Search Results (69)

Under the trend of AI-defined vehicles, multi-agent collaboration has become the core feature for intelligent vehicles to deliver superior user experience (UX). Traditional linear and independent evaluation methods can no longer adapt to the new technical characteristics and logic. Taking the agents of four functional domains—intelligent driving, intelligent cockpit, intelligent vehicle control, and intelligent connectivity—and their cross-domain collaborative relationships as research objects, this study constructs a UX evaluation index system consisting of five primary indicators and 14 secondary indicators. Innovatively, the analytic network process is adopted for indicator weight allocation, which effectively characterizes the interdependencies among indicators caused by multi-agent collaboration. Meanwhile, the coupling coordination theory is introduced to construct a comprehensive UX index, enabling quantitative evaluation of the balanced development level across the five dimensions. The results show that in intelligent vehicle UX, excellence in a single dimension does not equal excellent overall UX. Only through the collaborative upgrading of multiple agents and balanced development of the five dimensions can the comprehensive UX be maximized. This study further reveals the UX mechanism of multi-agent collaboration in intelligent vehicles and determines the optimal collaborative evolution path based on the dynamic programming algorithm, providing theoretical support and practical guidance for automakers in rational product development planning. Full article

In the digital era, online attention has become a key indicator of tourism competitiveness and destination visibility. This study proposes a two-dimensional framework to evaluate the competitive state of online attention by combining its current magnitude and growth dynamics. Using Baidu Index data, the study applies the Boston Consulting Group (BCG) matrix and the coefficient of variation to analyze online attention patterns of Beijing’s 5A–level tourist attractions from 2011 to 2025. The results show clear polarization in online attention. A small number of iconic attractions consistently dominate digital visibility, while many other sites exhibit unstable and uneven attention trajectories. These patterns reflect the cumulative effects of consumer behavior, information-seeking preferences, and algorithmically mediated content environments, which reinforce attention concentration and competitive inequality over time. External shocks, particularly the COVID–19 pandemic, caused sharp declines in online attention in 2020, followed by an uneven recovery in subsequent years, highlighting the volatility of digital attention systems. The study also demonstrates the managerial value of the proposed framework. By classifying attractions according to attention levels and growth potential, the framework supports differentiated marketing and demand–redistribution strategies. For instance, increasing the visibility of high-potential but under-visited attractions can help redirect visitors away from overcrowded “Star/GC” sites and encourage more balanced spatial and temporal visitation. Overall, this study proposes a quantitative and replicable framework that integrates digital attention dynamics, algorithmic filtering, and consumer behavior into destination competitiveness analysis. The framework supports evidence-based and sustainability-oriented destination management by informing adaptive marketing and demand management strategies that can help alleviate overtourism and balance visitor flows. However, the study relies on a single digital platform and lacks direct sustainability indicators. Future research should integrate multi-platform data and link online attention metrics to measurable environmental, social, and economic sustainability outcomes. Full article

The Historic Urban Landscape (HUL) approach provides a vital and extensive framework for heritage conservation. However, local practices often struggle to spatially translate qualitative assessments into quantitative controls at the urban block level, the most effective basic scale for administrative implementation, thereby limiting effective responses to the Management of Change. By integrating HUL with the theory of Dynamic Integrity, this study constructs a multi-dimensional evaluation index system and proposes a HUL evaluation method based on Character-Based Zoning. Taking the 125 urban block units of the historic urban area of Jingdezhen as a case study, this research integrates historical mapping, GIS spatial analysis, and Co-occurrence Network Analysis to reveal the internal structural logic of the heritage system. The study finds that the HUL of Jingdezhen is a multi-nodal dynamic system driven by four core elements: ritual beliefs, administrative management, production activities, and commercial guilds. Critically, modern visual intrusions severely impact the core heritage components within this system, specifically the Dubang and ritual culture. Based on the three dimensions of Heritage Richness, Landscape Sensitivity and Value Centrality, the study systematically identifies a total of 11 types of urban block units within the plots that characterize distinct historic landscape features and transformation patterns. This research not only deepens the localized application of HUL theory but also provides a scientific basis and methodological support for the Management of Change and periodic assessment in dynamic heritage environments. Full article

