Search Results (85)

The paper focuses on exploring ways to achieve sustainability in the manufacturing process through targeted optimization and ergonomic improvements of the work environment. The introductory section emphasizes the importance of sustainability from the perspectives of worker well-being, occupational safety, and efficient resource utilization. The paper presents a digital approach to workstation design with an emphasis on sustainability, which includes the creation of a 3D model of the assembly station using SolidWorks (v.2017) and Jack software (v.8.3), where the work movements of a virtual mannequin with realistic parameters are simulated. The analytical section is dedicated to evaluating workstation ergonomics using the RULA (Rapid Upper Limb Assessment), SSP (Static Strength Prediction), OWAS (Ovako Working Posture Analysis), and Lower Back Analysis methods, with the aim of identifying operations that reduce the sustainability of the work process due to excessive physical strain. Badly designed operations have a negative impact on sustainability in the meaning of physical workload strain (social dimension), low effectivity and quality (economic dimension), and higher resource (material, energy, transport, etc.) usage (environmental dimension). All these dimensions can be measured and expressed by number, but this paper focuses on workload only. Based on the results, specific measures were proposed with a focus on sustainability—raising the working height of pallets, optimizing the positioning of tools, and adjusting work movements. Repeated analyses after the implementation of these changes confirmed not only a reduction in physical strain and increased safety but also the enhancement of the sustainability of the working environment and processes. The results of the article clearly demonstrate that digital simulation and ergonomic design, oriented toward sustainability, are of crucial importance for the long-term efficiency and sustainable development of manufacturing organizations. The novelty of the work is in contribution to empirical validation on the role of digital twins, virtual ergonomics, and human factors in Industry 5.0 contexts, where the synergy between technological efficiency and human-centric sustainability is increasingly emphasized. The proposed approach represents a practical model for further initiatives aimed at improving the sustainability of assembly workstations. Full article

Load forecasting is a key component of power network planning and an essential approach to achieving the efficient cooperative optimization of integrated economic energy services. To improve the accuracy of the power load prediction and ensure the stable dispatch of power grid, this paper takes County A as a case study. The fish bone diagram method is applied to analyze the influence of four categories of factors on the county’s power load, and stepwise regression, the unit energy consumption method, and an optimized grey model are adopted to forecast and analyze the planned load of the county over the past 5 years. In addition, the spatial load density method, the optimized grey prediction model, and the General Regression Neural Network (GRNN) are used to predict and analyze the county’s planned power grid load based on data from the past ten years. The Ordered Weighted Averaging (OWA) operator is then applied to integrate the results, and the predictive performance of different methods is assessed with an evaluation function. The results show that this combined multi-method approach achieves a higher accuracy. It also accounts for the evolving political, economic, and social conditions of the country, making the predictions more useful for power grid planning. Based on these findings, corresponding countermeasures and suggestions are proposed to support the improvement of spatial planning for electric power facilities in County A. Full article

Decision-making and planning in eco-wellness tourism can vary depending on time, resources, and the perspectives of stakeholders, as it is often challenging to generalize the results of decision-making models across different scenarios. Hence, the primary objective of this study was to propose a scenario-based framework for optimising eco-wellness tourism development. For this purpose, maps of 26 factors affecting the evaluation of nature-based eco-wellness tourism, including water, climatic, and kinetic therapies, were used in the Ardabil province of Iran. Weighted criteria maps are integrated into suitability maps for various wellness tourism products under different scenarios, ranging from very pessimistic to very optimistic, using the Ordered Weighted Averaging (OWA) operator. Then, to identify areas of consensus, scenario-based maps for water, climate, and kinetic therapies are combined. In the very pessimistic (optimistic) scenario, climate-only therapy accounts for 0.91% (2.23%), water-only therapy for 1.07% (8.44%), and kinetic-only therapy for 3.5% (5.81%) of the area. The most significant expansion is observed in areas integrating all three therapies—climate, water, and kinetic—which increase from 3.23% in the very pessimistic scenario to 14.5% in the very optimistic scenario. The findings have substantial insights for policymakers, tourism planners, and investors in developing and promoting unique eco-wellness experiences that benefit tourists. The methodical approach and choice of data and parameters in the study can be inspirational and adjustable for relevant studies. Full article

