In the pursuit of advancing health and rehabilitation, the quintessence of human motion recognition technology has been underscored through its quantitative contributions to physical performance assessment. This research delineates the inception of a novel fuzzy comprehensive evaluation-based recognition method that stands at the forefront of such innovative endeavours. By synergistically fusing multi-sensor data and advanced classification algorithms, the proposed system offers a granular quantitative analysis with implications for health and fitness monitoring, particularly rehabilitation processes. Our methodological approach, grounded in the modal separation technique and Empirical Mode Decomposition (EMD), effectively distills the motion acceleration component from raw accelerometer data, facilitating the extraction of intricate motion patterns. Quantitative analysis revealed that our integrated framework significantly amplifies the accuracy of motion recognition, achieving an overall recognition rate of 90.03 %, markedly surpassing conventional methods, such as Support Vector Machines (SVM), Decision Trees (DT), and K-Nearest Neighbors (KNN), which hovered around 80 %. Moreover, the system demonstrated an unprecedented accuracy of 97 % in discerning minor left-right swaying motions, showcasing its robustness in evaluating subtle movement nuances-a paramount feature for rehabilitation and patient monitoring. This marked precision in motion recognition heralds a new paradigm in health assessment, enabling objective and scalable analysis pertinent to individualized therapeutic interventions. The experimental evaluation accentuates the system\'s adeptness at navigating the dichotomy between complex, intense motions and finer, subtler movements with a high fidelity rate. It substantiates the method\'s utility in delivering sophisticated, data-driven insights for rehabilitation trajectory monitoring.

The environmental changes from climatic, terrestrial and anthropogenic drivers can significantly influence the groundwater quality that may pose a threat to human health. However, the driving mechanism of groundwater quality and potential health risk still remains to be studied. In this paper, 165 groundwater samples were analyzed to evaluate the groundwater quality, driving mechanism, and probabilistic health risk in the central Yinchuan Plain by applying fuzzy comprehensive evaluation method (FCEM), redundance analysis (RDA) and Monte Carlo simulation. The results showed that hydrochemical evolution of groundwater were strongly influenced by water-rock interaction, evaporation and human activities. While 55.2% of groundwater samples reached the drinking water quality standard (Class I, II and III), 44.8% of samples exceeded the standard limits of Class III water quality (Class IV and V), indicating a high pollution level of groundwater. Mn, TDS, NH4+, NO3-, Fe, F-, NO2-, As were among major indicators that influence the groundwater quality due to the natural and anthropogenic processes. The RDA analysis revealed that climatic factors (PE: 10.9%, PRE: 1.1%), GE chemical properties (ORP: 20.7%, DO: 2.4%), hydrogeological factors (BD: 16.5%, K: 4.1%), and terrestrial factors (elevation: 1.2%; distanced: 5.6%, distancerl: 1.5%, NDVI: 1.2%) were identified as major driving factors influencing the groundwater quality in the study area. The HHRA suggested that TCR values of arsenic in infants, children and teens greatly exceeded the acceptable risk threshold of 1E-4, indicating a high cancer risk with a basic trend: infants > children > teens, while TCR values of adults were within the acceptable risk level. THI values of four age groups in the RME scenario were nearly ten times higher than those in the CTE scenario, displaying a great health effect on all age groups (HQ > 1). The present study provides novel insights into the driving mechanism of groundwater quality and potential health hazard in arid and semi-arid regions.

BACKGROUND: In the era of burgeoning digital technology, healthcare is a challenging transformative change towards virtual and digital platforms. Internet-based healthcare services are emerging as a popular trend within the medical area. User experience (UX) is paramount for the healthcare service, as it significantly influences experience satisfaction and fosters user viscosity. Gaining a profound understanding of users\' demands and crafting services that align with their expectations is essential.
METHODS: Consequently, exploring an effective design approach for the digital healthcare service that prioritizes UX along with utilizing a comprehensive evaluation methodology to handle UX data, is of profound importance. This study introduces a design methodology for Internet-based healthcare products grounded in the UX and mental (UX-M) model. Aiming to refine the Internet-based healthcare product design by integrating insights from the experience data, it employs the Delphi-ANP and the fuzzy comprehensive evaluation to determine evaluation indexes and conduct experiential assessments.
RESULTS: The UX evaluation results of existing schemes are compared with the proposed design scheme of the intelligent guidance and internet hospital. The findings indicate that the UX evaluation of Internet-based medical services with the proposed method outperforms the existing schemes.
CONCLUSIONS: On the one hand, UX research of Internet-based healthcare products can significantly enhance service satisfaction for patients utilizing online medical treatments. On the other hand, the analysis of experience-based evaluation empowers designers to refine and improve UX design of Internet-based medical services. Such research endeavors are critical for enhancing the overall quality of service offerings and elevating user satisfaction in the digital healthcare landscape.

