Search Results (2,958)

The development of large-scale cascade hydropower complexes has improved the navigation conditions of mountainous rivers but creates unique “continuous dam zones,” presenting complex challenges for port site selection due to hydrological variability and geological risks. To address the lack of specialized evaluation tools for this specific context, this paper constructs a comprehensive evaluation indicator system tailored for mountainous reservoir areas. The proposed system explicitly integrates critical engineering and physical constraints—specifically fluctuating backwater zones, geological hazards, and dam-bypass mileage—alongside ecological and social requirements. The Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) are integrated using a Game Theory model to determine combined weights, and the Evaluation based on Distance from Average Solution (EDAS) model is applied to rank the alternatives. An empirical analysis of the Xiluodu Reservoir area on the Jinsha River demonstrates that operational efficiency, geological safety, and environmental feasibility constitute the critical decision-making factors. The results indicate that Option C (Majiaheba site) offers the optimal solution (ASi = 0.9695), effectively balancing engineering utility with environmental protection. Sensitivity analysis further validates the consistency and stability of this ranking under different decision-making scenarios. The findings provide quantitative decision support for project implementation and offer a replicable reference for infrastructure planning in similar complex mountainous river basins. Full article

Bovine Parainfluenza Virus Type 3 (BPIV3) is a critical pathogen in the Bovine Respiratory Disease Complex (BRDC), leading to significant economic losses in the cattle industry. However, the metabolic reprogramming induced by BPIV3 in cattle remains poorly understood. This study aimed to investigate the impact of BPIV3 infection on the serum metabolome of Simmental cattle using untargeted metabolomics and ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS/MS). The results revealed significant alterations in the lipidome, including the upregulation of phosphatidylcholine (PC) and phosphatidylglycerol (PG), and the downregulation of phosphatidylinositol (PI). Sphingolipid metabolism also showed considerable changes, with increased levels of Trihexosylceramide and D-erythro-Sphingosine C-17. Furthermore, metabolic pathway analysis identified enriched pathways related to lipid metabolism, amino acid metabolism, and energy sensing. These findings suggest that BPIV3 infection induces substantial shifts in lipid metabolism, which may facilitate viral replication and immune evasion. Our results provide a deeper understanding of the metabolic changes in BPIV3-infected cattle and propose potential targets for therapeutic intervention. Full article

Design education involves complex historical knowledge structures that often impose a high extraneous cognitive load on students. This study proposes and evaluates an intelligent instructional system that integrates Retrieval-Augmented Generation (RAG) with anthropomorphic digital humans to function as scalable cognitive scaffolding. We developed a locally deployed architecture utilizing the Qwen3-30B Large Language Model (LLM) for reasoning, BGE-Large-Zh for high-precision semantic embedding, and LiveTalking for real-time audiovisual generation. To validate the system’s pedagogical efficacy, a multi-center randomized controlled trial (RCT) was conducted across three universities ($N = 150$). The experimental group utilized the RAG-enhanced digital human system, while the control group received traditional instruction. Quantitative results demonstrate that the system significantly improved learning outcomes ($p<0.001, Cohen’s d=1.14$) and classroom engagement ($p<0.001, d=1.39$). Crucially, measurements using the Paas Mental Effort Rating Scale revealed a significant reduction in mental effort ($p<0.001, d=1.71$) for the experimental group. Instructional efficiency analysis (E) confirmed that the system successfully converted reduced extraneous load into germane learning gains (Experimental $E=+0.72$ vs. Control $E=-0.68$). These findings validate the technical feasibility and educational value of combining localized RAG architectures with embodied AI, offering a replicable framework for reducing cognitive load in intensive learning environments. Full article

