Search Results (147)

Drawing on diffusion of innovation theory, this cross-national study examines the association between cultural dimensions and artificial intelligence (AI) capability on a 78-country sample. This cross-country, worldwide approach enables a more comprehensive understanding of differences in cross-national AI capability, providing cultural explanations for a new perspective on the diffusion of novel technologies. Our main findings reveal that individualism demonstrates the most stable positive association across model specifications. Uncertainty avoidance and motivation towards achievement and success are significant in the baseline SEM, but the results become sensitive after adding country-level control variables. Long-term orientation is significant in some OLS models but not in the baseline SEM. Power distance and indulgence are not supported in the baseline SEM. Results suggest that cultural values should be considered alongside economic, infrastructural, and regional conditions when analyzing cross-national differences in AI capability. Our findings provide a contextual perspective for policymakers and managers that are developing strategies for achieving competitive advantage. Considering the turbulence of the business and social environments, we argue that cultural adaptive capabilities are essential for global competitiveness. Full article

The rapid transition toward renewable energy requires transparent and spatially explicit methods for identifying suitable photovoltaic (PV) development areas. This study develops a geospatial artificial intelligence (GeoAI) decision-support framework for PV site suitability assessment in Rajasthan, India. Eleven harmonized predictors were used: global horizontal irradiance (GHI), photovoltaic power output (PVOUT), temperature, wind speed, aerosol optical depth (AOD), elevation, slope, albedo, land use/land cover (LULC), distance to roads, and distance to power lines. Reference labels were generated from an explicit rule-derived suitability index, class thresholds, and exclusion logic; therefore, the machine-learning task was to reproduce a transparent suitability framework rather than to predict observed PV yield or project-level performance. Extreme Gradient Boosting (XGBoost) was compared with simpler baseline models, evaluated using random and spatial-block validation, and interpreted using SHapley Additive exPlanations (SHAP). Independent overlays with known solar-installation records, presence-background robustness testing, and uncertainty/sensitivity analysis were used to examine spatial plausibility, spatial autocorrelation, deterministic label effects, and parameter uncertainty. The resulting outputs include pixel-level suitability zones, contiguous candidate polygons, district-level capacity-oriented summaries, and planning-priority classes. The framework is intended as a risk-aware regional screening tool: high model agreement indicates consistency with the constructed suitability labels, while final project decisions require parcel-scale land, grid, environmental, social, and economic assessment. Full article

Phytoplankton communities in the proposed water source area of the Western Route of the South-to-North Water Diversion Project showed multi-level responses across monitoring-period groups and diversion areas. Based on 64 valid samples, total biomass ranged from 0.027 to 5.659 mg L⁻¹ and showed no consistent differences between monitoring-period groups or diversion areas, indicating site- and sampling-period-scale patchiness. Among the dominant biomass-contributing taxa, most were diatom taxa, and the relative contributions of the top ten dominant taxa and Other taxa were reorganized among monitoring-period–area combinations. NMDS, PERMANOVA, and PERMDISP showed that monitoring period was significantly associated with community structure, whereas diversion-area effects were not significant. dbRDA indicated significant environmental–spatial constraints on community composition, with an adjusted explanatory power of 28.2%; T, NH₄⁺–N, TN, NO₃⁻–N, EC, pH, DO, and DTN were significant predictors. VPA showed stronger pure environmental than pure spatial effects, while DDR and EDR revealed significant geographic and environmental distance relationships. Taxonomic Bray–Curtis null models suggested a predominance of stochastic-like taxonomic turnover signatures, with stronger deterministic-like deviations in the upper-line diversion area. GAM identified NH₄⁺–N, DO, and EC as significant biomass predictors. These findings support integrating biomass, community composition, measured physicochemical variables, and taxonomic assembly signatures into sustainability-oriented phytoplankton monitoring for high-elevation riverine water source areas, thereby providing ecological evidence for sustainable water source protection and adaptive management. Full article

