Miguel Torres-Ruiz

Reinforcement learning (RL) has emerged as a promising approach for autonomous surgical robotic subtasks. Recent advances include deep reinforcement learning (DRL), imitation learning (IL), and vision–language–action (VLA) models. However, current evidence remains fragmented across simulation benchmarks, task-specific demonstrations, and limited clinical studies. Existing reviews primarily focus on RL algorithms, while the broader pathway from algorithm development to clinically deployable surgical autonomy has not been comprehensively synthesised. This PRISMA 2020-guided systematic review examines RL, IL, safe RL, simulation-to-real (sim-to-real) transfer, foundation models, VLA systems, and regulatory readiness in surgical robotics. We searched IEEE Xplore, PubMed/MEDLINE, Embase, Scopus, Web of Science, the Cochrane Library, ACM Digital Library, arXiv, and medRxiv for studies published between January 2015 and March 2026, with additional studies identified through backward citation tracing. Eligible studies proposed novel RL, imitation learning, or foundation-model approaches for surgical robotics with empirical validation in simulation or on physical robotic platforms. Two reviewers independently extracted data using a predefined coding scheme, and a third reviewer resolved disagreements. Owing to substantial heterogeneity in platforms, tasks, and outcome measures, a quantitative meta-analysis was not feasible; therefore, the evidence was synthesised narratively using a comparative framework. A total of 220 studies met the inclusion criteria, covering eleven active surgical RL platforms, seven paired sim-to-real studies, emerging foundation-model architectures, and three FDA-cleared robotic systems exhibiting Level 3 autonomy. Available comparative studies suggest that hierarchical approaches can outperform flat policies in long-horizon tasks, while language-conditioned models demonstrated promising multi-step surgical capabilities. Seven paired simulation-to-real studies were identified, encompassing tissue retraction, guidewire navigation, and surgical cutting tasks. Sim-to-real performance gaps varied substantially by task and metric, with success-rate gaps ranging from −10 to 50 percentage points (negative values indicating better real-world than simulated performance), while paired mean spatial errors differed by at most 0.61 mm. Most studies employed domain randomization or visual domain adaptation; hierarchical reinforcement learning demonstrated advantages over flat policies in multi-step surgical tasks. Explicit safety-constrained methods (CPO, CBF, and SER), formal verification, and regulatory-aligned evaluation were reported in fewer than 3% of applied studies. Most evidence remained simulation-based, with no reported autonomous RL execution in vivo in humans. Overall, RL-based surgical robotics appears mature at the simulation stage but remains preclinical for autonomous clinical deployment. Future progress requires stronger sim-to-real validation, multimodal safety-aware architectures, alignment with IEC 62304, ISO 14971, FDA guidance, and the EU AI Act, and open benchmarks that jointly evaluate performance, safety, and surgeon trust.

Ensuring a high quality of life for citizens is a fundamental objective for every city, with public safety representing one of the most critical challenges to social sustainability and equitable urban development. This study analyzes the spatiotemporal dynamics of violent crime in Mexico City across pre-pandemic, pandemic, and post-pandemic periods (2019–2023) to evaluate how COVID-19 mobility restrictions were associated with changes in crime patterns. Using publicly available crime reports, we applied Seasonal-Trend decomposition using Loess (STL) and Moran’s I to examine four violent crimes: homicide, robbery, kidnapping, and rape. The results reveal crime-specific pandemic-related patterns. While robbery showed a sustained decline, its spatial clustering intensified significantly, with Moran’s I increasing from 0.22 to 0.59, indicating highly localized risk zones. Conversely, rape exhibited a steady increase that appeared unaffected by lockdown measures, while maintaining significant spatial autocorrelation. Likewise, Cuauhtémoc borough persisted as the main urban hotspot across all phases. Overall, crime did not decline uniformly during the pandemic; instead, mobility restrictions reshaped the geographic distribution and concentration of specific offenses. This research contributes to the understanding of crime dynamics during and after a public health emergency.

