Search Results (1,129)

The power system, as a critical national infrastructure, faces stealthy and persistent intrusions from Advanced Persistent Threat (APT) attacks. These attack chains span multiple stages and components, while heterogeneous data sources lack unified semantics, limiting the interpretability of current detection methods. To address this, we combine the W3C PROV-DM standard with power-specific semantics to map generic provenance data into standardized provenance graphs. On this basis, we propose a graph neural network framework that jointly models temporal dependencies and structural features. The framework constructs unified provenance graphs with snapshot partitioning, applies Functional Time Encoding (FTE) for temporal modeling, and employs a graph attention autoencoder with node masking and edge reconstruction to enhance feature representations. Through pooling, graph-level embeddings are obtained for downstream detection. Experiments on two public datasets show that our method outperforms baselines across multiple metrics and exhibits clear inter-class separability. In the context of scarce power-domain APT data, this study improves model applicability and interpretability, and it provides a practical path for provenance graph-based intelligent detection in critical infrastructure protection. Full article

Predicting treatment outcomes in Class III protraction therapy remains challenging. Although finite element analysis (FEA) helps in the study of biomechanics and planning of orthodontic treatment, its use in Class III protraction has mainly been in evaluating appliance designs rather than patient-specific anatomy. The predictive accuracy of FEA has not been validated in Class III protration therapy. In this study, ten patients (5 female, 5 male, aged 7–11 years) with Class III malocclusion received either facemask or mentoplate treatment. CT scans from four patients were used to construct simplified finite element models, and predictions were compared with one-year treatment outcomes from six additional patients. While stress patterns differed between treatments, patient-specific geometrical factors had a more significant impact on deformation than treatment type. FEM-predicted maxillary changes (mean: 0.352 ± 0.12 mm) were approximately one-tenth of actual changes (mean: 1.612 ± 0.64 mm), with no significant correlation. Current FEM approaches, though useful for understanding force distribution, cannot reliably predict clinical outcomes in growing Class III patients. The findings suggest that successful prediction models must incorporate biological and growth factors beyond pure biomechanics. Accurate prediction of treatment outcomes requires comprehensive models that integrate multiple biological and developmental factors. Full article

This study presents the development of hybrid masonry mortars by incorporating two waste materials: recycled plastic strips from disposable face masks (FM) as mechanical reinforcement and calcined oyster shell powder (OSP) as a filler. The objective was to evaluate the combined effect of FM and OSP on the mechanical behavior of mortars. Three types of mixes were prepared: a reference mix, a mix with 5% OSP (by cement weight), and mixes with 5% OSP reinforced with FM strips. FM strips were incorporated at three different lengths, dividing the FM-reinforced group into three subgroups (0.1%, 0.2%, 0.5%, and 0.8%). The results showed an approximately 10% increase in compressive strength with the addition of 5% OSP compared to the control mortar, as well as an improvement in bond strength of about 21%. Furthermore, an optimum content of 0.2% of 6 mm strips allowed for adequate dispersion and maintained indirect tensile strengths similar to the control + OSP. OSP acted as a reactive filler, increasing compressive strength and improving both density and adhesion. However, higher FM contents or longer strips increased porosity and water absorption, while reducing strength. This combination represents an innovative strategy for valorizing post-pandemic and marine waste. Full article

