Search Results (33,070)

Blue-green infrastructure in high-density communities has been found to be vital to the well-being of urban residents, particularly in 15 min walkable communities. However, residents’ environmental preferences for blue-green infrastructure in high-density urban areas have received little attention. This study uses a walking interview method with 90 participants to explore residents’ motivations, activities and preferences in both community and riverside green spaces. The study area centers on the Licun River and surrounding communities within a 15 min walking distance of the river in Qingdao, China, a high-density city promoting 15 min walkable communities. The findings showed that relaxation was the main reason for visiting both types of spaces. Riverside green spaces supported a wider variety of activities but notable differences in preferences for particular spaces, particularly across gender and age groups. Within community green spaces, artificial elements had a stronger impact on preferences, whereas in riverside green spaces, natural elements were more influential. Blue-green infrastructure planning in high-density cities should then consider diverse user needs by accounting for demographic differences and adapting design elements to various spatial contexts. Since a 15 min walk is not feasible for all residents, enhancing the safety, walkability and inclusivity of blue-green infrastructure is essential for everyday use. Full article

Accurately identifying an urban functional zone (UFZ) is crucial for rationally allocating urban land resources and optimizing urban spatial structure. Existing research based on Points of Interest (POIs) mostly uses the relationship between the number of various types of POIs as the basis for identification. However, this approach neglects the difference of physical surface property of urban functional zones—imperviousness. Based on the FD-CR method, this study proposes the RFD-ECR identification method by combining TF-IDF and ISI. This study divides research units according to OpenStreetMap (OSM), and reclassifies POI data. It then uses the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm to highlight the dominant function of study units and incorporates the impervious surface index (ISI) as a correction to recognize urban functional zones. Experiments conducted in the central urban area of Chengdu demonstrate that this method is effective in identifying urban functional zones, achieving an accuracy rate of 80.21%. Comparison with the Frequency Density-Category Ratio (FD-CR) method reveals that this method, through the TF-IDF algorithm and the impervious surface index constraint, effectively improves the classification accuracy of mixed commercial UFZs. This method broadens the scope of research on urban functional zone identification based on POI data, and also provides a valuable reference for other cities undertaking functional zone identification. Full article

(1) Background: This study investigates the development of spatial navigation and long-term memory in the leopard gecko (Eublepharis macularius) to address gaps in understanding reptilian cognitive ontogeny. We aimed to determine if early-life training enhances long-term memory retention and to evaluate the repeatability of individual cognitive performance over time. (2) Methods: Using a modified Morris Water Maze with visual landmarks, we tested 39 individuals across three life stages: juveniles (20 trials), subadults, and adults (10 trials in each later phase). Long-term memory retention was assessed after four and fourteen months. (3) Results: A strong learning effect was observed during the juvenile stage, with geckos significantly improving speed and navigational efficiency. Spatial memory remained stable at the subadult stage (four months post-training), but declined significantly by adulthood (fourteen months post-training), returning to baseline levels. Individual success rates were significantly repeatable during juvenile (R = 0.192) and subadult phases (R = 0.071), although this consistency disappeared in adulthood. (4) Conclusions: These findings indicate that leopard geckos possess substantial spatial learning abilities early in life and exhibit individual cognitive differences. However, spatial memory decays over time without reinforcement. The results highlight the importance of considering developmental stages when evaluating the evolutionary and ecological constraints of reptilian cognition. Full article

