Search Results (2,094)

Residential space heating in Northern Europe requires long-duration thermal storage to align summer solar gains with winter heating demand. This study investigates a compact sand-based seasonal thermal energy storage integrated with flat-plate solar collectors for an A+ class single-family house in Kaunas, Lithuania. An iterative co-design couples collector sizing with the seasonal charging target and a 3D COMSOL Multiphysics model of a 300 m³ sand-filled, phenolic foam-insulated system, with a 1D conjugate model of a copper pipe heat-exchanger network. The system was charged from March to September and discharged from October to February under measured-weather boundary conditions across three consecutive annual cycles. During the first year, the storage supplied the entire winter heating demand, though 35.2% of the input energy was lost through conduction, resulting in an end-of-cycle average sand temperature slightly below the initial state. In subsequent years, both the peak sand temperature and the residual end-of-cycle temperature increased by 3.7 °C and 3.2 °C, respectively, by the third year, indicating cumulative thermal recovery and improved retention. Meanwhile, the peak conductive losses rate decreased by 98 W, and cumulative annual losses decreased by 781.4 kWh in the third year, with an average annual reduction of 4.15%. These results highlight the progressive self-conditioning of the surrounding soil and demonstrate that a low-cost, sand-based storage system can sustain a complete seasonal heating supply with declining losses, offering a robust and scalable approach for residential building heating applications. Full article

Many studies on solar heating systems have examined individual techniques to enhance the performance of solar water collectors, such as flow obstructions, increased turbulence, nanofluids, and investment in thermal storage. The benefits of integrating these sustainability strategies into a single, sustainable system have yet to be fully established. This work displays a hybrid water-heating system that contains a solar water collector (SWC) and an electric water heater (EWH), a photovoltaic panel (PV), and nano-additives to increase the outlet water temperature and improve thermal efficiency. Numerical and experimental analyses were used to estimate the influence of water flow rate (2.5, 3.5, and 4.5 L/min) and different Al₂O₃ concentrations (0.1%, 0.2%, and 0.3%) on system performance using U-shaped pipe in SWC model. The results highlight that lower flow rates consistently yield higher ΔT values because water spends a longer time in the collector, allowing it to absorb more heat. Also, when using water only, the collector efficiency increases pro-aggressively with flow rate. A significant performance enhancement is observed upon incorporating Al₂O₃ nanoparticles into the fluid, with a 0.1% Al₂O₃ volume concentration improving efficiency by ~7.4% over water. At 0.3%, the highest improvement is recorded, yielding a ~9.3% gain in efficiency compared to the base case. Full article

A global interest in the cultivation of Kalibaus (Labeo calbasu) has emerged due to decreasing natural stocks and a consistent rise in market value and demand. Given these concerns, understanding the species’ physiological responses to environmental changes is crucial. The present research aimed to assess the effect of varying environmental temperatures on metabolism, haemato-biochemical indices, hormonal concentrations and immune responses in L. calbasu. This study was conducted in triplicate using 100 L glass aquariums at four different temperatures: 22, 26, 30, and 34 °C. The highest weight and length gain were observed at 30 °C, while the lowest occurred at 22 °C. Notably, the best feed conversion ratio (FCR) of 1.51 ± 0.03 was also recorded at 30 °C. Although haematological and biochemical parameters remained within normal ranges, they varied with temperature changes. Indicators of cold and heat stress were evident through lower hematocrit levels and higher white blood cell (WBC) counts. Biochemical indicators such as serum albumin (1.84 ± 0.05 g dL⁻¹), serum globulin (1.64 ± 0.06 gdL⁻¹), HCO₃ (30.93 ± 0.62), Na⁺ (115.60 ± 3.72 mmolL⁻¹), alkaline phosphatase (93.33 ± 9.39 AP, IUL⁻¹), and AST/SGOT (21.00 ± 4.55 UL⁻¹) were significantly higher at 30 °C. Regarding hormonal responses, peak levels of growth hormone (GH), triiodothyronine (T3) (1.44 ± 0.07 ngmL⁻¹), and thyroxine (T4) were recorded at 30 °C. Meanwhile, serum cortisol (1.62 ± 0.06 µgdL⁻¹) and adrenocorticotropic hormone (ACTH) (18.01 ± 3.26 pgmL⁻¹) were highest at 34 °C. Immune responses were strongest between 26 and 30 °C. In conclusion, the results suggest that L. calbasu should ideally be cultured between 26 and 30 °C for optimum growth and health, making it ideal for commercial farming. Full article

