With the rapid evolution of social computing, online sentiments have become valuable information for analyzing the latent structure of social networks. Sentiment leaders in social networks are those who bring in new information, ideas, and innovations, disseminate them to the masses, and thus influence the opinions and sentiment of others. Identifying sentiment leaders can help businesses predict marketing campaigns, adjust marketing strategies, maintain their partnerships, and improve their products’ reputations. However, capturing the complex sentiment dynamics from multi-hop interactions and trust/distrust relationships, as well as identifying leaders within sentiment-aligned communities while maximizing sentiment spread efficiently through both direct and indirect paths, is a significant challenge to be addressed. This paper pioneers a challenging and important problem of sentiment leader identification in social networks. To this end, we propose an original solution framework called “SentiRank” and develop the associated algorithms to identify sentiment leaders. SentiRank contains three key technical steps: (1) constructing a sentiment graph from a social network; (2) detecting sentiment communities; (3) ranking the nodes on the selected sentiment communities to identify sentiment leaders. Extensive experimental results based on the real-world datasets demonstrate that the proposed framework and algorithms outperform the existing algorithms in terms of both one-step sentiment coverage and all-path sentiment coverage. Furthermore, the proposed algorithm performs around 6.5 times better than the random approach in terms of sentiment coverage maximization.

The Controller Area Network (CAN) protocol lacks native authentication mechanisms, exposing modern vehicles to critical security threats. While deep learning-based intrusion detection systems show promise, existing solutions require computational resources far exceeding automotive-grade microcontroller constraints, hindering practical embedded deployment. This paper proposes LCSMC-Net, an ultra-lightweight neural architecture for resource-constrained CAN intrusion detection. The framework integrates three innovations: (1) Separable Multiscale Convolution Lite (SMC-Lite) blocks capturing multitemporal attack patterns with minimal parameters; (2) Lightweight Channel-Temporal Attention (LCTA) achieving linear $\mathcal{O}\left(N\right)$ complexity through adaptive pruning; and (3) 6-dimensional CAN-optimized features exploiting protocol-specific characteristics for aggressive compression. The framework employs Bayesian hyperparameter optimization and knowledge distillation for systematic model compression. Extensive experiments on CAN and CAN-FD datasets demonstrate that LCSMC-Net achieves 99.89% accuracy with only 9401 parameters and 2.84M FLOPs, outperforming existing solutions while meeting real-time constraints of automotive embedded systems, providing a viable edge AI deployment solution.

Chronic kidney disease (CKD) impacts more than 850 million people globally, yet existing machine learning methodologies for risk stratification encounter substantial challenges: computationally intensive hyperparameter tuning, model opacity that conflicts with clinical interpretability standards, and class imbalance leading to systematic prediction bias. We constructed an integrated architecture that combines XGBoost with Optuna-driven Bayesian optimization, evaluated against 19 competing hyperparameter tuning approaches and tested on CKD patients using dual-paradigm statistical validation. The architecture delivered 93.43% accuracy, 93.13% F1-score, and 97.59% ROC-AUC—representing gains of 6.22 percentage points beyond conventional XGBoost and 7.0–26.8 percentage points compared to 20 baseline algorithms. Tree-structured Parzen Estimator optimization necessitated merely 50 trials compared to 540 for grid search and 1069 for FLAML, whereas Boruta feature selection accomplished 54.2% dimensionality reduction with no performance compromise. Over 30 independent replications, the model exhibited remarkable stability (cross-validation standard deviation: 0.0121, generalization gap: −1.13%) alongside convergent evidence between frequentist and Bayesian paradigms (all p < 0.001, mean CI-credible interval divergence < 0.001, effect sizes d = 0.665–5.433). Four separate explainability techniques (SHAP, LIME, accumulated local effects, Eli5) consistently identified CKD stage and albumin-creatinine ratio as principal predictors, aligning with KDIGO clinical guidelines. Clinical utility evaluation demonstrated 98.4% positive case detection at 50% screening threshold alongside near-optimal calibration (mean absolute error: 0.138), while structural equation modeling revealed hyperuricemia (β = −3.19, p < 0.01) as the most potent modifiable risk factor. This dual-validated architecture demonstrates that streamlined hyperparameter optimization combined with convergent multi-method interpretability enables precise CKD risk stratification with clinical guideline alignment, supporting evidence-informed screening protocols.

A point-of-care pathogen-microscopy readout must signal when a sample falls outside what its detector was trained on. PathoSafe is an 816-byte int8-projection low-rank Mahalanobis out-of-distribution (OOD) head reading a frozen detector’s penultimate feature. It reaches a cross-site area under the receiver operating characteristic curve (AUROC) of 0.980 and multi-source AUROC of 0.964 (parasitic-egg-dominated), within 0.01 of a 16,896-byte full-precision dense baseline, and adds a measured 30.76 µs and 816 bytes of read-only memory (ROM) constants on an STM32H743 development board, so abstention adds only a small measured cost on-chip. The advantage is the trunk feature rather than the integer form. Certification is the harder problem and carries our central result: slide clustering silently breaks the standard independent-sample certificate, since distribution-free risk control assumes exchangeable samples that patch-clustered medical data do not supply. On a degraded-input risk certificate, the naive cell-level version holds on only 20% of leakage-free re-splits, whereas the correct slide-level one is valid at 0.266 with its width set by the slide count rather than by any bound we evaluate. The deployed threshold inherits this less severely. A Hoeffding–Bentkus budget gives an illustrative count of 38 independent slides under the stated assumptions (35 when five seeds are used), which is about $1.3\times$ what this benchmark provides. We release that leakage-free slide-disjoint benchmark: an in-distribution malaria task with cross-site (BBBC041) and multi-source (SIPaKMeD, parasitic egg, white blood cells) regimes.