Search Results (151)

The development in coastal engineering and maritime transport demands accurate wave height prediction. In this study, hybrid deep learning models, including CNN-LSTM, CNN-BiLSTM, CNN-GRU, and CNN-BiGRU, are employed to develop regional multivariate wave prediction models that incorporate multiple features, such as wave height, wind stress, water depth, pressure, and sea surface temperature (SST), for the entire Bay of Bengal area. Sensitivity analysis is performed to evaluate the accuracy using statistical metrics, such as the correlation coefficient, RMSE, and MAE. The findings demonstrate that regional multivariate models offer satisfactory results for the entire Bay of Bengal region. The multivariate model performs better compared to the univariate model as the forecast horizon increases. Performance assessment of each environmental factor, employing the integrated gradient method, reveals that sea surface temperature has the most significant influence, while wind stress is the least dominant factor in the wave prediction model. Among the tested models, the CNN-BiGRU has superior performance with a correlation of 0.9872, an RMSE of 0.1547, and an MAE of 0.1005 for the 3 h prediction and is proposed as the optimal model. This study contributes to assessing the contribution of each environmental feature and improving the accuracy of regional wave prediction. Full article

Air pollution monitoring in ungauged zones presents unique challenges yet remains critical for understanding environmental health impacts and socioeconomic dynamics in the Eastern Mediterranean region. This study investigates air pollution patterns in northwestern Syria during 2019–2024, analyzing NO₂ and CO concentrations using Sentinel-5P TROPOMI satellite data processed through Google Earth Engine. Monthly concentration averages were examined across eight key locations using linear regression analysis to determine temporal trends, with Spearman’s rank correlation coefficients calculated between pollutant levels and five meteorological parameters (temperature, humidity, wind speed, atmospheric pressure, and precipitation) to determine the influence of political governance, economic conditions, and environmental sustainability factors on pollution dynamics. Quality assurance filtering retained only measurements with values ≥ 0.75, and statistical significance was assessed at a p < 0.05 level. The findings reveal distinctive spatiotemporal patterns that reflect the region’s complex political-economic landscape. NO₂ concentrations exhibited clear political signatures, with opposition-controlled territories showing upward trends (Al-Rai: 6.18 × 10⁻⁸ mol/m²) and weak correlations with climatic variables (<0.20), indicating consistent industrial operations. In contrast, government-controlled areas demonstrated significant downward trends (Hessia: −2.6 × 10⁻⁷ mol/m²) with stronger climate–pollutant correlations (0.30–0.45), reflecting the impact of economic sanctions on industrial activities. CO concentrations showed uniform downward trends across all locations regardless of political control. This study contributes significantly to multiple Sustainable Development Goals (SDGs), providing critical baseline data for SDG 3 (Health and Well-being), mapping urban pollution hotspots for SDG 11 (Sustainable Cities), demonstrating climate–pollution correlations for SDG 13 (Climate Action), revealing governance impacts on environmental patterns for SDG 16 (Peace and Justice), and developing transferable methodologies for SDG 17 (Partnerships). These findings underscore the importance of incorporating environmental safeguards into post-conflict reconstruction planning to ensure sustainable development. Full article

Biofouling, the accumulation of marine organisms on submerged surfaces, presents a significant challenge to the design, performance, and maintenance of offshore wind turbines (OWTs). This work synthesizes current knowledge on the physical and operational impacts of biofouling on OWT marine substructures, with a particular focus on how it alters hydrodynamic loading, increases drag and mass, and affects fatigue and structural response. Drawing from experimental studies, computational modeling, and real-world observations, this paper highlights the critical need to integrate biofouling effects into design practices. Additionally, emerging mitigation strategies are explored, including advanced antifouling materials and AI-driven monitoring systems, which offer promising solutions for long-term biofouling management. By addressing both engineering and ecological perspectives, this paper underscores the importance of developing robust, adaptive approaches to biofouling that can support the durability, reliability, and environmental sustainability of the offshore wind industry. Full article

