Search Results (1,014)

Human pose estimation (HPE) in privacy-sensitive environments such as healthcare facilities and smart homes demands non-visual sensing solutions. Millimeter-wave (mmWave) radar emerges as a promising alternative, yet its development is hindered by the scarcity of high-fidelity datasets with accurate annotations. This paper introduces mmFree-Pose, the first dedicated mmWave radar dataset specifically designed for privacy-preserving HPE. Collected through a novel visual-free framework that synchronizes mmWave radar with VDSuit-Full motion-capture sensors, our dataset covers 10+ actions, from basic gestures to complex falls. Each sample provides (i) raw 3D point clouds with Doppler velocity and intensity, (ii) precise 23-joint skeletal annotations, and (iii) full-body motion sequences in privacy-critical scenarios. Crucially, all data is captured without the use of visual sensors, ensuring fundamental privacy protection by design. Unlike conventional approaches that rely on RGB or depth cameras, our framework eliminates the risk of visual data leakage while maintaining high annotation fidelity. The dataset also incorporates scenarios involving occlusions, different viewing angles, and multiple subject variations to enhance generalization in real-world applications. By providing a high-quality and privacy-compliant dataset, mmFree-Pose bridges the gap between RF sensing and home monitoring applications, where safeguarding personal identity and behavior remains a critical concern. Full article

The human sphenoid bone (SB), centrally located at the cranial base, is structurally and developmentally complex. It arises from multiple cartilaginous precursors and undergoes both endochondral and intramembranous ossification, forming essential elements such as the sella, orbital walls, and numerous foramina. This review integrates embryological, anatomical, and radiological findings to present a comprehensive view of SB development and variation. Embryological studies reveal a layered ossification sequence, with accessory centers in the presphenoid and basisphenoid that influence adult morphology and variants, such as the caroticoclinoid foramen. In adulthood, the SB consists of a central body, paired greater and lesser wings, and the pterygoid processes, which articulate with key craniofacial bones and transmit vital neurovascular structures. Notable variants include duplication or absence of foramina, ossification of ligaments such as the pterygoid and pterygospinous ligaments, and the formation of bony bridges among the clinoid processes. These variants may affect cranial nerve trajectories and surgical access, posing potential risks during neurosurgical, endoscopic, and dental interventions. Emissary structures such as the sphenoidal emissary foramen and the newly described sphenopterygoid canal underscore the region’s vascular complexity. Additionally, variations in the optic and Vidian canals, as well as the superior orbital fissure, can also impact surgical approaches to the orbit, sinuses, and skull base. Understanding the full spectrum of sphenoid bone embryogenesis and morphology is essential for safe clinical practice and practical radiological imaging. Full article

This study presents a self-contained derivation and solution of the Black and Scholes partial differential equation (PDE), replacing the standard Wiener process with a smoothed Wiener process, which is a differentiable stochastic process constructed via normal kernel smoothing. By presenting a self-contained, Itô-free derivation, this study bridges the gap between heuristic financial reasoning and rigorous mathematics, bringing forth fresh insights into one of the most influential models in quantitative finance. The smoothed Wiener process does not merely simplify the technical machinery but further reaffirms the robustness of the Black and Scholes framework under alternative mathematical formulations. This approach is particularly valuable for instructors, apprentices, and practitioners who may seek a deeper understanding of derivative pricing without relying on the full machinery of stochastic calculus. The derivation underscores the universality of the Black and Scholes PDE, irrespective of the specific stochastic process adopted, under the condition that the essential properties of stochasticity, volatility, and of no arbitrage may be preserved. Full article

