Search Results (5,265)

This study examines the campus-planning projects (1920–1937) of Kwan, Chu & Yang, Architects & Engineers (KCY), a major Chinese firm, against the backdrop of Sino-American cross-cultural knowledge transfer. It argues that their work exhibited a distinct compositional tendency derived from the partners’ U.S. Beaux-Arts education and contemporary American planning theory. Through historical analysis and case studies of four university projects, this research examines how composition-based spatial unity engaged with specific Chinese site conditions. The results indicate that early projects negotiated irregular boundaries, while later ones grappled with complex topography, such as historic gardens and hills. Although often unrealized, these grand schemes embodied a scientific planning methodology and served as aspirational blueprints. This study concludes that compositional practice was a significant part of China’s architectural modernization, representing both a professional design approach and a cultural response to the quest for modernity and national identity. Full article

Fenton-based processes are widely used advanced oxidation methods that are known for degrading persistent pollutants. However, these techniques often generate significant amounts of iron-containing sludge, which poses environmental disposal challenges due to its complex composition. Furthermore, the sludge produced by the Fenton process contains a high content of Fe(III) compounds, which can serve as an iron source to stimulate dissimilatory iron reduction (DIR), enhancing the performance of anaerobic digestion. Based on the characterization results from a previous study, this work investigated the use of the ferrous precipitate generated by the electrochemical peroxidation process applied to tannery wastewater treatment as an additive to enhance volatile fatty acids (VFAs) production during dark fermentation. The performance of ferrous precipitate (R-Fe₃O₄) was compared to that of conventional magnetite (Fe₃O₄) during dark fermentation under high organic loading conditions, emphasizing their potential to enhance hydrolysis efficiency and VFAs production yields, while promoting sustainable resource recovery and reuse within a circular bioeconomy framework. The results showed that the addition of both Fe₃O₄ and R-Fe₃O₄ significantly increased the VFAs yields, with a predominance of long-chain fatty acids. The presence of CaCO₃ in the ferrous precipitate contributed to maintaining a stable pH environment, supporting microbial activity and enhancing the hydrolysis of soluble compounds. Moreover, the availability of essential micronutrients within the ferrous precipitate favored greater microbial diversity. Consequently, the addition of R-Fe₃O₄ promoted VFAs production, even at higher organic loading rates, suggesting a promising application of Fenton-based by-products as functional additives to improve the economic and environmental performance of the dark fermentation process. Full article

Unmanned Aerial Vehicle (UAV) trajectory planning in complex three-dimensional environments with threats remains a challenging optimization problem requiring efficient algorithms and threat detection capabilities. This study proposes the Conservative Enhanced Dwarf Mongoose Optimization Algorithm (CEDMOA), which introduces four key innovations to the original DMOA: hybrid population initialization, adaptive vocalization parameters, elite-guided learning strategy, and intelligent restart mechanisms. This work proposed the integration of CEDMOA with a novel vision-based threat detection system using YOLO object detection technology, enabling the identification and incorporation of threats into the optimization process. CEDMOA was comprehensively evaluated on the CEC2022 benchmark test suite, demonstrating superior performance compared to other state-of-the-art algorithms in solution quality and convergence stability. The results show the approach successfully generates an optimal collision-free flight trajectory in complex environments in UAV trajectory planning with both static and dynamic threats. Combining metaheuristic optimization with computer vision technology provides a robust framework for autonomous navigation that adapts to changing threat conditions. Experimental results validate the effectiveness of both the enhanced algorithm and the vision-based threat integration approach for practical UAV operations. Full article

This work investigated the membrane fouling mechanisms during the microfiltration of oat protein–β-glucan complexes. Microfiltration experiments were conducted under various pH conditions, protein-to-polysaccharide ratios, and ionic strengths. The fouling behavior was analyzed using multiple membrane fouling models to systematically elucidate the relationships among the particle characteristics, rheological behaviors, and membrane fouling. When the pH was adjusted to 7.8, the multimodal particle size distribution of the complexes promoted the formation of a loosely structured cake layer on the membrane surface, accompanied by partial obstruction of membrane pore entrances. On the contrary, the complexes, shown as having a monomodal particle size distribution and similar particle size to the membrane pore, formed compact cake layers and strong membrane fouling resistance. At pH 4.8, protein hydrophobic aggregation generated large particulate clusters that formed a loose cake layer during microfiltration, resulting in a decrease in membrane fouling resistance. Increasing the β-glucan content reduced membrane resistance through enhancing steric hindrance and hydrophilicity. This research provides a theoretical foundation for optimizing membrane separation process parameters in the production of diversified oat-based products. Full article