With the natural evolution of the Arctic route and advancements in related technologies, the development of new green ice-class ships is becoming a key technological breakthrough for the global shipbuilding industry. As a special vessel form that must perform icebreaking operations and undertake long-distance ocean voyages, an ice-class ship requires sufficient icebreaking capacity to navigate ice-covered water areas. However, since such ships operate for most of their time under open water conditions, it is also crucial to consider their resistance characteristics in these environments. Firstly, this paper employs linear interpolation to extract wind, wave, and sea ice data along the route and calculates the proportion of ice-covered and open water area in the overall voyage. This provides data support for hull form optimization based on real sea state conditions. Then, a resistance optimization platform for ice-class ships is established by integrating hull surface mixed deformation control within a scenario analysis framework. Based on the optimization results, comparative analysis is conducted between the parent hull and the optimized hull under various environmental resistance scenarios. Finally, the optimization results are evaluated in terms of energy consumption using a fuel consumption model of the ship’s main engine. The optimized hull achieves a 16.921% reduction in total resistance, with calm water resistance and wave-added resistance reduced by 5.92% and 27.6%, respectively. Additionally, the optimized hull shows significant resistance reductions under multiple wave and floating ice conditions. At the design speed, calm water power and hourly fuel consumption are reduced by 7.1% and 7.02%, respectively. The experimental results show that the hull form optimization process in this paper can take into account both ice-region navigation and ice-free navigation. The design ideas and solution methods can provide a reference for the design of ice-class ships. Full article

Preserving heritage sites is a complex challenge that requires multidisciplinary approaches, combining scientific accuracy with cultural and historical sensitivity. In alignment with UNESCO’s conservation guidelines, this study investigated the airflow dynamics and wind-induced structural effects within ancient architecture using advanced computational fluid dynamics (CFD). The study site was the Na Phra Meru Historical Temple in Ayutthaya, Thailand, where the shear stress transport $k-\omega$ turbulence model was applied to analyze distinctive airflow patterns. A high-precision 3D computational domain was developed using Faro focus laser scanning technology, with the CFD results being validated based on onsite experimental data. The findings provided critical insights into the temple’s ventilation behavior, revealing strong correlations between turbulence characteristics, wind speed, temperature, and relative humidity. Notably, the small slit windows generated complex flow mixing, producing a large internal recirculation zone spanning approximately 70% of the central interior space. In addition to airflow distribution, the study evaluated the aerodynamic forces and rotational moments acting on the structure based on five prevailing wind directions. Based on these results, winds from the east and northeast generated the highest aerodynamic loads and rotational stresses, particularly in the lateral and vertical directions. Overall, the findings highlighted the critical role of airflow and wind-induced forces in the deterioration and long-term stability of heritage buildings. The study demonstrated the value of integrating CFD, environmental data, and structural analysis to bridge the gap between conservation science and engineering practice. Future work will explore further the interactions between wall moisture and the multi-layered pigments in mural paintings to inform preservation practices. Full article

This study presents a multi-objective predictive energy management system (EMS) for optimizing hybrid renewable energy systems (HRES) in autonomous marine vessels. The objective is to minimize fuel consumption and emissions while maximizing renewable energy usage and pure-electric sailing durations. The EMS combines nonlinear model predictive control (NMPC) with metaheuristic optimizers—Grey Wolf Optimization (GWO) and Genetic Algorithm (GA)—and is benchmarked against a conventional rule-based (RB) method. The HRES architecture comprises photovoltaic arrays, vertical-axis wind turbines (VAWTs), diesel engines, generators, and a battery storage system. A ship dynamics model was used to represent propulsion power under realistic sea conditions. Simulations were conducted using real-world operational and environmental datasets, with state prediction enhanced by an Extended Kalman Filter (EKF). Performance is evaluated using marine-relevant indicators—fuel consumption; emissions; battery state of charge (SOC); and emission cost—and validated using standard regression metrics. The NMPC-GWO algorithm consistently outperformed both NMPC-GA and RB approaches, achieving high prediction accuracy and greater energy efficiency. These results confirm the reliability and optimization capability of predictive EMS frameworks in reducing emissions and operational costs in autonomous maritime operations. Full article

