Search Results (64,415)

Groundwater ingress threatens the serviceability and durability of underground structures, particularly when hydrogeotechnical conditions and waterproofing details are considered separately. This study presents a diagnosis-driven assessment of a reconstructed underground shooting range tunnel where leakage persisted despite reconstruction and previous repairs. The objectives were to identify the cause-and-effect mechanism of water ingress and select a targeted, low-impact remediation strategy. The investigation combined archival analysis, three site inspections, ultrasonic testing at 24 locations, eight tomographic scans, targeted destructive verification, and three geotechnical boreholes extending to 7.5 m. Ultrasonic measurements indicated good concrete homogeneity, with a mean estimated compressive strength of 36.9 MPa and a coefficient of variation of 5.86%. Tomography indicated a 25 cm bottom slab and a 20 cm lean concrete layer, compared with the designed 30 cm and 10 cm, respectively. The original geotechnical investigation was too shallow, and the ground conditions should have been classified as difficult, corresponding to geotechnical category II. Finally, leakage was linked to groundwater underestimation, water accumulation in the backfilled excavation, absence of drainage, waterproofing discontinuities, and ineffective previous injections. Targeted reinjection and joint sealing were selected, demonstrating how integrated diagnostics can support proportionate remediation while limiting excavation, demolition, material use, and operational disruption. Full article

Cross-phase collaborative management in complex product systems (CoPS) development is inherently challenged by heterogeneous organizational coupling and stochastic disturbances. Although digital twin (DT) and model-based systems engineering (MBSE) technologies provide foundations for physical–virtual synchronization and model traceability, existing approaches remain fragmented in three respects: insufficient requirements-traceable architectural integration, limited cross-phase semantic interoperability and runtime evolution, and weak operational links between semantic reasoning and adaptive decision models. To address these gaps, this paper proposes an MBSE-driven digital twin framework with semantic enhancement. First, a four-layer architecture is derived using the MagicGrid methodology, encompassing physical–virtual mapping, semantic reasoning, decision support, and service interaction. Second, a collaboration-oriented SysML profile is developed to standardize the representation of tasks, resources, materials, disturbances, and management constraints across engineering phases. Third, a knowledge-driven adaptive collaboration mechanism maps runtime disturbance inputs into semantic states, propagates their cross-phase impacts, supports process-topology reconfiguration, and generates decision-ready constraints for adaptive management. A case-based prototype for aero-engine turbofan blade development demonstrates the feasibility of the mapping–reasoning–decision chain and provides case-level evidence of improved cross-phase coordination under controlled disturbance scenarios. The results indicate a feasible engineering pathway from perceptive DT functions toward reasoning-enabled collaborative decision support. Full article

This study investigates recycled low-density polyethylene (LDPE)–sand composites as cement-free materials for selected construction applications. The effects of plastic content (30–50 wt.%), processing temperature (220–260 °C), and particle size (319–1015 µm) on mechanical and physical properties were evaluated using a Taguchi L9 experimental design. Mechanical properties, including compressive, flexural, and tensile strength, and physical properties, including density and water absorption, were assessed using laboratory-scale specimens prepared from moulded composite panels. Processing temperature was the dominant factor controlling strength development and water absorption reduction. The best-performing experimental condition within the investigated range was 30 wt.% LDPE, 260 °C, and 1015 µm particle size, yielding an apparent compressive strength of 65.5 MPa, flexural strength of 20.7 MPa, tensile strength of 4.4 MPa, density of 1595.2 kg/m³, and water absorption of 0.7%. Cross-validation showed good predictive capability for density, tensile strength, flexural strength, and water absorption, but only moderate predictive capability for compressive strength and compressive modulus. Therefore, the regression models are presented as screening tools within the investigated parameter range rather than as general design models. The results indicate that recycled LDPE–sand composites have potential for selected non-structural and limited semi-structural applications, subject to further product-standard testing, durability assessment, fire performance evaluation, and environmental impact analysis. Full article

