Search Results (5,028)

Wind turbine blade damage detection is crucial for advancing wind energy as a sustainable alternative to fossil fuels. Existing methods based on image processing technologies face challenges such as limited adaptability to complex environments, trade-offs between model accuracy and computational efficiency, and inadequate real-time inference capabilities. In response to these limitations, we put forward MSWindD-YOLO, a lightweight real-time detection model for wind turbine blade damage. Building upon YOLOv5s, our work introduces three key improvements: (1) the replacement of the Focus module with the Stem module to enhance computational efficiency and multi-scale feature fusion, integrating EfficientNetV2 structures for improved feature extraction and lightweight design, while retaining the SPPF module for multi-scale context awareness; (2) the substitution of the C3 module with the GBC3-FEA module to reduce computational redundancy, coupled with the incorporation of the CBAM attention mechanism at the neck network’s terminus to amplify critical features; and (3) the adoption of Shape-IoU loss function instead of CIoU loss function to facilitate faster model convergence and enhance localization accuracy. Evaluated on the Wind Turbine Blade Damage Visual Analysis Dataset (WTBDVA), MSWindD-YOLO achieves a precision of 95.9%, a recall of 96.3%, an mAP@0.5 of 93.7%, and an mAP@0.5:0.95 of 87.5%. With a compact size of 3.12 MB and 22.4 GFLOPs inference cost, it maintains high efficiency. After TensorRT acceleration on Jetson Orin NX, the model attains 43 FPS under FP16 quantization for real-time damage detection. Consequently, the proposed MSWindD-YOLO model not only elevates detection accuracy and inference efficiency but also achieves significant model compression. Its deployment-compatible performance in edge environments fulfills stringent industrial demands, ultimately advancing sustainable wind energy operations through lightweight lifecycle maintenance solutions for wind farms. Full article

The textile industry is among the most resource-intensive sectors, heavily dependent on water, energy, and synthetic chemicals, particularly in wet processing stages such as desizing, scouring, bleaching, dyeing, printing, and finishing. Conventional practices generate vast amounts of contaminated wastewater, posing severe risks to ecosystems and human health. In recent years, growing environmental concerns and stricter regulations have accelerated the search for sustainable alternatives. Biotechnology offers promising solutions, including enzymes, biopolymers, plant- and agrowaste-derived materials, and microbial metabolites, which can replace conventional auxiliaries and reduce the ecological footprint of textile processing. This review provides a structured overview of recent advances in bio-based compounds applied across different stages of textile wet processing. Applications are critically assessed in terms of performance, efficiency, environmental benefits, and potential for industrial adoption. Current limitations, future outlooks, and examples of commercially available products are also discussed. By highlighting the most recent progress, this review underscores the potential of bio-based innovations to support the transition toward more sustainable and resource-efficient textile manufacturing. Full article

Accurate demand forecasting contributes to improved energy efficiency and the development of short-term strategies. Predictive management of energy storage using redox flow batteries is presented as a robust solution for optimizing the operation of microgrids from the demand side. This study proposes an intelligent architecture that integrates demand forecasting models based on artificial neural networks and active management strategies based on the instantaneous production of renewable sources within the microgrid. The solution is supported by a real-time monitoring platform capable of analyzing data streams using continuous evaluation algorithms, enabling dynamic operational adjustments and active methods for predicting the storage system’s state of charge. The model’s effectiveness is validated using performance indicators such as RMSE, MAPE, and MSE, applied to experimental data obtained in a specialized microgrid laboratory. The results also demonstrate substantial improvements in energy planning and system operational efficiency, positioning this proposal as a viable strategy for distributed and sustainable environments in modern electricity systems. Full article

Advances in machine learning have improved the ability to predict critical environmental conditions, including solar radiation levels that, while essential for life, can pose serious risks to human health. In Ecuador, due to its geographical location and altitude, UV radiation reaches extreme levels. This study presents the development of a chatbot system driven by a hybrid artificial intelligence model, combining Random Forest, CatBoost, Gradient Boosting, and a 1D Convolutional Neural Network. The model was trained with meteorological data, optimized using hyperparameters (iterations: 500–1500, depth: 4–8, learning rate: 0.01–0.3), and evaluated through MAE, MSE, R², and F1-Score. The hybrid model achieved superior accuracy (MAE = 13.77 W/m², MSE = 849.96, R² = 0.98), outperforming traditional methods. A 15% error margin was observed without significantly affecting classification. The chatbot, implemented via Telegram and hosted on Heroku, provided real-time personalized alerts, demonstrating an effective, accessible, and scalable solution for health safety and environmental awareness. Furthermore, it facilitates decision-making in the efficient generation of renewable energy and supports a more sustainable energy transition. It offers a tool that strengthens the relationship between artificial intelligence and sustainability by providing a practical instrument for integrating clean energy and mitigating climate change. Full article

