Search Results (1,317)

Visible light communication (VLC)-based Li-Fi systems suffer from limitations in transmission capacity expansion due to the restricted modulation bandwidth of LEDs. In this study, a latent representation-based NRZ-OOK Li-Fi transmission framework that exploits the statistical feature distribution of the latent space is proposed to improve transmission efficiency without expanding the physical bandwidth. An autoencoder is employed to transform input images into low-dimensional latent vectors, which are then quantized and modulated for transmission. At the receiver, hard decision and inverse quantization are performed, and the image is reconstructed through a trained decoder by leveraging the distribution characteristics of the latent representation. The effective transmission capacity gain $G_{\mathrm{c}\mathrm{a}\mathrm{p}}$ is defined to quantify the amount of representable information relative to the original data under the same physical link resources according to the latent dimension, achieving up to a 49-fold data representation efficiency. The experimental results over practical optical links (0.5–1.5 m) showed that, in short-range conditions, larger latent dimensions maintained higher reconstruction PSNR, whereas under channel degradation conditions, smaller latent dimensions exhibited higher robustness, demonstrating a performance inversion phenomenon. Furthermore, it was confirmed that the dominant factor governing reconstruction performance shifts from the representational capability of the data to error accumulation characteristics depending on the channel condition. These results suggest that the latent representation-based transmission framework is an effective Li-Fi strategy that can simultaneously consider transmission efficiency and channel robustness through information representation optimization in bandwidth-limited environments. Full article

Firefighters must locate victims reliably to carry out rescue operations within burning structures during urban firefighting events. Low visibility, reduced oxygen levels, weakened structural rigidity, and dense smoke make it difficult to locate victims. In addition to these challenges, victims may be unconscious and unable to report their locations to firefighters. This research work explores the Double Deep Q-Network (Double DQN), Dueling Deep Q-Network (Dueling DQN), and Dueling Double Deep Q-Network (D3QN) agents for an unmanned aerial vehicle (UAV) to navigate around a structure and locate trapped victims within it. The UAV’s position, Light Detection and Ranging (LiDAR), and infrared camera data are utilized as inputs for the Deep Q-Networks. The PER is used to store transitions and sample them according to priority for training. Python’s Pygame library is used in this research to create a simulated environment in which infrared camera and LiDAR data are simulated. The performance of the UAV agent is evaluated using cumulative maximum reward, reward distribution histogram, Temporal Difference (TD) error over time, and number of successful episodes. Among the three DQN UAV agents, the Dueling DQN and Double DQN have potential for real-world applications in firefighting. Full article

Accurately estimating calorie intake and nutrient composition from what we eat remains one of the most practical challenges in maintaining a healthy lifestyle. Manual food logging and database-based estimations are often inaccurate because ingredient proportions and preparation styles vary widely. This paper presents a lightweight, privacy-preserving framework that estimates calories and detailed nutrient values from a single image. The model uses a Mask R-CNN-based segmentation network to identify visible food components, measure their area, estimate their volume using preset height values, and map them to nutritional information obtained from reliable datasets such as USDA and Food-a-pedia. The system integrates federated learning (FL) to ensure privacy by allowing the model to improve collaboratively without sharing raw user data. The proposed architecture achieved a mean Average Precision (mAP) of 96% for detection and 92% for segmentation, confirming its precision and efficiency. The model is trained and evaluated on a curated pizza dataset consisting of 1107 images across 50 topping categories, using a standard train-validation-test split (666/219/222) to ensure reliable performance assessment. The proposed system also achieves low nutrition estimation error, with calorie and nutrient deviations remaining within approximately 3.8% to 11.1% across evaluated metrics. A lightweight mobile interface is demonstrated through a Figma-based prototype mockup to illustrate potential real-world deployment and user interaction. Full article