Maritime training increasingly employs conventional full-mission bridge simulators (FMBS) and virtual reality (VR). This study aims to compare the incidence and severity of simulator sickness induced by a conventional FMBS and an equivalent VR system during a maritime navigation task. Thirty-two final-year maritime students with no prior VR experience participated in two 30 min sessions (conventional FMBS first, then VR after a minimum two-day break), performing an identical navigational watch scenario as observers. Pre- and post-session SSQ scores were collected using a standardized questionnaire, supplemented by post-study qualitative interviews. The results indicated no significant change in SSQ scores following the conventional simulator session. In the VR session, a slight increase in Total SSQ and in Oculomotor and Disorientation subscales was observed (uncorrected p-values < 0.05), but none of these remained significant after applying a Holm–Bonferroni correction. The Nausea subscale did not increase in either condition. Notably, participants’ baseline SSQ scores were lower before the VR trial than before the conventional trial, suggesting a possible novelty effect. Qualitative feedback reflected VR’s high immersion yet also some physical discomfort (e.g., headset weight and visual resolution), with most participants preferring the conventional simulator for longer training. Overall, while VR’s current hardware had comfort limitations, using a standard calm-weather training scenario in VR produced only a minimal, statistically non-significant increase in simulator sickness compared to the conventional simulator. Full article

Innovative and reliable structural health monitoring (SHM) is indispensable for ensuring the safety, dependability, and longevity of urban infrastructure. However, conventional methods lack full efficiency, remain labor-intensive, and are susceptible to errors, particularly in detecting subtle structural anomalies such as micro-cracks. To address this issue, this study proposes a novel deep-learning framework based on a modified Detection Transformer (DETR) architecture. The framework is enhanced by integrating a Vision Transformer (ViT) backbone and a specially designed Local Feature Extractor (LFE) module. The proposed ViT-based DETR model leverages ViT’s capability to capture global contextual information through its self-attention mechanism. The introduced LFE module significantly enhances the extraction and clarification of complex local spatial features in images. The LFE employs convolutional layers with residual connections and non-linear activations, facilitating efficient gradient propagation and reliable identification of micro-level defects. Thorough experimental validation conducted on the benchmark SDNET2018 dataset and a custom dataset of damaged bridge images demonstrates that the proposed Vision-Local Feature Detector (ViLFD) model outperforms existing approaches, including DETR variants and YOLO-based models (versions 5–9), thereby establishing a new state-of-the-art performance. The proposed model achieves superior accuracy (95.0%), precision (0.94), recall (0.93), F1-score (0.93), and mean Average Precision (mAP@0.5 = 0.89), confirming its capability to accurately and reliably detect subtle structural defects. The introduced architecture represents a significant advancement toward automated, precise, and reliable SHM solutions applicable in complex urban environments. Full article

With the continuous depletion of natural river sand resources and the escalating ecological degradation caused by excessive sand mining, manufactured sand has emerged as a sustainable and environmentally favorable alternative aggregate, playing an increasingly important role in the advancement of green construction materials. Nevertheless, the shrinkage behavior of manufactured sand concrete (MSC) exhibits significant deviations from that of conventional natural sand concrete due to differences in the material characteristics. Existing shrinkage prediction models—such as ACI 209, CEB-FIP 2010, B3, and GL 2000—fail to adequately incorporate the specific properties and substitution effects of manufactured sand, thereby limiting their predictive accuracy and applicability. To bridge this gap, the present study conducted a systematic evaluation of the four aforementioned classical shrinkage prediction models based on experimental data from MSC specimens incorporating varying replacement rates of manufactured sand. The findings revealed that models such as B3 and CEB-FIP 2010 neglected the influence of critical characteristics of manufactured sand—namely, particle morphology, gradation, and stone powder content—on the cementitious matrix and interfacial transition zone, which led to substantial prediction discrepancies. Accordingly, a nonlinear regression-based correction function was developed, introducing the manufactured sand content as a key influencing variable to recalibrate and enhance the ACI 209 and GL 2000 models for a more accurate application to MSC. The modified models exhibited markedly improved fitting performance and predictive robustness across the full range of manufactured sand replacement ratios (0–100%), thereby offering a more reliable framework for modeling the shrinkage development of MSC. Full article