Evaluation of socio-ecological vulnerability is crucial for sustainable management in mining cities. This study selected Xintai City, China, as a case and constructed a comprehensive vulnerability assessment framework based on 2010–2020 multi-source data. By integrating the Geodetector, spatial autocorrelation analysis, and ordered weighted averaging (OWA), we systematically explored the spatio-temporal patterns and driving mechanisms of socio-ecological vulnerability. The Theil index at the village level revealed finer spatial heterogeneity than large-scale analyses. The results show the following: (1) Socio-ecological vulnerability in Xintai City is generally moderate, with high-vulnerability areas concentrated in the urban center and former coal mining zones. Over the past decade, high—vulnerability levels in these areas have improved, whereas the urban-rural fringe has experienced a significant increase in vulnerability, primarily driven by industrial transfer and uneven resource allocation. (2) Geodetector analysis indicated a shift in dominant drivers from natural to socio-economic factors, with population density and construction land proportion surpassing natural conditions such as average annual rainfall by 2020. Additionally, mining land proportion, land use change, and the spatial distribution of social services played key roles in shaping vulnerability patterns, while ecological and public service factors showed weaker explanatory power. (3) Scenario simulation based on OWA demonstrated that an economic-priority pathway leads to the outward expansion of vulnerable areas into mountainous regions, while an ecological-priority approach promotes spatial contraction and optimization of vulnerability zones. These findings provide scientific guidance for identifying key vulnerable areas and formulating differentiated management strategies, offering reference value for the sustainable development of resource-exhausted mining cities. Full article

New energy vehicles (NEVs) have gained increasing favor among NEV consumers due to their dual advantages of “low cost” and “environmental friendliness.” In recent years, the share of NEVs in the global automotive market has been steadily rising. For instance, in the Chinese market, the sales of new energy vehicles in 2024 increased by 35.5% year-on-year, accounting for 70.5% of global NEV sales. However, as the diversity of NEV brands and models expands, selecting the most suitable model from a vast amount of information has become the primary challenge for NEV consumers. Although online service platforms offer extensive user reviews and rating data, the uncertainty, inconsistent quality, and sheer volume of this information pose significant challenges to decision-making for NEV consumers. Against this backdrop, leveraging the strengths of the quasi OWA (QOWA) operator in information aggregation and interval basic uncertain linguistic information (IBULI) information aggregation and two-dimensional information representation of “information + quality”, this study proposes a large-scale group data aggregation method for decision support based on the IBULIQOWA operator. This approach aims to assist consumers of new energy vehicles in making informed decisions from the perspective of information quality. Firstly, the quasi ordered weighted averaging (QOWA) operator on the unit interval is extended to the closed interval $\left[0,\tau \right]$, and the extended basic uncertain information quasi ordered weighted averaging (EBUIQOWA) operator is defined. Secondly, in order to aggregate groups of IBULI, based on the EBUIQOWA operator, the basic uncertain linguistic information QOWA (BULIQOWA) operator and the IBULIQOWA operator are proposed, and the monotonicity and degeneracy of the proposed operators are discussed. Finally, for the problem of product decision making in online service platforms, considering the credibility of information, a product decision-making method based on the IBULIQOWA operator is proposed, and its effectiveness and applicability are verified through a case study of NEV product decision making in a car online service platform, providing a reference for decision support in product ranking of online service platforms. Full article