Virtual plant models combined with ray-tracing simulations are an established tool for evaluating plant–light interactions. Current approaches often simplify leaf surface properties by assuming diffuse reflectance behavior, despite experimental evidence that leaf reflectance is direction-dependent across much of the visible spectrum. This study investigates whether incorporating measured, spectrally resolved and direction-dependent (BRDF) reflectance properties into these models affects simulation outcomes. Using virtual 3D cucumber (Cucumis sativus) plant models with PhongShader-based optical leaf characteristics for BRDF consideration, light absorption and local photon flux densities were simulated under a wide range of lighting conditions, including diffuse and directed sunlight scenarios. While total light absorption at the leaf level is only marginally affected (mean absolute percentage error, MAPE < 2%), spectral distortions in leaf surroundings, especially under direct light, exceeded 8% in the blue wavelength range. Beyond their relevance for estimating photosynthetic rates, such distortions directly affect the spectral composition within the canopy, which is particularly critical in greenhouse applications where optical sensors are used to monitor spectral ratios and, therefore, require the accurate prior simulation of canopy light conditions. This is particularly relevant for setups with directional artificial lighting. The findings suggest that BRDF modeling is not critical for calculating photosynthetic rates under most conditions, but is required in spectral analyses or for optimizing artificial lighting designs. Full article

Digital literacy is widely promoted as enabling later-life inclusion, but its potential to generate trust-related vulnerabilities remains insufficiently examined, particularly in rapidly ageing societies. Using nationally representative data from the 2022 China Family Panel Studies including 1583 adults aged 60 and above, this study examines whether digital literacy is associated with older adults’ trust in strangers, which is interpreted here as a form of trust vulnerability in low-verifiability interactions. In this study, trust vulnerability is operationalised through respondents’ self-reported trust in strangers, while digital literacy is operationalised through behavioural competences spanning information and data literacy, communication and collaboration, content creation, digital transactions, and problem-solving. OLS regression models with Huber–White robust standard errors are employed. The findings indicate that higher levels of digital literacy are positively associated with stronger trust in strangers, suggesting that competence may heighten older adults’ inclination to trust and increase exposure to digital risks. This association is driven primarily by functional competences, with mobile payment use and problem-solving skills showing the largest and most consistent associations. By contrast, subjective well-being and perceived platform security show no significant mediating roles. The study recommends integrating functional training, trust calibration and risk-recognition education, alongside interpretable and verifiable safeguards that make security more visible to older adults. Full article

The lack of a reliable national sampling frame poses a major challenge for conducting representative population and household surveys, particularly in developing countries affected by displacement and rapid territorial change. This study addresses this gap by developing Armenia’s first digitized national sampling frame, where accessible survey frames are severely limited. We introduce an innovative pre-EA tool to semi-automatically construct the digital sampling frame using publicly available datasets. Compared with traditional approaches, this method outperforms in several ways: it enables rapid, semi-automated frame construction, minimizes resource requirements, eliminates geometric errors associated with manual digitization, and produces pre-census EAs (pre-EAs) that both nest within administrative boundaries and align with visible ground features. The approach also integrates gridded population data to reflect recent urbanization and migration, generating pre-census EAs and urban–rural classifications suitable for national surveys. The sampling frame was successfully applied in the World Bank’s “Listening to Armenia” survey. Overall, the study demonstrates that automated, data-driven approaches can efficiently produce accurate, scalable, and adaptable national sampling frames, offering potential utility in other countries facing similar constraints. Full article

Precise 3D reconstruction of non-cooperative space targets is a prerequisite for active debris removal and on-orbit servicing. However, this task is impeded by severe environmental challenges. Specifically, the limited dynamic range of visible light cameras leads to frequent overexposure or underexposure under extreme space lighting. Compounded by sparse textures and strong specular reflections, these factors significantly constrain reconstruction accuracy. While existing general-purpose feed-forward models such as MapAnything offer efficient inference, their geometric recovery capabilities degrade sharply when facing significant domain shifts. To address these issues, this paper proposes an enhanced 3D reconstruction framework tailored for the space environment named In-Orbit MapAnything. First, to mitigate data scarcity, we construct a high-quality space target dataset incorporating extreme illumination characteristics, which provides comprehensive auxiliary modalities including accurate camera poses and dense point clouds. Second, we propose the SatMap-Adapter module to mitigate feature degradation caused by severe specular reflections. This architecture employs a hierarchical cascade sampling strategy to align multi-level backbone features and utilizes a lightweight adaptive fusion module to dynamically integrate shallow photometric cues, intermediate structural information, and deep semantic features. Finally, we employ a weight-decomposed low-rank adaptation strategy to achieve parameter-efficient fine-tuning while strictly freezing the pre-trained backbone. Experimental results demonstrate that the proposed method decreases the absolute relative error and Chamfer distance by 15.23% and 20.02% respectively compared to the baseline MapAnything model, while maintaining a rapid inference speed. The proposed approach effectively suppresses reconstruction noise on metallic surfaces and recovers fine geometric structures, validating the effectiveness of our feature-enhanced framework in extreme space environments. Full article

