Search Results (79,210)

Building damage and collapse are the main sources of serious casualties and financial losses during earthquakes, which are among the most destructive natural disasters that endanger human life and property. Therefore, quick and precise post-earthquake building damage assessment is essential for risk assessment and emergency action. Convolutional neural networks (CNNs) primarily concentrate on local features and frequently ignore global contextual information within and across buildings, despite the fact that deep learning-based techniques allow automated damage identification. Transformer-based approaches, on the other hand, are good at capturing global dependencies, but their large memory and processing costs restrict their usefulness. As a result, existing networks still struggle to achieve an effective balance between accuracy and efficiency. To address this issue, this study proposes a lightweight and efficient network for post-earthquake building damage assessment. Specifically, we develop a two-stage method based on EfficientViM with an encoder–decoder architecture. In the encoder, Mamba is introduced to extract multi-scale change features with long-range dependencies, leveraging the state space model to preserve global modeling capability while significantly reducing computational complexity. In the decoder, two lightweight modules are designed to further enhance discriminative capability and computational efficiency. The network finally outputs building localization and pixel-level building damage, respectively. Experiments were conducted on four earthquake events from the BRIGHT dataset using a three-for-training and one-for-testing cross-event rotation evaluation strategy. The results demonstrate that LEViM-Net requires only 30.94 M parameters and 27.10 G FLOPs. In addition, for the Türkiye earthquake event, the proposed method achieves an F1 score of 80.49%, an overall accuracy (OA) of 88.17%, and a mean intersection over union (mIoU) of 49.73%. The proposed model enables efficient remote-sensing-based mapping of macroscopic and image-visible building damage, providing timely support for early-stage emergency response. Full article

Alveolar ridge preservation (ARP) aims to reduce post-extraction bone resorption and facilitate implant placement. Among alloplastic grafts, β-tricalcium phosphate (β-TCP) is widely used due to its osteoconductive properties and complete resorbability. This systematic review evaluated the clinical effectiveness of β-TCP for ARP, focusing on ridge dimensional changes assessed by cone–beam computed tomography (CBCT). Electronic searches were performed in major scientific databases up to April 2026. Randomized controlled trials (RCTs) reporting CBCT-based dimensional outcomes after at least 4 months were included. Five RCTs met the inclusion criteria. Considerable heterogeneity was observed in biomaterial formulations, socket management, and outcome assessment. When used alone, β-TCP showed variable results, ranging from greater ridge resorption compared with xenograft to outcomes comparable with those of freeze-dried bone allograft. More consistent findings were reported when β-TCP was used in combination with other biomaterials, with outcomes generally comparable to those of deproteinized bovine bone mineral (DBBM). Overall, β-TCP may have a potential role in alveolar ridge preservation; however, evidence remains limited and heterogeneous. Differences between β-TCP alone and composite formulations should be carefully considered, and no definitive conclusions can be drawn regarding its comparative predictability versus xenografts. Further RCTs are needed to clarify its clinical effectiveness and identify optimal applications. Full article