Early wildfire monitoring in power transmission corridors requires reliable detection of weak fire and smoke cues under complex field conditions and strict edge-computing constraints. To address these issues, this paper proposes an edge-deployable RGB–thermal framework based on visible and thermal infrared (TIR) imaging for unmanned aerial vehicle (UAV)-based corridor monitoring, including a spatial detector, YOLO-MMSC, and a temporal-enhanced version, YOLO-MMSC-T. The study also establishes a self-collected corridor-oriented RGB–thermal (RGB–T) dataset to complement public wildfire data. Unlike existing RGB–thermal wildfire datasets that mainly focus on forest or wildland fire scenes, the proposed dataset is specifically organized for complex-background power transmission-corridor monitoring, including continuous UAV sequences, nighttime conditions, smoke/vegetation occlusion, long-range small targets, and hard-negative interference. To the best of our knowledge, this is the first self-collected RGB–thermal wildfire dataset designed for this specific application scenario. The framework integrates a mobile inverted bottleneck convolution (MBConv) lightweight backbone, a Shallow Detail Fusion Module (SDFM) for shallow cross-modal alignment and denoising, a Content-Guided Attention (CGA) module for adaptive fusion, and normalized Wasserstein distance (NWD)-based box regression for long-range small-target localization. Experiments on public and self-collected datasets show that YOLO-MMSC achieves 94.6% mAP@0.5, 95.0% precision, and 93.9% recall while running at 60 FPS on Jetson Orin NX. With temporal fine-tuning, YOLO-MMSC-T reaches a continuous detection rate (CDR) of 95.6% with a jitter index of $2.8\times {10}^{-3}$. Field experiments using a DJI Matrice 4T further indicate a practical operating altitude of 120–180 m. These results support lightweight RGB–thermal remote sensing for real-time wildfire monitoring in complex transmission-corridor environments. Full article

Hydro-cultural heritage is shaped by strong hydrological dependence and historical accessibility. To address insufficient identification of river-basin heritage linkages and their weak translation into graded protection, this study develops an analytical framework integrating heritage-site evaluation, cultural source identification, resistance-surface construction, potential corridor extraction, network grading, and protection guidance, and applies it to the Xiangjiang River Basin, China. Heritage sites were evaluated by protection level, historical continuity, spatial proximity, and hydro-cultural relevance. Cultural source areas were identified using weighted kernel density analysis, potential corridors were extracted using the minimum cumulative resistance model, and the graded corridor network was examined using network-structure indices. The results show river-oriented clustering, localized nucleation, and belt-like extension. Eight primary and fourteen supplementary cultural source areas were identified. Potential corridors are concentrated along the Xiangjiang main stem and major tributaries. In the resistance-surface construction, distance to the water system received the highest AHP-derived resistance weight, while GeoDetector showed that it had the highest, although modest, single-factor explanatory power among the tested variables for corridor spatial differentiation. The corridor network exhibits a primary–secondary–tertiary graded structure. This study reveals the spatial continuity and hierarchy of hydro-cultural heritage corridors and provides a methodological reference for river-basin conservation. Full article

This paper extends cyclic permutation coding, previously applied for error correction in power-line communications (PLC), to synchronization-oriented sequence design by introducing a novel class of Non-Binary Cyclic Permutation Sequences (NCPS) for low-correlation multiuser synchronization. Unlike conventional Zadoff–Chu (ZC) and constant-amplitude zero-autocorrelation (CAZAC) sequences that rely on complex-valued phase laws, NCPS employ discrete modular permutations mapped to complex exponentials. Autocorrelation properties were analytically derived where tractable, while general correlation behavior was characterized through structural analysis and confirmed via simulation. Results demonstrated that NCPS achieved near-orthogonal cyclic correlation performance comparable to ZC sequences while preserving optimal Hamming distance, beneficial for error correction, and offering reduced implementation complexity. These characteristics highlight the potential of NCPS as synchronization preambles in PLC systems and other low-complexity or quantized communication platforms, including Internet of Things networks. Full article

