Search Results (59)

Accurate and interpretable brain tumour detection from magnetic resonance imaging (MRI) is important for the reliable use of computer-assisted diagnostic systems. This study examines whether image preprocessing can improve the localisation quality of explanations generated by convolutional neural network (CNN) classifiers while preserving high classification performance. Two pre-trained CNN architectures, ResNet50 and DenseNet121, were fine-tuned using the BRISC 2025 dataset, which contains 6000 annotated contrast-enhanced T1-weighted MRI images: 5000 training images and 1000 test images. The dataset includes four classes: glioma, meningioma, pituitary tumour, and healthy brain images. The original classification layers were replaced with custom fully connected heads designed for four-class classification. Model explanations were generated using Grad-CAM, Integrated Gradients, and LIME. Their localisation quality was evaluated against the available tumour segmentation masks using Intersection over Union (IoU), the Dice coefficient, and the Pointing Game metric. Tests show that both models (99.1% for ResNet50 and 99.2% for DenseNet121) perform well in terms of validation accuracy, but the explanation maps often operate in regions outside the clinically relevant area. To combat this issue, an image preprocessing pipeline utilising Otsu threshold masking, hole filling, and brightness–contrast jittering was implemented to filter noise from the background and isolate the focus area—brain region. After preprocessing, the validation accuracy of the DenseNet121 model was 99.6%, and the average Grad-CAM explainability metrics improved from 21.49% to 23.08% IoU, from 30.21% to 32.49% Dice coefficient, and from 48.33% to 51.67% Pointing Game score. The results indicate that conventional image preprocessing can moderately improve the spatial agreement between explanation maps and annotated tumour regions without reducing classification accuracy. Full article

Rapid filling of urban drainage pipelines during intense rainfall can compress entrapped air and trigger pressure surges or geysering when air release through manhole-cover openings is restricted. Unlike studies focusing on simplified pipes or isolated shafts, this work examines the coupled air–water response of a prototype-scale drainage section with drop structures, branch inflows, variable-diameter inverted siphons, multiple shafts, and restricted manhole-cover venting. A three-dimensional unsteady air–water two-phase model was established and applied to nine two-stage inflow scenarios after validation against published rapid-filling pressure data. The results show that hydraulic slugs segmented the continuous crown air layer into localized air pockets and produced a high-pressure concentration zone upstream of the downstream diameter change, with a maximum shaft-top pressure of 29.2 kPa under the representative high-flow condition. In local shafts, insufficient venting through small openings, bottom water sealing, and continuous air supply jointly induced delayed geysering cycles characterized by pressure accumulation, breakthrough, relief, and re-accumulation, with a period of 110–120 s and peak pressures of 19–21 kPa. The second-stage rapid-filling flow rate dominated downstream peak pressures; when it increased from 9 to 11 m³/s, the peak pressure at a representative downstream shaft rose from 16 to 34 kPa. These findings clarify the mechanisms of high-pressure concentration and delayed geysering under restricted venting and support the identification of pressure-sensitive nodes in complex drainage networks. Full article

Background: Malaria is a life-threatening disease caused by Plasmodium species, which is endemic in tropical and subtropical regions worldwide. In clinical settings, experienced parasitologists perform microscopic examinations of thick/thin blood slides. This method is labour-intensive and is adversely affected by inter- and intra-observer variability among the microscopists. The present study aimed to develop a malaria screening algorithm using computer vision to identify and classify malaria parasite-infected red blood cells (RBC) from microscopic blood slide images. Methods: The proposed classification methodology first employs digital image processing techniques, the watershed transform, to preprocess the raw images, followed by connected component labelling to accurately segment and isolate individual RBCs from the background. To classify these segmented cells as either normal or infected, convolutional neural networks (CNNs) were utilized, leveraging their ability to automatically extract relevant features through deep, hidden layers, thus eliminating the need for manual feature engineering. Results: To compare and determine the most effective classification engine, the study developed and evaluated five distinct models: four well-established transfer learning architectures (VGG16, VGG19, DenseNet121, and InceptionV3), alongside a newly proposed custom CNN model. A total of 2422 segmented RBC images were used for the training, and 692 different images were used for testing, with the VGG model showing the best accuracy at 99.57%. The proposed CNN architecture also showed competitive results with 99.14% accuracy. Conclusions: Transfer learning models demonstrated remarkable accuracy for malaria parasite classification from blood smear slides, with VGG19 (99.57%) achieving the highest accuracy on diverged datasets for the test images. The analysis demonstrates the potential of this approach as a computational aid for future image-based malaria screening in conjunction with existing diagnostic tests. Full article