The rapid proliferation of Internet of Things (IoT) devices in cloud-based e-learning platforms has posed significant security risks, particularly in protecting learner information, authentication of devices, and safe communication in the highly heterogeneous learning settings. Current cryptographic solutions are largely based on classical public-key infrastructure (PKI) protocols such as RSA and ECC, which will become vulnerable with the advent of large-scale quantum computers capable of executing Shor’s algorithm. In addition, traditional perimeter-based security models are inadequate for handling the dynamics, scattered, and resource-limited characteristics of IoT-enabled educational systems. As a solution to these problems, this paper introduces ZeroTrustEdu, a scalable zero-trust cryptographic solution that combines lightweight post-quantum key management with adaptive trust scoring of cloud-connected IoT e-learning infrastructure. The proposed framework makes three fundamental contributions namely: (1) a hierarchical zero-trust security model with no implicit trust, operating across device, edge, and cloud layers; (2) a lightweight key distribution protocol based on the Module-Lattice Key Encapsulation Mechanism (ML-KEM) compliant with NIST FIPS 203 standards and (3) an adaptive behavioral trust scoring engine that dynamically adjusts device and user trust levels based on real-time interaction analytics. The architecture is evaluated using extensive NS-3 network simulations with up to 100,000 concurrent IoT nodes with formal security analysis under Chosen Plaintext Attack (CPA) and Chosen Ciphertext Attack (CCA) threat models. Comparative evaluation against RSA-2048, ECC-P256, and AES-256 baselines demonstrates that, ZeroTrustEdu delivers a 62% ± 3% (95% CI, 10 independent runs) reduction in ML-KEM encapsulation latency (12.8 ms for key encapsulation/decapsulation, contributing to a complete device authentication latency of 47.3 ms including ML-DSA signature operations), 45% reduced communication overheads, and 38% reduction in energy consumption on ARM Cortex-M4 constrained devices compared to RSA-2048 and achieves provable post-quantum security reducible to the hardness of the Module Learning With Errors (MLWE) problem. These findings demonstrate that the proposed architecture provides a viable, scalable, and quantum-resilient security solution for next-generation IoT-enabled e-learning environments. The cryptographic security of ZeroTrustEdu is guaranteed at the primitive level through NIST-standardized ML-KEM (FIPS 203) and ML-DSA (FIPS 204), with IND-CCA2 and EUF-CMA security formally proven in the respective standards; full protocol-level formal verification using automated theorem provers (ProVerif, Tamarin) is identified as valuable future work to rule out protocol-composition vulnerabilities beyond primitive-level guarantees. Full article

With the development of autonomous driving and intelligent connected vehicle technologies, the vehicle cabin is shifting from a simple transportation space to an intelligent mobile space integrating infotainment, interaction, and rest, and passenger comfort has gradually become an important factor affecting user experience, system trust, and perceived safety. Focusing on three categories of cabin environmental factors, namely the acoustic, optical, and thermal environments, this study develops an experimental design and comprehensive modeling method for passenger comfort evaluation. First, controlled single-factor experiments were conducted to establish quantitative mapping relationships between physical environmental parameters and subjective comfort ratings. The analytic hierarchy process (AHP) was then used to determine the weights of each indicator, and a penalty-based aggregation mechanism was introduced to construct a comprehensive comfort evaluation model. Finally, external validation was performed on an independent vehicle platform to examine the model’s applicability and consistency. The results show that acoustic comfort decreases as the sound pressure level increases, whereas optical and thermal comfort exhibit nonlinear behavior with optimal intervals. AHP weight results show that the thermal environment has the highest weight (0.4280), followed by the acoustic environment (0.3305) and the optical environment (0.2415). The external validation results indicate that the proposed model exhibits good predictive consistency across three steady-state operating conditions, with a mean absolute error of 0.122, a root-mean-square error of 0.150, and a Pearson correlation coefficient of 0.960. The findings show that the penalty-based aggregation model can effectively characterize the limiting-factor effect under the joint action of multiple environmental factors, providing a computable and interpretable evaluation framework for intelligent cockpit environmental control and automotive engineering experimental teaching. The conclusions of this study are mainly applicable to the current experimental platform and steady-state operating conditions, and further validation is still required with more vehicle models, dynamic road scenarios, and complex multi-environment factor disturbances. Full article

