Search Results (282)

In the context of an increasingly complex and dynamic digital ecosystem, accurately assessing the value of Specialized, Refined, Differentiated, and Innovative (SRDI) enterprises is crucial for making effective decisions. Traditional valuation methods struggle to effectively address issues such as the high R&D expenditures and significant operational risks associated with these enterprises. This study proposes an interpretable intelligent decision-support framework for valuing SRDI enterprises listed on the Beijing Stock Exchange (BSE), constructing a multidimensional indicator system that encompasses solvency, profitability, and R&D capabilities. Feature importance screening using the XGBoost algorithm was conducted to identify key indicators as input variables for a backpropagation (BP) neural network. Concurrently, the Particle Swarm Optimization (PSO) algorithm was applied to the neural network to optimize initial weights and thresholds, thereby modeling nonlinear valuation relationships. Empirical analysis of 770 SRDI firms listed on the Beijing Stock Exchange from 2020 to 2024 indicates that the XGBoost-PSO-BPNN model achieved a coefficient of determination of 0.8083 on the test set, outperforming traditional linear models and benchmark models such as single-tree models. SHAP explainability analysis further reveals that current asset turnover, return on assets, and equity concentration are the primary value drivers. This study employs various clustering methods to further classify enterprises into three categories and proposes recommendations for differentiated regulatory policies, providing intelligent decision support for enterprises operating within complex digital ecosystems. Full article

Stone arch bridges represent culturally significant heritage assets that exhibit distinct regional characteristics. At present digital preservation largely attends to geometric modeling and typically neglects the identification and conformance of core culture genes. This oversight has resulted in a disconnect between technological application and core heritage values, a prevalent issue globally. To address this, this study employs cultural gene theory to formulate a systematic framework for investigating the architectural cultural genes of stone arch bridges from the three dimensions: material–morphological, technical–behavioral, and cultural–symbolic. This study takes the Hongji Bridge in Handan as an example and uses literature research and 3D laser scanning and UAV oblique photogrammetry and qualitative extraction and visual presentation of the architectural genetic characteristics of stone arch bridges. This study identifies 11 core genetic indicators from the dimensions of genetic architecture, inheritance, and evolution, for the architectural cultural genes for the Chinese stone arch bridges The Zhaozhou Bridge (China) and Serranos Bridge (Europe)’s cross-cultural comparative analyses are adopted to validate the generalizability of the framework and the genetic uniqueness of the Chinese stone arch bridge. This research introduces a gene-based model of digital conservancy that fosters the transition of heritage preservation from technology-driven to value-driven. Full article

3D scene understanding in industrial environments is crucial for effective operation and maintenance (O&M) and asset monitoring. However, accurate asset detection and localisation face significant challenges due to asset diversity and scene complexity in these environments. Existing learning-based methods rely heavily on labelled training datasets, which are limited for industrial settings due to asset variability and intricate geometries. To address these challenges, this paper presents a novel framework for industrial asset detection and localisation without requiring labelled training datasets, using only point cloud data. Experimental results demonstrate the competitive performance of the proposed framework, achieving an average precision at 25% intersection over union (AP25) of 48.13% and an AP50 of 34.98%, significantly outperforming state-of-the-art (SOTA) methods. This framework can be employed to generate 3D digital models of brownfield industrial plants that lack up-to-date spatial information, serving as a foundational spatial layer for the development of digital twins within industrial environments. Full article

3D Gaussian Splatting (3DGS) has emerged as a cornerstone technique for 3D asset acquisition. However, existing copyright protection methods for 3DGS predominantly rely on embedding watermarks directly into Gaussian primitives, which inevitably degrades rendering quality. To address this issue, this paper proposes a zero-watermarking framework. By directly mapping the inherent features of rendered images to copyright information without modifying Gaussian parameters, the framework achieves perfect visual fidelity. Conventional image zero-watermarking maps features of a single image to a dedicated watermark. In contrast, our method guarantees mapping consistency: features of rendered images from any unknown viewpoint can be mapped to the same copyright identifier. To address this cross-view consistency challenge, we introduce an uncertainty-guided strategy that scores individual pixels to guide the decoder to mine shared features across multiple perspectives. This strategy enables accurate watermark retrieval even from novel viewpoints. Extensive experiments on the Blender, LLFF, and MipNeRF-360 datasets demonstrate that our method achieves superior performance, characterized by high message capacity, strong adversarial robustness, and a low false positive rate (FPR), while fully maintaining the integrity of the original 3DGS model. Full article

