Search Results (1,237)

This study presents the results of chemical and morphological analyses of conductive layers, indium tin oxide (ITO) and silver, deposited on lead zirconium titanate (PZT) substrates, in the form of ITO/PZT, Ag/PZT, and PZT buffer samples. The buffer layer was also examined to assess any potential impacts on the interface and was obtained by etching silver-coated PZT discs in an acid sonification bath. The ITO/PZT discs were obtained by DC sputtering. Chemical and morphological analyses were conducted using Raman spectroscopy, X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDS). XRD analysis revealed distinct diffraction peaks corresponding to the composition and crystalline structure of the various discs. This established the presence of the expected face-centered cubic (FCC) structure of silver, the perovskite phase of PZT, and the cubic bixbyite structure of the conductive ITO layer. SEM/EDS illustrated the particle distribution and elemental composition of the samples. Raman spectroscopy further corroborated the presence and identity of the surface layers of the samples. The results demonstrate that ITO/PZT structures have the expected compositions and identified impurities. SEM results give insight into possible effects on piezoelectric effects and integration into opto-electronic devices. Full article

Understanding user behavior on the web is considered essential for personalization, recommendation, and anomaly detection. Centralized analytics approaches raise significant privacy risks and regulatory concerns, particularly when large volumes of interaction data are collected in the cloud. Federated learning offers a decentralized alternative but faces challenges in handling heterogeneous, Non-Independently and Identically Distributed (non-IID) web interaction data. This paper presents FogLearn-Web, a fog computing-based federated learning framework for privacy-preserving web user behavior analytics. The architecture employs hierarchical aggregation in which browser-embedded models train locally, fog nodes perform behavior-aware regional aggregation, and the cloud maintains a global model with formal differential privacy guarantees. A key contribution is the behavioral sketch, a compact representation of local interaction distributions that enables attention-weighted federated averaging without exposing raw data. Experiments on benchmark and real-world datasets show that FogLearn-Web achieves within 2.3% of centralized accuracy while reducing data transmission by 89% and improving convergence under non-IID settings by 34% over standard FedAvg. Full article

As oil and gas reservoirs progress into the mid-to-late stages of development, produced fluids increasingly exhibit high water-cut and complex flow regimes. Conventional water-cut measurement techniques based on capacitance, conductance, and resistance often face challenges in terms of accuracy, stability, and adaptability. In this study, a novel non-contact broadband microwave system, based on a ridged-horn antenna microwave transmission sensor (RHAMTS), is proposed to achieve highly sensitive full-range (0–100%) water-cut monitoring. The RHAMTS consists of two identical ridged-horn antennas, whose geometries are optimized through analytical design calculations and full-wave finite-element simulations. Numerical simulations are first performed to elucidate the sensing mechanism. Subsequently, static and dynamic experiments are conducted under two representative conditions: emulsified oil-water mixtures and stratified oil-water layers. The results indicate that the broadband spectral signatures of the RHAMTS can effectively characterize water-cut in both emulsified mixtures and stratified oil-water layers. For emulsified mixtures, both amplitude attenuation and phase shift vary systematically with water-cut, and the RHAMTS can still effectively characterize water-cut under saline conditions. For stratified oil-water flow, results from both static and dynamic experiments demonstrate that amplitude attenuation provides more robust features for practical water-cut discrimination. Compared with conventional methods, the proposed RHAMTS offers non-contact operation, rich spectral information, and compatibility with various flow regimes, providing a feasible and efficient approach for water-cut monitoring under complex field conditions. Full article

By comparing Buber and Levinas, this article aims to clarify the constitution of the self as a place of identity. Both authors recognise, at first, that the self is only defined in relation to others and, ultimately, in relation to the You. However, the definition of that relationship is opposite in both authors. Levinas’ interpretation echoes Luther and Kierkegaard, as it establishes an insurmountable difference between you and me. Buber, although he borrows many concepts from the Danish philosopher, fundamentally, the definition of the individual as a relationship, contradicts Kierkegaard by defining the relationship not as a simple ‘facing’, but as a third party between me and you. Finally, it is concluded that although the individual must be preserved in the I–You relationship, this cannot be understood as something in itself, as a third part, and, on the other hand, there must be a certain knowledge about the Thou for the relationship to be real. Full article

