Search Results (1,713)

This study introduces an enhanced Intrusion Detection System (IDS) framework for Denial-of-Service (DoS) attacks, utilizing network traffic inter-arrival time (IAT) analysis. By examining the timing between packets and other statistical features, we detected patterns of malicious activity, allowing early and effective DoS threat mitigation. We generate real DoS traffic, including normal, Internet Control Message Protocol (ICMP), Smurf attack, and Transmission Control Protocol (TCP) classes, and develop nine predictive algorithms, combining traditional machine learning and advanced deep learning techniques with optimization methods, including the synthetic minority sampling technique (SMOTE) and grid search (GS). Our findings reveal that while traditional machine learning achieved moderate accuracy, it struggled with imbalanced datasets. In contrast, Deep Neural Network (DNN) models showed significant improvements with optimization, with DNN combined with GS (DNN-GS) reaching 89% accuracy. However, we also used Recurrent Neural Networks (RNNs) combined with SMOTE and GS (RNN-SMOTE-GS), which emerged as the best-performing with a precision of 97%, demonstrating the effectiveness of combining SMOTE and GS and highlighting the critical role of advanced optimization techniques in enhancing the detection capabilities of IDS models for the accurate classification of various types of network traffic and attacks. Full article

Concrete is a fundamental material in structural engineering, widely used in critical infrastructure such as bridges, nuclear power plants, and dams. These structures may be subjected to extreme dynamic loads resulting from natural disasters, industrial accidents, or missile impacts. Therefore, a comprehensive understanding of concrete behavior under high strain rates is essential for safe and resilient design. Experimental investigations, particularly spalling tests, have highlighted the strain-rate sensitivity of concrete in dynamic tensile loading conditions. This study presents a macroscopic 3D discrete element model specifically developed to simulate the dynamic response of concrete subjected to extreme loading. Unlike conventional continuum-based models, the proposed discrete element framework is particularly suited to capturing damage and fracture mechanisms in cohesive materials. A key innovation lies in incorporating a physically grounded strain-rate dependency directly into the local cohesive laws that govern inter-element interactions. The originality of this work is further underlined by the validation of the discrete element model under dynamic tensile loading through the simulation of spalling tests on normalstrength concrete at strain rates representative of severe impact scenarios (30–115 s⁻¹). After calibrating the model under quasi-static loading, the simulations accurately reproduce key experimental outcomes, including rear-face velocity profiles and failure characteristics. Combined with prior validations under high confining pressure, this study reinforces the capability of the discrete element method for modeling concrete subjected to extreme dynamic loading, offering a robust tool for predictive structural assessment and design. Full article

Adeno-associated virus (AAV) vectors face a critical translational challenge in ocular gene therapy due to pre-existing neutralizing antibodies (NAbs) whose seroprevalence limits patient eligibility. Standard NAb detection using non-ocular cell models (Human Embryonic Kidney 293T) may inadequately predict retinal transduction inhibition due to cell type-related variations in receptor usage and immunogenicity. This study established parallel NAb detection platforms utilizing human retinal pigment epithelial (ARPE-19) cells and standard 293T cells to systematically evaluate clinical serum samples against ophthalmologically relevant AAV serotypes (2, 5, 8, 9) via luciferase reporter-based transduction inhibition assays. Comparative analysis demonstrated ARPE-19 exhibited 42–48% higher NAb titers against AAV5/9 compared to 293T cells, with distinct serotype-biased neutralization hierarchies observed between cellular models. Furthermore, female-derived sera exhibited significantly elevated NAbs against particular serotypes in the ARPE-19 system. Critically, inter-serotype cross-neutralization correlation patterns differed substantially between cellular platforms. These findings demonstrate that physiologically relevant retinal cellular models provide essential immunological profiling data, revealing NAb characteristics obscured in standard assays. Consequently, employing retinal cell-based platforms is crucial for optimizing AAV serotype selection, patient stratification, and predicting clinical outcomes in ocular gene therapy. Full article