Viral RNA structure plays a critical regulatory role in viral replication, serving as a dual-purpose mechanism for encoding genetic information and controlling biological processes. However, these structural elements also serve as pathogen-associated molecular patterns (PAMPs), which are recognized by pattern recognition receptors (PRRs) of the host innate immune system. This review discusses the complex and poorly understood relationship between viral RNA structure and recognition of RNA by PRRs, specifically focusing on Toll-like receptor 3 (TLR3) and Retinoic acid-inducible gene I (RIG-I). While current interaction models rely upon data generated from use of synthetic ligands such as poly(I:C) or perfectly base-paired double-stranded RNA stems, this review highlights significant gaps in our understanding of how PRRs recognize naturally occurring viral RNAs that fold into highly complex three-dimensional structures. Furthermore, we explore how viral evolution and nucleotide variations, such as those observed in influenza viruses, can drastically alter local and distal RNA structure, potentially impacting immune detection. We conclude that moving beyond synthetic models to understand natural RNA structural dynamics is essential for elucidating the mechanisms of viral immune evasion and pathogenesis. Full article

Coxsackieviruses possess two proteases that are engaged in cleaving viral polyprotein and hijacking host cell processes such as RNA biosynthesis. Integrator subunit 10 (INTS10), a subunit of the integrator complex, facilitates the processing of small nuclear RNAs (U1 and U2 snRNAs) to regulate cellular transcription. We found that INST10 can be cleaved by Coxsackievirus B (CVB). Hence, we hypothesized that INST10 may play a role in CVB infection. In this study, INTS10 is identified as the substrate of CVB3 protease 3C (3C^pro). The cleavage occurs at the residue Q221 and yields a fragment. Depletion of INTS10 enhanced CVB3 replication and blocked snRNA processing. Overexpression of U1 snRNA inhibited CVB3 infection, whereas its knockdown conversely enhanced it. Similarly, knockdown of U2 snRNA was found to promote CVB3 replication. Taken together, the 3C^pro-mediated cleavage of INTS10 disrupts U snRNA processing, which in turn counteracts the inhibitory effect of snRNA U1 and U2 on virus replication and subverts host defenses. Full article

Wildfires are increasingly recognized as a climatological hazard, able to threaten industrial and critical infrastructure safety and operations and lead to Natech disasters. Future projections of exacerbated fire regimes increase the likelihood of Natech disasters, therefore increasing expected direct damage costs, clean-up costs, and long-term economic losses due to business interruption and environmental remediation. While large industrial complexes, such as oil, gas, and chemical facilities have sufficient resources for the implementation of effective prevention and mitigation plans, small-to-medium-sized industrial hubs are particularly vulnerable due to their scattered distribution and limited resources for investing in comprehensive fire prevention systems. This study targets the vulnerability of these communities by proposing the deployment of Wireless Sensor Networks (WSNs) as cost-effective Early Wildfire Detection Systems (EWDSs) to safeguard wildland and industrial domains. The proposed approach leverages wildland–industrial interface (WII) geospatial data, simulated wildfire dynamics data, and mathematical optimization to maximize detection efficiency at minimal cost. The WII delimits the boundary where the presence of wildland fires impacts industrial activity, thus representing a proxy for potential Natech disasters. The methodology is tested in Cocentaina, Spain, a municipality characterized by a highly flammable Mediterranean landscape and medium-scale industrial parks. Results reveal the complex trade-offs between detection characteristics and the degree of protection in the combined wildland and WII areas, enabling stakeholders to make informed decisions. This methodology is easily replicable for any municipality and industrial installation, or for generic wildland–human interface (WHI) scenarios, provided there is access to wildfire dynamics data and geospatial boundaries delimiting the areas to protect. Full article

