Search Results (3,906)

In the event of power outages caused by natural disasters, accidents, or other emergencies, outdoor emergency lighting systems play a critical role in providing illumination to maintain spatial orientation, facilitate evacuation procedures, and help individuals avoid hazardous areas or locate safe shelters. Compared to traditional lighting technologies, LED-based outdoor emergency lighting offers several advantages, including compact size, long operational lifespan, low energy consumption, high safety, resistance to breakage, and the absence of chemical residue or pollution. These characteristics align with contemporary trends in environmental sustainability and energy efficiency. This study proposes a novel LED driver circuit architecture for outdoor emergency lighting applications. The primary circuit topology is based on an improved buck-boost converter integrated with a flyback converter, forming a hybrid buck-boost-flyback configuration. The proposed circuit is capable of recycling the energy stored in the transformer’s leakage inductance, thereby enhancing overall power conversion efficiency. A 12 W (20 V/0.6 A) prototype LED driver circuit was designed and implemented to validate the performance of the proposed system. Experimental measurements, including waveform analysis and efficiency evaluation, demonstrate that the driver circuit achieves a high efficiency exceeding 91%. These results confirm the practical feasibility and effectiveness of the proposed electronic driver for LED-based outdoor emergency lighting applications. Full article

This study reports on green-synthesized zinc oxide nanoparticles (ZnONPs), focusing on their physicochemical characterization, photocatalytic properties, and agricultural applications. Dynamic light scattering (DLS) analysis revealed a mean hydrodynamic diameter of 337.3 nm and a polydispersity index (PDI) of 0.400, indicating moderate polydispersity and nanoparticle aggregation, typical of biologically synthesized systems. High-resolution transmission electron microscopy (HR-TEM) showed predominantly spherical particles with an average diameter of ~28 nm, exhibiting slight agglomeration. Energy-dispersive X-ray spectroscopy (EDX) confirmed the elemental composition of zinc and oxygen, while X-ray diffraction (XRD) analysis identified a hexagonal wurtzite crystal structure with a dominant (002) plane and an average crystallite size of ~29 nm. Photoluminescence (PL) spectroscopy displayed a distinct near-band-edge emission at ~462 nm and a broad blue–green emission band (430–600 nm) with relatively low intensity. The ultraviolet–visible spectroscopy (UV–Vis) absorption spectrum of the synthesized ZnONPs exhibited a strong absorption peak at 372 nm, and the optical band gap was calculated as 2.67 eV using the Tauc method. Fourier-transform infrared spectroscopy (FTIR) analysis revealed both similarities and distinct differences to the pigeon extract, confirming the successful formation of nanoparticles. A prominent absorption band observed at 455 cm⁻¹ was assigned to Zn–O stretching vibrations. X-ray photoelectron spectroscopy (XPS) analysis showed that raw pigeon droppings contained no Zn signals, while their extract provided organic biomolecules for reduction and stabilization, and it confirmed Zn²⁺ species and Zn–O bonding in the synthesized ZnONPs. Photocatalytic degradation assays demonstrated the efficient removal of pollutants from sewage water, leading to significant reductions in total dissolved solids (TDS), chemical oxygen demand (COD), and total suspended solids (TSS). These results are consistent with reported values for ZnO-based photocatalytic systems, which achieve biochemical oxygen demand (BOD) levels below 2 mg/L and COD values around 11.8 mg/L. Subsequent reuse of treated water for irrigation yielded promising agronomic outcomes. Wheat and barley seeds exhibited 100% germination rates with ZnO NP-treated water, which were markedly higher than those obtained using chlorine-treated effluent (65–68%) and even the control (89–91%). After 21 days, root and shoot lengths under ZnO NP irrigation exceeded those of the control group by 30–50%, indicating enhanced seedling vigor. These findings demonstrate that biosynthesized ZnONPs represent a sustainable and multifunctional solution for wastewater remediation and agricultural enhancement, positioning them as a promising candidate for integration into green technologies that support sustainable urban development. Full article