Ischemic Heart Disease (IHD) is the leading cause of cardiovascular mortality worldwide. Early detection of ischemic changes using electrocardiogram (ECG) signals is vital for timely intervention and enhanced clinical outcomes. However, the diagnosis of IHD varies significantly between men and women. Women often present with atypical symptoms, and their cardiovascular risk is frequently underestimated, which leads to delayed diagnosis. Also, existing approaches face challenges in subtle early-stage abnormalities, single-lead ECG presentation, and the limited interpretability of deep learning models. These cause significant challenges to the accurate diagnosis of IHD. To address these, this study proposes a gender-aware framework, Gender-Aware Morphology Encoder Network (GAMENet), for early ischemic heart disease detection using 12-lead ECG signals with clinical metadata. A novel GAMENet is developed using the PTB-XL database. The Adaptive Morphology Deviation Encoder (AMDE) through Morphology Segment Extraction (MSEG-R) using R-Peak anchoring, isolates clinically relevant waveform components (P-wave, QRS complex, ST-segment, and T-wave) from the preprocessed ECG signals. The feature vector of morphology features is passed through dense layers with dropout regularization and a SoftMax classifier. Statistical and comparative analysis ensures that the proposed framework enables accurate IHD classification and improved interpretability. Full article

This paper evaluates a dense, total-variation-based optical flow method for retrieving wildfire smoke plume motion vectors from geostationary, deep-space, and airborne remote sensing imagery. Using multiple major fire events, we assess the robustness of the approach across a range of spatial resolutions and time intervals. The test cases include Geostationary Operational Environmental Satellite (GOES) observations of the 2025 Los Angeles Fires and the 2024 Park Fire, imagery from NASA’s Enhanced MODIS Airborne Simulator (eMAS) for the 2019 Sheridan and Williams Flats Fires, and a complementary Park Fire image pair from the Earth Polychromatic Imaging Camera (EPIC) aboard the Deep Space Climate Observatory (DSCOVR). Optical flow is computed directly on radiance fields, and smoke plumes are isolated using smoke masks derived from the Segmentation, Instance Tracking, and data Fusion Using multi-SEnsor imagery (SIT-FUSE) framework where available. Performance is evaluated by comparing the root mean square error (RMSE) between original image pairs and between the first image and the second image after warping with the retrieved motion field. RMSE is computed both globally and over smoke-only regions. Across GOES and eMAS cases, optical flow systematically reduces RMSE, often by more than a factor of two within smoke regions, indicating substantially improved frame-to-frame alignment of plume structures after motion correction. The DSCOVR/EPIC case, despite its coarser spatial resolution and longer temporal separation, also shows a marked reduction in global RMSE, demonstrating that the method remains informative under a broader range of observational conditions. For a selected subset of 10 consecutive GOES Park Fire pairs, we additionally compare the retrieved smoke motion vectors with collocated winds from the High-Resolution Rapid Refresh (HRRR) model and find the closest agreement in a broad lower-tropospheric layer centered near 875 hPa. These results show that dense optical flow can capture fine-scale plume evolution in high-temporal-resolution datasets while also providing useful motion estimates in coarser, global-view imagery. RMSE reduction is interpreted here as evidence of improved motion-compensated alignment, while the HRRR comparison provides initial physical context rather than independent validation. The resulting smoke motion vector fields provide a foundation for future comparison with model winds and for applications in plume analysis, fire hazard monitoring, and air quality studies. Full article

The global emergence of the avian influenza virus (AIV) H5N1 clade 2.3.4.4b since 2016 has caused substantial losses in wild bird and poultry populations, along with heightened risks of transmission to humans and other mammals. Vaccination of poultry has been a key strategy to curb the virus’s spread and mitigate its socioeconomic impact. This report describes an outbreak of high pathogenicity avian influenza virus (HPAIV) H5N1 clade 2.3.4.4b in a flock of 15,000 brown layer chickens (170 days old), all of which had received a four-dose vaccination regimen with H5N1/H5N8 commercial vaccines at 17, 50, 100, and 125 days of age. Despite this vaccination history, H5N1 infection was confirmed approximately seven weeks post-vaccination. H5N1 infection was confirmed by RT-qPCR, virus isolation, and full genome sequencing covering all eight gene segments, followed by phylogenetic and molecular analyses. Clinical signs included reduced feed intake, decreased egg production, and a cumulative mortality rate of 35% over 52 days. Hemagglutination inhibition (HI) testing with various H5 antigens revealed inconsistent antibody titers (geometric mean: 4.0 to 9.1 log2). Genetic analysis of the full-length HA and NA gene sequences further revealed strong similarity to contemporaneous H5N1 clade 2.3.4.4b strains circulating in Egypt, with multiple mutations in the HA head domain, particularly near immunogenic epitopes and receptor binding sites. These findings highlight the limitations of current vaccination strategies under conditions of antigenic mismatch and complex immunization schedules, emphasizing the need for improved vaccine matching and continuous molecular surveillance. To improve outbreak management in poultry, enhanced vaccination protocols, stringent biosecurity measures, and rigorous monitoring practices are critical. Full article

