Search Results (8,773)

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

In response to environmental pressures and the growing demand for sustainable materials, this study investigates the use of lignocellulosic fillers derived from sorghum (Sorghum bicolor L. Moench) biomass, specifically stems and leaves, as reinforcements in biodegradable polylactic acid (PLA) composites. The aim was to assess the effect of filler type and content (5, 10, and 15 wt.%) on the physicochemical properties of the composites. Sorghum was manually harvested in Greater Poland, separated, dried, milled, and fractionated to particles <0.25 mm. Composites were produced via extrusion and injection molding, followed by characterization using differential scanning calorimetry (DSC), dynamic mechanical thermal analysis (DMTA), thermogravimetric analysis (TGA), tensile and impact testing, density measurements, optical microscopy, and scanning electron microscopy (SEM). Results showed that stem-based fillers provided a better balance between stiffness and ductility, along with improved dispersion and interfacial adhesion. In contrast, leaf-based fillers led to higher stiffness but greater brittleness and agglomeration. All composites exhibited decreased impact strength and thermal stability compared to neat PLA, with the extent of these decreases depending on the filler type and loading. The study highlights the potential of sorghum stems as a viable, renewable reinforcement in biopolymer composites, aligning with circular economy and bioeconomy strategies. Full article

Nuts, including tree nuts such as almonds, walnuts, cashews, and macadamias, as well as peanuts, are widely consumed for their health benefits owing to their high-quality protein content. Globally, the nut industry represents a multi-billion-dollar sector, with increasing demand driven by consumer interest in nutrition, functional foods, and plant-based diets. Recent advances in proteomic technologies have enabled comprehensive analyses of nut seed proteins, shedding light on their roles in nutrition, allergenicity, stress responses, and food functionality. Seed storage proteins such as 2S albumins, 7S vicilins, and 11S legumins, are central to nutrition and allergenicity. Their behavior during processing has important implications for food safety. Proteomic studies have also identified proteins involved in lipid and carbohydrate metabolism, stress tolerance, and defense against pathogens. Despite technical challenges such as high lipid content and limited genomic resources for many nut species, progress in both extraction methods and mass spectrometry has expanded the scope of nut proteomics. This review underscores the central role of proteomics in improving nut quality, enhancing food safety, guiding allergen risk management, and supporting breeding strategies for sustainable crop improvement. Full article

The smart home contributions in energy management systems can help the microgrid operator overcome technical problems and ensure economically viable operation by flattening the load profile. The purpose of this paper is to propose a smart home energy management system (SHEMS) that enables smart homes to monitor, store, and manage energy efficiently. SHEMS relies heavily on energy storage systems (ESSs) and electric vehicles (EVs), which enable smart homes to be more flexible and enhance the reliability and efficiency of renewable energy sources. It is vital to study the optimal operation of batteries in SHEMS; hence, a multi-objective optimization approach for SHEMS and demand response programs is proposed to simultaneously reduce the daily bills, the peak-to-average ratio, and the number of battery discharging cycles of ESSs and EVs. An inverter-based air conditioner, photovoltaic system, ESS, and EV, shiftable and non-shiftable equipment are considered in the suggested smart home. In addition, the amount of energy purchased and sold throughout the day is taken into account in the suggested mathematical formulation based on the real-time market pricing. The suggested multi-objective problem is solved by an improved gray wolf optimizer, and various weather conditions, including rainy, sunny, and cloudy days, are also analyzed. Additionally, simulations indicate that the proposed method achieves optimal results, with three objectives shown on the Pareto front of the optimal solutions. Full article

The increasing demand for reliable, sensitive, and cost-effective gas sensors drives ongoing research in this field. Ideal gas sensors must demonstrate high sensitivity and selectivity, stability, rapid response and recovery times, energy efficiency, and affordability. One-dimensional (1D) metal oxide semiconductors (MOSs) are prominent candidates due to their excellent sensing properties and straightforward fabrication processes. The sensing efficacy of 1D MOSs is heavily dependent on their surface area and porosity, which influence gas interaction and detection efficiency. Polymeric templates serve as effective tools for enhancing these properties by enabling the creation of uniform, porous nanostructures with high surface area, thereby improving gas adsorption, sensitivity, and dynamic response characteristics. This review systematically examines the role of polymeric templates in the construction of 1D MOSs for gas sensing applications. It discusses critical factors influencing polymer template selection and how this choice affects key microstructural parameters, such as grain size, pore distribution, and defect density, essential to sensor performance. The recent literature highlights the mechanisms through which polymer templates facilitate the fine-tuning of nanostructures. Future research directions include exploring novel polymer architectures, developing scalable synthesis methods, and integrating these sensors with emerging technologies. Full article

