Search Results (489)

Resource-exhausted cities have long played a key role in national energy development. Urban renewal projects, such as the renovation of old underground commercial spaces, can improve urban vitality and promote sustainable development. However, in resource-based cities, traditional industries dominate, while new industries such as modern commerce develop slowly. This results in low economic dynamism and weak motivation for urban development. To address this issue, we propose a systematic method for analyzing construction risks during the decision-making stage of renovation projects. The method includes three steps: risk value assessment, risk factor identification, and risk weight calculation. First, unlike previous studies that only used SWOT for risk factor analysis, we also applied it for project value assessment. Then, using the Work Breakdown Structure–Risk Breakdown Structure framework method (WBS-RBS), we identified specific risk sources by analyzing key construction technologies throughout the entire lifecycle of the renovation project. Finally, to enhance expert consensus, we proposed an improved Delphi–Analytic Hierarchy Process method (Delphi–AHP) to calculate risk indicator weights for different construction phases. The risk analysis covered all lifecycle stages of the renovation and upgrading project. The results show that in the Fushun city renovation case study, the established framework—consisting of five first-level indicators and twenty s-level indicators—enables analysis of renovation projects. Among these, management factors and human factors were identified as the most critical, with weights of 0.3608 and 0.2017, respectively. The proposed method provides a structured approach to evaluating renovation risks, taking into account the specific characteristics of construction work. This can serve as a useful reference for ensuring safe and efficient implementation of underground commercial space renovation projects in resource-exhausted cities. Full article

The general framework of Entropic Dynamics (ED) is used to construct non-relativistic models of relational Quantum Mechanics from well-known inference principles—probability, entropy and information geometry. Although only partially relational—the absolute structures of simultaneity and Euclidean geometry are still retained—these models provide a useful testing ground for ideas that will prove useful in the context of more realistic relativistic theories. The fact that in ED the positions of particles have definite values, just as in classical mechanics, has allowed us to adapt to the quantum case some intuitions from Barbour and Bertotti’s classical framework. Here, however, we propose a new measure of the mismatch between successive states that is adapted to the information metric and the symplectic structures of the quantum phase space. We make explicit that ED is temporally relational and we construct non-relativistic quantum models that are spatially relational with respect to rigid translations and rotations. The ED approach settles the longstanding question of what form the constraints of a classical theory should take after quantization: the quantum constraints that express relationality are to be imposed on expectation values. To highlight the potential impact of these developments, the non-relativistic quantum model is parametrized into a generally covariant form and we show that the ED approach evades the analogue of what in quantum gravity has been called the problem of time. Full article

As a core component of automated logistics systems, delivery robots hold significant application value in the field of unmanned delivery. This research addresses the robot path planning problem, aiming to enhance delivery efficiency and reduce operational costs through systematic improvements to the RIME optimization algorithm. Through in-depth analysis, we identified several major drawbacks in the standard RIME algorithm for path planning: insufficient global exploration capability in the initial stages, a lack of diversity in the hard RIME search mechanism, and oscillatory phenomena in soft RIME step size adjustment. These issues often lead to undesirable phenomena in path planning, such as local optima traps, path redundancy, or unsmooth trajectories. To address these limitations, this study proposes the Multi-Strategy Controlled Rime Algorithm (MSRIME), whose innovation primarily manifests in three aspects: first, it constructs a multi-strategy collaborative optimization framework, utilizing an infinite folding Fuch chaotic map for intelligent population initialization to significantly enhance the diversity of solutions; second, it designs a cooperative mechanism between a controlled elite strategy and an adaptive search strategy that, through a dynamic control factor, autonomously adjusts the strategy activation probability and adaptation rate, expanding the search space while ensuring algorithmic convergence efficiency; and finally, it introduces a cosine annealing strategy to improve the step size adjustment mechanism, reducing parameter sensitivity and effectively preventing path distortions caused by abrupt step size changes. During the algorithm validation phase, comparative tests were conducted between two groups of algorithms, demonstrating their significant advantages in optimization capability, convergence speed, and stability. Further experimental analysis confirmed that the algorithm’s multi-strategy framework effectively suppresses the impact of coordinate and dimensional differences on path quality during iteration, making it more suitable for delivery robot path planning scenarios. Ultimately, path planning experimental results across various Building Coverage Rate (BCR) maps and diverse application scenarios show that MSRIME exhibits superior performance in key indicators such as path length, running time, and smoothness, providing novel technical insights and practical solutions for the interdisciplinary research between intelligent logistics and computer science. Full article

