Search Results (27,216)

This review provides a comprehensive synthesis of robotic fruit harvesting systems, with a particular focus on the system-level integration of perception, manipulation, and fruit detachment within autonomous harvesting environments. Recent advances in machine vision, deep learning, sensor fusion, robotic end-effectors, grasping strategies, and motion planning are critically analyzed alongside cutting, pulling, and vibration-based detachment mechanisms under unstructured orchard conditions. Beyond component-level analysis, this review emphasizes the critical role of perception–action coupling and highlights key system integration challenges, including localization errors, perception-to-action latency, and environmental variability, which continue to limit reliable field deployment. In addition, orchard and pre-harvest-related factors such as canopy structure, fruit distribution, and detachment force variability are examined in relation to their direct impact on system performance, robustness, and harvesting efficiency. Furthermore, the review extends toward system-level considerations by incorporating performance evaluation metrics, economic feasibility, and scalability constraints, which are essential for transitioning robotic harvesting systems from experimental prototypes to commercially viable solutions, including practical field deployment in distributed and multi-robot harvesting systems. Emerging technologies, including artificial intelligence, advanced sensing, digital agriculture, and energy-aware system design, are discussed as key enablers for achieving adaptive, data-driven, and scalable autonomous harvesting. The novelty of this work lies in proposing an integrated framework that explicitly links perception, manipulation, and detachment with orchard-level constraints and deployment requirements, thereby bridging the gap between algorithmic advancements and real-world implementation of autonomous fruit harvesting systems. Full article

China has established the world’s largest municipal wastewater treatment system through rapid infrastructure expansion over the past two decades. However, under the transition from infrastructure expansion toward urban renewal and low-carbon development, wastewater systems are increasingly challenged by regional imbalances and structural inefficiencies. Existing studies have primarily focused on individual facilities or specific operational issues, while multidimensional system-level assessments remain limited. To address this gap, this study proposed a multidimensional assessment framework for evaluating wastewater system development in China from three dimensions: infrastructure adequacy, operational performance, and adaptive capacity. Based on national and provincial statistical data, regional disparities and development patterns were systematically analyzed using correlation analysis and hierarchical cluster analysis. Results showed that treatment capacity expansion in several provinces outpaced sewer network development, resulting in low hydraulic loading rates and underutilized facilities. Extraneous water infiltration remained a widespread issue, increasing unnecessary wastewater handling, energy consumption, and treatment burden. Reclaimed water development was influenced more strongly by policy-oriented planning and water resource constraints than by economic level alone. In addition, eastern coastal provinces generally demonstrated stronger infrastructure adequacy and operational performance, whereas several western and northeastern provinces remained constrained by insufficient adaptive capacity and sewer coordination. Overall, China’s wastewater sector is transitioning from treatment-oriented expansion toward system-oriented renewal. Future strategies should prioritize sewer rehabilitation, hydraulic efficiency improvement, reclaimed water integration, and adaptive infrastructure planning. The proposed framework can support future infrastructure monitoring, regional policy evaluation, and low-carbon wastewater system transformation. Full article

A multi-analytical investigation was carried out to elucidate the deterioration processes affecting the stone materials of the Arca di Cansignorio della Scala in Verona (Italy) and to characterize the surviving traces of its original polychrome and gilded decoration. The study combined macroscopic mapping, stratigraphic sampling, optical microscopy (OM), environmental scanning electron microscopy coupled with energy-dispersive X ray spectroscopy (ESEM-EDS), X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and ion chromatography (IC). The monument, predominantly carved from Candoglia marble, exhibits three principal weathering patterns: (i) rain washed areas affected by marble decohesion, (ii) grey deposits corresponding to dirt accumulation areas; and (iii) sulphation-induced black crusts developed in dirt wetting areas. In addition, severe mechanical deterioration was found to be associated with early twentieth-century structural consolidation interventions involving embedded iron bars, whose corrosion-driven volumetric expansion generated vertical cracking. Stratigraphic and microanalytical investigations revealed the presence of original azurite-based polychromy, proteinaceous and lipidic binding media, lead white preparatory layers, and multiple applications of gold leaf. The analytical results highlight the complex interplay between environmental exposure, atmospheric pollution, the incompatibility of materials introduced during past restorations campaigns. Furthermore, they contribute to a better understanding of the composition, execution techniques and preservation state of the surviving decorative layers, providing a scientific basis for future conservation strategies. Full article