Recommendation systems applied to tourism are widely recognized for improving the visitor’s experience in tourist destinations, thanks to their ability to personalize the trip. This paper presents a hybrid approach that combines Machine Learning techniques with the Ordered Weighted Averaging (OWA) aggregation operator to achieve greater accuracy in user segmentation and generate personalized recommendations. The data were collected through a questionnaire applied to tourists in the different points of interest of the Special, Tourist and Cultural District of Riohacha. In the first stage, the K-means algorithm defines the segmentation of tourists based on their socio-demographic data and travel preferences. The second stage uses the OWA operator with a disjunctive policy to assign the most relevant cluster given the input data. This hybrid approach provides a recommendation mechanism for tourist destinations and their cultural heritage. Full article

In the context of rapidly evolving industrial ecosystems, the human–machine interaction (HMI) has shifted from basic interface control toward complex, adaptive, and human-centered systems. This review explores the multidisciplinary foundations and technological advancements driving this transformation within Industry 4.0 and the emerging paradigm of Industry 5.0. Through a comprehensive synthesis of the recent literature, we examine the cognitive, physiological, psychological, and organizational factors that shape operator performance, safety, and satisfaction. A particular emphasis is placed on ergonomic interface design, real-time physiological sensing (e.g., EEG, EMG, and eye-tracking), and the integration of collaborative robots, exoskeletons, and extended reality (XR) systems. We further analyze methodological frameworks such as RULA, OWAS, and Human Reliability Analysis (HRA), highlighting their digital extensions and applicability in industrial contexts. This review also discusses challenges related to cognitive overload, trust in automation, and the ethical implications of adaptive systems. Our findings suggest that an effective HMI must go beyond usability and embrace a human-centric philosophy that aligns technological innovation with sustainability, personalization, and resilience. This study provides a roadmap for researchers, designers, and practitioners seeking to enhance interaction quality in smart manufacturing through cognitive ergonomics and intelligent system integration. Full article

Conducting ecological restoration has emerged as a critical governance strategy for enhancing ecosystem diversity, stability, and sustainability. The scientific identification of priority restoration areas is a prerequisite for effective ecological restoration projects. Current research on identifying priority restoration zones predominantly relies on administrative-scale frameworks, and the reliability and scientificity of the identified results are somewhat insufficient. To address this gap, this study selected Dali Prefecture in Yunnan Province, a region characterized by dense river networks, as the research area to identify the priority areas of ecological restoration. In view of the application of the InVest model in watershed-scale restoration, biodiversity assessment, and other fields, we utilize sub-watershed units and the InVEST model, and five key ecosystem services—water conservation, water purification (N/P), habitat quality, climate regulation, and soil retention—were quantified. Temporal changes in these services from 2015 to 2020 were analyzed alongside ecological risk assessments and restoration zoning. Priority areas were further identified through Ordered Weighted Averaging (OWA) operators under varying decision-making preferences. The optimal threshold for watershed delineation was determined as 11.04 km², resulting in 1513 refined sub-watershed units after correction, with 71.59% concentrated in the 10–50 km² range. A spatial analysis revealed an east-to-west gradient in ecosystem service distribution, where eastern regions consistently exhibited lower values compared to central and western areas. From 2015 to 2020, soil retention per unit area increased by 5.09%, while water purification for N and P showed marginal improvements of 0.97% and 0.39%, respectively. Conversely, water conservation declined significantly by 10.00%, with carbon sequestration and biodiversity protection experiencing slight reductions of 1.74% and 1.92%, all within a 2% variation margin. Ecological risk zoning identified low-risk areas (grades 1–3) predominantly in western and northeastern Dali, encompassing 1094 sub-watersheds (77.36% by count and 73.92% by area), while high-risk zones (grades 4–5) covered 386 units (26.08% by area). Integrating ecological quality and risk levels, the study area was classified into four functional zones: Zone I (high quality, high risk), Zone II (low quality, high risk), Zone III (low quality, low risk), and Zone IV (high quality, low risk). With increasing risk tolerance, the priority restoration areas expanded from eastward to central regions. Based on the scenario simulations under ecological priority, status quo, and development-oriented policies, the critical restoration areas include the Sangyuan River Basin, mid-reach of the Juli River, and upper Miyu River. This methodology provides a theoretical and technical foundation for ecosystem service enhancement and degraded ecosystem rehabilitation in Dali Prefecture and similar regions. Full article

