Search Results (2,875)

Traditional information distillation networks using single-scale convolution and simple feature fusion often result in insufficient information extraction and ineffective restoration of high-frequency details. To address this problem, we propose a lightweight image super-resolution reconstruction network based on multi-order information optimization. The core of this network lies in the enhancement and refinement of high-frequency information. Our method operates through two main stages to fully exploit the high-frequency features in images while eliminating redundant information, thereby enhancing the network’s detail restoration capability. In the high-frequency information enhancement stage, we design a self-calibration high-frequency information enhancement block. This block generates calibration weights through self-calibration branches to modulate the response strength of each pixel. It then selectively enhances critical high-frequency information. Additionally, we combine an auxiliary branch and a chunked space optimization strategy to extract local details and adaptively reinforce high-frequency features. In the high-frequency information refinement stage, we propose a multi-scale high-frequency information refinement block. First, multi-scale information is captured through multiplicity sampling to enrich the feature hierarchy. Second, the high-frequency information is further refined using a multi-branch structure incorporating wavelet convolution and band convolution, enabling the extraction of diverse detailed features. Experimental results demonstrate that our network achieves an optimal balance between complexity and performance, outperforming popular lightweight networks in both quantitative metrics and visual quality. Full article

As contemporary society and the global economy become increasingly dependent on satellite-based systems, the need for reliable and resilient positioning, navigation, and timing (PNT) services has never been more critical. This study investigates the impact of the geomagnetic storm that occurred in May 2024 on the performance of global navigation satellite system (GNSS) low-cost permanent stations. The research evaluates the influence of ionospheric disturbances on both positioning performance and raw GNSS observations. Two days were analyzed: 8 May 2024 (DOY 129), representing quiet ionospheric conditions, and 11 May 2024 (DOY 132), coinciding with the peak of the geomagnetic storm. Precise Point Positioning (PPP) and static relative positioning techniques were applied to data from a low-cost GNSS station (DYVA), supported by comparative analysis using a nearby geodetic-grade station (TRDS00NOR). The results showed that while RMS positioning errors remained relatively stable over 24 h, the maximum errors increased significantly during the storm, with the 3D positioning error nearly doubling on DOY 132. Short-term analysis revealed even larger disturbances, particularly in the vertical component, which reached up to 3.39 m. Relative positioning analysis confirmed the vulnerability of single-frequency (L1) solutions to ionospheric disturbances, whereas dual-frequency (L1+L2) configurations substantially mitigated errors, highlighting the effectiveness of ionosphere-free combinations during storm events. In the second phase, raw GNSS observation quality was assessed using detrended GPS L1 carrier-phase residuals and signal strength metrics. The analysis revealed increased phase instability and signal degradation on DOY 132, with visible cycle slips occurring between epochs 19 and 21. Furthermore, the average signal-to-noise ratio (SNR) decreased by approximately 13% for satellites in the northwest sky sector, and a 5% rise in total cycle slips was recorded compared with the quiet day. These indicators confirm the elevated measurement noise and signal disruption associated with geomagnetic activity. These findings provide a quantitative assessment of low-cost GNSS receiver performance under geomagnetic storm conditions. This study emphasizes their utility for densifying GNSS infrastructure, particularly in regions lacking access to geodetic-grade equipment, while also outlining the challenges posed by space weather. Full article

Conformal mappings between Riemannian spaces ${\overline{\mathbb{R}}}_N$ and ${\mathbb{R}}_N$ are defined by the explicit transformation of the metric tensor of the space ${\overline{\mathbb{R}}}_N$ to the metric tensor of the space ${\mathbb{R}}_N$. Geodesic mapping between these two Riemannian spaces is a transformation that transforms any geodesic line of the space ${\overline{\mathbb{R}}}_N$ to a geodesic line of the space ${\mathbb{R}}_N$. In this research, we defined an m-conformal line of a Riemannian space, which is geodesic if $m=0$. Based on this definition, we involved the concept of $(\overline{m},m)$-conformal mapping as a transformation ${\overline{\mathbb{R}}}_N\to {\mathbb{R}}_N$ in which any $\overline{m}$-conformal line of the space ${\overline{\mathbb{R}}}_N$ transforms to an m-conformal line of the space ${\mathbb{R}}_N$. The result of this research is the establishment of three invariants for these mappings. At the end of this research, we gave an example of a scalar geometrical object which may be used in physics. Full article