Volumetric additive manufacturing (VAM) enables layerless and fast printing within seconds. However, print quality remains highly sensitive to the delivered energy. In this study, the effects of visible (460 nm) and ultraviolet (385 nm) projector power were evaluated in a dual-color VAM setup with a CQ/EDAB initiated TEGDMA/BisGMA resin with an o-Cl-HABI inhibitor. Cubes ($6\times 6\times 6.7 {\mathrm{mm}}^3$) were printed under controlled visible and ultraviolet power and exposure times, then evaluated using in situ shadowgraphy, three-dimensional metrology, and confocal microscopy. Higher visible power reduced the polymerization initiation time, but increasing the visible dose rapidly led to over-polymerization, resulting in dimensional growth, corner rounding, and increased surface roughness ($R_a$). The lowest lateral variation was observed at the shortest exposure times, with a maximum error of 1.8%. Ultraviolet illumination did not significantly change initiation time or reduce over-polymerization within the tested intensities and inhibitor concentration ranges. Surface evaluations revealed a periodic line texture with a pattern pitch of approximately 25 μm. By shifting the focal plane and using a low-resolution projector, the pattern pitch increased to about 150 μm. These values were aligned with the ${\mathrm{MTF}}_{50}$ spatial frequencies of each projector at different defocus positions. This study provides useful guidelines for adjusting intensity to achieve high-fidelity VAM printed parts. Full article

Environmental monitoring systems require robust uncertainty quantification for effective decision-making in complex ecological processes. Harmful algal blooms represent a critical challenge where prediction uncertainty directly impacts resource allocation and response timing, yet current remote sensing-based prediction systems provide only deterministic classifications without confidence measures. This gap between algorithmic predictions and actionable risk assessment limits operational utility for stakeholders managing water quality under varying risk tolerances. This study developed a transferable probabilistic forecasting framework integrating Sentinel-2 multispectral imagery with quantile regression and ensemble machine learning to generate continuous confidence indicators for cyanobacteria density prediction, demonstrated through its application to Lake Okeechobee, Florida. The methodology combines spectral indices extracted from Sentinel-2 data with XGBoost for quantile regression at 0.05, 0.50, and 0.95 probability levels, and LightGBM for multi-horizon temporal forecasting. Sentinel-2’s 13 spectral bands spanning visible to shortwave infrared wavelengths, combined with its 5-day revisit frequency provide a spectrally rich and temporally dense input space that is well-suited to gradient boosting methods such as XGBoost, which can exploit complex nonlinear interactions among spectral features to distinguish cyanobacterial signatures from background water constituents. LightGBM achieved mean absolute percentage errors of 2.9% for 10-day forecasts and 5.7% for 20-day forecasts, outperforming conventional regression models. The framework generates 90% prediction intervals that enable reliable risk classifications for operational bloom management. This approach bridges the gap between satellite-based algal bloom detection and actionable decision-making by quantifying predictive uncertainty, representing a shift from binary classifications to probability-based environmental monitoring systems that accommodate varying stakeholder risk tolerances in water quality management applications. Full article