The excessive use of fossil fuels and the increasing generation of solid waste, driven by population growth, industrialization, and economic development, have led to serious environmental, energy, and public health issues. In light of this problem, it is crucial to adopt sustainable solutions that promote the transition to renewable energy sources, such as biogas. Although progress has been made in optimizing biogas production, there is still no adaptable model for various environments that allows for the determination of optimal quantities of different organic wastes, simultaneously considering their composition, moisture content, and control of critical factors for biogas production, as well as the biodigester’s capacity and other relevant elements. In practice, the dosing of waste is conducted empirically, leading to inefficiencies that limit the potential for biogas production in real scenarios. The objective of this article is to propose a linear optimization model with nonlinear constraints that maximizes biogas production, considering fundamental parameters such as the moisture percentage, pH, carbon/nitrogen ratio (C/N), substrate volume, organic matter, volatile solids (VS), and biogas production potential from different wastes. The model estimates the optimal waste composition based on the biodigester capacity to ensure balanced substrates. The results for the proposed scenarios demonstrate its effectiveness: Scenario 1 achieved 3.42 m³ (3418.67 L) of biogas, while Scenario 2, with a greater diversity of waste, reached 8.06 m³ (8061.43 L). The model maintained pH (6.49–6.50), C/N ratio (20.00), and moisture (60.00%) within optimal ranges. Additionally, a Monte Carlo sensitivity analysis (1000 simulations) validated its robustness with a 95% confidence level. This model provides an efficient tool for optimizing biogas production and waste dosing in rural contexts, promoting clean and sustainable technologies for renewable energy generation. Full article

Nickel-based superalloys, serving as the preferred materials for hot-end structural components in aerospace engines, pose considerable challenges for the fabrication of high-quality gas film holes on their surfaces due to their inherent high hardness and strength. Water-jet-guided laser processing technology has exhibited notable potential in the realm of gas film hole fabrication; however, its engineering application is hindered by the lack of synergy between processing quality and efficiency. To tackle this issue, this study achieves efficient coupling between a 1064 nm high-power laser and a stable water jet, leveraging a multi-focal water–light coupling mode. Furthermore, an “inside-to-outside” multi-pass ring-cutting drilling strategy is introduced, and the controlled variable method is employed to investigate the influence of laser single-pulse energy, scanning speed, and pulse frequency on the surface morphology and geometric accuracy of micro-holes. Building upon this foundation, micro-holes fabricated using optimized process parameters are analyzed and validated using scanning electron microscopy and energy-dispersive spectroscopy. The findings reveal that single-pulse energy is a pivotal parameter for achieving micro-hole penetration. By moderately increasing the scanning speed and pulse frequency, melt deposition and thermal accumulation effects can be effectively mitigated, thereby enhancing the surface morphology and machining precision of micro-holes. Specifically, when the single-pulse energy is set at 0.8 mJ, the scanning speed at 25 mm/s, and the pulse frequency at 300 kHz, high-quality micro-holes with an entrance diameter of 820 μm and a taper angle of 0.32° can be fabricated in approximately 60 s. The micro-morphology and element distribution of the micro-holes affirm that water-jet-guided laser processing exhibits exceptional performance in minimizing recast layers, narrowing the heat-affected zone, and preserving the smoothness of the hole wall. Full article

