Search Results (1,078)

Social housing plays a key role in the Colombian residential market, showing a growing commitment to sustainability: currently, a high percentage of EDGE-certified homes belong to this segment. However, in hot and humid areas such as Montería, most VIS homes have deficiencies in their thermal envelopes and poor roof insulation, which leads to a heavy reliance on air conditioning. This study addresses the lack of practical and replicable methodologies for improving energy efficiency in social housing located in hot–humid climates. The research aims to develop and apply a methodological framework that integrates architectural rehabilitation strategies with quantitative evaluation using the EDGE App tool. The proposed approach was implemented in Montería, Colombia, through a case study that combines diagnostic analysis of existing housing conditions, simulation of energy-saving measures, and assessment of environmental and economic performance. A real home in Montería was used as a reference, and more than 600 simulations were carried out considering different orientations and passive strategies. Through a Pareto analysis, the three most efficient measures were identified: natural ventilation, high-solar-reflectance roofing, and moderate reduction in the U-value. Together, these measures reduced energy consumption by up to 50%, with minimal increases in construction costs (≤1.2% of the commercial value). It was also found that excessive insulation can induce unwanted nighttime heating demands, highlighting the need for adjustments to the climatic context. The results confirm the technical and economic feasibility of mass rehabilitation of VIS in hot and humid climates using standard passive measure packages, consolidating the role of the EDGE App as a key tool for guiding sustainable design, investment, and environmental certification decisions. Full article

Rice (Oryza sativa L.) production in the Philippines remains highly vulnerable to recurrent outbreaks of the Rice Black Bug (RBB; Scotinophara coarctata F.) and White Stemborer (WSB; Scirpophaga innotata W.), two of the most destructive pests in Southeast Asian rice ecosystems. Classical economic threshold levels (ETLs) are difficult to estimate in smallholder settings due to the lack of cost–loss data, often leading to either delayed or excessive pesticide application. To address this, the present study developed an adaptive outbreak-forecasting framework that integrates the Number–Size (N–S) fractal model with machine learning (ML) classifiers to define and predict pest regime transitions. Seven years (2018–2024) of light-trap surveillance data from the Philippine Rice Research Institute–Midsayap Experimental Station were combined with daily climate variables from the NASA POWER database, including air temperature, humidity, precipitation, wind, soil moisture, and lunar phase. The N–S fractal model identified natural breakpoints in the log–log cumulative frequency of pest counts, yielding early-warning and severe-outbreak thresholds of 134 and 250 individuals for WSB and 575 and 11,383 individuals for RBB, respectively. Eight ML algorithms such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Balanced Bagging, LightGBM, XGBoost, and CatBoost were trained on variance-inflation-filtered climatic and temporal predictors. Among these, CatBoost achieved the highest predictive performance for WSB at the 94.3rd percentile (accuracy = 0.932, F1 = 0.545, ROC–AUC = 0.957), while Logistic Regression performed best for RBB at the 75.1st percentile (F1 = 0.520, ROC–AUC = 0.716). SHAP (SHapley Additive exPlanations) analysis revealed that outbreak probability increases under warm nighttime temperatures, high surface soil moisture, moderate humidity, and calm wind conditions, with lunar phase exerting additional modulation of nocturnal pest activity. The integrated fractal–ML approach thus provides a statistically defensible and ecologically interpretable basis for adaptive pest surveillance. It offers an early-warning system that supports data-driven integrated pest management (IPM), reduces unnecessary pesticide use, and strengthens climate resilience in Philippine rice ecosystems. Full article