This paper presents a structured strategy for integrating the Sustainable Development Goals (SDGs) into university courses by linking them to competencies and learning outcomes. The proposed methodology, based on fuzzy logic, evaluates the degree of alignment between teaching activities and selected SDGs through matrices that connect competencies with assessment activities and expected learning outcomes, improving the gap regarding the inclusion of the SDGs and their articulation in terms of competencies. The approach was applied to two subjects from the Master’s Degree in Renewable Energy and Energy Efficiency at the Distance University of Madrid: “Electricity Market” and “Wind Energy”. In both cases, the learning outcomes were redesigned, and the activities were adjusted to ensure meaningful incorporation of sustainability principles into the curriculum. The method enables quantification of each activity’s contribution to the SDGs and supports a critical review of curriculum design to ensure coherent integration. The results indicate that project-based activities show the highest alignment with the SDGs, particularly with Goals 7, and 12, which achieve an average rating of 0.7 (high). The developed tool provides a practical and replicable solution for sustainability-oriented curriculum planning and can be adapted to other disciplines and educational programs. Full article

Urban areas are increasingly challenged by the combined effects of climate change, rapid population growth, and high energy demand. The integration of renewable energy systems, such as photovoltaic (PV) panels, and nature-based solutions, such as green roofs, represents a key strategy for sustainable urban development. This study evaluates the spatial potential for PV and green roof implementation in Iași, Romania, using moderate to high-resolution geospatial datasets, including the ALOS AW3D30 Digital Surface Model (DSM) and the Copernicus Urban Atlas 2018, processed in ArcMap 10.8.1 and ArcGIS Pro 2.6.0. Solar radiation was computed using the Area Solar Radiation tool for the average year 2023, while roof typology (flat vs. pitched) was derived from slope analysis. Results show significant spatial heterogeneity. The Copou neighborhood has the highest PV-suitable roof share (73.6%) and also leads in green roof potential (46.6%). Integrating PV and green roofs can provide synergistic benefits, improving energy performance, mitigating urban heat islands, managing stormwater, and enhancing biodiversity. These findings provide actionable insights for urban planners and policymakers aiming to prioritize green infrastructure investments and accelerate the local energy transition. Full article

Chitosan-based nanoparticles were prepared using an eco-friendly chemical procedure that conjugates natural fatty acids to the backbone of chitosan. This consists of reacting two molecules in the absence of a solvent and using microwaves to promote the chemical transformation. Both conditions make the whole chemical process more eco-compatible in terms of reagents and energy consumption. The chemical structure and the self-association behavior of chitosan–fatty acid conjugates were characterized by FT-IR, NMR, and dynamic light scattering. The conjugates displayed an enhanced solubility and efficient self-assembly in aqueous solution. The antimicrobial activity of the resulting nanoparticles was evaluated against Escherichia coli (Gram-negative) and Bacillus subtilis (Gram-positive). The micelles significantly inhibited E. coli growth (35–60%), even at relatively low concentrations, whereas negligible activity was observed against B. subtilis. The antibacterial efficacy appears to arise primarily from the ability of the developed nanostructured conjugates to perturb bacterial membranes. These results support the potential of chitosan–fatty acid conjugates as sustainable nanomaterials for biomedical applications, particularly as eco-friendly antimicrobial agents. Future work will evaluate their activity against other Gram-positive pathogens and explore their use in drug delivery. Full article

Waste management poses escalating threats to environmental sustainability, particularly with municipal solid waste (MSW) growth. Incineration, a widely adopted method for reducing waste volume, produces millions of tons of municipal solid waste incineration fly ash (MSWIFA) each year. Despite its high toxicity and classification as a hazardous solid waste, its ultrafine particle size and pozzolanic activity offer potential for its use in construction materials. In this study, MSWIFA was used to replace 6%, 12%, 18% and 24% of cementitious materials, and the effect of MSWIFA substitution rate on the workability, mechanical properties, microstructure, and durability of UHPC was studied. Furthermore, the study assessed the solidification capacity of the UHPC for heavy metal ions and quantitatively analyzed its eco-economic benefits. The results show that, under standard curing conditions, substituting 12% of cementitious materials with MSWIFA significantly modified UHPC hydration, shortened setting time, reduced fluidity, and increased wet packing density. The 28-day compressive strength reached 134.63 MPa, comparable to the control group. Concurrently, fluidity, durability, and heavy metal leaching all met the required standards, with energy consumption reduced by 14.86%, carbon emissions lowered by 12.76%, and economic costs decreased by 6.41%. This study provides a feasible solution for recycling MSWIFA into non-hazardous concrete, facilitating sustainable hazardous waste management and mitigating heavy metal-related environmental pollution. Full article