In recent years, advanced CO₂ cycles, including supercritical CO₂ power cycles, transcritical CO₂ power cycles and refrigeration cycles, have demonstrated significant potential for application across a broad spectrum of energy conversion processes, owing to their high efficiency and compact components that are environmentally benign and non-polluting. This study presents a comprehensive review of the dynamic performance and control strategies of these advanced CO₂ cycles. It details the selection of system configurations and various control strategies, detailing the principles behind different control strategies, their applicable scopes, and their respective advantages. Furthermore, this study conducts a comparison between the joint control strategy and single control strategies for CO₂ cycles, demonstrating the superiority of the joint control strategy in CO₂ cycles. It then delves into the potential of novel control technologies for CO₂ cycles, using model-based control technology powered by artificial intelligence as a case study. This study also offers an extensive overview of control theory, methodology, scope of application, and the pros and cons of various control strategies, with examples including extreme value-seeking control, model predictive control (MPC) based on an artificial neural network model, and MPC based on particle swarm optimization. Finally, it explores the application of AI-controlled CO₂ cycles in new energy vehicles, solar power generation, aerospace, and other fields. It also provides an outlook on the development direction of CO₂ cycle control strategies in light of the evolving trends in the energy sector and advancements in AI methodologies. Full article

By unraveling the electrifying nexus between ESG sustainability uncertainty, financial development, information technology, trade policy uncertainty, and economic growth, this study sheds light on how these forces collectively shape the trajectory of the United States’ energy transition. Utilizing quarterly data from 2002 Q1 to 2024 Q4, we employ the novel Quantile-on-Quantile ARDL (QQARDL) framework to capture the heterogeneous and distribution-dependent dynamics of these relationships. To the best of our knowledge, this is the first study to apply QQARDL in assessing the simultaneous effects of institutional uncertainty, financial and technological drivers, and macroeconomic growth on energy transition outcomes in the U.S. The QQARDL results confirm that ET is cointegrated with ESG uncertainty, ICT, FD, TPU, and economic growth, though the strength and direction of these relationships vary across quantiles. ICT and EG consistently promote ET, ESG, and TPU exert mixed effects, FD is generally constraining, and the negative, significant ECT confirms stable long-run convergence with faster adjustment at higher ET quantiles. Based on these findings, policies were formulated to reduce ESG uncertainty, align financial development with green priorities, expand ICT adoption, stabilize trade frameworks, and harness economic growth to accelerate the U.S. energy transition. Full article

Augmented reality (AR) technology blends digital content with the real world to create immersive experiences. Diffractive waveguides, a key AR component, are popular because they balance weight, size, image quality, and ease of production. However, traditional glass-based waveguides have drawbacks like narrow viewing angles, blurry images, and rainbow-like distortions. To solve these issues, we developed ultra-thin, lightweight AR glasses using silicon carbide (SiC) and a new design method called the Period-Limited theory (PL theory). Our simulations show that the system works efficiently across red, green, and blue colors, removing rainbow distortions while keeping images clear and vibrant. Tests confirm that the design eliminates rainbow effects, provides a wide 55° field of view (FOV), and keeps each lens extremely light (just 2.12 g). This work offers a practical way to make compact, high-performance AR glasses with excellent display quality. Full article

Currently, the integration of deep learning technology with metasurface holographic imaging technology has propelled the development of optical imaging. Owing to the precise control of metasurfaces over the characteristics of light waves, holographic imaging technology can produce corresponding three-dimensional images after processing. Therefore, their integration enables the acquisition of high-quality images. The number of articles on metasurface design using neural network-based deep learning methods is increasing day by day; however, reviews on this topic remain scarce. This review introduces the development of neural networks and the relevant content on metasurface design using the four types of networks and the applications of deep learning-designed metasurface holographic imaging technology, thereby enhancing readers’ systematic understanding of such technologies. Full article

