Search Results (416)

With the advance of digital transformation, the assessment of the environmental impact of digital tools and technologies grows more relevant. Considering the inflated expectations of environmental responsibility in higher education, this study analyses how websites of Polish universities conform to sustainable web design criteria. The sustainability audit employed a methodology encompassing carbon emissions measurement, technical website analysis, and SEO evaluation. The author analysed 63 websites of public universities in Poland using seven independent audit tools, including an original AI Custom GPT agent preconfigured in the ChatGPT ecosystem. The results revealed a substantial differentiation in CO₂ emissions and website optimisation, with an average EcoImpact Score of 66.41/100. Nearly every fourth website exhibited a significant carbon footprint and excessive component sizes, which indicates poor asset optimisation and energy-intensive design techniques. The measurements exposed considerable variability in emission intensities and resource intensity among the university websites, suggesting the need for standardised digital sustainability practices. Regulations on the carbon footprint of public institutions’ websites and mobile applications could become vital strategic components for digital climate neutrality. Promoting green hosting, “Green SEO” practices, and sustainability audits could help mitigate the environmental impact of digital technologies and advance sustainable design standards for the public sector. The proposed auditing methodology can effectively support the institutional transition towards sustainable management of digital infrastructure by integrating technical, sustainability, and organisational aspects. Full article

Objective: This study aimed to improve the understanding of the lives of patients with chronic neuropathic pain planned for invasive motor cortex stimulation (iMCS) and assess their expectations towards this intervention and its impact. Methods: Semi-structured face-to-face interviews were conducted until saturation of data was reached. Patients were recruited from one university medical center in the Netherlands. All interviews were audio-recorded, transcribed verbatim, and subjected to thematic analysis using iterative and inductive coding by two researchers independently. Results: Fifteen patients were included (11 females; mean age 63 ± 9.4 yrs). Analysis of the coded interviews revealed seven themes: (1) the consequences of living with chronic neuropathic pain; (2) loss of autonomy and performing usual activities; (3) balancing energy and mood; (4) intimacy; (5) feeling understood and accepted; (6) meaning of life; and (7) the expectations of iMCS treatment. Conclusions: This is the first qualitative study that describes the suffering of patients with chronic neuropathic pain, and their expectations prior to invasive brain stimulation. Significant themes in the lives of patients with chronic pain have been brought to light. The findings strengthen communication between physicians, caregivers, and patients. Practice Implications: The insights gathered from the interviews create a structured framework for comprehending the values and expectations of patients living with central pain and reveal the impact of symptoms due to the central pain. This knowledge improves the communication between physicians and caregivers on one side and the patient on the other side. Furthermore, the framework enhances the capacity for shared decision-making, particularly in managing expectations related to iMCS. Full article

Background: Midfacial fractures are common outcomes of facial trauma. While younger individuals typically sustain these injuries through high-energy events like assaults and traffic or sports accidents, elderly patients increasingly present with fractures from low-energy mechanisms, primarily falls. Purpose: The aim of this study was to analyze age- and gender-specific patterns in midfacial fractures over a 10-year period, with emphasis on elderly individuals and low-energy trauma. Methods: A retrospective review was performed of proven midfacial fractures between 2013 and 2022 at the Department of Oral and Maxillofacial Surgery (University of Pécs, Hungary). The patients were stratified by age (<65 vs. ≥65 years) and gender. The variables included the injury mechanism, fracture localization, the dental status, hospitalization, and the presence of associated injuries. Bivariate analyses were performed, and the significance level was set to p < 0.05. Results: A total of 957 radiologically confirmed midfacial fracture cases were evaluated, of whom 344 (35.9%) were ≥65 years old. In the elderly group, females had a 19-fold higher risk for midfacial trauma than younger females (OR: 19.1, 95%CI: 9.30–39.21). In the older group, a fall was significantly the most frequent injury mechanism (OR: 14.5; 95%CI: 9.9–21.3), responsible for 89.5% of the cases, while hospitalization (OR: 0.36; 95%CI: 0.23–0.56) was less characteristic. Most of the fractures occurred in the zygomatic bone, in the zygomaticomaxillary complex, or in the anterior wall of the maxilla. Associated injuries in the elderly group included mostly lower limb injuries—particularly pertrochanteric femoral fractures in females—and upper limb injuries, with a slight male dominance. Conclusions: Low-energy falls are the primary cause of midfacial fractures in elderly patients, particularly in women. Tailored prevention and management strategies are essential for improving the outcomes in this growing demographic group. Full article

