Search Results (28,866)

With the expansion of offshore oil and gas resources to deepwater areas, the problem of the vortex-induced vibration of marine risers, as a key structure connecting offshore platforms and subsea wellheads, has become increasingly prominent. At present, there are few reviews on the vortex-induced vibration of flexible risers. This review provides a detailed discussion of vortex-induced vibration in marine risers. This review begins with the engineering background. It then systematically analyzes the key factors that influence VIV response. These factors include the riser’s structural parameters, such as aspect ratio and mass ratio. They also include the external fluid environment. Next, this review evaluates current VIV suppression strategies by analyzing specific experimental results. It compares the effectiveness and trade-offs of passive techniques. It also examines the potential and limitations of active methods, which often use smart materials, like piezoelectrics. This study highlights the major challenges in VIV research today. These challenges relate to prediction accuracy and suppression efficiency. Key problems include model uncertainty at high Reynolds numbers and the practical implementation of suppression devices in engineering systems. Finally, this paper presents an outlook on the future directions. It concludes that an intelligent, full-lifecycle integrity management system is the best path forward. Full article

Vegetation succession is a critical indicator of ecosystem structure and function and is often disrupted by the expansion of invasive species. However, ecosystem-scale studies elucidating invasion-driven succession mechanisms remain limited. This research focused on the Yancheng coastal salt marsh and analyzed the distribution variation of invasive species (Spartina alterniflora) and native species (Suaeda salsa and Phragmites australis) from 1987 to 2022 via the Google Earth Engine and random forest method. Logistic/Gaussian models were used to quantify land–sea distribution changes and vegetation succession trajectories. By integrating data on soil salinity, invasion duration, and fractional vegetation cover, generalized additive models (GAMs) were applied to identify the main factors influencing vegetation succession and to explore how Spartina alterniflora invasion affects the succession of salt marsh vegetation. The results indicated that the areas of Spartina alterniflora and Phragmites australis significantly increased by 3787.49 ha and 3452.60 ha in 35 years, respectively, contrasting with Suaeda salsa’s 82.46% decline. The FVC in the area has significantly increased by 42.10%, especially in the coexisted areas of different vegetation communities, indicating intensified interspecific competition. The overall trend of soil salinity was decreasing, with a decrease in soil salinity in native species areas from 0.72% to 0.37%. From the results of GAMs, soil salinity, tidal action, and invasion duration were significant factors influencing the distribution of native species, but salinity was not a significant factor affecting the Spartina alterniflora distribution. The findings revealed that the expansion of Spartina alterniflora changed the soil salinity and interspecific interactions, thereby altering the original plant community structure and establishing a new vegetation succession. This study enhances the understanding of the impacts of invasive species on ecosystems and offers theoretical support for salt marsh restoration. Full article

The potential damage to aviation products caused by vibration and shock during road transportation has long been overlooked, despite structural failure under dynamic loading emerging as a critical technical challenge affecting product reliability. For aviation components, both stress and vibration analysis are essential prerequisites prior to formal assembly. This study investigates a symmetric vertical tail, a common aviation structure, employing an innovative model group analysis method to characterize its dynamic stress and strain distributions under real transportation conditions. Experimental measurements of vibration acceleration and impact loads during transport served as input data for constructing a numerical model based on stress and vibration theory. The model elucidates the mechanical responses of the tail in both modal and vibrational states, enabling effectively evaluation of dynamic vibrations on the tail and its critical subcomponents during road transport. The findings provide actionable insights for optimizing aviation component packaging design, mitigating vibration-induced damage, and enhancing transportation safety. Full article

This study examined the eco-friendly synthesis of zinc oxide (ZnO) nanoparticles using Cladophora glomerata extracts as reducing and stabilizing agents, comparing zinc acetate and zinc chloride precursors for biomedical and environmental applications. Zinc acetate-synthesized ZnO nanoparticles showed a significant absorption peak around 320–330 nm, indicating stable, quasi-spherical ZnO nanoparticles with a narrow size distribution, primarily around 100 nm. Zeta potential measurements revealed a value of −25 mV for these particles, suggesting moderate colloidal stability. XRD analysis confirmed a highly crystalline hexagonal wurtzite structure for zinc acetate-derived ZnO, and SEM images supported a proper microstructure with approximately 2 µm particle size. FTIR analysis indicated higher-quality ZnO from zinc acetate due to the absence of moisture and hydroxyl groups. Conversely, zinc chloride-derived ZnO particles displayed a broader absorption spectrum around 370 nm, indicative of significant aggregation. Their narrower zeta potential distribution around +10 mV suggested diminished colloidal stability and a heightened aggregation tendency. While a peak around 100 nm was observed, many particles exceeded 1000 nm, reaching up to 10,000 nm. XRD results showed that zinc chloride adversely affected crystallinity, and SEM analysis indicated smaller particles (approx. 1 µm). FTIR analysis demonstrated that zinc chloride samples retained hydroxyl groups. Both zinc acetate- and zinc chloride-derived ZnO nanoparticles produced notable inhibitory zones against Gram-positive (L. monocytogenes, S. aureus) and specific Gram-negative bacteria (E. coli, K. pneumoniae). Zinc acetate-derived ZnO showed a 21 mm inhibitory zone against P. vulgaris, while zinc chloride-derived ZnO showed a 10.1 mm inhibitory zone against C. albicans. Notably, zinc chloride-derived ZnO exhibited broad-spectrum antimicrobial activity. MIC readings indicated that zinc acetate-derived ZnO had better antibacterial properties at lower concentrations, such as 3.125 µg/mL against L. monocytogenes. These findings emphasize that the precursor material selection critically influences particle characteristics, including optical properties, surface charge, and colloidal stability. Full article

