.Netnography and semantic annotation for recognizing and categorizing the context of tweets are conducted to gain a better understanding of users’stress management practices.The aim is to analyze the tweets about stress management practices and to identify the context from the tweets.Thereafter,we map the tweets on pleasure and arousal to elicit customer insights.We analyzed a case study of a marketing strategy on the Twitter platform.Participants in the marketing campaign shared photos and texts about their stress management practices.Machine learning techniques were used to evaluate and estimate the emotions and contexts of the tweets posted by the campaign participants.The computational semantic analysis of the tweets was compared to the text analysis of the tweets.The content analysis of only tweet images resulted in 96%accuracy in detecting tweet context,while that of the textual content of tweets yielded an accuracy of 91%.Semantic tagging by Ontotext was able to detect correct tweet context with an accuracy of 50%.
文摘Behavioral operations management(BOM)is one of the new areas in operations management.In the past 12 years,the field has made huge progress and researchers have become interested in this new perspective to solving operational problems.BOM is now one of the major subfields of operations management.In this paper,we examine and categorize areas of BOM based on the mainstream literature.Key areas include behavioral issues in new product development and project management,quality management,production management,inventory management,service operations,and forecasting.Studies in each area are divided into three subcategories,including OM context,individual attributes,heuristics,and biases,and individual differences.In OM context category,feedback and reward,training,work monitoring,teamwork and group decision making,goal setting,task assignment,and flexibility are among the main topics.In individual attributes,heuristics,and biases category,sunk cost effect and escalation of commitment,endowment effect,overprecision bias,planning fallacy,pullto-center effect,anchoring and insufficient adjustment,and misperceptions of feedback are mainly discussed.In individual differences,analytic thinking and system thinking are mainly studied.New areas for research are suggested in each related section and are summarized in future directions and conclusion sections.In contexts such as new product development,project management,and inventory management,a shift to finding solution to performance improvement is beneficial instead of focusing on heuristics and biases and considering them as a deficiency in human decision making.Regarding individual differences category,a shift toward attributes other than cognitive abilities,such as global processing,creative thinking,and design thinking are recommended.
文摘The emergence of smart grids in India is propelled by an intricate interaction of market dynamics,regulatory structures,and stakeholder obligations.This study analyzes the primary factors that are driving the widespread use of smart grid technologies and outlines the specific roles and obligations of different stakeholders,such as government entities,utility companies,technology suppliers,and consumers.Government activities and regulations are crucial in facilitating the implementation of smart grid technology by offering financial incentives,regulatory assistance,and strategic guidance.Utility firms have the responsibility of implementing and integrating smart grid infrastructure,with an emphasis on improving the dependability of the grid,minimizing losses in transmission and distribution,and integrating renewable energy sources.Technology companies offer the essential hardware and software solutions,which stimulate creativity and enhance efficiency.Consumers actively engage in the energy ecosystem by participating in demand response,implementing energy saving measures,and adopting distributed energy resources like solar panels and electric vehicles.This study examines the difficulties and possibilities in India’s smart grid industry,highlighting the importance of cooperation among stakeholders to build a strong,effective,and environmentally friendly energy future.
文摘The Kosovo National Park is an area rich in exceptional natural values,including mountainous landscapes,rivers,green forests,and a rich biodiversity.This park offers opportunities for scenic views and is an ideal destination for those who wish to explore untouched nature.The region is rich in rare flora and fauna,which are important for the preservation of local ecosystems and the global environment.The main objective of this paper is to examine the opportunities and challenges of ecotourism development in the Kosovo National Park,focusing on balancing biodiversity conservation with economic benefits for the local community and sustainable tourism development.The methodology used in this paper is multidimensional,combining analysis of existing literature,similar case studies,and interviews with experts in the field and local stakeholders.This process allows for an in-depth understanding of the opportunities and challenges that ecotourism development presents.To collect reliable data,interviews were conducted with local authorities and environmental experts,and management policies and strategies of similar parks were analyzed.The data were analyzed through qualitative and quantitative methods.The research results show that the development of ecotourism in the Kosovo National Park can have a positive impact on nature conservation and can contribute to the economic development of the region.However,this development requires careful management and a balance between biodiversity conservation and economic benefits.Ecotourism activities should be guided by sustainable practices that respect the environment and provide long-term benefits.
文摘This study aims to assess the potential of natural tourism in Kosovo,especially in the Dukagjin region as a case study.The main objective is to identify and analyze this region’s tourism potential to understand its impact and importance.180 respondents from the Dukagjin region participated in this study,and the quantitative method was used as a methodology.The measurement instrument consisted of 30 closed questions,which aimed to collect detailed information on the potential of natural tourism in this area.The study results showed that mountain,cultural,winter,healing,and rural tourism are especially important for developing the Dukagjin region.Furthermore,the analysis shows that 30%of the tourism potential is explained by rural tourism,underlining its importance in the local economy.Finally,the study’s findings are discussed,emphasizing their practical value for promoting and developing sustainable tourism in the region.
文摘The study aims to identify and analyze the perceptions and expectations of tourists regarding tourism development in the Sharr Mountains Region.In the Sharr Mountains Region,sustainable tourism is another opportunity for economic growth,cultural integrity,and environmental conservation.Public investments are vital in enhancing this investment as they help to develop infrastructure,promote businesses,and even promote environment-friendly activities.The research seeks to understand the relationship between public investments and investments in sustainable tourism in the Sharr Mountains region,focusing on the direct and indirect relational aspects of the economy,conservation,and the people.Key questions,address their expectations from public investment,infrastructure improvements,involvement of local authorities and the community,as well as concerns regarding the potential negative impact of tourism on the natural environment and local culture.The methodology of this study includes in-depth research through the quantitative method.Based on the results of data analysis,recommendations include improving tourism infrastructure,increasing awareness of environmental impact,and developing environmental education programs for tourists.Among the proposed actions is the improvement of cooperation between local authorities,the local community,and the private sector for the sustainable development of tourism in the Sharr Mountains Region.Finally,this study provides an essential contribution to the understanding of the expectations,evaluations,and impacts of tourists in this region,defining the key points of sustainable tourism development.