In unmanned ground vehicle (UGV) swarm systems, comprehensive environmental awareness is critical for coordinated operations. Yet they are frequently deployed in occlusion-rich, constrained environments where multi-agent visual fusion is essential. However, existing methods are critically limited by offline-calibrated extrinsic parameters, hindering flexible deployment, and by a strong co-visibility assumption, which fails under severe occlusion. To overcome these constraints, we introduce an end-to-end, calibration-free framework for the joint registration of cameras and subjects. Our approach begins with a single-view module that estimates subjects’ poses and appearance features. Subsequently, a novel graph-based pose propagation module (GPPM) treats UGVs’ cameras as nodes in a graph, connecting them with edges when they share co-visible subjects identified via appearance matching. Breadth-first search (BFS) then finds the shortest registration path from any camera to a designated root camera, enabling pose propagation via local co-visibility links and global alignment of all subjects into a unified bird’s-eye-view (BEV) space. This strategy relaxes the stringent requirement of full co-visibility with the root node. A multi-task loss function is proposed to jointly optimize pose estimation and feature matching. Trained and evaluated on a synthetic dataset with occlusions (CSRD-O) collected by a UGV swarm system, our framework achieves mean camera pose errors of 1.57 m/8.70° and mean subject pose errors of 1.40 m/9.14°. Furthermore, we demonstrate a scene monitoring task using a UGV swarm system. Experiments show that the proposed method generates robust BEV estimates even under severe occlusion and low inter-view overlap. This work presents a purely visual, self-calibrating multi-view fusion perception scheme, demonstrating its potential to support cooperative perception, task-oriented monitoring, and collective situational awareness in UGV swarm systems. Full article

Large language models are increasingly used as automated graders, yet their reliability under answer-side manipulation and their behavior in multi-model panels remain insufficiently understood. This paper introduces EvalHack, a matrix benchmark in which a fixed committee of four LLMs grades university-level machine learning exam answers under a strict integer-only contract (0–10) grounded in instructor-authored rubric artifacts. The dataset comprises 100 students answering 10 short, open-ended items (1000 answers). For each answer, the evaluation includes a clean version and two content-preserving adversarial variants that operate only on the student text: A₁, a visible coercive suffix appended to the answer, and A₂, a stealth variant that uses Unicode control characters (e.g., zero-width and bidirectional marks) to embed an instruction. EvalHack instruments the full grading pipeline, recording item-level member scores, the committee aggregate, within-panel disagreement, and discrepancies to human grades. Empirically, answer-side edits induce systematic score inflation and stronger top-end concentration, with edited answers clustering near the upper end of the scale. Within-panel disagreement, measured as the range between the highest and lowest member score, varies across conditions, with median Consistency Spread values of 3.0 (clean), 2.0 (A₁), and 6.0 (A₂). Compared to human graders, the panel is more lenient on average (MAE = 1.897; bias human − panel = −1.345). Finally, grouping items by disagreement shows that low-disagreement items exhibit smaller human-panel errors, indicating that within-panel spread can serve as a practical uncertainty signal for routing difficult answers to human review or to larger/more specialized panels. Full article

Accurate meteorological visibility estimation is critical to the safety and reliability of transportation and environmental monitoring systems. Despite the prevalence of deep learning, models often struggle with the non-linear visual degradation caused by varying atmospheric conditions and a scarcity of instrument-calibrated datasets. This study makes two primary contributions. First, we introduce the Hong Kong Chu Hai College Visibility Dataset (HKCHC-VD) comprising 11,148 high-resolution images paired with precise visibility measurements from a Biral SWS-100 sensor. Second, we propose a Range-Aware Attention Framework (RAT-Attn), an adaptive attention mechanism that translates classical range-specific atmospheric modeling into differentiable deep learning operations. This is a domain-specific architectural optimization that integrates a dual-backbone architecture (CNN and Vision Transformer) with a learnable threshold mechanism. This design enables the model to dynamically prioritize spatial and channel-wise features based on estimated visibility intervals, specifically targeting the non-linear visual degradation unique to fog and haze. Experimental results demonstrate that our proposed approach outperforms existing baselines, including VisNet and landmark ANN-based methods. The ResNet + ViT (spatial-threshold) variant achieves the most balanced performance, recording a Mean Squared Error (MSE) of 5.87 km², a Mean Absolute Error (MAE) of 1.65 km, and a classification accuracy of 87.07%. In critical low-visibility conditions (0 to 10 km), the framework reduces regression error by over 75% compared to the baselines. These results confirm that range-aware adaptive feature fusion is essential for robust meteorological estimation in real-world environments. Full article