Citrus fruits and related processed products represent a major agricultural sector worldwide, contributing to food supply chains and to regional economies, particularly in Mediterranean and subtropical areas. Citrus processing generates significant amounts of post-processing waste, and their sustainable management is a critical challenge, driving growing scientific interest in exploring environmentally sustainable and profitable valorisation strategies. This study aimed at mapping the sustainability of post-processing citrus valorisation strategies documented in the scientific literature, through a scoping literature review based on the PRISMA-ScR model. Only peer-reviewed studies in English were selected from Scopus and Web of Science; in detail, 29 life cycle assessment studies (LCAs) focusing on the valorisation of citrus by-products have been analysed. Most of the studies were focused on essential oil extraction and energy production. Most of the biorefinery systems and valorisation aims proposed were found to be better than the business-as-usual solution. However, results are strongly influenced by the functional unit and allocation method. Economic allocation to the main product resulted in better environmental performances. The major environmental hotspot is the agrochemicals required for crop management. The analysis of LCAs facilitated the identification of valorisation strategies that deserve greater interest from the scientific community to propose sustainable bio-circular solutions in the agro-industrial and agricultural sectors. Full article

Due to rapid economic development, high energy consumption, and the depletion of natural resources, resulting in climate change, urban planners and architects face the difficult task of creating a new type of sustainable city that takes into account rapid population growth. The aim of this article is to examine the development of contemporary forms of high-rise architecture and the role of the vertical city in responding to shrinking space and developing a realistic strategy for sustainable urban development. Literature analysis, case studies, and multidisciplinary analysis are used. Pro-ecological solutions are identified and analyzed using the most representative buildings in Asia and a theoretical example in Nanjing. The examples are characterized by above-average height, unusual shapes, and the use of advanced pro-ecological strategies. Greenery plays a key role, including regulating the temperature inside the building. Vertical multifunctionality is becoming an increasingly common response to increasing spatial needs. The apparent lack of understanding of the development of high-rise buildings in cities around the world, extending beyond individual skyscrapers, constitutes a research gap. This article discusses Chongqing, an example of a vertical city, which is understood not only in terms of individual high-rise buildings but also as a whole structure. The study addresses the issue of a new type of compact city: the vertical city. The article provides key guidelines and constraints for creating densely populated, yet sustainable and environmentally friendly cities of the future. The practical application of the study can be utilized by urban planners and decision-makers. Full article

It is well known that the operation of a Borehole Heat Exchanger (BHE) can thermally induce groundwater convection in aquifers, enhancing the thermal performance of the BHE. However, the effect on the thermal performance of BHEs installed in low-permeable rock formations remains unclear. In this study, two BHEs were installed in a silty sandstone formation, one backfilled with high-permeable materials and the other grouted with sand–bentonite slurry. A Thermal Response Test (TRT) showed that the fluid outlet temperature of the high-permeable-material backfilled BHE was about 2.5 °C lower than that of the BHE refilled with sand–bentonite slurry, implying a higher thermal efficiency. The interpreted borehole thermal parameters also show a lower borehole thermal resistance in the high-permeable-material backfilled BHE. Physical model tests reveal that groundwater convective flow was induced in the high-permeable-material backfilled BHE. A test of BHEs with different borehole diameters shows that the larger the borehole diameter, the higher the thermal efficiency is. Thus, the thermal performance enhancement was attributed to two factors. First, the induced groundwater flow accelerates heat transfer by convection. Additionally, the increment of the thermal volumetric capacity of the groundwater stored inside a high-permeable-material refilled borehole stabilized the borehole’s temperature, which is key to sustaining high thermal efficiency in a BHE. The thermal performance enhancement demonstrated here shows potential for reducing reliance on fossil-fuel-based energy resources in challenging geological settings, thereby contributing to developing more sustainable geothermal energy solutions. Further validation in diverse field conditions is recommended to generalize these findings. Full article

The sudden increase in global energy demand has prompted the integration of Artificial Intelligence and the Internet of Things into the utility grid. The synergy of Artificial Intelligence and the Internet of Things in renewable energy sources has emerged as a promising solution for the development of smart grids and a transformative catalyst that restructures centralized power systems into resilient and sustainable systems. The state-of-the-art of the Internet of Things and Artificial Intelligence is presented in this paper to support the design, planning, operation, management and optimization of renewable energy-based power systems. This paper outlines the benefits of smart and resilient energy systems and the contributions of the Internet of Things across several applications, devices and networks. Artificial Intelligence can be utilized for predictive maintenance, demand-side management, fault detection, forecasting and scheduling. This paper highlights crucial future research directions aimed at overcoming the challenges that are associated with the adoption of emerging technologies in the power system by focusing on market policy and regulation and the human-centric and ethical aspects of Artificial Intelligence and the Internet of Things. The outcomes of this study can be used by policymakers, researchers and development agencies to improve global access to electricity and accelerate the development of sustainable energy systems. Full article

