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Structural brain alterations in frailty among end-stage renal disease patients

Lattice-based post-quantum image encryption with NDFA-driven spin-matrix transformation

Abstract The increasing reliance on visual data transmission across distributed and cloud-based environments requires image encryption frameworks that provide both strong cryptographic guarantees and practical computational efficiency. Conventional image encryption schemes frequently rely on heuristic transformations whose security is primarily evaluated through statistical analyses, often lacking formal resistance against emerging post-quantum threats. A layered post-quantum image encryption framework is presented, integrating lattice-based key establishment, deterministic image scrambling, and authenticated encryption within a unified architecture. A shared secret is established using the sntrup761 key encapsulation mechanism, providing IND-CCA security under lattice-based assumptions. The derived session key is expanded using HKDF into independent keys for permutation scheduling and encryption, enabling separation between cryptographic security and image transformation layers. A nondeterministic finite-state scheduling mechanism drives spin-matrix-based scrambling to enhance spatial diffusion, while authenticated encryption with associated data ensures confidentiality and integrity through strict verification prior to image recovery. Security analysis shows that post-quantum security comes from the lattice-based key encapsulation mechanism rather than the image-transformation layer, and demonstrates robustness against adaptive adversarial models, while computational evaluation indicates linear scalability with image size. Statistical and robustness analyses further confirm strong diffusion properties and stable scrambling-layer behavior under noise and occlusion conditions. The proposed framework bridges standardized post-quantum cryptography with lightweight image-oriented transformations, providing a structured and practical approach for secure visual data transmission.

A cross-sectional analysis of the associations between dry eye disease and depression

Abstract Dry Eye Disease (DED) is a common condition worldwide that is often associated with mental health conditions such as depression. The study evaluates the associations between dry eye disease and depression in the Hong Kong population, based on a large population-based cohort. A total of 614 participants were enrolled, and 1228 eyes were evaluated. The Ocular Surface Disease Index (OSDI) questionnaire was administered to assess subjective dry eye symptoms. Lipid layer thickness (LLT) was measured using the IDRA ocular surface analyzer, while tear meniscus height (TMH), first non-invasive tear break-up time (NITBUT), average NITBUT, Meiboscore, and redness were assessed with the Keratograph 5 M. The osmolarity was measured by the TearLab. Dry eye and non-dry eye were categorized based on the Tear Film and Ocular Surface Society Dry Eye Workshop II (TFOS DEWS II) diagnostic criteria. Depression classification was based on participant self-report, together with concurrent antidepressant use. For statistical analysis, the Kolmogorov–Smirnov test was used to examine normality. Fisher’s exact test was applied to categorical variables, and the Mann–Whitney U test was used for continuous variables in two-group comparisons. A Generalized Estimating Equation (GEE) model was constructed to explore associations between dry eye parameters and depression. Among the 614 participants recruited from the general Hong Kong population, 596 were classified as not having depression (1192 eyes; median age, 46 years), and 18 were classified as having depression (36 eyes; median age, 51 years). The OSDI score was higher among participants with depression than among participants without depression [20 (9–36) vs. 14 (6–23), P  = 0.004]. However, other parameters showed no significant differences between the two groups. In the multivariate model, the OSDI score was significantly associated with depression, showing an odds ratio (OR) of 1.03 (95% CI: 1.01–1.06; P  = 0.015). Furthermore, the severe subjective dry eye (OSDI > 32) was significantly associated with depression, with an OR of 3.00 (95% CI: 1.14–7.86; P  = 0.026). Severe DED symptom score was significantly associated with depression in the population of Hong Kong, highlighting the clinical relevance of the relationship between DED and mental health.

Normal inflammatory marker levels do not exclude metabolic disturbances in young male team sport athletes

Sickle cell disease premarital screening utilization and associated factors among youth corps members in Osun State, Nigeria

Modeling ultrafast laser excitation of fused silica with a hybrid Lorentz–Drude dielectric response and density-dependent two-temperature model

Abstract Ultrashort laser excitation of dielectric materials is governed by a complex interplay between nonlinear photoexcitation, transient optical response, and energy transfer to the lattice. A consistent physical description of these processes across different pulse durations remains challenging, as purely free-electron-based optical models and classical thermal approaches do not adequately capture the strongly dynamic and non-equilibrium nature of the excited electron system. In this work, a coupled modeling framework is developed for amorphous fused silica (SiO 2 ) that directly links the transient optical response to a density-dependent two-temperature description of the subsequent thermal evolution. The optical properties are described using a hybrid Lorentz–Drude formalism, enabling a consistent representation of bound, localized, and free electronic states during excitation. The resulting energy deposition is coupled to a modified thermal model incorporating electron-density-dependent material parameters derived from first-principles calculations. The model is evaluated by comparison with experimentally measured ablation geometries and single-pulse ablation thresholds for pulse durations in the femtosecond and picosecond range. The threshold analysis indicates that experimentally measurable material removal can occur below the model-internal phase-explosion criterion, particularly for longer pulse durations. In the femtosecond regime, both ablation depth and diameter are reproduced within a limited deviation range. In the picosecond regime, the ablation depth remains consistent with experimental observations, while systematic deviations in the lateral extent become apparent. At longer pulse durations, the experimentally observed structures are increasingly influenced by melt-mediated material redistribution, which is not captured by the present model and leads to pronounced deviations in the ablation geometry. These results demonstrate that the proposed framework provides an effective physical description of ultrafast laser–matter interaction in non-equilibrium and transition regimes, while also defining its limitations in thermally dominated regimes where hydrodynamic effects become significant.