Wearable devices increasingly capture biosignals such as electrocardiograms, photoplethysmograms, inertial signals, and electrodermal activity during daily life, enabling earlier detection and continuous monitoring outside the clinic. Real-time edge machine learning can convert these streams into timely, privacy-preserving inference by placing computation on a wearable (device-only) or a paired phone, with intermittent cloud assist used selectively for dashboards, summarisation, and lifecycle management. Clinical adoption remains uneven because free-living data are noisy, labels are often delayed, and device ecosystems evolve over time. This narrative review organises the literature as an end-to-end deployment pathway: sensing and artefact management, streaming windowing and multimodal alignment, and model families suited to on-device inference. We compare classical feature-based pipelines with learned representations, including compact CNN/TCN and recurrent and efficient attention-based models, and discuss when self-supervised pretraining and distillation are most useful in low-label settings. We then synthesise deployment engineering levers (quantisation, pruning, and distillation) and benchmarking requirements, emphasising runtime constraints that determine feasibility: latency per update, peak RAM, energy per inference, duty cycle, and thermal behaviour. Applications are grouped across cardiovascular monitoring, blood pressure and haemodynamics, sleep and respiration, and movement and stress, with explicit attention to false-alert burden, adherence, and workflow integration. To support translation, we provide a validation ladder and a reliability toolkit covering calibration, uncertainty-aware thresholds and deferral, drift monitoring triggers, and safe update governance. The novelty of this review is a deployment-oriented synthesis that ties modelling choices to edge tiers and resource budgets and provides reusable reporting templates, including an edge-cost card and comparative tables spanning modalities, models, deployment levers, applications, and reliability requirements. Full article

With the large-scale grid integration of renewable energy sources such as wind power and photovoltaics, power system net load fluctuations have become significantly more severe, imposing higher demands on system flexibility. Traditional optimal transmission switching (OTS) models require the simultaneous optimization of continuous and discrete variables, resulting in high computational complexity that renders them unsuitable for daily real-time scheduling in large-scale power systems. This paper develops a flexible real-time rolling optimization scheduling model that incorporates OTS and proposes a two-stage fast solution framework based on deep neural networks (DNN). In the offline training phase, a multilayer perceptron-based DNN is trained using load and renewable generation data to rapidly and accurately predict the optimal line switching scheme. In the online application phase, the network topology predicted by the DNN transforms the original mixed-integer linear programming problem into a standard linear programming problem, substantially reducing computational complexity and solution time. Case studies on the modified IEEE 118-bus and IEEE 300-bus systems show that the proposed method achieves high prediction accuracy, reduces solution time by up to 117 times, and maintains nearly identical system operating costs to the physics-driven approach in the majority of cases. The results demonstrate that the proposed approach effectively balances computational efficiency and economic performance, verifying the practical value of optimal transmission switching in enhancing large-scale renewable energy accommodation and overall power system flexibility. Full article

Cryptorchidism is one of the most frequently diagnosed developmental disorders of the male canine reproductive system, defined as the failure of one or both testes to descend into the scrotum. Physiologically, testicular descent is typically completed by six to eight weeks of age, although some authors extend this period to sixteen weeks. Failure of testicular descent beyond this timeframe is considered pathological. The condition has multiple causes and affects between 1% and 10% of the canine population. Genetics is the most significant factor, indicating the hereditary basis of cryptorchidism. In addition, increasing attention has been directed toward the potential impact of environmental and epigenetic factors on the incidence of cryptorchidism, suggesting that the condition may result from complex interactions between genetic predisposition and external influences. The effect of hormones (such as INSL3 and testosterone), mechanical factors (including narrowing of the inguinal canal, abnormalities of the gubernaculum, and shortening of the spermatic cord), and environmental factors (for example, exposure to external estrogens and maternal stress during pregnancy) all contribute to the development of this disorder. Recent results have emphasized the role of the orexin system, particularly the OX2R receptor, in regulating endocrine and reproductive functions in cryptorchid testes. Computed tomography is increasingly utilized in complex cases due to its high precision in localizing retained testes. Clinically, cryptorchidism may present unilaterally or bilaterally. Unilateral cryptorchidism may preserve partial fertility, whereas bilateral cryptorchidism results in complete infertility. Undescended testes may be located in the abdominal cavity or inguinal canal. Major complications include an increased risk of testicular cancer (Sertoli cell tumors and seminomas) and endocrine disorders leading to feminization. Diagnosis is based on clinical examination and imaging modalities such as ultrasound. Orchiectomy, involving the removal of both the retained and normally descended testicles, is thought to be the gold standard for treatment. This method helps avoid complications and the transmission of the defect to offspring. According to Fédération Cynologique Internationale (FCI) standards, affected individuals should not be used for breeding or shows. Early detection, surgical intervention, and consistent exclusion from breeding programs are the primary strategies for reducing the incidence of this disorder in the canine population. Full article