This paper presents a systematic experimental comparison of three sliding-mode-based current control strategies—traditional sliding mode control (SMC), fast terminal sliding mode control (FTSMC), and super-twisting sliding mode control (STSMC)—applied to a grid-connected five-level active neutral point clamped flying capacitor (5L-ANPC-FC) inverter. Unlike existing studies that typically investigate a single controller or topology, this work provides a fair, hardware-validated benchmark under identical operating conditions, enabling a clear assessment of convergence speed, harmonic performance, robustness, and implementation complexity. All controllers are designed within a unified framework and their stability is rigorously analyzed using Lyapunov theory. Experimental evaluations are conducted under steady-state operation, step changes in reference current, grid-voltage sag/swell, and DC-link voltage variations. The results demonstrate that while all three controllers ensure robust current tracking and inherent DC-side capacitor voltage balancing without additional control loops, FTSMC achieves the lowest grid-current total harmonic distortion (THD) and fastest convergence. STSMC effectively suppresses chattering, and traditional SMC offers a simple yet reliable baseline solution. The presented findings provide practical design guidelines for selecting appropriate sliding-mode controllers in high-performance multilevel inverter applications. Among the assessed control techniques, FTSMC has the most rapid dynamic response, characterized by a rise time of 0.1 ms and a minimal grid-current THD of 1.95%, indicating exceptional steady-state and transient performance. STSMC markedly diminishes chattering and ripple, attaining a THD of 2.04% with enhanced waveform smoothness relative to traditional SMC. Conversely, traditional SMC offers a more straightforward implementation but demonstrates elevated ripple and THD levels of around 2.29%, along with a peak current inaccuracy of 6–8%. The results underscore the trade-offs between implementation simplicity, dynamic responsiveness, and harmonic performance of the evaluated control techniques. Full article

Eye-tracking is a key enabling technology for smart eyewear, supporting hands-free interaction, accessibility, and context-aware human–machine interfaces under strict constraints on size, power consumption, and computational complexity. While camera-based solutions provide high accuracy, their integration into lightweight and low-power wearable platforms remains challenging. This paper is a feasibility study for the design, simulation, and experimental evaluation of a photosensor oculography (PSOG) eye-tracking system that is fully integrated into an eyewear frame, based on near-infrared (NIR) emitters and photodiodes. The proposed approach combines simulation-driven optimization of the optical constellation, a multi-frequency modulation and demodulation scheme enabling parallel source discrimination and robust ambient-light rejection, and a resource-efficient signal acquisition pipeline suitable for embedded implementation. Eye rotations in azimuth and elevation are inferred from differential reflectance patterns of ocular regions (sclera, iris, and pupil) using lightweight regression techniques, including shallow neural networks and Gaussian process regression, selected to balance estimation accuracy with computational and power constraints. System performance is evaluated using a controllable artificial-eye platform under defined geometric and illumination conditions, enabling repeatable assessment of gaze-estimation accuracy and algorithmic behavior. Sub-degree errors are achieved in this controlled setting, demonstrating the feasibility and potential effectiveness of the proposed architecture. Practical considerations for translation to real-world smart eyewear, including human-subject validation, anatomical variability, calibration strategies, and embedded deployment, are discussed and identified as directions for future work. By detailing the optical design methodology, modulation strategy, and algorithmic trade-offs, this work clarifies the distinct contributions of the proposed PSOG system relative to existing frame-integrated and camera-free eye-tracking approaches, and provides a foundation for further development toward wearable and augmented-reality applications. Full article