Large language models (LLMs), built upon Transformer architectures, have demonstrated remarkable performance in a wide range of natural language processing tasks. However, their practical deployment—especially in long-context scenarios—is often hindered by the computational and memory costs associated with managing the key–value (KV) cache during inference. Optimizing this process is therefore crucial for improving LLM efficiency and scalability. In this study, we propose a novel entropy-guided KV caching strategy that leverages the distribution characteristics of attention scores within each Transformer layer. Specifically, we compute the entropy of attention weights for each head and use the average entropy of all heads within a layer to assess the layer’s contextual importance. Higher-entropy layers—those exhibiting broader attention dispersion—are allocated larger KV cache budgets, while lower-entropy (sink-like) layers are assigned smaller budgets. Instead of selecting different key–value tokens per head, our method selects a common set of important tokens per layer, based on aggregated attention scores, and caches them uniformly across all heads within the same layer. This design preserves the structural integrity of multi-head attention while enabling efficient token selection during the prefilling phase. The experimental results demonstrate that our approach improves cache utilization and inference speed without compromising generation quality. For example, on the Qwen3 4B model, our method reduces memory usage by 4.18% while preserving ROUGE score, and on Mistral 0.1v 7B, it reduces decoding time by 46.6%, highlighting entropy-guided layer analysis as a principled mechanism for scalable long-context language modeling. Full article

HPMC, regulating slurry properties, is widely used in cement-based materials. Research on the application of HPMC in gangue slurry is still in its early stages. Moreover, the interactive effects of various factors on gangue slurry performance have not been thoroughly investigated. The work examined the effects of slurry concentration (X₁), maximum gangue particle size (X₂), and HPMC dosage (X₃) on slurry performance using response surface methodology (RSM). The microstructure of the slurry was characterized via scanning electron microscopy (SEM) and polarized light microscopy (PLM), while low-field nuclear magnetic resonance (LF-NMR) was employed to analyze water distribution. Additionally, industrial field tests were conducted. The results are presented below. (1) X₁ and X₃ exhibited a negative correlation with layering degree and slump flow, while X₂ showed a positive correlation. Slurry concentration had the greatest impact on slurry performance, followed by maximum particle size and HPMC dosage. HPMC significantly improved slurry stability, imposing the minimum negative influence on fluidity. Interaction terms X₁X₂ and X₁X₃ significantly affected layering degree and slump flow, while X₂X₃ significantly affected layering degree instead of slump flow. (2) Derived from the RSM, the statistical models for layering degree and slump flow define the optimal slurry mix proportions. The gangue gradation index ranged from 0.40 to 0.428, with different gradations requiring specific slurry concentration and HPMC dosages. (3) HPMC promoted the formation of a 3D floc network structure of fine particles through adsorption-bridging effects. The spatial supporting effect of the floc network inhibited the sedimentation of coarse particles, which enhanced the stability of the slurry. Meanwhile, HPMC only converted a small amount of free water into floc water, which had a minimal impact on fluidity. HPMC addition achieved the synergistic optimization of slurry stability and fluidity. (4) Field industrial trials confirmed that HPMC-optimized gangue slurry demonstrated significant improvements in both stability and flowability. The optimized slurry achieved blockage-free pipeline transportation, with a maximum spreading radius exceeding 60 m in the goaf and a maximum single-borehole backfilling volume of 2200 m³. Full article