Carbon pricing has emerged as a central policy tool for addressing climate change and advancing corporate environmental responsibility, yet its impact on firm-level investment and capital allocation remains insufficiently understood, particularly in emerging economies. This study examines whether emissions trading systems (ETS) influence corporate investment behavior and the reallocation of capital, using China as a representative case within the Asia-Pacific region. Employing a panel dataset of 5000 Chinese listed firms (approximately 80,000 firm-year observations) over the 2008–2023 period and a difference-in-differences framework, we identify the causal effect of carbon pricing on corporate investment decisions. The results show that firms exposed to carbon pricing experience a statistically significant reduction in investment following policy implementation. This effect is more pronounced in high-emission industries and operates through declines in profitability and tighter financial conditions. Asset-weighted measures indicate that the adjustment is concentrated among larger firms, and industry-level analysis shows that reductions are disproportionately driven by high-emission sectors. This pattern of heterogeneous investment responses is consistent with carbon pricing influencing investment allocation across firms; however, we do not directly observe the subsequent destination of capital or whether reduced investment in exposed firms is transferred toward cleaner activities. These findings provide new micro-level evidence that carbon markets shape corporate environmental management and real economic decisions by altering firm incentives, cost structures, and expectations. The study contributes to the literature by linking environmental policy tools to firm-level investment behavior and offers practical insights for policymakers and managers navigating the low-carbon transition in emerging economies. Full article

Ornamental palm pruning residues represent a locally abundant, underutilized lignocellulosic waste stream with potential as a waste-valorized epoxy reinforcement. This study investigates the flexural behavior of particulate epoxy composites reinforced with Washingtonia robusta leaf stalk residue, evaluating the influence of reinforcement granulometry on mechanical and microstructural response. Four specimen families were fabricated from a Bisphenol A/F epoxy resin cured with a cycloaliphatic amine hardener: neat resin (RS, reference) and composites reinforced with fine (RF), coarse (RG) and mixed-fraction (RM) particles at 20 vol.% loading. Flexural properties were assessed by three-point bending and fracture surfaces were characterized by SEM. The neat resin exhibited a non-monotonic, viscoelastic-dominated response with no fracture within the extended deformation range tested, whereas all reinforced systems fractured within a substantially narrower window (~8–14.5 mm). RF showed the highest observed flexural modulus (≈15.8 GPa), followed by RM (≈15.4 GPa) and RG (≈14.2 GPa). These differences were not statistically significant (one-way ANOVA, p > 0.05). Damage tolerance followed a similar descriptive trend: RG failed earliest, linked to large interfacial pull-out cavities; RF delayed fracture through crack deflection; and RM showed the most favorable overall balance, combining a modulus comparable to RF with superior crack path tortuosity. These results indicate the potential of Washingtonia robusta, particularly in mixed-granulometry form, as a candidate reinforcement for semi-structural epoxy biocomposites, pending further characterization of properties such as tensile strength, impact resistance, moisture absorption, and long-term durability. Full article

Emerging technology topic identification is an important research task in the field of scientific and technological intelligence. To achieve a more comprehensive identification of emerging technology topics, this study proposes a multi-source data fusion framework that integrates three types of data sources: academic publications, patent data, and data from the GitHub open-source platform. In addition, an evaluation indicator system is constructed from four dimensions: growth, novelty, continuity, and impact. During the identification process, the BERTopic topic modeling approach is employed to uncover latent topics within the data, while the entropy weight method is applied for objective weighting, ultimately enabling the identification of emerging technology topics. The results indicate that the identified emerging technology topics include, but are not limited to, large language model-driven intelligent interaction, embodied intelligence perception, context memory management, and multimodal generation. Among the data sources, GitHub data provide earlier signals of technological evolution. Incorporating open-source platform data into the framework can effectively alleviate the lagging issues associated with traditional data sources. The proposed framework provides a more comprehensive research perspective for emerging technology topic identification. Full article