This study investigates automated fake news detection as a reliability-oriented text classification problem in dynamic digital information environments. We propose H-FuseNet, a hybrid multi-representation fusion framework that combines pretrained transformer representations with deception-oriented handcrafted linguistic, stylistic, and semantic features. Using the WELFake dataset, we benchmark 15 baseline models, including classical classifiers, ensemble methods, recurrent and convolutional networks, and transformer fine-tuning models, under stratified 10-fold cross-validation with nested hyperparameter optimization. To examine generalization beyond a single benchmark, we train exclusively on WELFake and evaluate cross-dataset performance on three held-out external datasets: FakeNewsNet, CoAID, and LLM-generated misinformation. H-FuseNet integrates transformer document embeddings with a lightweight feature-processing MLP, optional contextual feature streams when metadata are available, and auxiliary supervision through pseudo-labeled headline body stance and clickbait signals. The proposed model achieves 98.9% mean accuracy and 0.998 ROC–AUC, while maintaining strong calibration, with a Brier score of 0.012 and Expected Calibration Error of 0.009, and low variance across folds. Cross-dataset evaluation yields accuracies of 87.34% on FakeNewsNet, 83.56% on CoAID, and 91.22% on LLM-generated misinformation, demonstrating robust generalization under distribution shift. Ablation analyses show that handcrafted features, auxiliary tasks, and learned fusion each contribute to performance, while Wilcoxon and McNemar tests indicate statistically significant differences against selected strong baselines. Error analysis shows that remaining failures mainly occur in professionally written misinformation that imitates neutral journalistic style. Overall, the results suggest that calibrated multi-representation fusion can improve the reliability of automated fake news detection systems.

This paper presents a supervised multi-label framework for detecting multidimensional perceived risk in HIV-related Reddit discourse. A longitudinal corpus of 329,707 texts collected from r/hivaids and r/HIV between 2015 and 2025 was analyzed to identify three risk dimensions: transmission risk, health deterioration risk, and social stigma risk. A stratified sample of 2,000 texts was annotated by domain experts, achieving substantial inter-annotator agreement (Cohen’s κ = 0.74–0.81). A RoBERTa-base model was fine-tuned using class-weighted binary cross-entropy loss and per-class threshold optimization. The proposed model achieved a macro-F1 score of 0.87 and a macro-AUC-ROC of 0.97, outperforming 12 baseline models, including traditional machine learning, neural network, and alternative transformer-based approaches. Ablation experiments confirmed the importance of transformer fine-tuning and class weighting, while also showing that handcrafted features provided only marginal gains. Applied to the full corpus, the model revealed significant upward trends in transmission risk and health deterioration risk, strong co-occurrence between transmission and stigma-related discourse, and distinct information-seeking patterns across risk categories. The findings demonstrate that transformer-based multi-label learning can support scalable, reproducible analysis of HIV-related health perceptions in online communities, with potential applications in public health surveillance, communication strategy design, and digital intervention planning.

As global populations grow and technology advances, daily life is increasingly shaped by digital systems such as computers and smart devices. However, prolonged device use has contributed to increasing physical and mental health concerns, particularly those associated with poor sitting posture. Posture-related strain is frequently overlooked and contributes to musculoskeletal discomfort, including back, neck, shoulder, and wrist pain, and may also be associated with sleep disturbances and elevated stress levels. To the best of our knowledge and based on the existing literature, this is the first study to introduce a machine learning-based framework for advanced muscle strain severity classification using Internet of Things (IoT) devices that integrates posture monitoring and muscle strain detection into a unified low-cost framework ($23 hardware cost). The primary objective of this work is accurate classification of muscle strain severity, while real-time alerts serve as a secondary ergonomic feedback mechanism. Specifically, this study makes four major contributions. First, we created a novel dataset through real-time acquisition of electromyography (EMG) and posture signals from participants in hospital and industrial environments, capturing diverse muscle strain patterns validated against clinical assessment procedures. Second, we designed a two-part hardware architecture consisting of posture detection (PD) and strain detection (SD) modules using a NodeMCU ESP8266, HC-SR04 ultrasonic sensor, EMG sensor, and buzzer for real-time physiological monitoring, incorporating EMG-specific preprocessing including band-pass filtering, rectification, and RMS smoothing. Third, we proposed and evaluated a hybrid machine learning framework integrating Vision Transformer (ViT) and XGBoost to classify strain severity into three study-specific categories: baseline (EMG RMS < 40 µV), compensatory strain (40–59 µV), and overload (≥60 µV). These categories were used as reproducible severity proxies for machine learning annotation and should not be interpreted as universal biomarkers of structural tissue damage. Finally, the proposed framework achieved a classification accuracy of 99.0% (95% CI: 98.5–99.5%) with an inference latency of 15.2 ms.