The sports service supply chain faces various potential risks, such as market fluctuations, logistics issues, and partner uncertainties. To address these risks effectively, this study employs a combination of fuzzy comprehensive evaluation (FCE) methods and intelligent neural networks to create an innovative risk management framework. By considering diverse uncertainties and leveraging the analytical power of intelligent neural networks, this study aims to optimize the operation of the sports service supply chain and explore the risk factors within the public service supply chain of stadiums. This framework provides policy references to promote the healthy and sustainable development of the sports service industry. The main empirical findings, based on a representative survey of experts in China, are as follows: (1) When determining the weights of risk indicators for managing the public service supply chain of stadiums using the FCE method, the customer risk indicator is of paramount importance, with a weight of 0.286, accounting for 95.2 % of the total significance; and (2) In evaluating various risk indicators of the public service supply chain of stadiums through the neural network method, the customer risk indicator scores the highest, achieving a score of 76.02. Notably, the customer complaint risk indicator scores slightly higher at 79.33. Based on these findings, the study recommends focusing on enhancing customer experience within risk management strategies. Additionally, it suggests strengthening the supervision of platforms and third-party activities to ensure the stability and efficient operation of the stadium service supply chain. This study aims to provide theoretical support and reference indicators for evaluating the public service capabilities of stadiums.

In maternity rooms, breastfeeding chairs are crucial pieces of equipment that greatly influence the breastfeeding experience. However, an abundance of data indicates that there are still issues with breastfeeding chairs, including inadequate support for breastfeeding, poor body type adaptability, and failure to adequately analyse the requirements of mothers and baby users. This study used an innovative research approach to breastfeeding chair design from the standpoint of user requirements to improve breastfeeding willingness and user experience. We propose a user requirement assessment design approach that integrates the Kano model, analytic hierarchy process (AHP), and quality function development (QFD) based on the requirements of mother and baby groups. This approach was intended to build a user experience evaluation model for mother- and baby-friendly products. Following this approach to breastfeeding chair design, a fuzzy comprehensive evaluation (FCE) was used to assess the chair. Compared to the original breastfeeding chairs, the designs of breastfeeding chairs that met important requirements for mothers and infants, such as safety, hygiene, and breastfeeding support, resulted in an approximately 23 % increase in user satisfaction. This effectively improved the user experience of both mothers and infants. This approach is centred on the basic requirements of mothers and babies. It evaluates the essential requirements that impact the breastfeeding experiences of mothers and babies and provides multifaceted data regarding the attributes of the different requirements of mothers and babies. This results in theoretical research references for ensuing user-driven design products that cater to the requirements of mothers and their infants and play a pivotal role in formulating design guidelines for mother- and baby-friendly products.

Underwater acoustic sensor networks have a wide range of applications in both civil and military fields, but the complex and changing underwater environment makes them vulnerable to multiple security threats. Trust mechanisms are effective ways to enhance network security and reliability. In order to improve the accuracy of trust evaluation and the detection rate of abnormal nodes, this paper proposes an adaptive trust evaluation model based on fuzzy logic. This model adopts a variable weight fuzzy comprehensive evaluation algorithm to dynamically adjust the weights of three direct trust indicators to ensure the accuracy of direct trust evaluation. Then, it uses fuzzy closeness to eliminate unreliable recommendation trust and adjusts the weight of recommendation trust through deviation to improve the accuracy of indirect trust. The simulation results show that the model can effectively improve the accuracy of trust evaluation and the detection rate of abnormal nodes. Especially when the link quality is unstable, the success rate of detecting abnormal nodes in this model is improved by more than 10% compared with the existing trust model.

Objective: To establish an evaluation model for occupational hazard prevention and control levels in coal mines, to explore the combination of Analytic Hierarchy Process (AHP) and fuzzy comprehensive evaluation, to evaluate the overall situation of occupational hazard prevention and control in coal mines. Methods: In November 2021, Collect information of occupational hazards and their prevention and control status in 30 coal mines. AHP model was first constructed for the elements of occupational hazard prevention and control in coal mines. Then, the AHP and fuzzy comprehensive evaluation method was applied to comprehensively evaluate and grade the occupational hazard prevention and control in coal mines, using the principles of maximum membership and weighted average. Results: The hierarchical fuzzy comprehensive evaluation results for typical coal mine were basically consistent with its occupational hazard prevention and control situation. The dust prevention and control situation was good, the noise prevention and control situation is average, the occupational health monitoring situation was good, the occupational health management situation was average, and the overall level of occupational hazard prevention and control was good. Conclusion: The hierarchical fuzzy comprehensive evaluation model for occupational hazard prevention and control levels, combined with the principles of maximum membership and weighted average, can objectively evaluate and reflect the overall situation of occupational hazard prevention and control in coal mines.
目的： 建立煤矿职业危害防治等级评估模型，探讨层次分析法和模糊综合评估结合应用，评估煤矿职业危害防治整体状况。 方法： 于2021年11月，收集30家煤矿的职业危害及其防治状况信息，构建煤矿职业危害防治工作要素的层次分析模型，应用层次分析和模糊综合评判法对煤矿的职业危害防治进行综合评估分级，评估原则为最大隶属度原则和加权平均原则。 结果： 针对典型煤矿单位的层次模糊综合评判结果与单位的职业危害防治情况基本一致，粉尘防治情况良好，噪声防控情况一般，职业健康监护情况良好，职业卫生管理情况一般，职业危害防治等级整体良好。 结论： 建立的职业危害防治等级的层次模糊综合评判模型，结合最大隶属度原则和加权平均原则，能客观地评估和反映煤矿职业危害防治的整体情况。.