To address the challenges of complex composition, high chemical oxygen demand (COD) content, and the difficulty of treating organic wastewater from brewery wastewater, as well as the limitations of traditional Fenton technology, including low catalytic activity and high material costs, this study proposes the use of biochemical sludge as a raw material. Coupled with iron salt activation and mechanical ball milling technology, a low-cost, high-performance iron-doped mesoporous nano-sludge biochar material is prepared. This material was employed as a particle electrode to construct a three-dimensional electro-Fenton system for the degradation of organic wastewater from sauce-flavor liquor brewing. The results demonstrate that the sludge-based biochar produced through this approach possesses a mesoporous structure, with an average particle size of 187 nm, a specific surface area of 386.28 m²/g, and an average pore size of 4.635 nm. Iron is present in the material as multivalent iron ions, which provide more electrochemical reaction sites. Utilizing response surface methodology, the optimized treatment process achieves a maximum COD degradation rate of 71.12%. Compared to the control sample, the average particle size decreases from 287 μm to 187 nm, the specific surface area increases from 44.89 m²/g to 386.28 m²/g, and the COD degradation rate improves by 61.1%. Preliminary investigations suggest that the iron valence cycle (Fe²⁺/Fe³⁺) and the mass transfer enhancement effect of the mesoporous nano-structure are keys to efficient degradation. The Fe-O-Si structure enhances material stability, with a degradation capacity retention rate of 88.74% after 30 cycles of use. When used as a particle electrode to construct a three-dimensional electro-Fenton system, this material demonstrates highly efficiency in organic matter degradation and shows promising potential for application in the treatment of organic wastewater from sauce-flavor liquor brewing. Full article

For decades, regeneration of alveolar bone defects has depended on traditional grafting options, such as autogenous/allogenic grafts or allografts. Recently, extracted teeth was introduced as an alternative graft source. Tooth autografts are being used and have gained significant attention due to their biocompatibility, osteoconductivity, osteoinductivity, and osteogenic properties. Furthermore, tooth allografts have potential to act as natural biocomposites for oral regeneration procedures and might be advantageous options in near future. Recent advances in tooth banking, including cryopreservation, can serve to maintain bioactivity and to improve the safety, viability, and regenerative potential of teeth. They might be revolutionary in oral surgery, offering a more sustainable solution to the growing demand for bone regeneration procedures. Nevertheless, challenges such as immunogenic responses, ethical issues, and regulatory constraints persist. Ongoing research and technological innovation continue to address these problems. To date, the success rates of tooth autografts are promising, and they are regarded as a reliable option in clinical practice, with predictable outcomes in alveolar ridge preservation, sinus augmentation, periodontal regeneration, guided bone regeneration (GBR), and endodontic surgery by providing natural scaffolds for cell integration and bone remodeling. However, the scientific literature on tooth allografts is lacking. Therefore, this review aimed to comprehensively evaluate the scientific literature for comparing the properties of tooth grafts with other grafting options, in terms of processing techniques, and various clinical applications, positioning them as versatile biocomposites for the future, bridging material science and regenerative dentistry. Furthermore, possible applications of allogenic tooth grafts and overcoming current limitations are also discussed. Full article

The multi-timescale optimization dispatch method for integrated energy systems proposed in this paper balances sustainability and low-carbon characteristics. It first incorporates shared energy storage resources such as electric vehicles into system dispatch, fully leveraging their spatiotemporal properties to enhance dispatch flexibility and rapid response capabilities for integrating renewable energy and enabling clean power generation. Second, an incentive-penalty mechanism enables effective interaction between the system and the green certificate–carbon joint trading market. Penalties are imposed for failing to meet renewable energy consumption targets or exceeding carbon quotas, while rewards are granted for meeting or exceeding targets. This regulates the system’s renewable energy consumption level and carbon emissions, ensuring robust low-carbon performance. Third, this strategy considers the close coordination between heating, cooling, and electricity demand response measures with the integrated energy system, smoothing load fluctuations to achieve peak shaving and valley filling. Finally, through case study simulations and analysis, the advantages of the multi-timescale dispatch strategy proposed in this paper, in terms of economic feasibility, low-carbon characteristics, and sustainability, are verified. Full article

