Search Results (1,601)

Super high-rise buildings of 400 m and above are currently rare globally, making their design and construction data invaluable. Due to their enormous size, the structural safety, architectural effect, and construction cost are key concerns of all parties. This study employs parametric analysis to research the lateral force-resisting system and key local structural issues of a 400 m under-construction super-high-rise structure. The overall analysis results show that the 8-mega-column scheme can relatively well balance architectural effect and structural performance; the 5-belt truss design minimizes the steel consumption. The local research results indicate that the inward inclination of bottom columns leads to increased axial forces in floor beams significantly, necessitating reinforcement; horizontal braces directly connected to the core tube enhance folded belt truss integrity under rare earthquakes; failure of bottom gravity columns in the folded secondary frame increases beam bending moments and axial forces substantially. Steel consumption sensitivity analysis shows that when the structural first-order period is reduced by 0.1 s, adjusting the section sizes of the members in the belt truss minimizes the increase in steel consumption, while adjusting steel beams maximizes it. These findings provide essential design insights for similar super-high-rise projects. Full article

This paper proposes a nonlinear control strategy for a microgrid, comprising a PV generator, wind turbine, battery, solid oxide fuel cell (SOFC), electrolyzer, and a three-phase four-leg voltage source inverter (VSI) with an LC filter. The microgrid is designed to supply unbalanced AC loads while maintaining high power quality. To address chattering and enhance control precision, a super-twisting algorithm (STA) is integrated, outperforming traditional PI, IP, and classical SMC methods. The four-leg VSI enables independent control of each phase using a dual-loop strategy (inner voltage, outer current loop). Stability is ensured through Lyapunov-based analysis. Scalar PWM is used for inverter switching. The battery, SOFC, and electrolyzer are controlled using integral backstepping, while the SOFC and electrolyzer also use Lyapunov-based voltage control. A hybrid integral backstepping–STA strategy enhances PV performance; the wind turbine is managed via integral backstepping for power tracking. The system achieves voltage and current THD below 0.40%. An energy management algorithm maintains power balance under variable generation and load conditions. Simulation results confirm the control scheme’s robustness, stability, and dynamic performance. Full article

Structured illumination microscopy (SIM) with π/2 phase-shift modulation traditionally relies on frequency-domain computation, which greatly limits processing efficiency. In addition, the illumination regime inherent in structured illumination techniques often results in poor visual quality of reconstructed images. To address these dual challenges, this study introduces DM-SIM-LLIE (Differential Low-Light Image Enhancement SIM), a novel framework that integrates two synergistic innovations. First, the study pioneers a spatial-domain computational paradigm for π/2 phase-shift SIM reconstruction. Through system differentiation, mathematical derivation, and algorithm simplification, an optimized spatial-domain model is established. Second, an adaptive local overexposure correction strategy is developed, combined with a zero-shot learning deep learning algorithm, RUAS, to enhance the image quality of structured light reconstructed images. Experimental validation using specimens such as fluorescent microspheres and bovine pulmonary artery endothelial cells demonstrates the advantages of this approach: compared with traditional frequency-domain methods, the reconstruction speed is accelerated by five times while maintaining equivalent lateral resolution and excellent axial resolution. The image quality of the low-light enhancement algorithm after local overexposure correction is superior to existing methods. These advances significantly increase the application potential of SIM technology in time-sensitive biomedical imaging scenarios that require high spatiotemporal resolution. Full article

This paper proposes a Fresnel-based Modified Moiré Varifocal Metalens (MMVL) addressing the inherent defocus at 0° rotation and significant focal quality degradation during varifocal operation in Traditional Moiré Varifocal Metalenses (TMVLs). The transmission function of the Fresnel-modified Moiré metalens combines a static term with a dynamic term, allowing the MMVLs to effectively overcome these limitations. Meanwhile, to minimize energy losses arising from polarization conversion and diffraction between the two metalenses, the nano-units on the metalenses are optimized by Particle Swarm Optimization (PSO) with FDTD simulations, maximizing the polarization conversion efficiency and transmittance. The simulation results demonstrate superior focal quality and stability in the MMVL throughout full rotational cycles, with super-diffraction-limited focusing maintained across all varifocal states. MMVLs have advantages in robustness; under axial distance variation (d = 0–20$d_0$, 0–3 $\mathsf{\mu }$m), they maintain on-axis focus without deviation; with centering error (p = 0–10$p_0$, 0–3 $\mathsf{\mu }$m), they sustain a clear focus at >36% efficiency. These results confirm that MMVLs have enhanced tolerance to manufacturing/assembly errors compared to TMVLs, delivering significantly stabilized optical performance. This advancement enables new possibilities for integrated micro-optics and optical tweezer applications. Full article