Silver conductive structures were fabricated on 96% alumina ceramic substrates by selectively irradiating a silver nitrate precursor liquid film using a 355 nm Nd:YAG nanosecond laser under ambient conditions, without the use of external reducing agents. The effects of laser energy density, scan number, precursor concentration, plasma pretreatment, and PVP-30 addition on the morphology, composition, electrical conductivity, and adhesion of the deposited structures were investigated using XRD, SEM, EDS, contact angle measurements, resistance measurements, and tape-peeling tests. XRD confirmed the formation of metallic Ag in the laser-scanned regions. Insufficient laser energy density led to incomplete Ag⁺ reduction and discontinuous conductive paths, whereas excessive energy input caused hollow formation and Ag edge accumulation. A laser energy density of 12.03 J/cm² provided a favorable balance among structural integrity, Ag enrichment, and electrical conductivity. Increasing the scan number promoted particle coalescence and conductive network formation, while 1000 scanning cycles provided a suitable balance between structural continuity and dimensional precision. As the AgNO₃ concentration increased, the deposited structures evolved from isolated particles into continuous and compact layers, with 5 mol/L showing favorable deposition performance. Plasma pretreatment combined with PVP-30 addition reduced the contact angle of the ceramic surface from 48.25° to 19.05°, thereby improving the continuity, uniformity, and compactness of the deposits. After the scan spacing was reduced to form continuous silver films, the samples retained more than 98% of their conductivity after five tape-peeling cycles, with a resistivity of 6.14 × 10⁻⁸ Ω·m. These results demonstrate that laser-induced deposition is a controllable strategy for fabricating conductive silver structures on ceramic surfaces. Full article

This paper presents the modelling and dynamic analysis of a Virtual Synchronous Generator (VSG) operating under three representative load models: constant impedance (Z), constant power load (CPL), and composite ZIP (constant impedance, constant current, and constant power) loads. The VSG control strategy enables voltage-source converters to emulate the inertial behavior of synchronous machines. However, load characteristics strongly affect the stability of such systems, and CPLs can be particularly destabilizing because of their negative incremental impedance. This study provides a theoretical and simulation-based analysis of VSG performance under Z-, CPL, and ZIP load conditions. A swing-equation-based control model is linearized to obtain a reduced-order small-signal stability model. The incremental impedance properties of the load types are evaluated analytically, showing that CPL behavior reduces effective damping and can destabilize the system. The resulting analytical stability condition provides a practical basis for selecting virtual inertia and damping parameters. Practical DC-side energy storage and current-limiting constraints associated with inertia emulation are also discussed. The analysis is supported by simulation studies that quantify the influence of load dynamics on frequency stability and transient response. In contrast to current research, this paper offers a single comparative framework in which all load types are analyzed under the same operating conditions and derives analytical stability conditions that inform the selection of virtual inertia and damping parameters. Full article

Carbon fiber-reinforced polymer (CFRP) composites are in urgent demand in the aerospace, new energy vehicle, and wind power sectors owing to their superior specific strength, specific modulus, and lightweight potential. However, molding defects, such as voids, dry spots, and delamination, arising from their anisotropy and weak interlaminar bonding, severely constrain their service performance. Advanced molding technologies represent the key to overcoming this bottleneck. This paper systematically reviews typical advanced molding technologies in the field of CFRP composites, including resin transfer molding (RTM) and vacuum-assisted resin transfer molding (VARTM) in liquid composite molding, autoclave molding and compression molding (CM) in prepreg molding, and automated fiber placement (AFP) and material extrusion (ME) in automated molding. From an integrated perspective of “technological evolution–process characteristics–defect mechanisms–optimization strategies,” this review summarizes the technical principles, development trajectories, and core advantages of each process, analyzes the formation mechanisms of typical defects, including voids, dry spots, delamination, wrinkles, warpage, and melt instability, and summarizes multidimensional optimization advances in process parameter regulation, numerical simulation, resin modification, equipment upgrading, path planning, and thermal management. Furthermore, the differences and complementarities among these processes in terms of molding precision, efficiency, cost, and applicable scope are compared. Finally, future development directions, including digital twins, green low-carbon manufacturing, ultra-large integrated structures, multi-process integration, standardized defect characterization, and low-cost collaborative design, are discussed. This paper aims to provide systematic theoretical references and technical support for the optimization and upgrading, process integration, and industrial application of advanced CFRP molding technologies. Full article