Mechanosensitive ion channels such as OSCA1.2 enable cells to sense and respond to mechanical forces by translating membrane tension into ionic flux. While lipid rearrangement in the inter-subunit cleft has been proposed as a key activation mechanism, the contributions of other domains to OSCA gating remain unresolved. Here, we combined the genetic encoding of the photoactivatable crosslinker p-benzoyl-L-phenylalanine (BzF) with functional Ca²⁺ imaging and molecular dynamics simulations to dissect the roles of specific residues in OSCA1.2 gating. Targeted UV-induced crosslinking at positions F22, H236, and R343 locked the channel in a non-conducting state, indicating their functional relevance. Structural analysis revealed that these residues are strategically positioned: F22 interacts with lipids near the activation gate, H236 lines the lipid-filled cavity, and R343 forms cross-subunit contacts. Together, these results support a model in which mechanical gating involves a distributed network of residues across multiple channel regions, allosterically converging on the activation gate. This study expands our understanding of mechanotransduction by revealing how distant structural elements contribute to force sensing in OSCA channels. Full article

This study provides user-centered insights into how inclusive forest design can support the physical, emotional, and social well-being of older adults. It operationalizes universal design principles in natural settings and confirms their relevance through empirical evidence. With the acceleration of global population aging, adapting forest recreation environments to meet the specific needs of older adults is increasingly urgent. This study investigates how infrastructure influences both participation and emotional well-being among older visitors to forest recreation areas. Data were collected from 446 participants aged 65 and older, using a structured survey distributed through in-person contact and digital snowball sampling. Participants reported their infrastructure preferences and their emotional responses related to forest visits. The findings show that older adults highly value site cleanliness, shaded seating, accessible restrooms, and clear signage. Expectations varied significantly according to health status, age group, and visitation frequency. Emotional well-being was positively associated with both comfort and visit frequency. These results demonstrate how inclusive infrastructure plays a vital role in supporting older adults’ access to and enjoyment of forest environments. The study affirms that universally designed forests not only reduce barriers but also promote psychological health and active aging, contributing to developing more equitable and sustainable nature-based recreation areas. Full article

This study introduces a new method for modeling the nonlinear behavior of adhesively bonded composite steel–concrete T-beam systems. The model characterizes the interfacial behavior between the steel beam and the concrete slab using a strain compatibility approach within the framework of linear elasticity. It captures the nonlinear distribution of shear stresses over the entire depth of the composite section, making it applicable to various material combinations. The approach accounts for both continuous and discontinuous bonding conditions at the bonded steel–concrete interface. The analysis focuses on the top flange of the steel section, using a T-beam configuration commonly employed in bridge construction. This configuration stabilizes slab sliding, making the composite beam rigid, strong, and resistant to deformation. The numerical results demonstrate the advantages of the proposed solution over existing steel beam models and highlight key characteristics at the steel–concrete interface. The theoretical predictions are validated through comparison with existing analytical and experimental results, as well as finite element models, confirming the model’s accuracy and offering a deeper understanding of critical design parameters. The comparison shows excellent agreement between analytical predictions and finite element simulations, with discrepancies ranging from 1.7% to 4%. This research contributes to a better understanding of the mechanical behavior at the interface and supports the design of hybrid steel–concrete structures. Full article

This paper investigates the existence and uniqueness of bounded nonoscillatory solutions for two classes of m-th-order nonlinear neutral differential equations that incorporate both discrete and distributed delays. By applying Banach’s fixed-point theorem, we establish sufficient conditions under which such solutions exist. The results extend and generalize previous works by relaxing assumptions on the nonlinear terms and accommodating a wider range of feedback structures, including positive, negative, bounded, and unbounded cases. The mathematical framework is unified and applicable to a broad class of problems, providing a comprehensive treatment of neutral equations beyond the first or second order. To demonstrate the practical relevance of the theoretical findings, we analyze a delayed temperature control system as an application and provide numerical simulations to illustrate nonoscillatory behavior. This paper concludes with a discussion of analytical challenges, limitations of the numerical scope, and possible future directions involving stochastic effects and more complex delay structures. Full article

Understanding the distribution patterns of soil bacterial community structure and diversity across different forest types is essential for elucidating the mechanisms underlying microbial community assembly and its ecological drivers, particularly under the pressures of climate change. In this study, we examined six forest types—including four monocultures and two mixed-species stands—to systematically evaluate the structural composition, diversity metrics, and functional potential of soil bacterial communities. Significant differences in microbial structure and functional composition were observed among forest types. Mixed forests exhibited higher soil nutrient levels, more complex structures, and greater water retention capacity, resulting in significantly higher bacterial and functional diversity compared to monoculture forests. Bacterial diversity was greater in subsurface layers than in surface layers. Surface communities in monoculture forests showed relatively high structural heterogeneity, whereas deeper communities in mixed forests displayed more pronounced differentiation. The dominant bacterial phyla were mainly related to carbon and nitrogen metabolism, compound degradation, and anaerobic photosynthesis. Surface bacterial communities were primarily influenced by catalase activity, alkali-hydrolysable nitrogen, bulk density, and pH, whereas subsurface communities were largely controlled by pH, with supplementary regulation by nitrogen and potassium availability. Therefore, forest type and soil depth jointly influence the diversity, composition, and functional attributes of soil microbial communities by modulating soil physicochemical conditions. Full article