Advances in regenerative medicine increasingly rely on human-relevant in vitro systems to model the multistage process of wound healing. However, the translation of research into effective therapies remains limited by the inability of traditional 2D cultures and animal models to faithfully replicate the structural and biochemical complexity of human skin. While existing reviews often focus on the structural composition of static skin equivalents, this review addresses a critical knowledge gap: the need for dynamic, time-dependent methodologies that can capture the spatiotemporal evolution of healing, from inflammation to remodeling, in both physiological and pathological conditions. To this end, we critically evaluate next-generation platforms, including 3D bioprinting, organ-on-chip systems, organoids, and iPSC-based models, highlighting their comparative advantages and technical hurdles like vascularization and scalability. The unique contribution of this work lies in providing a forward-looking framework that advocates for the convergence of bioengineering and computational modeling to move beyond “steady-state” snapshots toward predictive, high-resolution dynamic models. We conclude that the future of wound healing research depends on integrating vascular and immune components within these platforms to achieve truly human-relevant, personalized diagnostic and therapeutic tools. Full article

The control of oxygen stoichiometry via topochemical reduction offers a powerful route to manipulate the functional properties of complex oxides. Here, we investigate the chemical and structural evolution of epitaxial BiFeO₃ (BFO) thin films under CaH₂ treatment in a sealed tube, using a representative reduction condition of 365 °C for 2 h and a temperature window of 345 to 380 °C to probe the reduction dependent evolution. The inherent sensitivity of BFO’s multiferroic properties to oxygen vacancy formation and cation valence states makes it an ideal platform to probe reduction pathways. The aim of this work is to elucidate the detailed reduction pathway, including phase stability, valence changes in Bi and Fe, and the morphological consequences of oxygen extraction. Using a combination of spectroscopic, diffraction, and microscopic techniques, it was demonstrated that CaH₂ annealing does not yield a homogeneous oxygen-deficient perovskite. Instead, it triggers a decomposition into Bi₂O₃, metallic Bi, and FeO_x secondary phases, accompanied by severe surface roughening. This chemical reconstruction leads to a strong suppression of the ferromagnetic-like response and a redshift in the optical absorption edge. Full article

Pesticides constitute a critical class of anthropogenic contaminants whose pervasive occurrence in surface waters, groundwater, and drinking water distribution systems poses substantial ecological and public health risks. Their pronounced structural heterogeneity, spanning highly polar herbicides to hydrophobic fungicides, together with their co-occurrence at trace levels, requires analytical methodologies capable of delivering rapid, robust, and high-resolution separations. In this study, a UHPLC-based analytical strategy is presented as a methodological framework for the development and optimization of UHPLC methods targeting multiresidue pesticide mixtures of environmental interest. The framework integrates a two-factor, three-level Design of Experiments, quadratic response surface modeling, and a multicriteria global desirability function to optimize the chromatographic resolution of 27 environmentally relevant pesticides. Statistical modeling revealed significant linear and quadratic effects of flow rate and gradient duration, highlighting the importance of multivariate optimization for complex multiresidue separations. The optimized UHPLC conditions improved simultaneous resolution, particularly for structurally similar analytes prone to coelution under conventional HPLC conditions. Overall, this work provides a statistically supported and transferable methodology for chemometric optimization of UHPLC separations and establishes a basis for extending desirability-driven optimization to additional classes of organic contaminants. Full article

Silicone gel packaging for high-voltage power devices suffers severe insulation degradation under complex conditions involving sustained high voltages and steep pulses. DC superimposed pulse electric fields are the primary cause. However, existing research lacks a systematic quantitative analysis of key influencing factors. Motivated by this inadequacy, this study quantified the effects of four core factors via control variable-based electrical tree experiments and revealed the microscopic mechanism through charge vibration experiments. Results indicate that pulse voltage slew rate is the most critical factor, as the impact of altering the pulse voltage slew rate on the parameters of the electrical tree exceeds 200%, while the impacts of altering the superimposed DC amplitude and duty cycle are 49.92% and 77.56%, respectively. Further discussion demonstrates that pulse voltage slew rate reflects the charge dynamic behaviors, while DC amplitude and duty cycle reflect charge static accumulation, with charge dynamic behaviors posing a more significant effect. This work clarifies key control parameters for silicone gel insulation degradation and the intrinsic influence chain from influencing factors to molecular stress, charge dynamic behaviors, electrical tree growth and silicone gel insulation degradation, providing theoretical support and technical guidance for optimizing the design and enhancing the reliability of silicone gel in power electronic devices packaging insulation. Full article