Dynamic compaction (DC) is a widely used ground-improvement technique due to its cost-effectiveness, low environmental impact, and high adaptability. Despite its simple implementation, compaction efficiency is governed by multiple interacting factors, including tamping energy and soil properties, which poses challenges to practical design. Although numerous investigations have been reported, a comprehensive review systematically linking the various aspects of the DC technique through multiple approaches remains lacking. This paper addresses this gap by integrating and critically evaluating findings from field studies, controlled laboratory experiments, analytical studies, and numerical modeling to establish an effective framework for dynamic compaction applications. In addition, the environmental performance of DC is critically assessed, demonstrating its relatively low environmental footprint compared to material-intensive ground-improvement techniques, as impacts are primarily governed by construction energy rather than material production, although vibration and noise remain key considerations. The findings indicate that DC performance is controlled by the combined effects of the tamper mass, drop height, and geometry, together with impact spacing, number of blows, and initial soil properties. Field studies show that densification depth and uniformity are influenced by the fines percentage, drainage conditions, and applied energy levels, often requiring appropriate tamping strategies to mitigate pore water effects. Laboratory investigations highlight the dominant role of tamper mass over drop height in stress transmission and penetration depth and demonstrate how the tamper shape and impact sequence govern crater formation and strain localization. Numerical models employing finite element, discrete element, smoothed particle hydrodynamics, and hybrid approaches provide insight into stress wave propagation, pore pressure evolution, and soil–structure interaction. However, limitations remain in simulating sequential tamping, boundary conditions, and coupled hydro-mechanical behavior. This review highlights the need for cross-validated modeling, advanced instrumentation, and machine learning integration to support predictive, site-responsive dynamic compaction design in complex geotechnical settings. Full article

Two-Stage Anaerobic Digesters (TSADs) have emerged as an effective strategy for improving the stability and efficiency of biogas production from high-strength substrates such as food waste. The separation of acidogenic and methanogenic phases enables better environmental control for distinct microbial communities, thereby enhancing methane yield and reducing process instability. This study investigates the dynamics of microbial populations of acidogens, acetogens, and methanogens in a TSAD using an extended Anaerobic Digester Model No. 1 framework incorporating stage-specific microbial growth kinetics. Simulation scenarios were performed across a range of operational parameters, including OLR (1–8 kg VS/m³ day), pH (5.0–8.0), temperature (35 °C and 45 °C), and HRT (10–30 days). The results demonstrate that balanced microbial population dynamics and syntrophic interactions strongly influence methane production and overall digester performance. Optimal methane yields were achieved within an OLR range of 3.5–4.5 kg VS/m³ day under mesophilic conditions. Elevated loading rates led to VFA accumulation and pH decline, resulting in the inhibition of methanogenic populations and reduced methane output. Preliminary parametric analysis suggests that the acetoclastic methanogen growth rate and ammonia inhibition constants are influential parameters affecting system performance. The findings highlight the importance of integrating microbial population dynamics into AD models to enhance predictive accuracy and support the development of intelligent control strategies for sustainable waste-to-energy systems. Full article

With the large-scale integration of renewable energy sources, the stability and control of flexible DC (VSC-HVDC) grid-connected systems have become critical issues. This paper proposes an optimal configuration strategy for the control parameters of grid-forming VSC-HVDC systems considering multiple operational constraints. First, a state-space model of the grid-forming VSC-HVDC system connected to a wind farm is established, and the effects of key control parameters on the small-signal stability are analyzed using eigenvalue and participation factor methods. Then, based on the stability analysis, an optimization model is constructed with the objectives of minimizing the steady-state DC operating voltage under operational constraints and maximizing system damping. To solve the optimization problem, the NSGA-II genetic algorithm is employed. Case studies in MATLAB/Simulink demonstrate that the proposed method effectively enhances the small-signal stability of the system across various operating points, reduces overshoot and settling time during power step changes, and ensures stable operation under the maximum transferable power limit. The results verify the robustness and effectiveness of the proposed parameter configuration strategy, providing a practical approach for the design and tuning of grid-forming VSC-HVDC systems in renewable energy integration applications. Full article