Device-to-device (D2D) communication enhances network efficiency by enabling direct, low-latency links between nearby users or devices. While most existing research assumes that D2D connections occur with the nearest neighbor, this assumption often fails in real-world scenarios—such as dense indoor environments, smart buildings, and industrial IoT deployments—due to factors like channel variability, physical obstructions, or limited user participation. In this paper, we investigate the performance implications of connecting to the n-th nearest neighbor in a cellular network supporting underlay D2D communication. Using a stochastic geometry framework, we derive and analyze key performance metrics, including the coverage probability and average data rate, for both D2D and cellular links under proximity-aware connection strategies. Our results reveal that non-nearest-neighbor associations are not only common but sometimes necessary for maintaining reliable connectivity in highly dense or constrained spaces. These findings are directly relevant to IoT-enhanced localization systems, where fallback mechanisms and adaptive pairing are essential for communication resilience. This work contributes to the development of proximity-aware and spatially adaptive D2D frameworks for next-generation smart environments and 5G-and-beyond wireless networks. Full article

This research paper delves into the application of optimality results in orthogonal fuzzy metric spaces to demonstrate the existence and uniqueness of solutions of nonlinear differential equations with boundary conditions and nonlinear integral equations, emphasizing the importance of orthogonal fuzzy metric spaces in extending fixed-point theory. Through introducing this innovative concept, the study provides a theoretical framework for analyzing mappings in diverse scenarios. In this study, we introduce the concept of best proximity point (BPP) within the framework of orthogonal fuzzy metric spaces by employing orthogonal fuzzy proximal contractive mappings. Moreover, this research explores the implications of the established results, considering both self-mappings and non-self mappings that share the same parameter set. Additionally, some examples are provided to illustrate the practical relevance of the proven results and consequences in various mathematical contexts. The findings of this study can open up avenues for further exploration and application in solving real-world problems. Full article

As a convolutional operation in a space domain, Gaussian filtering involves a large number of computational operations, a number that increases when the sizes of images and the kernel size also increase. Thus, finding methods to accelerate such computations is significant for overall time complexity enhancement, and the current paper proposes the use of partial sums to achieve this acceleration. The MPI (Message Passing Interface) library and the C programming language are used for the parallel program implementation of Gaussian filtering, based on a 1D kernel and 2D kernel working with and without the use of partial sums, and then a theoretical and practical evaluation of the effectiveness of the proposed implementations is made. The experimental results indicate a significant acceleration of the computational process when partial sums are used in both sequential and parallel processing. A PSNR (Peak Signal to Noise Ratio) metric is used to assess the quality of filtering for the proposed algorithms in comparison with the MATLAB implementation of Gaussian filtering, and time performance for the proposed algorithms is also evaluated. Full article

To improve the dynamic performance of parallel robots in multi-dimensional space, a novel trajectory planning method of space discretization for parallel robots is proposed. First, the kinematic model of the 5PUS-RPUR parallel robot is established. Then, the normalized Jacobian condition number is obtained via the variable weighting matrix method, and is used as the performance metric of path optimization. The weighted sum method is utilized to construct a composite objective function for the trajectory that incorporates travel time and acceleration fluctuations. Next, the position space between the start and end points is discretized, and the robot pose space based on the position points is analyzed via the search method. The discrete pose point weights are assigned according to the condition number. Dijkstra’s algorithm is used to find the path with the minimum condition number. The trajectory optimization model is established by fitting the discrete path with a B-spline curve and optimized via genetic algorithm. Finally, comparative numerical simulations validate the proposed method, which reduces actuator RMS displacement difference by up to 32.9% and acceleration fluctuation by up to 25.6% against state-of-the-art techniques, yielding superior motion smoothness and dynamic stability. Full article

