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Design and evaluation of a resilient IBN architecture: Integrating post-quantum cryptography with adaptive threat detection using machine learning
As the domain of network security keeps on evolving rapidly, especially in sensitive areas such as healthcare systems, the demand for reliable device verification, controlling access, and spotting threats is growing sharply. This paper presents the design, implementation, and systematic evaluation of an improved Intent-Based Networking (IBN) system that integrates post-quantum cryptography, certificate-based identity management, and machine learning-based anomaly detection within a unified framework. The system incorporates SPHINCS+ post-quantum digital signatures for quantum-resilient authentication, X.509 certificate lifecycle management for establishing device trust, and hardware-aware cryptographic operations to maintain efficiency. It further enforces fine-grained access policies using Role-Based Access Control (RBAC) augmented with Multi-Factor Authentication (MFA), ensuring strong access governance across network segments. For early threat detection, machine learning models such as Isolation Forest and MiniBatch KMeans are employed to learn communication patterns and detect anomalous device behavior. Additionally, event logs are maintained using asynchronous, hash-chained logging mechanisms inspired by blockchain principles, ensuring auditability and data integrity. To address evaluation transparency and rigor, the framework is assessed using a controlled prototype testbed with explicitly defined traffic features and reproducible experimental settings. The evaluation considers cryptographic correctness, access control performance, anomaly detection capability, and scalability under increasing workloads. Experimental results demonstrate 100% success in post-quantum signature generation and verification, effective anomaly detection with no observed false negatives in the evaluated scenarios, and stable log-processing throughput as the number of events grows. Importantly, this work does not claim novelty in individual components, but contributes through the system-level integration and empirical evaluation of a quantum-safe, ML-assisted IBN security architecture. The findings highlight key trade-offs between security enforcement and usability, while also identifying limitations such as certificate expiry handling gaps, conservative policy behavior, and lack of large-scale statistical validation. These observations establish a reproducible baseline and motivate future work toward statistically rigorous validation, real-world deployment, and adaptive policy optimization.
Development and validation of a stability-indicating high-performance liquid chromatography method for iomeprol
Analyses of eye lens stable isotopes across ontogeny of trophically diverse freshwater salmonids
Ontogenetic niche shifts in fishes are nearly universal but remain poorly understood in many species despite being fundamentally important for the persistence, management, and conservation of fish populations, including those of vulnerable salmonids. Eye lens stable isotope analysis has proven useful in studying ontogeny in some marine species but has rarely been applied in freshwater fishes. We conducted among the first applications of eye lens stable isotope analysis in two salmonids, Arctic Charr ( Salvelinus alpinus ) and Brook Trout ( Salvelinus fontinalis ), in four North American lakes at the southern extent of the range of Arctic Charr (Maine, USA). Our goal was to determine if ontogenetic patterns varied between individuals and populations in ways that relate to differential vulnerability. Like studies in marine systems, we found patterns in lens isotopic values that agree with expected ontogenetic patterns to reach known adult trophic niches. Within lakes and individuals examined in this study, Arctic Charr appeared more dependent on pelagic resources than co-occurring Brook Trout through life. Using Bayesian hierarchical linear regressions, we found evidence that ontogenetic shifts in trophic position (measured by δ 15 N) of Arctic Charr may vary among lakes. Arctic Charr in some populations increased in trophic position through life (population lifetime δ 15 N posterior mean slope estimate = 1.01) while others showed no substantial changes (population lifetime δ 15 N posterior mean slope = 0.05), which may relate to differences in habitat and fish assemblage among our study lakes. Our study suggests that individual life stages and populations of salmonids are likely to respond to climate variability (e.g., basal resource shifts) differentially, which could warrant population and life-stage-specific management.