The diffusion of intelligent construction technologies has improved construction efficiency and information integration, while also increasing the complexity and uncertainty of governance decisions in megaprojects. In particular, selecting an appropriate Engineering Transaction Mode (ETM) under intelligent construction involves multiple conflicting criteria, expert judgments, and loss-averse risk preferences, which are not fully captured by conventional multi-criteria decision-making methods. This study proposes a decision-making model that combines Pythagorean fuzzy sets (PFSs) and prospect theory to support ETM selection for megaprojects under intelligent construction. The model constructs an ETM evaluation system grounded in a systematic literature review and questionnaire evidence, encodes expert judgments using PFSs, determines expert and criterion weights via information-utility and fuzzy-entropy measures, and aggregates perceived gains and losses relative to positive and negative ideal solutions through prospect theory. A mega-pumping station project with four ETM alternatives is used for validation. Results indicate that “Self-management + Network-based integrated application + Consultant assistance” achieves the highest prospect value and is consistently ranked first; the same ordering is obtained using TOPSIS and a fuzzy comprehensive evaluation method, demonstrating robustness. The study contributes to theory by coupling hybrid fuzzy representation with loss-aversion-based behavioral aggregation for ETM governance under intelligent construction and provides practitioners with a transparent, replicable decision tool to support ETM selection in complex, uncertainty-laden megaprojects. Full article

The origin of life embodies two fundamental questions: how and when did life begin? It is commonly conjectured that life began on Earth around 4 billion years ago. This requires that the complex organization of RNA, DNA, triplet codon, protein, and lipid membrane (RDTPM) architecture was easy to establish between the time the Earth cooled enough for liquid water and the time when early microorganisms appeared. These bracketing events create a narrow window of time to construct a completely operational self-replicating organic system of very high complexity. Another conjecture is that life did not begin on Earth but was seeded from life-bearing space objects (e.g., asteroids, comets, space dust), commonly referred to as panspermia. The second conjecture implies that life formed somewhere else and was part of the solar nebula, originating from an earlier generation star where there was more time available for the development of life. In this paper, the goal is to provide a hypothetical perspective related to the timing for the origin of pre-biotic chemistry and life itself. Using a form of complexity growth, biological features spanning from the present day back to early life on Earth were examined for trends across time. Genome sizes, gene number, protein–protein binding sites, energy for cell construction, mass of individual cells, the rate of cell mass growth, and a molecular complexity measure all yield highly significant regressions of linearly increasing complexity when plotted over the last 4 Gyr (billion years). When extrapolated back in time, intersections with simple complexities associated with each variable yield a mean value of 8.6 Gyr before the present time. This era coincides with the peak of star and planet formation in the universe. This speculative analysis is consistent with the second conjecture for the origin of life. The major assumptions of such an analysis are presented and discussed. Full article

Universal accessibility remains a critical challenge for effective public transport and urban equity. This study addresses the need for operational prioritisation tools by proposing a robust hybrid methodology to rank interventions at urban mobility nodes. The approach combines the Analytic Hierarchy Process (AHP) for integrating expert and participatory criteria weighting with four Multi-Criteria Decision-Making (MCDM) techniques (TOPSIS, VIKOR, COPRAS, and ARAS) to ensure solution reliability. Empirical validation, conducted on 30 bus stops in Segovia, Spain, confirmed the methodological soundness, evidenced by near-perfect correlations (ρ = 0.99) among the compromise and additive ratio models (TOPSIS–VIKOR and COPRAS–ARAS) and stability across over 85% of sensitivity simulations. The findings validate that the methodology effectively guides resource allocation towards interventions yielding maximum social impact and demonstrate its transferability to complex urban supply chain contexts, such as logistics microhubs. Ultimately, this replicable and adaptable model supports the transition towards more equitable, resilient urban systems, aligning directly with Sustainable Development Goal 11 (Sustainable Cities and Communities). Full article

