Search Results (13,328)

Energy security has emerged as a critical concern amid intensifying climate risks and surging energy demand driven by economic growth. This study examines the impact of climate risk on energy security by constructing a panel dataset covering 30 Chinese provinces from 2006 to 2022. Using the instrumental variable generalized method of moments (IV-GMM) model, we estimate the marginal impact of climate risk on energy security and further investigate its asymmetric, direct, and indirect relationships via panel quantile regression and mediation analysis. Our key findings are as follows: (1) Climate risk exerts a significant negative impact on energy security, indicating an inverse relationship. (2) The effect of climate risk is asymmetric, with a stronger adverse impact in regions with lower levels of energy security. (3) Climate risk undermines energy security by reducing energy accessibility, affordability, sustainability, and technological efficiency. (4) Energy transition and energy efficiency serve as critical mediators in the relationship between climate risk and energy security, offering insights into potential mitigation pathways. Unlike previous studies that primarily examine energy security in isolation or focus on single dimensions, this research integrates a multidimensional indicator system and advanced econometric techniques to uncover both direct and mediated pathways, thereby filling a key gap in understanding the climate–energy nexus at the provincial level in China. Based on these findings, we propose targeted policy recommendations to enhance energy security by improving climate resilience, accelerating the deployment of renewable energy, and optimizing energy infrastructure investments. Full article

The aim of the research presented in the article is to develop a method for modifying compound curves, i.e. geometric systems composed of two (or more) circular arcs with different radii, directed in the same direction and directly connected to each other. These curves are used when connecting two directions of the railway route where one circular arc is impossible due to permanent terrain obstacles. To solve the problem, an analytical method of designing track geometric systems was used, in which individual elements of these systems are described using mathematical equations. The modification itself involves introducing appropriate transition curves between the connecting arcs. Three possibilities for such a connection were presented, resulting from the method of considering conditions related to horizontal curvature of the track axis. A comparative analysis of the obtained solutions was conducted using the developed geometric test system. The analysis was based on the curvature values determined for the considered transition curves, after assuming varying lengths of these curves. For the recommended solution to the problem, it was necessary to verify the practical feasibility of horizontal ordinate values, which could not be too small relative to the implementation error. As stated, to limit the effects of this error, the transition curve lengths should be adjusted to specific geometric situations and excessively short curves should be avoided. As a result of the conducted research, the transition curve determined with strict curvature conditions was determined to be the most advantageous. It maintains curvature continuity along its entire length, there are no abrupt changes in curvature at the edges, and the changes in curvature along the length are much smoother than in the other curves considered. Therefore, this curve should be recommended for practical use. Full article

This study explores graph-based methods to model and analyze the semantic interplay between editorial taxonomies and user-generated folksonomies on the PornHub platform, using a dataset of over 97,000 videos (2015–2024). We construct and examine a graph of user-assigned tags and platform-defined categories, applying the Leiden community detection algorithm to uncover latent semantic groupings. To enrich the graph structure, we embed textual metadata using state-of-the-art language models (Qwen3-Embedding-4B and all-MiniLM-L6-v2), enabling the integration of natural language processing within graph-based learning. Our analysis reveals that folksonomies partially align with taxonomies through synonymous structures but also diverge by capturing nuanced attributes such as body features and aesthetic styles. These asymmetries highlight how folksonomies introduce higher-resolution semantic layers absent from fixed-category systems. By fusing graph mining, NLP-driven embeddings, and network-based clustering, this work contributes a hybrid methodology for semantic knowledge extraction in large-scale, user-generated content. It offers implications for graph-based recommendation, content moderation, and metadata enrichment—demonstrating the utility of graph-centric AI techniques in real-world multimedia data settings. Full article

