Search Results (1,478)

This study investigates the environmental dimension of sustainable development across fifteen post-Soviet republics in 2022. While sustainability is generally understood as a triadic construct—economic, social, and environmental—this paper isolates the ecological pillar to highlight cross-country differences shaped by industrial legacies, institutional capacity, and governance models. A composite Environmental Performance Index (EPI) is developed using the Mazziotta–Pareto Index (MPI), which captures both average performance and internal consistency across three SDG-related domains: SDG 6 (Clean Water and Sanitation), SDG 13 (Climate Action), and SDG 15 (Life on Land). The study adds to existing literature as it includes a non-compensatory composite index and cluster analysis, and in policy terms, it provides a benchmarking system for facilitating ecological transition in the post-Soviet context. The results reveal strong divergence across the region: Baltic countries and Moldova achieve higher scores, reflecting policy convergence with the European Union and stronger environmental institutions, while Central Asian republics lag due to resource dependence, water scarcity, and weaker governance. Geographic cluster analysis corroborates these differences, showing clear spatial patterns of environmental convergence and divergence. Correlation analysis further demonstrates that environmental sustainability is positively associated with GDP per capita, HDI, and life expectancy, while negatively linked with inequality and fertility rates. These findings stress the need for context-sensitive and evidence-based policies, intra-regional cooperation, and integrated governance mechanisms to advance ecological transition in line with the 2030 Agenda for Sustainable Development. Full article

High energy consumption in Chinese rural residential buildings, caused by rudimentary construction methods and the poor thermal performance of building envelopes, poses a significant challenge to national sustainability and “dual carbon” goals. To address this, this study proposes a comprehensive modeling and analysis framework integrating an improved Bio-inspired Black-winged Kite Optimization Algorithm (IBKA) with Support Vector Regression (SVR). Firstly, to address the limitations of the original B-inspired BKA, such as premature convergence and low efficiency, the proposed IBKA incorporates diversification strategies, global information exchange, stochastic behavior selection, and an NGO-based random operator to enhance exploration and convergence. The improved algorithm is benchmarked against BKA and six other optimization methods. An orthogonal experimental design was employed to generate a dataset by systematically sampling combinations of influencing factors. Subsequently, the IBKA-SVR model was developed for energy consumption prediction and analysis. The model’s predictive accuracy and stability were validated by benchmarking it against six competing models, including GA-SVR, PSO-SVR, and the baseline SVR and so forth. Finally, to elucidate the model’s internal decision-making mechanism, the SHAP (SHapley Additive exPlanations) interpretability framework was employed to quantify the independent and interactive effects of each influencing factor on energy consumption. The results indicate that: (1) The IBKA demonstrates superior convergence accuracy and global search performance compared with BKA and other algorithms. (2) The proposed IBKA-SVR model exhibits exceptional predictive accuracy. Relative to the baseline SVR, the model reduces key error metrics by 37–40% and improves the R² to 0.9792. Furthermore, in a comparative analysis against models tuned by other metaheuristic algorithms such as GA and PSO, the IBKA-SVR consistently maintained optimal performance. (3) The SHAP analysis reveals a clear hierarchy in the impact of the design features. The Insulation Thickness in Outer Wall and Insulation Thickness in Roof Covering are the dominant factors, followed by the Window-wall Ratios of various orientations and the Sun space Depth. Key features predominantly exhibit a negative impact, and a significant non-linear relationship exists between the dominant factors (e.g., insulation layers) and the predicted values. (4) Interaction analysis reveals a distinct hierarchy of interaction strengths among the building design variables. Strong synergistic effects are observed among the Sun space Depth, Insulation Thickness in Roof Covering, and the Window-wall Ratios in the East, West, and North. In contrast, the interaction effects between the Window-wall Ratio in the South and other variables are generally weak, indicating that its influence is approximately independent and linear. Therefore, the proposed bio-inspired framework, integrating the improved IBKA with SVR, effectively predicts and analyzes residential building energy consumption, thereby providing a robust decision-support tool for the data-driven optimization of building design and retrofitting strategies to advance energy efficiency and sustainability in rural housing. Full article

