Search Results (1,824)

As global warming worsens, reducing energy use is becoming increasingly crucial. In recent years, 34% of the world’s energy use has been consumed by buildings. Therefore, improving building energy efficiency is essential for halting climate change and promoting sustainability. In this regard, Building Information Modeling (BIM) is steadily emerging as a valuable tool for promoting energy efficiency. This research adopts a systematic review approach, and 87 articles were included for review. This research identified seven areas in which BIM plays a role in energy efficiency. For each area, workflows for the adoption of BIM were explored. Meanwhile, the advantages and disadvantages of each adoption of BIM were critically examined. In conclusion, visualization is the most helpful feature of BIM and is beneficial for almost all applications. In addition, software compatibility issues and high initial setup costs are the most common drawbacks of adopting BIM. This research makes several contributions to the literature. First, the results of this study help provide a better understanding of the importance of BIM in energy efficiency improvement. Secondly, our research supplements the energy field that identifies seven BIM use categories. Thirdly, this article critically examines the use of BIM in the building energy field. Full article

Against the background of global warming, the carbon emission of cities accounts for more than 70%, and its carbon sink increase and emission reduction have become the research focus. The water bodies and green spaces in the urban blue–green space have a synergistic carbon sequestration effect, but current research pays less attention to the small and medium scales. Therefore, taking the waterfront green space on both sides of Qinhuai New River in Nanjing as the research object, this paper explores the impact of the synergy between water and greenery on the carbon sequestration efficiency of green space. The study first estimates the carbon sequestration efficiency of green spaces by integrating measured Leaf Area Index (LAI) data with the mean carbon sequestration rate per unit leaf area for typical tree and shrub species. It then constructs a set of water–green spatial relationship indicators and applies a random forest regression model to identify the key factors influencing carbon sequestration efficiency. Finally, multiple scenario models are developed to simulate the effects of green spaces on CO₂ reduction, thereby validating the roles of the identified influencing factors. The study found that waterfront green spaces tended to exhibit slightly higher carbon sequestration efficiency compared with non-waterfront green spaces. The proportion of 10 m forest land area and the proportion of 10–20 m forest land area had a higher impact on the carbon sequestration capacity of waterfront green space; that is, the closer the distance between the green space and the water, the better the carbon sequestration capacity. In order to improve the carbon sequestration efficiency of the waterfront area, the green space should be arranged along the water bank as much as possible, the depth of the green space should be increased, the proportion of the forest land area should be increased, the arbor and shrub should be planted evenly, and ribbon planting should be avoided. The study confirmed the synergistic effect of water and greenery in carbon sequestration benefits, providing data support and theoretical reference for the optimization and renewal of urban waterfront green space, and contributing to the realization of urban waterfront green space planning, design, and renewal with the goal of a high carbon sink. Full article

In Northeast Brazil, climatic factors and technology synergistically enhance melon productivity and fruit quality. However, the region requires further research on the efficient use of water resources, particularly in determining the crop coefficient (Kc), which comprises the evaporation coefficient (Ke) and the transpiration coefficient (Kcb). Air temperature affects crop growth and development, altering the spectral response and the Kcb. However, the direct influence of air temperature on Kcb and spectral response remains underemphasized. This study employed unmanned aerial vehicle (UAV) with RGB and Red-Green-NIR sensors imagery to extract biophysical parameters for improved water management in melon cultivation in semiarid northern Bahia. Field experiments were conducted during two distinct periods: warm (October–December 2019) and cool (June–August 2020). The ‘Gladial’ and ‘Cantaloupe’ cultivars exhibited higher Kcb values during the warm season (2.753–3.450 and 3.087–3.856, respectively) and lower during the cool season (0.815–0.993 and 1.118–1.317). NDVI-based estimates of Kcb showed strong correlations with field data (r > 0.80), confirming its predictive potential. The results demonstrate that UAV-derived NDVI enables reliable estimation of melon Kcb across seasons, supporting its application for evapotranspiration modeling and precision irrigation in the Brazilian semiarid context. Full article

