Search Results (2,630)

The application of tuned mass dampers (TMDs) to high-rise buildings or slender bridges can significantly decrease the dynamical vibrations due to external excitation, such as wind or earthquake loads. However, the individual properties of a TMD such as mass, stiffness and damping have to be designed carefully with respect to the dynamical properties of the investigated structure. In real-world structures, the influence of uncertain system properties might be critical for the performance of a TMD and thus the whole structure. Therefore, the design under uncertainty of such systems is an important issue, which is addressed in the current paper. For our investigations, we consider linear single-degree-of-freedom (SDOF) systems, where analytical formulas for the deterministic design already exist, and linear multi-degree-of-freedom (MDOF) systems, where a time integration and numerical optimization algorithms are usually applied to obtain the optimal TMD parameters. If the numerical optimization should be coupled with a sampling-based uncertainty quantification method, such as Monte Carlo sampling, the design procedure would require the evaluation of a coupled double-loop approach, which is very demanding from the computation point of view. Therefore, we focus the following paper on an efficient analytical uncertainty quantification approach, which estimates the mean and scatter from a Taylor series expansion. Additionally, we introduce an efficient mode decomposition approach for MDOF systems with multiple TMDs, which estimates the maximum displacements using a modal analysis instead of a demanding time integration. Different optimal design problems are formulated as single- or multi-objective optimization tasks, where the statistical properties of the maximum displacements are considered as safety margins in the optimization goal functions. The application of numerical optimization algorithms is straightforward and not limited to specific algorithms. As numerical examples, we investigate an SDOF system with single TMD and a multi-story frame with multiple TMDs. The presented procedure might be interesting for the design process of structures, where the dynamical vibrations reach a critical threshold. Full article

This paper introduces the Intensional Conceptualization Model for Open Environments (ICMOE), a formal framework designed to enable semantic integration in dynamic and distributed systems. Grounded in intensional logic and formalized via a domain-specific language (ICMOE-L) built on Description Logic (DL), the model distinguishes between intensional and extensional semantics, allowing structured representation and evolution of concepts, relations, and domain rules under the open world assumption. ICMOE supports advanced semantic reasoning through an interpretation function that bridges relational data and ontological structures. A formal complexity analysis shows that reasoning with ICMOE-L has a worst-case complexity of O(n) ), where n is the total number of TBox and ABox axioms. To validate its effectiveness, ICMOE is evaluated using both qualitative and quantitative metrics. The model achieves a Concept Coverage score of 0.94, Semantic Depth of 0.89, Dynamic Adaptability Index of 0.91, Semantic Rule Density of 0.85, and Ontology Alignment Efficiency of 0.88. These results demonstrate ICMOE’s superior scalability, semantic richness, and adaptability when compared to foundational models such as those by Guarino and Bealer—making it a robust solution for open distributed environments. Full article

Urban thermal environments critically impact human settlements and sustainable urban development. In this study, a multi-index framework integrating Landsat TM/ETM+/OLI observations (2004–2019) is developed to quantify the contributions of “source–sink” landscapes to urban heat island (UHI) dynamics in Guangzhou, China, with direct implications for advancing sustainable development. Urban–rural gradient analysis was combined with emerging spatiotemporal hotspot modeling, revealing the following results: (1) there were thermal spatial heterogeneity with pronounced heat accumulation in core urban zones and improved thermal profiles in northern sectors, reflecting a transition from “more sources, fewer sinks” in the southwest to “fewer sources, more sinks” in the northeast; (2) UHIs were effectively mitigated within 25–35 km of the city center, with the landscape effect index (LI > 1) indicating successful sink-dominated cooling; (3) spatiotemporal hotspots were observed, including persistent UHIs in old urban areas contrasting with environmentally vulnerable coldspots in suburban mountainous regions, highlighting uneven thermal risks. This framework provides actionable strategies for sustainable urban planning, including optimizing green–blue infrastructure in UHI cores, enforcing cool material standards, and zoning expansion based on source–sink dynamics. This study bridges landscape ecology and sustainable development, offering a replicable model for cities worldwide to mitigate UHI effects through evidence-based landscape management. Full article