Relative age effect (RAE) and biological maturation can significantly impact athlete selection. This study examined the presence of RAE in youth team invasion sports and assessed whether the timing of maturity influences body size and lower-body explosive strength. Athletes were grouped by age category (U14, U16, U18), and the distribution of birth quartiles was analyzed. Maturity timing was estimated using age at peak height velocity (APHV), and lower-body explosive strength was evaluated through jumping performance. Comparisons across age groups showed progressive increases in body size and jumping performance with advancing age, consistent with pubertal changes in fat-free mass and neuromuscular function. The birthdate distribution indicated an overrepresentation of relatively older athletes (U16 and U18), suggesting that selection bias may intensify with age around the pubertal window. Regression analyses showed that earlier APHV was significantly associated with greater body size after controlling for chronological age (CA) (p ≤ 0.01), although CA remained the strongest predictor. In contrast, APHV did not significantly explain additional variance in jumping performance, suggesting that anthropometrics, training exposure, and movement technique likely contributed. The current findings underscore the role of biological maturation in body size and the need for youth sport systems to include structured strength and conditioning programs that optimize lower-body muscle power. Full article

Improving the understanding of hydrological drought mechanisms is paramount for drought resistance and early warning in a changing environment. The Upper Yellow River Basin (UYRB), the primary water-producing region of the Yellow River Basin, experiences hydrological droughts that are jointly influenced by climate variability and human activities. This study systematically investigated the spatiotemporal evolution of hydrological droughts in the UYRB and elucidated their underlying mechanisms from the perspectives of sub-basin contributions, reservoir regulation, and large-scale climate drivers. We found an increasing trend in yearly drought severity from 1956 to 2010 under the natural scenario, but large reservoirs significantly reduced the severity. The headwater region, Tao River Basin, and interval region 2 were identified as the key areas in drought formation of the whole UYRB. Reservoirs generally increased monthly drought intensity in summer and autumn but decreased drought intensity in spring and winter. For drought events lasting for a longer time, reservoirs interrupted their continuity, which reduced the average severity and duration but exaggerated peak intensity, especially for extreme events. The impacts of different reservoirs on drought variations showed distinct differences due to different operation regulations. Cascade reservoir regulation weakened and altered the relationships between hydrological drought and climate indices, including PDO, AMO, NAO, and ENSO, at the 8–16 and 32–64 month timescales. This finding indicates that the effects of large-reservoir regulation should be removed using naturalized streamflow when identifying teleconnection drivers of hydrological drought and conducting drought early warning. These findings provide new insights into the mechanisms governing hydrological drought under the combined influences of climate change and reservoir regulation and offer a scientific basis for drought early warning, reservoir operation, and integrated water resources management. Full article