The prediction of thin-bedded, favorable sand bodies within the Triassic Baikouquan Formation fan delta on the western slope of the Mahu Sag is challenging due to their strong spatial heterogeneity. To address this, we propose an integrated workflow that synergizes seismic sedimentology with geologically constrained seismic inversion. This study leverages well logging, core data, and 3D seismic surveys. Initially, seismic attribute analysis and stratal slicing were employed to delineate sedimentary microfacies, revealing that the fan delta front subfacies comprises subaqueous distributary channels, interdistributary bays, and distal bars. Subsequently, the planform distribution of these microfacies served as a critical constraint for the Seismic Waveform Indicative Inversion (SWII), effectively enhancing the resolution for thin sand body identification. The results demonstrate the following: (1). Two NW-SE trending subaqueous distributary channel systems, converging near the BAI65 well, form the primary reservoirs. (2). The SWII, optimized by our workflow, successfully predicts high-quality sand bodies with a cumulative area of 159.2 km², primarily located in the MAXI1, AIHU10, and AICAN1 well areas, as well as west of the MA18 well. This study highlights the value of integrating sedimentary facies boundaries as a geological constraint in seismic inversion, providing a more reliable method for predicting heterogeneous thin sand bodies and delineating future exploration targets in the Mahu Sag. Full article

To address the challenges of short dwell time near top dead center (TDC) and uneven heat release, this paper presents a comprehensive analysis of the effects of different ignition schemes on combustion characteristics, flame formation and development, and emissions. A three-dimensional model of coupled reaction’s kinetic mechanism was established using Converge 3.0 and validated by experimental data. The results show that ignition position, whether synchronous or asynchronous changes, significantly influence pressure. The pressure in synchronous cases can reach up to 62.5 bar, representing a 10.8% increase, exhibiting a distinct upward trend with advanced ignition position. In asynchronous cases, the pressure variation shows a distinct nonlinear characteristic due to the negative effects of in-cylinder airflow and flame core collision. When the ignition position is advanced, the ignition delay increases for both synchronous and asynchronous strategies. However, for synchronous cases, the combustion duration is reduced by up to 1.5 ms, whereas for asynchronous cases, the reduction is only 0.135 ms. Regardless of the schemes, the layout and the strong counterclockwise swirl lead to the flame core gradually developing from right to left, ultimately engulfing the left-side flame core. Compared then to that case, the left and right flame kernels may collide prematurely, leading to incomplete local combustion and consequently reducing combustion efficiency. Compared to synchronous changes, the emission differences during asynchronous changes are smaller and maintained at a relatively low level. Full article

With the large-scale deployment of 5G technology, the rationality of communication base station siting is crucial for network performance, construction costs, and operational efficiency. Traditional site selection methods rely heavily on manual experience, exhibiting strong subjectivity and difficulty in balancing multi-objective optimization. Existing heuristic algorithms suffer from slow convergence speeds and susceptibility to local optima. To address these challenges, this paper constructs a multi-objective base station site selection model that simultaneously minimizes costs, maximizes coverage contributions, and minimizes interference. It achieves quantitative balance among objectives through normalization and weight fusion, while introducing constraints to ensure engineering feasibility. Concurrently, the genetic algorithm underwent targeted optimization by introducing an adaptive migration strategy based on population diversity and a cosine-type parameter adjustment strategy. This approach was integrated with the particle swarm optimization algorithm to balance exploration and exploitation while mitigating premature convergence. Experimental validation demonstrates that the improved algorithm achieves faster convergence and greater stability compared to traditional genetic algorithms and particle swarm optimization, while satisfying engineering constraints such as base station quantity, coverage, and interference. This research provides an efficient and feasible solution for intelligent base station site planning. Full article

Mainlobe interference can severely degrade the performance of distributed phased-array radar systems in the presence of strong jamming or low-reflectivity targets. This paper introduces a signal–data dual-domain cooperative antijamming and localization (SDCAL) framework that integrates adaptive complete ensemble empirical mode decomposition with improved blind source separation and wavelet optimization (CEEMDAN-WOBSS) for signal-level denoising and separation. Following source separation, CFAR-based pulse compression is applied for precise range estimation, and multi-node data fusion is then used to achieve three-dimensional target localization. Under low signal-to-noise ratio (SNR) conditions, the adaptive CEEMDAN–WOBSS approach reconstructs the signal covariance matrix to preserve subspace rank, thereby accelerating convergence of the separation matrix. The subsequent pulse compression and CFAR detection steps provide reliable inter-node distance measurements for accurate fusion. The simulation results demonstrate that, compared to conventional blind-source-separation methods, the proposed framework markedly enhances interference suppression, detection probability, and localization accuracy—validating its effectiveness for robust collaborative sensing in challenging jamming scenarios. Full article

