Search Results (10,600)

This article introduces an automaton designed to improve feasible solutions to the Monge–Kantorovich (MK) problem, particularly effective when the cost function is continuous. To enhance its performance, a good initial solution is obtained using the discrete wavelet transform. Specifically, a transportation problem is solved where the cost matrix is composed of the approximation coefficients of the transform, reducing the number of variables to one quarter of the original discrete problem. The solution to this reduced problem is extended using the detail coefficients, yielding a feasible solution to the original problem. This solution serves as the initial state of the tuning automaton, whose final states provide approximations to the optimal solution of the transportation problem. Full article

Structural reconstruction helps infer the spatial relationships and object layouts in a scene, which is an essential computer vision task for understanding visual content. However, it remains challenging due to the high complexity of scene structural topologies in real-world environments. To address this challenge, this paper proposes RegionGraph, a novel method for structural reconstruction of buildings from a satellite image. It utilizes a layout region graph construction and graph contraction approach, introducing a primitive (layout region) estimation network named ConPNet for detecting and estimating different structural primitives. By combining structural extraction and rendering synthesis processes, RegionGraph constructs a graph structure with layout regions as nodes and adjacency relationships as edges, and transforms the graph optimization process into a node-merging-based graph contraction problem to obtain the final structural representation. The experiments demonstrated that RegionGraph achieves a 4% improvement in average F1 scores across three types of primitives and exhibits higher regional completeness and structural coherency in the reconstructed structure. Full article

Modeling the dynamic response of saturated marine soils is crucial yet computationally challenging for traditional methods. Meanwhile, purely data-driven models suffer from sparse data and lack of physical interpretability. To overcome these limitations, this study proposes an intelligent engineering framework based on a frequency-domain physics-informed neural network (FD-PINN) for the forward simulation and inverse parameter identification of saturated seabed soils. Constrained directly by physical laws during the learning process, FD-PINN remains highly reliable even when training data is sparse. By formulating the governing equations in the frequency domain, it directly predicts complex-valued displacement and pore-pressure phasors. Multiscale Fourier feature mappings mitigate spectral bias and capture boundary layers and high-frequency effects. For inverse problems, a phase-sensitive lock-in extraction strategy transforms time-domain measurements into robust frequency-domain targets, enabling the accurate and noise-tolerant identification of poroelastic parameters with clear physical meaning (nondimensional storage parameter S and permeability parameter $\Gamma$). Numerical experiments show that FD-PINN substantially outperforms conventional time-domain PINN, achieving relative $L^2$ errors of ${10}^{-2}\sim {10}^{-3}$ for single- and multi-frequency excitations typical of wave-induced loadings. In particular, $\Gamma$ is consistently recovered with sub-percent relative error, while S can be reliably identified with multi-frequency data. The framework offers a data-efficient, noise-robust approach for high-fidelity modeling and robust parameter inversion, which is particularly valuable in offshore environments where high-quality data is scarce. Full article

Code smells are indicators of potential design problems in software systems and are commonly used to guide refactoring activities. Recent advances in representation learning have enabled the use of embedding-based models for analyzing source code, offering an alternative to traditional approaches based on manually engineered metrics. However, the effectiveness of different embedding representations for multiclass code smell detection remains insufficiently explored. This study presents an empirical comparison of embedding models for the automatic detection of three widely studied code smells: Long Method, God Class, and Feature Envy. Using the Crowdsmelling dataset as an empirical basis, source code fragments were extracted from the original projects and transformed into vector representations using two embedding approaches: a general-purpose embedding model and the code-specialized CodeBERT model. The resulting representations were evaluated using several machine learning classifiers under a stratified group-based validation protocol. The results show that CodeBERT consistently outperforms the general-purpose embeddings across multiple evaluation metrics, including balanced accuracy, macro F1-score, and Matthews correlation coefficient. Dimensionality reduction analyses using PCA and t-SNE further indicate that CodeBERT organizes code smell instances in a more structured latent representation space, which facilitates the separation of smell categories. In particular, CodeBERT achieved a macro F1-score of 0.8619, outperforming general-purpose embeddings (0.7622) and substantially surpassing a classical TF-IDF baseline (0.4555). These findings highlight the value of this study as a controlled multiclass evaluation of embedding representations and demonstrate the practical value of domain-specific representations for improving automated code smell detection and class separability in real-world software engineering scenarios. Full article