Background: Adaptive radiation therapy (ART) can improve prognosis for nasopharyngeal carcinoma (NPC) patients. However, the inter-individual variability in anatomical changes, along with the resulting extension of treatment duration and increased workload for the radiologists, makes the selection of eligible patients a persistent challenge in clinical practice. The purpose of this study was to predict eligible ART candidates prior to radiation therapy (RT) for NPC patients using a classification neural network. By leveraging the fusion of medical imaging and clinical data, this method aimed to save time and resources in clinical workflows and improve treatment efficiency. Methods: We collected retrospective data from 305 NPC patients who received RT at Hong Kong Queen Elizabeth Hospital. Each patient sample included pre-treatment computed tomographic (CT) images, T1-weighted magnetic resonance imaging (MRI) data, and T2-weighted MRI images, along with clinical data. We developed and trained a novel multi-modal classification neural network that combines ResNet-50, cross-attention, multi-scale features, and clinical data for multi-modal fusion. The patients were categorized into two labels based on their re-plan status: patients who received ART during RT treatment, as determined by the radiation oncologist, and those who did not. Results: The experimental results demonstrated that the proposed multi-modal deep prediction model outperformed other commonly used deep learning networks, achieving an area under the curve (AUC) of 0.9070. These results indicated the ability of the model to accurately classify and predict ART eligibility for NPC patients. Conclusions: The proposed method showed good performance in predicting ART eligibility among NPC patients, highlighting its potential to enhance clinical decision-making, optimize treatment efficiency, and support more personalized cancer care. Full article

Personality is a fundamental psychological trait that exerts a long-term influence on human behavior patterns and social interactions. Automatic personality recognition (APR) has exhibited increasing importance across various domains, including Human–Robot Interaction (HRI), personalized services, and psychological assessments. In this study, we propose a multimodal personality recognition model that classifies the Big Five personality traits by extracting features from three heterogeneous sources: audio processed using Wav2Vec2, video represented as Skeleton Landmark time series, and text encoded through Bidirectional Encoder Representations from Transformers (BERT) and Doc2Vec embeddings. Each modality is handled through an independent Self-Attention block that highlights salient temporal information, and these representations are then summarized and integrated using a late fusion approach to effectively reflect both the inter-modal complementarity and cross-modal interactions. Compared to traditional recurrent neural network (RNN)-based multimodal models and unimodal classifiers, the proposed model achieves an improvement of up to 12 percent in the F1-score. It also maintains a high prediction accuracy and robustness under limited input conditions. Furthermore, a visualization based on t-distributed Stochastic Neighbor Embedding (t-SNE) demonstrates clear distributional separation across the personality classes, enhancing the interpretability of the model and providing insights into the structural characteristics of its latent representations. To support real-time deployment, a lightweight thread-based processing architecture is implemented, ensuring computational efficiency. By leveraging deep learning-based feature extraction and the Self-Attention mechanism, we present a novel personality recognition framework that balances performance with interpretability. The proposed approach establishes a strong foundation for practical applications in HRI, counseling, education, and other interactive systems that require personalized adaptation. Full article

Background/Objectives: The Jackson/Cubbin scale is a recommended tool to assess the risk of pressure injury in intensive care unit (ICU) patients. This scale is deemed to have superior predictive validity compared to the Braden scale. Many Italian nurses struggle with reading and applying the tool in English. This language barrier results in a lack of use of the Jackson/Cubbin scale clinically, meaning that patients potentially experience worse outcomes. This study aims to translate the original English version of the Jackson/Cubbin scale into the Italian language, conduct a cultural adaptation, and verify its content validity. Methods: An observational study was conducted using Beaton’s five-step methodology: (1) forward translation, (2) synthesis, (3) back-translation, (4) expert committee approval using Fleiss’ Kappa (κ) index, and (5) pre-testing, where participants assessed item clarity on a dichotomous scale (clear/unclear). Items deemed unclear by 20% or more of the sample were revised. Content validity was assessed using the Content Validity Index (CVI). Results: Fleiss’ κ index was 0.74. Item 3 “PMH-affecting condition” was unclear to 36% of the sample and required revision. The item-level CVI (I-CVI) was >0.78 for each item. The scale-level CVI (S-CVI) and the scale-level CVI using the average method (S-CVI-Ave) were 0.92 and 0.94, respectively. Conclusions: The translation process resulted in a linguistically accurate scale requiring content modifications to reflect current evidence and reduce inter-rater variability. This may improve implementation of the Jackson/Cubbin scale in clinical practice for Italian nurses and reduce the incidence of pressure injury for ICU patients. Full article

Expressive imitation in the performing arts is typically trained through animal behavior imitation, aiming not only to reproduce action trajectories but also to recreate rhythm, style and emotional states. However, evaluation of such animal imitation behaviors relies heavily on teachers’ subjective judgments, lacking structured criteria, exhibiting low inter-rater consistency and being difficult to quantify. To enhance the objectivity and interpretability of the scoring process, this study develops a machine learning and structured pose data-based auxiliary evaluation framework for imitation quality. The proposed framework innovatively constructs three types of feature sets, namely baseline, ablation, and enhanced, and integrates recursive feature elimination with feature importance ranking to identify a stable and interpretable set of core structural features. This enables the training of machine learning models with strong capabilities in structured modeling and sensitivity to informative features. The analysis of the modeling results indicates that temporal–rhythm features play a significant role in score prediction and that only a small number of key feature values are required to model teachers’ ratings with high precision. The proposed framework not only lays a methodological foundation for standardized and AI-assisted evaluation in performing arts education but also expands the application boundaries of computer vision and machine learning in this field. Full article