Brain–machine interfaces (BMIs) aim to decode motor intentions from neural activity to enable direct control of external devices. However, most existing decoders rely on monolithic architectures that fail to capture the distinct neural representations of different joint movement directions, limiting their generalizability. In this work, we propose a Single-Direction CNN-LSTM decoder inspired by motor cortex encoding mechanisms, which separately models extension and flexion dynamics through parallel CNN-LSTM branches. Each branch extracts spatial–temporal features from neural spike data and predicts directional joint variables, which are then combined by subtraction to yield the net angular velocity and torque of upper-limb joints. Using invasive recordings from a macaque during a 2D center-out reaching task, we demonstrate that our decoder achieves comparable performance to a conventional CNN-LSTM when trained on all tasks, while significantly outperforming both CNN-LSTM and linear regression baselines in cross-target generalization scenarios. Moreover, the model can capture physiologically meaningful co-contraction patterns, providing richer insights into motor control. These results suggest that incorporating neuroscience-inspired modular decoding into deep neural architectures enhances robustness and adaptability across tasks, offering a promising pathway for BMI applications in prosthetics and rehabilitation. Full article

This paper presents a quantum system designed to generate random, phase-manipulated emissions. A key feature of the proposed system is its ability to create a controllable electromagnetic center. To achieve this, the architecture utilizes two synchronized sources positioned at distinct spatial locations. A method is introduced where Quantum-generated keys are used to form a random sequence in real time to control digital phase manipulators. A block diagram of a quantum system for generating random phase-manipulated emissions with a controllable electromagnetic center has been developed that enables control of the main operating frequency, the length of the additionally generated random sequences controlling the modulations, the frequencies and phases of the emissions, the period and start of phase manipulations, as well as the power of the signals emitted by each of the channels. This way ensures uniformity or a controllable difference in the signals emitted by the two sources of the system upon their arrival at a predetermined point in space. A laboratory prototype of the quantum system has been developed, and tests have been conducted to confirm the feasibility of the proposed method and block diagram. The proposed research refers to a case of phase manipulation of transmitted signals with a preset clock frequency. The theoretical and technical solutions presented in the material can also be used to create systems with randomly frequency-manipulated signals, as well as systems in which the manipulation periods change randomly, determined by random quantum keys generated in real time. Full article

CNNs can effectively extract features with low computational costs, achieving significant progress in hyperspectral image classification. However, due to the limited receptive field of CNNs, they have difficulty in capturing the multi-scale structural and global contextual information. Moreover, the class imbalance in hyperspectral images often causes the model to focus disproportionately on certain spectral bands, thereby reducing the average accuracy. To address these challenges, a method called the Cascaded Spatial-Frequency Convolutional Network (CSFCNet) was proposed for hyperspectral image classification. It integrates rich spatial-domain information and frequency-domain information by jointly modeling both domains. Specifically, a Dual Spatial Fourier Convolution (DSF-Conv) module was proposed to project feature maps into parallel spatial and frequency representations. In the Spatial pathway, input features are grouped and processed with multi-scale convolutions to extract hierarchical structures; in the Fourier pathway, frequency-domain convolutions can aggregate the global context. Subsequently, a group-cascaded structure connects the DSF-Conv modules with residual connections, alleviating the class imbalance problem by promoting more balanced contributions from different spectral components. Additionally, we introduce a Lightweight Local Attention module to enhance the feature discrimination. Furthermore, experiments on three datasets achieved competitive accuracies, demonstrating the effectiveness of CSFCNet. Ablation studies further verify the effectiveness of the core components within the network. Full article

Regional sustainable development requires integrated assessments that capture ecosystem function, risk exposure, and recovery capacity. Conventional two-dimensional frameworks based on ecosystem services (ESs) and landscape ecological risk (ERI) often overlook the self-regulation potential of ecosystems following disturbance. This study proposes that incorporating RES as a third zoning dimension enables functional differentiation between areas that share similar ES–ERI profiles but differ substantially in recovery capacity, thereby revealing management priorities that a conventional two-dimensional framework cannot detect. This study develops a three-dimensional zoning framework integrating ES, ERI, and ecological resilience (RES) in the main urban area of Ganzhou City, a representative hilly city in southern China. Land-use dynamics from 1990 to 2020 and under four 2050 scenarios were simulated using a coupled PLUS-InVEST approach. Differentiated ecological zones were delineated, and the optimal-parameter geographic detector (OPGD) was applied to examine driving factor interactions. Results indicate that cultivated land and forestland dominated the study area throughout the period. ES supply remained favorable with stage-wise fluctuations, while ERI showed progressive convergence of high-risk patches toward the central basin. RES exhibited a sharp decline in higher-resilience areas during 1990–2000 (91.0%), followed by partial recovery during 2010–2020 (47.3%). The three-dimensional zoning delineated 35.9% of the area as Ecological control zones that may require priority intervention. Driver analysis revealed that DEM, precipitation, and river proximity, along with their interactions, strongly influenced regional ecological patterns. The proposed framework extends conventional ES-ERI assessments and provides spatial guidance for differentiated ecological management in hilly regions. Full article