In forest operations, traditional ergonomic studies have been carried out by assessing body posture manually, but such assessments may suffer in terms of efficiency and reliability. Advancements in machine learning provided the opportunity to overcome many of the limitations of the manual approach. This study evaluated the intra- and inter-reliability of postural assessments in manual and motor-manual forest operations using the Ovako Working Posture Analysing System (OWAS)—which is one of the most used methods in forest operations ergonomics—by considering the predictions of a deep learning model as reference data and the rating inputs of three raters done in two replicates, over 100 images. The results indicated moderate to almost perfect intra-rater agreement (Cohen’s kappa = 0.48–1.00) and slight to substantial agreement (Cohen’s kappa = 0.02–0.64) among human raters. Inter-rater agreement between pairwise human-model datasets ranged from poor to fair (Cohen’s kappa = −0.03–0.34) and from fair to moderate when integrating all the human ratings with those of the model (Fleiss’ kappa = 0.28–0.49). The deep learning (DL) model highly outperformed human raters in assessment speed, requiring just one second per image, which, on average, was 19 to 53 times faster compared to human ratings. These findings highlight the efficiency and potential of integrating DL algorithms into OWAS assessments, offering a rapid and resource-efficient alternative while maintaining comparable reliability. However, challenges remain regarding subjective interpretations of complex postures. Future research should focus on refining algorithm parameters, enhancing human rater training, and expanding annotated datasets to improve alignment between model outputs and human assessments, advancing postural assessments in forest operations. Full article

Informational divergence emerged as a significant phenomenon during the COVID-19 health crisis. This period was characterized by information overload and changes in the communication of public health recommendations and policies by authorities and media outlets. This study examines the impact of such divergence on the population’s mental health, focusing on primary emotions expressed in comments across digital ecosystems. A media EMIC approach was used to analyze digital ecosystems during March and April 2020. Data were collected from Twitter, YouTube, Instagram, official press websites, and internet forums, yielding 3,456,387 communications. These were filtered to extract emotion-expressing content, resulting in 106,261 communications. Communications were categorized into primary emotions (anger, disgust, joy, fear, and sadness) using an exclusionary emotion assignment procedure. Analysis techniques included polarity and term frequency calculation, content analysis using Natural Language Understanding, emotion intensity measurement using IBM Watson Analytics, and data reliability assessment using the ISMA-OWA operator. The findings suggest that exposure to informational divergence from governments, health organizations, and media negatively affected mental health, evidenced by sadness, fear, disgust, and anger, which are associated with elevated levels of stress, anxiety, and information fatigue. In contrast, information perceived as reflecting coordination, support, and solidarity elicited positive emotional responses, particularly joy. Full article

The Carlson–Shaffer operator plays an important role in the geometric theory of analytic functions. It is associated with the hypergeometric function and the incomplete beta function. The Carlson–Shaffer operator generalizes various other linear operators, such as the Ruscheweyh derivative operator, the Bernardi–Libera–Livingston operator, and the Srivastava–Owa operator. Ideas in the theory of analytic functions are often symmetrically transferred to the theory of harmonic functions. By using the Carlson–Shaffer operator, we introduce a class of harmonic functions defined by weak subordination. Next, we give some necessary and sufficient coefficient conditions for the class of functions. Furthermore, we determine coefficient estimates, distortion bounds, extreme points, and radii of starlikeness and convexity for the defined class. Full article