Monitoring students’ on-screen availability is increasingly critical for analyzing participation patterns in synchronous online learning, especially under videoconferencing conditions characterized by compressed video streams, low-resolution face regions, fluctuating bandwidth, and dynamically reconfigured grid layouts. This study introduces a practical computer vision pipeline that integrates deep learning-based face detection, lightweight embedding-based identity matching, and frame-level temporal aggregation to estimate students’ visual presence (VP) during live online classes. A real-world dataset comprising 27 participants and 16,200 frames was collected under authentic conditions, including codec compression, variable image quality, and dynamic layout changes. Four widely used face detection models (Haar Cascade, DSFD, MTCNN, and YuNet) were benchmarked on noisy and low-quality images. Quantitative evaluation on a manually annotated subset of 270 frames demonstrates that MTCNN and YuNet yield lower average VP estimation errors (27.63% and 22.20%, respectively) compared to Haar Cascade (75.34%) and DSFD (47.14%), with YuNet also achieving the shortest average processing time of 0.069 s per frame. While the pipeline is intentionally streamlined to facilitate practical use by instructors, the study provides clearly defined steps and parameter settings, establishing a reproducible procedure for benchmarking face detection performance in synchronous online class environments. Full article

In pedestrian re-identification (ReID) tasks, existing models face dual challenges: first, surveillance cameras capture images at long distances with low resolution and blurriness; second, image data suffers from insufficient samples, limited poses, and cross-domain adaptation issues. To address these issues, we propose a wavelet-convolution-based person re-identification framework assisted by a Stable Diffusion-based identity-preserving image generation module used only at inference time. This approach employs a dual-channel wavelet convolutional neural network for multi-scale feature extraction of pedestrian images, combined with cross-attention and gating mechanisms for dynamic data fusion. Additionally, we incorporate a pre-trained Pose2ID-based auxiliary generation branch that synthesizes identity-preserving pedestrian views with diverse poses under human keypoint guidance. These generated views are used only at inference time, where their WSNet features are fused with the feature of the original image to provide pose-complementary representation enhancement. Experiments on the Market-1501 and MSMT17 benchmark datasets demonstrate that our method achieves an mAP of 92.1% and a Rank-1 accuracy of 96.5% on Market-1501, and an mAP of 60.1% and a Rank-1 accuracy of 81.2% on MSMT17, with a WSNet backbone of 2.66 M parameters. Compared with the baseline models, the proposed method improves mAP by 5.1 and 7.6 percentage points on Market-1501 and MSMT17, respectively. Full article

The query propagation paradigm provides a unified theoretical framework for end-to-end multi-object tracking, yet it still faces challenges in complex scenarios involving multi-scale variations, dense interactions, and trajectory fragmentation, including insufficient query initialization quality, imprecise feature alignment, and difficult identity recovery. Building upon MOTRv2, this paper proposes three core improvements. First, we design a geometric prior injection strategy based on sine–cosine encoding, which explicitly encodes target location and scale information into detection queries, providing high-quality initialization for tracking queries. Second, we propose a width–height-modulated deformable attention mechanism that dynamically adjusts the sampling range of deformable convolution according to target size, enabling fine-grained feature matching for multi-scale targets. Third, we construct a motion-direction-consistency-based trajectory re-association module that leverages motion continuity to efficiently recover lost trajectories without introducing additional appearance models. Furthermore, we introduce a progressive joint training strategy that optimizes detection and tracking modules in stages, effectively mitigating gradient competition in multi-task learning. Extensive quantitative and qualitative experiments on the BEE24, UAVSwarm, and VTMOT infrared datasets validate the effectiveness of the proposed method. On the UAVSwarm dataset, our method achieves state-of-the-art performance with 52.4% HOTA, 72.1% MOTA, and only 51 identity switches. Ablation studies further reveal the synergistic enhancement mechanism among the proposed modules. Full article