Background: Many patients harbor minimal residual disease (MRD)—small clusters of residual tumor cells that survive therapy and evade conventional detection but drive recurrence. Although advances in molecular and computational methods have improved circulating tumor DNA (ctDNA)-based MRD detection, these approaches face challenges: ctDNA shedding fluctuates widely across tumor types, disease stages, and histological features. Additionally, low levels of driver mutations originating from healthy tissues can create background noise, complicating the accurate identification of bona fide tumor-specific signals. These limitations underscore the need for refined technologies to further enhance MRD detection beyond DNA sequences in solid malignancies. Methods: Profiling circulating cell-free mRNA (cfmRNA), which is hyperactive in tumor and non-tumor microenvironments, could address these limitations to inform postoperative surveillance and treatment strategies. This study reported the development of OncoMRD BREAST, a customized, gene signature-informed cfmRNA assay for residual disease monitoring in breast cancer. OncoMRD BREAST introduces several advanced technologies that distinguish it from the existing ctDNA-MRD tests. It builds on the patient-derived gene signature for capturing tumor activities while introducing significant upgrades to its liquid biopsy transcriptomic profiling, digital scoring systems, and tracking capabilities. Results: The OncoMRD BREAST test processes inputs from multiple cutting-edge biomarkers—tumor and non-tumor microenvironment—to provide enhanced awareness of tumor activities in real time. By fusing data from these diverse intra- and inter-cellular networks, OncoMRD BREAST significantly improves the sensitivity and reliability of MRD detection and prognosis analysis, even under challenging and complex conditions. In a proof-of-concept real-world pilot trial, OncoMRD BREAST’s rapid quantification of potential tumor activity helped reduce the risk of incorrect treatment strategies, while advanced predictive analytics contributed to the overall benefits and improved outcomes of patients. Conclusions: By tailoring the assay to individual tumor profiles, we aimed to enhance early identification of residual disease and optimize therapeutic decision-making. OncoMRD BREAST is the world’s first and only gene signature-powered test for monitoring residual disease in solid tumors. Full article

This study presents a performance-based concrete mix design methodology rooted in the paste–aggregate binary framework, aiming to reduce binder content while ensuring optimal workability and strength. We found that inter-particle spacing (SPT) and paste rheology jointly govern fresh concrete behavior, with slump increasing nonlinearly with SPT and a critical transition zone around 20–35 µm; paste yield stress controls slump, while plastic viscosity governs segregation resistance. A two-level strength model was developed to predict concrete strength from paste properties based on compactness and hydration (R² = 0.90). Fixing SPT at 25 µm was identified as optimal for achieving balanced flowability with minimal paste volume. This approach effectively decouples aggregate packing optimization from paste calibration, offering a physically interpretable and practical framework for designing sustainable, low-carbon, and low-shrinkage concrete. Full article

Fiber-reinforced composites contain microscale features such as variations in local fiber volume fraction, fiber clusters, and resin-rich regions, which may impact mechanical properties. Microscale models need to be large enough to capture these features while maintaining high fidelity to capture the localized fiber-to-fiber interactions. This makes it difficult to efficiently model regions with equivalent fiber morphologies to as-manufactured scans and to perform large statistical studies to examine how these features drive mechanical performance. This study uses a novel microstructure generator and an efficient micromechanical model along with a characterization method that measures the geometry of these features to simulate a wide range of microstructures for strength and stiffness. After understanding how the mechanical properties are affected by morphology through correlation matrices, equivalent microstructures were generated to regions of an as-manufactured composite. The generation of microstructures based on different morphological descriptors allows for an understanding of which features are valuable when modeling these materials. In comparing microstructures with different equivalent descriptors to the case with all six descriptors, it was found that only using local fiber volume fraction median resulted in over predictions of strength and stiffness. Once two descriptors or more were introduced, such as local fiber volume fraction median and inter-quartile range, there was no significant difference in strength and stiffness. This suggests that at least two descriptors should be considered when generating equivalent microstructures for mechanical properties. Full article