Objectives: Prevention of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the disease COVID-19 is a public health priority. The efficacy of non-pharmaceutical interventions such as wearing face masks to prevent SARS-CoV-2 infection has been well established in controlled settings. However, evidence for the effectiveness of face masks in preventing SARS-CoV-2 transmission within real-world settings is limited and mixed. The present systematic review evaluated the effectiveness of face mask policies and mask wearing to prevent SARS-CoV-2 transmission and COVID-19 in real-world settings. Methods: Following PRISMA guidelines, scientific databases, and gray literature, were searched through June 2023. Inclusion criteria were as follows: (1) studies/reports written in or translated to English; (2) prospectively assessed incidence of SARS-CoV-2 or COVID-19; (3) assessed the behavior and/or policy of mask-wearing; and (4) conducted in community/public settings (i.e., not laboratory). Studies were excluded if they did not parse out data specific to the effect of mask wearing (behavior and/or policy) and subsequent SARS-CoV-2 transmission or COVID-19 disease or if they relied solely on statistical models to estimate the effects of mask wearing on transmission. A total of 2616 studies were initially identified, and 470 met inclusion and exclusion criteria for full-text review. The vote counting method was used to evaluate effectiveness, and risk of bias was assessed using JBI critical appraisal tools. Results: A total of 79 unique studies met the final inclusion criteria, and their data were abstracted and evaluated. Study settings included community/neighborhood settings (n = 34, 43%), healthcare settings (n = 30, 38%), and school/universities (n = 15, 19%). A majority of studies (n = 61, 77%) provided evidence to support the effectiveness of wearing face masks and/or face mask policies to reduce the transmission of SARS-CoV-2 and/or prevention of COVID-19. Effectiveness of mask wearing did not vary substantially by study design (67–100%), type of mask (77–100%), or setting (80–91%), while 85% of masking policies specifically reported a benefit. Conclusions: This systematic literature review supports public health recommendations and policies that encourage the public to wear face masks to reduce the risk of SARS-CoV-2 infection and COVID-19 in multiple real-world settings. Effective communication strategies are needed to encourage and support the use of face masks by the general public, particularly during peak infection cycles. Full article

Tracheal intubation is a frequent and high-risk procedure in the intensive care unit (ICU). Unlike elective intubation in the operating room, ICU intubation is often performed under emergent conditions in physiologically unstable patients, leading to increased technical difficulty and higher complication rates. Among these, hypoxemia is particularly frequent and represents a major determinant of morbidity and mortality. Optimizing preoxygenation is therefore a cornerstone of safe airway management in critically ill patients. The aim of this review is to explore the advantages and limitations of each preoxygenation strategy and to provide clinicians with clear, practical guidance to optimize airway management in the ICU. Preoxygenation aims to increase oxygen reserves in order to prolong the duration of safe apnea. Conventional methods include high-flow oxygen delivery through a tightly fitted face mask, though efficacy depends on minimizing leaks. More advanced strategies include non-invasive ventilation (NIV), which improves both alveolar oxygen fraction and lung volume, and high-flow nasal cannula (HFNC), which additionally allows apneic oxygenation during intubation. Randomized controlled trials, including the recent PREOXY study, demonstrate the superiority of NIV over facemask preoxygenation in reducing peri-intubation desaturation, particularly in hypoxemic patients. HFNC is valuable when NIV is contraindicated, while combined approaches (NIV plus HFNC) may further enhance efficacy. Beyond technique, structured protocols and team organization are crucial to reduce complications. In conclusion, preoxygenation is an essential, patient-specific intervention that mitigates the risks of ICU intubation. Familiarity with available methods enables clinicians to tailor strategies, optimize oxygenation, and improve patient safety during this high-risk procedure. Full article

Extreme ultraviolet (EUV) lithography faces critical challenges in aberration control and patterning fidelity as technology nodes shrink below 3 nm. This work demonstrates how Source–Mask Optimization (SMO) simultaneously addresses both illumination and mask design to enhance pattern transfer accuracy and mitigate aberrations. Through a comprehensive optimization framework incorporating key process metrics, including critical dimension (CD), exposure latitude (EL), and mask error factor (MEF), we achieve significant improvements in imaging quality and process window for 40 nm minimum pitch patterns, representative of 2 nm node back-end-of-line (BEOL) requirements. Our analysis reveals that intelligent SMO implementation not only enables robust patterning solutions but also compensates for inherent EUV aberrations by balancing source characteristics with mask modifications. On average, our results show a 4.02% reduction in CD uniformity variation, concurrent with a 1.48% improvement in exposure latitude and a 5.45% reduction in MEF. The proposed methodology provides actionable insights for aberration-aware SMO strategies, offering a pathway to maintain lithographic performance as feature sizes continue to scale. These results underscore SMO’s indispensable role in advancing EUV lithography capabilities for next-generation semiconductor manufacturing. Full article