Uniform application of fertilizers and pesticides continues to dominate global agriculture despite significant spatial variability in soil and crop conditions. This mismatch results in avoidable yield gaps, excessive chemical waste, and environmental pressures, including nutrient leaching and greenhouse gas emissions. The integration of Artificial Intelligence (AI) and Remote Sensing (RS) has emerged as a transformative framework for diagnosing this variability and enabling site-specific, climate-responsive management. This systematic synthesis reviews evidence from 2000–2025 to assess how AI–RS technologies optimize agrochemical efficiency. A comprehensive search across Scopus, Web of Science, IEEE Xplore, ScienceDirect, and Google Scholar were used. Following rigorous screening and quality assessment, 142 studies were selected for detailed analysis. Data extraction focused on sensor platforms (Landsat-8/9, Sentinel-1/2, UAVs), AI approaches (Random Forests, CNNs, Physics-Informed Neural Networks), and operational outcomes. The synthesized data demonstrate that AI–RS systems can predict critical soil attributes, specifically salinity, moisture, and nutrient levels, with 80–97% accuracy in some cases, depending on spectral resolution and algorithm choice. Operational implementations of Variable-Rate Application (VRA) guided by these predictive maps resulted in fertilizer reductions of 15–30%, pesticide use reductions of 20–40%, and improvements in water-use efficiency of 25–40%. In fields with high soil heterogeneity, these precision strategies delivered yield gains of 8–15%. AI–RS technologies have matured from experimental methods into robust tools capable of shifting agrochemical science from reactive, uniform practices to predictive, precise strategies. However, widespread adoption is currently limited by challenges in data standardization, model transferability, and regulatory alignment. Future progress requires the development of interoperable data infrastructures, digital soil twins, and multi-sensor fusion pipelines to position these technologies as central pillars of sustainable agricultural intensification. Full article

The Space–Air–Ground Integrated Network (SAGIN), a core architecture for 6G, faces formidable routing challenges stemming from its high-dynamic topological evolution and strong heterogeneous resource characteristics. Traditional protocols like OSPF suffer from excessive convergence latency due to frequent topology updates, while existing intelligent methods such as DQN remain confined to a passive reactive decision-making paradigm, failing to leverage spatiotemporal predictability of network dynamics. To address these gaps, this study proposes an adaptive routing algorithm (GCN-T-PPO) integrating a GCN-Transformer hybrid encoder, Particle Swarm Optimization (PSO), and Proximal Policy Optimization (PPO) with spatiotemporal attention. Specifically, the GCN-Transformer encoder captures spatial topological dependencies and long-term temporal traffic evolution, with PSO optimizing hyperparameters to enhance prediction accuracy. The PPO agent makes proactive routing decisions based on predicted network states (next K time steps) to adapt to both topological and traffic dynamics. Extensive simulations on real dataset-parameterized environments (CelesTrak TLE data, CAIDA 100G traffic statistics, CRAWDAD UAV mobility models) demonstrate that under 80% high load and bursty Pareto traffic, GCN-T-PPO reduces end-to-end latency by 42.4% and packet loss rate by 75.6%, while improving QoS satisfaction rate by 36.9% compared to DQN. It also outperforms SOTA baselines including OSPF, DDPG, D2-RMRL, and Graph-Mamba. Ablation studies validate the statistical significance (p < 0.05) of key components, confirming the synergistic gains from spatiotemporal joint modeling and proactive decision-making. This work advances SAGIN routing from passive response to active prediction, significantly enhancing network stability, resource utilization efficiency, and QoS guarantees, providing an innovative solution for 6G global seamless coverage and intelligent connectivity. Full article

This study presents the experimental and modelling investigation of the performance of an SI methanol marine engine operating under lean conditions. The effects of spark timing and excess air ratio on combustion characteristics, engine performance, and emissions are explored. Multiple machine learning models, including Support Vector Machines (SVM), Artificial Neural Network (ANN), LightGBM, and Random Forest (RF), are employed to predict the engine performance and emission characteristics. Experimental results show that as spark timing advances, the combustion phase advances, with the burn duration being extended. When the excess air ratio is less than 1.35, there exists an optimal spark timing, corresponding to a maximum brake thermal efficiency. The optimal spark timing exhibits an advancing tendency along with increasing excess air ratio. HC emission is primarily determined by the excess air ratio and shows no significant variation under the different spark timings. NO_x emission is initially increased and then decreased with advancing spark timing. Compared with ANN, LightGBM, and RF, SVM demonstrates a superior predictive accuracy, with R² values for engine performance exceeding 0.98 and R² values for emissions above 0.92. Full article