This article presents a comprehensive review of methods used for forecasting electricity consumption. The studies analyzed by the authors encompass both classical statistical models and modern approaches based on artificial intelligence, including machine-learning and deep-learning techniques. Electricity load forecasting is categorized into four time horizons: very short term, short term, medium term, and long term. The authors conducted a comparative analysis of various models, such as autoregressive models, neural networks, fuzzy logic systems, hybrid models, and evolutionary algorithms. Particular attention was paid to the effectiveness of these methods in the context of variable input data, such as weather conditions, seasonal fluctuations, and changes in energy consumption patterns. The article emphasizes the growing importance of accurate forecasts in the context of the energy transition, integration of renewable energy sources, and the management of the evolving electricity system, shaped by decentralization, renewable integration, and data-intensive forecasting demands. In conclusion, the authors highlight the lack of a universal forecasting approach and the need for further research on hybrid models that combine interpretability with high predictive accuracy. This review can serve as a valuable resource for decision-makers, grid operators, and researchers involved in energy system planning. Full article

Background: There is little information about the effectiveness of oral nutritional supplements (ONS) in combatting nutrient inadequacies in primary care, where most malnutrition exists. Aim: To examine the extent to which readymade ONS add or displace the nutrients consumed in the diet and their impact on combatting dietary inadequacies. Methods: 308 free-living people >50 years with medium + high risk of malnutrition (Malnutrition Universal Screening Tool) were randomised to receive readymade low volume (2.4 kcal/mL), liquid ONS plus dietary advice (ONS + DA) or dietary advice alone (DA). Intake was assessed at baseline (24 h recall) and 4-weekly for 12 weeks (3-day diet record). Total nutrient intake was benchmarked against UK and European dietary reference values (DRVs). The proportion of energy and nutrients from the ONS that added or displaced those from the diet (net addition/displacement) was calculated. Results: ONS + DA led to significantly greater total energy and nutritional intakes, with 25/29 nutrient intakes significantly higher than with DA alone. There were no significant differences in dietary energy and nutrient intakes from food between the groups. There was little or no displacement of nutrients from the diet, with over 90% of the energy and nutrients consumed in the ONS additive to the diet. ONS + DA more than halved the number of people with nutrient intakes that failed to meet DRVs and the number of nutrients per person that did not meet DRVs compared to DA alone. Conclusions: Supplementation with readymade, low volume (2.4 kcal/mL) liquid ONS overcomes most nutrient intake inadequacies in malnourished older people in primary care without significantly reducing intake from the diet. This makes ONS an effective way to improve nutritional intakes above dietary advice alone to improve the outcomes for the management of older people at risk of malnutrition. Full article

To respond to the challenge of global climate change, universities should engage in carbon footprint research to identify effective strategies for mitigating greenhouse gas emissions. In this research, a comprehensive framework tailored for the study of carbon footprints in universities was constructed and used in a university in Northeast China for a case study, based on the GHG Protocol and emission factor methodology. The sources of GHG emissions at this university were identified by the GHG Protocol. Activity data were collected through questionnaire surveys and field visits. The results show that the university’s annual carbon footprint in 2022 stands at 172,473.77 t CO₂-eq, with the contributions of Scope 1, 2, and 3 accounting for 2.35%, 64.69%, and 32.96%, respectively. Based on the carbon footprint quantification results, campus carbon reduction strategies were put forward from four perspectives: individual activities, building energy management, energy-loss reduction, and carbon sink, in order to enhance the sustainability of this university. An important difference between this work and previous studies is the explicit emphasis on the necessity of the indicative role of the carbon footprint in carbon reduction efforts. The case demonstrates the application of research framework and methods, providing methodologies and case references for future research on the carbon footprint of universities. Full article