Underwater crack detection in dam structures is of significant importance to ensure structural safety, assess operational conditions, and prevent potential disasters. Traditional crack detection methods face various limitations when applied to underwater environments, particularly in high dam underwater environments where image quality is influenced by factors such as water flow disturbances, light diffraction effects, and low contrast, making it difficult for conventional methods to accurately extract crack features. This study proposes a dual-stage underwater crack detection method based on Cycle-GAN and YOLOv11 called Edge-Enhanced Underwater CrackNet (E²UCN) to overcome the limitations of existing image enhancement methods in retaining crack details and improving detection accuracy. First, underwater concrete crack images were collected using an underwater remotely operated vehicle (ROV), and various complex underwater environments were simulated to construct a test dataset. Then, an improved Cycle-GAN image style transfer method was used to enhance the underwater images. Unlike conventional GAN-based underwater image enhancement methods that focus on global visual quality, our model specifically constrains edge preservation and high-frequency crack textures, providing a novel solution tailored for crack detection tasks. Subsequently, the YOLOv11 model was employed to perform object detection on the enhanced underwater crack images, effectively extracting crack features and achieving high-precision crack detection. The experimental results show that the proposed method significantly outperforms traditional methods in terms of crack detection accuracy, edge clarity, and adaptability to complex backgrounds, effectively improving underwater crack detection accuracy (precision = 0.995, F1 = 0.99762, mAP@0.5 = 0.995, and mAP@0.5:0.95 = 0.736) and providing a feasible technological solution for intelligent inspection of high dam underwater cracks. Full article

Fermented vegetables, which are valued for their distinctive organoleptic properties and nutritional profile, are susceptible to quality deterioration during processing and storage because microorganisms inhabit vegetable raw materials. The metabolic processes of these microorganisms may induce texture degradation, chromatic alterations, flavor diminution, and spoilage. Conventional inactivation methods employing thermal sterilization or chemical preservatives achieve microbial control through nonselective inactivation, inevitably compromising the regional sensory characteristics conferred by indigenous fermentative microbiota. Recent advances in existing antimicrobial technologies offer promising alternatives for selective microbial management in fermented vegetable matrices. Existing modalities, including cold plasma, electromagnetic wave-based inactivation (e.g., photodynamic inactivation, pulsed light, catalytic infrared radiation, microwave, and radio frequency), natural essential oils, and lactic acid bacterial metabolites, demonstrate targeted pathogen inactivation while maintaining beneficial microbial consortia essential for quality preservation when properly optimized. This paper explores the applications, mechanisms, and targeted microbes of these technologies in fermented vegetable ingredients, aiming to provide a robust theoretical and practical framework for the use of selective inactivation strategies to manage the fermentation process. By assessing their impact on the initial microbial community, this review aims to guide the development of methods that ensure product safety while safeguarding the characteristic flavor and quality of fermented vegetables. Full article

Organ bioprinting is a rapidly evolving field designed to address the persistent shortage of donor organs by engineering patient-specific tissues that replicate the function and structure of natural organs. Despite significant technological advancements, bioprinting still faces major obstacles, including tissue rejection, inadequate vascularization, limited physiological functionality, and various ethical and translational challenges. In this review, we assess current bioprinting modalities, particularly extrusion-based printing, inkjet printing, laser-assisted bioprinting (LAB), and stereolithography/digital light processing (SLA/DLP), highlighting their individual strengths and limitations. We also explore different bioink formulations, focusing especially on hybrid bioinks as promising solutions to traditional bioink constraints. Additionally, this article thoroughly evaluates bioprinting strategies for four major organs: heart, liver, kidney, and pancreas. Each organ presents unique anatomical and physiological complexities, from cardiomyocyte immaturity and electromechanical mismatch in cardiac tissues to vascularization and zonation challenges in liver structures, intricate nephron patterning in kidney constructs, and immune rejection issues in pancreatic islet transplantation. Regulatory and ethical considerations critical for clinical translation are also addressed. By systematically analyzing these aspects, this review clarifies current gaps, emerging solutions, and future directions, providing a comprehensive perspective on advancing organ bioprinting toward clinical application. Full article

