Search Results (1,490)

Cross-platform cultural heritage systems must coordinate structured domain data, shared services, real-time 3D representation, and device-specific interaction. This paper presents a metadata-centered architecture for delivering a numismatic collection through responsive web, map, conventional 3D, and immersive WebXR interfaces. It combines a relational metadata and media layer, shared REST-based services, and device-adaptive clients. It was instantiated with 253 historical coins. Instead of storing a coin-specific 3D model for each record, Babylon.js generates lightweight geometry at runtime and maps paired obverse and reverse images onto its surfaces. Record identifiers, metadata, media references, and query logic are reused across desktop, mobile, and head-mounted display access. An implementation-level check confirmed that an updated record was available through the catalog, map-related view, and 3D/WebXR gallery without client-specific duplication. Complementary formative evaluations involved 97 web participants and 19 WebXR participants, with mean System Usability Scale scores of 80.41 and 83.55, respectively. Feedback identified object manipulation, information legibility, visual fidelity, and interaction comfort as refinement priorities. This study contributes a system-level approach that connects shared cultural heritage services with conventional and immersive clients without separate collection databases or content-management workflows. Full article

A series of polyurethane (PU) nanocomposites incorporating 4,4′-bis(hydroxymethyl)-2,2′-bipyridine (BBD) as a chain extender and a commercially supplied graphene/zinc oxide (G/ZnO) hybrid filler were successfully synthesized. The effects of G/ZnO loading (0–2.0 wt.%) on the structural, thermal, mechanical, surface-wettability, and antibacterial properties of the nanocomposites were systematically investigated. Fourier-transform infrared spectroscopy confirmed the formation of the polyurethane structure and revealed changes in characteristic absorption bands following G/ZnO incorporation. Morphological observation of the pristine G/ZnO hybrid filler revealed an irregular and aggregated morphology, while X-ray diffraction confirmed the presence of crystalline ZnO. Energy-dispersive X-ray spectroscopy and elemental mapping showed Zn-containing regions within the examined areas of the G/ZnO-containing PU samples. X-ray photoelectron spectroscopy further confirmed the surface presence of Zn-containing species, with the Zn atomic concentration increasing from 0 at.% in PU-01 to 0.82 at.% in PU-04. Thermogravimetric analysis showed modest changes in thermal decomposition behavior with increasing G/ZnO loading, while differential scanning calorimetry and dynamic mechanical analysis revealed shifts in glass-transition and relaxation behavior, consistent with changes in polymer-chain mobility and the local interfacial environment. The tensile strength increased from 2.68 MPa for neat PU to 11.76 MPa for the nanocomposite containing 2.0 wt.% G/ZnO, accompanied by an increase in Young’s modulus. The water contact angle increased from approximately 68° to 89°, indicating reduced apparent surface wettability with increasing G/ZnO loading. The nanocomposites also exhibited antibacterial activity against Escherichia coli and Staphylococcus aureus, with antibacterial efficiencies exceeding 95% at higher G/ZnO loadings. Overall, the incorporation of the commercial G/ZnO hybrid filler was associated with changes in the thermal, mechanical, surface, and antibacterial properties of the BBD-containing PU system. Because separate PU systems without BBD and individual graphene- and ZnO-containing controls were not included, the individual contributions of BBD, graphene, and ZnO, as well as any synergistic effect between graphene and ZnO, cannot be established from the present results. Further studies addressing filler leaching, long-term antibacterial stability, coating adhesion, environmental durability, and cytocompatibility are required to establish the practical applicability of these materials. Full article

Occupant-centric building operation requires timely information on occupant states and local indoor conditions, but fixed sensors provide limited spatial coverage, and wearables depend on user participation. This study develops a Robot Operating System 2 (ROS 2)-based wheeled mobile sensing platform for buildings. The main contribution is the integration of autonomous mapping and navigation, target approach, multisensor occupant-data acquisition, lightweight edge-based human detection, and return-to-dock operation on a Raspberry Pi 5. During operation, the robot patrols indoor locations, detects and approaches occupants, collects human and environmental data, uploads the data to a server, and returns to the charging dock. To support concurrent perception and navigation, YOLOv5n was compressed using structured channel pruning and multi-scale feature distillation. The compressed model reduced parameters and computation by approximately 53% and 61%, increased inference throughput from 7.5 to 16.5 frames per second, and enabled the robot to complete all 60 controlled trials across five locations and three postures. This study does not quantify HVAC energy savings or carbon-emission reductions. Instead, it validates a mobile sensing and edge-perception layer for future server-side thermal comfort inference, demand-responsive HVAC control, and evaluation of building energy and carbon performance. Full article

