Search Results (273)

This study introduces an innovative and scalable approach for automated road surface assessment by integrating Mobile Mapping System (MMS)-based LiDAR data analysis with an open-source WebGIS platform. In a U.S.-based case study, over 20 datasets were collected along Interstate I-65 in West Lafayette, Indiana, using the Purdue Wheel-based Mobile Mapping System—Ultra High Accuracy (PWMMS-UHA), following Indiana Department of Transportation (INDOT) guidelines. Preprocessing included noise removal, resolution reduction to 2 cm, and ground/non-ground separation using the Cloth Simulation Filter (CSF), resulting in Bare Earth (BE), Digital Terrain Model (DTM), and Above Ground (AG) point clouds. The optimized BE layer, enriched with intensity and color information, enabled crack detection through Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Random Forest (RF) classification, with and without intensity normalization. DBSCAN parameter tuning was guided by silhouette scores, while model performance was evaluated using precision, recall, F1-score, and the Jaccard Index, benchmarked against reference data. Results demonstrate that RF consistently outperformed DBSCAN, particularly under intensity normalization, achieving Jaccard Index values of 94% for longitudinal and 88% for transverse cracks. A key contribution of this work is the integration of geospatial analytics into an interactive, open-source WebGIS environment—developed using Blender, QGIS, and Lizmap—to support predictive maintenance planning. Moreover, intervention thresholds were defined based on crack surface area, aligned with the Pavement Condition Index (PCI) and FHWA standards, offering a data-driven framework for infrastructure monitoring. This study emphasizes the practical advantages of comparing clustering and machine learning techniques on 3D LiDAR point clouds, both with and without intensity normalization, and proposes a replicable, computationally efficient alternative to deep learning methods, which often require extensive training datasets and high computational resources. Full article

In response to the urgent demands of the Internet of Things for precise indoor target positioning and information interaction, this paper proposes a multi-band analog radio-over-fiber mobile fronthaul system. The objective is to obtain the target’s location in indoor environments while integrating remote beamforming capabilities to achieve wireless access to the targets. Vector signals centered at 3, 4, 5, and 6 GHz for indoor positioning and centered at 30 GHz for wireless access are generated centrally in the distributed unit (DU) and fiber-distributed to the active antenna unit (AAU) in the multi-band analog radio-over-fiber mobile fronthaul system. Target positioning is achieved by radiating electromagnetic waves indoors through four omnidirectional antennas in conjunction with a pre-trained neural network, while high-speed wireless communication is realized through a phased array antenna (PAA) comprising four antenna elements. Remote beamforming for the PAA is implemented through the integration of an optical true time delay pool in the multi-band analog radio-over-fiber mobile fronthaul system. This integration decouples the weight control of beamforming from the AAU, enabling centralized control of beam direction at the DU and thereby reducing the complexity and cost of the AAU. Simulation results show that the average accuracy of localization classification can reach 86.92%, and six discrete beam directions are achieved via the optical true time delay pool. In the optical transmission layer, when the received optical power is 10 dBm, the error vector magnitudes (EVMs) of vector signals in all frequency bands remain below 3%. In the wireless transmission layer, two beam directions were selected for verification. Once the beam is aligned with the target device at maximum gain and the received signal is properly processed, the EVM of millimeter-wave vector signals remains below 11%. Full article

