Search Results (654)

This paper presents the development of a fuzzy-PID control able to adapt to several robot–patient interaction modes by monitoring patient evolution during the rehabilitation procedure. This control system is designed to provide targeted rehabilitation therapy through three interaction modes: passive; active–assistive; and resistive. By integrating a fuzzy inference system into the classical PID architecture, the FPID controller dynamically adjusts control gains in response to tracking error and patient effort. The simulation results indicate that, in passive mode, the FPID controller achieves a 32% lower RMSE, reduced overshoot, and a faster settling time compared to the conventional PID. In the active–assistive mode, the FPID demonstrates enhanced responsiveness and reduced error lag when tracking a sinusoidal reference, while in resistive mode, it more effectively compensates for imposed load disturbances. A rehabilitation scenario simulating repeated motion cycles on a healthy subject further confirms that the FPID controller consistently produces a lower overall RMSE and variability. Full article

Advanced three-dimensional extended reality (XR) technologies are highly suitable for cultural heritage research and education. XR tools enable the creation of realistic virtual or augmented reality applications for curating and disseminating information about cultural artifacts and sites. Developing XR applications for cultural heritage requires interdisciplinary collaboration involving strong teamwork and soft skills to manage user requirements, system specifications, and design cycles. Given the diverse end-users, achieving high precision, accuracy, and efficiency in information management and user experience is crucial. Human–computer interaction (HCI) design and evaluation methods are essential for ensuring usability and return on investment. This article presents ten case studies of cultural heritage software projects, illustrating the interdisciplinary work between computer science and HCI design. Students from institutions such as the State University of New York (USA), Glasgow School of Art (UK), University of Granada (Spain), University of Málaga (Spain), Duy Tan University (Vietnam), Imperial College London (UK), Research University Institute of Communication & Computer Systems (Greece), Technical University of Košice (Slovakia), and Indiana University (USA) contributed to creating, assessing, and improving the usability of these diverse cultural heritage applications. The results include a structured typology of CH XR application scenarios, detailed insights into design and evaluation practices across ten international use cases, and a development framework that supports interdisciplinary collaboration and stakeholder integration in phygital cultural heritage projects. Full article

Underground hydrogen storage (UHS) in geological formations offers a promising solution for large-scale energy buffering, but its long-term safety and mechanical stability remain concerns, particularly in fractured rock environments. This study develops a fully coupled thermo-mechanical model to investigate the cyclic response of a dual-cavern hydrogen storage system with polymer-based sealing layers. The model incorporates non-isothermal gas behavior, rock heterogeneity via a Weibull distribution, and fracture networks represented through stochastic geometry. Two operational scenarios, single-cavern and dual-cavern cycling, are simulated to evaluate stress evolution, displacement, and inter-cavity interaction under repeated pressurization. Results reveal that simultaneous operation of adjacent caverns amplifies tensile and compressive stress concentrations, especially in inter-cavity rock bridges (i.e., the intact rock zones separating adjacent caverns) and fracture-dense zones. Polymer sealing layers remain under compressive stress but exhibit increased residual deformation under cyclic loading. Contour analyses further show that fracture orientation and spatial distribution significantly influence stress redistribution and deformation localization. The findings highlight the importance of considering thermo-mechanical coupling and rock fracture mechanics in the design and operation of multicavity UHS systems. This modeling framework provides a robust tool for evaluating storage performance and informing safe deployment in complex geological environments. Full article