Multi-modal remote sensing image (MRSI) matching suffers from severe nonlinear radiometric distortions and geometric deformations, and conventional feature-based techniques are generally ineffective. This study proposes a novel and robust MRSI matching method using the side window filter (MSWF). First, a novel side window scale space is constructed based on the side window filter (SWF), which can preserve shared image contours and facilitate the extraction of feature points within this newly defined scale space. Second, noise thresholds in phase congruency (PC) computation are adaptively refined with the Weibull distribution; weighted phase features are then exploited to determine the principal orientation of each point, from which a maximum index map (MIM) descriptor is constructed. Third, coarse position, orientation, and scale information obtained through global matching are employed to estimate image-pair geometry, after which descriptors are recalculated for precise correspondence search. MSWF is benchmarked against eight state-of-the-art multi-modal methods—six hand-crafted (PSO-SIFT, LGHD, RIFT, RIFT2, HAPCG, COFSM) and two learning-based (CMM-Net, RedFeat) methods—on three public datasets. Experiments demonstrate that MSWF consistently achieves the highest number of correct matches (NCM) and the highest rate of correct matches (RCM) while delivering the lowest root mean square error (RMSE), confirming its superiority for challenging MRSI registration tasks. Full article

We present novel proofs of the Riemann hypothesis by extending the standard complex Riemann zeta function into a quaternionic algebraic framework. Utilizing λ-regularization, we construct a symmetrized form that ensures analytic continuation and restores critical-line reflection symmetry, a key structural property of the Riemann ξ(s) function. This formulation reveals that all nontrivial zeros of the zeta function must lie along the critical line Re(s) = 1/2, offering a constructive and algebraic resolution to this fundamental conjecture. Our method is built on convexity and symmetrical principles that generalize naturally to higher-dimensional hypercomplex spaces. We also explore the broader implications of this framework in quantum statistical physics. In particular, the λ-regularized quaternionic zeta function governs thermodynamic properties and phase transitions in Bose–Einstein condensates. This quaternionic extension of the zeta function encodes oscillatory behavior and introduces critical hypersurfaces that serve as higher-dimensional analogues of the classical critical line. By linking the spectral features of the zeta function to measurable physical phenomena, our work uncovers a profound connection between analytic number theory, hypercomplex geometry, and quantum field theory, suggesting a unified structure underlying prime distributions and quantum coherence. Full article

Traditional motor temperature rise testing generally uses temperature sensors. To solve problems such as sensor detachment, aging, and space occupation, this study takes a three-phase asynchronous motor as an example to propose a method for building a temperature rise monitoring model driven by a multi-physics field model based on the digital twin framework of power equipment. A twin monitoring model with defined input–output parameters is constructed to solve the problems of measurement inconvenience in traditional methods. Firstly, the losses of the iron core and the winding copper in the motor were obtained through electromagnetic field simulation. Secondly, the temperature distribution of the motor stator was obtained based on the bidirectional coupling characteristics of the magnetic and thermal fields. Subsequently, a temperature field reduced-order model based on the proper orthogonal decomposition method was built in Twin Builder, achieving fast calculation of the motor stator temperature. Finally, using the YE3-80M1-4 motor as the experimental subject, the model’s output results were compared with and validated against the experimental results. The results indicate that the simulation time of the reduced-order model is 2.1 s, and the relative error compared with the test values is within 5%, which confirms the practical applicability of the proposed method. Full article