Underground spaces offer significant potential for sustainable urban development, particularly in cold climate regions where surface thermal fluctuations are extreme. However, optimizing the wind–heat environmental performance of such spaces remains insufficiently explored, especially in relation to spatial morphology. This study addresses this gap by investigating how underground spatial configurations influence thermal comfort and ventilation efficiency. Six representative spatial prototypes—fully enclosed, single-side open, double-side open, central atrium, wind tower, and earth kiln—were constructed based on common underground design typologies. Computational fluid dynamics (CFD) simulations were conducted to evaluate airflow patterns and thermal responses under winter and summer conditions, incorporating relevant geotechnical properties into the boundary setup. The results indicate that deeper burial depths enhance thermal stability, while central atrium and wind tower prototypes offer the most balanced performance in both ventilation and heat regulation. These findings provide valuable design guidance for climate-responsive underground developments and contribute to the interdisciplinary integration of building physics, spatial design, and geotechnical engineering. Full article

Enzyme-Induced Calcium Carbonate Precipitation (EICP) shows promise for desertification control. This study investigates the effects of solid-to-liquid ratio, calcium sources, Ca²⁺ concentration, temperature, enzyme-to-liquid ratio (ELR), and pH on the activity of soybean crude urease (SCU). Furthermore, the impact of EICP treatment cycles on the mechanical properties, compressive behavior, and wind erosion resistance of aeolian sand (AS) was systematically evaluated, with microstructural evolution and pore characteristics of cemented specimens analyzed through SEM and X-CT. Key findings reveal that SCU activity and the calcium carbonate precipitation rate (PR) reached optimal levels (80~99%) under conditions of a 1:10 solid-to-liquid ratio, 1.0~1.5 M CaCl₂ concentration, 35~70 °C temperature range, and pH 7. After seven EICP treatments, AS specimens exhibited complete cementation with an unconfined compressive strength (UCS) of 580 kPa and a reduced wind erosion rate of 0.151 g/min, effectively mitigating desertification. SEM and X-CT analyses confirmed significant pore infilling and bridging between particles, accompanied by a reduction in pore quantity and permeability coefficient by over two orders of magnitude. EICP demonstrates notable advantages in enhancing mechanical performance, environmental compatibility, and cost efficiency, positioning cemented AS as a viable construction material while offering insights for sand stabilization engineering. These findings provide essential technical support for material innovation, wind and sand disaster prevention, and the sustainable construction of desert highway bases and subbases. Full article

In the future, electricity generation through eco-friendly renewable energy will accelerate. Surrounded by sea on three sides, the Republic of Korea is gaining attention for offshore wind power as a future industry, leveraging advantages of its maritime environment. However, maritime navigation remains active in waters, with maritime transportation being crucial, as it accounts for over 95% of the country’s cargo volume. Therefore, ensuring the safety of vessel operations is vital when constructing offshore wind farms. This study proposed alternative routes to ensure the safety of vessels and secure existing routes in the waters of the southwestern sea, where intensive development of OWFs is expected. The routes were determined based on the Permanent International Association of Navigation Congresses (PIANC) Guidelines and Maritime Traffic Safety Act’s implementation guidelines. Clearance between a maritime route and OWF was set to the rule of 6 L + 0.3 NM + 500 m for safety. The route width was calculated while considering vessel maneuverability, environmental factors, seabed conditions, the depth-to-draft ratio, and two-way traffic. The alternative routes were categorized into four types—maritime highways, maritime provincial routes, approach routes for departure/arrival, and recommended routes based on vessel positions, engine status, and route function. By considering traffic flow and applying international and domestic standards, these routes will ensure safe, efficient, and orderly vessel operations. Full article

The reduction of harmful emissions is shaping trends across many industries, including architecture and building. With rising ecological awareness and the threat of climate change, architects, construction engineers, and developers are focusing on innovative solutions to minimize the construction sector’s environmental impact. This paper presents a technical and management approach system using renewable energy sources, based on an existing single-family house with known energy consumption. The aim is to achieve energy independence by relying solely on on-site electricity generation and storage, while remaining connected to water and sewage infrastructure. Utilizing renewable energy sources enhances self-sufficiency and investment profitability. The study evaluates the house’s energy consumption to optimally select electricity supply solutions, including a small wind farm and photovoltaic installation integrated with appropriate electricity storage. This is crucial due to the air heat pump used for heating and domestic hot water, which requires electricity. An hourly simulation of the system’s operation over a year verified the adequacy of the selected devices. Additionally, two different locations were analyzed to assess how varying climate and wind conditions influence the design and performance of off-grid energy systems. The analysis showed that solar and wind systems can meet annual energy demand, but limited storage capacity prevents full autonomy. Replacing the heat pump with a biomass boiler reduces electricity use by about 25% and battery needs by 40%, though seasonal energy surpluses remain a challenge. This concept aligns with the goal of achieving climate neutrality by 2050. Full article

