Sepsis is responsible for approximately 270,000 deaths annually in the United States. Conventional scoring systems, such as SOFA and qSOFA, are largely reactive and do not effectively leverage longitudinal ICU data for early prediction. This study aims to develop a deep learning framework capable of predicting sepsis onset up to 6 h before Sepsis-3 criteria are met while also providing clinically interpretable temporal explanations. The PhysioNet/CinC 2019 Challenge dataset, comprising 1,552,210 patient hours from 40,336 ICU patients, was utilized. A temporal transformer encoder (TTE) was trained using 12-h look-back windows with 92 engineered features. Severe class imbalance (2.6% positive rate) was addressed through weighted random sampling and focal loss. Fivefold patient-level cross-validation was employed to prevent temporal leakage. Platt scaling was applied for probability calibration. Grad-CAM was adapted for temporal explainability, while SHAP was used for feature-level attribution. BiLSTM-Attention and XGBoost models served as baseline comparators. The TTE model achieved a cross-validated AUROC of $0.8320\pm 0.0032$ and an AUPRC of $0.1505\pm 0.0148$, significantly outperforming BiLSTM-Attention (AUROC: 0.7859) and XGBoost (AUROC: 0.7731; DeLong $p<0.0001$). Platt scaling reduced the expected calibration error from 0.3154 to 0.0017. The median alert lead time was 46.5 h (IQR: 21–84 h), with 95.3% of septic patients receiving alerts at least 3 h before onset. Grad-CAM analysis identified time steps $t-10$ and $t-9$ as the most predictive. However, high-severity patients (SOFA proxy $\ge 3$) demonstrated substantially reduced performance (AUROC: 0.257). The proposed TTE framework demonstrated strong and well-calibrated early sepsis prediction with substantial clinical lead time. The concentration of predictive signals 10–11 h prior to alert generation supports the feasibility of continuous automated ICU monitoring from admission onward. Reduced performance in high-severity patients highlights the need for severity-stratified modelling in future research.

Background/Objectives: Sepsis is responsible for approximately 270,000 deaths annually in the United States. Conventional scoring systems, such as SOFA and qSOFA, are largely reactive and do not effectively leverage longitudinal ICU data for early prediction. This study aimed to develop a deep learning framework capable of predicting sepsis onset up to 6 hours before Sepsis-3 criteria are met, while also providing clinically interpretable temporal explanations. Methods: The PhysioNet/CinC 2019 Challenge dataset, comprising 1,552,210 patient-hours from 40,336 ICU patients, was utilized. A Temporal Transformer Encoder (TTE) was trained using 12-hour look-back windows with 92 engineered features. Severe class imbalance (2.6% positive rate) was addressed through weighted random sampling and focal loss. Five-fold patient-level cross-validation was employed to prevent temporal leakage. Platt scaling was applied for probability calibration. Grad-CAM was adapted for temporal explainability, while SHAP was used for feature-level attribution. BiLSTM-Attention and XGBoost models served as baseline comparators. Results: The TTE model achieved a cross-validated AUROC of 0.8320±0.0032 and an AUPRC of 0.1505±0.0148, significantly outperforming BiLSTM Attention (AUROC: 0.7859) and XGBoost (AUROC: 0.7731; DeLong p < 0.0001). Platt scaling reduced the Expected Calibration Error from 0.3154 to 0.0017. The median alert lead time was 46.5 hours (IQR: 21–84 h), with 95.3% of septic patients receiving alerts at least 3 hours before onset. Grad-CAM analysis identified timesteps t − 10 and t −9 as the most predictive. However, high-severity patients (SOFA proxy ≥ 3)demonstrated substantially reduced performance (AUROC: 0.257). Conclusions: The proposed TTE framework demonstrates strong and well-calibrated early sepsis prediction with substantial clinical lead time. The concentration of predictive signals 10–11 hours prior to alert generation supports the feasibility of continuous automated ICU monitoring from admission onward. Reduced performance in high-severity patients highlights the need for severity-stratified modelling in future research.