The widespread integration of power electronic devices and renewable energy sources into power systems has significantly exacerbated voltage and current waveform distortion issues, where asymmetric loads—including single-phase nonlinear equipment and unbalanced three-phase power electronic installations—serve as critical harmonic sources whose inherent nonlinear and asymmetric characteristics increasingly compromise power quality. To enhance power quality management, this paper proposes a universal harmonic source modeling and operational state identification methodology integrating physical mechanisms with data-driven algorithms. The approach establishes an RL-series equivalent impedance model as its physical foundation, employing singular value decomposition and Z-score criteria to accurately characterize asymmetric load dynamics; subsequently applies Variational Mode Decomposition (VMD) to extract time-frequency features from equivalent impedance parameters while utilizing Density-Based Spatial Clustering (DBSCAN) for the high-precision identification of operational states in asymmetric loads; and ultimately constructs state-specific harmonic source models by partitioning historical datasets into subsets, substantially improving model generalizability. Simulation and experimental validations demonstrate that the synergistic integration of physical impedance modeling and machine learning methods precisely captures dynamic harmonic characteristics of asymmetric loads, significantly enhancing modeling accuracy, dynamic robustness, and engineering practicality to provide an effective assessment framework for power quality issues caused by harmonic source integration in distribution networks. Full article

In order to address the growing urgency of energy-related carbon emission reduction and improve the construction of the existing public building carbon emission database model, this study constructs a public building carbon emission database model based on energy activity data by collecting the energy consumption data of relevant buildings in the region and classifying the building types, aiming to quantitatively analyze the carbon emission characteristics of different types of public buildings and provide data support for the national and local governments, enterprises, universities and research institutions, and the power industry. This study is divided into three phases: The first stage is the mapping of carbon emission benchmarks. The second stage is the analysis of multi-dimensional-building carbon emission characteristics. The third stage is to evaluate the design optimization plan and propose technical improvement suggestions. At present, this research is in the first stage: collecting and analyzing information data such as the energy consumption of different types of buildings, building a carbon emission database model, and extracting and analyzing the carbon emission benchmarks and characteristics of each building type from the data of 184 public buildings in a given area. Moreover, preliminary exploration of the second phase has been conducted, focusing on identifying key influencing factors of carbon emissions during the operational phase of public buildings. Office buildings have been selected as representative samples to carry out baseline modeling and variable selection using linear regression analysis. The results of this study are of great significance in the energy field, providing data support for public building energy management, energy policy formulation, and carbon trading mechanisms. Full article

As electric vehicles (EVs) become increasingly integrated into urban mobility, the load on electrical grids increases, prompting innovative energy management strategies. This paper investigates the deployment of vehicle-to-grid (V2G) services at the University of Rome Tor Vergata, leveraging high-resolution floating car data (FCD) to forecast and schedule energy transfers from EVs to the grid. The methodology follows a four-step process: (1) vehicle trip detection, (2) the spatial identification of V2G in the campus, (3) a real-time scheduling algorithm for V2G services, which accommodates EV user mobility requirements and adheres to charging infrastructure constraints, and finally, (4) the predictive modelling of transferred energy using ARIMA and LSTM models. The results demonstrate that substantial energy can be fed back to the campus grid during peak hours, with predictive models, particularly LSTM, offering high accuracy in anticipating transfer volumes. The system aligns energy discharge with campus load profiles while preserving user mobility requirements. The proposed approach shows how campuses can function as microgrids, transforming idle EV capacity into dynamic, decentralised energy storage. This framework offers a scalable model for urban energy optimisation, supporting broader goals of grid resilience and sustainable development. Full article