This paper presents a systematic literature review focusing on the application of machine learning techniques for deriving observational constraints in cosmology. The goal is to evaluate and synthesize existing research to identify effective methodologies, highlight gaps, and propose future research directions. Our review identifies several key findings: (1) Various machine learning techniques, including Bayesian neural networks, Gaussian processes, and deep learning models, have been applied to cosmological data analysis, improving parameter estimation and handling large datasets. However, models achieving significant computational speedups often exhibit worse confidence regions compared to traditional methods, emphasizing the need for future research to enhance both efficiency and measurement precision. (2) Traditional cosmological methods, such as those using Type Ia Supernovae, baryon acoustic oscillations, and cosmic microwave background data, remain fundamental, but most studies focus narrowly on specific datasets. We recommend broader dataset usage to fully validate alternative cosmological models. (3) The reviewed studies mainly address the $H_0$ tension, leaving other cosmological challenges—such as the cosmological constant problem, warm dark matter, phantom dark energy, and others—unexplored. (4) Hybrid methodologies combining machine learning with Markov chain Monte Carlo offer promising results, particularly when machine learning techniques are used to solve differential equations, such as Einstein Boltzmann solvers, prior to Markov chain Monte Carlo models, accelerating computations while maintaining precision. (5) There is a significant need for standardized evaluation criteria and methodologies, as variability in training processes and experimental setups complicates result comparability and reproducibility. (6) Our findings confirm that deep learning models outperform traditional machine learning methods for complex, high-dimensional datasets, underscoring the importance of clear guidelines to determine when the added complexity of learning models is warranted. Full article

Despite growing interest in carbon capture and utilization (CCU), the transformation of captured CO₂ into dry ice remains poorly studied, particularly from a systems integration and energy optimization perspective. While previous works have examined individual components such as CO₂ absorption, liquefaction, or refrigerant evaluation, no existing study has modeled the full dry ice production chain from capture to solidification within a unified simulation framework. This study presents the first complete simulation and optimization of a dry ice production process, incorporating CO₂ absorption, solvent regeneration, dehydration, multistage compression, ammonia-based external liquefaction, and expansion-based solidification using Aspen HYSYS. The process features ammonia as a working refrigerant due to its favorable thermodynamic performance and zero global warming potential. Optimization of heat integration reduced total energy consumption by 66.67%, replacing conventional utilities with water-based heat exchangers. Furthermore, solvent recovery achieved rates of 75.65% for MDEA and 66.4% for piperazine, lowering operational costs and environmental burden. The process produced dry ice with 97.83% purity and 94.85% yield. A comparative analysis of refrigerants confirmed ammonia’s superiority over R-134a and propane. These results provide the first system-level roadmap for producing dry ice from captured CO₂ in an energy-efficient, scalable, and environmentally responsible manner. Full article

Advances in smart animal husbandry necessitate efficient pig behavior monitoring, yet traditional approaches suffer from operational inefficiency and animal stress. We address these limitations through a lightweight YOLOv8n architecture enhanced with SPD-Conv for feature preservation during downsampling, LSKBlock attention for contextual feature fusion, and a dedicated small-target detection head. Experimental validation demonstrates superior performance: the optimized model achieves a 92.4% mean average precision (mAP@0.5) and 87.4% recall, significantly outperforming baseline YOLOv8n by 3.7% in AP while maintaining minimal parameter growth (3.34M). Controlled illumination tests confirm enhanced robustness under strong and warm lighting conditions, with performance gains of 1.5% and 0.7% in AP, respectively. This high-precision framework enables real-time recognition of standing, prone lying, lateral lying, and feeding behaviors in commercial piggeries, supporting early health anomaly detection through non-invasive monitoring. Full article