Background/Objectives: Ataxia is quite common in pediatric neuromotor disorders and has a highly heterogeneous etiology. Mobility difficulties and functional limitations reflect the lack of coordination in this population. The aim of this study is to assess the effectiveness of an intensive program of Functional Partial Body Weight Support Treadmill Training (FPBWSTT) on the mobility and functionality of children with ataxia. Methods: Through a stratified randomized control trial, a sample of 18 children with progressive and non-progressive ataxia and GMFCS II-IV (mean age: 14 years; standard deviation: 2.5) was assessed prior to the intervention, post-intervention, and 2 months after its end. Motor and functional skills were assessed with the Gross Motor Function Measure (GMFM, items D-E), the Pediatric Balance Scale (PBS), a 10 m walk test (10 MWT), a 6 min walk test (6 MWT), the Scale for Assessment and Rating Ataxia (SARA), the TimedUp and Go (TUG) test, spatiotemporal gait parameters, and kinetic and kinematic variables of the pelvis and lower limb. Results: Statistically significant interactions and changes in favor of the FPBWSTT were found in all functional assessments and spatiotemporal gait parameters (p < 0.05), the majority of which were maintained for two months. There was no statistical interaction or change in kinematic parameters (p > 0.05), while kinetic variables were insufficiently collected and were not statistically analyzed. Conclusions: The FPBWSTT is more effective on the mobility and functionality of children with ataxia who are 8–18 years old, compared to typical physiotherapy. Kinematic variables may not be sensitive indicators of change over a short period of time and/or in this population. Full article

During pipe-roof construction using pipe-jacking technology, lubricants are injected into the tail void to reduce pipe–soil friction and minimize soil loss. However, research on ground settlement caused by multiple adjacent pipe jackings remains limited, and the influence of lubricant Young’s modulus on ground settlement control is not clear. To address these gaps, this study conducts a comprehensive investigation using a Fast Lagrangian Analysis of Continua in 3 Dimensions (FLAC3D). Initially, the research model is validated against a pipe-roof case in Japan. Subsequently, ground response characteristics are simulated under lubricants with different Young’s moduli, considering four burial depths, two pipe-roof arrangements (“gate-shaped” and “horseshoe-shaped”), and two tail voids. The results indicate that low-stiffness lubricants mobilize greater surface settlement, while increasing the lubricant Young’s modulus more markedly optimizes the interaction among adjacent pipelines, thereby greatly alleviating the settlement. Nonetheless, the control effectiveness of lubricant on the settlement is influenced by other factors. Increasing burial depths and tail voids weaken the lubricant’s capacity to mitigate surface settlement. In contrast, the horseshoe-type arrangement is more conducive to the lubricant’s control effect on surface settlement than the gate-type system. Moreover, under these three cases, an increase in the lubricant Young’s modulus can more substantially reduce surface settlement. These findings provide valuable insights for controlling ground settlement during pipe-roof construction. Full article

Two-dimensional (2D) materials with dangling-bond-free surfaces have attracted considerable attention in next-generation electronic applications due to their distinctive properties, such as high carrier mobility, short-channel immunity, and strong light-matter interactions. Recently, various methods have been developed for the preparation of 2D materials, including mechanical exfoliation, chemical vapor deposition, and chemical solution-based processes. Among these, solution processing provides a cost-effective and low-temperature approach for large-scale production. This review highlights recent advancements in solution-processed 2D materials, focusing on preparation methods and their applications in electronic and optoelectronic devices. Additionally, future perspectives are proposed to advance the breakthrough and commercialization of solution technologies for value-added chemical production. Full article

LiDAR-based place recognition (LPR) is crucial for the navigation and localization of autonomous vehicles and mobile robots in large-scale outdoor environments and plays a critical role in loop closure detection for simultaneous localization and mapping (SLAM). Existing LPR methods, which utilize 2D bird’s-eye view (BEV) projections of 3D point clouds, achieve competitive performance in efficiency and recognition accuracy. However, these methods often struggle with capturing global contextual information and maintaining robustness to viewpoint variations. To address these challenges, we propose R2SCAT-LPR, a novel, transformer-based model that leverages self-attention and cross-attention mechanisms to extract rotation-robust place feature descriptors from BEV images. R2SCAT-LPR consists of three core modules: (1) R2MPFE, which employs weight-shared cascaded multi-head self-attention (MHSA) to extract multi-level spatial contextual patch features from both the original BEV image and its randomly rotated counterpart; (2) DSCA, which integrates dual-branch self-attention and multi-head cross-attention (MHCA) to capture intrinsic correspondences between multi-level patch features before and after rotation, enhancing the extraction of rotation-robust local features; and (3) a combined NetVLAD module, which aggregates patch features from both the original feature space and the rotated interaction space into a compact and viewpoint-robust global descriptor. Extensive experiments conducted on the KITTI and NCLT datasets validate the effectiveness of the proposed model, demonstrating its robustness to rotation variations and its generalization ability across diverse scenes and LiDAR sensors types. Furthermore, we evaluate the generalization performance and computational efficiency of R2SCAT-LPR on our self-constructed OffRoad-LPR dataset for off-road autonomous driving, verifying its deployability on resource-constrained platforms. Full article