Current research primarily focuses on using CFRP materials to strengthen small or medium-sized test specimens. To address this, our study employed ABAQUS software to analyze the axial compression behavior of large-scale reinforced concrete (RC) columns strengthened with prestressed carbon fiber reinforced polymer (CFRP) sheets. We conducted comparative analyses on key parameters: the prestress level applied to the CFRP, the width of CFRP strips, the spacing between strips, the confinement ratio, and the overall load–displacement curves of the columns. The results demonstrate that applying prestress significantly improves the efficiency of stress transfer in the CFRP sheet, effectively mitigating the stress lag phenomenon common in traditional CFRP strengthening, leading to a substantially enhanced strengthening effect. The CFRP wrapping method critically impacts performance: increasing the confinement ratio enhanced ultimate load capacity by 21.8–59.9%; reducing the strip spacing increased capacity by 21.8–50.4%; and widening the strips boosted capacity by 38.7–58%. Although full wrapping achieved the highest capacity increase (up to 73.2%), it also incurred significantly higher costs. To ensure the required strengthening effect while optimizing economic efficiency and CFRP material utilization, the strip wrapping technique is recommended. For designing optimal reinforcement, priority should be given to optimizing the confinement ratio first, followed by adjusting strip width and spacing. Proper optimization of these parameters significantly enhances the strengthened member’s ultimate load capacity, ductility, and energy dissipation capacity. This study enriches the theoretical foundation for prestressed CFRP strengthening and provides an essential basis for rationally selecting prestress levels and layout parameters in engineering practice, thereby aiding the efficient design of strengthening projects for structures like bridges, with significant engineering and scientific value. Full article

This article presents a new full-bridge converter with two series-connected transformers (TTFB), designed to meet the hold-up time requirements in power systems. The conventional TTFB topology offers low root mean square (RMS) output current, clamped voltage stress across the primary switches, and zero-voltage switching (ZVS) capability. However, under a wide input voltage range, it suffers from a significant circulating current during the freewheeling period, leading to efficiency degradation. To mitigate this issue, a new converter is proposed by integrating the TTFB with a boost circuit, which operates during the hold-up state when the input voltage drops below the nominal level. Thus, the proposed converter can increase the duty ratio under nominal input voltage conditions, thereby reducing the primary-side RMS current and improving efficiency. To validate the effectiveness of the proposed method, a prototype with a 12 V/400 W output was implemented. The proposed converter achieved a peak efficiency of 92.1% at 50% load, and maintained a higher efficiency across the entire load range compared to the conventional design. Thus, the proposed converter offers a solution for applications demanding extended hold-up time with improved efficiency. Full article

Generic and consistent formulations for measurement of the backscattering cross section $\left({\sigma }_{bs}\right)$ and the volume backscattering coefficient $\left(s_v\right)$ using broadband pulse compression and narrowband echo integration are derived, for small- and finite-amplitude sound propagation. The theory applies to backscattering operation of echosounders and sonars in general, with focus on fisheries acoustics. Formally consistent mathematical relationships for broadband and narrowband operation of such instruments are established that ensure consistency with the underlying power budget equations on average-power form, bridging a gap in prior literature. The formulations give full flexibility in choice of transmit signals and reference signals for pulse compression. Generic and general criteria for quantitative consistency between broadband and narrowband operation are derived, establishing new knowledge and analysis tools. These criteria become identical for small- and finite-amplitude sound propagation. In addition to general criteria, two special cases are considered, relevant for actual operation scenarios. The criteria serve to test and evaluate the extent to which the methods used in broadband pulse compression and narrowband echo integration operating modes are correct and consistent, and to identify and reduce experienced discrepancies between such methods. These are topics of major concern for quantitative acoustic stock assessment, underlying national and international fisheries quota regulations. Full article

