Search Results (9,614)

semantic communication (SemCom) has emerged as a task-oriented communication paradigm that prioritizes meaning delivery over exact bit recovery. The integration of generative artificial intelligence (GenAI) into SemCom further enables knowledge-guided inference, multimodal reconstruction, and semantic compression through architectures such as large language models, variational autoencoders, generative adversarial networks, and diffusion models. At the same time, this integration introduces new security and privacy risks, including semantic eavesdropping, model inversion, semantic jamming, covert backdoors, prompt manipulation, and knowledge-base leakage, which are not adequately captured by conventional communication security models. In this survey, we provide a security-centric review of GenAI-assisted semantic communication systems by organizing the literature according to threat models, attack surfaces, defence strategies, and semantic modalities across text, image, and multimodal settings. The survey was conducted using IEEE Xplore, ACM Digital Library, SpringerLink, arXiv, and Google Scholar. Approximately 180 papers were initially screened, and 53 representative studies published between 2021 and 2026 were selected for detailed review. Based on this analysis, we classify the major threats into adversarial perturbation, jamming, poisoning and backdoor attacks, privacy leakage and semantic eavesdropping, and generative-model-specific vulnerabilities involving diffusion, large language models, and multimodal foundation models. We further map the corresponding defences, including adversarial training, model ensembling, semantic-aware encryption, diffusion-guided denoising, privacy-preserving representation learning, and secure resource allocation. The survey also identifies persistent open challenges, including the lack of standardized semantic security metrics, unified benchmarks, cross-layer evaluation frameworks, and robust defences for GenAI-native and multimodal semantic communication systems. Overall, this work provides a structured reference for the design of secure, trustworthy, and attack-resilient generative semantic communication systems for future intelligent networks. Full article

Paraffins are attractive as phase-change materials (PCMs) due to their high latent heat capacity and adjustable phase transition temperatures. However, the individual high-purity paraffins, especially the long-chain ones, are labor-intensive and costly to produce and capable of storing and releasing latent heat only within a limited temperature range. Herein, we demonstrate the feasibility of a high-purity paraffin wax fraction (C13–C49) obtained via the Fischer–Tropsch (FT) process as a versatile latent heat storage additive within a wide range of phase transition temperatures (8.1–98.2 °C). To avoid the leakage, the FT wax was encapsulated via nanoemulsion interfacial polymerization of melamine formaldehyde (MF) shells with various core-to-monomer and melamine/formaldehyde ratios. Differential scanning calorimetry revealed that the latent heat storage capacity of the FT/MF capsules was 104.5–163.4 J/g depending on the FT loading efficiency, with the heat storage and release range of −0.7–100.2 °C and −9.8–85.8 °C, respectively. The capsules were tested as a thermoregulating additive to commercially available gypsum plaster. Unlike employment of the additives based on individual paraffins, the addition of FT/MF capsules led to a smooth reduction in heating/cooling rates of plaster layers in an extended temperature range. This makes FT/MF capsules a promising and versatile additive for a diversity of thermal energy storage applications. Full article

An alternative, low-cost, current device to be used in leakage detection is presented in this work. The main advantages, besides the cost and portability, are the high efficiency and ease of operation, enabling a simplified and effective implementation in energized electrical power systems. Its main purpose includes detections in direct current auxiliary systems (DCAS), whose reliable and continuous operation is essential to guarantee safety and robustness in a large variety of assets, such as large power plants, substations and even industry. Such effectiveness along with the proof of concept are demonstrated through tests and real maintenance situations exhibited in the final sections. Full article

Blade Tip Timing (BTT) is an essential non-contact technique for monitoring vibrations in rotating machinery, but its practical accuracy is often degraded by noise, undersampling, and spectral leakage. This paper proposes a multi-stage robust Bayesian high-resolution identification framework that systematically addresses these challenges. A recursive digital algorithm based on Kalman filtering estimates the rotational speed without requiring once-per-revolution probes, effectively suppressing sensor noise. An attention-enhanced dynamic convolutional autoencoder then generates channel-specific window functions to minimize spectral leakage. The core identification algorithm extracts phases via all-phase FFT and employs sub-bin interpolation to overcome the resolution limitation of conventional FFT. A Tukey-biweight-based robust aggregation strategy is used to suppress the influence of abnormal or unequal-quality sensor channels during multi-channel phase fusion. A Bayesian prior distribution over the vibration order guides the estimation toward physically plausible values under noisy conditions. Finally, a coarse-to-fine multi-stage search strategy drastically reduces computational burden while preserving accuracy. Experiments on a rotor-blade test bench at constant and variable speeds show that the method reduces the noise floor by about 60 dB, achieves a maximum frequency identification error of 7.84%, and accelerates the search by approximately 48.6% compared to exhaustive search. The proposed method provides a reliable and efficient solution for blade health monitoring. Full article