The rapid growth of the global population and associated increases in resource consumption have accelerated environmental degradation, making sustainable design and construction processes increasingly essential. The construction sector holds significant potential for reducing environmental impacts, especially through sustainability-focused certification systems such as LEED. This study evaluates the projected energy efficiency and sustainability performance of the Surgical Sciences Building at Istanbul University’s Çapa Campus, which was designed with the goal of achieving LEED Gold certification. The assessment is based on design-phase data and conducted prior to construction. Energy performance analyses were carried out using DesignBuilder software, supported by the LEED Assessment Report and Energy Audit Report. According to simulation results, approximately 30% savings in energy consumption and water usage are expected. In addition, the process-oriented LEED approach is expected to result in a total CO₂ emission savings of approximately 570 tonnes, while renewable energy systems are expected to meet approximately 13% of the building’s primary energy demand and reduce CO₂ emissions by approximately 151 tonnes per year. Waste management strategies developed for both the construction and operational phases are aligned with LEED criteria and aim to achieve up to 80% recycling rates. The findings demonstrate that LEED certification, when employed as a process-oriented design and decision-making tool rather than a result-oriented label, can enable sustainable strategies to be integrated from the earliest stages of project development. Particularly for complex healthcare buildings, embedding LEED principles into the design process has strong potential to enhance environmental performance. Although based on a single case study, this research provides valuable insight into the broader applicability of LEED in diverse building types and geographic contexts. Full article

The Brazilian semi-arid Northeast plays a critical role in regional hydrology, where rainfall is marked by pronounced temporal variability and short duration, presenting significant challenges for understanding and managing hydrological and erosive processes. This study aims to evaluate the performance of empirical models for estimating rainfall kinetic energy (KE) and erosivity index (EI₃₀) in this region, for all events and erosive events, using high-resolution rainfall data collected at the Federal University of Cariri (UFCA), Ceará. A total of 283 natural rainfall events were analyzed, with KE and EI₃₀ values calculated using multiple models: Wischmeier and Smith, USDA, Van Dijk, a temporal variation-based model (KE_VT), and a regional model developed for Brazil’s semi-arid zone, which served as the reference. The results show a predominance of small rainfall events (<5.2 mm), though maximum EI₃₀ values exceeded 1300 MJ ha⁻¹ mm h⁻¹, highlighting the potential for extreme erosive events. Comparative analysis revealed that all international models significantly underestimated KE and EI₃₀ values compared to the regional reference, with the KE_VT model showing the closest approximation (13% underestimation), for all events and erosive events. Statistical assessments using the Wilcoxon test, Nash–Sutcliffe efficiency, and Willmott concordance index confirmed the superior performance of the KE_VT, for all events and erosive events. These findings underscore the importance of considering intra-event rainfall variability and regional calibration when modeling erosivity in semi-arid climates, contributing to more effective soil conservation and hydrological planning. Full article

This study systematically reviews the development and application of remote sensing technology in monitoring and evaluating urban heat island (UHI) effects. The urban heat island effect, characterized by significantly higher temperatures in urban areas compared to surrounding rural regions, has become a widespread environmental issue globally, with impacts spanning public health, energy consumption, ecosystems, and social equity. The paper first analyzes the formation mechanisms and impacts of urban heat islands, then traces the evolution of remote sensing technology from early traditional platforms such as Landsat and NOAA-AVHRR to modern next-generation systems, including the Sentinel series and ECOSTRESS, emphasizing improvements in spatial and temporal resolution and their application value. At the methodological level, the study systematically evaluates core algorithms for land surface temperature extraction and heat island intensity calculation, compares innovative developments in multi-source remote sensing data integration and fusion techniques, and establishes a framework for accuracy assessment and validation. Through analyzing the heat island differences between metropolitan areas and small–medium cities, the relationship between urban morphology and thermal environment, and regional specificity and global universal patterns, this study revealed that the proportion of impervious surfaces is the primary driving factor of heat island intensity while simultaneously finding that vegetation cover exhibits significant cooling effects under suitable conditions, with the intensity varying significantly depending on vegetation types, management levels, and climatic conditions. In terms of applications, the paper elaborates on the practical value of remote sensing technology in identifying thermally vulnerable areas, green space planning, urban material optimization, and decision support for UHI mitigation. Finally, in light of current technological limitations, the study anticipates the application prospects of artificial intelligence and emerging analytical methods, as well as trends in urban heat island monitoring against the backdrop of climate change. The research findings not only enrich the theoretical framework of urban climatology but also provide a scientific basis for urban planners, contributing to the development of more effective UHI mitigation strategies and enhanced urban climate resilience. Full article