Hydrogen is increasingly recognized as a clean energy vector and storage medium, yet its viability and strategic role in the Western Balkans remain underexplored. This study provides the first comprehensive techno-economic, environmental, and strategic evaluation of hydrogen production pathways in Albania. Results show clear trade-offs across options. The levelized cost of hydrogen (LCOH) is estimated at 8.76 €/kg H₂ for grid-connected, 7.75 €/kg H₂ for solar, and 7.66 €/kg H₂ for wind electrolysis—values above EU averages and reliant on lower electricity costs and efficiency gains. In contrast, fossil-based hydrogen via steam methane reforming (SMR) is cheaper at 3.45 €/kg H₂, rising to 4.74 €/kg H₂ with carbon capture and storage (CCS). Environmentally, Life Cycle Assessment (LCA) results show much lower Global Warming Potential (<1 kg CO₂-eq/kg H₂) for renewables compared with ~10.39 kg CO₂-eq/kg H₂ for SMR, reduced to 3.19 kg CO₂-eq/kg H₂ with CCS. However, grid electrolysis dominated by hydropower entails high water-scarcity impacts, highlighting resource trade-offs. Strategically, Albania’s growing solar and wind projects (electricity prices of 24.89–44.88 €/MWh), coupled with existing gas infrastructure and EU integration, provide strong potential. While regulatory gaps and limited expertise remain challenges, competition from solar-plus-storage, regional rivals, and dependence on external financing pose additional risks. In the near term, a transitional phase using SMR + CCS could leverage Albania’s gas assets to scale hydrogen production while renewables mature. Overall, Albania’s hydrogen future hinges on targeted investments, supportive policies, and capacity building aligned with EU Green Deal objectives, with solar-powered electrolysis offering the potential to deliver environmentally sustainable green hydrogen at costs below 5.7 €/kg H₂. Full article

Climate change and global warming are concerning issues impacting various industries. In the aquaculture industry, these issues are more important in coldwater species, like rainbow trout Oncorhynchus mykiss. Hence, strategies to control these negative effects are worthy of study. Herbal feed additives are reliable tools to increase fish growth and health, thereby mitigating the drawbacks of climate change on fish. In this study, three diets containing 100 (100EO), 200 (200EO), and 400 (400EO) mg/kg essential oil of Abies sibirica (SBF) along with a control diet (CTL; unsupplemented) were fed to triplicate groups of fish for 60 days. Then the fish were exposed to a 96 h heat stress (25 °C) to monitor their survival and biochemical responses. The results showed that growth performance, feed efficiency, heat stress resistance, intestinal activity of digestive enzymes, and skin mucus immunological parameters significantly (p < 0.05) increased in the SBF essential oil treatments, and the highest increases were observed in the 100EO treatment, followed by the 200EO group. Dietary supplementation with SBF essential oil significantly (p < 0.05) mitigated heat stress-induced increases in plasma cortisol and glucose. Moreover, dietary SBF essential oil significantly (p < 0.05) enhanced immunological parameters such as plasma and intestinal lysozyme and immunoglobulin levels, and improved hepatic antioxidant defenses (including catalase, glutathione peroxidase, total antioxidant capacity, and reduced glutathione), while reducing lipid peroxidation. These effects were most pronounced in the 100EO and 200EO treatments, with the highest performance being observed in the former group. In conclusion, dietary SBF essential oil at 100 mg/kg is capable of augmenting growth performance, immunity, and antioxidant capacity, and suppressing physiological stress, thereby augmenting fish resilience against heat stress. Full article

