Lingyu Wang

High-altitude environments characterized by low air pressure, hypoxia, and strong solar radiation have a significant impact on human thermal comfort; however, existing thermal comfort theories and evaluation models are primarily developed under low-altitude climatic conditions, and their applicability in plateau regions remains limited. With the acceleration of urbanization and the increase in residential, tourism, and occupational activities in high-altitude areas, systematically reviewing the research progress on thermal comfort in such environments is of great practical significance. This study combines systematic literature retrieval and bibliometric analysis, based on the Web of Science Core Collection and China National Knowledge Infrastructure (CNKI) databases, to analyze relevant studies published since 2001. Using CiteSpace, research hotspots, collaboration networks, and evolutionary trends are visualized. The results indicate that current research hotspots mainly focus on physiological responses and thermal adaptation mechanisms under low-pressure and hypoxic conditions, thermal comfort regulation strategies for high-altitude buildings and environments, and the applicability and modification of conventional thermal comfort models. Emerging trends include multi-environmental factor coupling analysis, adaptive model development, region-specific building design approaches, and health-oriented comprehensive evaluation frameworks. The findings provide valuable references for building thermal environment design, regional revision of thermal comfort evaluation standards, and policy-making in high-altitude regions.

As technologies become increasingly diverse and complex, Intelligent Massage Systems (IMS) are evolving from traditional mechanically executed modes toward personalized and predictive health interventions. However, the field still lacks a unified grading standard for intelligence, making it difficult to quantitatively assess a system’s overall intelligence level. To address this gap, this paper proposes a task-driven six-level (L0–L5) classification framework and constructs a Massage-Driven Task (MDT) model that decomposes the massage process into six subtasks (S1–S6). Building on this, we design a three-dimensional evaluation scheme comprising a Functional Delegation Structure (FDS), an Anomaly Perception Mechanism (APM), and a Human–Machine Interaction Boundary (HMIB), and we select eight key performance indicators to quantify IMS intelligence across the perception–decision–actuation–feedback closed loop. We then determine indicator weights via the Delphi method and the Analytic Hierarchy Process (AHP), and obtain dimension-level scores and a composite intelligence score S0 using normalization and weighted aggregation. Threshold intervals for L0–L5 are defined through equal-interval partitioning combined with expert calibration, and sensitivity is verified on representative samples using ±10% data perturbations. Results show that, within typical error ranges, the proposed grading framework yields stable classification decisions and exhibits strong robustness. The framework not only provides the first reusable quantitative basis for grading IMS intelligence but also supports product design optimization, regulatory certification, and user selection.

In the face of increasing environmental challenges, carbon emissions from industrial parks have become a global focal point, particularly as electricity consumption serves as a major source of carbon emissions that requires effective management. Despite proactive efforts by governments and industry stakeholders to transition industrial parks toward cleaner production methods, traditional energy management systems exhibit significant limitations in data collection, real-time monitoring, and intelligent analysis, making it difficult to meet the urgent demands for carbon reduction. To address these challenges, this study proposes a carbon data management approach for industrial parks based on digital twin technology and develops an intelligent system that integrates monitoring, environmental surveillance, energy management, and carbon emission monitoring. The system supports efficient energy-saving and carbon-reducing decision making by real-time collection of energy consumption data. By incorporating Building Information Modeling (BIM) and Internet of Things (IoT) technologies, the system facilitates the integration and visualization of multi-source data, significantly enhancing the transparency of carbon data. The results of the carbon reduction validation system demonstrate that the application of this platform and its associated facilities can significantly reduce carbon emissions in the park, providing robust support for the transition of industrial parks toward low-carbon and sustainable development.

As a pivotal node in both urban and rural power grids, the box-type substation not only serves the functions of power conversion and distribution but also need to provide structural support and environmental adaptability. However, deficiencies in strength, stiffness, or vibration characteristics may lead to vibration and noise issues, and extreme environmental changes can pose risks of structural damage. This study aims to verify and optimize the seismic resistance and environmental adaptability of box-type substations through finite element simulation methods. Using SOLIDWORKS, a three-dimensional model of the box-type substation was constructed, and static and dynamic analyses were conducted using Ansys Workbench to comprehensively evaluate the dynamic response of the box-type substation under wind, snow loads, and seismic action. Through iterative simulations and a comparison of multiple design solutions, the structural optimization of the substation was achieved. The optimized structure balances strength and stiffness, significantly reducing the weight of the substation body, with the wall thickness reduced by 60%. Additionally, the phenomenon of stress concentration on the side walls was eliminated, ensuring that the equivalent stress is below the material yield strength. This research provides methods and empirical results for enhancing the performance and reliability of box-type substations under seismic conditions, confirming the feasibility of a lightweight design, while ensuring structural safety.

With the continued application of data visualization technology in sustainable development, the construction of carbon emission monitoring platforms is becoming increasingly popular in industrial parks. However, there are many kinds of such interfaces, the usability of which remains unclear. Therefore, in order to explore the usability of current carbon emission visualization interfaces in parks and put forward humanized optimization strategies for their subsequent design, this study used eye-tracking technology to analyze the data readability of six types of layouts from three aspects of visual perception features: integrity, understandability, and selectivity. Quantitative data from eye movement experiments and visual perception characteristics were evaluated using a Likert scale in an analysis of different layouts, and the correlation data between three visual perception characteristics and the readability of different layout data were obtained using an SPSS tool. The results show that, compared with a layout containing 3D graphics, the pure data type of interface has a shorter task completion time and higher readability; however, it provides fewer choices for users and is less interesting. In addition, there is a significant negative correlation between integrity and task completion time; the more complete the interface layout, the shorter the task completion time. In summary, a certain correlation was found between visual perception characteristics and the readability of interface layout using this method. At the same time, the advantages and disadvantages of different interface layouts were also analyzed, and more humanized optimization directions and strategies were devised. This is vital for aiding subsequent research on the influence of specific layout elements to optimize visualization interfaces that display carbon emission data.