This paper argues that digital identity in AI-mediated environments has become a central mechanism through which contemporary societies organise recognition, participation, and belonging. Digital identity is no longer simply a technical representation of the individual. It is produced through infrastructural processes of classification, ranking, and credibility signalling that determine who becomes visible, who is treated as legitimate, and who is able to participate meaningfully in social and civic life. The paper develops a conceptual framework that treats AI-driven platforms as social infrastructures rather than neutral intermediaries. It shows how identity is inferred through data-driven systems rather than negotiated through social interaction, how recognition is operationalised through visibility and credibility metrics rather than ethical judgement, and how participation becomes conditional on algorithmic allocation of attention rather than guaranteed by access alone. Visibility is identified as the key conversion point through which inferred identity becomes social consequence. Drawing on interdisciplinary literature, the analysis demonstrates that misrecognition, exclusion, and inequality in platform environments are not primarily the result of isolated error or intentional bias. They are patterned outcomes of ordinary optimisation processes that distribute legitimacy and opportunity unevenly across social groups. These dynamics reshape group formation, harden social boundaries, and concentrate risk among populations that are already more vulnerable to misrecognition and reduced contestability. The paper concludes that governing digital identity is a societal challenge rather than a purely technical one. As platforms increasingly perform institutional functions without equivalent accountability, digital identity governance becomes a critical site of social ordering. Addressing this challenge requires public standards for how visibility, recognition, and participation are allocated, meaningful avenues for contestation, and protections against the normalisation of stratified belonging in AI-mediated societies. Full article

To investigate the mechanical performance and failure modes of Prestressed Concrete Cylinder Pipe (PCCP) bell-and-spigot joints under conditions such as differential settlement, this study conducted a full-scale rotation test on a DN1400 PCCP joint and established a three-dimensional non-linear finite element model using ABAQUS. The experimental results indicate that when the relative rotation angle reaches approximately 1.92°, the primary failure mode is the slipping of the rubber gasket from the spigot groove, leading to sealing failure. Meanwhile, the strains in the concrete, mortar coating, and prestressing wires at the joint increase significantly with the rotation angle. The finite element simulation results align well with the experimental data, with an average error of 1.88%. Based on the validated model, a parametric analysis was performed on PCCP joints with diameters ranging from 1400 mm to 4000 mm. The study determined the ultimate relative rotation angle for different diameters based on the concrete visible crack criterion and revealed a significant size effect, characterized by a decrease in the ultimate rotation angle with increasing pipe diameter. These findings provide a theoretical basis for the design, construction, and safety assessment of PCCP pipelines. Full article

Anticipation allows individuals to prepare actions by predicting upcoming events, yet its influence on motor learning and its practical relevance for rehabilitation remain unclear. This study investigates how anticipation mechanisms shape motor learning and skill acquisition in a virtual leader–follower task and explores their potential for adaptive training. Forty-nine healthy adults performed a joystick-controlled tracking task in virtual reality, following a dynamic leader that was always visible (Control), became invisible at regular intervals (Deterministic Anticipation), or disappeared randomly (Stochastic Anticipation) to elicit anticipatory behavior. Kinematic and kinetic metrics and time-series analysis were used to evaluate synchrony, smoothness, and coordination. Performance improved from baseline to retention, with no distinct differences in final performance between the groups. However, slope-based analyses found that anticipation-based training accelerated learning, especially in the novice subgroup (baseline score < 35), with marked improvements in metrics such as score pause duration, temporal lag, and spatial error. Although participants reached similar final performance levels across protocols, the rate and pattern of learning differed across training protocols. Anticipation accelerates early-stage improvements, with the strongest effects observed in novice participants. The paradigm provides a high-resolution framework for adaptive motor training and assessment. Full article