Background/Objectives: Artificial intelligence (AI) has rapidly expanded across medical imaging with proposed applications in diagnosis, prognostication, and surgical planning. Concerns remain regarding methodological robustness and clinical readiness for many published models. This systematic review aimed to conduct a methodological audit of AI imaging studies relevant to contemporary neurosurgical practice—including intracranial, cerebrovascular, spinal, and connectomics-based applications—published in 2025. Methods: Following PRISMA guidelines and PROSPERO registration (CRD420261284068), PubMed was searched for studies published in 2025 evaluating machine learning or deep learning applications in MRI- or CT-based imaging. Three reviewers independently extracted data on validation strategy, data leakage risk, human comparator use, calibration reporting, and CLAIM/TRIPOD-AI adherence. Risk of bias was assessed using PROBAST+AI. Results: Of 1776 screened records, 91 studies met the inclusion criteria. China led contributions (54.9%), oncology was the most common domain (37.4%), and MRI was the predominant modality (67.0%). External validation was reported in 75.8% of studies, and 66.0% used multicenter cohorts. Data leakage risk was low in 93.4%. However, only 18.7% included human comparators, calibration was reported in 30.8%, and none achieved full CLAIM/TRIPOD-AI compliance. Conclusions: AI imaging studies published in 2025 demonstrate encouraging progress in multicenter design and external validation. However, persistent gaps in human benchmarking, calibration, and reporting suggest further methodological development is needed. Full article

Background/Objectives: A disadvantage of Descemet stripping automated endothelial keratoplasty (DSAEK) in eyes with prior glaucoma filtration surgery is the difficulty in maintaining air tamponade during the procedure. Herein, we report the use of bleb compressive sutures in managing air tamponade in the anterior chamber during DSAEK in eyes with blebs following trabeculectomy. Methods: This retrospective case series included 34 eyes of 33 patients that developed bullous keratopathy following trabeculectomy. Bleb compression suturing was performed using a 10-0 nylon suture in eyes with an intraocular pressure (IOP) < 10 mmHg or a fragile ischemic bleb. Postoperative IOP, air ingress into the bleb, rebubbling, bleb leakage, and bleb damage were evaluated. Results: Of the 34 eyes, 13 underwent bleb compression suturing before DSAEK (suture group), whereas 21 eyes did not (non-suture group). The mean preoperative IOP was 7.5 ± 2.5 mmHg and 11.2 ± 4.2 mmHg in the suture and the non-suture groups, respectively. The IOP was measured 2 h postoperatively in 14 eyes, increasing by 18 ± 9.3 and 11.7 ± 3.1 mmHg in the suture and non-suture groups, respectively, compared to the preoperative IOP, with no significant differences. At 2 h postoperatively, two eyes in the suture group and one eye in the non-suture group exhibited an IOP spike (≥30 mmHg). One eye in the non-suture group required rebubbling owing to air ingress into the bleb. The mean IOP was 7.1 ± 3.2 and 9.4 ± 4.6 mmHg in the suture and non-suture groups, respectively, 1–2 weeks postoperatively. Preoperative and postoperative IOPs did not significantly differ in either group, and no suture-related complications were observed. Conclusions: For eyes with blebs, bleb compression suturing in DSAEK provides effective air tamponade during graft adhesion. Full article

In order to explore the influence of baffle structure on the oil–gas two-phase flow and leakage characteristics of aero-engine bearing chamber sealing systems, based on the VOF two-phase flow model, this paper systematically carried out a transient numerical simulation of the bearing chamber sealing systems with conventional configurations and baffle configurations. The oil distribution, leakage and flow evolution of the two types of configurations under different baffle heights, sealing pressure differences and rotational speeds were compared and analyzed. The results show that the higher the height of the baffle, the more obvious the accumulation effect of the lubricating oil and the greater the leakage. The increase in sealing pressure difference helps to suppress leakage and reduce leakage fluctuation. The increase in rotational speed aggravates the centrifugal effect of the lubricating oil and makes the leakage increase significantly. This paper reveals the multi-parameter coupling mechanism of the baffle structure on the leakage control of the bearing chamber sealing system, and it provides a theoretical basis for the optimal design of the bearing chamber sealing structure of the aero-engine. Full article

Risk identification in ramp merging is often compromised by complex vehicle interactions and ‘future information’ leakage. To resolve this, we decouple the process using an ‘observation-conflict’ mechanism. By extracting kinematic features solely from the merging preparation phase, the framework predicts continuous risks during actual execution without temporal bias. Furthermore, we stabilize risk detection by integrating cpTTC thresholds with duration constraints into a three-level risk labeling scheme, ensuring the results align with real-world physical dynamics. Multiple machine learning models are comparatively evaluated using group-based data partitioning. Results indicate that the XGBoost model achieves the best overall performance, yielding an overall accuracy of 0.8182 and a multiclass AUC (OvR) of 0.8898. Furthermore, time cross-domain validation under varying macroscopic traffic flow states demonstrates that the framework exhibits reliable statistical stability; when reconstructed into a binary classification task, it maintains a risk recall of 0.9978. These findings provide a reliable methodological basis and early-warning feature reference for dynamic traffic risk assessment in merging scenarios. Full article