The accurate simulation of sub-GeV particle detectors is essential for interpreting experimental data and optimizing detector design. This work identifies and addresses several critical aspects in modeling such detectors, taking as a case study the High-Energy Particle Detector (HEPD-02), a space-borne instrument developed within the CSES-02 mission to measure electrons in the ∼3–100 MeV range, protons and light nuclei in the ∼30–200 MeV/n. The HEPD-02 instrument consists of a silicon tracker, plastic and LYSO scintillator calorimeters, and anticoincidence systems, making it a representative example of a complex low-energy particle detector operating in Low Earth Orbit. Key challenges arise from replicating intricate detector geometries derived from CAD models, selecting appropriate hadronic physics lists for low-energy interactions, and accurately describing the detector response—particularly quenching effects in scintillators and digitization in solid-state tracking planes. Particular attention is given to three critical aspects: the precise CAD-level geometry implementation, the impact of hadronic physics models on the detector response, and the parameterization of scintillation quenching. In this study, we present original solutions to these challenges and provide data–MC comparisons using data from HEPD-02 beam tests. Full article

This study investigates water-based gel and gel-like cleaning treatments on Superficie 553, an oil painting on canvas by Giuseppe Capogrossi, using portable NMR to assess their impact. The objective was to evaluate the effects of four cleaning systems composed of a buffer solution released in free form and combined with xanthan gum, a cross-linked silicone polymer gel, and an agar gel matrix. Two distinct NMR experiments were conducted. The first involved the acquisition of ¹H depth profiles to detect the distribution of the cleaning solution within the painted layer and the thickness variations resulting from cleaning procedures. The second employed the acquisition of relaxation times, facilitating the investigation of molecular mobility within the organic components of the paint layer. NMR results indicated that the agar gel system caused negligible structural changes, whereas the silicone gel induced rigidification, and the other systems permanently increased molecular mobility. These measurements provided insights into alterations in the dynamic behavior of the polymerized oil. A key strength of this investigation lies in the direct application of diagnostic methods on Superficie 553, made possible by the non-invasive nature and portability of the NMR-MOUSE system. Additionally, portable FTIR was used to detect residues and obtain chemical information, confirming that the silicone gel left detectable residues and identifying the agar gel as the most conservative cleaning method. This enabled in situ analysis of the original artwork without sampling or relocation—a crucial advantage given the difficulty of replicating the complex physicochemical conditions of historical paint surfaces under laboratory constraints. Such real-time, on-site monitoring ensured an authentic evaluation of the treatment effects, preserving the integrity of the artwork throughout the conservation process. Full article

Global climate change has emerged as a critical challenge for human society in the 21st century. As hubs of population and economic activity, urban and rural areas are increasingly exposed to complex and compounded disaster risks. To systematically evaluate the role of educational intervention in climate adaptability capacity building, this study employs a case study approach, focusing on the “Climate Change Adaptation Education and Teaching Alliance Program” launched in Taiwan in 2014. Through a comprehensive analysis of its institutional structure, curriculum, alliance network, and practical activities, the study explores the effectiveness of educational innovation in cultivating climate resilience talent. The study found that the program, through interdisciplinary collaboration and a practice-oriented teaching model, successfully integrated climate adaptability content into 57 courses, training a total of 2487 students. Project-based learning (PBL) and workshops significantly improved students’ systems thinking and practical abilities, and many of its findings were adopted by local governments. Based on these empirical results, the study proposes that urban and rural planning education should be promoted in the following ways: first, updating teaching materials to reflect regional climate characteristics and local needs; second, enhancing curriculum design by introducing core courses such as climate-resilient planning and promoting interdisciplinary collaboration; third, enriching hands-on learning through real project cases and participatory workshops; and fourth, deepening integration between education and practice by establishing multi-stakeholder partnerships supported by dedicated funding and digital platforms. Through such an innovative educational framework, we can prepare a new generation of professionals capable of supporting global sustainable development in the face of climate change. This study provides a replicable model of practice for education policymakers worldwide, particularly in promoting the integration of climate resilience education in developing countries, which can help accelerate the achievement of UN Sustainable Development Goals (SDG11) and foster interdisciplinary collaboration to address the global climate crisis. Full article

