Search Results (8,884)

Background: Exercise-induced fatigue arises primarily from energy substrate depletion and the accumulation of metabolites such as lactate and ammonia, which impair performance and delay recovery. Emerging evidence implicates gut microbiota modulation—particularly via probiotics—as a means to optimize host energy metabolism and accelerate clearance of fatigue-associated by-products. Objective: This study aimed to determine whether live or heat-inactivated Lacticaseibacillus paracasei NB23 can enhance exercise endurance and attenuate fatigue biomarkers in a murine model. Methods: Forty male Institute of Cancer Research (ICR) mice were randomized into four groups (n = 10 each) receiving daily gavage for six weeks with vehicle, heat-killed NB23 (3 × 10¹⁰ cells/mouse/day), low-dose live NB23 (1 × 10¹⁰ CFU/mouse/day), or high-dose live NB23 (3 × 10¹⁰ CFU/mouse/day). Forelimb grip strength and weight-loaded swim-to-exhaustion tests assessed performance. Blood was collected post-exercise to measure serum lactate, ammonia, blood urea nitrogen (BUN), and creatine kinase (CK). Liver and muscle glycogen content was also quantified, and safety was confirmed by clinical-chemistry panels and histological examination. Results: NB23 treatment produced dose-dependent improvements in grip strength (p < 0.01) and swim endurance (p < 0.001). All NB23 groups exhibited significant reductions in post-exercise lactate (p < 0.0001), ammonia (p < 0.001), BUN (p < 0.001), and CK (p < 0.0001). Hepatic and muscle glycogen stores rose by 41–59% and 65–142%, respectively (p < 0.001). No changes in food or water intake, serum clinical-chemistry parameters, or tissue histology were observed. Conclusions: Our findings suggest that both live and heat-treated L. paracasei NB23 may contribute to improved endurance performance, increased energy reserves, and faster clearance of fatigue-related metabolites in our experimental model. However, these results should be interpreted cautiously given the exploratory nature and limitations of our study. Full article

Background/Objectives: Previous studies assessing dietary intake have used self-report methods, prone to misreporting. Using researcher-conducted weighed food records, we assessed rural and urban mothers’ energy and nutrient intakes of concern and compared them to recommended nutrient intakes (RNIs). Methods: This cross-sectional study was conducted in rural (Asaase Kokoo) and urban (University of Ghana Staff Village) communities. Dietary data were collected from fifty-four mothers (26 rural, 28 urban) on 2 weekdays and 1 weekend day, analyzed with software, and programmed with West African, FNDDS, Kenyan, Ugandan, and USDA food composition databases. Results: Mean (SD) ages (years) were 35.8 (11.6) and 44.4 (7.6), and mean energy intakes (kcal) were 2026 (461) and 1669 (385) for rural and urban mothers, respectively. Mean percentage contributions of macronutrients to energy intake were within recommended ranges for rural and urban mothers. All participants met or exceeded vitamin A RNI, irrespective of location. While all rural mothers met or exceeded iron RNI, some urban mothers (14.3%) did not. Few rural (7.7%) and urban mothers (10.7%) did not meet zinc RNI. About half of rural (46.2%) and urban mothers (53.6%) did not meet folate RNI. Most rural (96.1%) and urban mothers (92.8%) met or exceeded fiber RNI. Conclusions: Overall, rural mothers had higher energy and nutrient intakes than urban mothers. While most met RNIs, there were some micronutrient inadequacies, particularly folate, where almost half of rural and urban mothers consumed below RNI. Our findings indicate the need for tailored interventions to prevent nutrient deficiencies or excesses in Ghanaian mothers. Full article