In the era of urban stock development, the regeneration of historic districts must abandon the misguided approach of large-scale, sweeping transformations and shift toward a micro-regeneration model characterized by small-scale, precise, and incremental interventions. However, as urban renewal enters this stock-based phase, the issues of “physical dissonance” and “cultural discontinuity” in the heritage landscapes of historic districts are becoming increasingly pronounced. To solve this problem, this paper aims to identify the heritage landscape genes of historical districts, explore the characteristics of historical districts, provide operational targets for the micro-renewal of historical districts, guide the implementation of micro-regeneration policies of historical districts, and then improve the quality of historical district heritage landscapes. Taking the Chinese Baroque Block in Harbin as an example, this paper proposes a genetic recognition method for the heritage landscape of historical districts based on the spatial translation of historical information, spatial topology analysis, an improved U-Net deep learning model, and text mining theme analysis. The micro-regeneration path of historical blocks of “gene identification-feature mining-targeted operation-quality improvement” is proposed. The micro-regeneration countermeasures of “gene replacement and texture repair in open space, gene repair and targeted acupuncture in street and alley, gene embedding and catalyst adjustment in courtyard layout, gene recombination and embroidery treatment of architectural style, and retrospective and contextual narrative of intangible genes” are formulated. The heritage landscape gene of historical districts is conducive to the refined control of the characteristics and quality of historical districts and provides new ideas for the micro-regeneration of historical districts in the stock era. Full article

Rapid population growth is intensifying global energy demand and waste generation. Slaughterhouse waste is creating important environmental problems. Transforming this into renewable energy through technologies like anaerobic digestion offers a sustainable pathway to reduce environmental impacts and support the energy transition. The main objective of this study was to examine the biodegradability of the slaughterhouse semi-liquid fraction (S), slaughterhouse liquid fractions (L), and their mixtures (25%, 50%, and 75%) through a two-phase anaerobic co-digestion (TPAcD) process. Batch reactors were operated in two separate microbiological and thermal phases. In the first, a thermophilic 55 °C–acidogenic stage, biochemical hydrogen potential (BHP) assays were conducted to evaluate green hydrogen production, while in the second, a mesophilic 35 °C–methanogenic stage, biochemical methane potential (BMP) assays were carried out to assess biomethane generation. The most relevant findings revealed that while liquid fractions maximized hydrogen recovery, overall yields remained limited due to competitive metabolic pathways. Notably, the 25L:75S configuration optimized hydrolysis, with a 1280% increase in soluble COD, establishing the semi-liquid fraction as a critical organic reservoir for thermophilic–acidogenic activity. In the subsequent stage, the acidogenic pre-treatment significantly enhanced methanogenesis, where the same 25L:75S mixture exhibited a synergistic methane yield of 495.46 mL CH₄/g VS. This 13.8% improvement over the theoretical additive potential confirms that strategic substrate balancing overcomes individual feedstock limitations, maximizing energy recovery in sequential anaerobic digestion. These results highlight the potential of phase-separated anaerobic co-digestion as a strategy to improve the valorization of slaughterhouse wastes. Full article

This study aims to automate the generation of execution plans for university projects by transforming BPMN-based process models into hierarchical planning representations that can be executed by HTN planners. Effective implementation of university extension projects requires explicit management of objectives, dependencies, and operational constraints, yet this process is often carried out manually and without formal planning support. To address this problem, the paper proposes PlanProjU, a web-based platform that captures project knowledge through BPMN and translates it into HDDL domain and problem files for execution with SHOP2 and PyHOP. The system was evaluated through real university project cases and a comparative analysis of alternative generated plans. The results show that BPMN-based project knowledge can be operationalized into executable hierarchical planning structures and that different planners may produce distinct plan alternatives depending on project characteristics. The originality of the study lies in the design of a traceable BPMN-to-HDDL workflow for university project planning, implemented in an integrated platform that connects business process modeling with HTN automated planning the originality of the study lies in the design of a traceable BPMN-to-HDDL workflow for university project planning, implemented in an integrated platform that connects business process modeling with HTN automated planning in a domain that has received limited attention in prior research. In this sense, the proposal serves both as an innovative research contribution and as a practical alternative for structuring implementation decisions in institutional settings. Full article

Satellite–terrestrial integrated networks provide seamless global coverage, especially in remote areas where terrestrial deployment is costly. Integrated sensing and communications (ISAC) enhances spectral efficiency by merging both functions on a single platform. This paper proposes a novel integrated satellite–terrestrial ISAC architecture, where a satellite performs simultaneous communication and sensing. The satellite transmits communication signals and sensing waveforms to an Earth Station, which then relays them to a terrestrial base station to serve multiple users. We formulate a joint beamforming design problem to maximize the sum rate of users under quality-of-service constraints, backhaul capacity limits, beampattern requirements for sensing, and power budgets. With perfect channel state information, the non-convex problem is transformed into a difference-of-convex form and solved via the convex–concave procedure. For imperfect channel state information, a robust method combining successive convex approximation and the S-procedure is developed. Simulations show the proposed design outperforms benchmarks and is suitable for low-Earth orbit satellite systems. Full article