With the continuous scaling-down of integrated circuit feature sizes, inverse lithography technology (ILT), as the most groundbreaking resolution enhancement technique (RET), has become crucial in advanced semiconductor manufacturing. By directly optimizing mask patterns through inverse computation rather than rule-based local corrections, ILT can more accurately approximate target design patterns while extending the process window. However, current mainstream ILT approaches—whether machine learning-based or gradient descent-based—all face the challenge of balancing mask optimization quality and computational time. Moreover, ILT often faces a trade-off between imaging fidelity and manufacturability; fidelity-prioritized optimization leads to explosive growth in mask complexity, whereas manufacturability constraints require compromising fidelity. To address these challenges, we propose an iterative deep learning-based ILT framework incorporating a lightweight model, ghost and adaptive attention U-net (EAAUnet) to accelerate runtime and reduce computational overhead while progressively improving mask quality through multiple iterations based on the pre-trained network model. Compared to recent state-of-the-art (SOTA) ILT solutions, our approach achieves up to a 39% improvement in mask quality metrics. Additionally, we introduce a mask constraint scheme to regulate complex SRAF (sub-resolution assist feature) patterns on the mask, effectively reducing manufacturing complexity. Full article

This study investigates the microenvironment under face masks and respirators, focusing on the influence of material and design characteristics on carbon dioxide (CO₂) concentration, temperature, and relative humidity. Masks and respirators were selected from the three main types of personal protective equipment—textile non-medical masks, surgical masks and respirators. Experimental measurements were conducted to assess how geometric parameters (thickness, bulk density), mass (mass per unit area), and structural parameters (air permeability, face cover area) influence the microclimatic conditions during wear. The results show that thickness, mass per unit area, and bulk density have a significant effect on temperature but negligible influence on CO₂ concentration and humidity. In contrast, air permeability and face cover area play a key role in CO₂ and moisture accumulation: higher permeability significantly reduces both, while larger coverage tends to increase them. These findings support the need for an integrated design approach that balances filtration efficiency with respiratory and thermophysiological comfort, contributing to improved protective performance and user acceptance. Full article

Hyperedge prediction is crucial for uncovering higher-order relationships in complex systems but faces core challenges, including unmodeled node influence heterogeneity, overlooked hyperedge order effects, and data sparsity. This paper proposes Order propagation Fusion Self-supervised learning for Hyperedge prediction (OFSH) to address these issues. OFSH introduces a hyperedge order propagation mechanism that dynamically learns node importance weights and groups neighbor hyperedges by order, applying max–min pooling to amplify feature distinctions. To mitigate data sparsity, OFSH incorporates a key node-guided augmentation strategy with adaptive masking, preserving core high-order semantics. It identifies topological hub nodes based on their comprehensive influence and employs adaptive masking probabilities to generate augmented views preserving core high-order semantics. Finally, a triadic contrastive loss is employed to maximize cross-view consistency and capture invariant semantic information under perturbations. Extensive experiments on five public real-world hypergraph datasets demonstrate significant improvements over state-of-the-art methods in AUROC and AP. Full article