The closed-loop testing of autonomous driving systems critically depends on large-scale libraries of diverse and realistic 3D vehicle assets, yet current pipelines still rely on labor-intensive modeling or multi-view capture, making efficient construction a key bottleneck. To overcome this bottleneck and enable convenient, cost-effective 3D asset generation, we propose a semantic prior-guided framework for accurate and robust vehicle point cloud reconstruction from casually captured single-view photographs. Our framework is built on a diffusion backbone but is fundamentally driven by two forms of prior knowledge: First, geometric and appearance priors from camera-aware image features, masks, and distance-transform maps are projected onto the evolving point cloud, compensating for the severe information loss in single-view inputs. Second, we introduce distillation-style regulators—pretrained neural networks that encode vehicle type and model semantics; they act as teacher networks that impose high-level constraints on the generated point clouds, transferring rich semantic knowledge and effectively regularizing the learning process. With these priors, our model infers vehicle-specific semantics from limited observations and reconstructs high-quality 3D point cloud assets. On the 3DRealCar++ dataset, our method clearly surpasses state-of-the-art point cloud baselines in both F-score and Chamfer Distance. Full article

Global urbanization is shifting from incremental expansion to stock optimization, and old residential communities have become important spatial units for low-carbon transition. However, in existing built environments, traditional process-based inventory methods face practical constraints, including missing original drawings, complex site conditions, and severe vegetation obstruction. As a result, systematic accounting of buildings, landscapes, and natural carbon sinks remains difficult. This study integrates life cycle assessment (LCA), BIM reverse modeling, 3D point clouds, DesignBuilder simulation, inventory-based accounting, and i-Tree Eco to construct a life cycle carbon emission accounting framework for old residential communities. The framework links current-condition data reconstruction, quantity take-off, operational energy simulation, landscape inventory accounting, and vegetation carbon sequestration assessment. It is applied to Nanyuan Xincun in Hefei to quantify the community-scale carbon source–sink structure. The results show that Nanyuan Xincun presents a clear operation-led emission pattern, with the operation and maintenance phase accounting for 82.52% of total positive emissions. Within architectural engineering, operation and maintenance accounts for 82.91%, while material production accounts for 13.28%. Landscape engineering shows a more mixed structure, with operation and maintenance accounting for 52.95% and material production accounting for 36.49%. Vegetation carbon sequestration analysis shows that mature trees and shrubs are the main ecological carbon assets. Annual sequestration reaches 16.95 t-CO₂e/a, and trees and shrubs contribute 92.85% of total vegetation carbon storage. Under current vegetation conditions, annual sequestration is equivalent to 32.99% of annual landscape operation emissions, indicating considerable ecological compensation potential. Based on these findings, this study proposes four optimization pathways: operational energy reduction, low-carbon material substitution, construction and demolition waste recycling, and mature tree protection. These pathways provide data support for refined carbon management and low-carbon renewal in existing communities. Full article

This study investigates the key determinants of firm profitability in the global automotive sector, examining whether superior returns on assets (ROA) stem from operational efficiency, strategic leverage, or innovation intensity, and highlighting the potential trade-off between efficiency and investment in capital-intensive industries. Analysing a global panel dataset of 192 automotive firms from 38 countries/regions over 2010–2024, a fixed effects regression model with Driscoll–Kraay standard errors was applied to control for unobserved heterogeneity, heteroskedasticity, and cross-sectional dependence across 11 financial and strategic variables. The findings reveal that firm size and inventory turnover are significant positive drivers of profitability, while research and development (R&D) intensity exerts a strong negative impact. The positive association with the effective tax rate reflects reverse causality, where more profitable firms incur higher tax burdens, rather than a causal effect of taxation on performance. Notably, working capital management, leverage, sales growth, and capital expenditure showed no statistically significant effects after controlling for firm and time effects. Temporal fluctuations, including a marked profitability decline in 2024, underscore the sector’s sensitivity to macroeconomic shocks. This study contributes robust, large-scale empirical evidence on the short-term profitability trade-off associated with R&D intensity in a globally integrated industry, addressing cross-sectional dependence through its methodological approach. Full article

The efficient development of low-permeability reservoirs is critically constrained by severe geological heterogeneity, marginal permeability (<10 mD), and the consequent prevalence of low-productivity wells. Conventional intermittent production management, reliant on empirical fixed-cycle schedules, fails to adapt to dynamic reservoir behavior and wellbore conditions, leading to suboptimal energy efficiency and recovery. This study presents a physics-constrained, data-driven framework for adaptive intermittent production optimization, specifically designed to address the coupled geological-engineering complexities of such reservoirs. The methodology integrates three core innovations: (1) a hybrid flowing bottomhole pressure (FBHP) decline model coupling a “Three-Segment” wellbore pressure calculation with inflow performance relationship (IPR) curves, enabling dynamic characterization of pressure depletion; (2) a shut-in pressure buildup prediction framework combining a physically interpretable dual-exponential recovery mechanism—representing near-wellbore elastic expansion and far-field formation recharge—with a Random Forest Regression algorithm to capture the influence of geological and operational heterogeneity; and (3) a “Dual-Curve Intersection Method” that autonomously determines optimal pumping and shut-in durations by intersecting predicted pressure decline and recovery curves under geological constraints. Field implementation on 15 low-production wells in the Jiyuan Oilfield—a representative low-permeability asset—demonstrated robust performance: average pump efficiency improved from 14.3% to 14.49%, and average single-well electricity savings reached 15.61%. This work establishes a closed-loop intelligent control framework that bridges reservoir geology, wellbore hydraulics, and machine learning, offering a scalable solution for enhancing energy efficiency and production sustainability in low-permeability and unconventional resources. Full article