Frost accumulation on heat exchangers severely limits the efficiency and reliability of air-source heat pumps (ASHPs) in cold, humid environments. Superhydrophobic coatings fabricated via electrostatic spraying offer a promising energy-free strategy for frost suppression. In this study, a robust superhydrophobic coating was deposited on the heat exchanger of a residential ASHP using this scalable technique. Under low-temperature heating conditions (2/1 °C), the coated exchanger delayed frost completion by a factor of 2.83 and shortened defrosting time by 33.3% compared to a conventional hydrophilic counterpart. These improvements translated to a 6.24% increase in average heating capacity and a 2.83% gain in the coefficient of performance (COP). Although the thicker superhydrophobic coating resulted in a marginal 3.1% reduction in cooling capacity during free-cooling operation, the significant enhancements in frost resistance and heating performance underscore its practical value. This work demonstrates that electrostatic spraying is a viable and effective method for fabricating high-performance superhydrophobic heat exchangers, paving the way for more efficient and frost-resistant ASHPs. Full article

Metabolic syndrome (MetS) is a multifactorial disorder characterized by central obesity, insulin resistance, dyslipidemia, and hypertension, all of which increase the risk of type 2 diabetes and cardiovascular diseases. This study investigates the potential complementary effects of the standardized green and black ADM ComplexTea Extract (CTE) and the heat-treated postbiotic (BPL1^® HT) on the cardiometabolic alterations associated with MetS in a murine model. C57BL/6J mice were fed a high-fat/high-sucrose (HFHS) diet and treated with CTE, BPL1^® HT, or their combination for 20 weeks. Metabolic, inflammatory, oxidative, vascular parameters, and fecal microbiota composition were assessed. Both CTE and BPL1^® HT individually attenuated weight gain, organ hypertrophy, insulin resistance, and inflammation. However, their combined administration exerted synergistic effects, fully normalizing body weight, adipocyte size, lipid profiles, HOMA-IR index, and insulin sensitivity to levels comparable to lean controls. Co-treatment also restored PI3K/Akt signaling in liver and muscle, reduced hepatic steatosis, and normalized the expression of inflammatory and oxidative stress markers across multiple tissues. Furthermore, vascular function was significantly improved, with enhanced endothelium-dependent relaxation and reduced vasoconstrictor responses, particularly to angiotensin II. CTE, BPL1^®HT, and the blend prevented bacterial richness reduction caused by HFHS; the blend achieved higher bacterial richness than mice in Chow diet. Additionally, the blend prevented the increase in Flintibacter butyricus, which is associated with MetS clinical parameters, and showed a tendency to increase the abundance of Bifidobacterium. These findings suggest that the combination of CTE and BPL1^® HT offers a potential nutritional strategy to counteract the metabolic and cardiovascular complications of MetS through complementary mechanisms involving improved insulin signaling, reduced inflammation and oxidative stress, enhanced vascular function, and modulation of gut microbiota. Full article

Modern gastronomy strives to combine high-quality food with the preservation of nutritional value, microbiological safety, and the sustainable use of raw materials. With the development of culinary technologies, precise heat treatment methods are gaining increasing importance, enabling better process control and more consistent quality results. This analysis aims to present the effects of the sous-vide (SV) method on the quality of vegetables in comparison with conventional heat treatment methods, such as boiling in water, steaming, cooking under increased pressure, cooking in a microwave oven, baking, grilling, and the cook-vide method. Analysis of the scientific literature has shown that the sous-vide method usually allows for the retention of greater amounts of vitamins (especially vitamin C), phenolic compounds and minerals, resulting in products with higher nutritional value and bioavailability of bioactive ingredients. Maintaining a controlled, low temperature in a vacuum environment reduces the loss of water and volatile components, which has a positive impact on the process yield as well as the color, texture, and aroma of vegetables. SV processing enhances product digestibility, preserves natural appearance, and improves food safety. Due to its hermetic packaging and limited oxygen access, this method ensures good microbiological quality and extends product shelf life. In the food service industry, SV allows for repeatable results, high sensory and technological quality, and reduced food waste. In the context of contemporary nutritional challenges and the experiences of the COVID-19 pandemic, sous-vide technology is gaining importance as a method supporting food safety, sustainability, and efficient resource management in the food service industry. Full article