Compact 5G millimeter-wave (mm-Wave) multiple-input multiple-output (MIMO) systems face a serious challenge as high isolation is required for high spectral efficiency. This paper presents a novel computational design framework for enhancing the isolation of a two-port ultra-wideband (UWB) MIMO antenna, specifically targeting the 5G n257 band (26.5–29.5 GHz). A pixelated metasurface is presented and optimized with the help of a binary-coded Grey Wolf Optimizer (B-GWO) algorithm through a MATLAB-Computer Simulation Technology (CST) co-simulation interface, which is used in contrast to some conventional decoupling structures. A Geometric Mirror Symmetry method is used to accelerate the optimization process, which halves the number of optimization variables and significantly reduces the computational load. Crucially, this symmetry is also a fundamental requirement to ensure that the reflection coefficients ($S_{11}$, $S_{22}$) of the antennas remain identical. The proposed design achieves isolation levels better than 20 dB across the entire target band, reaching a peak isolation of $32.58$ dB at $28.67$ GHz, while maintaining reflection coefficients ($S_{11}$, $S_{22}$) below $-10$ dB. The MIMO diversity performance is comprehensively validated with an Envelope Correlation Coefficient (ECC) $<0.005$, a Diversity Gain (DG) of $9.99$ dB, and a Total Active Reflection Coefficient (TARC) $<-10$ dB. Moreover, the suppression of surface waves enhances the realized gain to $4.51$ dBi, providing a $0.57$ dB improvement over the reference antenna. In addition, an equivalent passive RLC circuit model is constructed to observe the physical process of the pixelated surface, which shows the optimized structure as a band stop filter at the coupling frequency. The high correlation of the Equivalent Circuit Model and full-wave simulation outcomes confirms that the suggested design procedure is a strong verification alternative to physical fabrication. Full article

Rapid urban expansion and the growing spatial requirements of utility-scale photovoltaic (PV) deployment compete for the same category of land—flat, accessible, and high-insolation terrain—yet the scale, trajectory, and planning-sensitivity of this conflict remain poorly characterised at the regional level. This study quantifies the spatiotemporal competition between urban construction land and PV-suitable land in the Yangtze River Delta (YRD) from 2000 to 2020 and projects its evolution to 2030 under three development scenarios. Built-up areas were extracted for three epochs using a Random Forest (RF) classifier on the Google Earth Engine (GEE) platform, achieving overall accuracies of 87.7–94.5% and Kappa coefficients of 0.718–0.739. PV site suitability was evaluated through a hybrid Multi-Criteria Decision Analysis (MCDA) framework combining Boolean exclusion constraints with an Analytic Hierarchy Process (AHP)-based Weighted Linear Combination model; the weight structure was validated by a Consistency Ratio of 0.006, and a One-At-a-Time sensitivity analysis confirmed spatial robustness across threshold scenarios. Spatial overlay analysis reveals that the cumulative area of PV-suitable land occupied by urban built-up uses grew from 15,862 km² in 2000 to 23,872 km² in 2020, representing an incremental loss of 8010 km² over two decades. Future conflict was projected using the PLUS model, calibrated on 2010–2020 observed expansion and validated against the 2020 classified map (OA = 93.99%, Kappa = 0.91). Under the Business-as-Usual (BAU) scenario, 33,368 km² of currently open PV-suitable land faces urban encroachment by 2030; the Ecological Conservation Priority (ECP) scenario reduces this figure to approximately 30,767 km², while the Economic Development (ED) scenario yields a near-identical outcome to BAU, indicating that development velocity alone does not determine the spatial extent of conflict—the allocation of growth does. These findings provide a quantitative basis for designating energy-strategic reserve zones within national spatial planning frameworks and demonstrate that targeted spatial governance, applied at high-pressure locations, can substantially slow the erosion of the region’s solar energy land base. Full article

Existing buildings with large glazing ratios within subtropical Mediterranean climates face substantial challenges for thermal and visual control of their indoor environment. Previous research by the same authors has already identified the potential of incorporating both solar–PDLC (polymer-dispersed liquid crystal) and SPD (suspended particle device) switchable films within facades exposed to high solar insolation to provide a wide dynamic range of visual transparencies. This paper identifies a novel application for switchable laminates within a dynamic external shading device that permits the casting of a shadow on demand onto existing fenestration. This study compares the degree of glare within an enclosed space attained by a conventional opaque overhang over a window to that achieved with glass shading overhangs incorporating two types of switchable films. Using a scale model in a field test setting, indoor illumination and glare measurements are investigated under different states of switchable films and compared to those provided by conventional static glazing, with and without ordinary external overhangs under identical field test conditions. Results show that switchable overhangs in their transparent/bleached state can allow the ingress of daylight without creating excessive glare, whereas in their translucent/tinted state, switchable shades can deliver a level of glare protection similar to that provided by an opaque shading overhang. Full article