Electrical Impedance Tomography (EIT) is a promising non-invasive sensing technique, yet its practical application in resource-constrained environments is often limited by the high computational cost of inverse image reconstruction. To address this challenge, we focus on specific sensing objectives rather than full image recovery. In this study, we propose a lightweight, task-oriented inference framework for object localization in EIT that bypasses the need to solve computationally expensive inverse reconstruction problems. This approach addresses the high computational demands and hardware complexity of conventional iterative methods, which often hinder real-time monitoring in resource-constrained edge computing environments. Training datasets were generated via finite element method (FEM) simulations for Opposite and Adjacent current injection configurations. A feedforward neural network was developed to independently estimate the radial and angular object positions as probability distributions. Our systematic evaluation revealed that the localization performance depends on the injection configuration and model depth; notably, the Opposite method achieved perfect classification accuracy (1.00) for radial estimation with an optimized architecture of four hidden layers, whereas the Adjacent method exhibited higher ambiguity. Results quantitatively evaluated using the Wasserstein distance show that the Opposite configuration produces more localized, unimodal probability distributions than the Adjacent configuration by utilizing current fields that traverse the entire domain. Compared with existing image-based reconstruction methods, including the conventional electrical impedance tomography and diffuse optical tomography reconstruction software (EIDORS ver.3.12), the proposed framework reduced energy consumption from 3.09 to 0.96 Wh, demonstrating an approximately 70% improvement in energy efficiency while maintaining a high localization accuracy without the need for iterative Jacobian updates. This task-oriented framework enables reliable, high-speed, and energy-efficient localization, making it well-suited for low-power EIT applications in mobile and embedded sensor systems. Full article

Synthetic aperture radar (SAR) imagery plays a vital role in critical applications such as military reconnaissance and disaster monitoring. These applications require high detection accuracy. Therefore, rotated object detection has gained increasing attention. By predicting an object orientation angle, it offers advantages over horizontal bounding boxes, especially for elongated structures such as ships and bridges in SAR scenes. However, challenges such as speckle noise and complex backgrounds in SAR imagery still hinder high-precision detection. To address this, we propose WE-KAN, a novel rotated object detection framework based on wavelet features and Kolmogorov–Arnold network (KAN) prediction. First, we enhance the backbone by incorporating wavelet domain features from SAR grayscale images. The extracted wavelet domain features and image features are fused by a proposed attention module. Second, considering the sensitivity to angle prediction, we design a angle predictor based on KAN. This architecture provides a powerful and dedicated solution for accurate angle regression. Finally, for precise rotated bounding box regression, we employ a joint loss function combining a rotated intersection over union (RIoU) with a Gaussian distance loss function. These designs improve the model’s robustness to noise and its perception of fine object structures. When evaluated on the large-scale public RSAR dataset, our method achieves an AP50 of 70.1 and a mAP of 35.9 under the same training schedule and backbone network, significantly outperforming existing baselines. This demonstrates the effectiveness and robustness of our method for dense, small, and highly oriented objects in complex SAR scenes. Full article

Miniaturised proximity Time-of-Flight (ToF) sensors are attractive for robotics applications due to their low cost, compact size, and low power consumption, which makes them suitable for direct distribution on the robot body. However, both the accuracy and the reliability of their measurements are influenced by operating conditions and target properties. These aspects are not fully investigated in the manufacturer’s datasheet, yet they play a crucial role in downstream robotic tasks. To address this gap, we mounted three VL53L5CX sensors, an Ambient Light Sensor, and a thermistor on a robotic manipulator in a controlled laboratory setup and executed a series of experiments to characterise sensor performance. Specifically, experiments were conducted to quantify sensor drift over time, the influence of ambient illumination under three office lighting conditions, within-frame beam variability, depth accuracy over the 20–800 mm range for different materials, orientation sensitivity at different distances, and an empirical signal-to-noise ratio. The results reveal a transient warm-up effect at startup, after which measurements stabilise, a near-linear range-dependent bias with substantially larger uncertainty for dark targets, limited within-frame variability, and an invalid measurement rate consistently below 10%. Overall, the VL53L5CX provides repeatable measurements, and the findings of this work can be leveraged to derive more faithful sensor models, apply range bias correction, and broaden the range of robotic applications. Full article