A naive Bayesian classifier (NBC) combined with principal component analysis (PCA) effectively differentiates the dose-dependent effects of certain groups of psychoactive drugs based on their impact on the amplitude–spectral characteristics of electrocorticograms (ECoG) in rats. This approach has been shown to be useful for pharmacological screening of agents with unknown or poorly understood activity. Despite previously obtained optimistic results, classification determination for some drugs was inaccurate, necessitating the search for possible ways to improve the predictive effectiveness of the proposed algorithm. One possible approach would be to use as input quantitative data not only the impact of the psychoactive drugs studied on the amplitude–spectral characteristics of ECoG but also connectivity changes, including the average coherence power of different pairs of leads. The aim of this study was to compare the accuracy of NBC in classifying the pharmacological mechanism of action of agents with well-known mechanisms (test set) using pharmaco-EEG data on changes in the amplitude–spectral characteristics of ECoG, coherence, and the combined use of two data sets. Materials and methods. Experiments were performed on Wistar rats with chronically implanted ECoG electrodes. The training set, relative to which the effects of the pharmacological agents from the test set were classified, were the matrices of effects of 12 pharmacological agents: the NMDA antagonist dizocilpine, the D2/D3 antagonists haloperidol and sulpiride, the M-anticholinergic tropicamide, the H1/5HT2A receptor blocker hydroxyzine, the acetylcholinesterase inhibitor galantamine, the alpha-2 adrenergic agonist dexmedetomidine, the alpha-2 adrenergic antagonist atipamezole, the adenosine receptor blocker caffeine and the GABA-mimetics aminophenylbutyric acid (phenibut), bromdihydrochlorophenylbenzodiazepine (phenazepam) and 5-ethyl-5-phenyl-2,4,6(1H,3H,5H)-pyrimidinetrione. The test set included various drugs with tropism for the targets of the training set drugs: dopamine receptor antagonists chlorpromazine, droperidol, tiapride and raclopride, H1-histamine blockers diphenhydramine and promethazine, 5-HT2-receptor blockers ritanserin and glemenserin, acetylcholinesterase inhibitor ipidacrine, alpha2-adrenergic receptor antagonist yohimbine, alpha2-adrenergic agonists medetomidine and xylazine, GABA-mimetics 5-ethyl-5-(1-methylbutyl)-2,4,6(1H,3H,5H)-pyrimidinetrione and chloral hydrate. The analysis of the ECoG signal included the calculation of 132 amplitude–spectral characteristics and 75 coherence indicators, which, using the PCA, led to new integrative indicators used for further classification of the NBC. Results and discussion. For each drug in the test set, the median similarity probability with a particular group from the training set was calculated, which was used to assess the classification quality. It was found that, when using the amplitude–spectral characteristics of ECoG, the proposed methodological approach allows for the identification of the ECoG effects of several groups of psychoactive drugs, including D2/D3-dopamine, M-cholinergic, H1-histamine, and 5-HT2-serotonin receptor blockers, AChE inhibitors, GABA-mimetics, and alpha-2-adrenergic receptor agonists and antagonists. This approach enabled the correct classification of 18 of 24 groups in the test set. When using changes in coherence indices as the initial data, the classification accuracy also amounted to 18 of 24 groups. When combining the two data sets, the number of correctly identified NBC groups was 20 of 24 groups. When comparing the classification during training (confusion matrix), it was found that coherence data or adding coherence data to the data based on changes in amplitude–spectral characteristics leads to a statistically significant (p < 0.01 in both cases) increase in accuracy. Conclusions. The obtained data demonstrated high accuracy in classifying the pharmacological activity of the test sample drugs using any of the three compared approaches. Despite the lack of statistically significant differences between them, classification based on the combined dataset demonstrated a higher number of “correct” similarities. This allows us to recommend the approach based on combined data of drug effects on amplitude–spectral characteristics and coherence as the most promising for further studies using pharmaco-EEG screening. Full article

Climate risks drive dynamic adjustments to global climate policies, creating significant climate policy uncertainty (CPU). This uncertainty profoundly affects enterprises’ survival and sustainable development. A key question emerges: how do enterprises’ perception of CPU influence their resilience? To answer this, this study adopts a capability perspective and empirically examines the impact of climate policy uncertainty perception (CPUP) on enterprise resilience (RESI) and the underlying mechanism. Using panel data on Chinese A-share listed companies on the Shanghai and Shenzhen Stock Exchanges from 2009 to 2023, the study defines CPUP as the interaction between a news-based provincial CPU index and the frequency of climate risk words in annual report texts, and measures RESI with the entropy weight method across four dimensions (business volatility, long-term growth, short-term performance, and enterprise survival). Panel regression with fixed effects indicates that CPUP significantly enhances RESI. A one-standard-deviation increase in CPUP raises RESI by approximately 0.0019 index units, equivalent to about 2.2% of the standard deviation of RESI. This effect is more pronounced for enterprises in the eastern and central regions and in high-carbon industries. Mechanism tests confirm that CPUP boosts RESI by optimizing management capabilities and strengthening development capabilities, revealing a capability-driven path between CPUP and RESI. This study enriches the theoretical understanding of CPU’s economic consequences and RESI antecedents from a capability perspective. It also provides empirical references for enterprises to build resilience amid policy fluctuations and for policymakers to formulate regionally differentiated climate policies. Full article