Background/Objectives: The rise in urinary tract infections caused by multidrug-resistant (MDR) bacteria presents a serious public health challenge across the Balkans, a region already burdened by aging populations, healthcare resource limitations, and fragmented antimicrobial surveillance systems. Methods: This review explores the epidemiology, risk factors, and consequences of MDR UTIs, particularly in the context of the COVID-19 pandemic, which significantly accelerated antimicrobial resistance (AMR) due to widespread, inappropriate antibiotic use. Results: The paper discusses region-specific data on resistance trends, highlights the gaps in diagnostic infrastructure, and evaluates emerging clinical strategies including antimicrobial stewardship (AMS), rapid diagnostic technologies, novel antibiotics, and non-antibiotic alternatives such as bacteriophage therapy and vaccines. Conclusions: Policy recommendations are provided to strengthen surveillance, promote evidence-based treatment, and ensure equitable access to diagnostic and therapeutic tools. A multidimensional and regionally coordinated response is essential to curb the MDR UTI burden and safeguard public health across the Balkans. Full article

The deposition of large-sized cuttings (or blocky cuttings) is a critical risk factor for stuck pipe incidents during the drilling of deep and extended-reach wells. This risk is particularly pronounced in well sections with long borehole trajectories and low drilling fluid return velocities, where it poses a substantial threat to wellbore cleanliness and the safe operation of the drill string. This study utilizes a self-developed visual experimental platform to simulate the deposition evolution of large-sized cuttings (20–40 mm in diameter) in the annulus under various wellbore inclinations and drilling fluid parameters. The stable height, lateral distribution characteristics, and response patterns of the resulting cuttings bed under different conditions were quantitatively characterized. Building upon this, a theoretical contact friction model between the drill string and the cuttings bed was employed to investigate how the bed height influences hook load during tripping and rotary torque during top drive operation. A mapping relationship was established between cuttings bed structural parameters and the resulting additional loads and torques. Results reveal significant interactive effects among drilling fluid velocity, fluid density, drill pipe rotation speed, and wellbore inclination on both cuttings bed development and associated drill string loads. Strong correlations were identified among these parameters. Based on these findings, a stuck pipe early-warning indicator system is proposed using frictional load thresholds, with clearly defined safety limits for cuttings bed height. Recommendations for optimizing cuttings transport parameters through coordinated control of fluid velocity, density, and rotary speed are also provided, offering theoretical support and engineering guidance for borehole cleaning strategies and stuck pipe risk prediction in large cuttings scenarios. Full article

The article examines the application of controlling in energy and heating (E&H) companies, with particular emphasis on diagnosing the extent to which reporting and management controlling are implemented, as well as identifying barriers that limit the development of their managerial functions. The aim of the study was to determine the extent to which management controlling is applied in the managerial practice of the E&H sector and how its use differs from practices observed in other sectors of the economy. The research employed a mixed methods approach, including a literature review, a case study of controlling implementation in a selected energy company, and a quantitative analysis based on the Managerial Controlling Index (MCI). The central research question addressed the impact of legal, market, and organisational conditions on the scope of controlling in the E&H sector. The findings indicate that E&H companies record lower MCI scores than companies in other industries, regardless of their size, age, or business profile. The article concludes with a set of managerial recommendations outlining directions for the development of management controlling as a tool for supporting decision-making and enhancing integration with the overall management system. Full article

As brain–computer interface (BCI) technology continues to advance, research on human brain function has gradually transitioned from theoretical investigation to practical engineering applications. To support EEG signal acquisition in a variety of real-world scenarios, BCI electrode systems must demonstrate a balanced combination of electrical performance, wearing comfort, and portability. Dry electrodes have emerged as a promising alternative for EEG acquisition due to their ability to operate without conductive gel or complex skin preparation. This paper reviews the latest progress in dry electrode EEG systems, summarizing key achievements in hardware design with a focus on structural innovation and material development. It also examines application advances in several representative BCI domains, including emotion recognition, fatigue and drowsiness detection, motor imagery, and steady-state visual evoked potentials, while analyzing system-level performance. Finally, the paper critically assesses existing challenges and identifies critical future research priorities. Key recommendations include developing a standardized evaluation framework to bolster research reliability, enhancing generalization performance, and fostering coordinated hardware-algorithm optimization. These steps are crucial for advancing the practical implementation of these technologies across diverse scenarios. With this survey, we aim to offer a comprehensive reference and roadmap for researchers engaged in the development and implementation of next-generation dry electrode EEG-based BCI systems. Full article