Lung cancer remains the leading cause of cancer mortality, hindered by drug resistance, limited targeting, and low immunotherapy response. This review presents biomimetic superparamagnetic iron-oxide nanoparticles (SPIONs) as a next-generation theranostic platform. By cloaking SPIONs with cell membranes—macrophage, neutrophil, or cancer cell—we endow them with biological targeting, immune evasion, and deep lung penetration. Coupled with magnetic field-guided retention and real-time imaging, these systems enable precision hyperthermia, on-demand drug release, and immune microenvironment reprogramming. We critically compare membrane types, outline translational challenges, and propose a regulatory-aligned safety framework. This biomimetic strategy offers a dual diagnostic–therapeutic solution for lung cancer and potentially other solid tumors. Full article

Hybrid cascaded multi-terminal HVDC systems represent a significant advancement in HVDC transmission technology. A notable real-world implementation of this concept is the bipolar hybrid cascaded multi-terminal high voltage direct current (MTDC) project in China, which successfully transmits hydropower from Baihetan to Jiangsu. This system combines MMCs for system support with LCCs for high-power transmission, offering both flexibility and efficiency in long-distance power delivery. This research explores the characteristics of main DC fault types in such systems, classifying faults based on sections and modes while analyzing their unique outcomes depending on DC fault locations. By focusing on the DC-side terminal behavior of the MMCs and LCCs, the main response processes to asymmetrical DC faults are investigated in detail. This study offers a detailed analysis of asymmetrical DC faults in bipolar HVDC systems, proposing a new classification based on fault characteristics such as current, voltage, active power, and reactive power. A supporting theoretical analysis is also presented. It identifies specific control demands needed for effective fault mitigation. PSCAD/EMTDC simulation results demonstrate that DC faults with similar characteristics can be consistently grouped into distinct categories by this new classification method. Each category is further linked to specific control demands, providing a strong basis for developing advanced protection strategies and practical solutions that enhance the stability and reliability of hybrid cascaded HVDC systems. Full article

This study addresses the Capacitated Vehicle Routing Problem (CVRP) known to be NP-hard. In this problem, a set of customers with varying demands is considered. To solve the problem, routes were generated for several vehicles with identical capacity, which were responsible for delivering products to a set of geographically dispersed customers. The purpose of the problem is to minimize the total cost of all routes. This problem was solved by applying the metaheuristic Simulated Annealing (SA) and incorporating four different neighborhoods to improve the initial solution generated randomly. In the SA, a set of cooling factors is used. The best solution obtained by SA is refined by the use of Hill Climbing using a double neighborhood. The algorithm was tested with instances from the literature in order to measure its effectiveness in solution quality and execution time. We tested the approach with 106 instances from the literature and obtained the optimum in 93 instances. The average time in most instances was less than five minutes. Delivery companies can benefit from this approach. They only need to identify the depot, the clients, and the distance between locations, and this approach can be used with relative ease. Full article

Intelligent ventilation systems can optimize airflow regulation to enhance mining safety and reduce energy consumption, driving green development in mineral resource extraction. This paper systematically elaborates on the overall architecture, cutting-edge advances, and core technologies of current intelligent mining ventilation. Building upon this foundation, a comprehensive intelligent mine ventilation solution encompassing the entire process of ventilation design, optimization, and operation is constructed based on a five-layer architecture, integrating key technologies such as intelligent sensing, real-time solving, airflow regulation, and remote control, providing an overarching framework for smart mine ventilation development. To address the computational efficiency bottleneck of traditional methods, an improved loop-solving method based on minimal independent closed loops is realized, achieving near real-time analysis of ventilation networks. Furthermore, a multi-level airflow regulation strategy is realized, including the methods of optimization control based on mixed integer linear programming and equipment-driven demand-based regulation, effectively resolving the challenges of calculating nonlinear programming models. Case studies indicate that the intelligent ventilation system significantly enhances mine safety and efficiency, leading to approximately 10–20% energy saving, a 40–60% quicker emergency response, and an average increase of about 20% in the utilization of fresh air at working faces through its remote and real-time control capabilities. Full article