Bayesian analysis of viscous modified cosmic Chaplygin gas in FRW universe with a cosmological constant

A blockchain-driven forensic framework for secure cyberbullying evidence collection in IoT environments

Adaptive deep reinforcement learning–based secure routing for wireless sensor networks

Research on loss optimization calculation and cooling structure design of high-speed permanent magnet motor rotor for integrated starter-generator

Phishing webpage detection using structured URL generation

Imbalance fault diagnosis based on iterative attention wavelet packet decomposition and weighted ensemble classification algorithm

Prioritising construction accident risk factors using an integrated Random Forest, XGBoost, and ANN framework with sensitivity analysis

TrafCopAgent: synergizing reinforcement learning and multi-agent collaboration for adaptive emergency traffic control

Diagenetic processes in quaternary vertebrate fossils from coastal environments

Abstract The fossilization of Quaternary mammals in coastal environments of southern Brazil results from the interaction between taphonomic and diagenetic processes, strongly controlled by hydrological dynamics, interstitial fluid chemistry, and sedimentary reworking. Through the integration of macro- and microscopic analyses with SEM, XRD, and Raman spectroscopy, two main diagenetic pathways — Route A and Route B — were identified. Route A develops in silica-rich environments, characterized by mechanical sediment infiltration and precipitation of siliceous phases. These processes promote increased structural disorder in apatite, reduction in crystallite size, pronounced removal of B-type carbonate, and the development of vibrational bands associated with phosphate–silicate interactions. In parallel, Route B is associated with carbonate-supersaturated solutions. In this pathway, calcite precipitation and the formation of larger crystallites indicate a buffered geochemical system capable of delaying apatite decarbonation. Evidence of this buffering is preserved in Raman data, which reveal a diagenetic gradient marked by narrowing of the ν₁(PO₄³⁻) band (decreased FWHM) together with progressive but partial depletion of B-type carbonate. XRD data confirm the formation of fluorapatite through diagenetic substitution of OH⁻ by F⁻, as well as variations in crystallite size consistent with distinct fluid–mineral interaction histories. Taphonomic evidence indicates variable redox conditions and intense pore-fluid circulation. The distinction between Route A and Route B provides a useful interpretative framework for understanding bone preservation in Quaternary coastal environments.

LIR-Mamba: consolidating YOLO and selective SSM with global-local scanning for robust infrared small target detection under laser interference

Hydrodynamic characteristics during floating and installation of high blockage-ratio immersed tunnels in Inland Rivers

A graph-guided cross-modal attention network for multimodal emotion recognition via emotion-shift modeling

Integrated high density seismic imaging and structural characterization of the Yahu deep brine system, Qaidam Basin

Explainable hybrid deep learning for automated cervical cytology classification

Abstract Accurate and explainable automated analysis of cervical cytology remains a major challenge for artificial intelligence (AI)-assisted cervical cancer screening, particularly in low-resource settings where access to expert cytotechnologists is limited. Although deep convolutional neural networks (CNNs) have substantially improved classification accuracy, their limited interpretability hinders clinical trust and routine adoption. Conversely, neuro-fuzzy systems provide transparent decision-making but lack the ability to learn complex cytomorphological representations directly from image data. Here, we present PapsAI XNet , a novel explainable hybrid deep learning framework that integrates attention-guided CNN feature extraction with an Adaptive Neuro-Fuzzy Inference System (ANFIS) to simultaneously achieve high diagnostic accuracy and interpretable decision-making. PapsAI XNet incorporates a Channel Attention Module and feature normalization strategy to enhance morphological feature discrimination and stabilize downstream fuzzy inference. A compact ANFIS rule base initialized through data-driven clustering enables transparent reasoning while avoiding the rule explosion commonly associated with high-dimensional neuro-fuzzy systems. The framework was evaluated on the publicly available Herlev dataset comprising seven cervical cytology classes and benchmarked against ResNet-18 and MobileNetV2 using accuracy, sensitivity, specificity, precision, F1-score, receiver operating characteristic (ROC) analysis, computational complexity, and explainability analyses. PapsAI XNet achieved an overall accuracy of 97.8% , sensitivity of 96.4% , specificity of 98.6% , precision of 97.1% , and F1-score of 96.7% , significantly outperforming both benchmark CNN architectures. The greatest improvements were observed for mild and moderate dysplasia, where gradual morphological transitions frequently challenge conventional deep learning models. Class-wise area under the ROC curve exceeded 0.97 for all abnormal cytological categories. Furthermore, learned Gaussian membership functions, fuzzy-rule surfaces, and rule activation patterns provided clinically meaningful explanations linking deep morphological features to diagnostic outcomes while maintaining a lightweight computational footprint suitable for real-time deployment. These findings demonstrate that integrating attention-guided deep feature learning with interpretable neuro-fuzzy reasoning enables accurate, explainable, and computationally efficient cervical cytology classification. PapsAI XNet provides a practical framework for trustworthy AI-assisted cervical cancer screening and represents an important step toward clinically deployable explainable AI for digital cytopathology, particularly in resource-constrained healthcare systems.