This study presents a comprehensive framework for agro-meteorological prediction, combining stochastic modeling, machine learning techniques, and environmental feature engineering to address challenges in yield prediction and wind behavior modeling. Focused on mango cultivation in the Mediterranean region, the workflow integrates diverse datasets, including satellite-derived variables such as NDVI, soil moisture, and land surface temperature (LST), along with meteorological features like wind speed and direction. Stochastic modeling was employed to capture environmental variability, while a proxy yield was defined using key environmental factors in the absence of direct field yield measurements. Machine learning models, including random forest and multi-layer perceptron (MLP), were hybridized to improve the prediction accuracy for both proxy yield and wind components (U and V that represent the east–west and north–south wind movement). The hybrid model achieved mean squared error (MSE) values of 0.333 for U and 0.181 for V, with corresponding R² values of 0.8939 and 0.9339, respectively, outperforming the individual models and demonstrating reliable generalization in the 2022 test set. Additionally, although NDVI is traditionally important in crop monitoring, its low temporal variability across the observation period resulted in minimal contribution to the final prediction, as confirmed by feature importance analysis. Furthermore, the analysis revealed the significant influence of environmental factors such as LST, precipitable water, and soil moisture on yield dynamics, while wind visualization over digital elevation models (DEMs) highlighted the impact of terrain features on the wind patterns. The results demonstrate the effectiveness of combining stochastic and machine learning approaches in agricultural modeling, offering valuable insights for crop management and climate adaptation strategies. Full article

This study introduces an innovative Emotional Artificial Neural Network (EANN) model to predict ship fuel consumption with high accuracy, addressing the challenges posed by complex environmental conditions and operational variability. This research examines the impact of climate change on maritime operations and fuel efficiency by analyzing climatic variables such as wave period, wind speed, and sea-level rise. The model’s performance is assessed using two ship types (bulk carrier and container ship with max 60,000 dead weight tonnage (DWT)) under various climate scenarios. A comparative analysis demonstrates that the EANN model significantly outperforms the conventional Feedforward Neural Network (FFNN) in predictive accuracy. For bulk carriers, the EANN achieved a Root Mean Squared Error (RMSE) of 5.71 tons/day during testing, compared to 9.91 tons/day for the FFNN model. Similarly, for container ships, the EANN model achieved an RMSE of 5.97 tons/day, significantly better than the FFNN model’s 10.18 tons/day. A sensitivity analysis identified vessel speed as the most critical factor, contributing 33% to the variance in fuel consumption, followed by engine power and current speed. Climate-change simulations showed that fuel consumption increases by an average of 22% for bulk carriers and 19% for container ships, highlighting the importance of operational optimizations. This study emphasizes the efficacy of the EANN model in predicting fuel consumption and optimizing ship performance. The proposed model provides a framework for improving energy efficiency and supporting compliance with International Maritime Organization Standards (IMO) environmental standards. Meanwhile, the Carbon Intensity Indicator (CII) evaluation results emphasize the urgent need for measures to reduce carbon emissions to meet the IMO’s 2030 standards. Full article