Background: The overwhelming majority of the antimicrobial peptides (AMPs) studied in mussels (Mytilus spp.) so far are specifically expressed by hemocytes and display compact disulfide-stabilized structures. However, gill-specific myticalins play a role in mucosal immunity and are one of the very few examples of known molluscan AMPs lacking cysteine residues. Methods: We investigate the molecular evolution of myticalins, compiling a collection of sequences obtained by carefully annotating 169 genome assemblies of different Mytilus species. We determine the gene presence/absence patterns and gene expression profiles for the five myticalin subfamilies, including the newly reported myticalin E. Results: All sequences are deposited in MyticalinDB, a novel database that includes a total of 100 unique mature myticalin peptides encoded by 215 protein precursors, greatly enriching the compendium of these molecules from previous reports. Among the five subfamilies, myticalin A and C are the most widespread and highly expressed across all Mytilus species. Interestingly, structural prediction reveals a previously unreported strong amphipathic nature for some myticalins, which may be highly relevant for their biological activity. Conclusions: The results reported in this work support the role of myticalins in gill-associated mucosal immunity and highlight the importance of inter-individual molecular diversity in establishing an efficient response to microbial infections. The newly established MyticalinDB provides a valuable resource for investigating the evolution and extraordinary molecular diversity of this AMP family. Full article

Background: Renal tumors, encompassing benign, malignant, and normal variants, represent a significant diagnostic challenge in radiology due to their overlapping visual characteristics on computed tomography (CT) scans. Manual interpretation is time consuming and susceptible to inter-observer variability, emphasizing the need for automated, reliable classification systems to support early and accurate diagnosis. Method and Materials: We propose KidneyNeXt, a custom convolutional neural network (CNN) architecture designed for the multi-class classification of renal tumors using CT imaging. The model integrates multi-branch convolutional pathways, grouped convolutions, and hierarchical feature extraction blocks to enhance representational capacity. Transfer learning with ImageNet 1K pretraining and fine tuning was employed to improve generalization across diverse datasets. Performance was evaluated on three CT datasets: a clinically curated retrospective dataset (3199 images), the Kaggle CT KIDNEY dataset (12,446 images), and the KAUH: Jordan dataset (7770 images). All images were preprocessed to 224 × 224 resolution without data augmentation and split into training, validation, and test subsets. Results: Across all datasets, KidneyNeXt demonstrated outstanding classification performance. On the clinical dataset, the model achieved 99.76% accuracy and a macro-averaged F1 score of 99.71%. On the Kaggle CT KIDNEY dataset, it reached 99.96% accuracy and a 99.94% F1 score. Finally, evaluation on the KAUH dataset yielded 99.74% accuracy and a 99.72% F1 score. The model showed strong robustness against class imbalance and inter-class similarity, with minimal misclassification rates and stable learning dynamics throughout training. Conclusions: The KidneyNeXt architecture offers a lightweight yet highly effective solution for the classification of renal tumors from CT images. Its consistently high performance across multiple datasets highlights its potential for real-world clinical deployment as a reliable decision support tool. Future work may explore the integration of clinical metadata and multimodal imaging to further enhance diagnostic precision and interpretability. Additionally, interpretability was addressed using Grad-CAM visualizations, which provided class-specific attention maps to highlight the regions contributing to the model’s predictions. Full article

High-quality growth of the construction industry and an improved human settlements environment are essential to sustainable urbanization. Existing studies have paid limited systematic attention to the spatial and temporal dynamics of the coordinated development between the construction industry and human settlements, as well as the underlying factors driving regional disparities. This gap restricts the formulation of precise, differentiated sustainable policies tailored to regions at different development stages and with varying resource endowments. Southern China, characterized by pronounced spatial heterogeneity and unique development trends, offers a natural laboratory for examining the spatio-temporal interaction between these two dimensions. Using panel data for 15 southern provinces (2013–2022), we applied the entropy method, coupling coordination model, Dagum Gini coefficient, spatial trend surface analysis, gravity model, and grey forecasting to evaluate current conditions and predict future trends. The main findings are as follows. (1) The coupling coordination degree rose steadily, forming a stepped spatial pattern from the southwest through the center to the southeast. (2) The coupling coordination degree appears obvious polarization effect, presenting a spatial linkage pattern with Jiangsu-Shanghai-Zhejiang, Hubei-Hunan-Jiangxi, and Sichuan-Chongqing as the core of the three major clusters. (3) The overall Dagum Gini coefficient declined, but intra-regional disparities persisted: values were highest in the southeast, moderate in the center, and lowest in the southwest; inter-regional differences dominated the total inequality. (4) Forecasts for 2023–2027 suggest further improvement in the coupling coordination degree, yet spatial divergence will widen, creating a configuration of “eastern leadership, central catch-up acceleration, and differentiated southwestern development.” This study provides an evidence base for policies that foster high-quality construction sector growth and enhance the living environment. The findings of this study indicate that policymaking should prioritize promoting synergistic regional development, enhancing the radiating and driving role of core regions, and establishing a multi-level coordinated governance mechanism to bridge regional disparities and foster more balanced and sustainable development. Full article