Data on work accidents reflect the incidence of harm to workers’ health and occupational diseases, supported by studies that indicate the influence of length of service on service, age, and dominant skills as contributing factors to occupational accidents. This study aimed to assess whether the working environment conditions were favorable to workers and to determine whether gender, age, and length of service influenced the occurrence of work-related accidents. The goal was to identify and mitigate risk factors to improve worker health. Descriptive statistics techniques, including Pearson correlation, Analysis of Variance, the Tukey’s test, and Cluster Analysis were applied. Additionally, a categorical variable analysis (survey) was conducted to assess the work environment, alongside postural analysis using the OWAS (Ovako Working Posture Analyzing System) method. The results revealed noise levels exceeding recommended limits in almost all investigated sectors, as well as inadequate illuminance and temperature conditions on the production line. The clustering analysis identified three distinct groups. Group 1: Individuals aged 18 to 27 with little experience in the activity, of whom 42% reported pain or discomfort. Group 2: Older operators with 62% experiencing pain or discomfort. Group 3: Young male workers with experience in the role, a higher incident of work accidents, and alcohol consumption up to three times a week, of whom 50% reported pain or discomfort. Statistical inference allowed the identification of process deficiencies and a detailed analysis of work-related pain through self-perceived diagnosis, enabling corrective actions to similar processes and contributing to existing research. Full article

Area-wide coordinated urban regeneration is a strategic approach to upgrading urban functions, enhancing the allocation efficiency of land resources, and enhancing the overall urban environment from a project portfolio perspective. However, implementing area-wide coordinated urban regeneration faces significant challenges, including project delays, terminations, and difficulties in achieving investment returns. These challenges are particularly acute in smaller Chinese cities. While most previous research has paid attention to large Chinese cities, they usually neglect the risks associated with urban regeneration from an area-wide project portfolio perspective. To address this gap, this research develops a comprehensive list of risk indicators for area-side coordinated urban regeneration based on project portfolio management theory. Stakeholder opinions on the likelihood and impact of these risk indicators were collected by a questionnaire survey. A risk evaluation method, integrating the C-OWA operator and grey cluster analysis, was proposed to assess these risks. Risk management and control strategies were then proposed based on different risk levels. A case study of the coordinated urban regeneration of Yancheng’s Chaoyang area was conducted to evaluate comprehensive risk levels and provide tailored recommendations for risk control. This study offers practical guidance for urban planners and policymakers to improve decision-making in small cities and contributes new insights into risk management in the field of urban development. Full article

Forest operations often expose workers to physical risks, including posture-related disorders such as low back pain. The Ovako Working Posture Assessment System (OWAS) is widely used to assess postures in forest operations, but it requires expertise and significant resources. In this study, the use of image embedding and transfer learning was explored to automate OWAS classification. Over 5000 images from motor–manual cross-cutting operations were analyzed using two models: Google’s Inception V3 and SqueezeNet, both of which were integrated with neural networks via the Orange Visual Programming platform. The image vectors were fed into a locally run neural network (a multilayer perceptron with backpropagation) that was optimized for architecture and hyperparameters. The models were trained and tested using 20-fold cross-validation on the Posture and Action datasets, achieving accuracies of 84% and 89%, respectively, with Inception V3 outperforming SqueezeNet on both datasets. Predictions on unseen images yielded lower accuracies (50%–60%), highlighting the challenge of domain differences. These results demonstrate the potential of embedding-based transfer learning to automate postural classification with high accuracy, thereby reducing the need for expertise and resources. However, further research is needed to improve performance on unseen data and to explore alternative classifiers and embedding methods for better representation. Full article

Ordered functional weighted averaging (OFWA) operators are a generalization of the well-known ordered weighted averaging (OWA) operators in which functions, instead of single values, are considered as weights. This fact offers an extra level of flexibility; for example, in multi-criteria decision-making, it can be used to aggregate available information and provide recommendations. This paper furthers the analysis of these general operators, studying how they can be combined to obtain conservative and aggressive perspectives from experts and studying the algebraic structure of the whole set of these operators. Full article