As social infrastructure intensively developed during the high economic growth period of the 1970s faces simultaneous aging, there is an urgent need to transition from conventional reactive maintenance to preventive maintenance utilizing various data (data-driven asset management. However, the greatest barrier in practice is that inspection data is unevenly distributed in analog formats such as paper and unstructured files, and heavily relies on the subjective visual evaluation of expert engineers (e.g., discrete graded evaluations from A to D). The intervention of this “Assessor Bias” makes it difficult to ensure the robustness required for direct statistical analysis. This paper serves as a bridge between this analog expert knowledge and quantitative data science. It formulates human cognitive conflicts (true state, peer pressure, avoidance of cognitive load) using the distance-decay model of the Analytic Hierarchy Process (AHP) and the Softmax function, constructing a micro-macro link model accompanied by stochastic variations. Through large-scale multi-agent simulations ($N={10}^7$) validating the model’s convergence, it was demonstrated that in long-tail distributions formed under peer pressure, macroscopic statistical distance metrics such as the Kullback-Leibler (KL) divergence ignore the fact that a small number of true signals are non-linearly suppressed, causing a statistical misinterpretation that “the error is within an acceptable range”. This implies that as long as macroscopic statistical indicators are over-trusted, signs of critical deterioration (minorities) will be structurally marginalized. Returning to the debate on “Homogeneity (Homogenität)” in German social statistics, this paper advocates that in order to realize objective “Micro-segmentation of Homogeneous Statistical Populations,” a paradigm shift from qualitative methods relying on human intuition to quantitative methods incorporating multi-criteria decision making is essential, rather than simply expanding the sample size. Full article

Electricity infrastructure is widely regarded as a fundamental prerequisite for supporting sustainable industrial development and driving corporate digital transformation under energy constraints. Taking the quasi-natural experiment of changes in electricity supply resulting from the cross-regional power transmission of the Three Gorges Project, and using data from China’s A-share listed manufacturing companies over the period 2000 to 2023, this paper constructs a multi-period difference-in-differences model to investigate whether improvements in electricity infrastructure promote corporate digital transformation and to examine their potential role in supporting sustainable economic development. The empirical results indicate that improvements in electricity infrastructure significantly enhance the level of corporate digital transformation. In the mechanism analysis, the alleviation of financing constraints and the increase in R&D investment play important mediating roles in the process through which electricity infrastructure affects corporate digital transformation. Further heterogeneity analysis reveals that the above effects are more pronounced in non-STAR Market enterprises, labor-intensive enterprises, asset-intensive enterprises, state-owned enterprises, and regions characterized by relatively lower levels of marketization. This study reveals the intrinsic relationship between electricity infrastructure and corporate digital transformation at the micro level, provides empirical evidence for understanding how energy infrastructure supports sustainable digital transformation and enhances long-term economic resilience, and offers policy implications for promoting the coordinated development of energy security and the digital economy. Full article

Scalable production of interactive 3D assets is a key requirement for XR-based applications, yet the functional integration of GenAI-generated assets into game engines remains challenging for non-expert users. This article proposes and validates a Prompt-to-Trigger workflow that links GenAI-based asset ideation and generation with the implementation of basic interactive behaviors (triggers) in accessible XR platforms. The study adopted a qualitative and exploratory approach, using systematic observation throughout a two-stage development process. This process included an initial phase where 3D assets were generated and refined using tools such as Tripo AI and Meshy, followed by an optimization stage to ensure compatibility with Blender and XR environments like A-Frame and Godot, and subsequently, the creation of AI-powered activation scripts. The results show that GenAI’s current 3D outputs frequently exhibit topological inconsistencies and rigging errors that compromise performance and real-time interoperability, requiring cleanup and optimization before deployment. The Prompt-to-Trigger workflow formalizes this bridge, positioning AI assistance as a functional layer for iterative logic generation. The resulting model provides non-expert creators with structured, actionable framework to prototype complex XR experiences for applied domains like education and multimedia communication. Full article