Background/Objectives: Urinary incontinence (UI) is a frequent condition in postmenopausal women and is associated with a substantial negative impact on quality of life. Conservative management can include pelvic floor muscle training (PFMT) and high-intensity focused electromagnetic stimulation (HIFEM); however, data regarding the potential benefit of combining these modalities remain limited. This study aimed to evaluate whether the addition of a structured Kegel exercise program to EMSELLA-based electromagnetic stimulation is associated with enhanced clinical outcomes in postmenopausal women with urinary incontinence. Methods: This prospective comparative study included 99 postmenopausal women with stress, urgency, or mixed urinary incontinence and an International Consultation on Incontinence Questionnaire–Urinary Incontinence Short Form (ICIQ-UI SF) score ≥ 6. Participants received either EMSELLA therapy alone (Group A, n = 49) or EMSELLA combined with a standardized Kegel exercise program (Group B, n = 50) over a three-month period. Symptom severity was assessed at baseline and at three months using the ICIQ-UI SF. Between-group comparisons were performed using analysis of covariance, adjusting for baseline scores. Results: Both therapeutic approaches were associated with clinically meaningful improvement in urinary incontinence symptoms. After adjustment for baseline severity, lower follow-up ICIQ-UI SF scores, greater mean symptom reduction, and higher response rates were observed in the combined-therapy group. Across all menopausal-duration subgroups, outcomes consistently favored the association of EMSELLA therapy with Kegel exercises. No treatment-related adverse events were reported. Conclusions: The association of EMSELLA electromagnetic stimulation with a structured Kegel exercise program was associated with greater symptom improvement than electromagnetic stimulation alone, suggesting an additive therapeutic effect of voluntary pelvic floor muscle training. This combined conservative approach was well tolerated and may represent a useful management strategy for postmenopausal urinary incontinence. Full article

Many MRI-based Alzheimer’s disease (AD) classification studies report near-perfect accuracy; however, these results are often inflated by data leakage caused by slice-level splitting, where correlated slices of the same subject appear in both the training and test sets. In this study, we introduce AttentionMS-Net, an attention-enhanced multi-scale deep learning architecture that combines channel-spatial attention using the Convolutional Block Attention Module (CBAM) with multi-scale feature aggregation from intermediate EfficientNet-B3 layers for binary AD classification (Non-Demented vs. Demented). Using strict subject-level 10-fold cross-validation on the OASIS dataset (347 subjects, 86,437 slices), our experiments clearly show the impact of data leakage: image-level 10-fold CV achieves about 99.9% accuracy, whereas subject-level 10-fold CV with the same model results in 80.8% accuracy—a reduction of roughly 19 percentage points. Aggregating predictions at the subject level further improves accuracy to 82.4% (AUC: 0.889), suggesting that prediction errors are mainly slice-specific rather than subject-specific. Systematic ablation reveals a complementary interaction between attention and multi-scale components, neither of which performs as well alone. Post-hoc Grad-CAM++ visualization and SHAP analysis suggest that AttentionMS-Net’s attention patterns are focused on ventricular regions—visual biomarkers indicative of overall brain atrophy rather than early hippocampal degeneration. These findings highlight the unreliability of current benchmarks and establish methodologically rigorous baselines for future AD classification research. Full article

As digital payment infrastructures expand, the incidence of card-not-present fraud has become a major source of operational and financial risk for banks, payment processors, and merchants. In response, financial institutions increasingly rely on data-driven decision systems, yet fraudsters continuously adapt their strategies to evade conventional rule-based controls. A promising way to strengthen risk management is to model transactional data so as to uncover non-trivial, high-dimensional patterns characteristic of fraudulent behavior and to embed these models into real-time decision pipelines. In this work, we develop and compare a suite of learning-based fraud detectors, including a convolutional neural network and several machine learning classifiers, within a unified quantitative risk-management framework. The problem is formulated as a supervised classification task within a quantitative risk management framework, where the cost of missed fraud is particularly critical. The mathematical contribution is methodological rather than architectural: we design a leakage-safe and prevalence-faithful evaluation protocol for extremely imbalanced binary classification, combine cross-validated hyperparameter optimization with risk-aligned model selection based on metrics such as recall and Matthews correlation coefficient, and quantify uncertainty by bootstrap confidence intervals and paired McNemar tests. In addition, we connect statistical evaluation with deployment-time decisioning through a decision-theoretic, cost-sensitive threshold rule, showing how institution-specific false-positive and false-negative costs determine the operating point of the classifier. Because fraudulent transactions constitute only a small proportion of the total volume, we employ resampling strategies to mitigate severe class imbalance and systematically calibrate the models via cross-validated hyperparameter optimization. The empirical analysis on real transaction data shows that carefully tuned deep and ensemble methods can achieve strong fraud-detection performance, while the proposed framework clarifies which performance differences are statistically meaningful and which operating points are most suitable under institution-specific false-positive and false-negative costs. Full article