Background/Objectives: Spinal cord injury (SCI) leads to long-term changes in mobility, bodily function, and everyday participation, extending beyond physical impairment to affect autonomy, identity, and social inclusion. In Greece, limited community-based rehabilitation services, environmental inaccessibility, and fragmented follow-up care further shape the lived experience of individuals with SCI. This study aimed to explore the lived experiences and perceived rehabilitation needs of people with paraplegia living in the community, adopting a phenomenological approach to understand rehabilitation as an ongoing process of reclaiming autonomy, dignity, and participation. Methods: A qualitative phenomenological design was employed. In-depth semi-structured interviews were conducted with fourteen individuals with paraplegia following SCI. Data were analyzed using Braun and Clarke’s reflexive thematic analysis, supported by ATLAS.ti software. Results: Participants described living with SCI as a ‘Socially lived disability: a daily confrontation with an inadequate system and the ongoing struggle for accessibility, autonomy, and dignity’ (Overarching Theme). Participants’ experiences were organized into six themes: (A) facing the new reality, (B) barriers and facilitators of independent living, (C) role and importance of rehabilitation, (D) me and others around me, my difference, (E) the need for adequately trained and informed health professionals and caregivers, (F) ageing as an additional challenge. Conclusions: Living with SCI is experienced as an ongoing process of embodied and social reorientation, in which autonomy, participation, and dignity are continuously negotiated rather than restored once and for all. Rehabilitation emerges as a lifelong, person-centered process that extends beyond functional recovery to support bodily confidence, accessibility, social inclusion, and quality of life across the life course. These findings highlight the need for coordinated, community-based rehabilitation systems, accessible environments, and adequately trained health professionals capable of addressing the evolving functional, social, and existential realities of individuals living with SCI. Full article

2S/LGBTQIA+ survivors of intimate partner violence (IPV) face multiple, intersecting barriers to accessing care, yet little is known about how these barriers are shaped by IPV-caused brain injury (IPV-BI). Background/Objectives: This study aimed to explore how stigma and institutional trust influence 2S/LGBTQIA+ survivors’ experiences of help-seeking following IPV-BI. Guided by a Community Advisory Board, four semi-structured focus groups were conducted with 29 2S/LGBTQIA+ IPV-BI survivors. Methods: Reflexive thematic analysis was used to examine participants’ help-seeking accounts, with attention to minority stress and intersecting stigmas related to IPV, BI, and 2S/LGBTQIA+ identity. Results: The findings indicate that survivors navigated compounded stigmas that limited access to safe, affirming services and heightened vulnerability during help-seeking. Institutional trust was central to participants’ decisions to disclose sensitive information and engage in care, with confidentiality emerging as a critical determinant of perceived safety. Participants described negotiating disclosure, anticipating discrimination, and avoiding services when systems were perceived as unsafe or unresponsive. Conclusions: These findings highlight the need for service systems to integrate IPV-BI into screening and support protocols, provide training on the intersections of IPV, BI, and 2S/LGBTQIA+ identities, and centre confidentiality as a condition for trust and access, ultimately fostering safer, more responsive systems of care. Full article