Compared to other multi-source image fusion tasks, visible and SAR image fusion faces a lack of training data in deep learning-based methods. Introducing structural priors to design fusion networks is a viable solution. We incorporated the feature hierarchy concept from computer vision, dividing deep features into low-, mid-, and high-level tiers. Based on the complementary modal characteristics of SAR and visible, we designed a fusion architecture that fully analyze and utilize the difference of hierarchical features. Specifically, our framework has two stages. In the cross-modal enhancement stage, a CycleGAN generator-based method for cross-modal interaction and input data enhancement is employed to generate pseudo-modal images. In the fusion stage, we have three innovations: (1) We designed feature extraction branches and fusion strategies differently for each level based on the features of different levels and the complementary modal features of SAR and visible to fully utilize cross-modal complementary features. (2) We proposed the Layered Strictly Nested Framework (LSNF), which emphasizes hierarchical differences and uses hierarchical characteristics, to reduce feature redundancy. (3) Based on visual saliency theory, we proposed a Gradient-weighted Pixel Loss (GWPL), which dynamically assigns higher weights to regions with significant gradient magnitudes, emphasizing high-frequency detail preservation during fusion. Experiments on the YYX-OPT-SAR and WHU-OPT-SAR datasets show that our method outperforms 11 state-of-the-art methods. Ablation studies confirm each component’s contribution. This framework effectively meets remote sensing applications’ high-precision image fusion needs. Full article

To address the issue of deformation mismatch between the exterior wall panels and the resilient frame structure under large deformations, two novel flexible devices (FDs) with different working principles are proposed in this paper. These FDs enable the exterior wall panels to achieve cooperative deformation with frame columns or beams under horizontal loads, thus improving the seismic performance of the frame structure with additional exterior wall panels. This study begins by explaining the specific design thought of the FDs based on examining the deformation characteristics of frame structures. Then, a series of low-cycle loading tests are conducted on frame specimens to demonstrate the effectiveness of the FDs. The experimental results indicate that the FDs can improve the interaction between the exterior wall panels and the main frame, reduce plastic damage to the wall panels, and increase the peak load-bearing capacity of the overall structure by approximately 17–21%. In addition, a refined finite element modeling method for the proposed FDs is presented using the ABAQUS software, providing a basis for further research on frame structures with additional exterior wall panels. Full article

It is increasingly recognized that the twin transitions of digitalization and green transformation are pivotal to achieving sustainable development. This study examines how digital trade affects corporate green total factor productivity (GTFP), using panel data from Chinese A-share listed firms and 287 prefecture-level cities in Mainland China from 2012 to 2022. The results demonstrate that digital trade exerts a significant positive impact on GTFP, primarily through improvements in technical efficiency, with heterogeneous effects across different stages of the corporate life cycle. Endogeneity concerns are carefully addressed through instrumental variable estimation and quasi-experimental designs, and robustness checks confirm the reliability of the findings. Mechanism analyses further reveal that digital trade enhances GTFP by stimulating green technological innovation and optimizing supply chain management. Importantly, threshold regression reveals non-linear effects. Both the level of digital trade and institutional factors, such as environmental regulation, intellectual property protection, and market integration, moderate the relationship between digital trade and GTFP in U-shaped, N-shaped, and other positive non-linear patterns. These insights enhance the understanding of how digitalization interacts with institutional contexts to drive sustainable productivity growth, providing practical implications for policymakers seeking to optimize digital trade strategies and complementary regulatory frameworks. Full article

This paper is a case study of supporting students in developing their Generative Artificial Intelligence (GAI) literacy as well as guiding them to use it ethically, appropriately, and responsibly in their studies. As part of the study, a law coursework assignment was designed utilising a four-step Problem, AI, Interaction, Reflection (PAIR) framework that included a problem-solving task that required the students to use GAI tools. The students were asked to use one or two GAI tools of their choice early in their assessment preparation to research and were given a set questionnaire to reflect on their experience. They were instructed to apply Gibbs’ or Rolfe’s reflective cycles to write about their experience in the reflective part of the assessment. This study found that a GAI-enabled assessment reinforced students’ understanding of the importance of academic integrity, enhanced their research skills, and helped them understand complex legal issues and terminologies. It also found that the students did not rely on GAI outputs but evaluated and critiqued them for their accuracy and depth referring to primary and secondary legal sources—a process that enhanced their critical thinking and problem-solving skills. Full article