This study investigated how super-chilled (SC) storage at −3 °C combined with deoxygenated packaging (DO) affects quality degradation in yellowtail (Seriola quinqueradiata), dorsal ordinary muscle, and dark muscle. Sensory evaluation showed that DO significantly suppressed spoilage odor intensity in both muscle types, with enhanced effects under SC conditions. Spoilage in air-stored samples was primarily driven by Pseudomonas growth, whereas DO (especially SC) maintained microbial diversity by inhibiting bacterial proliferation and delaying spoilage. Volatile compound profiles differed markedly between the DO and air-stored samples. Despite these changes, DO-induced volatile compound alterations in the dorsal ordinary and dark muscles had minimal effects on perceived odor. Although DO prevented the accumulation of thiobarbituric acid reactive substances in both muscles, it did not suppress trimethylamine formation. These results demonstrate that SC-DO synergistically extends the shelf life of yellowtail by mitigating microbial spoilage and lipid oxidation, particularly during odor deterioration. Full article

To resolve the conflict between global structure modeling and local detail preservation in image super-resolution, we propose SwinT-SRGAN, a novel framework integrating Swin Transformer with GAN. Key innovations include: (1) A dual-path generator where Transformer captures long-range dependencies via window attention while CNN extracts high-frequency textures; (2) An end-to-end Detail Recovery Block (DRB) suppressing artifacts through dual-path attention; (3) A triple-branch discriminator enabling hierarchical adversarial supervision; (4) A dynamic loss scheduler adaptively balancing six loss components (pixel/perceptual/high-frequency constraints). Experiments on CelebA-HQ and Flickr2K demonstrate: (1) Very good performance (max gains: 0.71 dB PSNR, 0.83% SSIM, 4.67 LPIPS reduction vs. Swin-IR); (2) Ablation studies validate critical roles of DRB. This work offers a robust solution for high-frequency-sensitive applications. Full article

By integrating cathodic arc evaporation (CAE) with magnetron sputtering (MS) or high-power impulse magnetron sputtering (HiPIMS), hard coatings with diverse multicomponent compositions can be fabricated. Depending on the deposition conditions, the coatings with nano-composite or nano-multilayered microstructures are produced. During the mixing deposition conditions, nano-composite coatings are fabricated, which can be tailored to possess combining properties of super hardness, low friction coefficient, and excellent thermal/chemical stability. For the deposition with larger rotating periods, layer-by-layer deposition was observed. By the nano-multilayered coating design, superior mechanical properties (hardness ≥ 35 GPa), modulated residual stresses, and enhanced high-temperature properties can be obtained. In addition, lubricious elements, low friction (friction coefficient < 0.4), and low wear (<10⁻⁵ mm³/N∙m) both at ambient temperature and high temperature can be realized. Among these coatings, some have been specifically designed to achieve outstanding cutting performance in high-speed cutting applications. Several nitride and oxide hard coatings, such as AlTiN, TiAlN/TiSiN, AlCrN/Cu, and AlCrO, were deposited using a hybrid industrial physical vapor deposition (PVD) coating system. The microstructure, mechanical properties, and cutting performance of these coatings will be discussed. Full article

Against the dual backdrop of iterative AI advancement and deepening green development imperatives, AI-driven industrial intelligence (INT) has emerged as a pivotal force in driving sustainable economic growth. While the existing literature has explored the correlation between INT and green total factor productivity (GTFP), significant gaps remain in the design of multidimensional variables, analysis of environmental regulation (ER), and capture of dynamic effects. From the perspective of ER, this study utilizes provincial panel data from China (2012–2023) to construct an 11-indicator evaluation system for INT development and employs the EBM super-efficiency model to measure GTFP. Furthermore, a two-way fixed effects model combined with a moderated mediation model is established to systematically elucidate the intrinsic linkage mechanism between INT and GTFP. The key findings are as follows: First, INT has a significant positive impact on GTFP. Second, green innovation and spatio-economic synergy are crucial pathways through which INT empowers GTFP. Third, ER exhibits a substitution effect within both the direct and indirect impacts of INT on GTFP, where intensified ER significantly attenuates INT’s positive impacts. Fourth, the enhancement effect of INT on GTFP remains statistically significant with a one-year lag, and the substitution effect of ER persists. This study provides an in-depth analysis of the mechanisms of INT-driven green economic transformation, offering valuable insights for governments to implement differentiated environmental governance strategies tailored to local conditions. Full article