Small-target detection in sea clutter is a key challenge in marine radar surveillance, crucial for maritime safety and target identification. This study addresses the challenge of weak feature representation in one-dimensional (1D) sea clutter time-series analysis and suboptimal detection performance for sea surface small targets. A novel dual-feature image detection method incorporating an improved mobile vision transformer (MobileViT) network is proposed to overcome these limitations. The method converts 1D sea clutter signals into two-dimensional (2D) fused images by means of a Gramian angular difference field (GADF) and recurrence plot (RP), enhancing the model’s key-information extraction. The improved MobileViT architecture enhances detection capabilities through multi-scale feature fusion with local–global information interaction, integration of coordinate attention (CA) for directional spatial feature enhancement, and replacement of ReLU6 with SiLU activation in MobileNetV2 (MV2) modules to boost nonlinear representation. Experimental results on the IPIX dataset demonstrate that dual-feature images outperform single-feature images in detection under a ${10}^{-3}$ constant false-alarm rate (FAR) condition. The improved MobileViT attains 98.6% detection accuracy across all polarization modes, significantly surpassing other advanced methods. This study provides a new paradigm for time-series radar signal analysis through image-based deep learning fusion. Full article

(1) Research Highlights: Existing studies primarily focus on the supply-side evaluation of urban forest accessibility, emphasizing physical proximity while often overlooking real-time usage patterns and demographic-specific exposure. This study shifts the focus to social exposure, analyzing how diverse population groups interact with urban forests across different times of the day, integrating dynamic accessibility metrics. (2) Background and Objectives: Unlike previous research that predominantly assessed urban forest accessibility through conventional models based on static spatial proximity, this study incorporates spatiotemporal population distribution data to capture actual utilization patterns. By introducing a dynamic, exposure-based framework, this research aims to facilitate equitable and temporally sensitive access to green spaces across diverse demographic groups. (3) Materials and Methods: This study focuses on Seoul, South Korea, and applies the Gaussian-based Two-Step Floating Catchment Area (G2SFCA) methodology to assess urban forest accessibility. Living population data (2021–2024) from mobile telecommunications sources were used as demand indicators, while OpenStreetMap (OSM) green space data were utilized as supply indicators. Realistic travel distances were calculated using OSM pedestrian networks and Dijkstra’s algorithm, incorporating slope effects and distance decay functions. A K-means clustering algorithm was applied to classify four distinct exposure types based on demographic and temporal variations. (4) Results: The findings reveal significant disparities in urban forest exposure based on age group and time of day. Four major urban forest exposure patterns were identified: Type A—school-age children, with peak usage around midday; Type B—working-age adults, frequenting mid-sized urban forests during commuting and leisure hours; Type C—elderly individuals, utilizing large-scale urban forests and neighborhood parks mainly in the morning; Type D—young adults, engaging with small urban parks and rest areas at various times. (5) Conclusions: Urban forest management must move beyond the quantitative expansion of green spaces and instead implement customized policies that optimize accessibility and equitable distribution based on distinct temporal and demographic patterns of social exposure. By integrating real-time urban mobility data into urban forest planning, policies can better align green space supply with actual usage, fostering a more equitable, data-driven, and sustainable urban green infrastructure. Full article