Leakage localization in water distribution networks (WDN) is crucial for reducing water losses, conserving energy, and improving system efficiency. This work presents the application of an integrated approach for leakage localization, relying on sub-daily pressure and inflow data, water-consumption data obtained by means of smart meters, and the WDN digital twin. Observed and simulated pressure data are iteratively compared across multiple leakage scenarios to identify the spatial distribution that minimizes discrepancies, thus pinpointing likely leakage areas. The approach was tested on a real-world WDN where about one-third of users are equipped with smart meters. Results demonstrated its effectiveness in accurately localizing leakages and supporting prompt, targeted repairs. Full article

Spiral-groove dry gas seals are widely used in various rotating machinery, and their performance prediction is of great significance for structural design and operational optimization. Existing studies still face several limitations, including the limited fidelity of numerical simulations, the insufficient number of experimental samples, and the restricted generalization capability of models based on a single data source. To address these issues, this study constructed a multi-source data system integrating numerical simulation data and experimental data, and systematically compared four representative data fusion methods, namely the uncertainty-weighted fusion algorithm, TrAdaBoost, MFDNN, and CoKriging, with analysis of their applicability and predictive performance. The results show that multi-source data fusion can effectively exploit the complementary advantages of different data sources and improve the prediction accuracy of dry gas seal performance. In terms of the comparison of data fusion methods, all four methods achieved good results for the groove-depth problem; however, for the spiral-angle and groove-number problems, which exhibit stronger nonlinear characteristics, clear differences were observed among the methods. Among them, TrAdaBoost showed the best overall performance, followed by MFDNN, then CoKriging, while the uncertainty-weighted method was relatively weaker. In terms of seal performance, the influence of groove depth on seal performance was relatively direct; the spiral angle is recommended to be controlled within 10–14°, and the groove number within 12–16, so as to balance opening force and leakage rate. This study can provide a reference for the rapid performance prediction and parameter optimization of spiral-groove dry gas seals. Full article

Emergency leakage repair in subway shield tunnels requires a technique to encapsulate highly reactive sodium silicate that is simple and field-deployable, yet no mature solution currently exists. The challenge lies in sodium silicate’s strong alkalinity and high osmotic pressure, both of which corrode most shell materials. This study proposes a “composite core” concept—functionally re-engineering the core rather than relying on complex shell chemistries. Using hydroxypropyl methylcellulose (HPMC) as the key material, temperature-triggered microcapsules with a nano-silica shell and sodium silicate–HPMC core were fabricated via low-temperature shear. Low temperature (10–15 °C) is critical: it suppresses side reactions and tunes viscosity to 2000–5000 cP, facilitating shear dispersion. The resulting microcapsules exhibit well-defined morphology with a dense shell. Temperature response tests reveal distinct release onset at ~30 °C (HPMC’s LCST): HPMC chain collapse generates internal stress that ruptures the shell, driving progressive sodium silicate release. Alkaline resistance tests confirm that intact microcapsules remain stable in high-pH environments (pH ≈ 13.2) for 30 min. This work validates the “composite core” concept and provides a simple, field-operable route to fabricate temperature-triggered microcapsules for emergency repair applications. Full article

This paper introduces a reliability-centric stacking framework for smart-grid stability prediction, considered here as the binary classification of stable versus unstable operating conditions in a benchmark smart-grid dataset. A key methodological strength of the proposed approach is its leakage-safe evaluation protocol, which combines strict outer cross-validation with out-of-fold meta-features so that performance is assessed without reusing validation information. The meta-learner is trained with Tempered Fractional Gradient Descent (TFGD), a history-aware optimizer designed to stabilize meta-level learning and improve the trustworthiness of probability estimates. On the UCI Electrical Grid Stability dataset, the proposed TFGD-stacking framework preserves strong discrimination performance while delivering clear gains in calibration and probabilistic quality compared with soft voting and strong single learners. In particular, it improves Balanced Accuracy and substantially reduces calibration error and proper scoring losses, leading to more reliable probability estimates for threshold-based decision-making. Risk–coverage analysis further shows that these reliability gains translate into better decision support under uncertainty. These findings support TFGD-stacking as a practical and principled approach for smart-grid applications where calibrated probabilities are essential for operational supervision and intervention. Full article