Star sensors, as the most precise attitude measurement devices currently available, play a crucial role in spacecraft attitude estimation. However, traditional frame-based cameras tend to suffer from target blur and loss under high-dynamic maneuvers, which severely limit the applicability of conventional star sensors in complex space environments. In contrast, event cameras—drawing inspiration from biological vision—can capture brightness changes at ultrahigh speeds and output a series of asynchronous events, thereby demonstrating enormous potential for space detection applications. Based on this, this paper proposes an event data extraction method for weak, high-dynamic space targets to enhance the performance of event cameras in detecting space targets under high-dynamic maneuvers. In the target denoising phase, we fully consider the characteristics of space targets’ motion trajectories and optimize a classical spatiotemporal correlation filter, thereby significantly improving the signal-to-noise ratio for weak targets. During the target extraction stage, we introduce the DBSCAN clustering algorithm to achieve the subpixel-level extraction of target centroids. Moreover, to address issues of target trajectory distortion and data discontinuity in certain ultrahigh-dynamic scenarios, we construct a camera motion model based on real-time motion data from an inertial measurement unit (IMU) and utilize it to effectively compensate for and correct the target’s trajectory. Finally, a ground-based simulation system is established to validate the applicability and superior performance of the proposed method in real-world scenarios. Full article

With the rapid urbanization and increasing development of underground spaces, foundation pit groups in complex geological environments encounter considerable challenges in deformation control. These challenges are especially prominent in cases of adjacent constructions, complex geology, and environmentally sensitive areas. Nevertheless, existing research is lacking in systematic analysis of construction sequencing and the interaction mechanisms between foundation pit groups. This results in gaps in comprehending stress redistribution and optimal excavation strategies for such configurations. To address these gaps, this study integrates physical model tests and PLAXIS 3D numerical simulations to explore the Nanjing Jiangbei New District Phase II pit groups. It concentrates on deformations in segmented and adjacent configurations under varying excavation sequences and spacing conditions. Key findings reveal that simultaneous excavation in segmented pit groups optimizes deformation control through symmetrical stress relief via bilateral unloading, reducing shared diaphragm wall displacement by 18–25% compared to sequential methods. Sequential excavations induce complex soil stress redistribution from asymmetric unloading, with deep-to-shallow sequencing minimizing exterior wall deformation (≤0.12%He). For adjacent foundation pit groups, simultaneous excavation achieves minimum displacement interference, while phased construction requires prioritizing large-section excavation first to mitigate cumulative deformations through optimized stress transfer. When the spacing-to-depth ratio (B/He) is below 1, horizontal displacements of retaining structures increase by 43% due to spacing effects. This study quantifies the effects of excavation sequences and spacing configurations on pit group deformation, establishing a theoretical framework for optimizing construction strategies and enhancing retaining structure stability. The findings are highly significant for underground engineering design and construction in complex urban geological settings, especially in high-density areas with spatial and geotechnical constraints. Full article

Amid global efforts to balance sustainable development and food security, ecosystem service value (ESV), a critical bridge between natural systems and human well-being, has gained increasing importance. This study explores the spatiotemporal dynamics and driving factors of land use changes and ESV from a food security perspective, aiming to inform synergies between ecological protection and food production for regional sustainability. Using Guangdong Province as a case study, we analyze ESV patterns and spatial correlations from 2005 to 2023 based on three-phase land use and socioeconomic datasets. Key findings: I. Forestland and cropland dominate Guangdong’s land use, which is marked by the expansion of construction land and the shrinking of agricultural and forest areas. II. Overall ESV declined slightly: northern ecological zones remained stable, while eastern/western regions saw mild decreases, with cropland loss threatening grain self-sufficiency. III. Irrigation scale, forestry output, and fertilizer use exhibited strong interactive effects on ESV, whereas urban hierarchy influenced ESV independently. IV. ESV showed significant positive spatial autocorrelation, with stable agglomeration patterns across the province. The research provides policy insights for optimizing cropland protection and enhancing coordination between food production spaces and ecosystem services, while offering theoretical support for land use regulation and agricultural resilience in addressing regional food security challenges. Full article