This study presents a novel and efficient iterative scheme in the setting of CAT(0) spaces and investigates the convergence properties for a generalized class of mappings satisfying the Garcia–Falset property using the proposed iterative scheme. Strong and weak convergence results are established in CAT(0) spaces, generalizing many existing results in the literature. Furthermore, we discuss the stability and data dependence of the new iterative process. Numerical experiments include an analysis of error values, the number of iterations, and computational time, providing a comprehensive assessment of the method’s performance. Moreover, graphical comparisons demonstrate the efficiency and reliability of the approach. The obtained results are utilized in solving integral equations. Additionally, the paper concludes with a polynomiographic study of the newly introduced iterative process, in comparison with standard algorithms, such as Newton, Halley, or Kalantari’s $B_4$ iteration, emphasizing symmetry properties. Full article

In this paper, we develop a fully discrete finite element scheme, based on a second-order backward differentiation formula (BDF2), for numerically solving the three-dimensional incompressible Navier–Stokes equations. Under the assumption that the fully discrete solution remains bounded in a certain norm, we establish that any smooth initial data necessarily gives rise to a unique strong solution that remains smooth. Moreover, we demonstrate that the fully discrete numerical solution converges strongly to this exact solution as the temporal and spatial discretization parameters approach zero. Full article

With rapid urbanization and industrialization in China, existing gas supply networks urgently require renewal and optimization. This paper proposes a Gray Wolf Optimizer (GWO)-based method for reducing calculation errors and a Zebra Optimization Algorithm (ZOA)-based approach for gas supply pressure distribution. For error correction, the pipe friction coefficient is adjusted to minimize the deviation between calculated and actual flows. The GWO reduces average relative error to 0.01% with satisfactory iteration speed and efficiency. For pressure distribution, supply-end pressures are optimized to reduce energy consumption and enhance system performance. The ZOA shows strong convergence and global search capabilities. These methods provide valuable theoretical and practical insights for optimizing gas supply networks, supporting green transformation and sustainable development. Full article

This paper presents a rigid–flexible coupling dynamic modeling framework and a fixed-time control strategy for a flexible space-tethered satellite (STS) system. A high-fidelity rigid–flexible coupling dynamic model of STS is developed using the finite element method, accurately capturing the coupled attitude dynamics of the satellite platform and flexible tether. Leveraging a simplified representation of the STS model, a nonsingular terminal sliding-mode controller (NTSMC) is synthesized via fixed-time stability theory. Uncertainties and disturbances within the system are compensated for by a radial basis function neural network (RBFNN), ensuring strong robustness. The controller’s fixed-time convergence property—with convergence time independent of initial conditions—is established using Lyapunov stability theory, enabling reliable operation in complex space environments. Numerical simulations implemented on the STS rigid–flexible coupling model validate the controller’s efficacy. Comparative analyses demonstrate superior tracking performance and enhanced practicality over conventional sliding-mode controllers, especially in the aspect of chattering suppression for the satellite thrusters. Full article

This study advances the unmanned aerial vehicle (UAV) localization technology within the framework of a low-altitude economy, with particular emphasis on the accurate and real-time identification and tracking of unauthorized (“black-flying”) drones. Conventional YOLOv8s-based target detection algorithms often suffer from missed detections due to their reliance on single-frame features. To address this limitation, this paper proposes an improved detection algorithm that integrates a long-short-term memory (LSTM) network into the YOLOv8s framework. By incorporating time-series modeling, the LSTM module enables the retention of historical features and dynamic prediction of UAV trajectories. The loss function combines bounding box regression loss with binary cross-entropy and is optimized using the Adam algorithm to enhance training convergence. The training data distribution is validated through Monte Carlo random sampling, which improves the model’s generalization to complex scenes. Simulation results demonstrate that the proposed method significantly enhances UAV detection performance. In addition, when deployed on the RK3588-based embedded system, the method achieves a low false negative rate and exhibits robust detection capabilities, indicating strong potential for practical applications in airspace management and counter-UAV operations. Full article