Canopy gaps are vital microhabitats for forest carbon cycling and species regeneration, whose accurate extraction is crucial for ecological modeling and smart forestry. However, traditional monitoring methods have notable limitations: ground-based measurements are inefficient; remote-sensing interpretation is susceptible to terrain and spectral interference; and traditional algorithms exhibit an insufficient feature representation capability. Aiming at overcoming the bottleneck issues of canopy gap identification in mountainous forest regions, we constructed a multi-task deep learning model (ES-Net) integrating an edge–semantic collaborative perception mechanism. First, a refined sample library containing multi-scale interference features was constructed, which included 2808 annotated UAV images. Based on this, a dual-branch feature interaction architecture was designed. A cross-layer attention mechanism was embedded in the semantic segmentation module (SSM) to enhance the discriminative ability for heterogeneous features. Meanwhile, an edge detection module (EDM) was built to strengthen geometric constraints. Results from selected areas in Yunnan Province (China) demonstrate that ES-Net outperforms U-Net, boosting the Intersection over Union (IoU) by 0.86% (95.41% vs. 94.55%), improving the edge coverage rate by 3.14% (85.32% vs. 82.18%), and reducing the Hausdorff Distance by 38.6% (28.26 pixels vs. 46.02 pixels). Ablation studies further verify that the synergy between SSM and EDM yields a 13.0% IoU gain over the baseline, highlighting the effectiveness of joint semantic–edge optimization. This study provides a terrain-adaptive intelligent interpretation method for forest disturbance monitoring and holds significant practical value for advancing smart forestry construction and ecosystem sustainable management. Full article

Generative Artificial Intelligence (GenAI)-assisted Virtual Reality (VR) heritage serious game design constitutes a complex adaptive socio-technical system in which natural language prompts act as control levers shaping designers’ cognition and action. However, the systemic effects of prompt type on agency construction, decision boundaries, and process strategy remain unclear. Treating the design setting as adaptive, we captured real-time interactions by collecting think-aloud data from 48 novice designers. Nine prompt categories were extracted and their cognitive effects were systematically analyzed through the Repertory Grid Technique (RGT), principal component analysis (PCA), and Ward clustering. These analyses revealed three perception profiles: tool-based, collaborative, and mentor-like. Strategy coding of 321 prompt-aligned utterances showed cluster-specific differences in path length, first moves, looping, and branching. Tool-based prompts reinforced boundary control through short linear refinements; collaborative prompts sustained moderate iterative enquiry cycles; mentor-like prompts triggered divergent exploration via self-loops and frequent jumps. We therefore propose a stage-adaptive framework that deploys mentor-like prompts for ideation, collaborative prompts for mid-phase iteration, and tool-based prompts for final verification. This approach balances creativity with procedural efficiency and offers a reusable blueprint for integrating prompt-driven agency modelling into GenAI design workflows. Full article

Background/Objectives: To investigate associations between Follistatin (FST) gene polymorphisms (SNPs) and baseline musculoskeletal traits, and their interactions with 16-week exercise interventions. Methods: A cohort of 470 untrained Northern Han Chinese adults (208 males, 262 females), sourced from the “Research on Key Technologies for an Exercise and Fitness Expert Guidance System” project, was analyzed. These participants were previously randomly assigned to one of four exercise groups (Hill, Running, Cycling, Combined) or a non-exercising Control group, and completed their respective 16-week protocols. Body composition, bone mineral content (BMC), bone mineral density (BMD), and serum follistatin levels were all assessed pre- and post-intervention. Dual-energy X-ray absorptiometry (DXA) was utilized for the body composition, BMC, and BMD measurements. FST SNPs (rs3797296, rs3797297) were genotyped using matrix assisted laser desorption/ionization time-of-flight mass spectrometer (MALDI-TOF MS) or microarrays. To elucidate the biological mechanisms, we performed in silico functional analyses for rs3797296 and rs3797297. Results: Baseline: In females only, the rs3797297 T allele was associated with higher muscle mass (β = 1.159, 95% confidence interval (CI): 0.202–2.116, P_{_adj} = 0.034) and BMC (β = 0.127, 95% CI: 0.039–0.215, P_{_adj} = 0.009), with the BMC effect significantly mediated by muscle mass. Exercise Response: Interventions improved body composition, particularly in females. Gene-Exercise Interaction: A significant interaction occurred exclusively in women undertaking hill climbing: the rs3797296 G allele was associated with attenuated muscle mass gains (β = −1.126 kg, 95% CI: −1.767 to −0.485, P_{_adj} = 0.034). Baseline follistatin correlated with body composition (stronger in males) and increased post-exercise (primarily in males, Hill/Running groups) but did not mediate SNP effects on exercise adaptation. Functional annotation revealed that rs3797297 is a likely causal variant, acting as a skeletal muscle eQTL for the mitochondrial gene NDUFS4, suggesting a mechanism involving muscle bioenergetics. Conclusions: Findings indicate that FST polymorphisms associate with musculoskeletal traits in Northern Han Chinese. Mechanistic insights from functional annotation reveal potential pathways for these associations, highlighting the potential utility of these genetic markers for optimizing training program design. Full article