Under the background of global green sustainable development and the urgent need to understand complex regional innovation systems, it is crucial to scientifically assess China’s Tourism Green Innovation Efficiency (TGIE) as a dynamic networked system and reveal its system-level evolution driving mechanism. This article presents the construction of the TGIE evaluation indicator system, measures the inter-provincial TGIE in China in 2011–2023 based on the three-stage super-efficiency SBM-DEA model, analyzes the spatial correlation network characteristics of TGIE by using the motif analysis method and the social network analysis method, and explores the evolutionary driving mechanism by using the time-exponential random graph model (TERGM). The study shows the following: (1) The TGIE of China exhibits a regional distribution pattern characterized by “high in the east and low in the west.” The efficiency of the eastern coastal region is significantly higher than that of the central and western regions, and the overall efficiency shows a fluctuating upward trend. (2) The local structure of China’s TGIE network is dominated by the chain structure, and the partially closed structure is gradually enhanced. It indicates that the bridge role of intermediary nodes in the cross-regional flow of innovation resources is becoming more and more significant. (3) The overall network evolves from a single center to a polycentric collaboration model. High-efficiency regions attract low-efficiency regions to collaborate through high connectivity, and intermediary nodes play a key role in connecting high- and low-efficiency regions. (4) The evolution of China’s TGIE network is driven by both exogenous and endogenous dynamics, showing significant path dependence and path creation characteristics. This study enhances the theoretical framework of complex systems in tourism innovation and offers theoretical support and policy insights for optimizing the network structure of China’s TGIE as a complex adaptive system and maximizing regional cooperation networks. Full article

This paper addresses the three-dimensional trajectory tracking control problem for the underactuated Autonomous Underwater Vehicle (AUV) operating in complex ocean environments characterized by dynamic disturbances and model uncertainties. A super-twisting extended state observer (STESO) was designed to accurately estimate and compensate for external disturbances and unmodeled dynamics in finite time. A fractional-order proportional–integral–derivative (FOPID) controller was then developed based on the disturbance estimates provided by the STESO. Leveraging the superior frequency-domain tuning flexibility of fractional calculus, the controller enhances tracking precision and robustness against dynamic disturbances. Furthermore, a strict Lyapunov-based stability analysis is presented, and the tracking error converges to zero asymptotically when disturbance estimation errors vanish. Numerical simulations validated the effectiveness and robustness of the proposed control strategy under various disturbance scenarios. Full article

Image super-resolution (SR) techniques can significantly enhance visual quality and information density. However, existing methods often rely on large amounts of paired low- and high-resolution (LR-HR) data, which limits their generalization and robustness when faced with data scarcity, distribution inconsistencies, and missing high-frequency details. To tackle the challenges of image reconstruction in data-scarce scenarios, this paper proposes a semi-supervised learning-driven multi-fidelity fusion (SSLMF) method, which integrates multi-fidelity data fusion (MFDF) and semi-supervised learning (SSL) to reduce reliance on high-fidelity data. More specifically, (1) an MFDF strategy is employed to leverage low-fidelity data for global structural constraints, enhancing information compensation; (2) an SSL mechanism is introduced to reduce data dependence by using only a small amount of labeled HR samples along with a large quantity of unlabeled multi-fidelity data. This framework significantly improves data efficiency and reconstruction quality. We first validate the reconstruction accuracy of SSLMF on benchmark functions and then apply it to image reconstruction tasks. The results demonstrate that SSLMF can effectively model both linear and nonlinear relationships among multi-fidelity data, maintaining high performance even with limited high-fidelity samples. Finally, its cross-disciplinary potential is illustrated through an audio restoration case study, offering a novel solution for efficient image reconstruction, especially in data-scarce scenarios where high-fidelity samples are limited. Full article