The construction of the Grand Ethiopian Renaissance Dam (GERD) on the Blue Nile has heightened transboundary water tensions in the Nile River Basin, particularly affecting downstream Sudan and Egypt. This study leverages African Earth Observation Data Cubes, specifically Digital Earth Africa’s Water Observations from Space (WOfS) platform, to quantify the hydrological impacts of GERD’s three filling phases (2019–2022) on Sudan’s Roseires Dam. Using Sentinel-2 satellite data processed through the Open Data Cube framework, we analyzed water extent changes from 2018 to 2023, capturing pre- and post-filling dynamics. Results show that GERD’s water spread area increased from 80 km² in 2019 to 528 km² in 2022, while Roseires Dam’s water extent decreased by 9 km² over the same period, with a notable 5 km² loss prior to GERD’s operation (2018–2019). These changes, validated against PERSIANN-CDR rainfall data, correlate with GERD’s filling operations, alongside climatic factors like evapotranspiration and reduced rainfall. The study highlights the potential of Earth Observation (EO) technologies to support transparent, data-driven transboundary water governance. Despite the Cooperative Framework Agreement (CFA) ratified by six upstream states in 2024, mistrust persists due to Egypt and Sudan’s non-ratification. We propose enhancing the Nile Basin Initiative’s Decision Support System with EO data and AI-driven models to optimize water allocation and foster cooperative filling strategies. Benefit-sharing mechanisms, such as energy trade from GERD, could mitigate downstream losses, aligning with the CFA’s equitable utilization principles and the UN Watercourses Convention. This research underscores the critical role of EO-driven frameworks in resolving Nile Basin conflicts and achieving Sustainable Development Goal 6 for sustainable water management. Full article

The rapid development of underground engineering contributes significantly to achieving China’s “dual carbon” strategic goals. However, during the construction and operation phases, this engineering project faces diverse risks and challenges related to disasters. Consequently, enhancing the evaluation capability for underground engineering resilience is imperative. Based on the characteristics of resilience evaluation and enhancement in underground engineering, this study defines the concept and objectives of resilience evaluation for underground space engineering and analyzes corresponding enhancement methods. By considering aspects such as the magnitude of collapse disaster risk in underground engineering, its vulnerability, resistance capacity, adaptability to disasters, recovery ability, and economic feasibility, a comprehensive index system for evaluating the resilience of collapse disaster risks in underground engineering has been established. This research suggests that disaster risk management should shift from passive to active prevention. Through resilience evaluation case applications, it is possible to improve the design objectives of underground engineering towards “structural recoverability”, “ease of damage repair”, and “controllable consequences after a disaster”. The integration of intelligent static assessment models based on artificial intelligence algorithms can effectively enhance the accuracy of resilience evaluations. Furthermore, dynamic assessments using multiple data fusion techniques combined with numerical simulations represent promising directions for improving the overall resilience of underground engineering. Full article

The Visegrád Group (V4), as China’s key economic and trade partner in Central and Eastern Europe, plays a pivotal role in enhancing the effectiveness of sustainable development within the China-Central and Eastern Europe cooperation (China-CEEC) framework through its comprehensive green initiatives. This study analyzes export data and environmental product classifications from major countries in the CEPII-BACI database, covering the period from 2003 to 2022, to construct a green product space network. The analysis reveals the evolutionary patterns of the green industry and the collaborative transformation mechanisms between China and the V4 countries. The findings indicate the following: (1) The green product space network displays a “core-periphery” structural framework, wherein China has expanded its core product offerings by leveraging technological advancements in the photovoltaic sector, while the V4 countries enhance their resource allocation by systematically phasing out peripheral products. (2) The Green Complexity Index (GCI) suggests that China’s green production capacity has significantly improved, thereby narrowing the technological gap with Poland and Slovakia. (3) According to the Green Competition Index, a strategic complementary space exists between the two parties in the domain of medium- to high-complexity products. This study recommends extending green cooperation to higher value chain segments by establishing a collaborative innovation network for green technologies, developing a dynamic capacity optimization mechanism, and deepening the joint research and development of core products. This article offers a decision-making framework based on production capacity endowments to facilitate multinational collaborative transformations in the green industry. Full article