Mathematical models for infectious diseases, particularly autonomous ODE models, are generally known to possess simple dynamics, often converging to stable disease-free or endemic equilibria. This paper investigates the dynamic consequences of a crucial, yet often overlooked, component of pandemic response: the saturation of public health testing. We extend the standard SIR model to include compartments for ‘Confirmed’ (C) and ‘Monitored’ (M) individuals, resulting in a new SICMR model. By fitting the model to U.S. COVID-19 pandemic data (specifically the Omicron wave of late 2021), we demonstrate that capacity constraints in testing destabilize the testing-free endemic equilibrium ($E_1$). This equilibrium becomes an unstable saddle-focus. The instability is driven by a sociological feedback loop, where the rise in confirmed cases drive testing effort, modeled by a nonlinear Holling Type II functional response. We explicitly verify that the eigenvalues for the best-fit model satisfy the Shilnikov condition (${\lambda }_u>{\lambda }_s$), demonstrating the system possesses the necessary ingredients for complex, chaotic-like dynamics. Furthermore, we employ Stochastic Differential Equations (SDEs) to show that intrinsic noise interacts with this instability to generate ’noise-induced bursting,’ replicating the complex wave-like patterns observed in empirical data. Our results suggest that public health interventions, such as testing, are not merely passive controls but active dynamical variables that can fundamentally alter the qualitative stability of an epidemic. Full article

Three-dimensional biomaterial scaffolds with controlled geometry and surface nanoarchitecture are essential for advancing polymer processing strategies in tissue engineering. Conventional electrospinning generates nanofibrous structures but has limited ability to reproduce defined three-dimensional shapes or achieve high pattern fidelity. This study aimed to develop a scalable processing method for producing biodegradable scaffolds with precisely controlled microstructure and geometry using phase separation–assisted electrospray. Poly (lactic acid) microcapsules with tunable diameters and porous nanofibrous surfaces were fabricated under controlled humidity and deposited onto conductive molds to obtain two- and three-dimensional scaffold shapes. The manufacturing process required only simple electrospray equipment and static molds, without mechanically complex collectors or moving stages. The resulting scaffolds replicated mold features with resolutions down to 200 μm and achieved thickness up to 600 μm. The nanofibrous microcapsule surfaces supported strong adhesion and metabolic activity of HepG2 cells, while cellular penetration into deeper scaffold regions remained limited to approximately 80 μm. These findings indicate that electrospray-mediated microcapsule deposition is a practical polymer-processing approach that integrates nanofibrous surface formation with mold-defined shaping, offering a reproducible and scalable method for fabricating structurally precise and biologically compatible three-dimensional scaffolds. Full article

Accurate assessment of rice resistance to Sogatella furcifera (Horváth) is essential for breeding insect-resistant cultivars. Traditional assessment methods rely on manual scoring of damage severity, which is subjective and inefficient. To overcome these limitations, this study proposes an automated resistance evaluation approach based on multi-view 3D reconstruction and deep learning–based point cloud segmentation. Multi-view videos of rice materials with different resistance levels were collected over time and processed using Structure from Motion (SfM) and Multi-View Stereo (MVS) to reconstruct high-quality 3D point clouds. A well-annotated “3D Rice WBPH Damage” dataset comprising 174 samples (15 rice materials, three replicates each, 45 pots) was established, where each sample corresponds to a reconstructed 3D point cloud from a video sequence. A comparative study of various point cloud semantic segmentation models, including PointNet, PointNet++, ShellNet, and PointCNN, revealed that the PointNet++ (MSG) model, which employs a Multi-Scale Grouping strategy, demonstrated the best performance in segmenting complex damage symptoms. To further accurately quantify the severity of damage, an adaptive point cloud dimensionality reduction method was proposed, which effectively mitigates the interference of leaf shrinkage on damage assessment. Experimental results demonstrated a strong correlation (R² = 0.95) between automated and manual evaluations, achieving accuracies of 86.67% and 93.33% at the sample and material levels, respectively. This work provides an objective, efficient, and scalable solution for evaluating rice resistance to S. furcifera, offering promising applications in crop resistance breeding. Full article