Arbuscular mycorrhizal fungi (AMF) play a crucial role in the soil–plant interface, yet the combined effects of AMF inoculation and phosphorus (P) addition on soil–plant nitrogen (N) and P, as well as oat grain yield, under drought stress remain unclear. Experiments were conducted during the 2021 and 2022 oat-growing seasons, applying AMF (40 g inoculum per pot; sterilized inoculum as the NAMF control) and P (0, 20, and 40 mg kg⁻¹ soil, designated P0, P1, and P2) under 75% and 55% relative water content. This study found that AMF inoculation at the P1 level significantly improved the AMF colonization rate, grain yield, and partial factor productivity of P (PFP_P) of oat. The grain yield increased by 6.2% (2021) and 9.8% (2022) under drought stress compared to the AMF-free treatment. AMF inoculation and P addition showed interactive effects on soil–plant N and P dynamics, which significantly increased microbial biomass phosphorus (MBP), nitrate N, and the available P content in oat soil. P1AMF significantly increased the total N and P contents under drought stress compared to P1NAMF, with maximum increments of 40.7% (N) and 11.1% (P) in 2021 and 15.4% (N) and 32.3% (P) in 2022. Moreover, the P1AMF treatment significantly improved P recovery efficiency (PRE), achieving a maximum increase of 48.4% across the two-year study. The analysis revealed that soil MBP was the key factor influencing oat grain yield, as well as the total N and P content in oat plants. It was concluded that AMF inoculation with a moderate amount of P addition could effectively regulate soil N and P availability and enhance plant N and P contents, as well as P productivity and use efficiency, thereby improving oat drought tolerance. Soil MBP acted as a vital bridge in the oat soil–plant continuum. Full article

Coordinated adaptive signal control is a proven strategy for improving traffic efficiency and minimizing vehicular delays. First, we develop a Queue Evolution and Delay Model (QEDM) that establishes the relationship between detector-measured queue lengths and model parameters. QEDM accurately characterizes residual queue dynamics (accumulation and dissipation), significantly enhancing delay estimation accuracy under oversaturated conditions. Secondly, we propose a novel intersection-level signal optimization method that addresses key practical challenges: (1) pedestrian stages, overlap phases; (2) coupling effects between signal cycle and queue length; and (3) stochastic vehicle arrivals in undersaturated conditions. Unlike conventional approaches, this method proactively shortens signal cycles to reduce queues while avoiding suboptimal solutions that artificially “dilute” delays by extending cycles. Thirdly, we introduce an adaptive coordination control framework that maintains arterial-level green-band progression while maximizing intersection-level adaptive optimization flexibility. To bridge theory and practice, we design a cloud–edge–terminal collaborative deployment architecture for scalable signal control implementation and validate the framework through a hardware-in-the-loop simulation platform. Case studies in real-world scenarios demonstrate that the proposed method outperforms existing benchmarks in delay estimation accuracy, average vehicle delay, and travel time in coordinated directions. Additionally, we analyze the influence of coordination constraint update intervals on system performance, providing actionable insights for adaptive control systems. Full article

Nutrition plays a pivotal role in preventive health, yet existing digital solutions often lack personalization and accessibility. This study presents an AI-driven framework that integrates machine learning (ML) and natural language processing (NLP) to deliver dynamic, user-centric dietary recommendations. A gradient boosting model, trained on NHANES demographic and anthropometric data, predicts caloric needs with an MAE of 132 kcal, while a locally deployed LLM (Mistral 7B) interprets free-text dietary constraints with 91% accuracy. Rule-based filtering from the USDA database ensures nutritional balance. A pilot usability test (n = 5) confirmed the system’s practicality and satisfaction. The proposed framework addresses key gaps in scalability, privacy, and adaptability, demonstrating the potential of hybrid AI techniques in applied nutrition science. By bridging computational methods with food science, this work offers a reproducible, modular solution for personalized health applications. Full article