The growing need for rapid screening of adsorption energies in organic materials has driven substantial progress in developing various architectures of equivariant graph neural networks (eGNNs). This advancement has largely been enabled by the availability of extensive Density Functional Theory (DFT)-generated datasets, sufficiently large to train complex eGNN models effectively. However, certain material groups with significant industrial relevance, such as aromatic compounds, remain underrepresented in these large datasets. In this work, we aim to bridge the gap between limited, domain-specific DFT datasets and large-scale pretrained eGNNs. Our methodology involves creating a specialized dataset by segregating aromatic compounds after a targeted ensemble extraction process, then fine-tuning a pretrained model via approaches that include full retraining and systematically freezing specific network sections. We demonstrate that these approaches can yield accurate energy and force predictions with minimal domain-specific training data and computation. Additionally, we investigate the effects of augmenting training datasets with chemically related but out-of-domain groups. Our findings indicate that incorporating supplementary data that closely resembles the target domain, even if approximate, would enhance model performance on domain-specific tasks. Furthermore, we systematically freeze different sections of the pretrained models to elucidate the role each component plays during adaptation to new domains, revealing that relearning low-level representations is critical for effective domain transfer. Overall, this study contributes valuable insights and practical guidelines for efficiently adapting deep learning models for accurate adsorption energy predictions, significantly reducing reliance on extensive training datasets. Full article

Underground gas storage (UGS) in heterogeneous carbonate reservoirs is crucial for energy security but frequently faces wellbore instability challenges, which traditional static methods struggle to address due to dynamic full life cycle changes. This study systematically analyzes the dynamic evolution of wellbore stress in the Bs8 well (Qianmiqiao carbonate UGS) during drilling, acidizing, and injection-production operations, establishing a quantitative risk assessment model based on the Mohr–Coulomb criterion. Results indicate a significantly higher wellbore instability risk during drilling and initial gas injection stages, primarily manifested as shear failure, with greater severity observed in deeper well sections (e.g., 4277 m) due to higher in situ stresses. During acidizing, while the wellbore acid column pressure can reduce principal stress differences, the process also significantly weakens rock strength (e.g., by approximately 30%), inherently increasing the risk of wellbore instability, though the primary collapse mode remains shallow shear breakout. In the injection-production phase, increasing formation pressure is identified as the dominant factor, shifting the collapse mode from initial shallow shear failure to predominant wide shear collapse, notably at 90°/270° from the maximum horizontal stress direction, thereby significantly expanding the unstable zone. This dynamic assessment method provides crucial theoretical support for full life cycle integrity management and optimizing safe operation strategies for carbonate gas storage wells. Full article

Conventional immunotherapy, including immune checkpoint blockade and chimeric antigen receptor (CAR)-T cells, has revolutionized cancer therapy over the past decade. Yet, the efficacy of these therapies is limited by tumor resistance, antigen escape mechanisms, poor persistence, and T-cell exhaustion, particularly in the treatment of solid tumors. The emergence of unconventional immunotherapies offers novel opportunities by leveraging diverse immune cell subsets and synthetic biologics. This review explores various immunotherapy platforms, including gamma delta T cells, invariant natural killer T cells, mucosal-associated invariant T cells, engineered regulatory T cells, and universal CAR platforms. Additionally, it expands on biologics, including bispecific and multispecific antibodies, cytokine fusions, agonists, and oncolytic viruses, showcasing their potential for modular engineering and off-the-shelf applicability. Distinct features of unconventional platforms include independence from the major histocompatibility complex (MHC), tissue-homing capabilities, stress ligand sensing, and the ability to bridge adaptive and innate immunity. Their compatibility with engineering approaches highlights their potential as scalable, efficient, and cost-effective therapies. To overcome translational challenges such as functional heterogeneity, immune exhaustion, tumor microenvironment-mediated suppression, and limited persistence, novel strategies will be discussed, including metabolic and epigenetic reprogramming, immune cloaking, gene editing, and the utilization of artificial intelligence for patient stratification. Ultimately, unconventional immunotherapies extend the therapeutic horizon of cancer immunotherapy by breaking barriers in solid tumor treatment and increasing accessibility. Continued investments in research for mechanistic insights and scalable manufacturing are key to unlocking their full clinical potential. Full article