Ensuring hygiene compliance in regulated environments—such as food processing facilities, hospitals, and public indoor spaces—requires reliable detection of personal protective equipment (PPE) usage, including gloves, face masks, and hairnets. Manual inspection is labor-intensive and unsuitable for continuous, real-time enforcement. This study benchmarks three lightweight object detection models—YOLOv8n, YOLOv10n, and YOLOv12n—for automated PPE compliance monitoring using a large curated dataset of over 31,000 annotated images. The dataset spans seven classes representing both compliant and non-compliant conditions: glove, no_glove, mask, no_mask, incorrect_mask, hairnet, and no_hairnet. All evaluations were conducted using both detection accuracy metrics (mAP@50, mAP@50–95, precision, recall) and deployment-relevant efficiency metrics (inference speed, model size, GFLOPs). Among the three models, YOLOv10n achieved the highest mAP@50 (85.7%) while maintaining competitive efficiency, indicating strong suitability for resource-constrained IoT-integrated deployments. YOLOv8n provided the highest localization accuracy at stricter thresholds (mAP@50–95), while YOLOv12n favored ultra-lightweight operation at the cost of reduced accuracy. The results provide practical guidance for selecting nano-scale detection models in real-time hygiene compliance systems and contribute a reproducible, deployment-aware evaluation framework for computer vision in hygiene-critical settings. Full article

Face recognition systems typically face actual challenges like facial pose, illumination, occlusion, and ageing that significantly impact the recognition accuracy. In this paper, a robust face recognition system that uses Multi-task Cascaded Convolutional Networks (MTCNN) for face detection and face alignment with an enhanced FaceNet for facial embedding extraction is presented. The enhanced FaceNet uses attention mechanisms to achieve more discriminative facial embeddings, especially in challenging scenarios. In addition, an Adaptive Feature Fusion module synthetically combines identity-specific embeddings with context information such as pose, lighting, and presence of masks, hence enhancing robustness and accuracy. Training takes place using the CelebA dataset, and the test is conducted independently on LFW and IJB-C to enable subject-disjoint evaluation. CelebA has over 200,000 faces of 10,177 individuals, LFW consists of 13,000+ faces of 5749 individuals in unconstrained conditions, and IJB-C has 31,000 faces and 117,000 video frames with extreme pose and occlusion changes. The system introduced here achieves 99.6% on CelebA, 94.2% on LFW, and 91.5% on IJB-C and outperforms baselines such as simple MTCNN-FaceNet, AFF-Net, and state-of-the-art models such as ArcFace, CosFace, and AdaCos. These findings demonstrate that the proposed framework generalizes effectively between datasets and is resilient in real-world scenarios. Full article

Indoor sports facilities present unique challenges for air quality management due to high crowd densities and limited ventilation. This study investigated air quality in a municipal athletic facility in Komotini, Greece, focusing on concentrations of airborne particulate matter (PM_1.0, PM_2.5, PM₁₀), humidity, and temperature across spectator zones, under varying mask scenarios. Sensing devices were installed in the stands to collect high-frequency environmental data. The system, based on optical particle counters and cloud-enabled analytics, enabled real-time data capture and retrospective analysis. The main experiment investigated the impact of spectators wearing medical masks during two basketball games. The results show consistently elevated PM levels during games, often exceeding recommended international thresholds in the spectator area. Notably, the use of masks by spectators led to measurable reductions in PM_1.0 and PM_2.5 concentrations, because they seem to have limited the release of human-generated aerosols as well as the amount of movement among spectators, supporting their effectiveness in limiting fine particulate exposure in inadequately ventilated environments. Humidity emerged as a reliable indicator of occupancy and potential high-risk periods, making it a valuable parameter for real-time monitoring. The findings underscore the urgent need for improved ventilation strategies in small to medium-sized indoor sports facilities and support the deployment of low-cost sensor networks for actionable environmental health management. Full article