Background: Iodinated contrast media are essential for oncologic imaging but raise specific safety concerns because cancer patients are often exposed to repeated contrast-enhanced computed tomography, nephrotoxic drugs, immune-modulating therapies, and, in selected cases, radioiodine-dependent diagnostic or therapeutic pathways. Methods: We performed a narrative review based on an exploratory search followed by a focused search targeting iodinated contrast use in oncology-related settings. Studies were included if they addressed renal risk and post-contrast acute kidney injury, hypersensitivity and acute adverse reactions, or thyroid dysfunction with radioiodine-related implications. We also considered clinically relevant studies on drug interactions, isotope studies, and laboratory confounding. Results: The evidence base was methodologically heterogeneous, with renal safety as the predominant domain. Kidney injury after contrast-enhanced imaging in cancer patients appeared frequently multifactorial, supporting the broader concept of post-contrast acute kidney injury rather than automatic attribution to contrast alone. Hypersensitivity reactions to modern nonionic iodinated contrast media were generally uncommon, with severe reactions rare, although immune-modulating therapies may alter risk. Thyroid-related effects were usually transient but relevant in patients with thyroid autonomy and in differentiated thyroid carcinoma, where contrast exposure may affect scintigraphy and radioiodine planning. Conclusions: In oncology, iodinated contrast use requires individualized, field-specific risk stratification instead of reflexive avoidance. Full article

Transient analyses of liquid-fueled molten salt reactors involve strong coupling among neutronics, delayed neutron precursor transport, thermal–hydraulics, and solid heat transfer, leading to high computational costs for repeated high-fidelity simulations. To enable fast multi-physics prediction at unseen operating conditions, a parametric non-intrusive reduced-order model (ROM) combining proper orthogonal decomposition (POD) and higher-order dynamic mode decomposition (HODMD) is developed. Coupled full-order snapshots generated from an OpenFOAM-based one-eighth symmetric core model based on a simplified MSRE benchmark configuration are used to construct reduced representations for 11 physical fields. The POD truncation rank, HODMD delay dimension, and interpolation model are selected using leave-one-out cross-validation, with polynomial, radial basis function, and Gaussian process regression models considered as interpolation candidates. For unseen parameter points, the model maintains high accuracy in both the interpolation stage and the temporal extrapolation stage. In the temporal extrapolation stage, the highest mean relative L₂ error for the inlet-temperature-step case is 2.112%, whereas all mean relative L₂ errors for the inlet-velocity-step case remain below 0.177%. The results indicate that, under the present cases and parameter settings, the proposed framework provides an accurate and rapid surrogate for multi-physics transient prediction. Full article

The elevation datum is a critical element in surveying and mapping, as variations in elevation systems can lead to discrepancies between Digital Surface Model (DSM) products generated from satellite imagery. To eliminate these differences and ensure high-precision data consistency, this study constructs an elevation datum conversion scheme for multi-source DSM products using the SGG-UGM-2 (2190 degree) global gravity field model to calculate elevation anomalies. While traditional serial algorithms suffer from significantly decreased efficiency as the volume of DSM image files increases, this paper proposes a novel HDC-MPI elevation datum conversion algorithm based on Message Passing Interface (MPI) parallel technology. By leveraging distributed memory parallel computing, the processing task is partitioned into multiple sub-tasks, substantially enhancing overall throughput. Experimental results demonstrate that: (1) the HDC-MPI algorithm improves conversion efficiency by approximately 8 times compared to the serial approach when processing 12 image scenes; (2) the algorithm’s efficiency is primarily governed by image memory usage rather than terrain complexity; and (3) the conversion accuracy of the HDC-MPI algorithm remains fully consistent with serial results, ensuring the reliability of the elevation datum transformation. Full article