This paper analyzes the long-term thermal effect of newly applied fiber-reinforced concrete link slabs on an existing steel bridge for a rehabilitation project of the Maryland Transportation Authority. To enhance structural resilience, thermal movement is one of the major concerns in concrete link slab design. To accommodate the global thermal expansion of a full bridge, the existing fixed bearings were modified to expansion bearings to release the longitudinal thermal movement of the super-structure. Their movements were measured by the installed LVDT devices. In this pilot study for the Maryland Transportation Authority (MDTA), engineered cementitious composite (ECC) and ultra-high-performance concrete (UHPC) were selected as candidate materials for link slabs to replace traditional steel expansion joints. To evaluate the performances of ECC and UHPC, built-in strain gauges were implemented for long-term field monitoring to ensure the durability of link slabs. For comparison, the measured data were collected over two full years to study their thermal effects in order to evaluate their sustainability. The novelty of the study is in comparing the performance of different materials side-by-side using true sensor measurements and numerical simulation. Thermal movement performance, including thermal cracking, will be one of the major selection criteria for the link slab material. Full article

Enhancing tourism eco-efficiency (TEE) is crucial for achieving China’s “dual carbon” objectives. This study examines nine provinces in the Yellow River Basin from 2010 to 2022, employing a super-efficiency SBM model, kernel density estimation, gravity center migration, standard deviation ellipse, Tobit regression, and fuzzy-set Qualitative Comparative Analysis (fsQCA) to investigate spatial-temporal variations and influencing factors. The results show that TEE increased steadily before 2019, declined during the COVID-19 pandemic, and recovered after 2021. Spatially, widening disparities and a polarization trend were observed, with the efficiency center remaining relatively stable in Shaanxi Province. Factors such as advancements in tourism economic development, regional economic growth, technological innovation, and infrastructure improvements significantly promote TEE, whereas stringent environmental regulations and greater openness exert constraints, and the impact of human capital remains uncertain. Four types of condition combinations were identified—economic-driven, market-innovation-driven, scale-innovation-driven, and balanced development. Managerial implications highlight the need for region-specific pathways and regional cooperation, with a dual focus on technological and institutional drivers as well as ecological value orientation, to sustainably enhance TEE in the Yellow River Basin. Full article

As critical infrastructure, hydropower dams require efficient and accurate detection of underwater structural surface defects to ensure their safety. This paper presents the design and implementation of a robotic control system specifically developed for underwater dam inspection in hydropower stations, aiming to enhance the robot’s operational capability under harsh hydraulic conditions. The study includes the hardware design of the control system and the development of a surface human–machine interface unit. At the software level, a modular architecture is adopted to ensure real-time performance and reliability. The solution employs a hierarchical architecture comprising hardware sensing, real-time interaction protocols, and an adaptive controller, and the integrated algorithm combining a fixed-time disturbance observer with adaptive super-twisting controller compensates for complex hydrodynamic forces. To validate the system’s effectiveness, field tests were conducted at the Baihetan Hydropower Station. Experimental results demonstrate that the proposed control system enables stable and precise dam inspection, with standard deviations of multi-degree-of-freedom automatic control below 0.5 and hovering control below 0.1. These findings confirm the system’s feasibility and superiority in performing high-precision, high-stability inspection tasks in complex underwater environments of real hydropower dams. The developed system provides reliable technical support for intelligent underwater dam inspection and holds significant practical value for improving the safety and maintenance of major hydraulic infrastructure. Full article

This paper introduces a novel autoencoder-based channel estimation framework specifically designed for OFDM systems in the complex and rapidly time-varying maritime channel. We design a novel autoencoder architecture that integrates attention mechanisms with long short-term memory networks to adapt to the challenges posed by maritime communication. Additionally, to enhance the OFDM system’s ability to acquire precise channel response and improve operational efficiency, we introduce an improved fast super-resolution convolutional neural network. This enhancement is achieved through the incorporation of a residual denoising module specifically designed to mitigate the adverse effects of additive noise. By jointly training the autoencoder and the channel estimation network, we significantly enhance the reliability of maritime OFDM communication systems. Simulation results demonstrate that the proposed channel estimation network accurately estimates channel response across different pilot numbers, and the joint channel estimation method based on the autoencoder can be extended to accommodate different transmission rates and sea states. Full article