This study evaluates the positional accuracy of Global Navigation Satellite Systems (GNSS) and Unmanned Aerial vehicle (UAV)-based LiDAR systems in terrain modeling, using a total station as a reference. The research was conducted over 17 Ground Control Points (GCPs), with measurements obtained using a CHCNAV i50 GNSS receiver and a DJI Zenmuse L1 Light Detection and Ranging (LiDAR) sensor mounted on a UAV. Accuracy was assessed for horizontal (X, Y) and vertical (Z) components by comparing the results against total station data. Errors were quantified using statistical metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and RMS at 1σ. GNSS exhibited superior horizontal accuracy with an RMS 1σ of 1.1 cm, while LiDAR achieved 1.7 cm. In contrast, GNSS outperformed LiDAR in vertical precision, achieving a 1σ RMS of 6.4 cm compared to 6.6 cm for LiDAR. These findings align with manufacturer specifications and international standards such as those of the American Society for Photogrammetry and Remote Sensing (ASPRS). The results highlight that GNSS is preferable for applications requiring high horizontal precision, while LiDAR is better suited for vertical modeling and terrain analysis. The combination of both systems may offer enhanced results for comprehensive geospatial surveys. Overall, both technologies demonstrated sub-decimetric accuracy suitable for precision agriculture, civil engineering, and environmental monitoring. Full article

Accurate and rapid detection of surface targets is a key technology for autonomous navigation of intelligent and unmanned ships. Faced with complex maritime environments and ever-changing maritime targets, it is impossible to consistently obtain accurate target detection results based on a single sensor. Infrared and visible light have strong complementarity. By fusing infrared and visible images, a more comprehensive and prominent fused image can be obtained, effectively improving the accuracy of target detection. This article constructs a lightweight convolutional neural network image fusion model based on the fusion framework of convolutional neural networks and then uses the constructed water surface dataset for comprehensive experimental testing of image fusion and object detection. The test results show that the object detection model trained using fused images has better detection performance than the object detection model trained using infrared and visible light images alone. So, integrating two types of images can provide better results for object detection and help promote the development of related technologies. Full article

In this study, we present the development of a high-average-power, exceptionally stable extreme-ultraviolet (EUV) laser source based on a high-order harmonic generation (HHG) technique. The spectrum of an ytterbium-doped laser is broadened through self-phase modulation (SPM) in a gas-filled hollow fiber and compressed down to 25.3 fs for efficient harmonic generation. The high harmonics are generated in a krypton (Kr) gas cell, delivering a total power of 241 μW within the 30–60 nm spectral range, corresponding to a single harmonic output of 166 μW at a central wavelength of 46.8 nm. Notably, the system demonstrates good power stability with a root-mean-square (RMS) deviation of only 1.95% over 12 h of continuous operation. This advanced light source holds great potential for applications in nano- and quantum-material development and in semiconductor wafer defect detection. Future work aims to further enhance the output power in the 30–60 nm band to the milliwatt level, which would significantly bolster scientific research and technological development in related fields. Full article

Unmanned aerial vehicle (UAV)-based light detection and ranging (LiDAR) technology enables the acquisition of high-precision three-dimensional point clouds of the Earth’s surface. These data serve as a fundamental input for applications such as digital terrain model (DTM) construction and terrain analysis. Nevertheless, accurately extracting ground points in densely vegetated areas remains challenging. This study proposes a point cloud filtering method for the separation of ground points by integrating elevation frequency histograms and a multi-feature Gaussian mixture model (GMM). Firstly, local elevation frequency histograms are employed to estimate the elevation range for the coarse identification of ground points. Then, GMM is applied to refine the ground segmentation by integrating geometric features, intensity, and spectral information represented by the green leaf index (GLI). Finally, Mahalanobis distance is introduced to optimize the segmentation result, thereby improving the overall stability and robustness of the method in complex terrain and vegetated environments. The proposed method was validated on three study areas with different vegetation cover and terrain conditions, achieving an average $OA$ of 94.14%, $IoUg$ of 88.45%, $IoUng$ of 88.35%, and F1-score of 93.85%. Compared to existing ground filtering algorithms (e.g., CSF, SBF, and PMF), the proposed method performs well in all study areas, highlighting its robustness and effectiveness in complex environments, especially in areas densely covered by low vegetation. Full article