Several D-amino acid residue-containing peptides (DAACPs) with antimicrobial, cardio-excitatory, and neuronal activities have been identified in various species. The L-Asn-D-Trp-L-Phe-NH₂ (N(dW)F) tripeptide, derived from Aplysia kurodai, exhibits cardiac activity in invertebrates. The chirality of the tryptophan residue at the second position in N(dW)F influences its conformation and biological characteristics. We demonstrated the chiral separation of N(dW)F and its diastereomer NWF using (S)-3,3′-diphenyl-1,1′-binaphthyl-20-crown-6-ether columns (CR-I(+)). A reduction in the ratio of acetonitrile and methanol in the mobile phase allowed the complete separation of N(dW)F and its diastereomer, improving the separation factor (α) from 0.96 to 6.28. Molecular dynamics simulations revealed that the interaction of N(dW)F with CR-I(−) was more favorable than with CR-I(+). These findings indicate that the structure of the CR-I column stereoselectively recognizes peptides and facilitates the separation of naturally occurring D-amino acid residue-containing tripeptides. Full article

A robust and elegant approach, based on the Two-Zone Moment Analysis (TZMA) method, is proposed to assess the contributions of the mobile and stationary zones, $H_{Cm}$ and $H_{Cs}$, to the C term $H_C$ in the van Deemter equation for plate height. The TZMA method yields two formulations for $H_{Cm}$ and $H_{Cs}$, both fully equivalent in terms of $H_C$, yet offering different decompositions of the contributions from the mobile and stationary zones. The first formulation proposes an expression for the term $H_{Cs}$ that has strong similarities, but also significant differences, from the well-known and widely used one proposed by Giddings. While it addresses the inherent limitation of Giddings’ approach—namely, the complete decoupling of transport phenomena in the moving and stationary zones—it introduces the drawback of a non-unique decomposition of $H_C$. Despite this, it proves highly valuable in highlighting the limitations and flaws of Giddings’ method. In contrast, the second formulation not only properly accounts for the interaction between the moving and stationary zones, but provides a unique and consistent decomposition of $H_C$ into its components. Three different geometries are investigated in detail: the 2D triangular array of cylinders (pillar array columns), the 2D array of rectangular pillars (radially elongated pillar array columns) and the 3D face-centered cubic array of spheres. It is shown that Giddings’ approach significantly underestimates the $H_{Cs}$ term, especially for porous-shell particles. Its accuracy is limited, being reliable only when intra-particle diffusivity ($D_s$) and the zone retention factor ($k^{″}$) are very low, or when axially invariant systems are considered. Full article

As urban environments become increasingly interconnected, the demand for precise and efficient pedestrian solutions in digitalized smart cities has grown significantly. This study introduces a scalable spatial visualization system designed to enhance interactions between individuals and the street in outdoor sidewalk environments. The system operates in two main phases: the spatial prior phase and the target localization phase. In the spatial prior phase, the system captures the user’s perspective using first-person visual data and leverages landmark elements within the sidewalk environment to localize the user’s camera. In the target localization phase, the system detects surrounding objects, such as pedestrians or cyclists, using high-angle closed-circuit television (CCTV) cameras. The system was deployed in a real-world sidewalk environment at an intersection on a university campus. By combining user location data with CCTV observations, a 4D+ virtual monitoring system was developed to present a spatiotemporal visualization of the mobile participants within the user’s surrounding sidewalk space. Experimental results show that the landmark-based localization method achieves a planar positioning error of 0.468 m and a height error of 0.120 m on average. With the assistance of CCTV cameras, the localization of other targets maintains an overall error of 0.24 m. This system establishes the spatial relationship between pedestrians and the street by integrating detailed sidewalk views, with promising applications for pedestrian navigation and the potential to enhance pedestrian-friendly urban ecosystems. Full article