With the rapid development of renewable energy sources in power generation, utility boilers need to perform load regulation over a wide range to maintain the stability of the power supply system. Preheating combustion technology is a potential approach to achieve wide load range operation, improve combustion stability, and lower NO_x emissions from utility boilers. Preheating combustion devices (PCDs) were designed and installed in the reduction zone of a boiler. These devices preheated the coal at an excess air ratio ranging from 0.35 to 0.7 to generate high-temperature gas and char, which effectively reduced NO_x formation in the furnace. Numerical studies were conducted to evaluate the combustion performance and nitrogen oxides emissions of a 330 MW utility boiler retrofitted with PCDs at different loads. The simulations were conducted over a load range of 20% to 100% of the rated load, corresponding to an electrical power of 66 MW to 330 MW. The preheated combustion device’s previous experimental data served as the boundary conditions of the preheated product nozzles. The simulation results demonstrated that the retrofitted boiler could operate stably from 20% to 100% of the rated load, maintaining acceptable combustion efficiency and lower NO_x emissions. The combustion efficiency gradually dropped with decreasing boiler load, reaching a minimum value of 95.6%. As the load declined, the size of the imaginary tangent circle of the boiler shrank, while the ignition distance increased. Additionally, the variation in NO_x concentration with load was complex. The NO_x concentration at the furnace outlet was between 102.7 and 220.3 mg/m³, and the preheated products effectively reduced the nitrogen oxides produced by combustion in the furnace at all loads. Full article

An epoxy resin can be crosslinked with an imidazole-based ionic liquid (IL), whose excess, provided its high melting temperature, can potentially form a dispersed phase to store thermal energy and produce a phase-change material (PCM). This work investigates the crosslinking of diglycidyl ether of bisphenol A (DGEBA) using 1-ethyl-3-methylimidazolium chloride ([EMIM]Cl) at its mass fractions of 5, 10, 20, 40, and 60%. The effect of [EMIM]Cl on the viscosity, curing rate, and curing degree was studied, and the thermophysical properties and morphology of the resulting crosslinked epoxy polymer were investigated. During the curing, [EMIM]Cl changes its role from a crosslinking agent (an initiator of homopolymerization) and a diluent of the epoxy resin to a plasticizer of the cured epoxy polymer and a dispersed phase-change agent. An increase in the [EMIM]Cl content accelerates the curing firstly because of the growth in the number of reaction centers, and then the curing slows down because of the action of the IL as a diluent, which reduces the concentration of reacting substances. In addition, a rise in the proportion of [EMIM]Cl led to the predominance of the initiation over the chain growth, causing the formation of short non-crosslinked molecules. The IL content of 5% allowed for curing the epoxy resin and elevating the stiffness of the crosslinked product by almost 7 times compared to tetraethylenetriamine as a usual aliphatic amine hardener (6.95 GPa versus 1.1 GPa). The [EMIM]Cl content of 20–40% resulted in a thermoplastic epoxy polymer capable of flowing and molding at elevated temperatures. The formation of IL emulsion in the epoxy matrix occurred at 60% [EMIM]Cl, but its hygroscopicity and absorption of water from surrounding air reduced the crystallinity of dispersed [EMIM]Cl, not allowing for an effective phase-change material to be obtained. Full article

Compressed air energy storage in aquifers (CAESA) is a promising technology for large-scale, long-duration energy storage. The initial bubble, also known as cushion gas, is a prerequisite for system operation, as it creates the storage space and provides pressure support. However, the optimal amount of cushion gas needed to satisfy both energy storage scale and system cycle time (SCT) remains insufficiently studied. In this work, we investigate the relationship between cushion-gas masses and SCT under various energy storage scales using numerical simulations, and further analyze its impact on the maximum achievable energy storage scale through an orthogonal design encompassing nine geological conditions. Simulation results indicate that aquifer permeability, depth, and thickness impose a physical upper limit on achievable storage scales. Below this threshold, increasing cushion-gas mass approximately linearly enhances SCT, while beyond it, performance gains saturate. The effect of the air bubble on system performance is also influenced by well screen length. Sensitivity analysis suggests that larger injection masses are beneficial under high-permeability and deeper burial conditions, whereas excessive injection under unfavorable geological conditions can lead to inefficiency and wasted resources. Based on these findings, the recommended injection gas masses for different energy storage scales under the ideal model are provided, along with suggestions for gas injection configurations based on various geological conditions. This work provides a new approach for the design of initial bubble injection for a CAESA system. Full article