The process of mining activities often causes the formation of acid mine drainage (AMD). Through rock fractures and underground rivers, AMD can easily enter the groundwater environment near mines and cause serious pollution to water quality. In order to effectively evaluate the quality of polluted mine water and to understand its threat to the ecosystem around the mine. In this study, four AMD pollution distribution areas, Guiyang City, Bijie City, Qianxinan Prefecture, and Qiandongnan Prefecture in Guizhou Province, were used as the study area. A composite model for mine water quality evaluation was constructed using factor analysis (FA), analytic hierarchy process (AHP), and fuzzy comprehensive evaluation (FCE). Furthermore, by introducing the weighted average method and the level characteristic value (J), the water quality type and the water body environmental quality were evaluated comprehensively, respectively. Compared with the traditional evaluation model, the AHP-FA-FCE model has obvious advantages in the selection of evaluation indicators, the determination of indicator weights, and the comprehensive evaluation of water quality types, and the evaluation results obtained are more reasonable and accurate. Three common factors mainly controlled by mineral oxidation factor, human activity factor, and mineral dissolution factor were extracted by dimension reduction of the original hydrochemical data by FA. The water quality of the mine water samples was evaluated using SO4 2- , Fe, Al, Mn, Na, and F- as evaluation indicators, and the results showed that the mine water samples in the study area as a whole were dominated by class V water, which accounted for 77.78% of the total. Based on the statistical analysis of the original data, it was found that influenced by the water-rock interactions in the study area and the AMD pollution components, the hydrochemical type of the mine water is mainly SO4 2- -Ca-Mg type. The water body environmental quality of mine water in four areas, Guiyang City, Qianxinan Prefecture, Bijie City, and Qiandongnan Prefecture, is from excellent to poor. The average level characteristic value of all the areas is more than 3, and the overall environmental quality of the water body is poor. The strong water-rock interaction and mining activities in the study area may be the main cause of AMD pollution. The results of this study may provide some theoretical reference for the water quality evaluation of AMD-polluted areas. PRACTITIONER POINTS: A composite model for mine water quality evaluation was constructed. A factor analysis-based evaluation indicator selection method is proposed. This study improved the weighting process of the traditional fuzzy comprehensive evaluation. A water quality discriminant based on the weighted average method is proposed. The water environmental quality of various types of mine water was evaluated.

Child health is an important public health issue in China and the Chinese government always attached great importance to child health care. With the implementation of a series of medical and health reforms in China in recent decades, the status of child health improved year by year. Objectives This study aims to comprehensively evaluate if the measures implemented in the medical and health reforms effectively promoted the development of Chinese child health care in recent years and provide theoretical support for future decision-making on the policies of child health care in China. Methods A total of six indicators were selected from the China Health Statistics Yearbook. Based on the multi-criteria decision analysis (MCDA) algorithm, three different evaluation methods were applied in the study, which are the weighted technique for order preference by similarity to an ideal solution (TOPSIS) method, the weighted rank-sum ratio (RSR) method, and the fuzzy comprehensive evaluation (FCE). Each indicator\'s weight was calculated by the entropy weight methods objectively. The sensitivity analysis was conducted to validate the stability and accuracy of the rank results. Results The results indicated that the rank values of each year\'s child health care calculated by the different evaluation methods were not exactly the same, but the overall trend is consistent which is that child health care in China improved year by year from 2000 to 2020. The top 5 were ranked from 2016-2020 and the bottom 5 were ranked from 2000-2004. Conclusions The results indicated that the policies and measures implemented in the medical and health reforms, as well as improved sanitation conditions, availability of healthy food and water, etc., have jointly promoted the development of child health care in China in the past 20 years, providing a scientific theoretical basis for future policy-making to promote child health care.

Sweetpotato variety breeding is always a long process. Screening of hybrid offspring is dominated by empirical judgment in this process. Data analysis and decision fatigue have been troubling breeders. In recent years, the low-efficiency screening mode has been unable to meet the requirements of sweetpotato germplasm innovation. Therefore, it is necessary to construct a high-efficiency method that can screen germplasms for different usages, for mining elite genotypes, and to create dedicated sweetpotato varieties. In this article, the multicriteria decision-making (MCDM) model was constructed based on six agronomic traits, including fresh root yield, vine length, vine diameter, branch number, root number and the spatial distribution of storage roots, and five quality traits, including dry matter content, marketable root yield, uniformity of roots, starch content and the edible quality score. Among these, the edible quality score was calculated by using fuzzy comprehensive evaluation to integrate the sensory scores of color, odor, sweetness, stickiness and fibrous taste. The MCDM model was compared with the traditional screening method via an evaluation in 25 sweetpotato materials. The interference of subjective factors on the evaluation results was significantly reduced. The MCDM model is more overall, more accurate and faster than the traditional screening method in the selection of elite sweetpotato materials. It could be programmed to serve the breeders in combination with the traditional screening method.