Lipids play a central role in poultry nutrition by modulating energy utilization, nutrient digestibility, and metabolic processes related to lipid absorption and deposition. This review synthesizes current knowledge on the main dietary lipid sources used in poultry nutrition and their effects on performance, lipid metabolism, and egg yolk fatty acid composition. Conventional lipid sources, including vegetable oils and animal fats, differ in fatty acid profile, degree of saturation, and digestibility, which directly influence metabolic efficiency and productive responses. In addition, the strategic use of lipid sources enables the modulation of fatty acid profiles in poultry products, particularly through the enrichment of polyunsaturated fatty acids such as omega-3. These effects are associated with mechanisms involving lipid digestion, absorption, and hepatic lipoprotein synthesis, which regulate fatty acid deposition in tissues and egg yolks. However, responses to dietary lipids are influenced by factors such as inclusion level, oxidative stability, and lipid composition. Overall, dietary lipid manipulation represents an effective strategy to optimize production efficiency and enhance the nutritional quality of poultry-derived foods. Full article

With the increasing penetration of renewable energy systems, multilevel inverters have been widely adopted to meet the growing demand for high-power and high-quality energy conversion. Among various multilevel topologies, cascaded H-bridge multilevel inverters (CHMIs) are particularly attractive due to their modular structure and improved output voltage quality. However, the increased number of power semiconductor devices and switching states significantly complicates fault diagnosis under practical operating conditions. Currently, most existing neural networks for fault diagnosis are manually designed based on domain expertise. This may limit their adaptability to task-specific fault patterns as well as edge-side inference performance. To reduce the dependence on manually designed diagnostic networks, an edge-oriented fault diagnosis framework based on differentiable architecture search (DARTS) is proposed to automatically design task-specific diagnostic networks. A simplified special cell search strategy is adopted to improve search efficiency and facilitate practical deployment. The searched architectures are lightweight and suitable for deployment on edge platforms. The experiments show that the proposed method achieves an average diagnostic accuracy of 99.44% on the test set under the RL load of $(7\mathrm{\Omega },6\mathrm{mH})$. Furthermore, the searched model contains only 0.2417 M trainable parameters, and edge deployment experiments on the Jetson Orin Nano platform show low-latency inference capability. Full article

This study introduces a greentelligent scheduling approach to enhance energy efficiency in the aluminum extrusions casting workshop (ACW), addressing the high energy consumption and low efficiency inherent in these processes. Global energy consumption is significantly attributed to the manufacturing sector, with aluminum extrusions being one of the most common products, particularly in energy-intensive casting workshops. Given the considerable demand and potential for energy savings in aluminum extrusions manufacturing (AEM), this study proposes an intelligent scheduling approach to minimize non-processing energy consumption (NPE) while also reducing average completion time (ACT). Utilizing industrial internet of things (IIoT) technologies, practical production data is acquired to support a bi-objective scheduling model. An empirical knowledge-based evolution algorithm (EBA) with an improvement strategy (SO-EBA) is developed to efficiently solve this complex, NP-hard problem. A production case in an ACW demonstrates the effectiveness of the SO-EBA. Compared to benchmark algorithms, the SO-EBA achieves significant reductions in optimal NPE by more than 39.41%, while maintaining production efficiency. This work advances greentelligent manufacturing by integrating IIoT and intelligent algorithms, offering a scalable solution for sustainable production in energy-intensive industries. Full article