Railway bogies are typically engineered with conservative safety margins, which frequently results in the premature disposal of components retaining significant structural integrity. This study proposes a comprehensive 3D modelling framework designed to accurately predict and extend the Remaining Useful Life (RUL) of the bogie structure. To achieve this, a Building Information Modelling (BIM) approach was used, not only for the bogie, but for all train, using a rolling stock in Portugal as a case study. The use of both real and virtual sensors installed in the bogie, with data collected with a sampling rate according to the specificity of each sensor and, next, managed through machine learning tools, allows to implement a predictive maintenance (PdM) policy that aid to extend the RUL. The proposed approach demonstrates that extending the operational life of the bogie is both feasible and safe. This facilitates a strategic transition from the current practices to new approaches that improve the Availability of the Physical Assets, including through the metaverse. Full article

Efficiently converting natural capital into economic assets is a critical challenge in urban–rural transformation, yet the interactive mechanism between institutional land reform and physical spatial restructuring remains underexplored. While traditional frameworks emphasize institutional design, this study identifies a “Spatial-First” mechanism where physical reconfiguration serves as a spatial mediator to catalyze property rights breakthroughs. Using an entropy-weighted coupling coordination model, we analyzed policy dynamics in Nanhai District, China, a unique “dual-pilot” zone, from 2020 to 2024. The results indicate a nonlinear leap in the Coupling Coordination Degree (D) from 0.100 to 0.978. We interpret this surge as a policy-driven shock during the intensive pilot phase, where substantive spatial integration (0.719) effectively bypassed high transaction costs inherent in collective tenure, outpacing institutional progress (0.281). However, an Ecological Lag was observed; the disproportionately low weighting of the ecological carrier index (7.09%) suggests that current gains are primarily driven by green industrialization rather than the expansion of absolute ecological stock. This study concludes that while spatial tools can effectively unlock natural capital value in the short term, long-term sustainability necessitates a strategic shift from administrative-led economic efficiency to market-based ecological restoration. Full article

Cultural heritage, in all its tangible and intangible expressions, is undergoing a process of renewal driven by the integration of digital technologies and participatory approaches. This study presents a pilot project developed within the SAMOTHRACE Fundation, focused on the design of a Serious Game dedicated to the Norman Castle of Aci Castello in Sicily. The project explores how game-based learning and interactive storytelling can enhance visitor engagement, accessibility, and understanding of small-scale heritage sites that are often excluded from major cultural circuits. Using Unity and Blender, the prototype combines historical research, 3D reconstruction, and narrative interaction to transform the castle into an immersive educational environment. This initial phase also served as the basis for an academic thesis, laying the methodological groundwork for future expansion and evaluation. The results of this pilot provide preliminary quantitative evidence that serious games can support cultural communication strategies, foster emotional engagement, and stimulate curiosity toward minor heritage sites, while remaining compatible with the constraints of modest institutions. Ultimately, the project illustrates how even modest institutions can leverage digital innovation to revitalize their heritage assets, promote inclusive participation, and explore new models of interactive archaeology and community-centered cultural engagement. Full article

The nature of nonlinear dynamics of financial markets results in fractal geometry and chaotic behavior that can be viewed on a variety of scales in time. This paper conducts research on the fractal characteristics of the stock network and its contribution to the price of assets based on the Fractal Market Hypothesis (FMH). A multiscale network centrality measure is built based on high-frequency return dependencies to measure the self-similar, scale-invariant nature of inter-stock dependencies. The network factor and portfolio returns are then broken down with the wavelet packet decomposition (WPD) to obtain frequency-domain profiles, which characterize the variability of risk transmission in relation to investment horizons. The profiles are consistent with scaling properties of fractal, but the decomposition does not identify causal pathways on its own. Estimation of fractal dimension by use of the box-counting technique aided by the Hurst exponent analysis reveals that the A-share of China market exhibited long-range dependence and multifractal scaling. Network factor has the largest explanatory power in mid-frequency between the D5 and D6 bands of 32 to 128 days. This intermediary frequency concentration is consistent with the hypothesis of heterogeneous markets, in which the groups of investors with varying time horizons generate scale-related price dynamics. The addition of the network factor to a 6-factor specification lowers the GRS under the 5-factor specification by 31.45 to 17.82 on the same test-asset universe, indicating better cross-sectional coverage in the sample. The estimates of the Lyapunov exponents (0.039) as well as the correlation dimension ($D_2=4.7$) confirm the presence of low-dimensional chaotic processes of the network factor series, but these values are specific to the Chinese A-share market over the 2005–2023 sample period. These results provide a frequency-disaggregated use of network-based factor modeling and suggest that it can be applicable in multiscale portfolio risk management where the investor horizon is not uniform. Full article