To meet the requirements of quantitative elemental analysis in the ultraviolet (UV) spectrum, a UV single-photon imaging system was developed, integrating a digital micromirror device (DMD) and a single photon-counting imaging detector, enabling high sensitivity, high resolution, and a wide dynamic range. However, intrinsic geometric distortion poses a significant challenge to accurate spectral calibration. A hybrid correction framework is proposed, cascading polynomial coarse correction with multilayer perceptron (MLP) fine regression, improving calibration accuracy. The method utilizes a full-field dot-array mask projected by the DMD to acquire distortion-reference image pairs. The polynomial model rapidly captures the dominant high-order distortion, while a lightweight MLP performs non-parametric fine regression of residual displacements, achieving a mean error of 0.84 pixels. This approach reduces the root mean square (RMS) error to 1.01 pixels, outperforming traditional direct linear transformation (5.35 pixels) and pure polynomial models (1.33 pixels), while the nonlinearity index decreases from 0.35° to 0.05°. In addition, the method demonstrates stable performance across multi-scale checkerboard patterns ranging from 128 to 280 pixels, with RMS errors remaining around the 1-pixel level. These results validate the high-precision distortion suppression and robust cross-scale performance of the proposed framework. By leveraging DMD-generated patterns for self-calibration, this method eliminates the need for external targets, offering a scalable solution for high-end spectrometer calibration. Full article

Epigenetic clocks have successfully estimated biological age by identifying CpG sites whose DNA methylation levels correlate with chronological age. However, these statistical models provide limited mechanistic insight into the biological underpinnings of ageing. While they capture the “pace” of ageing, they fail to quantify the “resilience” of biological systems—the capacity to recover, reorganize, and maintain homeostasis under stress. To overcome this limitation, we introduce EpiAge-R (Epigenetic Age with Resilience), a mechanistic framework that shifts the focus from passive correlation to active recovery potential. The EpiAge-R framework integrates multilayered biological information—including long-read methylation sequencing, chromatin context, histone modification balance, 3D genome topology, and mitochondrial dynamics—into a unified Resilience Index. By distinguishing between degenerative methylation drift (damage) and adaptive repair processes (resilience), EpiAge-R aligns with nonlinear multi-omics ageing trajectories. This framework provides a quantitative foundation for next-generation biomarkers and precision longevity interventions, aiming to define optimal health rather than statistical normality. Full article

Unconsolidated sandstone reservoirs, particularly in offshore and marine environments, are highly susceptible to sand production, which leads to flow-capacity degradation, plugging evolution, sand-retention instability, and erosion–corrosion damage in gravel-pack completion systems. To address the lack of system-level and quantitative integrity evaluation methods, a unified assessment framework is developed by coupling flow behavior, sand-retention mechanisms, and erosion–corrosion damage processes. The gravel-pack completion system is idealized as a concentric multilayer porous-medium structure under steady-state radial Darcy flow, and an equivalent radial permeability model is established to characterize flow capacity and anti-plugging performance, which enables consistent comparison of different completion schemes under identical plugging conditions. Based on sand-retention mechanisms, a sand-retention capacity index is proposed by integrating formation particle size distribution, screen aperture, gravel size, and sand-leakage risk. An erosion–corrosion coupled damage model is further developed to predict screen damage rates in CO₂-containing environments, and an integrity index is formulated to link damage evolution with long-term service performance. By integrating flow capacity, anti-plugging performance, sand-retention capacity, and structural integrity using a weighted geometric mean, a comprehensive evaluation index is established for overall system integrity assessment. Using the proposed framework, a representative formation sand with d₁₀ = 30  μm, d₅₀ = 180  μm, and d₉₀ = 500 μm  is evaluated. The optimal sand control design corresponds to a gravel median size of 971.53 μm (equivalent to a standard 16/20 mesh gravel) and an optimal screen aperture of 523.11 μm, with a screen porosity of 0.56. Under these conditions, the selected screen aperture and gravel size are well matched with the formation sand size, falling within recommended engineering ranges and achieving a favorable balance among sand retention, flow capacity, anti-plugging performance, and structural integrity. The proposed framework provides a quantitative and engineering-applicable basis for the optimization and integrity classification of offshore gravel-pack sand control completions under multi-constraint operating conditions. Full article