Atrial fibrillation (AF) detection from short single-lead electrocardiogram (ECG) recordings requires a balance between rhythm discrimination and computational efficiency. This study compares a one-dimensional ResNet-34 (1D-ResNet34), a Temporal Convolutional Network (TCN), and MambaECG, a compact Mamba-inspired gated temporal model, under the same reported training protocol on the PhysioNet Computing in Cardiology Challenge 2017 corpus. The PhysioNet dataset contains 8528 recordings. For independent dataset evaluation, the Chapman–Shaoxing database contains 45,152 recordings, of which 35,339 were used: 1780 AF records and 33,559 selected sinus-rhythm comparator records after excluding 9813 other-arrhythmia records. Models trained on PhysioNet were evaluated on Chapman Lead I without target-domain fine-tuning, forming a zero-shot evaluation on an independent but simplified AF-versus-selected-sinus task. On PhysioNet, TCN achieved the highest AUROC (0.957), F1 (0.594), sensitivity (0.883), and specificity (0.914) with 0.19 M parameters. ResNet1D34 achieved AUROC 0.955 with 35.34 M parameters, while MambaECG achieved AUROC 0.928 with 0.65 M parameters and the lowest reported GPU inference latency of 0.04 ms per recording. Chapman selected-task AUROCs were 0.948, 0.963, and 0.961 for MambaECG, ResNet1D34, and TCN, respectively. Because the PhysioNet and Chapman non-AF comparator definitions differ, these AUROCs are not treated as evidence that performance improved across domains. Grad-CAM examples are used as qualitative interpretability illustrations rather than clinical validation. The results support a controlled comparison of accuracy, parameter efficiency, and measured latency while identifying limitations in label harmonisation, padding, partition documentation, and the Mamba-inspired proxy implementation.

Background: Cloud computing, remote workforces, and Internet of Things (IoT) deployments have eroded the enterprise network perimeter, exposing the limits of security models that grant implicit trust to internal actors. Zero Trust Architecture (ZTA), governed by the principle of "Never Trust, Always Verify," addresses this through continuous verification, least-privilege access, micro-segmentation, and adaptive monitoring. Objectives: This paper synthesizes reported evidence on ZTA across three research questions: effectiveness relative to perimeter models, the implementation challenges enterprises face, and the security contribution of individual components. Methods: A structured literature review was conducted on a predefined corpus of 37 ZTA-related studies. A rubric-based composite model incorporating relevance, methodology quality, recency, and measurability of outcomes characterized each study. All 37 met the appraisal threshold of S >= 3.0, the lowest composite score being 3.55, so the rubric characterized the corpus rather than functioning as an exclusion filter. Results: Reported findings suggest that ZTA can improve breach detection, reduce unauthorized access, and limit attack propagation in specific empirical and simulation contexts. Headline values, including 75% improvement in breach detection, 66.7% reduction in unauthorized access, up to 80% attack-surface reduction, and 100% repulsion of simulated malicious packet injections, are drawn from a limited number of studies and need replication. Implementation barriers include performance overhead in IoT and edge environments, high transition costs, legacy migration complexity, interoperability gaps, and cultural resistance. Conclusions: ZTA is a promising and structurally well-aligned security framework for modern distributed environments, but the strength of evidence varies by component and deployment context.

Atrial fibrillation (AF) affects more than 37 million people worldwide and substantially raises the risk of stroke, heart failure, and premature death. Wearable single-lead ECG devices offer a natural platform for continuous AF surveillance, yet automated detection from short, noisy recordings remains an unsolved clinical engineering problem. This paper presents a sys tematic benchmark of three deep learning architectures—a one dimensional ResNet-34 (1D-ResNet34), a Temporal Convolutional Network (TCN), and a novel State-Space Model (MambaECG)— evaluated under identical training protocols on two indepen dent public datasets: the PhysioNet Computing in Cardiology Challenge 2017 corpus (8,528 recordings) and the Chapman Shaoxing 12-lead ECG database (45,152 recordings, Lead I used). All models were trained exclusively on single-lead PhysioNet recordings and tested on Chapman–Shaoxing Lead I without fine-tuning, constituting the first cross-institutional generalisation study for this architectural family. The TCN achieves the highest AUROC of 0.957 with only 0.19M parameters, outperforming 1D-ResNet34 (AUROC 0.955, 35.34M parameters) and MambaECG (AUROC 0.928, 0.65M parameters). Notably, all three architectures demonstrate positive zero-shot transfer to Chapman–Shaoxing, with AUROCs of 0.961, 0.963, and 0.948 respectively—without any target-domain exposure during training. MambaECG achieves the fastest in ference at 0.04 ms per recording, enabling real-time deployment on resource-constrained wearable hardware. Gradient-weighted Class Activation Mapping (Grad-CAM) analysis confirms that MambaECG correctly localises clinically meaningful ECG fea tures: QRS complexes and P-waves for normal rhythm, irregular RR intervals and fibrillatory baseline for AF, and premature ventricular complex morphology for Other rhythm. These results provide evidence-based architectural guidance for wearable AF screening systems and demonstrate that lightweight temporal models match or exceed deep residual networks at a fraction of the computational cost.