The thermal data of the hypersonic wind tunnel field accurately reflect the aerodynamic performance and key parameters of the aircraft model. However, the prediction of the temperature in hypersonic wind tunnels has problems such as a large delay, nonlinearity and multivariable coupling. In order to reduce the influence brought by temperature changes and improve the accuracy of temperature prediction in the field control of hypersonic wind tunnels, this paper first combines kernel principal component analysis (KPCA) with phase space reconstruction to preprocess the temperature data set of wind tunnel tests, and the processed data set is used as the input of the temperature-prediction model. Secondly, support vector regression is applied to the construction of the temperature prediction model for the hypersonic wind-tunnel temperature field. Meanwhile, aiming at the problem of difficult parameter-combination selection in support vector regression machines, an Improved African Vulture Optimization Algorithm (IAVOA) based on adaptive chaotic mapping and local search enhancement is proposed to conduct combination optimization of parameters in support vector regression. The improved African Vulture Optimization Algorithm (AVOA) proposed in this paper was compared and analyzed with the traditional AVOA, PSO (Particle Swarm Optimization Algorithm) and GWO (Grey Wolf Optimizer) algorithms through 10 basic test functions, and the superiority of the improved AVOA algorithm proposed in this paper in optimizing the parameters of the support vector regression machine was verified in the actual temperature data in wind-tunnel field control. Full article

This study systematically investigates the magnetic ordering and magnetocaloric properties of a series of polycrystalline compounds, Gd_1-xDy_xScO₃ (x = 0, 0.1, 0.2 and 1). X-ray powder diffraction (XRD) analysis confirms that all samples exhibit an orthorhombic perovskite structure with a space group of Pbnm. The zero-field cooling and field cooling magnetization curves demonstrate a transition from antiferromagnetic to paramagnetic phases, with Néel temperatures of about 3 K for GdScO₃ and 4 K for DyScO₃. The doping of Dy³⁺ weakened long-range antiferromagnetic order and enhanced short-range magnetic disorder in GdScO₃, leading to vanished antiferromagnetic transition between 2 and 100 K for the sample of x = 0.2. Using the Arrott–Noakes equation, we constructed Arrott plots to analyze the system’s critical behavior. Both the compounds with x = 0.1 and x = 0.2 conform to the 3D-Heisenberg model. These results indicate the weakened long-range antiferromagnetic order induced by Dy³⁺ doping. Significant maximal magnetic entropy change (−Δ$S_{\mathrm{M}}^{\mathrm{M}\mathrm{a}\mathrm{x}}$) of 36.03 J/kg K at 3 K for the sample Gd_0.9Dy_0.1ScO₃ is achieved as the magnetic field changes from 0 to 50 kOe, which is higher than that of GdScO₃ (−Δ$S_{\mathrm{M}}^{\mathrm{M}\mathrm{a}\mathrm{x}}$ = 34.32 J/kg K) and DyScO₃ (−Δ$S_{\mathrm{M}}^{\mathrm{M}\mathrm{a}\mathrm{x}}$ = 15.63 J/kg K). The considerable magnetocaloric effects (MCEs) suggest that these compounds can be used in the development of low-temperature magnetic refrigeration materials. Full article

Organic framework membranes (OFMs) have emerged as transformative materials for separation technologies due to their tunable porosity, structural diversity, and stability, yet their design and optimization face challenges in navigating vast chemical spaces and complex performance trade-offs. This review highlights the pivotal role of machine learning (ML) in overcoming these limitations by integrating multi-source data, constructing quantitative structure–property relationships, and enabling the cross-scale optimization of OFMs. Methodologically, ML workflows—spanning data construction, feature engineering, and model optimization—accelerate candidate screening, inverse design, and mechanistic interpretation, as demonstrated in gas separations and nascent liquid-phase applications. Key findings reveal that ML identifies critical structural descriptors and environmental parameters, guiding the development of high-performance membranes that surpass traditional selectivity–permeability limits. Challenges persist in liquid separations due to dynamic operational complexities and data scarcity, while emerging frameworks offer untapped potential. The integration of interpretable ML, in situ characterization, and industrial scalability strategies is essential to transition OFMs from laboratory innovations to sustainable, adaptive separation systems. This review underscores ML’s transformative capacity to bridge computational insights with experimental validation, fostering next-generation membranes for carbon neutrality, water security, and energy-efficient industrial processes. Full article