Background and Objectives: Conduction disorders (CDs) and permanent pacemaker implantation (PPI) remain among the most common complications after transcatheter aortic valve implantation (TAVI). Given their potential impact on long-term outcomes, improved preprocedural identification of patients at high risk for new-onset CDs is increasingly important. We investigated whether selected preprocedural inflammatory and fibrotic biomarkers, along with echocardiographic indices of regional myocardial remodeling, were associated with new-onset CDs after TAVI. Materials and Methods: This single-center prospective observational study included 112 patients with severe aortic stenosis undergoing TAVI. Peripheral blood samples were obtained within 24 h before TAVI for measurement of inflammatory and fibrotic biomarkers, including interleukin-6 (IL-6), C-reactive protein (CRP), CRP-to-albumin ratio (CAR), procalcitonin, ferritin, lactate dehydrogenase, and transforming growth factor-β1. The primary outcome was the occurrence of new-onset CDs during the index hospitalization or within three months after TAVI. Results: New-onset CDs occurred in 58 patients (51.8%). IL-6 showed the strongest association with the outcome in univariable analysis (OR per 1-SD increase, 9.55; 95% CI 3.00–30.40; p < 0.001), remained associated after adjustment for selected clinical and procedural predictors in exploratory models (adjusted OR 10.70; 95% CI 2.43–47.07; p = 0.002), and demonstrated the highest, although moderate, discriminatory performance among the evaluated biomarkers (AUC 0.730; 95% CI 0.637–0.823). CRP and CAR were higher in patients with CDs and were significant in univariable analysis, but showed weaker and less consistent adjusted associations. Among echocardiographic markers, AB strain ratio ≥ 2 was the most consistent imaging correlate in exploratory biomarker–echocardiographic models. Conclusions: Elevated preprocedural IL-6 was the biomarker most consistently associated with new-onset CDs after TAVI. These findings suggest that higher preprocedural IL-6 levels may reflect patient-specific susceptibility to new-onset CDs after TAVI and could have potential value as an adjunctive biomarker, alongside echocardiographic assessment, for preprocedural risk evaluation, with further validation required in larger prospective studies. Full article

The perimenopausal phase represents a critical window for early intervention against accelerated bone loss, highlighting an urgent need for safe and accessible non-pharmacological strategies. Although mind–body exercises are widely recommended for healthy aging, their specific structural and metabolic impacts on the perimenopausal skeleton yield conflicting results and remain poorly understood. To address this gap, this systematic review and three-level meta-analysis evaluated the effects of mind–body modalities on bone health in perimenopausal women, utilizing this model to account for statistical dependencies among multiple effect sizes within individual studies. Quantitative synthesis suggested that mind–body exercise may offer modest structural benefits, indicated by positive effects on bone mineral density (SMD = 0.55, p < 0.01) and bone mineral content (SMD = 1.63, p < 0.01); however, these findings must be interpreted with caution as the accompanying certainty of evidence is low to very low. Conversely, these structural adaptations were not accompanied by stable alterations in bone turnover markers, which remained consistently unchanged (SMD = −0.10, p > 0.05). Furthermore, bone mineral metabolism was not statistically significant in the primary analysis, with a confidence interval that included the null (SMD = 0.90, p = 0.079) and demonstrated limited stability across robustness checks. Assessed via the GRADE framework, this overall very low certainty was primarily due to risk-of-bias concerns, inconsistency, imprecision, suspected publication bias, and clinical heterogeneity. Consequently, given the current evidence limitations, mind–body exercise is best conceptualized as a supportive lifestyle component of a healthy aging trajectory rather than a potent osteogenic therapy. It offers a holistic preventative approach that might support bone health while concurrently supporting overall musculoskeletal resilience, though firm clinical recommendations cannot yet be made. Future well-powered trials must adopt rigorous designs with precise endocrine staging to better clarify the underlying mechanisms of skeletal adaptation. Full article