Industrial processes like farming, food processing, petroleum refinery, and leather manufacturing produce a lot of high-salinity wastewater. This wastewater presents serious environmental risks, such as soil degradation, eutrophication, and water salinization, if it is released without adequate treatment. The sources and features of high-salinity wastewater are outlined in this review, along with the main methods for removing organic pollutants, such as physicochemical, biological, and combined treatment approaches. Membrane separation, coagulation–flocculation, and advanced oxidation processes are the primary physicochemical techniques. Anaerobic and aerobic technologies are the two categories into which biological treatments fall. Physicochemical–biological combinations and the fusion of several physicochemical techniques are examples of integrated technologies. In order to achieve sustainable and effective treatment and resource recovery of high-salinity wastewater, this review compares the effectiveness and drawbacks of each method and recommends that future research concentrate on the development of salt-tolerant catalysts, anti-fouling membrane materials, halophilic microbial consortia, and optimized hybrid treatment systems. Full article

This study aims to develop a performance evaluation system specifically for the design phase of high-rise building projects within the architecture, engineering, and construction industry, where performance is often only measured during construction. The research process included three stages: identification of 21 key performance indicators through a literature review and expert validation; development of standardized indicator sheets detailing calculation protocols and data collection procedures; and creation of a functional dashboard-based evaluation system using Excel. The system was validated through expert review and tested with a simulated project generated using an AI-based language model. The evaluation system proved functional, accessible, and effective in detecting performance issues across five core categories: planning, cost, time, quality, and people. The results from the simulated application highlighted strengths in quality and stakeholder collaboration but also revealed significant gaps in cost and time performance. This study addresses a gap in the existing literature by focusing on performance evaluation during the design phase of construction projects, a stage often underrepresented in performance studies. The resulting system offers a structured, practical tool adaptable to real-world projects. The validation relied on a limited number of expert participants and a simulated project. Future research should recommend broader international validation and real-world application. Full article

Self-efficacy is related to a specific domain and is a result of capabilities and beliefs of one’s own performance in a specific domain given a specific task, depending on the levels of anxiety, motivation, feeling of success, and positive and negative rewards. Computer experience, the learning of information and communication technology tasks at school, and the use of general applications in class are known to be related to computer and information literacy. This study investigates the mediation effect of student computer self-efficacy in using general applications in these relationships using a structural equation model. The data used in this study stems from nine European educational systems participating in the International Computer and Information Literacy Study in 2018. The results show that in nearly all educational systems, the self-efficacy regarding the use of general applications has significant mediation effects in the relationship between computer and information literacy and each of the three information and communication technology variables in the model. The mediation effects are strongest for general applications in class and weakest for learning of information and communication technology tasks at school. The results are discussed against the educational systems’ context with recommendations for improving student computer self-efficacy. Full article

The aim of this study was to examine teachers’ belief levels regarding education for sustainable development (ESD), to identify the factors behind these beliefs, and to reveal their suggestions for improving the quality of ESD. The study employed an explanatory sequential mixed-methods design. Data were collected from 409 teachers working at primary and secondary schools in Türkiye through the “Beliefs About Education for Sustainable Development Scale” and semi-structured interviews. The quantitative data were analyzed using an independent samples t-test, one-way Analysis of Variance (ANOVA), and Pearson product-moment correlation, and the qualitative data were analyzed through content analysis. The results indicated that the participants’ beliefs in ESD practices were high, but these beliefs were negatively affected by challenges due to SD goals, policymakers, students, and parents. In addition, the participants made recommendations for improving the quality of ESD to policymakers, the Turkish Council of Higher Education, the Ministry of National Education, and their colleagues. The findings of the study present significant implications for policymakers and educators for more effective implementation of ESD in the education system. Full article