Since the global zero carbon goal was proposed, most higher education institutions around the world are still in the process of transitioning towards carbon neutrality. However, there is still a research gap in the systematic design strategy for a zero carbon campus. This study adopts a qualitative literature analysis approach to establish a theoretical framework for a zero carbon campus design. The framework hierarchically outlines the One Top-Down vision of carbon neutrality, two complementary paths of carbon emission reduction and carbon sink, specific implementation strategies based on the coupling of the social, technological, and ecological dimensions, as well as the establishment of a carbon-neutral smart services platform. Subsequently, a case study was conducted at the Fahua campus of Shanghai Jiao Tong University, guided by this theoretical framework. This study not only completed the modeling and visualization of the carbon-neutral systematic design of the campus but also attempted to conceive of people-centered services under the zero carbon commitment and emphasized the critical role of university campus culture and historical connotations in the carbon-upgrading process. The results showed that the establishment of this theoretical framework can inspire innovative localized carbon-neutral solutions for campus, empower the replicability of advanced zero carbon campuses, and more effectively promote the carbon neutrality development of communities and cities.

Global responses to climate change have accomplished certain reductions in carbon emissions. However, current efforts are insufficient for reaching the worldwide objective of carbon neutralization. Industrial parks that integrate industrial and economic resources are priority entities concerning the achievement of national carbon mitigation. The implementation of carbon neutralization at an industrial park level is unclear. This paper used a bibliometric approach to analyze articles related to carbon emissions reduction in industrial parks. From 2001 to 2022, 114 publications were collected from the WoS database. Descriptive statistical analysis, network analysis, keyword co-occurrence network analysis, keyword clustering, co-citation analysis, and burst detection were employed to summarize the research hotspots and evolution trends in this field. The results showed that the research hotspots were energy management, industrial symbiosis, economics and development, and carbon emission assessments in industrial parks. Emerging trends are the management of integrated energy systems, circular economy, renewable energy, economic analysis, and the validation of various models. Based on these analyses, four challenges and prospects were proposed for the construction of zero-carbon industrial parks. Finally, this paper presents a systematic guidance framework for carbon neutralization in industrial parks, which provides important references for future theoretical and practical research on industrial parks.

Background: Vibratory massage is now widely used to alleviate muscle fatigue. The effects of different vibration massage intensities on left and right upper trapezius (UT) fatigue have not been examined. Therefore, the present study first examined whether a vibration massage intervention had an effect on UT muscles and second compared the effects of 2 different levels (36 Hz and 46 Hz) of vibratory massage on the right and left oblique muscles under 3 different fatigue conditions. Methods: A total of 23 participants (12 female, 11 male; age: 26.5 ± 3.9 year, height: 170.5 ± 1.6 cm, mass: 57.5 ± 1.5 kg, BMI: 24.3 ± 1.6 kg/m²) were randomly divided into intervention and control groups. The 2 groups of subjects completed isometric contraction fatigue tasks of 30 s of fatigue, 60 s of fatigue and 90 s of fatigue in turn. The specific task of isotonic contraction was performed by subjects holding a 1 kg dumbbell in each hand and performing a straight arm weighted lateral supination exercise. After each exercise, the intervention group was randomized to apply a massage device with a vibration intensity of 36 Hz or 46 Hz on the left and right UT muscles for 5 min each. The control group did not receive any treatment. Both groups then repeated the same fatigue task as before. Then, the effects of different vibration massage interventions on UT muscles were derived by analyzing the changes in maximal voluntary contraction percentage (MVC%) of surface electromyography (sEMG) signals before and after the intervention, and the most effective vibration massage program for relieving left and right UT fatigue was summarized. Then, four classification algorithms were used to label and classify the collected sEMG data, and finally a UT muscle fatigue identification and vibration massage model was constructed. Results: After using the vibration massage level 1 (36 Hz) intervention, the MVC% of the right UT muscle showed significant reductions in the 30 s fatigue task, the 60 s fatigue task and the 90 s fatigue task (R1: p = 0.022, R2: p = 0.005, R3: p = 0.049). After using the vibration massage level 3 (46 Hz) intervention, the MVC% of the right UT muscle showed a significant decrease in both the 60 s fatigue task and the 90 s fatigue task (R2: p = 0.033, R3: p = 0.028). Significant decreases in MVC% for the left UT muscle were found only in the 90 s fatigue task (L3: p = 0.040). Then, by comparing the different performances of four commonly used classification algorithms, it was found that the bagging (accuracy = 0.860) algorithm had higher accuracy. Therefore, the bagging algorithm was used for the UT fatigue identification and vibration massage models. Conclusions: This was the first study to show the impacts of different levels of vibration massage on fatigue alleviation in the left and right UT muscles. Furthermore, the bilateral UT fatigue identification and vibration massage model developed in this study can help people to choose the most appropriate massage protocol for quick relief and relaxation of the UT muscles under three different fatigue tasks.