文摘Breast cancer’s heterogeneous progression demands innovative tools for accurate prediction.We present a hybrid framework that integrates machine learning(ML)and fractional-order dynamics to predict tumor growth across diagnostic and temporal scales.On the Wisconsin Diagnostic Breast Cancer dataset,seven ML algorithms were evaluated,with deep neural networks(DNNs)achieving the highest accuracy(97.72%).Key morphological features(area,radius,texture,and concavity)were identified as top malignancy predictors,aligning with clinical intuition.Beyond static classification,we developed a fractional-order dynamical model using Caputo derivatives to capture memory-driven tumor progression.The model revealed clinically interpretable patterns:lower fractional orders correlated with prolonged aggressive growth,while higher orders indicated rapid stabilization,mimicking indolent subtypes.Theoretical analyses were rigorously proven,and numerical simulations closely fit clinical data.The framework’s clinical utility is demonstrated through an interactive graphics user interface(GUI)that integrates real-time risk assessment with growth trajectory simulations.
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.
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.
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.
基金The authors extend their appreciation to Prince Sattam bin Abdulaziz University for funding this research work through the project number(PSAU/2024/01/32082).
文摘In Human–Robot Interaction(HRI),generating robot trajectories that accurately reflect user intentions while ensuring physical realism remains challenging,especially in unstructured environments.In this study,we develop a multimodal framework that integrates symbolic task reasoning with continuous trajectory generation.The approach employs transformer models and adversarial training to map high-level intent to robotic motion.Information from multiple data sources,such as voice traits,hand and body keypoints,visual observations,and recorded paths,is integrated simultaneously.These signals are mapped into a shared representation that supports interpretable reasoning while enabling smooth and realistic motion generation.Based on this design,two different learning strategies are investigated.In the first step,grammar-constrained Linear Temporal Logic(LTL)expressions are created from multimodal human inputs.These expressions are subsequently decoded into robot trajectories.The second method generates trajectories directly from symbolic intent and linguistic data,bypassing an intermediate logical representation.Transformer encoders combine multiple types of information,and autoregressive transformer decoders generate motion sequences.Adding smoothness and speed limits during training increases the likelihood of physical feasibility.To improve the realism and stability of the generated trajectories during training,an adversarial discriminator is also included to guide them toward the distribution of actual robot motion.Tests on the NATSGLD dataset indicate that the complete system exhibits stable training behaviour and performance.In normalised coordinates,the logic-based pipeline has an Average Displacement Error(ADE)of 0.040 and a Final Displacement Error(FDE)of 0.036.The adversarial generator makes substantially more progress,reducing ADE to 0.021 and FDE to 0.018.Visual examination confirms that the generated trajectories closely align with observed motion patterns while preserving smooth temporal dynamics.
文摘Lassa Fever(LF)is a viral hemorrhagic illness transmitted via rodents and is endemic in West Africa,causing thousands of deaths annually.This study develops a dynamic model of Lassa virus transmission,capturing the progression of the disease through susceptible,exposed,infected,and recovered populations.The focus is on simulating this model using the fractional Caputo derivative,allowing both qualitative and quantitative analyses of boundedness,positivity,and solution uniqueness.Fixed-point theory and Lipschitz conditions are employed to confirm the existence and uniqueness of solutions,while Lyapunov functions establish the global stability of both disease-free and endemic equilibria.The study further examines the role of the Caputo operator by solving the generalized power-law kernel via a two-step Lagrange polynomial method.This approach offers practical advantages in handling additional data points in integral forms,though Newton polynomial-based schemes are generally more accurate and can outperform Lagrange-based Adams-Bashforth methods.Graphical simulations validate the proposed numerical approach for different fractional orders(ν)and illustrate the influence of model parameters on disease dynamics.Results indicate that increasing the fractional order accelerates the decline of Lassa fever in both human and rodent populations.Moreover,fractional-order modeling provides more nuanced insights than traditional integer-order models,suggesting potential improvements for medical intervention strategies.The study demonstrates that carefully chosen fractional orders can optimize convergence and enhance the predictive capacity of Lassa fever models,offering a promising direction for future research in epidemiological modeling.
文摘The life cycle phase of fossil fuel extraction is mainly considered in the life cycle assessment(LCA)when evaluating the energy production processes.It is then only one of many unit processes,which contribute to the blurring of mining-relevant results.There are few items in the literature focusing exclusively on the lignite mining phase and analysing the specific mining conditions and associated environmental impacts.The article focuses on the LCA of lignite mining processes on the basis of data coming from a Polish mine.The technology for opencast lignite mining is noted for its high production efficiency,high level of recovery and lower risk as regards the safety of workers when compared with underground mining systems.However,the need to remove large amounts of overburden to uncover the deposit contributes to a much greater degradation of the landscape.Analysing the results obtained,several key(hot spot)elements of the lignite mining operations were distinguished for modelling the environmental impact,i.e.:calorific value,the amount of electricity consumption,the manner in which waste and overburden are managed.As a result there is a high sensitivity of the final indicator to changes in these impacts.
文摘The principal component analysis (PCA) is a kind of algorithms in biometrics. It is a statistics technical and used orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables. PCA also is a tool to reduce multidimensional data to lower dimensions while retaining most of the information. It covers standard deviation, covariance, and eigenvectors. This background knowledge is meant to make the PCA section very straightforward, but can be skipped if the concepts are already familiar.