Aiming to address the problem of extracting the remote sensing FTIR spectral characteristics of the hot jet of a certain type of aero-engine under different working conditions, this paper proposes a feature construction algorithm for the remote sensing FTIR spectral characteristics of the aero-engine hot jet based on the fusion of the original spectral features and the deep spectral features. The infrared spectrum was collected at a distance of 280 m, covering the spectral range of 2.5–15 μm with a resolution of 1 cm⁻¹. The Neighborhood–Autoencoder Integration Dual-Branch Network (NAIDN) feature construction algorithm is proposed. This algorithm contains a neighborhood integration branch and an autoencoder branch. The neighborhood integration branch converts the radiation intensity values of discrete wavenumber points into local energy aggregation features through a sliding window, accurately extracting the key physical information in the original spectrum. The autoencoder branch uses a three-layer fully connected neural network architecture to mine the deep spectral features of the spectral data. The algorithms of the two branches not only retain the physical interpretability of spectral analysis but also capture the multi-parameter coupling information hidden in the hot jet spectrum through the representation learning ability of the autoencoder, achieving feature fusion across spatial dimensions. Compared with traditional feature construction algorithms, the dual-branch feature construction algorithm proposed in this paper has stronger comprehensive representation capabilities. The content of carbon dioxide (CO₂) and cyanide groups (-C≡N) in the hot jet under different operating conditions varies significantly. In the experiment, an unsupervised clustering algorithm, the Agglomerative Clustering classifier, is selected, and the classification accuracy of the features extracted by the algorithm in this paper reaches 92.97% on this classifier, thereby verifying the effectiveness of the algorithm in this paper. Full article

The precise identification of terminal area arrival procedures is crucial for airspace planning, traffic management, and safety analysis. Traditional methods are limited in automatically detecting specific procedural maneuvers from large amounts of trajectory data. This paper proposes a methodology with knowledge-based filtering to automatically identify three common air traffic control arrival procedures, namely Point Merge System, Vector for Space, and Trombone, from historical trajectory data. After clustering the landing trajectories in the terminal area, we identify the predominant flight patterns. Then, a knowledge-based filtering algorithm, designed based on knowledge of the procedure and geometry criteria, is employed to precisely extract trajectories with different procedure patterns. Experimental results demonstrate that this method effectively identifies the distinct procedural trajectories. An in-depth analysis of the extracted trajectories reveals significant characteristics and differences in their spatial distribution, trajectory structure, and operational efficiency. This work provides data-driven decision support for evaluating terminal area operational performance and arrival procedures. Full article