Background and Objectives: Blood-borne inflammatory ratios have been proposed as inexpensive prognostic tools across a range of diseases, but their role in pulmonary tuberculosis (TB) remains uncertain. In this retrospective case–control analysis, we explored whether composite indices derived from routine haematology—namely the neutrophil-to-lymphocyte ratio (NLR), the platelet-to-lymphocyte ratio (PLR), the systemic immune–inflammation index (SII) and a novel CRP–Fibrinogen Index (CFI)—could enhance risk stratification beyond established cytokine measurements among Romanian adults with culture-confirmed pulmonary T. Materials and Methods: Data were drawn from 80 consecutive TB in-patients and 50 community controls. Full blood counts, C-reactive protein, fibrinogen, and four multiplex cytokines were extracted from electronic records, and composite indices were calculated according to standard formulas. The primary outcomes were in-hospital mortality within 90 days and length of stay (LOS). Results: Among TB patients, the median NLR was 3.70 (IQR 2.54–6.14), PLR was 200 (140–277) and SII was 1.36 × 10⁶ µL⁻¹ (0.74–2.34 × 10⁶), compared with 1.8 (1.4–2.3), 117 (95–140) and 0.46 × 10⁶ µL⁻¹ (0.30–0.60 × 10⁶) in controls. Those with SII above the cohort median exhibited more pronounced acute-phase responses (median CRP 96 vs. 12 mg L⁻¹; fibrinogen 578 vs. 458 mg dL⁻¹), yet median LOS remained virtually identical (29 vs. 28 days) and early mortality was low in both groups (8% vs. 2%). The CFI showed no clear gradient in hospital stay across its quartiles, and composite ratios—while tightly inter-correlated—demonstrated only minimal association with cytokine levels and LOS. Conclusions: Composite cell-count indices were markedly elevated but did not predict early death or prolonged admission. In low-event European cohorts, their chief value may lie in serving as cost-free gatekeepers, flagging those who should proceed to more advanced cytokine or genomic testing. Although routine reporting of NLR and SII may support low-cost surveillance, validation in larger, multicentre cohorts with serial sampling is needed before these indices can be integrated into clinical decision-making. Full article

Background/Objectives: Cxbladder^® Triage Plus is a multimodal urinary biomarker assay that combines reverse transcription-quantitative analysis of five mRNA targets and droplet-digital polymerase chain reaction (ddPCR) analysis of six DNA single-nucleotide variants (SNVs) from two genes (fibroblast growth factor receptor 3 (FGFR3) and telomerase reverse transcriptase (TERT)) to provide risk stratification for urothelial carcinoma (UC) in patients with hematuria. This study evaluated the analytical validity of Triage Plus. Methods: The development dataset used urine samples from patients with microhematuria or gross hematuria that were previously stabilized with Cxbladder solution. Triage Plus was evaluated for predicted performance, analytical criteria (linearity, sensitivity, specificity, accuracy, and precision), extraction efficiency, and inter-laboratory reproducibility. Results: The development dataset included 987 hematuria samples. Compared with cystoscopy (standard of care), Triage Plus had a predicted sensitivity of 93.6%, specificity of 90.8%, positive predictive value (PPV) of 46.5%, negative predictive value of 99.4%, and test-negative rate of 84.1% (score threshold 0.15); the PPV increased to 74.6% for the 0.54 score threshold. For the individual FGFR3 and TERT SNVs, the limit of detection (analytical sensitivity) was a mutant-to-wild type DNA ratio of 1:440–1:1250 copies/mL. Intra- and inter-assay variance was low, while extraction efficiency was high. All other pre-specified analytical criteria (linearity, specificity, and accuracy) were met. Triage Plus showed good reproducibility (87.9% concordance between laboratories). Conclusions: Cxbladder Triage Plus accurately and reproducibly detected FGFR3 and TERT SNVs and, in combination with mRNA expression, provides a non-invasive, highly sensitive, and reproducible tool that aids in risk stratification of patients with hematuria. Full article