The rational design of a bimetallic nanostructure with a phase separation and interface is of great importance to enhance electrocatalytic performance. Herein, PdNi heterostructures with controlled elemental distributions were constructed via a seeded growth strategy. Partially coated Ni islands in the Pd-Ni nanowire and strained Pd branches in the Pd-NiPd nanowires are revealed, respectively. Impressively, Pd-NiPd nanowires with abundant branches exhibit a superior mass current density and cycling stability toward an ethanol oxidation reaction (EOR) and ethylene glycol oxidation reaction (EGOR). The highest mass activities of 8.63 A mg_Pd⁻¹ and 12.53 A mg_Pd⁻¹ for EOR and EGOR, respectively, are realized on the Pd-NiPd nanowires. Theoretical calculations indicate that the Pd (100)-PdNi (111) interface stands out as an active site for enhancing OH adsorption and the decreasing CO bonding interaction. This study not only puts forward a simple method to construct bimetallic nanostructures with desired elemental distributions and interfaces but also demonstrates the significance of interface engineering in regulating the catalytic activity of metallic nanomaterials. Full article

Acid deposition, a major environmental issue causing soil acidification and microbial suppression, impacts forest nutrient cycling. Meanwhile, nitrogen (N) fertilization is widely applied in subtropical forests, yet its interaction with acid deposition on litter decomposition is unclear. We conducted a field experiment using two common tree species, Cunninghamia lanceolata and Cinnamomum camphora, and applied three acid deposition levels (0, 0.25, and 0.50 g H⁺ m⁻² month⁻¹) and four N fertilization levels (0, 3, 6, and 9 g N m⁻² year⁻¹) in a factorial design. Our results showed that acid deposition alone significantly reduced litter decomposition rates, with maximum mass loss decreasing by 23.6% for Cunninghamia and 36.3% for Cinnamomum (p < 0.05). Urea fertilization alone also suppressed decomposition, reducing maximum mass loss by 27.3% for Cunninghamia and 37.3% for Cinnamomum (p < 0.05). However, when combined, urea fertilization mitigated the suppressive effect of acid deposition, particularly under severe acid conditions, where maximum mass loss increased by 18.5% for Cunninghamia and 43.1% for Cinnamomum (p < 0.05). Acid deposition reduced microbial respiration and enzyme activities related to carbon cycling, while urea fertilization showed both positive and negative effects depending on the acid levels (p < 0.05). Urea can enhance the litter layer’s acid-buffering capacity, offering potential management insights for acid deposition-affected forests. Further research on microbial mechanisms across ecosystems is recommended. Full article