Variable-rate pesticide spraying and fertilizer application require coordinated operation of heterogeneous agricultural machines, particularly in irregular fields where task demands, vehicle mobility, payload capacity, energy consumption, and resupply requirements vary spatially. However, most existing studies optimize aerial spraying or ground fertilization separately and do not jointly consider prescription-map demands, air–ground synchronization, pesticide-drift risk, and agricultural vehicle constraints. This study formulates collaborative UAV spraying and UGV fertilization as a multi-objective mixed-integer nonlinear programming problem with three objectives: minimizing system makespan, weighted energy consumption, and pesticide-drift penalty. A prescription-map-guided bi-level planning framework is proposed. At the upper level, the problem-specific TNSAOO solver determines UAV and UGV task sequences and collaborative resupply-point activation. At the lower level, adaptive Theta* and row-constrained Hybrid A* generate UAV spraying and UGV fertilization trajectories, respectively, while prescription-dependent application commands are assigned along active operation segments and a time-window mechanism detects and corrects residual air–ground conflicts. The framework was evaluated using 30 real farmland boundaries and 90 randomized prescription scenarios. Mean geometric coverage rates reached 98.82% for UAV spraying and 98.95% for UGV fertilization, while the mean prescription-compliance errors were 6.21% and 2.13%, respectively. In addition, 96.7% of the batch runs contained no more than one detected air–ground conflict, with a mean corrective waiting time of 1.07 s. Compared with traditional independent operation, collaborative planning reduced mean system makespan by 9.32%, weighted energy consumption by 7.21%, modeled drift penalty by 3.62%, and total path length by 5.79%. In the multi-objective comparison, TNSAOO obtained a mean hypervolume of 0.597 and a mean inverted generational distance of 0.375, showing competitive Pareto-search performance relative to established comparison algorithms, particularly NSGA-II. Additional terrain and drift sensitivity analyses produced systematic changes in energy, completion time, and modeled drift risk under controlled parameter perturbations. These findings demonstrate the simulation-based feasibility of jointly planning heterogeneous variable-rate spraying and fertilization under a shared prescription map. Physical field experiments remain necessary to validate spray deposition, fertilizer-distribution uniformity, terrain effects, and model calibration under environmental uncertainty. Full article

Reinforcement learning (RL) has become an effective paradigm for enabling autonomous robots to acquire navigation policies directly from interaction with complex and uncertain environments. Nevertheless, autonomous path planning for Skid-Steer Mobile Manipulators (SSMMs) remains a challenging problem because it requires the coordinated control of the non-holonomic mobile base and the manipulator while simultaneously accounting for obstacle avoidance and wheel–terrain interaction effects. This paper presents and evaluates RL-based path planning strategies for SSMMs, explicitly incorporating coupled dynamics of the mobile platform and manipulator to generate collision-free trajectories under varying terrain conditions. The proposed framework incorporates a slip-aware reward formulation that penalizes discrepancies between commanded and measured robot motion while accounting for longitudinal and lateral slip resulting from wheel–terrain interaction. The main contributions are (i) a unified RL-based framework based on actor–critic techniques for SSMM path planning, integrating the mobile base and manipulator dynamics within a coupled system representation; (ii) a physics-aware multi-objective reward formulation that incorporates wheel–terrain interaction into policy learning; and (iii) the implementation via simulation and field validation of the proposed policies under progressively complex navigation conditions and real underground mining scenarios. The framework is evaluated using four RL algorithms across multiple environments and maps from real mining scenarios, encompassing diverse navigation conditions and start-to-goal configurations. The evaluated methods include Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimization (PPO), Soft Actor–Critic (SAC), and Twin Delayed DDPG (TD3). Experimental field results show that SAC achieves the lowest planning time, reducing the planning time by 127.3%, 24.1%, and 2.52% compared with PPO, TD3, and DDPG, respectively. SAC also achieves the shortest path, reducing the average path length by 20.32%, 8.58%, and 1.90% compared with PPO, DDPG, and TD3, respectively. Moreover, SAC generates smoother control profiles for both the mobile base and the manipulator arm, while TD3 exhibits competitive performance across several navigation metrics. The proposed framework demonstrates the potential of slip-aware RL for coordinated SSMM navigation, providing a practical foundation for improving the safety, energy efficiency, and operational autonomy of mobile manipulators exposed to complex mining environments. Full article