Major depressive disorder remains difficult to diagnose objectively, as routine assessment is still largely dependent on clinical interview and rating scales. Resting-state electroencephalography (EEG) is an attractive complementary modality because it is non-invasive, low-cost, and compatible with wearable sensing, but many reported EEG classification results are weakened by segment-level leakage and unclear subject identity handling. This study evaluates whether depression can be distinguished from healthy controls using a compact eight-channel resting-state EEG configuration under a strictly leakage-free subject-wise protocol. Using a widely used public EEG dataset, we first corrected a previously overlooked subject-identity ambiguity by constructing a class-aware composite key, yielding 56 valid unique participants. We then applied ten repeated subject-wise holdout splits and compared five compact baselines spanning Extra Trees and a multi-layer perceptron on asymmetry-aware spectral features and three convolutional networks on raw signals, including the EEG-specific EEGNet and ShallowConvNet architectures. Uncertainty was quantified through 95% bootstrap confidence intervals of the mean across repeats. The best model, an Extra Trees classifier using eight-channel spectral and asymmetry features, achieved a mean balanced accuracy of 93.5% with a 95% bootstrap confidence interval of 89.6% to 96.8% and a mean area under the receiver operating characteristic curve of 98.6% with a 95% bootstrap confidence interval of 96.2% to 100.0%. A connectivity ablation showed that inter-channel coherence was informative in isolation but did not improve performance when naively fused with spectral features. A feature-selection ablation did not show evidence that the 90-dimensional spectral representation was dominated by noisy or uninformative dimensions under this evaluation protocol. These results support compact, subject-wise evaluated EEG screening pipelines while highlighting the importance of rigorous leakage control. Full article

Modern enterprise breaches are no longer isolated events but coordinated, multi-stage campaigns whose success depends on the defender’s inability to translate detection into timely containment. While existing frameworks—such as attack-lifecycle models, Zero Trust architectures, and detection-driven systems—provide valuable capabilities, they lack a formal mechanism for coupling inferred adversarial state with coordinated, cross-layer enforcement. This paper presents AMNDA, an Adaptive Multi-layer, stage-aware Network Defense Architecture that operationalizes lifecycle-aware defense through explicit state-to-control mapping and executable orchestration. Adversarial progression is modeled as a probabilistic state-transition process, and inferred states are systematically mapped to synchronized controls across edge protection, identity governance, internal segmentation, and behavioral detection. A formally defined orchestration function transforms detection outputs into stage-conditioned policy updates, enforcing monotonic tightening of containment as adversarial capability escalates. AMNDA is implemented and validated in a reproducible Microsoft Azure environment. Empirical results show that stage-aligned enforcement actions execute within 1.0–3.1 s, while detection latency remains the dominant constraint, with a median of 1034 s across the validation corpus. This separation reveals a critical operational insight: in modern cloud environments, the limiting factor in lifecycle defense is not enforcement capability but detection timing. The contribution of AMNDA is therefore not a new detection technique but a formal, deployable architecture that converts attack-stage inference into coordinated, low-latency containment. By bridging lifecycle modeling, Zero Trust principles, and automated orchestration, the proposed approach establishes a practical foundation for state-aware, adaptive cyber defense. Full article