This article proposes a novel time-space two-grid high-order compact difference scheme for the one-dimensional nonlinear Schrödinger equation subject to Dirichlet boundary conditions. In comparison with the fully nonlinear compact difference scheme, the proposed methodology combines a small-scale nonlinear fourth-order compact difference algorithm on a time-space coarse grid and a large-scale linearized correction compact difference algorithm on a fine grid. In contrast to the time two-grid compact difference method, the proposed scheme applies the two-grid technique in both the spatial and temporal domains, thereby further improving computational efficiency. Solutions from the coarse grid are projected onto the fine grid via a temporally linear and spatially cubic Lagrange interpolation operator. Unconditional stability and optimal convergence rates, which are fourth-order in space and second-order in time, are proven in both the discrete $L^2$ and $L^{\infty }$ norms, without any constraints on the grid ratio. In addition to the standard techniques of the energy method, a discrete Sobolev inequality and an a priori error estimate are employed to demonstrate stability and high-order convergence. Finally, the theoretical results are validated through numerical experiments, which confirm the robustness and reliability of the proposed approach. A single-soliton experiment demonstrates that, compared with the fully nonlinear compact difference scheme, the proposed method achieves a significant reduction in CPU time while maintaining a comparable level of accuracy. Additional experiments further illustrate the algorithm’s effectiveness in simulating two-soliton interactions and soliton birth. These findings establish the proposed scheme as a highly efficient alternative to conventional nonlinear approaches. Full article

The daily activity patterns of wild animal species are driven by environmental conditions and plant productivity although the degree of dependence varies according to their ecological niche. Bear ecology is intrinsically linked to seasonal vegetative availability. As omnivores with high metabolic demands, these species rely heavily on botanical resources including fruits, seeds, and roots. Consequently, differences in primary productivity across the landscape influence how individuals distribute their circadian activity patterns. The Enhanced Vegetation Index (EVI) is a tool that quantifies the quality and vigor of vegetation. Relating the EVI to activity patterns allows us to understand how vegetation dynamics and conditions influence the use of time at different times of the day. This study analyzes the daily activity pattern of the American black bear (Ursus americanus) in the El Cielo Biosphere Reserve (ECBR) using camera traps and its association with spatial variations in the Enhanced Vegetation Index (EVI). The results show that the daily activity pattern of the American black bear in the ECBR exhibits a diurnal–crepuscular tendency. In areas with high primary productivity and higher temperatures, activity occurs before sunrise and at sunset, with low activity during the rest of the day. In contrast, in areas with less vegetation and lower temperatures, activity occurs throughout the day. This suggests that, in the ECBR, the activity pattern of black bears could be modulated by temperature variations related to changes in vegetation productivity. Full article

This study proposes an integrated analytical framework (IAF) as a tool to simultaneously assess vulnerable social groups within their administrative context. This study hypothesizes that analyzing vulnerable groups through socio-spatial delineation reveals subnational disparities and sub-regional heterogeneity in energy poverty (EP) indicators, associated with additional context-sensitive environmental consequences of energy use. Using Hungarian deprived rural settlements (DRSs) (n = 300) as an example, mixed methods were applied to examine national–regional disparities, intra-regional variations, and the environmental implications of extreme household energy use practices. Results show that both socio-economic indicators and building energy efficiency, and energy-use profiles, fall short of national indicator performance. The sample outlined by the IAF performed homogeneously regarding socio-economic circumstances and showed mild differences in housing quality and energy access. These results indicate not structural differences but variation in underlying regional drivers, highlighting the region-specific manifestation of EP. The energy-use-related environmental assessment was performed using a parametrized building-stock model and the two most extreme energy-use scenarios for households relying on solid fuels. The results suggest that the use of substitute fuels substantially increases the combined emissions of CO₂, CO, PM, NOx, and SOx by up to 32 percentage points. Although limitations constrain the reporting of empirically representative results, findings underscore the potential policy relevance of DRSs in national climate objectives. Full article