Offshore wind turbines (OWTs) operate under harsh marine conditions involving strong winds, waves, and salt-laden air, which increase the risk of excessive vibrations and structural failures such as tower collapse. To ensure structural safety and achieve effective vibration control, accurate modal parameter identification is essential. In this study, a vibration monitoring system was developed, and the Bayesian Spectral Decomposition (BSD) method was applied for the operational modal analysis of a 5.5 MW monopile OWT. The monitoring system consisted of ten uniaxial accelerometers mounted at five elevations along the tower, with two orthogonally oriented sensors at each level to capture horizontal vibrations. Due to continuous nacelle yawing, the measured accelerations were projected onto the structural fore–aft (FA) and side–side (SS) directions prior to modal analysis. Two days of vibration and SCADA data were collected: one under rated rotor speed and another including one hour of idle state. Data preprocessing involved outlier removal, low-pass filtering, and directional projection. The obtained data were divided into 20-min segments, and the BSD approach was applied to extract the primary modal parameters in both FA and SS directions. Comparison with results from the Stochastic Subspace Identification (SSI) technique showed strong consistency, verifying the reliability of the BSD method and its advantage in uncertainty quantification. The results indicate that the identified modal frequencies remain relatively stable under both rated and idle conditions, whereas the damping ratios increase with wind speed, with a more significant growth observed in the FA direction. Full article

Air-assisted sprayers are widely used in orchards to ensure deep canopy penetration and effective pesticide coverage, yet excessive or misdirected airflow often causes spray drift and ground losses. This study evaluated spray deposition efficiency, drift, and environmental performance of a novel double-tower orchard sprayer (DIVENT) equipped with two independently driven axial fans allowing separate airflow adjustment on each side. Field experiments were conducted in apple orchards under crosswind conditions using the following three airflow emission scenarios (air volume to the LEFT/RIGHT side of sprayer): symmetrical (100%/100%), compensating crosswind (30%/100%), and one-sided (0%/100%). Measurements of spray deposition within the canopy, ground losses, and off-target deposition drift were performed using fluorescent tracer, and power consumption was recorded to estimate fuel use and CO₂ emissions. The compensating airflow setting significantly improved spray targeting, reducing both in-orchard ground losses and off-target drift by up to 60%, while maintaining uniform canopy coverage comparable to the conventional symmetrical mode. The one-sided emission scenario achieved the highest drift reduction (67.8%) and the lowest power and CO₂ emissions, though at the cost of reduced canopy deposition. Overall, the study demonstrates that independent fan control allows effective adaptation of spraying to weather and canopy conditions, providing substantial environmental and energy benefits without compromising spray efficiency. Full article

Excessive corporate use of fossil fuels has significantly worsened global air quality. In response, many governments, including China’s, have implemented tax incentives to promote sustainable development, though their effectiveness at the firm level remains unclear. This study empirically examines the relationship between tax incentives and corporate green transition using a panel of 30,483 firm-year observations from Chinese A-share non-financial listed firms spanning 2009–2023. We construct a Green Sustainable Development Performance (GSDP) index based on green patent applications and environmental disclosure and identify innovation investment as the main transmission mechanism. The results show that stronger tax incentives are associated with higher GSDP scores. This relationship is largely driven by innovation: after controlling R&D input, the direct effect of tax incentives declines, while the indirect effect through innovation remains both statistically and economically significant. The effect is more evident in large firms and those in eastern provinces, but weaker in regions with higher financial constraints with limited time lags. The findings offer practical implications for designing targeted, verifiable, and innovation-oriented tax instruments to foster high-quality, sustainable corporate development. Full article