Biodiversity in arid river basins is highly climate-sensitive, yet the multi-pathway relations among the environment, landscape structure, connectivity, and plant diversity remain unclear. Framed by a scale–place–space sustainability perspective, we evaluated, in the Hotan River Basin (NW China), how the environmental factors affect plant diversity directly and indirectly via the landscape configuration and functional connectivity. We integrated Landsat images (2000, 2012, and 2023), 57 vegetation plots, topographic and meteorological data; computed the landscape indices and Conefor connectivity metrics (PC, IIC); and fitted a partial least squares structural equation model (PLS-SEM). From 2000 to 2023, the bare land declined, converted mainly into shrubland and cropland; the construction land is projected to expand under SSP1-2.6/SSP2-4.5/SSP5-8.5 by 2035 and 2050. The landscape metrics showed a rising PD, DIVISION, and SHDI/SHEI, and a declining AI and CONTAG, indicating finer, more heterogeneous mosaics. Plant diversity peaked on low–moderate slopes and with ~32–36 mm annual precipitation. The PLS-SEM revealed significant direct effects on diversity from environmental factors (positive), landscape structure (negative), and connectivity (positive). The dominant chained mediation (environment → structure → connectivity → diversity) indicated that environmental constraints first reconfigure the spatial structure and then propagate to community responses via connectivity, highlighting connectivity’s role in buffering climatic stress and stabilizing communities. The findings provide a quantitative framework to inform biodiversity conservation and sustainable landscape planning in arid basins. Full article

While technologies like renewable energy and low-carbon transportation are known to mitigate carbon emissions from urban expansion, achieving carbon neutrality during this process remains a critical unresolved challenge. This issue is particularly pressing for developing countries striving to balance urbanization with carbon reduction. Taking Qionglai City as a case study, this study simulated the territorial spatial functional patterns (TSFPs) and carbon emission distribution for 2025 and 2030. Based on the key drivers of carbon emissions from urban expansion identified through the Geographical and Temporal Weighted Regression (GTWR) model, carbon-neutral pathways were designed for two scenarios: urban expansion scenarios under historical evolution patterns (Scenario I) and urban expansion scenarios optimized under carbon neutrality targets (Scenario II). The results indicate that (1) urban space is projected to expand from 6094.73 hm² in 2020 to 6249.77 hm² in 2025 and 6385.75 hm² in 2030; (2) total carbon emissions are forecasted to reach 1.25 × 10⁶ t (metric tons) and 1.40 × 10⁶ t in 2025 and 2030, respectively, exhibiting a spatial pattern of “high in the central-eastern regions, low in the west”; (3) GDP, Net Primary Productivity (NPP), and the number of fuel vehicles are the dominant drivers of carbon emissions from urban expansion; and (4) a four-pronged strategy, optimizing urban green space vegetation types, replacing fuel vehicles with new energy vehicles, controlling carbon emissions per GDP, and purchasing carbon credits, proves effective. Scenario II presents the optimal pathway: carbon neutrality in the expansion zone can be achieved by 2025 using the first three measures (e.g., optimizing 66.73 hm² of green space, replacing 800 fuel vehicles, and maintaining emissions at 0.21 t/10⁴ CNY per GDP). By 2030, carbon neutrality can be achieved by implementing all four measures (e.g., optimizing 67.57 hm² of green space, replacing 1470 fuel vehicles, and achieving 0.15 t/10⁴ CNY per GDP). This study provides a methodological basis for local governments to promote low-carbon urban development and offers practical insights for developing nations to reconcile urban expansion with carbon neutrality goals. Full article