The increasing use of UAVs has reshaped urban logistics, enabling sustainable alternatives to traditional deliveries. To address critical issues inherent in the system, the proposed study presents the design and evaluation of an innovative unmanned aerial vehicle (UAV) prototype that integrates advanced electronic components and artificial intelligence (AI), with the aim of reducing environmental impact and enabling autonomous navigation in complex urban environments. The UAV platform incorporates brushless DC motors, high-density LiPo batteries and perovskite solar cells to improve energy efficiency and increase flight range. The Deep Q-Network (DQN) allocates energy and selects reference points in the presence of wind and payload disturbances, while an integrated sensor system monitors motor vibration/temperature and charge status to prevent failures. In urban canyon and field scenarios (wind from 0 to 8 m/s; payload from 0.35 to 0.55 kg), the system reduces energy consumption by up to 18%, increases area coverage by 12% for the same charge, and maintains structural safety factors > 1.5 under gust loading. The approach combines sustainable materials, efficient propulsion, and real-time AI-based navigation for energy-conscious flight planning. A hybrid methodology, combining experimental design principles with finite-element-based structural modelling and AI-enhanced monitoring, has been applied to ensure structural health awareness. The study implements proven edge-AI sensor fusion architectures, balancing portability and telemonitoring with an integrated low-power design. The results confirm a reduction in energy consumption and CO₂ emissions compared to traditional delivery vehicles, confirming that the proposed system represents a scalable and intelligent solution for last-mile delivery, contributing to climate resilience and urban sustainability. The findings position the proposed UAV as a scalable reference model for integrating AI-driven navigation and renewable energy systems in sustainable logistics. Full article

Processing economically and socio-culturally significant Amazonian fruits—andiroba (Carapa guianensis Aubl.), açai (Euterpe oleracea Mart.), and babassu (Attalea speciosa Mart. ex Spreng.)—generates substantial biomass waste, posing critical environmental and waste management challenges. This study explored the valorization of these abundant residual biomasses as sustainable feedstocks for biosurfactant production by bacterium Pseudomonas aeruginosa P23G-02, while simultaneously profiling their nutritional value and broader implications for a circular bioeconomy. Through liquid fermentation, biosurfactants were produced at an approximate yield of 6 mg/mL. The isolated biosurfactants exhibited favorable properties, including emulsification indices of around 60% and surface tension reduction to below 30 mN/m, with the andiroba-derived biosurfactant identified as a rhamnolipid type. Nutritional profiling of the residues revealed significant energy values, reaching up to 656 kcal/100 g, with açai and babassu residues being carbohydrate-rich (exceeding 80%), and andiroba residues exhibiting a high lipid profile (up to 57%). These distinct compositions critically influenced biosurfactant yield. These findings underscore the viability of Amazonian fruit biomass as valuable resources for developing eco-friendly bioproducts and innovative waste management solutions. While highlighting a promising pathway for circular bioeconomy development, future research should address biosafety and explore alternative microbial hosts for applications in sensitive sectors such as food and nutrition. Full article

The rapid growth of the human population and industrialization has intensified anthropogenic activities, leading to the release of various toxic chemicals into the environment, triggering significant risks to human health and ecosystem stability. One sustainable solution to remove toxic chemicals from various environmental matrices, such as water, air, and soil, is bioremediation, an approach utilizing biological agents. Microalgae, as the primary producers of the aquatic environment, offer a versatile bioremediation platform, where their metabolic processes break down and convert pollutants into less harmful substances, thereby mitigating the negative ecological impact. Besides the CO₂ sequestration potential, microalgae are a source of renewable energy and numerous high-value biomolecules. Additionally, microalgae can mitigate various toxic chemicals through biosorption, bioaccumulation, and biodegradation. These remediation strategies propose a sustainable and eco-friendly approach to address environmental pollution. This review evaluates the microalgal mitigation of major environmental contaminants—heavy metals, pharmaceuticals and personal care products (PPCPs), persistent organic pollutants (POPs), flue gases, microplastics, and nanoplastics—linking specific microalgae removal mechanisms to pollutant-induced cellular responses. Each section explicitly addresses the effects of these pollutants on microalgae, microalgal bioremediation potential, bioaccumulation process, the risks of trophic transfer, and biomagnification in the food web. Herein, we highlight the current status of the microalgae-based bioremediation prospects, pollutant-induced microalgal toxicity, bioaccumulation, and consequential biomagnification. The novelty of this review lies in integrating biomagnification risks with the bioremediation potential of microalgae, providing a comprehensive perspective not yet addressed in the existing literature. Finally, we identify major research gaps and outline prospective strategies to optimize microalgal bioremediation while minimizing the unintended trophic transfer risks. Full article