With increasingly stringent requirements for fuel sulfur content in ship emission control areas, traditional manual onboard inspection methods struggle to meet the demands for real-time supervision. This study proposes a hybrid method for monitoring and matching excessive exhaust emissions from ships underway using Automatic Identification System (AIS) data. A ship emission calculation model is applied to obtain the real-time SO₂ emission source strength for each vessel. Then, an improved Gaussian puff model, considering the moving characteristics of ships, is established to calculate time-series SO₂ diffusion concentrations at monitoring points for each ship within the study area. Finally, a matching algorithm for identifying ships with excessive emissions, based on grey relational analysis, is designed. This algorithm matches the computed time-series diffusion concentration of each ship with the monitored concentration, enabling precise traceability of ships using fuel with excessive sulfur content under multi-ship conditions. This study uses the Nanjing Dashengguan Yangtze River Bridge area as the experimental region and employs measured SO₂ data from monitoring points to verify the method’s feasibility and effectiveness. The results demonstrate that this method can effectively identify ships with excessive emissions, providing crucial technical support for the green development of shipping and for the prevention and control of air pollution. Full article

The increasing global demand for sustainable energy has increased the importance of solar photovoltaic thermal (PV/T) systems, which simultaneously increase electrical efficiency by removing excess heat and utilizing it for beneficial purposes. Although the addition of fins is generally known to increase efficiency, the influence of Z-finned geometries on PV/T system performance has not yet been fully characterized. In this study, the performance of conventional (PV/T-C) and Z-finned (PV/T-F) air-cooled PV/T systems was numerically investigated through comprehensive energy, exergy, and sustainability analyses. Simulations were conducted using ANSYS Fluent 2025 R1. The results revealed that, compared to the PV/T-C system, the PV/T-F system achieved an increase of 17.18% in overall efficiency. Furthermore, the incorporation of fins enhanced the overall exergy efficiency by 2.57% and improved the sustainability index by 0.32%. The findings demonstrate that Z-shaped fins improve the overall, exergy, and sustainability performances of air-cooled PV/T systems under the climatic conditions of Malatya, Türkiye. This study highlights the critical role of fin geometry in enhancing PV/T system performance and contributes valuable insights for the design of more efficient and sustainable solar energy systems. Full article

The aim of the study is to analyze the impact of climate change on modern low-energy construction. The authors attempted to answer the question whether an existing single-family building that meets the current requirements for a low-energy facility can be called such in terms of ongoing long-term climate changes. Therefore, on the model of the building in question, the ESP-r program analyzed the impact of climate change on energy consumption for both heating and cooling purposes. The SSP2-4.5 scenario (RCP 4.5 according to the IPCC 5th Assessment Report) was adopted, generating future climate parameters for 2050 and 2080, using the HadCM3 model. In order to validate the model, the actual energy consumption values were compared with the values obtained from numerical modeling in the ESP-r program. The final task was to analyze the impact of ongoing climate changes on energy parameters and comfort of use of the facility. Based on the results obtained, the authors concluded that the effect of the changes that take place is the need to introduce an air conditioning system into it in the summer, because the currently existing solution, using a mechanical ventilation system to maintain thermal comfort, is unable to provide the required parameters in the rooms. We are dealing with the phenomenon of excessive temperature increase in the building in the summer. Therefore, the facility currently designed as a low-energy building will require the installation of additional installation systems in the coming years, primarily cooling rooms, which will involve increased energy consumption. Full article

Air transportation has accelerated international trade, and the efficient use of cargo aircraft capacity supports logistics operations, reduces expenses, and benefits the environment. In this study, we formulate a mathematical programming model to solve the cargo aircraft capacity optimization problem and propose simplified approaches for practical applications. We investigate Mixed-Integer Linear Programming (MILP), Genetic Algorithm (GA), and Large Neighborhood Search (LNS) techniques. MILP yields optimal solutions for small instances but cannot handle large-scale, real-world problems due to excessive computation time; therefore, we combine the GA and LNS. The GA provides acceptable solutions rapidly, and LNS refines them by exploring larger solution spaces. Thus, this hybrid approach leverages the GA’s exploration capability and LNS’s exploitation ability to produce high-quality solutions efficiently. Our experimental results show that the hybrid GA-LNS method outperforms the MILP and single approaches in terms of capacity usage, loading duration, and computational time. This study provides an applicable model with practical constraints and guidelines for air cargo and cost reduction, operational efficiency, and safety. Full article