Existing CO₂ leakage risk assessment frameworks for CO₂ capture, geological storage and utilization (CCUS) projects face limitations due to subjective biases and poor adaptability to long-term scale sequestration. This study proposed a dynamic risk assessment method for CO₂ leakage based on a timeliness analysis of different leakage paths and accurate time-dependent numerical simulations, and it was applied to the first CO₂ enhanced gas recovery (CCUS-EGR) pilot project of China in the Maokou carbonate gas reservoir in the Wolonghe gas field. A 3D geological model of the Maokou gas reservoir was first developed and validated. The CO₂ leakage risk under different scenarios including wellbore failure, caprock fracturing, and new fracture activation were evaluated. The dynamic CO₂ leakage risk of the CCUS-EGR project was then quantified using the developed method and numerical simulations. The results revealed that the CO₂ leakage risk was observed to be the most pronounced when the caprock integrity was damaged by faults or geologic activities. This was followed by leakage caused by wellbore failures. However, fracture activation in the reservoir plays a neglected role in CO₂ leakage. The CO₂ leakage risk and critical risk factors dynamically change with time. In the short term (at 5 years), the project has a low risk of CO₂ leakage, and well stability and existing faults are the major risk factors. In the long term (at 30 years), special attention should be paid to the high permeable area due to its high CO₂ leakage risk. Factors affecting the spatial distribution of CO₂, such as the reservoir permeability and porosity, alternately dominate the leakage risk. This study established a method bridging gaps in the ability to accurately predict long-term CO₂ leakage risks and provides a valuable reference for the security implementation of other similar CCUS-EGR projects. Full article

The growing use of weathering steel in bridge engineering has highlighted the increasing impact of fatigue damage caused by the combined effects of welding residual stress and vehicular loading. This study investigates the fatigue performance of Q500qENH weathering steel bridge decks by proposing a coupled analysis method for residual stress and fatigue crack growth, utilizing collaborative simulations with Abaqus 2023 and Franc3D 7.0. An interaction model integrating welding-induced residual stress fields and dynamic vehicular loads is developed to systematically examine crack propagation patterns in critical regions, including the weld toes of the top plate and the weld seams of the U-ribs. The results indicate that the crack propagation rate at the top plate weld toe exhibits the most rapid progression, reaching the critical dimension (two-thirds of plate thickness) at 6.98 million cycles, establishing this location as the most vulnerable failure point. Residual stresses significantly amplify the stress amplitude under tension–compression cyclic loading, with life degradation effects showing 48.9% greater severity compared to pure tensile stress conditions. Furthermore, parametric analysis demonstrates that increasing the top plate thickness to 16 mm effectively retards crack propagation, while wheel load pressures exceeding 1.0 MPa induce nonlinear acceleration of life deterioration. Based on these findings, engineering countermeasures including welding defect control, optimized top plate thickness (≥16 mm), and wheel load pressure limitation (≤1.0 MPa) are proposed, providing theoretical support for fatigue-resistant design and maintenance of weathering steel bridge decks. Full article

The oral microbiota, long recognized for their role in local pathologies, are increasingly implicated in systemic disorders, particularly cardiovascular disease (CVD). This review focuses on emerging evidence linking oral dysbiosis to neuroglial activation and autonomic dysfunction as key mediators of cardiovascular pathology. Pathogen-associated molecular patterns, as well as gingipains and leukotoxin A from Porphyromonas gingivalis, Fusobacterium nucleatum, Treponema denticola, Aggregatibacter actinomycetemcomitans, etc., disrupt the blood–brain barrier, activate glial cells in autonomic centers, and amplify pro-inflammatory signaling. This glia driven sympathetic overactivity fosters hypertension, endothelial injury, and atherosclerosis. Crucially, sex hormones modulate these neuroimmune interactions, with estrogen and testosterone shaping microbial composition, glial reactivity, and cardiovascular outcomes in distinct ways. Female-specific factors such as early menarche, pregnancy, adverse pregnancy outcomes, and menopause exert profound influences on oral microbial ecology, systemic inflammation, and long-term CVD risk. By mapping this oral–brain–heart axis, this review highlights the dual role of oral microbial virulence factors and glial dynamics as mechanistic bridges linking periodontal disease to neurogenic cardiovascular regulation. Integrating salivary microbiome profiling with glial biomarkers [e.g., GFAP (Glial Fibrillary Acidic Protein) and sTREM2 (soluble Triggering Receptor Expressed on Myeloid cells 2)] offers promising avenues for sex-specific precision medicine. This framework not only reframes oral dysbiosis as a modifiable cardiovascular risk factor, but also charts a translational path toward gender tailored diagnostics and therapeutics to reduce the global CVD burden. Full article