Volatile organic compounds (VOCs, dominated by xylene, toluene, and benzene) and paint mist emissions from ship painting represent a major environmental and health concern, posing a critical bottleneck to the green transformation of the shipbuilding industry. To tackle this challenge, this study presents an integrated recovery system designed specifically for ship automatic-spraying robots. Guided by the synergistic principle of “air-curtain containment, multi-stage adsorption, and negative-pressure recovery,” the system features a modular design that ensures full compatibility with the robots’ spraying trajectory without operational interference. Core adsorption materials, namely glass fiber filter cotton and honeycomb activated carbon fiber, were selected to suit the high-humidity and high-pollutant-concentration environment typical of ship painting. An appropriately matched axial flow fan maintains stable negative pressure throughout the system. Furthermore, the design integrates an air curtain isolation subsystem and an automated control subsystem, enabling coordinated operation and real-time adjustment. Using ANSYS Fluent, geometric and flow field simulation models were established to analyze airflow distribution and pollutant adsorption behavior, which led to the optimization of key structural and material parameters. Field experiments conducted in shipyard environments demonstrated the system’s superior performance: it achieved a VOC removal efficiency of 88.4% and a paint mist capture efficiency of 85.7% under optimal working conditions, with a maximum simulated paint mist capture efficiency of 86.2%. The system maintained stable performance under complex vertical and overhead spraying conditions, with an efficiency attenuation of less than 1.5%, and its outlet emissions fully complied with the mandatory limits specified in the Emission Standard of Air Pollutants for the Shipbuilding Industry (GB 30981.2-2025). The relative error between experimental data and simulation results is less than 2%, confirming the reliability and practicality of the proposed system. This research provides an efficient and adaptable pollution control solution for green shipbuilding and offers valuable technical insights for the sustainable upgrading of automated painting processes in heavy industries. Full article

This work presents a portable polarimetric multispectral imaging (PMSI) system operating in the visible to shortwave infrared range (VNIR–SWIR: 400–1700 nm) and its application to target detection, discrimination from aquatic backgrounds, and polymer identification. The instrument integrates two synchronized cameras with motorized bandpass filters and piezoelectric polarization control, enabling the acquisition of 48 wavelength–polarization measurements per capture. This configuration allows the extraction of both intensity-based and polarimetric features, including the degree of linear polarization (DoLP). A complete radiometric and polarimetric calibration framework is implemented, encompassing system response characterization, polarization-dependent gain correction, and reflectance normalization under variable illumination. Experiments conducted on a representative set of 16 polymer materials show that polarimetric information consistently improves class separability compared to intensity-only features, with a mean gain of 6.9 (95% CI: 6.35–8.47). Although the correlation between intensity- and DoLP-based separability is moderate (r = 0.44), the results indicate complementary identification capability. Material recoverability was further evaluated using spectral unmixing techniques (VCA, N-FINDR, and PPI), with VCA offering the best accuracy–complexity trade-off on the calibrated Stokes reflectance dataset. Despite these gains, identification among chemically similar polyethylene variants remains challenging due to limited spectral and polarimetric contrast. An underwater detectability study under natural illumination reveals strong wavelength-dependent constraints: SWIR penetration is limited to 4 cm, whereas VNIR bands (430–550 nm) preserve detectability up to 20 cm, with DoLP enhancing edge visibility. These results motivate future validation in more complex aquatic conditions and with increased spectral dimensionality. Full article