Refinery blending is a routine operation, yet small changes in crude mix can strongly affect product yield and fuel quality. In this work, Aspen HYSYS v12.1 was used to model and optimize the blending of four Nigerian crude oils—Antan, Usan, Bonga, and Forcados—processed at about 150,000 barrels per day. The study examined how adjustments in blend ratio and feed temperature influence gasoline output and energy use in the distillation unit. The best result was obtained at a blend of Antan 10%, Usan 37.45%, Bonga 10%, and Forcados 42.55%, where gasoline yield increased by roughly 5.6% compared with the equal-blend case. Product properties remained within Nigerian fuel standards (RON ≈ 92, sulphur ≈ 0.038 wt%), showing that quality was not affected by the optimizations. Economic estimates also indicated higher annual revenue and a modest reduction in furnace heat duty, suggesting lower fuel consumption. Although the work was limited to steady-state simulation without plant-scale validation, it provides practical evidence that systematic crude blend optimizations can deliver measurable gains in yield and energy efficiency for refineries using mixed feedstocks. Full article

Urbanization and climate change have intensified the urban heat island (UHI) effect, increasing the demand for sustainable cooling solutions. Greenery, particularly in urban settings, has gained attention as a passive design strategy to enhance urban thermal comfort. This study systematically reviews peer-reviewed literature published in the last decade to assess the effectiveness of greenery in mitigating urban heat. Using a precise selection process, studies indexed in Web of Science (WOS), ScienceDirect, and Scopus were analyzed to identify key findings, methodologies, and gaps in existing research. The results highlight the impact of green facades, green walls, and urban greenery on surface and air temperature reduction, energy efficiency, and microclimate regulation. Furthermore, the study examines variations in performance based on climate zones, vegetation types, and urban configurations. Findings suggest that while greenery significantly improves urban thermal comfort, further research is needed to standardize assessment methods and optimize implementation strategies. This review contributes to the growing body of knowledge on nature-based solutions and provides insights for policymakers, urban designers, and researchers aiming to integrate greenery into sustainable urban planning. Full article

Urban heat islands (UHIs) have intensified in rapidly urbanizing regions like New York, exacerbating thermal discomfort, public health risks, and energy consumption. While previous research has highlighted various environmental and socioeconomic contributors, most existing studies lack interpretable, fine-scale models capable of quantifying the effects of specific drivers—limiting their utility for targeted planning. To address this challenge, we develop an interpretable machine learning framework using Random Forest and XGBOOST to predict land surface temperature across 1800+ census tracts in the New York metropolitan area, incorporating vegetation indices, water proximity, urban morphology, and socioeconomic factors. Both models performed strongly (mean R² ≈ 0.90), with vegetation coverage and water proximity emerging as the most influential cooling factors, while built form features played supporting roles. Socioeconomic vulnerability indicators showed weak correlations with temperature, suggesting a relatively equitable thermal landscape. Optimization simulations further revealed that increasing vegetation to a threshold level could lower average surface temperatures by up to 6.38 °C, with additional but smaller gains achievable through adjustments to water access and urban form. These findings provide evidence-based guidance for climate-adaptive urban design and green infrastructure planning. More broadly, the study illustrates the potential of explainable machine learning to support data-driven environmental interventions in complex urban systems. Full article