Rolling bearings are prone to failure, meaning that research on intelligent fault diagnosis is crucial in relation to this key transmission component in rotating machinery. The application of deep learning (DL) has significantly advanced the development of intelligent fault diagnosis. This paper proposes a novel method for rolling bearing fault diagnosis based on the fractional constant Q non-stationary Gabor transform (FCO-NSGT) and VMamba-Conv. Firstly, a rolling bearing fault experimental platform is established and the vibration signals of rolling bearings under various working conditions are collected using an acceleration sensor. Secondly, a kurtosis-to-entropy ratio (KER) method and the rotational kernel function of the fractional Fourier transform (FRFT) are proposed and applied to the original CO-NSGT to overcome the limitations of the original CO-NSGT, such as the unsatisfactory time–frequency representation due to manual parameter setting and the energy dispersion problem of frequency-modulated signals that vary with time. A lightweight fault diagnosis model, VMamba-Conv, is proposed, which is a restructured version of VMamba. It integrates an efficient selective scanning mechanism, a state space model, and a convolutional network based on SimAX into a dual-branch architecture and uses inverted residual blocks to achieve a lightweight design while maintaining strong feature extraction capabilities. Finally, the time–frequency graph is inputted into VMamba-Conv to diagnose rolling bearing faults. This approach reduces the number of parameters, as well as the computational complexity, while ensuring high accuracy and excellent noise resistance. The results show that the proposed method has excellent fault diagnosis capabilities, with an average accuracy of 99.81%. By comparing the Adjusted Rand Index, Normalized Mutual Information, F1 Score, and accuracy, it is concluded that the proposed method outperforms other comparison methods, demonstrating its effectiveness and superiority. Full article

Governments worldwide have set ambitious targets for decarbonising energy grids, driving the need for increased renewable energy generation and improved energy efficiency. One key strategy for achieving this involves enhanced energy management in buildings, often using machine learning-based forecasting methods. However, such methods typically rely on extensive historical data collected via costly sensor installations—resources that many buildings lack. This study introduces a novel forecasting approach that eliminates the need for large-scale historical datasets or expensive sensors. By integrating custom-built models with existing energy data, the method applies calculated weighting through a distance matrix and accuracy coefficients to generate reliable forecasts. It uses readily available building attributes—such as floor area and functional type to position a new building within the matrix of existing data. A Euclidian distance matrix, akin to a K-nearest neighbour algorithm, determines the appropriate neural network(s) to utilise. These findings are benchmarked against a consolidated, more sophisticated neural network and a long short-term memory neural network. The dataset has hourly granularity over a 24 h horizon. The model consists of five bespoke neural networks, demonstrating the superiority of other models with a 610 s training duration, uses 500 kB of storage, achieves an R² of 0.9, and attains an average forecasting accuracy of 85.12% in predicting the energy consumption of the five buildings studied. This approach not only contributes to the specific goal of a fully decarbonized energy grid by 2050 but also establishes a robust and efficient methodology for maintaining standards with existing benchmarks while providing more control over the method. Full article

Enhancing the real-world performance of sustainably designed and certified green buildings remains a significant challenge, particularly in hot climates where efforts to improve thermal comfort often conflict with energy efficiency goals. In the United Arab Emirates (UAE), even newly constructed facilities with green building certifications present opportunities for retrofitting and performance optimization. This study investigates the energy and thermal comfort performance of a LEED Gold-certified, mixed-use university campus in Dubai through a calibrated digital twin developed using IES thermal modelling software. The analysis evaluated existing sustainable design strategies alongside three retrofit energy conservation measures (ECMs): (1) improved building envelope U-values, (2) installation of additional daylight sensors, and (3) optimization of fan coil unit efficiency. Simulation results demonstrated that the three ECMs collectively achieved a total reduction of 15% in annual energy consumption. Thermal comfort was assessed using operative temperature distributions, Predicted Mean Vote (PMV), and Predicted Percentage of Dissatisfaction (PPD) metrics. While fan coil optimization yielded the highest energy savings, it led to less favorable comfort outcomes. In contrast, enhancing envelope U-values maintained indoor conditions consistently within ASHRAE-recommended comfort zones. To further support energy reduction and progress toward Net Zero targets, the study also evaluated the integration of a 228.87 kW rooftop solar photovoltaic (PV) system, which offset 8.09% of the campus’s annual energy demand. By applying data-driven thermal modelling to assess retrofit impacts on both energy performance and occupant comfort in a certified green building, this study addresses a critical gap in the literature and offers a replicable framework for advancing building performance in hot climate regions. Full article