The vibration signals of Electro-Hydrostatic Actuators (EHAs) exhibit strong non-linearity and non-stationarity, particularly under complex coupling mechanisms, making the extraction of intrinsic fault features computationally challenging. Conventional deep learning approaches often lack mathematical interpretability and struggle to decouple superimposed fault signatures from incomplete datasets. To address these issues, this paper proposes the Enhanced Continuous Wavelet Transform Capsule Network (ECWTCN), an intelligent decoupled diagnosis framework designed for multiscale signal analysis. The architecture integrates a wavelet-kernel convolution layer to extract physically interpretable time–frequency features across multiple scales, effectively capturing transient impulses associated with incipient faults. Furthermore, a novel maximized aggregation routing algorithm is introduced to optimize the dynamic routing process, enhancing global feature aggregation. A distinct advantage of the ECWTCN is its capability to generalize distinct fault patterns, enabling the identification of unseen compound faults by training exclusively on normal and single-fault samples. Comparative experiments show that the proposed method delivers strong multi-label classification performance under operating condition A, achieving a Subset Accuracy of 93.7% and a Label Ranking Average Precision of 0.998. Complexity analysis further confirms the method’s efficiency in terms of FLOPs and parameter size. This work presents a robust, lightweight, and mathematically interpretable solution for the analysis of complex signals in high-reliability equipment. Full article

The aviation sector faces the challenge of reducing emissions while meeting growing demand for passenger transport. Recent research has proposed a hybridised axial compressor concept using a vaneless, counter-rotating configuration with independently electrically driven rotors. Earlier work showed the aerodynamic feasibility of this approach and identified the need for extended compressor maps to capture performance variations with hybridisation degree and speed ratio. This study explores the operational potential of such compressors in greater depth, focusing on how variable rotor speeds can unlock aerodynamic benefits and expand the operating envelope for hybrid-electric propulsion in regional aircraft and rotorcraft. Using mean line analysis, it is shown that independently driven rotors can operate effectively across a wide range of speed ratios. This flexibility enables the compressor to maintain high efficiency over diverse operating conditions, including part-load scenarios, typical of hybrid-electric missions. Independent speed control also offers a means of actively managing compressor stability. Compared to the conventional design the operating range can be significantly increased without relying on traditional stability measures such as variable stator vanes or bleed valves, reducing system weight and complexity. In this way the operating range of the hybrid compressor could be increased by up to 50%, while the number of blade rows could be reduced by up to 30% and the mass flow range increased by up to 33%. Together with the potential efficiency gains of counter-rotating concepts, this underscores its promise for future low-emission propulsion systems. Full article

Scalability of the original test design for the box assembly with removable component (BARC) structure is of interest in the field of experimental structural analysis. As complex structures become increasingly difficult to test experimentally the larger they become, it is a common test practice to use a scaled-down representative model to understand the characteristics of these systems. For complex structures with non-rigid boundary conditions, there exists a gap in understanding the effects of scalability and welding. To gain a better understanding of the outcomes of this phenomenon, the dynamical effects of upscaling the dimensions of the BARC structure are analyzed. Three variations of the BARC are investigated experimentally and computationally, namely, the original BARC system, the BARC system upscaled at 1.5 times the size of the original model, and the BARC system upscaled at two times the size of the original model. The original BARC is tested to investigate the properties of the predetermined boundary conditions. Because the upscaled BARC systems are manufactured using welding, an investigation of the variability of results due to welding imperfections is conducted to evaluate its effects on the vibrational properties of the systems. The dominant resonant frequencies of the three systems are identified through an impact hammer test. The results are then compared to those obtained through finite element analysis, in which both datasets show agreement. In general, as the BARC system is upscaled, the resonant frequencies decrease without inducing mode switching for the selected boundary conditions, indicating that the larger systems are less rigid. To understand the trends of nonlinear softening/hardening and nonlinear damping, forced vibration experiments conducted in the form of true random and controlled stepped-sine excitations are performed. The results show that, in general, as the BARC system is upscaled, changes in the nonlinear properties of the system are induced. With regard to the effects of using welding to manufacture BARC systems, the results prove that variations in welding can lead to non-negligible variations in the vibratory responses of the BARC system. Additionally, several types of harmonic vibrational testing are investigated to understand the physics behind their varied responses. Overall, this work shows that upscaling the BARC system can be beneficial to researchers who require a less rigid system for investigations and that manufacturing of BARC systems by welding can be a cost-effective alternative to subtractive manufacturing. Full article