In the present work, we design a laminated composite composed of molybdenum–rhenium alloy and silicon carbide ceramics for use in space reactors as a candidate structural material with neutron spectral shift properties. The influence of the internal microstructure on the mechanical properties is investigated by finite element simulation based on scale separation. The results of the study showed that the incorporation of gradient transition layers between the metallic and ceramic phases effectively mitigates thermally induced local stresses arising from mismatches in coefficients of thermal expansion. By optimizing the composition of the gradient transition layers, the stress distribution within the composite under operating conditions has been adjusted. As a result, the stress experienced by the alloy phase is significantly reduced, potentially extending the high-temperature creep rupture life. Full article

Risk zone delineation and mobility behavior control constitute critical measures in pandemic containment. Numerous studies utilize static demographic data or dynamic mobility data to calculate the high–risk zones present in cities; however, these studies fail to concurrently consider activity and mobility patterns of populations in both space and time, which results in many studies only being able to employ static geostatistical analytical methods, neglecting the transmission risks associated with human mobility. This study utilized the mobile phone signaling data of Shenzhen residents from 2019 to 2020 and developed a CP tensor decomposition algorithm to decompose the long-sequence spatiotemporal trajectory data to detect high risk zones in terms of detecting overlapped community structures. Tensor decomposition algorithms revealed community structures in 2020 and the overlapping regions among these communities. Based on the overlap in spatial distribution and the similarity in temporal rhythms of these communities, we identified regions with spatiotemporal co-location as high–risk zones. Furthermore, we calculated the degree of population mixing in these areas to indicate the level of risk. These areas could potentially lead to rapid virus spread across communities. The research findings address the shortcomings of currently used static geographic statistical methods in delineating risk zones, and emphasize the critical importance of integrating spatial and temporal dimensions within behavioral big data analytics. Future research should consider utilizing non-aggregated individual trajectories to construct tensors, enabling the inclusion of individual and environmental attributes. Full article

Virtual power plants (VPPs) enable the efficient participation of distributed renewable energy resources in electricity markets by aggregating them. However, the profitability of VPPs is challenged by market volatility and regulatory constraints, such as price caps and imbalance penalties. This study examines the joint impact of varying price cap levels and imbalance penalty structures on the bidding strategies and revenues of VPPs. A stochastic optimization model was developed, where a three-stage scenario tree was utilized to capture the uncertainty in electricity prices and renewable generation output. Simulations were performed under various market conditions using real-world price and generation data from the Korean electricity market. The analysis reveals that higher price cap coefficients lead to greater revenue and more segmented bidding strategies, especially under asymmetric penalty structures. Segment-wise analysis of bid price–quantity pairs shows that over-bidding is preferred under upward-only penalty schemes, while under-bidding is preferred under downward-only ones. Notably, revenue improvement tapers off beyond a price cap coefficient of 0.8, which indicates that there exists an optimal threshold for regulatory design. The findings of this study suggest the need for coordination between price caps and imbalance penalties to maintain market efficiency while supporting renewable energy integration. The proposed framework also offers practical insights for market operators and policymakers seeking to balance profitability, adaptability, and stability in VPP-integrated electricity markets. Full article

Many challenges in solving large graph coloring through parallel strategies remain unresolved. Previous algorithms based on Pregel-like frameworks, such as Apache Giraph, encounter parallelism bottlenecks due to sequential execution and the need for a full graph traversal in certain stages. Additionally, GPU-based algorithms face the dilemma of costly and time-consuming processing when moving complex graph applications to GPU architectures. In this study, we propose Spardex, a novel parallel and distributed graph coloring optimization algorithm designed to overcome and avoid these challenges. We design a symmetry-driven optimization approach wherein the EdgePartition1D strategy in GraphX induces partitioning asymmetry, leading to overlapping locally symmetric regions. This structure is leveraged through asymmetric partitioning and symmetric reassembly to reduce the search space. A two-stage pipeline consisting of partitioned repaint and core conflict detection is developed, enabling the precise correction of conflicts without traversing the entire graph as in previous algorithms. We also integrate symmetry principles from combinatorial optimization into a distributed computing framework, demonstrating that leveraging locally symmetric subproblems can significantly enhance the efficiency of large-scale graph coloring. Combined with Spark-specific optimizations such as AQE skew join optimization, all these techniques contribute to an efficient parallel graph coloring optimization in Spardex. We conducted experiments using the Aliyun Cloud platform. The results demonstrate that Spardex achieves a reduction of 8–72% in the number of colors and a speedup of 1.13–10.27 times over concurrent algorithms. Full article