This paper presents field investigations of a corrugated steel soil–steel arch structure with a span of 25.7 m and a rise of 9.0 m—currently the largest single-span structure of its kind in Europe. The structure, serving as a wildlife crossing along the DK16 expressway in northeastern Poland, was constructed using deep corrugated steel plates (500 mm× 237 mm) made from S315MC steel, without additional reinforcements such as stiffening ribs or geosynthetics. The study focused on monitoring the structural behavior during the critical backfilling phase. Displacements and strains were recorded using 34 electro-resistant strain gauges and a geodetic laser system at successive backfill levels, with particular attention to the loading stage at the crown. The measured results were compared with predictions based on the Swedish Design Method (SDM). The SDM equations did not accurately predict internal forces during backfilling. At the crown level, bending moments and axial forces were overestimated by approximately 69% and 152%, respectively. At the final backfill level, the SDM underestimated bending moments by 55% and overestimated axial forces by 90%. These findings highlight limitations of current design standards and emphasize the need for revised analytical models and long-term monitoring of large-span soil–steel structures. Full article

Dual-active bridge (DAB) converters have emerged as a preferred topology in electric vehicle charging and energy storage applications, owing to their structurally symmetric configuration and intrinsic galvanic isolation capabilities. However, conventional triple-phase shift (TPS) control strategies face significant challenges in maintaining high efficiency across ultra-wide output voltage and load ranges. To exploit the inherent structural symmetry of the DAB topology, a symmetric optimization strategy based on triple-phase shift (SOS-TPS) is proposed. The method specifically targets the forward buck operating mode, where an optimization framework is established to minimize the root mean square (RMS) current of the inductor, thereby addressing both switching and conduction losses. The formulation explicitly incorporates zero-voltage switching (ZVS) constraints and operating mode conditions. By employing the Karush–Kuhn–Tucker (KKT) conditions in conjunction with the Lagrange multiplier method (LMM), the refined control trajectories corresponding to various power levels are analytically derived, enabling efficient modulation across the entire operating range. In the medium-power region, full-switch ZVS is inherently satisfied. In the low-power operation, full-switch ZVS is achieved by introducing a modulation factor λ, and a selection principle for λ is established. For high-power operation, the strategy transitions to a conventional single-phase shift (SPS) modulation. Furthermore, by exploiting the inherent symmetry of the DAB topology, the proposed method reveals the symmetric property of modulation control. The modulation strategy for the forward boost mode can be efficiently derived through a duty cycle and voltage gain mapping, eliminating the need for re-derivation. To validate the effectiveness of the proposed SOS-TPS strategy, a 2.3 kW experimental prototype was developed. The measured results demonstrate that the method ensures ZVS for all switches under the full load range, supports ultra-wide voltage conversion capability, substantially suppresses RMS current, and achieves a maximum efficiency of 97.3%. Full article

The presented research examines the performance characteristics of Brazed Plate Heat Exchangers through computational fluid dynamics (CFD), focusing on pressure drop calculations for single-phase flow within full channels of plates featuring dimpled corrugation. This work aims to bridge gaps in the literature, particularly regarding the underexplored behavior near the ports for the studied technology and establishing a framework for future conjugate heat transfer studies. A methodology for the domain generation was developed, integrating a preliminary forming simulation to reproduce the complex plate geometry. Comprehensive sensitivity analyses were conducted to evaluate the influence of different parameters and identify the optimal settings for obtaining reliable results. The findings indicate that the $k-\epsilon$ realizable turbulence model with enhanced wall treatment offers superior accuracy in predicting pressure drops, with errors within ±4.4%. Additionally, leveraging the information derived from CFD, a strategy to estimate contributions from different channel sections without a direct reliance on those simulations was developed, offering practical implications for plate design. Full article