In recent years, the deterioration of social infrastructure such as bridges has become a serious issue in many countries around the world. To maintain the functionality of aging bridges over the long term, it is necessary to conduct regular inspections, detect damage at an early stage, and perform timely repairs. However, inspections require significant cost and time, and ensuring the safety of inspectors remains a major challenge. As a result, inspection using robots has attracted increasing attention. This study focuses on a wire-driven bridge inspection robot designed to inspect the underside of bridge girders. To use this robot, wires must be installed in the space beneath the girders. However, it is difficult to install wires over areas such as rivers. To address this problem, we developed a robotic arm capable of throwing a spear attached to a string. In order to throw the spear accurately to the target location, a three-dimensional dynamic model of the spear in flight was constructed, considering the tension of the string. Using this model, we accurately estimated the required throwing conditions and confirmed that the robotic arm could successfully throw the spear to the target location. Full article

Potato (Solanum tuberosum L.) is a globally significant staple crop that faces constant threats from Phytophthora infestans, the causative agent of late blight (LB). The battle between Phytophthora infestans and its host is driven by the molecular interplay of RXLR-encoded avirulence (PiAvr) effectors and nucleotide-binding leucine-rich repeat (NLR) immune receptors in potato. This review provides a comprehensive analysis of the structural characteristics, functional diversity, and evolutionary dynamics of RXLR effectors and the mechanisms by which NLR receptors recognize and respond to them. The study elaborates on both direct and indirect modes of effector recognition by NLRs, highlighting the gene-for-gene interactions that underlie resistance. Additionally, we discuss the molecular strategies employed by P. infestans to evade host immunity, including effector polymorphism, truncation, and transcriptional regulation. Advances in structural biology, functional genomics, and computational modeling have provided valuable insights into effector–receptor interactions, paving the way for innovative resistance breeding strategies. We also discuss the latest approaches to engineering durable resistance, including gene stacking, synthetic NLRs, and CRISPR-based modifications. Understanding these molecular mechanisms is critical for developing resistant potato cultivars and mitigating the devastating effects of LB. This review aims to bridge current knowledge gaps and guide future research efforts in plant immunity and disease management. Full article

Exoskeleton robots function as augmentation systems that establish mechanical couplings with the human body, substantially enhancing the wearer’s biomechanical capabilities through assistive torques. We introduce a lumbar spine-assisted exoskeleton design based on Variable-Stiffness Pneumatic Artificial Muscles (VSPAM) and develop a dynamic adaptation mechanism bridging the pneumatic drive module with human kinematic intent to facilitate human–robot cooperative control. For kinematic intent resolution, we propose a multimodal fusion architecture integrating the VGG16 convolutional network with Long Short-Term Memory (LSTM) networks. By incorporating self-attention mechanisms, we construct a fine-grained relational inference module that leverages multi-head attention weight matrices to capture global spatio-temporal feature dependencies, overcoming local feature constraints inherent in traditional algorithms. We further employ cross-attention mechanisms to achieve deep fusion of visual and kinematic features, establishing aligned intermodal correspondence to mitigate unimodal perception limitations. Experimental validation demonstrates 96.1% ± 1.2% motion classification accuracy, offering a novel technical solution for rehabilitation robotics and industrial assistance. Full article