This study investigates the impact of circumferential angle (φ) and interaction angle (θ) between hydrogen jets and diesel sprays in a co-axial hydrogen–diesel injector on combustion and emissions in a hydrogen–diesel dual-fuel engine using 3D CFD simulations. The results demonstrate that a co-axial dual-layer nozzle design significantly enhances combustion performance by leveraging hydrogen jet kinetic energy to accelerate fuel–air mixing. Specifically, a co-axial alignment (φ = 0°) between hydrogen and diesel sprays achieves optimal combustion characteristics, including the highest in-cylinder pressure (20.92 MPa), the earliest ignition timing (−0.3° CA ATDC), and the maximum indicated power of the high-pressure cycle (47.26 kW). However, this configuration also results in elevated emissions, with 29.6% higher NOx and 34.5% higher soot levels compared to a φ = 15° arrangement. To balance efficiency and emissions, an interaction angle of θ = 7.5° proves most effective, further improving combustion efficiency and increasing indicated power to 47.69 kW while reducing residual fuel mass. For applications prioritizing power output, the φ = 0° and θ = 7.5° configuration is recommended, whereas a φ = 15° alignment with a moderate θ (5–7.5°) offers a viable compromise, maintaining over 90% of peak power while substantially lowering NOx and soot emissions. Full article

Glutamate is one of the major naturally occurring non-essential amino acids. The aim of this review is to provide a comprehensive analysis of the role of glutamate as a key metabolite in the metabolism of plant and animal organisms. Its role in nutrition and neurotransmission has intrigued researchers for many years. In both plants and animals, glutamate primarily exists in a monoanionic form characterised by unique physical and chemical properties. In plants, it is involved in the glutamine synthetase/glutamate synthase (GS/GOGAT) cycle, while in animals, it plays a role in the glutamine/glutamate cycle, which is closely related to the urea cycle. Glutamate is also closely linked to the Krebs cycle in both groups of organisms through α-ketoglutarate. Glutamate is essential in both biosynthetic and catabolic pathways and participates in numerous physiological processes in plants and animals. Animals acquire glutamate from food, while plants acquire it from the soil; however, both also synthesise it de novo. Once present in the body, it is transported across cell membranes by specific transporters driven by ionic gradients (a mechanism known as secondary active transport). It is involved in cellular and systemic signalling pathways by interacting with ionotropic and metabotropic receptors. Additionally, glutamate is an important ‘building block’ of many proteins, including storage proteins. It also occurs in the form of monosodium glutamate (MSG), a flavour enhancer that is widely used but often criticised. Due to its important role in metabolism and signalling, the significance of glutamate in nutrition and its impact on human health are vital areas of research in food biochemistry. These investigations contribute to the development of nutritious food products and the design of effective pharmaceuticals. In this paper, we also address unresolved questions in glutamate research and consider its practical applications. Full article

Using the combination of Concentrated solar power (CSP) and calcium looping (CaL) technology is an effective way to solve the problems of intermittent solar energy, but calcium-based materials are prone to sintering due to the densification of the surface structure during high-temperature cycling. In this study, the enhancement mechanism of Co and Cr doping in terms of the adsorption properties of CaO was investigated by Density Functional Theory (DFT) calculations. The results indicate that Co and Cr doping shortens the bond length between metal and oxygen atoms, enhances covalent bonding interactions, and reduces the oxygen vacancy formation energy. Meanwhile, the O²⁻ diffusion energy barrier decreased from 4.606 eV for CaO to 3.648 eV for Co-CaO and 2.854 eV for Cr-CaO, which promoted CO₂ adsorption kinetics. The CO₂ adsorption energy was significantly increased in terms of the absolute value, and a partial density of states (PDOS) analysis indicated that doping enhanced the C-O orbital hybridization strength. In addition, Ca₄O₄ cluster adsorption calculations indicated that the formation of stronger metal–oxygen bonds on the doped surface effectively inhibited particle migration and sintering. This work reveals the mechanisms of transition metal doping in optimizing the electronic structure of CaO and enhancing CO₂ adsorption performance and sintering resistance, which provides a theoretical basis for the design of efficient calcium-based sorbents. Full article