Forest aboveground biomass (AGB) products serve as essential references for research on carbon cycle and climate change. However, significant uncertainties exist regarding forest AGB products and their evaluation methods. This study aims to evaluate AGB products in the context of discrepancies in plot size and product scales, while also investigate the applicability of large-scale AGB products at a regional level. The National Aeronautics and Space Administration (NASA)’s Global Ecosystem Dynamics Investigation (GEDI) and the European Space Agency (ESA)’s Climate Change Initiative (CCI) biomass data were evaluated using sample plots from the National Forest Inventory (NFI). The study was conducted in Jilin Province, located in Northeast China, which is predominantly covered by natural forests. Spatial representativeness evaluation indicators for sample plots were established, followed by a comprehensive representativeness assessment and the selection of sample plots based on the criteria importance through the intercriteria correlation (CRITIC) method. Additionally, the study conducted an overall evaluation of the products, as well as evaluations across different biomass ranges and various forest types. The results indicate that the accuracy metrics demonstrated improved performance when using representative plots compared to all plots, with the R² increasing by 15.38%. Both products demonstrated optimal accuracy and stability in the 50–150 Mg/ha range. GEDI and CCI biomass data indicated an overall underestimation, with biases of −25.68 Mg/ha and −83.95 Mg/ha, respectively. Specifically, a slight overestimation occurred in the <50 Mg/ha range, while a gradually increasing underestimation was observed in the ≥50 Mg/ha range. This study highlights the advantages of spatial representativeness analysis in mitigating evaluation uncertainties arising from scale mismatches and enhancing the reliability of product evaluation. The accuracy trends of AGB products offer significant insights that could facilitate improvements and enhance their application. Full article

The Sparrow Search Algorithm (SSA), a metaheuristic renowned for rapid convergence, good stability, and high search accuracy in continuous optimization, faces inherent limitations when applied to discrete multi-variable combinatorial optimization problems like feature selection. To enable effective multi-variable evaluation and discrete feature subset selection using SSA, a novel binary variant, Position Binarization-based Sparrow Search Algorithm (BSSA), is proposed. BSSA employs a sigmoid transformation function to convert the continuous position vectors generated by the standard SSA into binary solutions, representing feature inclusion or exclusion. Recognizing that the inherent exploitation bias of SSA and the complexity of high-dimensional feature spaces can lead to premature convergence and suboptimal solutions, we further enhance BSSA by introducing stochastic Gaussian noise (zero mean) into the sigmoid transformation. This strategic perturbation actively diversifies the search population, improves exploration capability, and bolsters the algorithm’s robustness against local optima stagnation during multi-variable evaluation. The fitness of each candidate feature subset (solution) is evaluated using the classification accuracy of a Support Vector Machine (SVM) classifier. The BSSA algorithm is compared with four high-performance optimization algorithms on 12 diverse benchmark datasets selected from the UCI repository, utilizing multiple performance metrics. Experimental results demonstrate that BSSA achieves superior performance in classification accuracy, computational efficiency, and optimal feature selection, significantly advancing multi-variable evaluation for feature selection tasks. Full article

Background: Traditional molecular optimization methods struggle with high data dependency and significant computational demands. Additionally, conventional genetic algorithms often produce solutions with high similarity, leading to potential local optima and reduced molecular diversity, thereby limiting the exploration of chemical space. Methods: In order to address the above issues, this paper proposes an improved genetic algorithm for multi-objective drug molecular optimization (MoGA-TA). It uses the Tanimoto similarity-based crowding distance calculation and a dynamic acceptance probability population update strategy. The study employs a decoupled crossover and mutation strategy within chemical space for molecular optimization. The proposed crowding distance calculation method better captures molecular structural differences, enhancing search space exploration, maintaining population diversity, and preventing premature convergence. The dynamic acceptance probability strategy balances exploration and exploitation during evolution. Optimization continues until a predefined stopping condition is met. To assess MoGA-TA’s effectiveness, the algorithm is evaluated using metrics like success rate, dominating hypervolume, geometric mean, and internal similarity. Results: Experimental results show that compared to the comparative method, MoGA-TA performs better in drug molecule optimization and significantly improves the efficiency and success rate. Conclusions: The method described in this paper has been proven to be an effective and reliable method for multi-objective molecular optimization tasks. Full article

