Search Results (80)

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

This paper introduces a fatigue detection model specifically designed for immersive virtual reality (VR) environments, aimed at facilitating upper limb rehabilitation for individuals with spinal cord injuries (SCIs). The model’s primary application centers on the Box-and-Block Test, providing healthcare professionals with a reliable tool to monitor patient progress and adapt rehabilitation routines. At its core, the model employs data fusion techniques via ordered weighted averaging (OWA) operators to aggregate multiple metrics captured by the VR rehabilitation system. Additionally, fuzzy logic is employed to personalize fatigue assessments. Therapists are provided with a detailed classification of fatigue levels alongside a video-based visual representation that highlights critical moments of fatigue during the exercises. The experimental methodology involved testing the fatigue detection model with both healthy participants and patients, using immersive VR-based rehabilitation scenarios and validating its accuracy through self-reported fatigue levels and therapist observations. Furthermore, the model’s scalable design promotes its integration into remote rehabilitation systems, highlighting its adaptability to diverse clinical scenarios and its potential to enhance accessibility to rehabilitation services. Full article

Some new techniques of aggregating hesitant fuzzy numbers (HFNs) by using monotonic dependent OWA (MDOWA) operators are investigated. By utilizing the score value of HFN, the concepts of hesitant fuzzy configuration vector and hesitant fuzzy hybrid configuration vector are proposed. Then, some methods of calculating variable weights related to the MDOWA operators under hesitant fuzzy environments are presented. Further, some operators, including hesitant fuzzy monotonic dependent OWA (HFMDOWA) operators and hesitant fuzzy hybrid monotonic dependent OWA (HFHMDOWA) operators, are developed, such as balanced HFMDOWA operators, rewarded HFMDOWA operators, balanced HFHMDOWA operators, rewarded HFHMDOWA operators, and so on. These developed operators are applied to multiple criteria group decision making (MCGDM), and a novel MCGDM algorithm is presented. By using the presented operators and algorithm, we can obtain symmetric decision-making results. Finally, an application example is provided to demonstrate the effectiveness of the developed MCGDM techniques. Full article

The evolution of beyond fifth generation (B5G) wireless networks poses significant technical and economic challenges across urban, suburban, and rural areas, demanding increased capacity for users whose positions continually change. This study investigated the dynamic positioning of an unmanned aerial vehicle (UAV), acting as a mobile base station (MoBS) to enhance network efficiency and meet ground terminals (GTs) expectations for data rates, particularly in emergency scenarios or temporary events. While UAVs show great promise, existing research often assumes certainty in network architecture, overlooking the complexities of unpredictable user movements. We introduce a decision-making framework utilizing the ordered weighted averaging (OWA) operator to address uncertainties in GT locations, enabling the optimization of UAV trajectories to maximize the overall network data rate. An optimization problem is formulated by modeling GT dynamics through a Markov process and discretizing UAV movements while accounting for communication thresholds and movement constraints. Extensive simulations reveal that our approach significantly improves expected data rates by up to 48% compared to traditional fixed base stations (BSs) and predefined UAV movement patterns. This research underscores the potential of UAV-assisted networks to bolster communication reliability while effectively managing dynamic user movements to maintain optimal quality of service (QoS). Full article

The main objective of this article is to present the formulation of a Capital Asset Pricing Model ordered weighted average CAPM_OWAand its extensions, called CAPM-induced OWA (CAPM_IOWA), CAPM Bonferroni OWA (CAPM_Bon-OWA), and CAPM Bonferroni-induced OWA CAPM_Bon-IOWA. A step-by-step process for applying this new proposal in a real case of formulating investment portfolios is generated. These methods show several scenarios, considering the attitude, preferences, and relationship of each argument, when underestimation or overestimation of the information by the decision maker may influence the decision-making process regarding portfolio investments. Finally, the complexity of the method and the incorporation of soft information into the modeling process lead to generating a greater number of scenarios and reflect the attitudes and preferences of decision makers. Full article

When information is incomplete or uncertain, Fermatean hesitant fuzzy sets (FHFSs) can provide more information to help decision-makers deal with more complex problems. Typically, determining attribute weights assumes that each attribute has a fixed influence. Introducing probability information can enable one to consider the stochastic nature of evaluation data and better quantify the importance of the attributes. To aggregate data by considering the location and importance degrees of each attribute, this paper develops a Fermatean hesitant fuzzy multi-attribute decision-making (MADM) method with probabilistic information and an ordered weighted averaging (OWA) method. The OWA method combines the concepts of weights and sorting to sort and weigh average property values based on those weights. Therefore, this novel approach assigns weights based on the decision-maker’s preferences and introduces probabilities to assess attribute importance under specific circumstances, thereby broadening the scope of information expression. Then, this paper presents four probabilistic aggregation operators under the Fermatean hesitant fuzzy environment, including the Fermatean hesitant fuzzy probabilistic ordered weighted averaging/geometric (FHFPOWA/FHFPOWG) operators and the generalized Fermatean hesitant fuzzy probabilistic ordered weighted averaging/geometric (GFHFPOWA/GFHFPOWG) operators. These new operators are designed to quantify the importance of attributes and characterize the attitudes of decision-makers using a probabilistic and weighted vector. Then, a MADM method based on these proposed operators is developed. Finally, an illustrative example of selecting the best new retail enterprise demonstrates the effectiveness and practicality of the method. Full article