To evaluate the coupled deformation of existing shield tunnels induced by multi-segment excavations with isolation piles, this study develops an integrated analytical framework combining a Kerr three-parameter foundation-plate model with a three-dimensional image-source solution. A closed-form expression for the soil displacement field is first derived by incorporating layered soil conditions, staged excavation, and associated spatial effects. The soil–pile interaction of isolation piles is then modeled using the Kerr foundation, and the flexural response is obtained through variational formulation and finite-difference discretization. These responses are sequentially propagated through the excavation stages, enabling the superposition of multi-pit effects on the final retaining-wall deformation. The image-source method and a volume-equivalent transformation are further used to convert wall deformation into an additional stress field acting on the tunnel, which is ultimately coupled with a tunnel–soil deformation–coordination model to compute horizontal tunnel displacements. This unified workflow establishes a continuous mechanical transfer chain—from excavation-induced soil loss to isolation-pile bending and finally tunnel deformation. Parametric analyses show that lateral displacement of the retaining structure is jointly governed by wall bending and pit-bottom uplift, producing a right-skewed “S-shaped” profile. The bending-moment peak shifts toward earlier-excavated zones, indicating a memory effect of excavation sequencing. Two engineering cases verify that the proposed method accurately reproduces the magnitude and depth of measured wall deflections, while predicted tunnel displacements show a near-Gaussian pattern with high accuracy near the peak. The analytical framework provides a robust theoretical basis for optimizing pit segmentation and excavation sequencing adjacent to shield tunnels. Full article

With the widespread adoption of container technology, its shared kernel architecture has made abnormal file access behavior a key precursor to container escape and lateral attacks, necessitating precise and efficient runtime detection mechanisms. However, existing monitoring methods typically suffer from issues such as insufficient granularity in data collection, limited path semantic modeling capabilities, and low anomaly detection accuracy. To address these challenges, this paper proposes an eBPF-based method for detecting abnormal file access in containers. A lightweight kernel-level monitoring mechanism is constructed to capture access behavior in real time at the system call level, effectively enhancing both the granularity of data collection and the completeness of context. At the feature modeling layer, a multimodal path semantic representation method is designed, combining risk-layer rules and semantic vectorization strategies to enhance the hierarchical expression of path structures and improve context modeling ability. In the detection layer, an attention-enhanced autoencoder model is introduced, achieving high-precision identification of abnormal access behavior and low false-positive monitoring under unsupervised conditions through a path segment attention mechanism and weighted reconstruction loss function. Experiments in real container environments show that the proposed method achieves a recall rate of $82.0\%$, a false-positive rate of $0.79\%$, and a Matthews correlation coefficient of 0.852, significantly outperforming mainstream unsupervised detection methods such as Isolation Forest, One-Class SVM, and Local Outlier Factor. These results verify the advantages of the proposed method in terms of detection accuracy, real-time performance, and system friendliness, providing an efficient and feasible solution for enhancing the detection of unknown attacks in container runtimes. Full article

Evaluating pedestrian comfort in high-density cities requires methods integrating subjective experience with urban morphology. This study develops an integrated framework combining pairwise comparison scoring, semantic segmentation (DeepLabv3+), ensemble learning (Random Forest), and SHAP-based interpretability. EfficientNet-B7 is used to expand pairwise datasets and derive continuous comfort scores across Macau’s street network. Four experiential street types are identified: historical–cultural districts, urban lifestyle areas, natural corridors, and leisure zones. SHAP analysis illustrates stable associations between predicted comfort scores and multi-layered spatial configurations, including cultural legibility and sequencing in historic cores, moderate greenery with functional anchoring in residential areas, and scene coherence in tourism zones. Semantic features serve as effective morphological proxies within the modeling framework. Methodologically, the framework demonstrates how explainable machine learning can be applied to dense Asian cities under observational conditions. Design implications emphasize interface continuity, microclimate adaptation, and functional enrichment, suggesting that pedestrian comfort is closely related to coherent spatial–experiential structures rather than isolated environmental upgrades. Full article

Safe exploitation of the marine natural gas hydrate (NGH) resource is essential to meet the demand of the future energy requirement. To enable real-time monitoring of methane leakage during the production test of NGH, an ocean stereoscopic monitoring system based on underwater single-point mooring structure is developed, which supports in situ monitoring of marine environment at the sea-air interface, the euphotic zone, and the seabed boundary layer. Numerical simulations were conducted to evaluate the effect of mooring configuration, cable lengths, and buoyancy settings on the mooring stability of the system against the current and waves. Based on the simulation result, an optimized segmented inverse-catenary mooring configuration is developed to achieve a balance between the performance and cost. The designed submersible relay buoy isolates the upper dynamic S-shaped cable from the lower static straight electro-optical-mechanical (EOM) cable, thereby improving system stability. The monitoring system based on the optimized mooring structure is successfully deployed at the NGH zone in the northern South China Sea at the water depth of 1330 m confirming its working stability in harsh sea conditions. Full article