The rapid transition toward electrified mobility and climate neutrality has prioritized the structural and environmental optimization of battery electric vehicle (BEV) subsystems. While vehicle lightweighting enhances operational efficiency, the production phase of structural enclosures and battery cells frequently introduces severe environmental and economic impacts and supply chain vulnerabilities. This study presents a comprehensive cradle-to-gate environmental life cycle assessment (LCA), life cycle costing (LCC), and semi-quantitative social assessment of alternative battery housing materials and battery cell architectures. To achieve a functionally accurate comparison, alternative materials, including a novel recyclable thermoplastic acrylic sheet molding compound (SMC), commercial thermoset SMCs, aluminum (AlMg3), and stainless steel, are evaluated using an analytical stiffness- and strength-equivalent methodology across three real-world geometric demonstrators. Simultaneously, lithium iron phosphate (LFP) liquid electrolyte prismatic cells and solid-state polymer pouch cells are assessed. Material-level results indicate that, while aluminum minimizes the structural mass, primary aluminum manufacturing exhibits the highest global warming potential and processing costs. Conversely, Polytec SMC and Elium SMC achieve the lowest environmental impacts alongside competitive total production costs. At the cell level, prismatic LFP architectures display superior environmental performance compared to solid-state pouch cells, which suffer from energy-intensive processing and lower volumetric capacity normalization. Demonstrator-level aggregation reveals that the electrochemical cells heavily dominate the environmental and economic footprint of the complete assembly, with the housing accounting for less than 5% of the total global warming potential (GWP) and 1% of the total costs. The social assessment reveals moderate and comparable performance across all systems, with slight advantages for thermoplastic composite-based configurations in terms of circularity potential and innovation perception. Overall, the study highlights the critical importance of the cell architecture and manufacturing processes in determining battery system sustainability, while demonstrating the relevance of lightweight composite housings in reducing the structural mass with a minimal environmental penalty. Full article

The rapid growth of rural e-commerce is reshaping rural development, yet its implications for territorial sustainability remain unclear. Using panel data from 1503 Chinese counties from 2014 to 2022, the effects of Taobao Village development and the mechanism of territorial sustainability are examined through causal, spatial, and mechanism analyses. The results show the following: (1) Taobao Village development significantly improves territorial sustainability, with stronger effects observed in central and northeastern China, while the impacts vary considerably across regions due to differences in economic foundations, digital infrastructure, and land-use conditions. (2) Spatial analysis reveals that the sustainability-enhancing effects of Taobao Villages are mainly localized, with no significant spillover effects to neighboring counties, indicating the constraints of existing administrative and spatial governance systems. (3) The ecological land-use change induced by Taobao Village development is characterized by quantity reduction with potential quality upgrading. The development of Taobao Village has reduced the ecological land area, but it does not mean a decline in ecological functions. The converted land may be composed of low-quality ecological plots, and the fiscal benefits driven by e-commerce and China’s land use compensation policy may maintain or enhance the overall ecological function. These findings highlight the importance of integrating digital rural development with ecological sustainability and territorial spatial governance. The study provides policy implications for promoting resilient and sustainable rural transformation through differentiated land-use strategies. Full article

The introduction of Connected and Autonomous Vehicles (CAVs) into the existing traffic system represents one of the greatest challenges of modern road traffic engineering. Beyond their role as active traffic participants, CAVs can also be regarded as mobile (floating) sensors, effectively turning the vehicle fleet itself into a distributed, city-wide and motorway-wide sensing infrastructure. The transition from fully human-driven vehicles to fully autonomous vehicles will take decades, giving rise to a prolonged mixed-traffic period in which vehicles with different levels of automation share the same road space. This paper analyses the parameters and measures used for evaluating the throughput, environmental impact, and safety of traffic networks at different CAV penetration rates. It further reviews studies that rely exclusively on data collected from CAVs acting as mobile sensors, examining data-aggregation and traffic-state-estimation methods used to reconstruct macroscopic traffic parameters such as flow, density, headway, and speed. Additionally, measures for evaluating specific use cases for CAVs including mobility-on-demand services and their cost comparison with human-driven taxi operations are also addressed. The energy and emissions implications of CAV deployment, including the added burden of sensing hardware and system-level rebound effects, are also examined. Based on the synthesis performed, a set of representative CAVs penetration rates is proposed as a standardised framework for future mixed-traffic flow evaluations. Full article