Mobile edge computing storage is increasingly used to support immersive services and Internet of Things applications that generate continuous real-time data streams. Sustained availability must therefore be maintained under both abrupt failures and software aging. Prior studies often evaluate reactive mechanisms (e.g., failover and live migration) and preventive mechanisms (e.g., software rejuvenation) separately, so their combined effect in microservice-based distributed edge storage is still unclear. We develop a Stochastic Reward Net (SRN) model for a multi-node edge storage architecture that captures hardware and software failures, software aging, high availability, live migration, and rejuvenation at both node and microservice levels. Using the model, we compare six policy scenarios and quantify Capacity-Oriented Availability COA), defined as the expected number of usable microservices while the storage layer is operational. Steady-state and sensitivity analyses over twelve timing parameters show that policies including live migration achieve the highest, or effectively tied-highest, COA across wide ranges of failure and repair rates. They also show that uncoordinated rejuvenation schedules can reduce availability when rejuvenation starts before live migration completes and terminates services prior to evacuation, a phenomenon we refer to as a Proactive Crash (PC). Across the tested ranges, edge/storage failure rates and rejuvenation trigger intervals dominate availability, while detection delays, repair times, and rejuvenation duration have a smaller influence. These results give guidelines for configuring proactive high availability so that migration completes before rejuvenation and rejuvenation is neither too frequent nor too sparse. Full article

Series arc faults on the DC side of photovoltaic (PV) systems are a critical hazard that can trigger system fires. Conventional contact-based detection methods suffer from cumbersome installation and high retrofit cost, whereas existing non-contact approaches mostly rely on megahertz-level high-frequency sampling and therefore require expensive radio-frequency instrumentation or high-performance computing platforms. As a result, it remains difficult to simultaneously achieve strong interference immunity and real-time performance on low-cost embedded devices with limited resources. To address this engineering paradox between high-frequency sampling and constrained computational capability, this paper proposes a fully embedded, non-contact arc fault detection system based on a 12–80 kHz low-frequency sub-band selection strategy. By exploiting the physical characteristic of broadband energy elevation induced by arc faults, the proposed strategy avoids dependence on high-bandwidth hardware. Guided by this strategy, a Moebius-topology coaxial shielded loop antenna is employed as the near-field sensor, while an ultra-simplified passive analog front end is constructed directly by using the on-chip programmable gain amplifier and analog-to-digital converter of the microcontroller unit, enabling efficient signal acquisition and fast Fourier transform processing within the target sub-band. To cope with complex background noise in the low-frequency range, an environment-adaptive baseline mechanism based on exponential moving average and exponential absolute deviation is developed for dynamic decoupling. In addition, a lightweight INT8-quantized multilayer perceptron is introduced as a nonlinear auxiliary module, thereby forming a robust hybrid decision architecture with complementary rule-based and artificial intelligence components. Experimental results show that, under the tested household, laboratory, and PV-site conditions, the proposed system achieved an overall detection rate of 97%, while the remaining 3% mainly corresponded to failed ignition or non-sustained arc attempts rather than persistent false triggering during normal monitoring. Full article