Against the backdrop of rapid development in the IST industry, addressing issues such as regional homogeneity and uneven spatiotemporal development requires scientific identification and analysis of related resources to support sustainable regional IST development and promote high-quality regional economic growth. This study proposes a framework based on “policy orientation–theoretical support–regional adaptation,” utilizing machine learning to construct a multi-dimensional evaluation index system for IST development potential. By combining subjective and objective criteria to determine indicator weights, a scientific evaluation system is established, with visual analysis conducted through Geographic Information System (GIS). The research selects 22 indicator factors across four dimensions: natural environmental suitability, socio-economic support capacity, regional transportation accessibility, and tourism appeal. Through weighted superposition analysis, it achieves visual representation of spatial differentiation characteristics in the development potential levels of IST in Heilongjiang Province. Results demonstrate a distinct “V”-shaped distribution of high development potential, primarily concentrated in the Greater Khingan Range region, Harbin–Mudanjiang border zone, and Jiamusi, with gradual decline from the core “V”-shaped area to both sides. The proposed evaluation index system provides scientific quantitative decision-making support for regional IST planning, and this methodology also holds reference value for evaluating other tourism industry developments. Full article

The project cycle of complex construction projects covers the whole process from project decision-making, design, bidding, construction, completion acceptance, and the initial stage of operation. Among them, the investment risk assessment of complex construction projects focuses on the early decision-making stage of the project, aiming to provide a basis for investment feasibility analysis. The investment risk of complex construction projects is highly nonlinear and uncertain, and the traditional risk assessment methods have limitations in model generalization ability and prediction accuracy. To improve the accuracy and reliability of quantitative risk assessment, this study proposed a novel investment risk assessment model based on the perspective of investors. Firstly, through literature research, a multi-dimensional comprehensive risk assessment index system covering policies and regulations, economic environment, technical management, construction safety, and financial cost was systematically identified and constructed. Subsequently, the Least Squares Support Vector Machine (LSSVM) was used to establish a nonlinear mapping relationship between risk indicators and final risk levels. Aiming at the problem that the parameter selection of the standard LSSVM model has a significant impact on the performance, this paper proposed an improved Firefly Algorithm (IFA) to automatically optimize the penalty factor and kernel function parameters of LSSVM, so as to overcome the blindness of artificial parameter selection and improve the convergence speed and generalization ability of the model. Compared with the classical Firefly Algorithm, IFA strengthens learning and adaptive strategies by adding depth. The conclusions are as follows. (1) Compared with the Backpropagation Neural Network (BPNN), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost), this model showed higher prediction accuracy on the test set, and its accuracy was reduced by about 3%. (2) Compared with FA, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), IFA had a stronger global retrieval ability. (3) The model could effectively fit the complex risk nonlinear relationship, and the risk assessment results were highly consistent with the actual situation. Therefore, the risk assessment model based on the improved LSSVM constructed in this study not only provides a more scientific and accurate quantitative tool for investment decision-making of construction projects, but also has important theoretical and practical significance for preventing and resolving significant investment risks. Full article

This study presents a multi-index performance system that is systematically used to assess the binder synergy and fly ash reactivity of eco-sustainable cementitious composite (ESCC) using the Strength Activity Index (SAI) as a reference in line with ASTM C618. The partial replacements of fly ash with high and low calcium fly ash (HCFA and LCFA) were added to the fly-ash-to-sand (FA/S) ratios of 0, 10, 20, and 30% with a constant mix parameter, such as a 50% ratio of water to slag and a 20% ratio of activator to slag. The Initial Flow Index (IFI) and Flow Retention Index (FRI) were used to measure fresh-state performance, and compressive-, tensile-, and flexural-based indices, i.e., the SAI, Tensile Strength Index (TSI), and Flexural Strength Index (FSI), were used to measure mechanical performance. The results indicate that flowability and workability retention decrease with an increase in the FA/S ratio, with LCFA-based mixtures having better flow retention than HCFA systems. The optimum mechanical performance at a replacement level of 20% FA/S produced the maximum SAI values of about 112% HCFA and 110% LCFA with a consistent increase in TSI and FSI values at 28 days. When the replacement levels were increased (30% FA/S), all strength indices decreased with the effect of dilution and decreased the packing efficiency of the binder. Comparisons of the SAI with the respective TSI and FSI values through correlation analysis showed that the quantitative relationship between compressive, tensile, and flexural behavior was definite and showed that compressive strength alone is not enough to extrapolate mechanical performance. Collectively, the proposed framework provides a reasonable performance-based basis for the manner in which fly ash could be utilized in the most effective way in eco-sustainable cementitious compositions. Full article