Monitoring the condition of rolling bearings is critical for industrial reliability, yet traditional contact-based accelerometers can be impractical in confined or hazardous environments. This study investigates the use of microphones as a non-invasive diagnostic alternative, focusing on the impact of sensor distance and spatial placement on fault detection sensitivity across various rotational speeds and load conditions. Using an accelerometer mounted directly on the bearing as a benchmark, acoustic data were acquired on a test bench under different speed and load conditions. The experimental setup evaluated three distinct microphone positions and five distances relative to the source to assess spatial influence. Analysis was conducted comparing scalar indicators, such as Root Mean Square (RMS), kurtosis and Crest Factor (CF) values, with advanced diagnostic techniques, specifically the High-Frequency Resonance Technique (HFRT) for envelope spectrum extraction. Results indicate that while the signal-to-noise ratio (SNR) predictably decreases with distance, diagnostic performance is significantly compromised by acoustic shielding effects caused by bearing housing. Moreover, while simple statistical factors (RMS, kurtosis, CF) show limited reliability across varying distances and noise floors, HFRT-based envelope analysis yields robust fault identification even at the maximum sensor distance. The study concludes that optimal microphone placement is essential for reliable remote monitoring. Particularly, these findings suggest that a preliminary spatial characterization of the acoustic field can significantly enhance the effectiveness of non-contact diagnostic systems in industrial applications. Full article

Automated pixel-level detection of steel surface defects is a critical challenge in manufacturing quality control, complicated by the variation in defect size and shape, low contrast with background textures, and the diversity of defect patterns. This paper proposes ID-MSNet, an enhanced version of the UNet3+ architecture, designed specifically for the segmentation of three common steel surface defect types: inclusions, patches, and scratches. The proposed architecture introduces three targeted modifications: (1) a multi-scale feature learning module (MSFLM) in the encoder that uses dilated convolutions at multiple rates to capture contextual features across different scales, combined with DropBlock regularization and batch normalization to improve generalization; (2) an improved down-sampling (IDS) module that replaces standard max-pooling with learnable strided convolutions fused via 1 × 1 convolution, preserving richer feature representations; and (3) a convolutional block attention module (CBAM) integrated into the skip connections to selectively focus the model on spatially and channel-wise relevant defect regions. Experiments on the publicly available SD-saliency-900 dataset demonstrate that ID-MSNet achieved an 86.19% mIoU, outperforming all compared state-of-the-art segmentation models while using only 6.7 million parameters—approximately 75% fewer than the original UNet3+. These results establish ID-MSNet as a strong and efficient baseline for steel surface defect segmentation, with potential applicability to automated quality inspection in broader manufacturing contexts. Full article

Understanding knee kinematics during gait is essential for the design of prostheses, orthoses, and biomimetic mechanisms. In many biomechanical analyses, tibiofemoral motion is simplified to the sagittal plane, allowing the locus of the instantaneous center of rotation (ICR) to describe joint kinematics derived from the instantaneous axis of rotation (IAR). However, it remains unclear whether ICR trajectories obtained from simplified flexion–extension tasks can represent those observed during gait. This study analyzes the sagittal-plane trajectory of the tibiofemoral ICR during gait swing, standing swing, seated swing, and squat. Motion data from 21 healthy participants were captured using videogrammetry, and the instantaneous axis of rotation (IAR) was computed from homogeneous transformation matrices using the Mozzi–Chasles theorem. Sagittal-plane ICR trajectories were derived and compared within subjects across tasks. Significant differences were found between gait and all other movements in both trajectory shape and spatial position. The shape metric (S), which quantifies differences in trajectory geometry, showed mean values ranging from 0.82 to 1.04 with very large effect sizes (Cohen’s d = 2.90 to 4.47, p < 0.0001). The centroid distance metric (M), which measures the overall spatial displacement between trajectories, indicated positional differences ranging from 8.15 mm to 12.37 mm between trajectories also showing very large effect sizes (Cohen’s = 1.72–3.40, p < 0.0001). Additionally, the mean deviation of the IAR from the sagittal plane ranged from 14° to 18° during gait, whereas smaller deviations were observed in non–weight-bearing swing movements. These results demonstrate that tibiofemoral ICR trajectories are task-dependent and that simplified flexion–extension tasks do not fully reproduce the knee kinematics observed during gait. Consequently, the use of gait-derived ICR trajectories, together with their variability, provides a more suitable basis for the design and optimization of polycentric mechanisms, enabling the development of devices that more closely replicate real biomechanics and are potentially better adapted to the user. Full article