The growing demand for energy-efficient buildings necessitates innovative approaches to reduce energy consumption during early design stages. While traditional physics-based simulations remain time- and expertise-intensive, automated machine learning (AutoML) offers a promising alternative by enabling data-driven building performance prediction with minimal human intervention. This study conducts a benchmark evaluation of AutoML’s potential in building energy applications through three objectives: (1) a literature review revealing AutoML’s nascent adoption (10 identified studies) and primary use cases (heating/cooling prediction, energy demand forecasting); (2) a benchmark comparing three AutoML frameworks (AutoGluon, H2O, Auto-sklearn) against baseline and ensemble ML models using R², RMSE, MSE, and MAE metrics; and (3) SHAP (SHapley Additive exPlanations)-based interpretability analysis. Results demonstrate AutoGluon’s superior accuracy (R² = 0.993, RMSE = 2.280 kWh/m²) in predicting energy performance, outperforming traditional methods. Key influential features, including solar heat gain coefficient (SHGC) and U-values, were identified through SHAP, offering actionable design insights. The primary novelty of this work lies in its two-step methodology: a focused review to identify pertinent AutoML frameworks, followed by a comparative benchmarking of these frameworks against traditional ML for early-stage prediction. This process substantiates AutoML’s potential to democratize energy modeling and deliver practical, interpretable workflows for architectural design. Full article

The three key design criteria for nearly zero-energy buildings (nZEBs) and climate-neutral buildings are minimizing energy use, ensuring high occupant comfort, and reducing environmental impact. Thermal comfort is one of the main components of indoor environmental quality (IEQ), strongly affecting occupants’ health, well-being, and productivity. As energy-efficiency requirements become more demanding, the appropriate selection of heating systems, their automated control, and the management of solar heat gains are becoming increasingly important. This study investigates the influence of two low-temperature radiant heating systems—underfloor and wall-mounted—and the use of Venetian blinds on perceived thermal comfort in a highly glazed public nZEB building located in a densely built urban area within a temperate climate zone. The assessment was based on the PMV (Predicted Mean Vote) index, commonly used in IEQ research. The results show that both heating systems maintained indoor conditions corresponding to comfort or slight thermal stress under steady state operation. However, during periods of strong solar exposure in the room without blinds, PMV values exceeded 2.0, indicating substantial heat stress. In contrast, external Venetian blinds significantly stabilized the indoor microclimate—reducing PMV peaks by an average of 50.2% and lowering the number of discomfort hours by 94.9%—demonstrating the crucial role of solar protection in highly glazed spaces. No significant whole-body PMV differences were found between underfloor and wall heating. Overall, the findings provide practical insights into the control of thermal conditions in radiant-heated spaces and highlight the importance of solar shading in mitigating heat stress. These results may support the optimization of HVAC design, control, and operation in both residential and non-residential nZEB buildings, contributing to improved occupant comfort and enhanced energy efficiency. Full article

Machine learning (ML) is becoming a key enabler in building energy management systems (BEMS), yet most existing reviews focus on simulations and fail to reflect the realities of real-world deployment. In response to this limitation, the present work aims to present a systematic review dedicated entirely to experimental, field-tested applications of ML in BEMS, covering systems such as Heating, Ventilation & Air-conditioning (HVAC), Renewable Energy Systems (RES), Energy Storage Systems (ESS), Ground Heat Pumps (GHP), Domestic Hot Water (DHW), Electric Vehicle Charging (EVCS), and Lighting Systems (LS). A total of 73 real-world deployments are analyzed, featuring techniques like Model Predictive Control (MPC), Artificial Neural Networks (ANNs), Reinforcement Learning (RL), Fuzzy Logic Control (FLC), metaheuristics, and hybrid approaches. In order to cover both methodological and practical aspects, and properly identify trends and potential challenges in the field, current review uses a unified framework: On the methodological side, it examines key-attributes such as algorithm design, agent architectures, data requirements, baselines, and performance metrics. From a practical standpoint, the study focuses on building typologies, deployment architectures, zones scalability, climate, location, and experimental duration. In this context, the current effort offers a holistic overview of the scientific landscape, outlining key trends and challenges in real-world machine learning applications for BEMS research. By focusing exclusively on real-world implementations, this study offers an evidence-based understanding of the strengths, limitations, and future potential of ML in building energy control—providing actionable insights for researchers, practitioners, and policymakers working toward smarter, grid-responsive buildings. Findings reveal a maturing field with clear trends: MPC remains the most deployment-ready, ANNs provide efficient forecasting capabilities, RL is gaining traction through safer offline–online learning strategies, FLC offers simplicity and interpretability, and hybrid methods show strong performance in multi-energy setups. Full article