Hybrid AC/DC microgrids have emerged as a promising solution for integrating diverse renewable energy sources, enhancing efficiency, and strengthening resilience in modern power systems. However, existing control schemes exhibit critical shortcomings that limit their practical effectiveness. Traditional linear controllers, designed around nominal operating points, often fail to maintain stability under large load and generation fluctuations. Optimization-based methods are highly sensitive to model inaccuracies and parameter uncertainties, reducing their reliability in dynamic environments. Intelligent approaches, such as fuzzy logic and ML-based controllers, provide adaptability but suffer from high computational demands, limited interpretability, and challenges in real-time deployment. These limitations highlight the need for robust control strategies that can guarantee reliable operation despite disturbances, uncertainties, and varying operating conditions. Numerical performance indices demonstrate that the reviewed robust control strategies outperform conventional linear, optimization-based, and intelligent controllers in terms of system stability, voltage and current regulation, and dynamic response. This paper provides a comprehensive review of recent robust control strategies for hybrid AC/DC microgrids, systematically categorizing classical model-based, intelligent, and adaptive approaches. Key research gaps are identified, including the lack of unified benchmarking, limited experimental validation, and challenges in integrating decentralized frameworks. Unlike prior surveys that broadly cover microgrid types, this work focuses exclusively on hybrid AC/DC systems, emphasizing hierarchical control architectures and outlining future directions for scalable and certifiable robust controllers. Also, comparative results demonstrate that state of the art robust controllers—including H∞-based, sliding mode, and hybrid intelligent controllers—can achieve performance improvements for metrics such as voltage overshoot, frequency settling time, and THD compared to conventional PID and droop controllers. By synthesizing recent advancements and identifying critical research gaps, this work lays the groundwork for developing robust control strategies capable of ensuring stability and adaptability in future hybrid AC/DC microgrids. Full article

Modern energy systems are undergoing a profound transformation characterized by the active replacement of conventional fossil-fuel-based power plants with renewable energy sources. This transition aims to reduce the carbon emissions associated with electricity generation while enhancing the economic performance of electric power market players. However, alongside these benefits come several challenges, including reduced overall inertia within energy systems, heightened stochastic variability in grid operation regimes, and stricter demands on the rapid response capabilities and adaptability of emergency controls. This paper presents a novel methodology for selecting effective control laws for low-inertia energy systems, ensuring their dynamic stability during post-emergency operational conditions. The proposed approach integrates advanced techniques, including feature selection via decision tree algorithms, classification using Random Forest models, and result visualization through the Mean Shift clustering method applied to a two-dimensional representation derived from the t-distributed Stochastic Neighbor Embedding technique. A modified version of the IEEE39 benchmark model served as the testbed for numerical experiments, achieving a classification accuracy of 98.3%, accompanied by a control law synthesis delay of just 0.047 milliseconds. In conclusion, this work summarizes the key findings and outlines potential enhancements to refine the presented methodology further. Full article

In response to the increasing demand for environmentally friendly and cost-effective adsorbents in wastewater treatment, this study reports the green synthesis, characterization, and application of a magnetic epichlorohydrin Rosa canina (m-ECH-RC) nanocomposite for removing Safranin O (SO), a commonly used cationic dye in textile effluents. The synthesized material was characterized using Brunauer–Emmett–Teller (BET), Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), X-ray diffraction (XRD), and zeta potential analyses to reveal its surface morphology, pore structure, functional groups, crystallinity, and colloidal stability. Adsorption performance was systematically tested under various conditions, including pH, adsorbent dose, contact time, ionic strength, and initial dye concentration. Kinetic analyses revealed that the adsorption process of Safranin O dye mainly obeys pseudo-second-order kinetics, but intraparticle and film diffusion also contribute to the process. As a result of the Isotherm analysis, it was found that the adsorption process conformed to the Langmuir model. Testing on real textile wastewater samples demonstrated a removal efficiency of 75.09% under optimized conditions. Reusability experiments further revealed that the material maintained high adsorption–desorption performance for up to five cycles, emphasizing its potential for practical use. These findings suggest that m-ECH-RC is a viable and sustainable adsorbent for treating dye-laden industrial effluents. Full article