NucVerse3D: generalizable 3D nuclear instance segmentation across heterogeneous microscopy modalities
Factors that influence care for second and third trimester termination of pregnancy for medical reasons in Canada: A qualitative investigation
Background Termination of pregnancy in the 2 nd /3 rd trimester for medical reasons is an essential health service. Our objective was to explore the factors that influence 2 nd /3 rd trimester abortion care for medical reasons in Canada from the perspective of healthcare providers. Methods We conducted one-on-one, semi-structured interviews with healthcare providers across the 10 Canadian academic maternal fetal medicine (MFM) centres. The interviews were conducted in English or French, and we collected participant demographic information through an online survey. We used reflexive thematic analysis to identify healthcare providers’ perspectives of barriers and facilitators that impact care for 2 nd /3 rd trimester abortion for medical reasons at their centres. Our analysis was informed by a realist standpoint. We used NVivo14 Software to organize our analysis. Findings We recruited 28 participants – 10 MFM specialists, 9 medical geneticists/genetic counsellors, and 9 nurses/social workers. We identified six main themes describing factors that influence termination care: 1) Provider commitment amid burnout and discomfort regarding abortion care; 2) Provider availability and team structure ; 3) Logistical factors that support coordinated and compassionate care, including the availability of a dedicated care coordinator, are instrumental to how care is delivered; 4) Advantages and challenges of local guidelines highlights a tension between desiring structured equitable guidelines and facilitating flexibility to tailor care to meet individual needs; 5) Considerations for patient equity by adjusting care based on patients’ lived experiences; 6) Communicating with and supporting patients emphasizes the importance of patient-centered communication and opportunities to help patients navigate their grief and bereavement. Conclusions Healthcare providers identified key factors that promote or hinder 2 nd /3 rd trimester abortion care for medical reasons in Canada. Understanding which barriers to address and facilitators to amplify will inform efforts to optimize equitable, patient-centred, supportive, and comprehensive healthcare services.
Transformer-based iris verification with attention-guided segmentation and Siamese learning
Spatiotemporal localization of sex difference in plantar pressure distribution during self paced walking in healthy Japanese adults
Objectives There is notable sex difference in the manner of locomotion, but researchers are yet to obtain the comprehensive information about the biomechanical difference in gait pattern between males and females. The present study aimed to investigate sex differences in plantar pressure distribution during self-paced walking using a cluster-based permutation approach. Method Plantar pressure data were collected from 24 healthy males and 68 healthy females using a pressure-sensor footplate. Group comparisons were conducted for basic gait parameters as well as pressure-related metrics, including maximum force, peak pressure, and contact area size. Additionally, time-series characteristics of total force were compared between sexes. A cluster-based permutation test was used to identify spatial and temporal regions with significant sex differences in plantar pressure distribution at high resolution. Results Analyses revealed that females exhibited shorter step durations and higher cadence compared to males, attributable in part to differences in the duration of the late stance phase. Females also demonstrated higher weight-normalized plantar pressures across most of the stance phase. Sex-specific differences in plantar pressure distribution were localized to the calcaneal and second metatarsal regions. Conclusion Spatially localized pattern of sex difference in the plantar pressure indicates that biomechanical factors may contribute to the sex difference in the incidence rates of clinical conditions such as stress fracture. There was also a hitherto unreported pattern of sex difference in the time-series of total force, that may be related to sex-specific strategy to keep a self-preferred walking speed.
Correction: Intrabasin Variability of East Pacific Tropical Cyclones During ENSO Regulated by Central American Gap Winds
ThermiQuant™ AquaStream: A portable instrument for quantitative colorimetric isothermal nucleic acid amplification reactions in paper and tube formats
Microfluidic paper-based analytical devices (µPADs) are an attractive format for colorimetric nucleic acid amplification tests (NAATs) because they enable low-cost, portable diagnostics in resource-limited settings. However, researchers often optimize colorimetric assays in liquid reactions in tubes before translating them to µPADs. Since both formats require separate instruments for incubation and real-time sensing, direct comparison of reactions between the two formats is difficult. To address these cross-platform limitations, we developed ThermiQuant™ AquaStream, a portable benchtop device (15 × 20 × 16 cm, ~ 5 kg; cost: USD 327) that supports seamless colorimetric loop-mediated isothermal amplification (LAMP) reactions in both µPADs and tubes under a common workflow. The system enables real-time reaction tracking (every 30 seconds) through onboard image processing, precise isothermal control (±0.5 °C) using a repurposed consumer-grade sous-vide heater, and medium-throughput (24 tubes or 42 µPADs). Testing with synthetic SARS-CoV-2 orf7ab DNA fragments demonstrated a limit of detection corresponding to a 95% probability of detection (LOD95) of 110 copies per reaction in tube (22 copies/µL) and 39 copies per reaction in µPADs (5 copies/µL), estimated using probit regression. In both formats the limit of quantification (LOQ), defined as the lowest concentration yielding a coefficient of variation (CV) of quantification time (Tq) ≤ 10%, was 250 copies/reaction resulting in a strong linear (R 2 = 0.98 & R 2 = 0.96 respectively for tube and µPADs) standard calibration curves. ThermiQuant™ AquaStream provides an affordable and versatile benchtop platform capable of supporting both tube- and µPAD-based colorimetric LAMP assays, serving as a proof-of-concept research tool for assay development and molecular diagnostics in One Health settings such as clinics, farms, and field environments.