Background and Objectives: Shortages of dermatologists create significant barriers to care, particularly for inflammatory and history-dependent conditions where image-only artificial intelligence (AI) classifiers have limited applicability. Current teledermatology solutions largely focus on single-task, morphology-based neoplasm classifiers, leaving the vast majority of dermatologic presentations underserved. This study evaluated the diagnostic accuracy and management plan quality of Dermflow (Prava Medical, Delaware, USA), a proprietary dermatology-focused Multimodal Image-and-Data Assistant (MIDA) that autonomously gathers dermatology-specific history, integrates data with patient-submitted images, and outputs structured differential diagnoses and management summaries. Materials and Methods: Two AI systems, Dermflow and Claude Sonnet 4 (Claude, a leading vision–language model), analyzed 87 clinical images from the Skin Condition Image Network and Diverse Dermatology Images databases, representing 10 inflammatory dermatoses and 9 neoplastic conditions stratified across Fitzpatrick Skin Tone (FST) categories (I–II, III–IV, V–VI). For the diagnostic comparison, Dermflow received images and autonomously gathered clinical history, while Claude received identical images without history. For the management plan comparison, both systems received the correct diagnosis and the clinical histories gathered by Dermflow. The primary outcome was diagnostic accuracy. The secondary outcome was management plan quality, assessed by two blinded dermatologists across eight clinical dimensions using 5-point Likert scales. Chi-square tests compared diagnostic accuracy between models; t-tests and ANOVA compared management quality scores. Results: Dermflow achieved markedly superior diagnostic accuracy compared to Claude (86.2% vs. 24.1%, p < 0.001). Both models maintained consistent diagnostic performance across FST categories without significant within-model differences (Dermflow p = 0.924; Claude p = 0.828). Management plan quality showed no significant overall differences between models. However, composite management quality scores declined significantly for darker skin tones across both systems: Dermflow scored 4.20 (FST I–II), 3.99 (FST III–IV), and 3.47 (FST V–VI); Claude scored 4.35, 3.97, and 3.44, respectively (p < 0.001 for most pairwise FST comparisons within each model). Conclusions: Multimodal AI integrating targeted history with image analysis achieves substantially higher diagnostic accuracy than image-only approaches across both inflammatory and neoplastic dermatologic conditions. Autonomous history gathering addresses fundamental limitations of morphology-only classifiers and enables scalable, patient-facing triage across the full spectrum of dermatologic disease. However, both models demonstrated reduced management plan quality for darker skin tones despite receiving the correct diagnosis, suggesting persistent training data limitations that require targeted bias-mitigation strategies beyond domain-specific instruction. Full article

Tangyue Village in Huizhou, China, is renowned for its monumental Bao-family archway complex and well-preserved ancestral halls, which host and memorial activities embodying rich clan traditions and regional cultural identity. However, these traditional spaces face contemporary challenges, including functional obsolescence, high energy consumption, and limited sustainability. Focusing on the memorial spaces of Tangyue Village, this study explores low-carbon design strategies for their renewal by developing a comprehensive research framework that integrates multi-stakeholder demand analysis, weighting evaluation, case-based design, and performance verification. Initially, user needs were identified through semi-structured interviews and behavioral observations, followed by the application of the Fuzzy Kano (FKANO) model to classify and filter these requirements. Subsequently, a multi-level evaluation system was established, encompassing low-carbon performance, spatial functionality, cultural continuity, and community participation. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach combined with the entropy weight method was then employed to determine the relative importance of each indicator. The results indicate that the organization of memorial spaces, the application of low-carbon materials, rainwater harvesting, and spatial accessibility represent key design priorities. Space syntax simulations conducted via DepthmapX further demonstrate that the optimized design significantly improves spatial accessibility, permeability, and vitality while enhancing the overall low-carbon performance. Ultimately, this study proposes practical low-carbon renewal strategies for memorial spaces in traditional settlements, offering a systematic approach that balances cultural heritage preservation with environmental sustainability. Full article

Injection molding process optimization has predominantly relied on simulation-generated data, which cannot capture machine-specific variability and stochastic process noise inherent in real manufacturing environments. This paper presents an experiment-driven machine learning framework for multi-objective optimization of injection molding process parameters targeting volumetric shrinkage, warpage, cycle time, and part weight. Physical experiments were conducted on an industrial injection molding machine using high-density polyethylene with a face-centered central composite design. Systematic benchmarking of four machine learning algorithms under identical cross-validation protocols identified Gaussian process regression as the best-performing surrogate model for the majority of quality metrics, while warpage prediction remained challenging across all algorithms due to its complex thermo-mechanical origins. Permutation-based feature importance analysis established a clear parameter hierarchy, identifying holding time as the dominant factor governing multiple quality responses. Constrained Bayesian optimization with progressive constraint tightening was employed to identify optimal parameter sets and fundamental process capability boundaries. The resulting parameter configurations were validated against a held-out test set. This work demonstrates that rigorous, data-driven optimization using exclusively experimental data provides a viable and practically achievable alternative to simulation-based approaches, contributing to experiment-centric smart manufacturing in polymer processing. Full article