An important task for the European Union is to transpose agreements and international standards in regulation and directives that are binding on member states. The resultant European action plans and directives identify priority areas in the building and energy sectors where circular economy principles can be applied. Italy records a general circular materials rate of 20.8%, surpassing the mean European value. But low recycling rates are still registered in the construction sector. This paper aims to assess the position of Italy with respect to the European regulatory framework on circularity in the energy transition of buildings. Firstly, the government’s initiatives and technical standards are introduced and commented upon. Secondly, the study illustrates the current Italian platforms, networks, and public and private initiatives highlighting opportunities and obstacles that the energy sector has to overcome in the area of circularity. It emerges that Italian policies still use voluntary tools that are not sufficiently in line with an effective circular economy model. Moreover, data collection plays a crucial role in accelerating the implementation of future actions. Italy should consider the foundation of a National Observatory for the Circular Economy to elaborate European directives, harmonize regional policies, and promote the implementation of effective practices. Full article

Introduction: This study evaluated the performance of automated dual-energy computed tomography (DECT)-based quantification of perfusion defects (PDs) in acute pulmonary embolism and examined its correlation with clinical parameters. Methods: We retrospectively analyzed data from 171 patients treated for moderate-to-severe acute pulmonary embolism, who underwent DECT imaging at two separate time points. PDs were quantified using a fully automated AI-based segmentation method that relied exclusively on iodine perfusion maps. This was compared with a semi-automatic clinician-guided segmentation, where radiologists manually adjusted thresholds to eliminate artifacts. Clinical variables including the Miller obstruction score, right-to-left ventricular diameter ratio, oxygen saturation, and patient-reported symptoms were also collected. Results: The semiautomatic method demonstrated stronger correlations with embolic burden (Miller score; r = 0.4, p < 0.001 at follow-up) and a negative correlation with oxygen saturation (r = −0.2, p = 0.04). In contrast, the fully automated AI-based quantification consistently produced lower PD values and demonstrated weaker associations with clinical parameters. Conclusions: Semiautomatic quantification of PDs currently provides superior accuracy and clinical relevance for evaluating lung PDs in acute pulmonary embolism. Future multimodal AI models that incorporate both anatomical and clinical data may further enhance diagnostic precision. Full article

Objectives: This study aimed to evaluate the effect of nutritional education on nutritional knowledge, nutritional status, and quality of life in patients with liver cirrhosis. Methods: Thirty patients participated. At baseline, assessments were conducted to collect data on demographics, physical activity, anthropometric and biochemical measures, dietary habits, 24 h food intake, nutritional status, quality of life, and nutritional knowledge. Participants received a 30 min face-to-face nutritional education session by a registered dietitian, repeated after one month. A follow-up phone call was conducted one month later to reinforce the education. Final evaluations were completed one month after the call. Results: A significant upward trend was detected in nutritional knowledge scores after the intervention period (from 7.4 ± 2.76 to 9.2 ± 3.45). The physical component of quality of life improved, while the mental component showed a slight decline. Dietary changes included reduced energy and protein intake among females and increased protein intake in males. In both genders, fat intake increased and carbohydrate intake decreased. Biochemical improvements were observed, including significant reductions in gamma-glutamyl transferase, aspartate aminotransferase, alanine aminotransferase, and triglycerides in females and alanine aminotransferase and gamma-glutamyl transferase in males. Conclusions: Structured nutritional education may improve nutritional knowledge, dietary behavior, and biochemical markers in cirrhosis patients. Longer follow-up durations may further enhance these improvements. Full article

The kallikrein–kinin system and its B1 and B2 receptors are key regulators in metabolic disorders such as obesity, diabetes, and insulin resistance. Obesity, a chronic and multifactorial condition often associated with comorbidities like type 2 diabetes and dyslipidemia, remains poorly understood at the metabolic level. The kinin B2 receptor (B2R) is involved in blood pressure regulation and glucose metabolism, promoting glucose uptake in skeletal muscle via bradykinin. Studies in B2R-KO mice demonstrate that the absence of this receptor predisposes animals to glucose intolerance under a high-fat diet and impairs adaptive thermogenesis, indicating a protective role for B2R in metabolic homeostasis and insulin sensitivity. In contrast, the kinin B1 receptor (B1R) is inducible under pathological conditions and is activated by kinin metabolites. Mouse models lacking B1R exhibit improved metabolic profiles, including protection against high-fat diet-induced obesity and insulin resistance, enhanced energy expenditure, and increased leptin sensitivity. B1R inactivation in adipocytes enhances insulin responsiveness and glucose tolerance, supporting its role in the development of insulin resistance. Moreover, B1R deficiency improves energy metabolism and thermogenic responses to adrenergic and cold stimuli, promoting the activation of brown adipose tissue and the browning of white adipose tissue. Collectively, these findings suggest that B1R and B2R represent promising therapeutic targets for the treatment of metabolic disorders. Full article