Sports and exercise and changes in college students’ phubbing behavior: based on latent growth and cross-lagged models
RF-SVR-based prediction methodology for metal tube-bending rebound: Handling non-uniformity and limited sample challenges
This paper explores a prediction algorithm for determining the rebound angle of non-uniform and small-sample tubes. To address the issues of non-uniform and small-sample data, this paper proposes an algorithm based on Random Forest-Support Vector Regression (RF-SVR). Firstly, the polynomial feature generation method is introduced to solve the problem of non-uniform data. Secondly, after obtaining the data generated by the polynomial features, RF (Random Forest, an algorithm based on classification trees) is introduced to select the rebound features of the tubes, so that the features that have a profound influence on the rebound Angle can be retained. After obtaining the new data set, SVR (Support Vector Regression, an algorithm specifically designed for solving regression problems) is used to predict the rebound model of the bending of the metal tubes. The experimental results show that the RF-SVR method is superior to the traditional RF-BP and SVR methods, achieving higher prediction accuracy on small samples and non-uniform datasets.
Understanding spatial distribution variability of surface soil reaction and electrical conductivity in cultivated soils of India as influenced by some environmental factors
Feasibility and reliability of decentralized HIV-1 viral load monitoring on self-sampled blood
Background Home-based blood self-sampling for HIV-1 viral load (VL) monitoring has the potential to alleviate pressure on healthcare systems by reducing clinic visits for people with HIV. This study evaluates the feasibility and reliability of HIV-RNA measurements in self-sampled blood and in viremic samples. Methods Between September 2024 and March 2025 participants were recruited at the outpatient clinic. Self-sampled blood was collected using the TassoPlus device at home and compared to conventional HIV-RNA testing from the same individual. Only samples containing a minimum volume of 200 μL plasma were deemed eligible for analysis. Also, 30 stored viremic samples (HIV-1 RNA 100–950 copies/mL) aliquoted into 200 μL volumes were included and compared to conventional HIV-RNA testing. The Alinity m HIV-1 assay (Abbott), validated for low plasma volumes, was used for all samples. Agreement was assessed using Pearson correlation and Bland-Altman analysis. Sample quality was assessed based on plasma volume and clotting. User-friendliness of the TassoPlus was evaluated through a questionnaire. Results Of the 62 participants (median age 56 [46–66]; 81% male), 63% (n = 38) returned the sample, yielding a mean plasma volume of 207 μL (range:10–550). Of these, 18 samples (29%) were excluded due to insufficient volume for analysis. HIV-RNA in both self-sampled and stored samples (n = 50), correlated with conventional samples (r = 0.800). However, in four low-volume viremic samples, HIV-RNA was <100 copies/mL, while conventional sampling detected 148, 260, 501 and 759 copies/mL, respectively. Bland-Altman analysis showed a mean difference of 0.08 log copies/mL (95%LOA:-0.79–0.95) between conventional sampled blood and low-volume samples (self-sampled/stored samples). No clotting was observed. Furthermore, 57% of participants expressed interest in using the TassoPlus, and 48% rated its usability as easy or very easy. Discussion Self-sampling with TassoPlus presents challenges, including high rates of unsuccessful sampling and potential failure to detect low-level viremia. Therefore, significant refinements are essential for reliable clinical use.