Due to its efficiency, advanced oxidation processes (AOP), such as photo-Fenton, have become an alternative for removing emerging contaminants like paracetamol. The objective of this work was to perform a life cycle assessment (LCA) according to ISO 14040/44 for a heterogeneous photo-Fenton process catalyzed by Cu/Fe-pillared clays (PILC) for the removal of paracetamol from water. The study covered catalyst synthesis and four treatment scenarios, with inventories built from experimental data and ecoinvent datasets; treatment time was 120 min per functional unit. Environmental impacts for catalyst synthesis were quantified with ReCiPe 2016 (midpoint), while toxicity-related impacts of the degradation stage were assessed with USEtox™ (human carcinogenic toxicity, human non-carcinogenic toxicity, and freshwater ecotoxicity). Catalyst synthesis dominated most midpoint categories, the global warming potential for 1 g of Cu/Fe-PILC was 10.98 kg CO₂ eq. Toxicity results for S4 (photo-Fenton Cu/Fe PILC) showed very low values: 9.73 × 10⁻¹² CTUh for human carcinogenic and 1.29 × 10⁻¹³ CTUh for human non-carcinogenic. Freshwater ecotoxicity ranged from 5.70 × 10⁻⁴ PAF·m³·day at pH 2.7 (≥60 min) to 1.67 × 10⁻⁴ PAF·m³·day at pH 5.8 (120 min). Overall, optimizing pH and reaction time, are key levers to improve the environmental profile of AOP employing Cu/Fe-PILC catalysts. Full article

The growing demand for radiation-shielded infrastructure highlights the need for materials that balance shielding performance with environmental and economic sustainability. Heavyweight self-compacting concretes (HWSCC), commonly produced with barite (BaSO₄) or magnetite (Fe₃O₄) aggregates, lack systematic life cycle comparisons. The aim of this study is to systematically compare barite- and magnetite-based HWSCC in terms of life cycle environmental impacts, life cycle cost, functional performance (strength and shielding), and end-of-life circularity, in order to identify the more sustainable and cost-effective material for radiation shielding infrastructure. This study applies cradle-to-grave life cycle assessment (LCA) and life cycle cost analysis (LCC), in accordance with ISO 14040/14044 and ISO 15686-5, to evaluate barite- and magnetite-based HWSCC. Results show that magnetite concrete reduces global warming potential by 19% eutrophication by 24%, and fossil resource depletion by 23%, while lowering life cycle costs by ~23%. Both concretes achieve comparable compressive strength (~48 MPa) and shielding efficiency (µ ≈ 0.28–0.30 cm⁻¹), meeting NCRP 147 and IAEA SRS-47 standards. These findings demonstrate that magnetite-based HWSCC offers a more sustainable, cost-effective, and ethically sourced alternative for radiation shielding in healthcare, nuclear, and industrial applications. In addition, the scientific significance of this work lies in establishing a transferable methodological framework that combines LCA, LCC, and performance-normalized indicators. This enables scientists and practitioners worldwide to benchmark heavyweight concretes consistently and to adapt sustainability-informed material choices to their own regional contexts. Full article

The delayed coking unit (DCU) is a critical technology in Mexican refineries for upgrading heavy crude oil into lighter, high-value products. Despite its economic relevance, the process is energy-intensive, generates substantial emissions, and produces significant coke, challenging its sustainability. This study proposes a multi-objective optimization framework to enhance DCU performance by integrating Aspen Plus^® v.12.1 simulations with sustainability metrics. Five key indicators were considered: Global Warming Potential (GWP), Specific Energy Intensity (SEI), Mass Intensity (MI), Reaction Mass Efficiency (RME), and Product Yield. A validated Aspen Plus^® model was combined with sensitivity analysis to identify critical decision variables, which were optimized through the $ϵ$-constraint method. Strategic adjustments in reflux flows, split ratios, and column operating conditions improved separation efficiency and reduced energy demand. Results show GWP reductions of 15–25% and SEI improvements of 5–18% for light and heavy gas oils, with smaller gains in MI and trade-offs in RME. Product yield was preserved under optimized conditions, ensuring economic feasibility. A key limitation is that this study did not model coking reactions; instead, optimization focused on the separation network, using reactor effluent as a fixed input. Despite this constraint, the methodology demonstrates a replicable path to improve refining sustainability. Full article