Heating, Ventilation, and Air Conditioning (HVAC) systems represent a significant portion of global energy use, yet they are often operated without optimized control strategies. This study explores the application of deep learning to accurately model heating system behavior as a foundation for predictive control and energy-efficient HVAC operation. Experimental data were collected under controlled laboratory conditions, and 288 unique hyperparameter configurations were developed. Each configuration was tested three times, resulting in a total of 864 artificial neural network models. Five key hyperparameters were varied systematically: number of epochs, network size, network shape, learning rate, and optimizer. The best-performing model achieved a mean squared error of 0.469 and featured 17 hidden layers, a left-triangle architecture trained for 500 epochs with a learning rate of 5 × 10⁻⁵, and Adam as the optimizer. The results highlighted the importance of hyperparameter tuning in improving model accuracy. Future research should extend the analysis to incorporate cooling operation and real-world building operation data for broader applicability. Full article

Background/Objectives: Cancer is a complex disease that affects patients’ nutritional and psychological status. This study aimed to assess the nutritional status of patients diagnosed with lung and gastrointestinal system cancers and evaluate its association with anthropometric measurements, nutrient intake, and psychological symptoms. Methods: This cross-sectional study was conducted with 180 patients with lung and gastrointestinal system cancers. Data were collected face-to-face by a questionnaire that included the Subjective Global Assessment-(SGA), Cachexia Assessment Criteria, 24 h Food Consumption Record, and Symptom Checklist-90-Revised-(SCL-90-R). Some anthropometric measurements were collected. Results: Body Mass Index (BMI) was found to be significantly lower (p < 0.001) in SGA-B (moderately malnourished) and SGA-C (severely malnourished) compared to those in SGA-A (well-nourished). The calf circumference was significantly lower (p = 0.002) in SGA-C compared to those in SGA-A and SGA-B. The mean SGA scores were found to be higher in cachexia-diagnosed participants (p < 0.001). The energy intake of SGA-C was significantly lower than SGA-A and SGA-B (p < 0.001). In addition, the energy intake of SGA-B was lower than SGA-A (p < 0.001). The protein intake of SGA-C was lower than SGA-A and SGA-B (p < 0.001). The protein intake of SGA-B was lower than SGA-A (p < 0.001). Regarding the intake of vitamins A, C, E, B1, and B6 and carotene, folate, potassium, magnesium, phosphorus, iron, and zinc, SGA-B and SGA-C were significantly lower than SGA-A (p < 0.001). Additionally, only phobic anxiety was found to be significantly higher in SGA-B than in SGA-A (p: 0.024). Conclusions: As the level of malnutrition increased, a reduction in some nutrient intake and anthropometric measurements was observed. No significant difference was found in any psychological symptoms except phobic anxiety. With this in mind, it is important that every cancer patient, regardless of the stage of the disease, is referred to a dietitian from the time of diagnosis. Full article