Against deglobalization and intensifying geopolitical conflicts, maritime bulk commodity supply chain vulnerability and resilience governance are strategic priorities for 75% of countries. To tackle rising global uncertainty, this study proposes the country-level risk identification, monitoring, and extrapolation (RIME) framework for such supply chains, which aligns with the theoretical demand for macro, end-to-end risk integration beyond the traditional firm-level focus. Based on the “supplier country–shipping route–importing country” spatiotemporal linkage, we construct the first standardized country-level vulnerability index. It overcomes the limitations of existing static and localized assessments by integrating spatiotemporal, multi-source risks across the full physical chain, thereby enabling dynamic, macro-level monitoring and supporting systematic diagnostics and trend tracking of national supply chain security. We also develop an emergent risk simulation technique to quantify the direction and intensity of compound disturbances as well as the system’s dynamic responses. Empirical validation with China’s iron ore imports shows that the index effectively captures risk evolution, while the simulations confirm that sudden disruptions amplify systemic risk. This framework fills national strategic security theoretical gaps and provides governments with dynamic monitoring, quantitative assessment, and policy forecasting tools. Full article

Cultural ecosystem services (CESs) represent a critical link between ecosystems and human well-being and constitute a core foundation for regional sustainable development. The balance between CES supply and demand directly affects the coordination efficiency between ecological conservation and socio-economic development, making it a key prerequisite for ecosystem management, conservation planning, and policy formulation. This study focuses on the circum-Taihu region and integrates multi-source data to assess public perceived demand and spatial supply capacity of CESs. Supply–demand matching relationships are examined across three dimensions, namely, scenic beauty, cultural heritage, and recreation, through the construction of a region-specific CES quantitative indicator system. The impacts of multiple environmental factors on CES supply–demand dynamics are further explored to provide scientific support for coordinated ecological, cultural, and economic sustainability at the regional scale. The findings demonstrate the following: (1) the proposed methodology effectively quantifies CES perception and supply capacity in the circum-Taihu region. Scenic beauty exhibits the highest perception levels, whereas cultural heritage and recreation show lower perception. Cultural heritage displays the strongest supply capacity, whereas scenic beauty and recreation exhibit weaker supply. (2) Significant spatial imbalances exist between CES perception levels and supply capacity across the circum-Taihu region. Areas exhibiting mismatches constitute the largest proportion for cultural heritage CESs, followed by scenic beauty, with recreation displaying the smallest amounts of imbalance. (3) Environmental drivers exert differentiated effects on CES supply–demand relationships. Slope, road network density, and elevation have significant positive effects, whereas the normalized difference vegetation index (NDVI), distance to water bodies, and distance to roads exhibit significant negative effects. Distance to roads imposes the strongest inhibitory influence on CES perception, whereas elevation emerges as the most influential driver of public perceived CES levels. Full article

The Iraqi coastline in the northern Persian Gulf is highly vulnerable to the impacts of future sea level rise. This study introduces a novel approach in the Arc Geographic Information System (ArcGIS) for inundation risk of the 58 km Iraqi coast of the northern Persian Gulf through a combination of multi-data sources, machine-learning predictions, and hydrological connectivity by Landsat. The Prophet/Neural Prophet time-series framework was used to extrapolate future sea level rise with 11 satellite altimetry missions that span 1993–2023. The coastline was obtained by using the Landsat-8 Operational Land Imager (OLI) imagery based on the Normalised Difference Water Index (NDWI), and topography was obtained by using the ALOS World 3D 30 m DEM. Global Land Use and Land Cover (LULC) projections (2020–2100) and population projections (2020–2100) were used as future inundation values. Two scenarios were compared, one based on an altimeter-based projection of sea level rise (SLR) and the other based on the National Aeronautics and Space Administration (NASA) high-emission scenario, Representative Concentration Pathway 8.5 (RCP8.5). It is found that, by the IPCC AR6 end-of-century projection horizon (relative to 1995–2014), 154,000 people under the altimeter case and 181,000 people under RCP8.5 will have a risk of being inundated. The highest flooded area is the barren area (25,523–46,489 hectares), then the urban land (5303–5743 hectares), and finally the cropland land (434–561 hectares). Critical infrastructure includes 275–406 km of road, 71–99 km of electricity lines, and 73–82 km of pipelines. The study provides the first hydrologically verified Digital Elevation Model (DEM)-refined inundation maps of Iraq that offer a baseline, in the form of a comprehensive and quantitative base, to the coastal adaptation and climate resilience planning. Full article