Energy efficiency in hospitals—where continuous operation with high internal gains and strict comfort needs—demands facade strategies tailored to climate. This study quantifies how the window-to-wall ratio (WWR) distribution and city-specific envelope properties affect the annual heating and cooling loads of a four-story, 3000 m² hospital in Turkey. Energy simulations were conducted using DesignBuilder (2021) with EnergyPlus under a controlled modeling framework, following ASHRAE healthcare guidelines for internal loads and TS 825:2024 for envelope compliance. Three locations were selected to span national variability: Bursa (Marmara—temperate/transition), Mersin (Mediterranean—hot–humid), and Kars (humid continental—cold). Scenario 1 (S1) assigned a graduated WWR on the south facade by floor—20%, 30%, 40%, and 50% from ground to top—while the north, east, and west facades were held at 20%, 30%, and 20%. Scenario 2 (S2) preserved the same geometry and WWR values but applied the graduated WWR to the north facade instead, keeping the south at 20%, east at 30%, and west at 20%. Within each city, opaque and glazing properties were kept constant across scenarios to isolate WWR–orientation effects. For every city–scenario combination, annual space-heating and space-cooling loads were computed, and window heat gains and losses were analyzed on the facade with variable WWR to support interpretation of performance mechanisms. The results indicate that S2 outperforms S1 in Mersin, S1 outperforms S2 in Kars, and S2 offers a moderate advantage in Bursa. Full article

Austempered ductile iron has gained increased interest as an ideal choice of material for mechanical components exposed to dynamic loading. Apart from strength and ductility, material damping is also one of the important demands imposed on the machine components exposed to noise and vibrations. In the present study, the damping characteristics of austempered ductile iron with varying levels of Mn variations are investigated. Samples were produced with three levels of Mn variations: 0.31 wt%, 0.60 wt%, and 0.92 wt%, and they were subjected to austenitization and austempering heat treatment. Variation in the damping characteristics of austempered ductile iron were studied before and after the heat treatment by performing the impact hammer test. The microstructure and the hardness variations of the samples were studied to interpret the mechanisms associated with variation in the damping characteristics. The studies revealed that Mn addition and heat treatment contribute to the variation in the microstructure and the mechanical properties. These properties have a contribution to the variation in the damping characteristics of ADI. Additionally, the study also revealed the existence of an optimum level of Mn that could yield better damping characteristics. Full article

Background/Objectives: The poultry industry faces severe heat-stress challenges that threaten both economic sustainability and animal welfare. Embryonic thermal manipulation (ETM) has been proposed as a thermal programming strategy to enhance chick heat tolerance, yet its efficacy in layers requires verification, and its effects on growth performance and neurodevelopment remain unclear. Methods: White Leghorn embryos at embryonic days 13 to 18 (ED 13–18) were exposed to 39.5 °C (ETM). Hatch traits and thermotolerance were recorded, and morphometric and histopathological analyses were performed on brain sections. Transcriptome profiling of the whole brains and hypothalami was conducted to identify differentially expressed genes (DEGs). Representative pathway genes responsive to ETM were validated by RT-qPCR. Results: ETM reduced hatchability, increased deformity rate, and decreased hatch weight and daily weight gain. During a 37.5 °C challenge, ETM chicks exhibited delayed panting and lower cloacal temperature. Histopathology revealed impaired neuronal development and myelination. Transcriptomic analysis of ED18 whole brains showed DEGs enriched in neurodevelopment, stimulus response, and homeostasis pathways. RT-qPCR confirmed hypothalamic sensitivity to ETM: up-regulation of heat-shock gene HSP70, antioxidant gene GPX1, the inflammatory marker IL-6, and apoptotic genes CASP3, CASP6, CASP9; elevated neurodevelopmental marker DCX, indicative of a stress-responsive neuronal state; and reduced orexigenic neuropeptide AGRP. Conclusions: ETM improves heat tolerance in layers but compromises hatching performance and brain development, with widespread perturbation of hypothalamic stress responses and neurodevelopmental gene networks. These findings elucidate the mechanisms underlying ETM and provide a reference for enhancing thermotolerance in poultry. Full article