Real-time bioluminescence imaging of mycobacteria with Akaluc: a novel method for monitoring drug efficacy
Deep learning–based region merging with adaptive threshold optimization for building segmentation in remote sensing images
Precise extraction of buildings from high-resolution remote sensing images is essential for urban analysis and land management. However, accurately extracting buildings as a region of interest (ROI) from remote sensing (RS) images remains challenging. This difficulty arises from the spectral similarity of other objects, such as roads, cars, or trees, along with limited information on building boundaries and small buildings. Traditional image segmentation methods often rely on a fixed threshold value, making optimisation difficult in cases of over-segmented regions. As a result, region merging is subsequently performed on the region adjacency graph (RAG). Consequently, building segmentation in RS images becomes problematic and can lead to inaccurate boundary delineation or region classification. To overcome these limitations, we propose a novel segmentation approach that incorporates an adaptive thresholding optimisation technique and a merging criterion (MC) based on deep features extracted via a convolutional neural network (CNN)-based AttentionU-Net architecture. This ensures that merging decisions are guided by intrinsic region-level characteristics and refined through deep feature representations. Beginning with initial segmentation generated by the simple linear iterative clustering (SLIC) algorithm, the AttentionU-Net architecture is applied to high-resolution RS images to extract deep features, respectively. As a result, our approach combines both low and high-level feature information, reducing misalignment during merging and enhancing traditional region merging strategies. To validate this approach, the WHU buildings’ RS images dataset was utilised. Experimental results demonstrate that our approach achieves superior segmentation accuracy in building delineation while eliminating the need for rigid thresholds. Finally, the results were compared with those obtained using the multiresolution segmentation (MRS) algorithm implemented in eCognition software on the same WHU buildings RS images, where our approach performs better. Specifically, the proposed approach attained a higher segmentation accuracy, with an F-measure of 0. 91 and a goodness of segmentation score G s of 0.92, compared to 0.52 and 0.83, respectively, achieved by the MRS algorithm.
Establishment and characterization of NKMS-1, a novel mouse NK cell line
Modeling treatment and temperature effects on dengue transmission at the division level in Bangladesh
Dengue fever remains a growing public health threat in Bangladesh, with urbanization, temperature variability, and limited healthcare resources exacerbating recurrent outbreaks. Although many studies have modeled dengue dynamics, the explicit role of treatment under temperature variability remains poorly quantified. Here, we present a simple and interpretable Susceptible–Infected–Treated–Recovered–Susceptible (SITRS) model for humans, coupled with a mosquito Susceptible–Infected (SI) model incorporating a temperature-dependent biting rate. This framework captures how treatment access and efficacy interact non linearly with temperature-driven changes in mosquito biting behavior. Unlike typical dengue models that assume homogeneous recovery, our formulation distinguishes natural recovery from supportive care, reflecting healthcare disparities between urban and rural regions. Using Lyapunov stability theory, we establish threshold conditions for endemicity. We calibrate the model using division-level dengue surveillance and temperature data across Bangladesh. The results show that limited treatment access substantially amplifies outbreak peaks, whereas timely supportive care reduces epidemic intensity even under high-transmission conditions. Short-term forecasts for 2025 identify Dhaka Metropolitan as both treatment-sensitive and a hotspot, highlighting significant regional inequities in transmission risk. Beyond Bangladesh, this modeling framework offers a generalizable approach for integrating treatment capacity with temperature-sensitive vector dynamics, providing actionable insights for epidemic preparedness in resource-limited settings.
Molecular characterization of Echinococcus granulosus sensu lato genotypes circulating in small ruminants in north India
Deep learning-based bimodal speech and facial expression recognition of miners’ unsafe emotions
Under the influence of unsafe emotions, miners’ ability to perceive risks is hindered, which can easily lead to decision-making errors and safety accidents. To recognize unsafe emotions exhibited by miners during operations, this study proposes a deep learning-based bimodal framework that integrates speech and facial expression features. A convolutional neural network (CNN) combined with a bidirectional long short-term memory (Bi-LSTM) network is employed to model local spectral patterns and temporal dependencies in speech signals, and ShuffleNet-V2 is used to capture deep facial features. In addition, three feature enhancement strategies are proposed to improve the generalization ability of the model. By constructing a dataset containing five categories of miners’ unsafe emotions for network training, the model achieves a mean recognition accuracy of 85.56%. Furthermore, we conducted a preliminary field test of the bimodal model in a real mining environment. The results provide preliminary evidence of its potential applicability in real-world mining conditions.