A nonlinear ultrasonic time-domain identification method based on chaos sensitivity was proposed in this study. The Duffing chaotic system was introduced into the weak second harmonic identification to realize early detection and quantitative evaluation of fatigue damage in U71Mn steel. First, to ensure the reliability of nonlinear ultrasonic testing, a probe-pressure monitoring device was designed. Through pressure-stability experiments, 16 N was determined as the optimal pressure, which effectively suppresses contact nonlinearity interference and ensures coupling stability. Subsequently, the Duffing chaos detection system was established. The signal-system frequency-matching problem was resolved through time-scale transformation. Simultaneously, the issue of unknown initial phases was resolved using phase traversal compensation. Based on the chaotic system’s sensitivity to specific frequency signals and immunity to noise, the amplitudes of the fundamental wave and second harmonics in the target signals were quantified to calculate the nonlinear coefficient. Experimental results demonstrate that the proposed method can extract these amplitudes directly in the time domain, thereby effectively overcoming the spectral leakage inherent in traditional frequency-domain methods. The nonlinear coefficient of U71Mn steel exhibits a “double-peak” characteristic as fatigue damage increases. Specifically, the first peak appears at approximately 50% of fatigue life, while the second occurs at approximately 80%. This phenomenon is closely correlated with the distinct stages of internal fatigue crack propagation, reflecting a complex damage-evolution mechanism. This study not only provides a novel method for the precise extraction of weak nonlinear signals but also establishes a critical theoretical and experimental foundation for accurate fatigue life prediction for U71Mn rail steel. Full article

With the rapid growth of passenger demand, the imbalance between transport capacity and passenger flow has become increasingly severe. Existing studies seldom consider the impacts induced by passenger demand uncertainty under a flexible train composition mode. To address this issue, this study investigates the integrated optimization of train timetabling and rolling stock circulation planning under a flexible train composition mode. The objective is to minimize the number of stranded passengers and operational costs. A scenario-based robust optimization framework is introduced, and a mean risk objective is formulated by combining the expected objective value with the expected absolute deviation of each scenario’s objective value from the expectation. By using linearization techniques, the model is transformed into a mixed integer programming (MIP) problem, which balances the operating cost and robustness while satisfying safety and service level requirements. The model is validated through a case study of Shanghai Metro Line 16. Numerical experimental results indicate that, in a single scenario, compared with the fixed train composition scheme, the proposed scheme reduces the objective function value by 28.3%. Simultaneously, it can enhance the robustness of the train timetable and rolling stock circulation plan under the condition of uncertain passenger demands. The related findings provide decision support for the design of urban rail transit operating plans. Full article

Deep learning systems deployed in regulated settings require explanations that are accurate and stable under nuisance transformations, yet classical post hoc transition matrices rely on fidelity-only fitting that fails to guarantee consistent explanations under spatial rotations or other group actions. In this work, we propose Equivariant Transition Matrices, a post hoc approach that augments transition matrices with Lie-group-aware structural constraints to bridge this research gap. Our method estimates infinitesimal generators in the formal and mental feature spaces, enforces an approximate intertwining relation at the Lie algebra level, and solves the resulting convex Least-Squares problem via singular value decomposition for small networks or implicit operators for large systems. We introduce diagnostics for symmetry validation and an unsupervised strategy for regularization weight selection. On a controlled synthetic benchmark, our approach reduces the symmetry defect from 13,100 to 0.0425 while increasing the mean squared error marginally from 0.00367 to 0.00524. On the MNIST dataset, the symmetry defect decreases by 72.6 percent (141.19 to 38.65) with changes in structural similarity and peak signal-to-noise ratio below 0.03 percent and 0.06 percent, respectively. These results demonstrate that explanation-level equivariance can be reliably imposed post-training, providing geometrically consistent interpretations for fixed deep models. Full article

Foreign direct investment (FDI) plays a vital role in Africa’s economic development; however, the rapidly expanding body of literature on this topic remains highly fragmented. This dispersion creates a significant research problem, obscuring structural evolution, persistent thematic gaps, and collaborative networks within the field. To address this, a bibliometric analysis is necessary, as it provides an objective, macro-level methodology capable of synthesising vast amounts of publication data and uncovering hidden intellectual structures that traditional systematic reviews cannot easily capture. Consequently, this study maps the development of FDI research in Africa by analysing and visualising scientific publications to reveal the structure, evolution, and interdisciplinary nature of the field, identifying leading scholars, collaboration networks, and core thematic areas. Using data from the Scopus database, the study examines 2003 documents through Biblioshiny and VOSviewer. The findings are presented in three sections. The descriptive analysis shows a steady rise in FDI publications from 1986 to 2024, with strong growth in the past two decades. The most productive institutions are in South Africa and Nigeria, while major contributing countries include South Africa, the United States, China, and the United Kingdom. Keyword and collaboration analyses highlight themes such as Sub-Saharan Africa, economic growth, capital flow, renewable energy, and natural resources. Ultimately, this mapping goes beyond descriptive trends to provide critical analytical insights, revealing a significant thematic shift from traditional economic paradigms toward sustainable development and environmental economics. Practically, these findings offer strategic guidance for policymakers and investors by identifying key institutional hubs and regional knowledge gaps. Scientifically, the study establishes a foundation for future research by directing attention toward underexplored, emerging issues such as climate resilience, digital transformation, and subnational FDI dynamics. Full article