Information announcement is the process of propagating and synchronizing the information of Computing Resource Nodes (CRNs) within the system of the Computing Networks. Accurate and timely acquisition of information is crucial to ensuring the efficiency and quality of subsequent task scheduling. However, existing announcement mechanisms primarily focus on reducing communication overhead, often neglecting the direct impact of information freshness on scheduling accuracy and service quality. To address this issue, this paper proposes a hierarchical and clustering-based announcement mechanism for the wide-area Computing Networks. The mechanism first categorizes the Computing Network Nodes (CNNs) into different layers based on the type of CRNs they interconnect to, and a top-down cross-layer announcement strategy is introduced during this process; within each layer, CNNs are further divided into several domains according to the round-trip time (RTT) to each other; and in each domain, inspired by the “Six Degrees of Separation” concept from social propagation, a RTT-aware fast clustering algorithm canopy is employed to partition CNNs into multiple overlap clusters. Intra-cluster announcements are modeled as a Traveling Salesman Problem (TSP) and optimized to accelerate updates, while inter-cluster propagation leverages overlapping nodes for global dissemination. Experimental results demonstrate that, by exploiting shortest path optimization within clusters and overlapping-node-based inter-cluster transmission, the mechanism is significantly superior to the comparison scheme in key indicators such as convergence time, Age of Information (AoI), and communication data volume per hop. The mechanism exhibits strong scalability and adaptability in large-scale network environments, providing robust support for efficient and rapid information synchronization in the Computing Networks. Full article

Benford’s law states that in many real-world datasets, the probability that the leading digit is d equals ${log}_{10}((d+1)/d)$ for all $1\le d\le 9$. We call this weak Benford behavior. A dataset is said to follow strong Benford behavior if the probability that its significand (i.e., the significant digits in scientific notation) is at most s equals ${log}_{10}\left(s\right)$ for all $s\in [1,10)$. We investigate Benford behavior in a multi-proportion stick fragmentation model, where a stick is split into m substicks according to fixed proportions at each stage. This generalizes previous work on the single proportion stick fragmentation model, where each stick is split into two substicks using one fixed proportion. We provide a necessary and sufficient condition under which the lengths of the stick fragments converge to strong Benford behavior in the multi-proportion model. Full article

In the context of public space research, numerous studies highlight its vital role in fostering public life and social interaction. With urbanization on the rise and most people living in cities, acknowledging public spaces, and especially public squares, as key components of the urban realm is more important than ever. The success of space is frequently determined by its capacity to meet human needs, a condition that, in turn, is largely contingent upon specific design qualities. Literature identifies key qualities such as inclusiveness, accessibility and connectivity, sociability, vitality, perceptual and esthetic satisfaction, and participatory characteristics. While many studies explore these factors, little attention has been given to whether users and designers assign equal importance to them. This research addresses the question: To what extent do experts’ and users’ perceptions converge regarding the variables that determine the success of public spaces? To explore this, the study applies MICMAC method structural analysis that prioritizes variables based on their interdependence and dependence. The method is used with both design experts and public space users. Findings reveal convergence in perceptions regarding key parameters; specifically, strong convergence is observed in the qualities of participation and vitality, followed by sociability and perceptual and esthetic satisfaction. Moreover, the expert group prioritizes parameters related to sociability, accessibility and connectivity, and inclusiveness, reflecting contemporary design principles aimed at creating equitable, easily accessible, and inclusive spaces. In contrast, the user group focuses more on the experiential and esthetic dimension of space, adding variables related to perceptual and esthetic satisfaction and vitality. The study aims to inform more user-responsive public space design by bridging gaps between expert and user perspectives. Full article

Introduction: Exposure to violence is an adverse experience associated with the perpetration of violent future behaviors such as child-to-parent violence. Objective: The objectives were to analyze the psychometric properties of the Violence Exposure Scale (VES) in a sample of Ecuadorian adolescents as well as its measurement invariance by sex and age; analyze the differences in exposure to violence across four settings (home, school, street, and TV), in two time frames (last year and childhood), according to sex and age; and analyze the relationship between exposure to violence in the four settings and in both time frames with child-to-parent violence. Methods: A cross-sectional study was conducted using a probabilistic sample of 2150 Ecuadorian adolescents (55% female), aged 12 to 18 years (M = 14.53; SD = 1.55). Participants completed the adapted version of the VES and the Child-to-Parent Violence Questionnaire (CPV-Q). Confirmatory factor analyses, reliability testing, convergent and discriminant validity analyses, and measurement invariance assessments were performed. Results: The VES showed excellent model fit in both versions, VES¹ (last year) and VES² (before age 10), with strong goodness-of-fit indices (VES¹: CFI = 0.988; RMSEA = 0.055; VES²: CFI = 0.994; RMSEA = 0.044). Reliability was good (α_o and ω_o ≤ 0.80; G.6 and CR ≤ 0.70). Effect sizes ranged from 0.11 to 0.31 for violence by children toward parents and reached up to 0.83 among the different forms of victimization. Conclusions: The adaptation of the VES in Ecuadorian adolescents showed validity and reliability in assessing exposure to violence. Girls were more at risk at home, while boys were more exposed at school and in the community. Full article