Reliable fault detection along transmission corridors is essential for preventing small defects from developing into long outages and costly emergency operations. This study aims to improve the field reliability of an open vocabulary vision language backbone without retraining the large model in an end-to-end manner. The work focuses on four operational fault classes in multi-region corridor imagery collected during routine inspections and uses a Florence-2 vision language model as the base recognizer. On top of this backbone, three domain-specific components are introduced. A subclass-aware fusion scheme keeps probability mass within the active parent concept so that insulator icing and conductor icing produce stable, action-oriented decisions. A Power-Line Focus Then Crop normalization uses an attention-guided corridor window together with isotropic resizing so that thin conductors and small fittings remain visible in the processed image. A corridor geo prior reduces scores as the distance from the mapped centerline increases and in this way suppresses detections that lie outside the corridor. All methods are evaluated under a shared preprocessing and scoring pipeline in training-free and parameter-efficient tuning modes. Experiments on unseen regions show higher accuracy for skinny and low-contrast faults, fewer false alarms outside the right-of-way, and improved score calibration in the confidence range used for triage, while keeping throughput and memory usage suitable for unmanned aerial vehicles and substation edge devices. Full article

Wireless power transfer (WPT) systems are generally sensitive to variations in separation distance and coil alignment, which result in reduced power transfer efficiency and delivered power. Various approaches based on control system and active matching circuits have resulted in more complex implementations. This work, by contrast, presents a full DC–DC inductively coupled WPT system employing coupled nonlinear resonators to automatically adapt the system for variations in transfer coil separation and orientation, maintaining high transfer efficiency at a constant output power level. With entirely passive circuit components, the nonlinear resonators suppress the frequency-splitting phenomenon typical of WPT systems that leads to efficiency degradation. A class-EF power amplifier used in the transmitter experiences an approximately constant impedance, providing a constant output power while maintaining high efficiency. On the receive side, a class-E rectifier operates at a constant input power, achieving high overall efficiency without active control. An experimental demonstration delivers 5 W with a 6.12% power variation over a 1 to 9 cm distance variation and achieves a peak DC–DC efficiency of 71.6%. The response of the system to changes in coil separation is compared with a conventional linear WPT circuit, showing a constant-power and high-efficiency operation. Full article

The sport of orienteering requires athletes to reach specific points marked on a map (called “punching stations”) in the shortest possible time. Currently, the recording of athletes’ passages through the stations is performed offline. In addition to delays in generating intermediate and final rankings, this approach often leads to detection errors and potential cheating related to the lack of authentication of an athlete’s actual passage at a given station. This paper aims to define and design a system enabling three main functionalities: 1. real-time monitoring of athletes’ trajectories through a sensor network connected to control stations; 2. multi-modal authentication of athletes at each station; and 3. immutable certification of each athlete’s passage through blockchain-based recording. System performance is evaluated in terms of wireless network coverage and data collection efficiency across three representative environments: urban, rural, and forested areas. Results are obtained through a measurement campaign for two dedicated wireless technologies: ZigBee for local mesh network and LoRa for long-range links to connect local mesh networks to the cloud over the Internet, which is then accessed by the race organizers. Furthermore, two supporting subsystems are described, addressing athlete authentication and data integrity assurance, as well as a blockchain recording for the overall event management framework. Results are in terms of coverage distances for both technologies, proving highly effective across varied terrains. Field tests demonstrated significant communication capabilities, achieving distances of up to 1800 m in open spaces. Even in challenging, dense wooded environments, the system maintained reliable coverage, reaching transmission distances of up to 600 m. Local ZigBee links between punching stations achieved ranges between 70 and 150 m in forested areas. These findings validate the use of a wireless multi-hop network designed to minimize packet loss and ensure reliable data delivery in competitive scenarios. The feasibility is also investigated in terms of WSN performance, delay analysis and power consumption evaluation. Full article