Bionic intelligent robots operating in unstructured dynamic environments require perception systems that regulate uncertain observations according to their reliability and avoid converting transient disturbances into persistent spatial references. Inspired by reliability-weighted multisensory integration and by a functional abstraction of persistence-based evidence consolidation in biological navigation, this paper proposes a bio-inspired reliability-constrained LiDAR–inertial odometry framework. Each point-to-map observation is evaluated using residual consistency, local geometric quality, and voxel-level temporal stability. The fused reliability score regulates both the ESIKF state update and incremental map maintenance: low-confidence observations are continuously down-weighted, highly reliable points are admitted to the persistent map, ambiguous points are retained in short-term candidate memory for multi-frame verification, and unreliable points are rejected. The framework translates biological design principles into an engineering perception–memory architecture rather than reproducing a specific neural circuit. Repeated experiments on public datasets and a wheeled mobile robot platform show comparable accuracy in two normal sequences. Across five dynamic sequences, the complete method reduces localization RMSE by 14.08–28.88% relative to Fast-LIO2 and achieves lower mean RMSE than Dynamic-LIO on all five evaluated dynamic sequences. Frozen-map evaluation further yields 7.77–15.71% lower point-to-map RMSE together with higher consistent-correspondence ratios and coverage, while the maximum mean RMSE deviation in the parameter-sensitivity study remains below 7.2%. The maximum measured processing time is 18.69 ms per scan, maintaining real-time operation for a 10 Hz LiDAR. Full article

This paper aims to evaluate the usability and user experience of ad hoc augmented reality systems in the learning experience of mechanical engineering students, focusing on fluid machinery education. Three mobile augmented reality applications were developed to support teachers in explaining the main parts of an impeller blade and its fluid interaction, integrating computer-aided design (CAD) models with velocity and pressure maps derived from computational fluid dynamics (CFD) simulations. By providing multiple means of representation, these tools were developed to support the explanation of complex 2D concepts without requiring specialized hardware. The obtained results revealed that the usability of the developed applications, assessed through the System Usability Scale (SUS), was remarkably effective. Furthermore, the User Experience Questionnaire (UEQ) showed that the average scores for each evaluation criterion were highly positive, especially in the “Stimulation” and “Novelty” areas. Accurate statistical analyses revealed that students’ feedback was not influenced by users’ familiarity with virtual and augmented reality tools. In conclusion, since no objective learning gains were evaluated, this investigation’s outcomes indicate that the developed applications provide an engaging tool with high usability and positive user experience, framing inclusive education as a fundamental design rationale rather than an empirically demonstrated outcome and laying the groundwork for future studies to objectively measure cognitive impact. Full article

This work presents the design, construction and operation of the SKD-1, a modular skid-steer unmanned ground vehicle (UGV) developed as a low-cost research platform for mobile robotics applications. The platform integrates a differential skid-steer drive system, a Raspberry Pi-based onboard computer, and a microcontroller-based control layer implemented using an STM32 microcontroller. The sensing system includes light detection and ranging (LiDAR), global navigation satellite system (GNSS), and an inertial measurement unit (IMU), enabling experiments in localization, mapping, and autonomous navigation. The software architecture is based on the Robot Operating System (ROS) 2 framework, relying on standard ROS 2 packages for perception, mapping, and path planning, with the custom hardware-interface layer being the only non-standard software component. The mechanical and electronic subsystems were designed with a modular architecture that facilitates maintenance, sensor replacement, and hardware upgrades. The primary contribution of this work is the open-hardware design, integration, and documentation of a reproducible robotics testbed, motivated by the prohibitive cost of commercial platforms in resource-constrained research contexts. Indoor and outdoor experiments—covering velocity-tracking, SLAM, and waypoint-navigation trials—demonstrate the functional integration of the sensing, actuation, and computing subsystems. Full article