Background/Objectives: Skeletal Class III malocclusion in growing patients presents therapeutic challenges. While traditional tooth-anchored facemask (FM) therapy is widely used, it may induce undesired dental effects. Bone-anchored maxillary protraction (BAMP), using either miniscrews (MSs) or miniplates (MPs), has been proposed to enhance skeletal outcomes and minimize dental compensation. The objective is to compare the efficacy of MS and MP as skeletal anchorage in the orthopedic treatment of the Class III growing patients. Methods: This systematic review and meta-analysis followed PRISMA guidelines. Five databases and manual searches were conducted without restrictions. Inclusion criteria encompassed randomized and non-randomized controlled trials assessing cephalometric outcomes in growing patients treated with MS or MP. Risk of bias was assessed with RoB 2 and ROBINS-I tools, and evidence certainty was evaluated using GRADE. A meta-analysis was performed, collecting all the statistically significant results that emerged in the 11 articles between skeletal anchorage and controls, comparing the values of the MP group with the MS group. Results: Eleven studies (seven MP, four MS) met the inclusion criteria. Both MS and MP groups showed significant maxillary advancement and improved maxillo–mandibular relationships compared to controls. Regarding vertical values, studies have reported contrasting outcomes. Soft tissue improvements were consistent in both MS and MP devices. Statistical analysis has highlighted how MP devices demonstrated more pronounced skeletal effects, while MS systems were associated with more dental effects. Conclusions: MP may be preferable when the aim is to maximize skeletal correction with fewer dental side effects, while MS can be considered in cases favoring less invasive approaches; long-term follow-up and high-quality clinical studies are needed to confirm these clinical assessments. Full article

Background/Objectives: Sarcopenia, defined as the progressive loss of muscle mass and function, is increasingly recognized in pediatric cancer patients as a significant clinical and prognostic factor. Sarcopenia in children arises from malignancy-related inflammation, malnutrition, and treatment toxicity, negatively affecting treatment response, recovery, and quality of life. Methods: We searched MEDLINE and Scopus for English-written articles published over the last five years using synonyms for the terms “sarcopenia” and “pediatric cancer”. Screening and data extraction were performed in a duplicate-blinded method. We qualitatively synthesized eligible articles. Results: Recent studies identify pre-treatment sarcopenia as a marker of poor prognosis, especially in hepatoblastoma and neuroblastoma. Total psoas muscle area (tax) and skeletal muscle index (SMI) are emerging diagnostic tools, though standardized methods remain lacking. Sarcopenia’s etiology is multifactorial, involving impaired mitochondrial metabolism, chemotherapy-induced appetite loss, and systemic inflammation. Sarcopenic obesity is common, particularly among leukemia survivors, often masked by normal BMI. Survivors also face reduced bone density, impaired immunity, and persistent muscle loss, linked to prior therapies such as radiotherapy and hematopoietic stem cell transplantation. Increase in muscle mass post-treatment correlates with better survival outcomes. Conclusions: Early detection of sarcopenia can support timely interventions such as nutritional support and physical activity. Yet, significant diagnostic heterogeneity across existing studies hampers definitive conclusions regarding its true prevalence and the optimal assessment method. Standardized diagnostic criteria are urgently needed to enable more reliable prevalence estimates and evidence-based clinical strategies. Full article

Surface defect detection serves as one of the crucial auxiliary components in the quality control of varistors, and it faces real challenges such as the scarcity of defect samples, high labelling cost, and insufficient a priori knowledge, which makes unsupervised deep learning-based detection methods attract attention. However, existing unsupervised models have problems such as inaccurate defect localisation and a low recognition rate of subtle defects in the detection results. To solve the above problems, an unsupervised detection method (Var-MNDR) is proposed to reconstruct the normal distribution of surface defects of varistors under mask constraints. Firstly, on the basis of colour space as well as morphology, an image preprocessing method is proposed to extract the main body image of the varistor, and a mask-constrained main body pseudo-anomaly generation strategy is adopted so that the model focuses on the texture distribution of the main body region of the image, reduces the model’s focus on the background region, and improves the defect localisation capability of the model. Secondly, Kolmogorov–Arnold Networks (KANs) are combined with the U-Network (U-Net) to construct a segmentation sub-network, and the Gaussian radial basis function is introduced as the learnable activation function of the KAN to improve the model’s ability to express the image features, so as to realise more accurate defect detection. Finally, by comparing the four unsupervised defect detection methods, the experimental results prove the superiority and generalisation of the proposed method. Full article