Energy harvesting in the automotive sector is a rapidly growing field aimed at improving vehicle efficiency and sustainability by recovering wasted energy. Various technologies have been developed to convert mechanical, thermal, and environmental energy into electrical power, reducing dependency on traditional energy sources. This manuscript provides a comprehensive review of energy harvesting applications/methodologies, aiming to trace the research lines and future developments. This work identifies the main categories of harvesting solutions, namely mechanical, thermal, and hybrid/environmental solar–wind systems; each section includes a detailed review of the technical and scientific state of the art and a comparative analysis with detailed tables, allowing the state of the art to be mapped for identification of the strengths of each solution, as well as the challenges and future developments needed to enhance the technological level. These improvements focus on energy conversion efficiency, material innovation, vehicle integration, energy savings, and environmental sustainability. The mechanical harvesting section focuses on energy recovery from vehicle vibrations, with emphasis on regenerative suspensions and piezoelectric-based solutions. Specifically, solutions applied to suspensions with electric generators can achieve power outputs of around 1 kW, while piezoelectric-based suspension systems can generate up to tens of watts. The thermal harvesting section, instead, explores methods for converting waste heat from an internal combustion engine (ICE) into electrical power, including thermoelectric generators (TEGs) and organic Rankine cycle systems (ORC). Notably, ICEs with TEGs can recover above 1 kW of power, while ICE-based ORC systems can generate tens of watts. On the other hand, TEGs integrated into braking systems can harvest a few watts of power. Then, hybrid solutions are discussed, focusing on integrated mechanical and thermal energy recovery systems, as well as solar and wind energy harvesting. Hybrid solutions can achieve power outputs above 1 kW, with the main contribution from TEGs (≈1 kW), compared to piezoelectric systems (hundreds of W). Lastly, a section on commercial solutions highlights how current scientific research meets the automotive sector’s needs, providing significant insights for future development. For these reasons, the research results aim to be guidelines for a better understanding of where future studies should focus to improve the technological level and efficiency of energy harvesting solutions in the automotive sector. Full article

This study investigated the impact of grassland and cropland expansion on carbon (C) and nitrous oxide (N₂O) emissions using remote sensing data and machine learning models. The research focused on agricultural land-use changes in South Sumatra from 1992 to 2018, utilizing Landsat satellite imagery and Google Earth Engine (GEE) for spatial and temporal analysis. Machine learning algorithms, including gradient boosting trees (GBT), random forest (RF), support vector machines (SVM), and classification and regression trees (CART), were employed to estimate greenhouse gas emissions based on multiple environmental parameters. These parameters include enhanced vegetation index (EVI), land surface temperature (LST), normalized difference vegetation index (NDVI), albedo, elevation, humidity, population density, precipitation, soil moisture, and wind speed. The results revealed a strong correlation between agricultural expansion and increased C and N₂O emissions, with RF and GBT models demonstrating superior predictive accuracy. Specifically, GBT and RF achieved the highest R² value (0.71, 0.59) and the lowest error metrics in modeling emissions, whereas SVM performed poorly across all cases. The study highlights the effectiveness of machine learning in quantifying emission dynamics and underscores the necessity of sustainable land management strategies to mitigate greenhouse gas emissions. By integrating remote sensing and data-driven methodologies, this research contributes to climate change mitigation policies and precision agriculture strategies aimed at balancing food security and environmental sustainability. Full article

Since the 1990s, Polish energy companies have been using new technologies to build wind farms, consisting of large devices. Over the years, the power and the size of installations have increased, and it continues to do so. In Poland, as well as in other countries, a problem with the post-use management of wind turbine blades has appeared. The recycling of wind turbine blades has remained challenging hitherto. The utilization of many different materials and changes in the dimensions cause multi-material waste. Since there are no economically viable recycling technologies available for such large-scale composite products, other treatment strategies for disposed WTBs have to be considered. This study explores the repurposing of WTBs as a pro-environmental alternative approach from a technological and architectural point of view. For this purpose, the study is guided by an analysis of wind turbine locations in reference to the impending need for waste management of wind blades in Poland. Well-profiled blades help transfer a large portion of wind energy to turbine rotors, which is why their construction is a challenge when it comes to designing new objects or elements thereof from decommissioned blades. They have a continuous curvature, where both the cross-section and thickness change, which is why, in the design of architectural or engineering objects, they are cut into smaller parts. This solution makes it possible to optimize the load-bearing properties of individual segments, ensuring a more stable system. Smaller elements also provide greater freedom in shaping architectural forms, which is associated with better control of the final effect from the aesthetic side. The potential of repurposing WTBs is shown, for example, in the design concept for the Archery Centre in Poznan (Poland). Full article