Urban expansion, peri-urban growth, and evolving mobility service systems are reshaping the functional links between urban and rural areas. As these relationships become increasingly complex, conventional urban–rural classifications may underestimate the spatial differences in everyday accessibility, environmental conditions, and demographic exposure. To capture these interrelated dimensions, this study develops a GeoAI-enabled Accessibility–Environment–Equity (AEE) framework to analyse the urban–rural continuum as a multidimensional functional space. Using Odense municipality as a case study, the framework incorporates network accessibility indicators, environmental benefit and pressure proxies, and demographic exposure indicators within a hexagonal tessellation. The indicators are combined into a standardised AEE feature matrix, interpreted through a principal component analysis, and classified using Gaussian mixture modelling with posterior-probability uncertainty mapping. The resulting typology shows a distinct but non-uniform core–periphery gradient: the central areas exhibit high accessibility and population density but lower green space availability and higher environmental pressure proxies, while the peripheral areas are greener but less accessible by non-car modes. The benchmarking against single-domain reference classifications reveals that the functional typology is primarily shaped by accessibility and demographic density structures rather than by greenness alone. This study advances urban–rural continuum (URC) research by demonstrating how GeoAI-enabled workflows can identify functional transition zones and accessibility–environment mismatches, supporting more evidence-based planning for medium-sized European cities and their surrounding urban–rural transition zones. Full article

Industrial digital transformation has become a strategic priority for manufacturing organizations, yet many initiatives still struggle to move from digital ambition to organizational execution. In Industry 4.0-oriented contexts, this gap persists because transformation depends not only on technologies, processes and investments, but also on the critical knowledge required to sustain success factors across socio-technical systems. This study develops and evaluates a socio-technical systems framework for critical knowledge mapping in industrial digital transformation. Grounded in Design Science Research, the study combines structured literature-based construct development, expert validation and application in an industrial organization. The empirical demonstration was conducted in a large Brazilian-born multinational food and beverage ingredients manufacturing organization, whose identity has been anonymized. The framework connects critical success factors, knowledge domains and knowledge vulnerabilities, enabling a more actionable system-level diagnosis of knowledge readiness, defined as the organizational capacity to identify, mobilize and protect the critical knowledge required for transformation. The application showed that strategic and governance-related factors were relatively more consolidated in the analyzed organization, while the main vulnerabilities were concentrated in human and organizational dimensions, especially knowledge sharing, talent management, workforce readiness and industrial-digital literacy. By linking Industry 4.0 readiness to human-aware and resilient transformation concerns associated with Industry 5.0, the study shows how critical knowledge mapping can support the transition from readiness assessment to socio-technical execution. Full article

Intelligent Transportation Systems (ITS) increasingly combine sensing, communication, computation, data platforms, and mobility services. This article presents a structured, literature-based mapping review of ITS applications from a standards-oriented perspective. The study analyzes 42 studies organized into five thematic groups and evaluated through 63 article–standard assessments using selected ITU-T Recommendations as an analytical lens. The rubric used in this work examined whether each study reported, or allowed reviewers to infer, evidence on architecture, data handling, interoperability, security and privacy, deployment assumptions, and digital-twin capabilities. Partial alignment was the most frequent outcome, accounting for 25 of 63 article–standard assessments (39.7%). At group level, satisfactory or optimal alignment occurred in 5 of 8 assessments (62.5%) in the digital-twin group and in 3 of 13 (23.1%) in the Big Data group; in the latter, 6 of 13 assessments (46.2%) showed limited or no alignment. Stronger alignment was usually found when studies described architectures, data flows, sensing mechanisms, service workflows, physical–virtual modeling, or system-management components relevant to the Recommendation, and weaker alignment when they focused mainly on algorithms, datasets, prediction accuracy, authentication, or secure dissemination without sufficient detail on interfaces, data governance, gateway roles, deployment conditions, or platform integration. The review proposes a five-dimension standards-facing reporting checklist addressing interoperability, data lifecycle and governance, security and privacy, operational readiness, and standards-facing evidence. It supports traceable reporting through explicit evidence-status categories and locations, and can be implemented as a Standards and Interoperability Reporting Statement (SIRS) for authors, reviewers, and editors. Overall, the findings show that standards-oriented assessment depends not only on technical performance but also on explicit and traceable integration evidence, while the proposed reporting profile provides a practical mechanism for making such evidence more systematically visible in future ITS studies. Full article