Methane (CH₄) emissions from the wetlands of the Tibetan Plateau (TP) remain poorly quantified, particularly regarding their historical dynamics, spatial heterogeneity, and response to climate change. This study provides the high-resolution, observation-driven reconstruction of TP wetland CH₄ emissions over the past four decades, integrating a machine learning model with 108 flux measurements from 67 sites. This unique combination of field-based data and fine-scale mapping enables unprecedented accuracy in quantifying both emission intensity and long-term trends. We show that current TP wetlands emit 5.87 ± 1.43 g CH₄ m⁻² yr⁻¹, totaling 97.3 Gg CH₄ yr⁻¹, equivalent to 7.8% of East Asia’s annual wetland emissions. Despite a climate-driven increase in per-unit-area CH₄ fluxes, a 19.8% (8432.9 km²) loss of wetland area since the 1980s has reduced total emissions by 15%, counteracting the enhancement from warming and moisture increases. Our comparative analysis demonstrates that existing land surface models (LSMs) substantially underestimate TP wetland CH₄ emissions, largely due to the inadequate representation of TP wetlands and their dynamics. Projections under future climate scenarios indicate a potential 8.5–21.2% increase in emissions by 2100, underscoring the importance of integrating high-quality, region-specific observational datasets into Earth system models. By bridging the gap between field observations and large-scale modeling, this work advances understanding of alpine wetland–climate feedback, and provides a robust foundation for improving regional carbon budget assessments in one of the most climate-sensitive regions on Earth. Full article

Background: Viral–bacterial co-infections can amplify disease severity through complex biological mechanisms. Mathematical models are critical tools for understanding these threats, but it is unclear how well they capture the underlying biology. This systematic review addresses a central question: to what extent does the current generation of models mechanistically represent co-infections, or do the mathematical assumptions underlying these models adequately represent the known biological mechanisms? Methods: Following PRISMA guidelines, we systematically reviewed the literature on mechanistic models of human virus–bacteria co-infections. A systematic search of articles on the scientific literature repositories PubMed, Scopus, and Dimensions was conducted and data on study objectives, model structure, assumptions about biological interactions (e.g., susceptibility, mortality), control measures (if evaluated), and the empirical sources used for key parameters were extracted. Results: We identified 72 studies for inclusion in this analysis. The reviewed models are consistently built on the established premise that co-infection alters disease severity and host susceptibility. However, we found they incorporate these dynamics primarily through high-level mathematical shortcuts, such as applying static “multiplicative factors” to transmission or progression rates. Our quantitative analysis also revealed questionable approaches; for example, 79% (57) of these studies relied on non-empirical sources (assumed or borrowed values) for parameter values including interaction parameters (e.g., increased susceptibility to a secondary pathogen following primary infection, or elevated mortality rates in co-infected individuals). Conclusions: An apparently unjustified practice exists in co-infection modeling, where complex biological processes are simplified to fixed numerical assumptions, often without empirical support. This practice limits the predictive reliability of current models. We identify an urgent need for data-driven parameterization and interdisciplinary collaboration to bridge the gap between biological complexity and modeling practice, thereby enhancing the public health relevance of co-infection modeling. Full article

In recent years, cross-modal retrieval has garnered significant attention due to its potential to bridge heterogeneous data modalities, particularly in aligning visual content with natural language. Despite notable progress, existing methods often struggle to accurately capture user intent when queries are expressed through complex or evolving instructions. To address this challenge, we propose a novel cross-modal representation learning framework that incorporates an instruction-aware dynamic query generation mechanism, augmented by the semantic reasoning capabilities of large language models (LLMs). The framework dynamically constructs and iteratively refines query representations conditioned on natural language instructions and guided by user feedback, thereby enabling the system to effectively infer and adapt to implicit retrieval intent. Extensive experiments on standard multimodal retrieval benchmarks demonstrate that our method significantly improves retrieval accuracy and adaptability, outperforming fixed-query baselines and showing enhanced cross-modal alignment and generalization across diverse retrieval tasks. Full article