Deploying deep learning (DL) models in real-world environments remains a major challenge, particularly under resource-constrained conditions where achieving both high accuracy and compact architectures is essential. While effective, Conventional pruning methods often suffer from high computational overhead, accuracy degradation, or disruption of the end-to-end training process, limiting their practicality for embedded and real-time applications. We present Dynamic Attention-Guided Pruning (DAGP), a Dynamic Attention-Guided Soft Channel Pruning framework that overcomes these limitations by embedding learnable, differentiable pruning masks directly within convolutional neural networks (CNNs). These masks act as implicit attention mechanisms, adaptively suppressing non-informative channels during training. A progressively scheduled L1 regularization, activated after a warm-up phase, enables gradual sparsity while preserving early learning capacity. Unlike prior methods, DAGP is retraining-free, introduces minimal architectural overhead, and supports optional hard pruning for deployment efficiency. Joint optimization of classification and sparsity objectives ensures stable convergence and task-adaptive channel selection. Experiments on CIFAR-10 (VGG16, ResNet56) and PlantVillage (custom CNN) achieve up to 98.82% FLOPs reduction with accuracy gains over baselines. Real-world validation on an enhanced PlantDoc dataset for agricultural monitoring achieves 60 ms inference with only 2.00 MB RAM on a Raspberry Pi 4, confirming efficiency under field conditions. These results illustrate DAGP’s potential to scale beyond agriculture to diverse edge-intelligent systems requiring lightweight, accurate, and deployable models. Full article

The fast fashion industry has significantly increased global textile demand, driving a surge in fiber production. However, only a minimal portion of this fiber comes from recycled sources. In the United States alone, a vast amount of textile waste is generated annually, with over half ending up in landfills, contributing to environmental degradation and global warming. These developments underscore the urgent need for scalable and efficient textile recycling solutions to address both economic and ecological challenges in the fashion industry. Among recycling methods, mechanical recycling stands out for its low cost and simplicity, making it suitable for processing various types of textile waste. This article reviews current knowledge, identifies key research gaps, and provides direction for future studies in mechanical textile recycling. Despite progress, significant challenges remain in improving the quality and efficiency of recycled fiber. This study shows the importance of advancing pretreatment methods and sorting technologies, and highlights understanding regarding shredding, opening processes, and fabric structural properties. Full article

As an important component of the Stirling-type pulse tube cryocooler (SPTC), an efficient phase shifter can significantly improve the cooling capacity. This paper combines the advantages of the cold inertance tube and reservoir (ITR) and the active warm displacer (AWD) in an 8 K Stirling-type pulse tube cryocooler. Through numerical simulation methods, the influence of structural parameters of the cold ITR and operating parameters of AWD on acoustic power and impedance was studied. The results indicate that the length and diameter of the inertance tube, as well as the displacement and phase of the AWD, will affect the distribution of PV power inside the middle heat exchanger. The impedance distribution inside the pulse tubes of the higher-temperature section and the lower-temperature section changes in opposite directions. Through experiment, the effectiveness of the cold ITR and the adjustment function of the AWD were verified. A cooling capacity of 74 mW at 8 K can be obtained with the electric power of 177.5 W and a precooling capacity of 9.1 W/70 K. The AWD has a significant adjustment effect on T1 and T2, reaching the lowest no-load temperature at 2.13 mm and 48°, respectively, with a minimum no-load temperature of 5.13 K. Full article

This study investigates the future temperature changes in the climate-vulnerable region of the Eastern Mediterranean. The results from seventeen (17) CMIP6 (6th Phase of Coupled Model Intercomparison Project) model simulations are analyzed. The analysis is focused on the SSP2-4.5 and SSP5-8.5 scenarios. The ERA5 reanalysis is used as a reference dataset to investigate the performance of CMIP6 simulations to accurately reproduce the mean temperature in the Eastern Mediterranean region. The results show that CMIP6 model simulations vary regarding their efficiency for capturing the mean temperature. Future projections show that significant warming is shown during the last period of the 21st century. The continental Balkan and Turkish regions are recognized as the most affected areas regarding future warming. The increase in temperature spatially ranges (in local scale) from 1.5 °C to 4.5 °C for the SSP2-4.5 scenario and from 3.0 °C to 8.0 °C for the SSP5-8.5 scenario. Finally, the seasonal analysis indicates that summer (JJA) shows the maximum temperature increase compared with the other seasons. Full article