Background: Rehabilitation of edentulous mandibles in a post-oncologic setting remains a major clinical challenge. In such situations, placement of conventional endosseous implants may be compromised by severe bone deficiency, a history of peri-implant infection, and constraints related to reconstructive soft tissues. Customized titanium subperiosteal implants, made possible by three-dimensional imaging, computer-aided design, and additive manufacturing, represent a potential alternative when conventional options are unfavorable. This case report describes a full-arch fixed rehabilitation of an edentulous mandible in a patient previously treated for squamous cell carcinoma of the floor of the mouth. Methods: A patient-specific titanium additively manufactured subperiosteal jaw implant (AMSJI) made of biocompatible titanium was designed using a digital planning workflow. Implant placement was performed in a single surgical session under general anesthesia, with fixation using osteosynthesis screws. A screw-retained full-arch provisional prosthesis was delivered intraoperatively, allowing immediate loading with adjustments aimed at avoiding compression of the healing soft tissues. Results: The patient achieved satisfactory functional and esthetic rehabilitation. Postoperative follow-up showed overall favorable mucosal tolerance; an early, limited peri-abutment mucosal dehiscence was observed and managed with suturing under local anesthesia, without compromising implant stability. Conclusions: This case highlights the clinical interest of patient-specific titanium subperiosteal implants as a fixed rehabilitation option in post-oncologic patients with major osseous and mucosal constraints and a history of reconstructive procedures. The combination of accurate digital planning and custom-made manufacturing may avoid the need for extensive bone grafting. However, these findings should be interpreted with caution due to the short-term follow-up and the inherent limitations of a single-case report, which limit the level of evidence and generalizability. Full article

Objective: Continuous pressure-flow cerebral bio-signals are critical for monitoring cerebrovascular dynamics but are often disrupted by missing data segments caused by artifacts from a variety of sources. These missing segments refer to segments of the signal that do not contain any valid physiological data. Such interruptions fragment the signals, resulting in discontinuities that compromise their overall integrity. Therefore, reconstructing missing values and preserving signal continuity are essential for ensuring the stable computation of signal trajectories and the accuracy of derived cerebrovascular indices. Methods: To address this issue, this systematic scoping review aimed to identify and synthesize existing interpolation and imputation strategies for handling missing segments in continuous pressure-flow cerebral bio-signals. Following the Cochrane and Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, a comprehensive search of five electronic databases was conducted from their inception to 24 September 2024, and updated on 16 June 2025, using a detailed search string. Results: The initial searches yielded 19,403 results, and 8 studies were filtered and included in the review. All included studies employed interpolation techniques, such as linear and spline interpolation algorithms, to correct distorted signal segments. However, none of the included studies directly utilized interpolation or imputation strategies to reconstruct or completely fill missing data segments. Conclusions: This reveals a critical knowledge gap, as no study has systematically addressed the utilization of interpolation or imputation strategies for missing segments in pressure-flow cerebral bio-signals. Therefore, this systematic review emphasizes the need for specialized methodologies and standardized frameworks to enable reliable recovery of missing data segments in pressure-flow cerebral bio-signals, which is critical for advancing real-time neurocritical care monitoring and experimental neuroscience/psychological research. Significance: This systematic review lays the groundwork for future research into physiologically informed interpolation and imputation strategies for pressure-flow cerebral bio-signals in clinical and research applications. Full article

Molecular reconstitution of petroleum is a computer simulation based on petroleum characterization data and applying various computational techniques to generate individual molecules, predict their properties, and construct a composition model. The accuracy of molecule physical property calculation affects the exactness of the whole process of building the molecular composition model of petroleum. To the best of our knowledge, no report has appeared yet that deals with the accuracy of the calculation of the properties of the predefined molecules used in the process of molecular reconstitution of oil. To bridge this gap we used three of the most employed group contribution methods of Joback and Reid (1987) (J&R), Abdulelah–Gani (2022) (A&G) and Constantinou–Gani (1994) (C&G) to calculate the properties of 110 molecules from gasoline range and 139 molecules from diesel range, whose measured properties were found in the Design Institute for Physical Properties (DIPPR) and API Technical Databook databases, and which were used by Xie et al. (2026) to reconstruct the molecular composition of 22 crude oils. It was found that no single group contribution method was universally superior. The J&R method performed better for most heteroatomic compounds, while the A&G method outperformed it for certain hydrocarbon classes. Only the A&G method can be used to calculate specific gravity. In general, the error of physical property calculations was variable, reaching as high as 20% (absolute) depending on the molecular weight of the molecules and their chemical class. The implementation limitations of these methods in software libraries must be carefully considered during the process of molecular reconstitution of petroleum. Full article