The 10-m walk test (10MWT) is a stopwatch-based clinical mobility assessment. To better understand mobility limitations, 10MWT test completion times may be complemented with gait parameters like step length. State-of-the-art augmented reality (AR) glasses can potentially do this given their unique 3D-positional data from which gait parameters may be derived. We examined the test-retest reliability, concurrent validity, and face validity of gait parameters derived from AR glasses during a 10MWT in 20 people with Parkinson’s disease, performed at self-selected comfortable and fast-but-safe walking speeds. AR-derived 10MWT completion times and gait parameters (mean step length, cadence, and maximal gait speed) were compared across repetitions and with lab-based (Interactive Walkway) and clinical (stopwatch) reference systems. Good-to-excellent test-retest reliability statistics were observed for test completion times and gait parameters for all systems and conditions alike. Concurrent validity was demonstrated between AR, lab-based, and clinical references for test completion times (good-to-excellent agreement: ICC > 0.879) and gait parameters (excellent agreement: ICC > 0.942). Face validity was confirmed by significant differences in test completion times and gait parameters between speed conditions in a-priori expected directions. These findings support the conclusion that gait parameters can be derived reliably and validly from AR glasses in people with Parkinson’s disease. Full article

This paper presents a case study from the Archaeological and Via Flaminia Museum in Cagli (Italy), developed within the ERASMUS+ Next-Museum project, which explores inclusive approaches through the digital transformation of small museums and their connection to the surrounding territory. A key goal was to “return” bronze statuettes to the museum, symbolically compensating the community for their absence. The initiative integrates accessibility and multisensory storytelling following “Design for All” principles. Three installations were implemented: tactile replicas of the statuettes produced through 3D printing, a sensorized table for interactive storytelling, and a story map displayed on a touchscreen for exploring local archaeological heritage. The design prioritized inclusivity, particularly for visitors with visual impairments, while addressing practical constraints such as the need for a mobile and flexible setup within a limited budget. Verification and validation tests were conducted with visually impaired participants during the pre-opening phase, and the installations were later evaluated using the User Experience Questionnaire, complemented by qualitative feedback. These evaluations highlight the potential of phygital experiences to foster engagement with cultural heritage while addressing technological and design challenges. Full article

This article presents the design and implementation of an API that delivers real-time promotional notifications to mobile devices based on their proximity to shopping centers, calculated using the Haversine formula. Developed in Laravel, the API determines whether a mobile device is within a 600 m radius of any registered shopping center, such as Soriana, GranD, and HEB, and sends the relevant promotional information. The system uses Petri nets to model asynchronous behavior, enabling efficient concurrency management between the mobile application and the API. This structure ensures optimized message delivery, preventing communication collisions and delays. The mobile application, developed in Kotlin, integrates geolocation services to capture and update the user’s location in real time. The results indicate an improvement in response time and proximity detection accuracy, highlighting the effectiveness of the Petri net model for systems requiring concurrent interaction. The combination of Laravel, Kotlin, and formal modeling with Petri nets proves to be an effective and scalable solution for proximity-based mobile applications. Full article

Debris flows propagating in natural environments often encounter irregular terrain features, such as bottom roughness and man-made structures like groundsills, which significantly influence their behavior and dynamics. In practice, groundsills are commonly used as debris flow mitigation structures. This study examines the effects of a beam-type groundsill array on the flow behavior of sediment mixtures in an inclined channel using numerical simulations. The sediment mixtures, modeled as Bingham fluids, were tested as they flowed over groundsill arrays with varying densities, characterized by the spacing-to-height ratio ($d/h$) ranging from 2 to 10. The findings indicate that interaction with the groundsills produces a hydraulic jump-like flow, reaching a height approximately 2.2 times the approach flow depth across different array densities. High-density arrays ($d/h\le 4$) substantially hindered flow propagation, reducing front velocities but leading to sediment buildup upstream of the groundsills. Conversely, low-density arrays ($d/h>4$) facilitated smoother flow with higher velocities. These insights into the relationship between array density, flow behavior, and sediment trapping provide valuable guidance for optimizing groundsill array designs to effectively reduce the mobility of gravity-driven flows of non-Newtonian fluids (such as snow avalanches, debris, lava, or mudflows) and mitigate the associated risks. Full article