Elite female tennis players are among those at high risk for developing the Female Athlete Triad (Triad), characterized by three interrelated conditions: energy deficiency/low energy availability, menstrual dysfunction, and low bone mineral density. From 1996 to 2022, the Women’s Tennis Association (WTA) developed and implemented prevention, education, and management plans for female athletes at risk for, or exhibiting symptoms of, the Triad. This article reviews the WTA Triad protocol, developed in 2018 and utilized through 2022, in collaboration with subject matter experts in the Women’s Health and Exercise Laboratory at The Pennsylvania State University. The WTA Triad protocol (1996–2022) includes prevention and management programs implemented by a multidisciplinary Performance Health Team to include screening for “red flags” during annual physicals or upon clinical presentation of a menstrual problem, bone disorder, or nutritional concern; targeted education for players, coaches, and other support team members with handouts and lectures on nutrition and body image to prevent energy deficiency; and a multidisciplinary protocol to guide treatment and return-to-play decisions. Other sport governing bodies can adopt similar multi-layered programs and practices for their athletes, coaches, and support teams to educate, screen, manage, and help to prevent the development of the Triad. Full article

The awareness of global warming has boosted research on methods to reduce energy consumption and greenhouse gas (GHG) emissions. Livestock buildings, although essential for food production, represent a sustainability challenge due to their high maintenance energy costs, GHG emissions, and impact on the environment and rural landscapes. Since the environment, cultural heritage, and community identity deserve protection, research trends and current knowledge on livestock buildings, building sustainability, energy efficiency strategies, and landscape management were investigated using the Web of Science and Scopus search tools (2005–2025). Research on these topics was found to be uneven, with limited focus on livestock buildings compared to food production and animal welfare, and significant interest in eco-sustainable building materials. A total of 96 articles were selected after evaluating over 5400 records. The analysis revealed a lack of universally accepted definitions for building design strategies and their rare application to livestock facilities, where passive solutions and insulation prevailed. The application of renewable energy was rare and limited to rural buildings, as was the application of sustainable building materials to livestock, agriculture, and vernacular buildings. Conversely, increased attention was paid to the definition and classification of vernacular architecture features aimed at enhancing existing buildings and mitigating or facilitating the landscape integration of those that diverge most from them. Although not exhaustive, this review identified some knowledge gaps. More efforts are needed to reduce environmental impacts and meet the milestones set by international agreements. Research on building materials could benefit from collaboration with experts in cultural heritage conservation because of their command of traditional materials, durability-enhancing methods, and biodeterioration. Full article

The capacity estimation of lithium-ion batteries, serving as an auxiliary power source in fuel cell vessels, is crucial for ensuring system stability and enhancing operational efficiency. Accurate capacity estimation technology not only helps extend battery lifespan but also enhances the energy management and scheduling capabilities of the entire vessel. To address the challenge of accurately estimating lithium-ion battery capacity under complex operating conditions, this study extracts universal health factors from battery data under varied charging and discharging scenarios and combines these with a deep learning model to enhance prediction accuracy. First, battery data from three complex conditions are analyzed, extracting partial charge and discharge data. The distance correlation coefficient calculates the correlation between each factor and the capacity sequence, informing the priority of universal health factors. A TCN-BiGRU model is then developed, with hyperparameters determined by the Kepler optimization algorithm (KOA). Cells from a battery pack under consistent conditions are used for training, while other cells in the same pack serve as the test set. Evaluation metrics include mean absolute error (MAE) and root-mean-square error (RMSE). The testing shows that the MAE and RMSE for full-life capacity estimation remain around 1%, with most cells achieving values under 1%. The results indicate that the proposed method effectively aids in accurate capacity estimation for individual cells in complex operating environments. Full article