We developed an AI-assisted zero-trust control system at low capital expenditure to retrofit brownfield Ethernet environments without disruptive hardware upgrades or costly software-defined networking migration. Legacy network infrastructures in small and medium-sized enterprises (SMEs) lack the flexibility and programmability required by modern zero-trust architectures, creating a persistent security gap between static Layer-1 deployments and dynamic cyber threats. The developed system addresses this gap through a modular architecture that integrates genetic-algorithm-based virtual local area network (VLAN) optimization, large language model-guided firewall rule synthesis, threat-intelligence-driven policy automation, and telemetry-triggered adaptive isolation. Network assets are enumerated and evaluated through a risk-aware clustering model to enable micro-segmentation that aligns with the principle of least privilege. Optimized segmentation outputs are translated into pfSense firewall policies through structured prompt engineering and dual-stage validation, ensuring syntactic correctness and semantic consistency. A retrieval-augmented generation pipeline connects live telemetry with historical vulnerability intelligence, enabling rapid policy adjustments and automated containment responses. The system operates as an overlay on existing managed switches, orchestrating configuration changes through standards-compliant interfaces such as simple network management protocol and network configuration protocol. Experimental evaluation in a representative SME testbed demonstrates substantial improvements in segmentation granularity, refining seven flat subnets into thirty-four purpose-specific VLANs. Compliance scores improved significantly, with the International Organization for Standardization/International Electrotechnical Commission 27001 rising from 62.3 to 94.7% and the National Institute of Standards and Technology Cybersecurity Framework alignment increasing from 58.9 to 91.2%. All 851 automatically generated firewall rules passed dual-agent validation, ensuring reliable enforcement and enhanced auditability. The results indicate that the system developed provides an operationally feasible pathway for legacy networks to achieve zero-trust segmentation with minimal cost and disruption. Future extensions will explore adaptive learning mechanisms and hybrid cloud support to further enhance scalability and contextual responsiveness. Full article

Generative AI (GenAI) now produces text, images, audio, and video that can be perceptually convincing at scale and at negligible marginal cost. While public debate often frames the associated harms as “deepfakes” or incremental extensions of misinformation and fraud, this view misses a broader socio-technical shift: GenAI enables synthetic realities—coherent, interactive, and potentially personalized information environments in which content, identity, and social interaction are jointly manufactured and mutually reinforcing. We argue that the most consequential risk is not merely the production of isolated synthetic artifacts, but the progressive erosion of shared epistemic ground and institutional verification practices as synthetic content, synthetic identity, and synthetic interaction become easy to generate and hard to audit. This paper (i) formalizes synthetic reality as a layered stack (content, identity, interaction, institutions), (ii) expands a taxonomy of GenAI harms spanning personal, economic, informational, and socio-technical risks, (iii) articulates the qualitative shifts introduced by GenAI (cost collapse, throughput, customization, micro-segmentation, provenance gaps, and trust erosion), and (iv) synthesizes recent risk realizations (2023–2025) into a compact case bank illustrating how these mechanisms manifest in fraud, elections, harassment, documentation, and supply-chain compromise. We then propose a mitigation stack that treats provenance infrastructure, platform governance, institutional workflow redesign, and public resilience as complementary rather than substitutable, and outline a research agenda focused on measuring epistemic security. We conclude with the Generative AI Paradox: as synthetic media becomes ubiquitous, societies may rationally discount digital evidence altogether, raising the cost of truth for everyday life and for democratic and economic institutions. Full article

Glucagon-like peptide-2 (GLP-2) has been reported to cause gastrointestinal relaxation by interfering with enteric inhibitory neurotransmitters, including vasoactive intestinal peptide (VIP). However, the involvement of VIP in the GLP-2’s actions on isolated ileal preparations has never been explored. In this study, we investigated whether VIP contributes to the inhibitory effects of GLP-2 on spontaneous and neurally evoked contractions in mouse ileal segments. Functional experiments showed that VIP, as well as GLP-2, depresses both spontaneous and electrically induced contractile responses. The VIP antagonist, VIP 6–28, slightly increased the amplitude of the neurally induced contractile responses. VIP 6–28 did not alter the hormone’s effects on the spontaneous activity, but reduced its inhibitory action on the neurally evoked contractions. In GLP-2-exposed specimens, immunohistochemistry showed a significant decrease in VIP-positivity in nerve fibers located in the muscle layers. These results provide the first evidence that in isolated mouse ileal preparations VIP contributes to the inhibitory effects of GLP-2 on the neurally induced contractile responses. From a physiological point of view, such depressant effects of the hormone may represent a mechanism aimed at slowing intestinal transit and optimizing nutrient absorption. Full article