With the rapid development of cross-sea infrastructure, the immersed tube method has been increasingly applied to deep-water immersed-tube tunnel construction. However, when the construction depth reaches the scale of one hundred meters, issues such as high hydrostatic pressure, complex hydrological conditions, and limited construction windows significantly elevate project risks. Against this backdrop, this study systematically reviews relevant domestic and international research findings in the context of 100-m-deep water environments and constructs a comprehensive risk index system covering the construction processes of the WBS breakdown system based on the WBS-RBS decomposition method within the HSE framework. A risk index weighting analysis combines quantitative and qualitative analysis, categorizing the indicators into qualitative and quantitative categories. Quantitative analysis employs threshold determination and the LEC method; qualitative analysis utilizes expert surveys and the G1 method. Ultimately, a model that combines multiple methods for a 100-m-deep water environment, integrating subjective expertise and objective data, is developed. On this basis, multi-level prevention and control measures are proposed for hundred-meter-deep water-immersed tube construction. The results demonstrate that the proposed system can effectively identify key risk sources under deep-water conditions and provide practical countermeasures, offering significant guidance for ensuring construction safety and engineering quality in hundred-meter immersed-tube tunnel projects. Full article

High-voltage bushings are critical insulation components, yet conventional PRPD-based severity assessment methods that rely on global pattern morphologies such as “rabbit ears” and “tortoise shell” remain coarse, lack local sensitivity, and fail to track continuous degradation. This paper proposes a dynamic severity assessment method that shifts the focus from global contours to dense partial discharge (PD) clusters, defined as high-density aggregations of PD pulses in specific phase–magnitude regions of PRPD patterns. Each dense cluster is treated as the statistical projection of a physical discharge channel, and the evolution of its number, intensity, location, and shape provides a fine-scale description of defect development. A multi-level relative density and morphological image processing algorithm is used to extract dense clusters directly from PRPD histograms, followed by a 20-dimensional feature set and a five-index system describing discharge activity, development speed, complexity, instability, and evolution trend. A fuzzy comprehensive evaluation model further converts these indices into three severity levels with confidence measures. Long-term degradation tests on defective bushings demonstrate that the proposed method captures key turning points from dispersed multi-cluster patterns to a single dominant cluster and yields a stable, stage-consistent severity evaluation, offering a more sensitive and physically interpretable tool for condition monitoring and early warning of HV bushings. The method achieved a high evaluation confidence (average 60.1%), which rose to 100% at the critical failure stage. It successfully identified three distinct degradation stages (stable, accelerated, and critical) across the 49 test intervals. A quantitative comparison demonstrated significant advantages: 8.3% improvement in early warning (4 windows earlier than IEC 60270), 50.6% higher monotonicity, 125.2% better stability, and 45.9% wider dynamic range, while maintaining physical interpretability and requiring no training data. Full article

To address the limitations of single evaluation methods, complex risk factors, and subjective weight allocation in university mining lab safety management, this study proposes an improved EW-FCE model integrating entropy weighting and fuzzy comprehensive evaluation. A multi-level evaluation index system was developed, covering personnel status, hazardous objects, operating environment, and lab standardization (4 secondary and 24 tertiary indicators). By combining objective entropy weights with quantitative risk affiliation from fuzzy evaluation, the model overcomes traditional subjectivity. Applied to a key mining lab in Shanxi, it calculated indicator weights and overall risk values using survey data. Key risk factors identified include special equipment operation certification (weight 0.0909), heavy machinery maintenance records (0.0813), and radioactivity detector qualification (0.0761). The model enables scientific risk ranking and aligns closely with actual lab safety conditions, offering a practical tool for safety management and supporting AI-assisted decision-making in engineering universities. Full article