We have studied the extinction power flow for a dipole in a laser beam, and embedded in a dissipating medium. The power flows along the field lines of the Poynting vector. We have shown that near the particle, the field lines form closed loops, which start and end at the location of the dipole. A closed-form expression for these loops has been derived, and we have shown how the orientation direction of a loop is determined by the permittivities and permeabilities of the host medium and the particle. It is also shown that the spatial extent of these loops is determined by singularities in the flow pattern. It is shown that the extent of the loop structure near the dipole diminishes strongly when there is dissipation in the medium. This is due to the appearance of singularities very close to the particle, which are due to the damping. At greater distances, flow lines run off to the far field or they come in from the far field. Most flow lines change from incoming to outgoing, or vice versa, so they turn around somewhere in the flow field. Singularities, points where the Poynting vector vanishes, appear on the coordinate axes. At these points, field lines split. Off the axes, singularities appear as the centers of vortices. Near a vortex, energy swirls around the singular point. Field lines can come out of the center of a vortex or end there. Full article

As artificial intelligence (AI) systems grow more powerful, autonomous, and embedded in critical infrastructure, their identification and traceability become foundational to regulatory oversight and sustainable digital governance. In digitally transformed enterprises, long-term sustainability depends on transparent, accountable, and lifecycle-governed AI systems, all of which require verifiable identity. This study proposes a conceptual and architectural framework for AI identification, combining technical and governance mechanisms to support lifecycle accountability. The framework integrates five components: model fingerprinting, cryptographic hashing, blockchain-based registration, zero-knowledge proof (ZKP)-based proof of possession, and post-deployment structural change screening. We introduce a dual-layer identifier, consisting of a machine-verifiable primary hash and a human-readable secondary identifier, anchored in a tamper-resistant registry. Identity validation is supported by selective ZKP-based verification at governance-defined checkpoints, while post-deployment changes are monitored using Lempel-Ziv Jaccard Distance (LZJD) as a governance-oriented screening signal rather than a semantic performance metric. The framework establishes an enforceable and transparent identity infrastructure that enables continuity, auditability, and policy-aligned oversight across AI system lifecycles. By embedding AI identification within enterprise architecture and governance processes, the proposed approach supports sustainable innovation, strengthens institutional accountability, and provides a foundation for selective, policy-defined verification during digital transformation. Full article

DNA nanotechnology offers an unprecedented level of structural programmability for organizing metallic nanoparticles into precisely defined architectures, providing a powerful platform for plasmonic biosensing. In particular, gold and silver nanoparticles assembled on DNA nanostructures enable nanometer-scale control over interparticle distance, orientation, and spatial symmetry, which directly govern collective plasmonic behaviors and optical signal transduction. This review summarizes recent advances in DNA nanostructure-mediated assembly of metal nanoparticles, with an emphasis on design principles and assembly strategies that enable static and dynamic control of nanoparticle organization. Representative examples are discussed to illustrate how well-defined plasmonic assemblies give rise to tunable optical responses, including localized surface plasmon resonance modulation, chiroptical signals, fluorescence enhancement or quenching, and surface-enhanced Raman scattering. The role of structural programmability and stimulus-responsive reconfiguration in translating molecular recognition events into amplified optical outputs is highlighted in the context of biosensing. Finally, current challenges and future perspectives are outlined, focusing on structural robustness, signal reproducibility, and integration toward practical and multiplexed biosensing platforms. Full article