The global renewable energy sector now represents the world’s fastest-growing sector, with growth projected to more than double by 2030 and expected to exceed 4600 GW between 2025 and 2030. This is driven by falling costs, increasing consumer awareness, sustainable energy production models, and national and international climate commitments. This review study aims to discuss the transformation initiatives in the renewable energy sector with net-zero targets. A total of 89 studies published between 2020 and 2026 were identified for this literature review. The results indicate that digital transformation has the potential to significantly optimize the performance of the renewable energy sector by resolving its sustainability issues. This study discusses the waste types and waste management strategies in the renewable energy sector. It also highlights the indicators, barriers, and drivers of sustainable performance in the renewable energy sector by integrating advanced technological solutions in manufacturing, supply chain management, maintenance, monitoring, and the management of renewable energy equipment. The study findings demand global commitment and policy coordination in achieving the goals of decarbonization. The literature insights highlight future core research fields and can guide international organizations, industrial policymakers, and academic scholars towards a better and more sustainable future. Full article

Direct emission of low-concentration methane not only aggravates global warming but also causes serious energy waste. Catalytic combustion is considered an effective strategy for methane abatement because it enables methane oxidation at relatively low temperatures. In this work, a series of Mn–Ce–Cu/γ-Al₂O₃ catalysts with different nominal Mn/Ce ratios were prepared by the incipient wetness impregnation method and applied to low-concentration methane catalytic combustion. The results showed that Mn–Ce co-modification significantly improved the activity of Cu/γ-Al₂O₃ catalysts, and the catalytic performance strongly depended on the Mn/Ce ratio. Among all samples, 7Mn-3Ce-10Cu exhibited the best activity, with the temperatures required for 10%, 50% and 90% methane conversion (T₁₀, T₅₀ and T₉₀) of 380.8, 427.3 and 478.7 °C, respectively. Apparent activation energy (E_a) analysis further showed that 7Mn-3Ce-10Cu possessed the lowest E_a value of 83.81 kJ mol⁻¹, indicating that the optimized Mn/Ce ratio effectively lowered the apparent kinetic barrier for methane oxidation. X-ray diffraction (XRD), transmission electron microscopy (TEM) and nitrogen (N₂) adsorption–desorption results suggested that Mn–Ce co-modification changed the phase composition, improved the dispersion state of active oxide species and generated a more favorable pore structure for reactant diffusion. Oxygen temperature-programmed desorption (O₂-TPD) and X-ray photoelectron spectroscopy (XPS) results further indicated that the enhanced activity of 7Mn-3Ce-10Cu was closely associated with improved oxygen desorption behavior, a higher proportion of surface oxygen species and favorable surface redox characteristics of Cu, Mn and Ce species. Moreover, 7Mn-3Ce-10Cu maintained methane conversion above 90% during a 50 h stability test at 500 °C, and the inhibition caused by 5% H₂O was partially reversible. These results demonstrate that Mn–Ce co-modification is an effective strategy for improving low-cost Cu-based catalysts for low-concentration methane combustion. Full article

Monopile offshore wind turbines are vulnerable to excessive vibration under coupled wind, wave, and seismic loading because of their slender and flexible structural characteristics. This study investigates a single-sided vibro-impact nonlinear energy sink (SSVI NES) installed inside the nacelle of a 5 MW monopile offshore wind turbine. A reduced-order ten-degree-of-freedom dynamic model is established using the Euler-Lagrange formulation, and turbulent wind, irregular wave, and seismic inputs are generated using TurbSim, the Kaimal and JONSWAP spectra, the Morison equation, and 15 PEER ground-motion records. The proposed SSVI NES is compared with an optimized tuned mass damper (TMD) under nominal and frequency-detuned conditions. Under the nominal design condition, the optimized TMD and the representative SSVI NES reduce the RMS nacelle fore-aft displacement by approximately 55% and 50%, respectively, indicating that the SSVI NES provides near-benchmark vibration mitigation. Meanwhile, the maximum absorber stroke of the SSVI NES is reduced by approximately 40% compared with that of the optimized TMD, which is beneficial for nacelle-integrated implementation. Under frequency detuning, the response-reduction effectiveness of the TMD decreases from approximately 55% to 20%, whereas the SSVI NES retains approximately 80% of its nominal RMS-based control effectiveness. These quantified results show that the SSVI NES offers a balanced combination of competitive nominal response reduction, reduced absorber motion demand, and improved robustness against structural-frequency variations. The proposed device therefore provides a promising passive-control strategy for enhancing the serviceability and multi-hazard resilience of monopile offshore wind turbines. Full article