The green transformation of traditional energy structures and the development of the new energy industry are crucial drivers of sustainable development in the country. The ASEAN Plan of Action for Energy Cooperation (2016–2025; APAEC [2016–2025]), established in 2016, has significantly promoted the growth of the new energy sector and enhanced energy structures across Association of Southeast Asian Nations (ASEAN). This initiative has also inspired these countries to develop corresponding industrial policies aimed at supporting the new energy vehicle (NEV) industry, resulting in significant growth in this sector within the ASEAN region. This paper analyzes the factors influencing the development of the NEV industry in the context of ASEAN’s new energy policies, drawing empirical insights from data collected across six ASEAN countries from 2013 to 2024. Following the implementation of the APAEC (2016–2025), it was observed that ASEAN countries reached a consensus on energy development and cooperation, collaboratively advancing the NEV industry through regional policies. Furthermore, factors such as national governance, financial development, education levels, and the size of the automotive market positively contribute to the growth of the NEV industry in ASEAN. Conversely, high energy consumption can hinder its progress. Additionally, further research indicates that the APAEC (2016–2025) has exerted a more pronounced impact on countries with robust automotive industry foundations or those prioritizing relevant policies. The findings of this paper offer valuable insights for ASEAN countries in the formulating policies for the NEV industry, optimizing energy structures, and achieving low-carbon energy transition and sustainable development. Full article

Outdoor air pollution poses a major global health risk, yet monitoring remains insufficient, especially in regions with limited infrastructure. Solar-powered monitors could allow for increased coverage in regions lacking robust connectivity. However, reliable sample collection can be challenging with these systems due to extreme temperatures and insufficient solar energy. Proper planning can help overcome these challenges. Air Sampler Solar and Thermal Optimization for Reliable Monitoring (Air-STORM) is an open-source tool that uses meteorological and solar radiation data to identify temperature and solar charging risks for air pollution monitors based on the target deployment area. The model was validated experimentally, and its utility was demonstrated through illustrative case studies. Air-STORM simulations can be customized for specific locations, seasons, and monitor configurations. This capability enables the early detection of potential sampling risks and provides opportunities to optimize monitor design, proactively mitigate temperature and power failures, and increase the likelihood of successful sample collection. Ultimately, improving sampling success will help increase the availability of high-quality outdoor air pollution data necessary to reduce global air pollution exposure. Full article

High semen quality is vital for reproductive success in the swine industry; however, seasonal fluctuations often compromise this quality. The molecular mechanism underlying these seasonal effects on semen quality remains largely unclear. This study employed untargeted metabolomic profiling of boar seminal plasma (SP) to identify metabolites and metabolic pathways associated with semen quality during the summer and winter months. Semen samples were collected from mature Duroc boars at a commercial boar stud and classified as Passed or Failed based on motility and morphology. SP from five samples per group was analyzed using ultra-high-performance liquid chromatography–mass spectrometry (UHPLC-MS). In total, 373 metabolites were detected in positive ion mode and 478 in negative ion mode. Several differentially expressed metabolites (DEMs) were identified, including ergothioneine, indole-3-methyl acetate, and avocadyne in the summer, as well as LysoPC, dopamine, and betaine in the winter. These metabolites are associated with key sperm functions, including energy metabolism, antioxidant defense, and capacitation. KEGG pathway analysis indicated enrichment in starch and sucrose metabolism, pyrimidine metabolism, and amino acid metabolism across the seasons. Overall, the results reveal that SP metabolomic profiles vary with the season, thereby influencing semen quality. The identified metabolites may serve as potential biomarkers for assessing semen quality and enhancing reproductive efficiency in swine production. Full article

This study empirically evaluates the energy and carbon reduction effects of an envelope retrofit applied to an aging public building in South Korea. Unlike previous studies that primarily relied on simulation-based analyses, this work fills the empirical research gap by using actual utility billing data collected over one pre-retrofit year (2019) and two post-retrofit years (2023–2024). The retrofit included improvements to exterior walls, roofs, and windows, aiming to enhance thermal insulation and airtightness. The analysis revealed that monthly electricity consumption was reduced by 14.7% in 2023 and 8.0% in 2024 compared to that in the baseline year, with corresponding decreases in electricity costs and carbon dioxide emissions. Seasonal variations were evident: energy savings were significant in the winter due to reduced heating demand, while cooling energy use slightly increased in the summer, likely due to diminished solar heat gains resulting from improved insulation. By addressing both heating and cooling impacts, this study offers practical insights into the trade-offs of envelope retrofitting. The findings contribute to the body of knowledge by demonstrating the real-world performance of retrofit technologies and providing data-driven evidence that can inform policies and strategies for improving energy efficiency in public buildings. Full article