The COVID-19 pandemic highlighted the need for a scientific framework that enables quantitative assessment and control of airborne infection risks in indoor environments. This study identifies limitations in the traditional Wells–Riley model—specifically its assumptions of perfect mixing and steady-state conditions—and addresses these shortcomings by adopting the REHVA (Federation of European Heating, Ventilation and Air Conditioning Associations) infection risk assessment model. We propose a five-tier risk classification system (Monitor, Caution, Alert, High Risk, Critical) based on two key metrics: the probability of infection (Pₙ) and the event reproduction number (R_event). Unlike the classical model, our approach integrates airborne virus removal mechanisms—such as natural decay, gravitational settling, and filtration—with occupant dynamics to reflect realistic contagion scenarios. Simulations were conducted across 10 representative indoor settings—such as classrooms, hospital waiting rooms, public transit, and restaurants—considering ventilation rates and activity-specific viral emission patterns. The results quantify how environmental variables (ventilation, occupancy, time) impact each setting’s infection risk level. Our findings indicate that static mitigation measures such as mask-wearing or physical distancing are insufficient without dynamic, model-based risk evaluation. We emphasize the importance of incorporating real-time crowd density, occupancy duration, and movement trajectories into risk scoring. To support this, we propose integrating computer vision (CCTV-based crowd detection) and entry/exit counting sensors within a live airborne risk assessment framework. This integrated system would enable proactive, science-driven epidemic control strategies, supporting real-time adaptive interventions in indoor spaces. The proposed platform could serve as a practical tool for early warning and management during future airborne disease outbreaks. Full article

With the growing adoption of connected and autonomous vehicles (CAVs), their market penetration is expected to rise. This study investigates the mixed traffic flow dynamics of human-driven vehicles (HDVs) and CAVs at airport terminal curbsides. A two-lane parking simulation model is developed, integrating the intelligent driver model, PATH-calibrated cooperative adaptive cruise control, and a degraded adaptive cruise control model to capture different driving behaviors. The model accounts for varying time headways among HDV drivers based on their information acceptance levels and imposes departure constraints to enhance safety. Simulation results show that the addition of CAVs can significantly increase the average speed of vehicles and reduce the average delay time. Two metrics are inversely proportional. Specifically, as illustrated by a curbside length of 400 m and a parking demand of 1300 pcph, when the CAV penetration rate p is 10%, 30%, 50%, 70%, and 100%, respectively, compared to p = 0, the average traffic flow speed increases by 1.7%, 6.4%, 15.0%, 27.2%, and 48.7%, respectively. The average delay time decreases by 2.8%, 6.4%, 10.5%, 13.5%, and 20.0%, respectively. Meanwhile, CAVs and HDVs exhibit consistent patterns in terms of parking space utilization: the first stage (0–30% of parking spaces) showed a stable and concentrated trend; the second stage (30–70% of parking spaces) showed a slow downward trend but remained at a high level; the third stage (70–100% of parking spaces) showed a rapid decline at a steady rate. Full article

We explore the sparsity and prunability of multi-layer perceptrons (MLPs) trained using the Forward-Forward (FF) algorithm, an alternative to backpropagation (BP) that replaces the backward pass with local, contrastive updates at each layer. We analyze the sparsity of the weight matrices during training using multiple metrics, and test the prunability of FF networks on the MNIST, FashionMNIST and CIFAR-10 datasets. We also propose FFLib—a novel, modular PyTorch-based library for developing, training and analyzing FF models along with a suite of FF-based architectures, including FFNN, FFNN+C and FFRNN. In addition to structural sparsity, we describe and apply a new method for visualizing the functional sparsity of neural activations across different architectures using the HSV color space. Moreover, we conduct a sensitivity analysis to assess the impact of hyperparameters on model performance and sparsity. Finally, we perform pruning experiments, showing that simple FF-based MLPs exhibit significantly greater robustness to one-shot neuron pruning than traditional BP-trained networks, and a possible 8-fold increase in compression ratios while maintaining comparable accuracy on the MNIST dataset. Full article