In recommender systems research, the data sparsity problem has driven the development of hybrid recommendation algorithms integrating multimodal information and the application of graph neural networks (GNNs). However, conventional GNNs relying on homogeneous Euclidean embeddings fail to effectively model the non-Euclidean geometric manifold structures prevalent in real-world scenarios, consequently constraining the representation capacity for heterogeneous interaction patterns and compromising recommendation accuracy. As a consequence, the representation capability for heterogeneous interaction patterns is restricted, thereby affecting the overall representational power and recommendation accuracy of the models. In this paper, we propose a hyperbolic graph neural network model with contrastive learning for rating–review recommendation, implementing a dual-graph construction strategy. First, it constructs a review-aware graph to integrate rich semantic information from reviews, thus enhancing the recommendation system’s context awareness. Second, it builds a user–item interaction graph to capture user preferences and item characteristics. The hyperbolic graph neural network architecture enables joint learning of high-order features from these two graphs, effectively avoiding the embedding distortion problem commonly associated with high-order feature learning. Furthermore, through contrastive learning in hyperbolic space, the model effectively leverages review information and user–item interaction data to enhance recommendation system performance. Experimental results demonstrate that the proposed algorithm achieves excellent performance on multiple real-world datasets, significantly improving recommendation accuracy. Full article

Migrant children’s discrimination perceptions directly affect their cultural adaptation in the city of influx. In response to migrant children, cities in China have issued relevant urban education policies such as the different-location college entrance examination policy. This study aims to investigate the impact of China’s urban educational policies on the relationship between perceptions of discrimination and acculturation among migrant children. The research sample for this paper was drawn from nine cities that pioneered the policy reform, and a total of 1436 questionnaires were collected. This study analyzed the data using multiple regression analysis and mediation effect tests. This study reveals the following: (a) Migrant children’s educational policy identity has a significant positive impact on their acculturation, whereas their perception of discrimination has a significant negative effect on their acculturation. (b) As the influence of urban educational policies increases, the negative effects of discrimination perceptions on migrant children’s school cultural adaptation, community cultural adaptation, and customs and language adaptation will all diminish. To further explore the facilitating effect of urban educational policies on the cultural adaptation of migrant children, this study proposes recommendations for the household registration system, college entrance examination admission system, and child protection system. This paper not only puts forward policy recommendations for cities of inflow but also provides a Chinese research horizon for the urban cultural adaptation of migrant children in cities of inflow. Full article

Although the ultimate drivers of bat fatalities at wind turbines are still not well understood, the foraging behavior of insectivorous bats puts them at increased risk of collision with rotating blades. Wind energy facilities are commonly located in agriculture fields where bats can exploit periodic superabundant insect emergence events in the late summer and early autumn. Thermal imaging, acoustic monitoring, and bat carcass stomach content analyses show that bats prey upon insects on and near wind turbine towers. Studies have shown a positive association between insect abundance and bat activity, including in agricultural systems. We conducted a systematic review of bat diets for four common bat species in the Midwest and northern Great Plains to synthesize existing knowledge across species, assess the extent to which these bat focal species consume crop pests, and evaluate the potential for crop pest emergence models to predict temporal and spatial patterns of bat fatalities in this region. Big brown bats and eastern red bats consumed a variety of crop pests, including some for which emergence models may be available. In contrast, there were few studies for hoary bats or silver-haired bats, and the dietary evidence available has insufficient taxonomic resolution to conclude that crop pests were consumed. To augment existing data and illuminate relationships, we recommend that genetic diet analyses for bats, specifically hoary and silver-haired, be conducted in the late summer and autumn in this region. The results of these studies may provide additional candidate insect models to evaluate for predicting bat fatalities at wind turbines and clarify if the superabundant insect emergence hypothesis warrants further investigation. Full article

This study aims to develop, implement, and evaluate an Early Warning System (EWS) to alert communities and government agencies in KwaZulu-Natal, South Africa, about extreme weather events (EWEs) and related disease outbreaks. The project focuses on eThekwini and Ugu municipalities, using a participatory, co-creation approach with communities and health providers. A systematic review will be undertaken to understand the impact of climate change on disease outbreaks and design an EWS that integrates data from rural and urban healthcare and environmental contexts. It will assess disease burden at primary healthcare clinics, examine health needs and community experiences during EWEs, and evaluate health system resilience. The project will also evaluate the design, development, and performance of the EWS intervention, including its implementation costs. Ethical approval will be sought, and informed consent obtained from participants. Based on the findings, recommendations will be made to the Department of Health to enhance early warning systems and health system resilience in response to EWEs and disease outbreaks. Full article