Accurate localization and mapping are essential for autonomous mobile robots operating in unknown environments. This study investigates the impact of Extended Kalman Filter (EKF)-based sensor fusion on the performance of three widely used two-dimensional (2D) LiDAR Simultaneous Localization and Mapping (SLAM) algorithms: GMapping, Karto SLAM, and SLAM Toolbox. Wheel encoder longitudinal velocity and inertial measurement unit (IMU) yaw angular velocity were fused using an EKF and compared with raw wheel odometry using the MIT Stata Center dataset. Localization performance was evaluated both before and after SLAM using translational and rotational Absolute Pose Error (APE) across multiple trajectory segments. Five repeated executions were performed for each SLAM configuration to characterize run-to-run variability. Prior to SLAM, EKF-filtered odometry reduced translational APE root mean square error (RMSE) by approximately 61–75% and rotational APE RMSE by approximately 65–77% relative to raw odometry. After SLAM, translational differences between the two odometry sources were substantially smaller and varied according to the evaluated algorithm and trajectory, while rotational performance exhibited larger and less consistent changes. These results demonstrate that substantial improvements in upstream odometry accuracy do not necessarily produce proportional improvements in final SLAM localization and that the influence of sensor fusion varied across the evaluated SLAM algorithm and trajectory segments, providing practical guidance for selecting localization strategies in autonomous mobile robots. Full article

Urban micromobility systems have become increasingly important for short-distance and last-mile travel, particularly with the growing use of electric bicycles in dense urban environments. However, e-bike performance is not determined only by distance or travel time; road slope, segment direction, rolling resistance, aerodynamic drag and regenerative braking potential also affect energy demand. This paper develops a GIS-based, segment-level framework for mapping slope-dependent energy consumption and regenerative energy recovery in an urban e-bike network. Road segments were enriched with length, elevation-difference and slope information, and energy indicators were calculated using a simplified physics-based model. Energy consumed, energy regained, and net energy values were visualized as GIS thematic maps. The results show that energy performance is spatially heterogeneous and strongly sensitive to slope direction. The proposed workflow provides a practical spatial decision-support layer for energy-aware micromobility planning, route evaluation and sustainable urban mobility assessment. Full article

Unmanned aerial vehicles (UAVs) are increasingly considered as enabling technologies for last-mile delivery, emergency medical response, and smart-city applications. However, drone-based logistics, emergency communication, sensing activities, and future aerial mobility are often addressed as separate research domains. This article proposes a four-dimensional planning framework for drone-enabled mobility, integrating goods, information, sensing, and human mobility within a unified conceptual structure. The framework is developed through a literature-informed conceptual analysis and previous applied research experiences related to drone-assisted logistics and emergency communication. Goods mobility includes parcel delivery, medical logistics, emergency supply transport, and hybrid operational models involving trucks, public transport, depots, and micro-hubs. Information mobility refers to the use of drones as mobile communication tools for emergency warnings, citizen interaction, drone-to-infrastructure communication, and infomobility services. Sensing mobility concerns traffic monitoring, environmental observation, disaster mapping, crowd monitoring, and infrastructure inspection. Human mobility is considered as an emerging extension related to urban air mobility (UAM), electric vertical take-off and landing (eVTOL) systems, and low-altitude aerial corridors. Cross-cutting issues such as energy autonomy, solar-assisted drones, multimodal integration, safety, communication, regulation, sustainability, and public acceptance are discussed. The proposed framework provides a structured basis for assessing drones as components of sustainable, resilient, and multimodal mobility systems. Full article

TA-BI (trust-aware behavioral intelligence) is a conceptual–formal observation-to-decision contract for trust-aware routing support in reactive multi-hop wireless sensor networks (WSNs) and mobile wireless sensor networks (MWSNs). It specifies evidence provenance and availability, separates current Behavioral Information from historical node Trust, maps these states to route candidates, and controls when RouteScore may affect a native routing decision. In the reference AODV adapter, declared validity checks, destination-sequence freshness, and hop-count precedence are applied before RouteScore on a finite frozen candidate set. A complete common component basis prevents asymmetric missing evidence from favoring a candidate; a closed gate disables TA-BI re-ranking but retains a total routing outcome through baseline ordering or native fallback. Nine propositions establish bounded contract properties, including candidate-admissibility preservation rather than equivalence of the complete AODV transition system. A deterministic Python oracle evaluates ten groups, with explicit T3/T4 subcases for stale, version-inconsistent, missing, low-confidence, and restored evidence. The evidence is separated into formal correctness, executable conformance, historical integration reachability, and empirical network performance. The supplied formal and deterministic artifacts support only the first two levels; the bounded historical OMNeT++/INET record supports integration reachability only. Packet delivery, delay, throughput, energy, overhead, scalability, and mobility performance are not evaluated. Full article