Amid the new wave of technological revolution and industrial transformation, new quality productive forces (NQPFs) have become the key to a firm’s sustainable development. To help enterprises accelerate the improvement of their NQPFs, grounded in signaling theory, this paper takes data of China’s A-share listed companies from 2015 to 2024 as the research sample and uses a two-way fixed effects model to empirically examine whether and how superior ESG performance, serving as a high-quality signal, fosters NQPFs. The results show the following. There is a significant positive relationship between corporate ESG performance and NQPFs. This finding remains robust across a series of checks, including replacing the key explanatory variable, removing outlier years and cities, and addressing endogenous problems through instrumental-variable estimation. Heterogeneity tests reveal that the effect is more pronounced among non-state-owned firms and those located in Northeast China, whereas it is statistically insignificant for firms in Western China. Mechanism analysis indicates that ESG performance boosts NQPFs indirectly by raising analyst attention and investor confidence. Overall, this paper not only enriches research perspectives on the relationship between corporate ESG performance and NQPFs, but also offers theoretical support and practical reference for the formulation of corporate ESG strategies and the precise policy-making of governments. Full article

Vision-based pose estimation has been widely applied in unmanned aerial vehicle (UAV) navigation. However, existing visual pose estimation algorithms are highly sensitive to camera imaging distortion, which degrades estimation accuracy, and often suffer from noticeable jitter between frames in dynamic scenarios. To address these issues, this paper proposes an improved visual pose estimation algorithm built upon the Perspective Similar Triangle (PST) geometric model. Using a planar fiducial marker as the observation target, the single-frame pose estimation problem is reformulated as a hierarchical geometric inference framework, including image point distortion correction, depth recovery based on planar similar triangle constraint, and rigid transformation estimation between the camera and world coordinate systems. This formulation improves pose estimation accuracy under distorted imaging conditions. To accommodate distortion variations in practical scenarios, a radial distortion coefficient update method is further designed to adaptively adjust the radial distortion parameters under single-frame observations, ensuring that the distortion model remains consistent with the actual imaging distortion and providing reliable model inputs for distortion correction in pose estimation. In addition, to enhance pose stability in dynamic scenarios, a multi-frame optical center consistency constraint (MOCCC) method is introduced to optimize the pose estimation for more stability. By constraining pose estimation across adjacent frames using the mean optical center over multiple frames as the optimization objective, the proposed method effectively suppresses pose jitter caused by single-frame observation noise. Finally, a three-degree-of-freedom (3-DOF) attitude motion platform is established, and both static and dynamic experimental scenarios are designed to validate the accuracy and stability of the proposed algorithm. Experimental results demonstrate that the proposed algorithm achieves high accuracy and high stability pose estimation under imaging distortion and small perturbations, exhibiting good robustness and suitability for practical UAV visual navigation applications. Full article

Intelligent-assisted assessment of pilot flight training ability is a method of automating the evaluation of pilots’ flight skills using artificial intelligence. Currently, using AI to assist or replace human instructors in flight skill assessment has become a mainstream research direction in the field of intelligent aviation. Existing flight skill assessment methods suffer from limitations in data types and insufficient assessment accuracy. To address these issues, we evaluate and predict pilot performance in simulated flight missions based on physiological signals. Following the “OODA loop” theory, we established a multimodal dataset including pilot eye movement, electroencephalogram (EEG), electrocardiogram (ECG), electrodermal signaling (EDS), heart rate, respiration, and flight attitude data. This dataset records changes in physiological rhythms and flight behaviors during pilots’ flight training at different difficulty levels. To enhance the signal-to-noise ratio, we propose an enhanced wavelet fuzzy thresholding denoising algorithm utilizing LSTM optimization. We address the problem of isolated features across different time frames in multimodal data modeling by introducing a multi-feature fusion algorithm based on STFT. Furthermore, by combining a high-efficiency sub-attention mechanism with a Transformer network, we construct a multi-classification network for intelligent-assisted assessment of pilot flight training ability, further improving the output accuracy of each category. Experiments show that our designed algorithm can achieve a classification accuracy of up to 85% on the dataset (5-fold cross-validation), which meets the requirements for auxiliary assessment of flight capabilities. Full article