Jose Cornejo

Type 2 diabetes mellitus (T2DM) affects hundreds of millions of people worldwide, and nearly half of all cases remain undiagnosed. This technical review, conducted under conducted under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and structured around the Population, Intervention, Comparison, Outcome and Context (PICOC) framework, identifies and synthesises the evidence on artificial intelligence (AI) algorithms for the early detection of T2DM and the risk stratification of its complications, evaluating their performance against conventional diagnostic methods and characterising their clinical implementation contexts. A systematic search in Scopus with a four-stage screening process, synthesised across five research dimensions, yielded 255 records, of which 47 studies met the inclusion criteria (2025–2026). Ensemble methods based on gradient boosting, particularly particularly extreme gradient boosting (XGBoost), emerged as the dominant primary modelling approach, while logistic regression was the most frequently reported algorithm, predominantly as a baseline comparator, with area under the receiver operating characteristic curve (AUC-ROC) values ranging from 0.63 to 0.97. AI models consistently outperformed glycated haemoglobin (HbA1c), whose reported sensitivity for prediabetes was only 49%, reaching accuracies of up to 96.7%, although that upper figure comes from a narrowly defined cohort with a high baseline prevalence and does not represent general screening performance. Multimodal approaches achieved the highest discrimination, combining wearables, continuous glucose monitoring, and genomics (AUC 0.96 internal, 0.90 external, for the separation of self-reported glycaemic states), and a real-world clinical deployment integrated into electronic health records improved glycaemic control outcomes among high-risk patients. An assessment of the eight anchor studies with thePrediction model Risk Of Bias ASsessment Tool updated for AI (PROBAST + AI) found high concern regarding quality in six of them, driven mainly by cross-sectional designs and self-reported outcomes. Overall, AI stands out as a viable and scalable complement to conventional screening, although gaps remain in multicentre prospective validation, reporting standardisation, and algorithmic equity across populations.

Enterprise Resource Planning (ERP) systems have become a widely adopted management technology, even among small enterprises, driven by the expansion of cloud-based solutions that reduce infrastructure barriers. However, many implementations fail to sustain their long-term benefits, particularly in small enterprises, where the post-deployment stage is often faced with limited technical resources and poorly formalized IT support architectures. Although the literature has extensively examined critical success factors during implementation, the role of IT support as a permanent function sustaining system continuance and software evolution has received less attention. This study analyzes the role of IT support architecture in the operational continuity of ERP systems across distributed cloud and on-premises computing environments in small enterprises via a literature review following PRISMA and a PICOC approach, using Scopus as the main database. After applying inclusion and exclusion criteria, 45 studies published between 2020 and 2025 were selected and classified into five thematic categories: post-implementation critical success factors (38%), operational continuity and organizational performance (25%), incident management and technical support (13%), cloud ERP adoption (13%), and user training and change management (11%). Results show that IT support is a cross-cutting factor determining post-deployment lifecycle resilience, ERP stability, and availability, whose effectiveness depends on top management backing and organizational structure. Beyond synthesizing existing critical success factors, this study proposes an IT support maturity model, comprising Reactive, Formalized, and Preventive/Analytical levels, that offers small enterprises a diagnostic instrument to assess their current support architecture and identify concrete pathways toward more resilient ERP post-deployment lifecycle dynamics.

Autonomous subterranean mobility remains one of the least unified domains in robotics because locomotion emerges from coupled interactions among deformable geomaterials, structural mechanics, energy dissipation, and environment-dependent sensing constraints. This foundational pioneer technical review synthesizes 35 years of research on burrowing and underground robotic systems through a terradynamic and multiphysics perspective. Following PRISMA guidelines, 143 peer-reviewed studies were analyzed across granular soils, cohesive sediments, saturated media, fractured geomaterials, and extraterrestrial regolith analogs. The review evaluates six dominant locomotion classes, including peristaltic, undulatory, fluidization-assisted, excavation-based, tip-extension, and hybrid architectures. Results demonstrate that locomotion performance is governed primarily by regulation of substrate response rather than propulsion generation alone. Across all architectures, mobility depends on the coupled evolution of confinement-dependent stress redistribution, yielding mechanics, pore-pressure dynamics, fracture propagation, structural stability, thermomechanical loading, and energy partitioning. The analysis further reveals a convergence toward stress-regulated locomotion, where successful systems minimize drag accumulation, control force-chain evolution, and adapt to changing terradynamic conditions. Major unresolved challenges include the absence of transferable scaling laws, standardized benchmarking methodologies, predictive terradynamic models, and multi-medium autonomy. The review concludes by proposing the foundations of a unified multiphysics terradynamic robotics paradigm capable of linking robot design, substrate mechanics, control, and deployment across terrestrial and planetary subsurface environments.

This paper presents the Micro Video Guidance Sensor Version 2 (uVGS-2), a ROS-based vision navigation framework for real-time six-degrees-of-freedom pose estimation in drones, spacecraft, and autonomous robotic platforms operating in GNSS-denied environments. The system evolves from the previous Smartphone Video Guidance Sensor (SVGS) architecture through a modular C++ implementation, including advanced image preprocessing, deterministic blob sorting, and an optimized perspective-4-point solver using a Lie-algebra-based analytical Jacobian formulation. The proposed architecture achieves computationally efficient photogrammetric state estimation using onboard camera and processor resources, enabling deployment in resource-constrained systems. Experimental validation was conducted in NASA’s Astrobee free-flying robot, both at the International Space Station (ISS), for SVGS, and by ground testing through real-time sensor-fusion with Astrobee’s graph-based localizer (Astroloc), for uVGS-2. Results demonstrate robust centimeter-level accuracy in relative position and attitude estimation under illumination disturbances, partial occlusions, and intermittent loss of line-of-sight. The framework can be used in robotic platforms and autonomous UAV operations, including precision landing, formation flight, and cooperative navigation in environments where GNSS signals are unavailable or intermittent.

Stroke is the second leading cause of death globally and a major contributor to lower-limb disability, affecting gait, balance, and functional independence in elderly populations. While robot-assisted rehabilitation has demonstrated effectiveness in motor recovery, access remains limited due to high costs and geographic barriers, particularly in Latin America. This study presents ExoKnee, a low-cost knee exoskeleton designed through biomimetic principles and 3D-printed fabrication as a proof-of-concept device targeting gait rehabilitation in elderly adults. The system integrates a single-degree-of-freedom pneumatic actuator controlled by electromyography (EMG) signals from the quadriceps muscle, enabling knee flexion and extension (90° to 180°). The design was evaluated through finite element analysis and dynamic simulations in MATLAB/Simulink R2024a under constant, stepwise, and sinusoidal reference inputs in a digital-twin environment. Expert validation using the Content Validity Coefficient yielded a mean score of 0.8747, reflecting preliminary expert agreement on the conceptual design’s coherence and relevance. The prototype demonstrated controlled movements through a 6-bar pneumatic system with EMG-triggered relay activation, validated at the proof-of-concept level through simulation and single-subject threshold calibration. ExoKnee addresses critical gaps by offering an anthropometrically informed, biosignal-driven, and locally manufacturable rehabilitation platform for low- and middle-income countries, pending clinical validation. Future work will focus on clinical trials and adaptive EMG control strategies.

This paper presents the Micro Video Guidance Sensor (UVGS-2), an advanced evolution of the Smartphone Video Guidance Sensor (SVGS), a vision-based 6-DOF pose estimation system for proximity operations in spacecraft, UAVs, and mobile robot platforms. The system is based on the photogrammetric estimation of a beacon’s relative position and attitude in a camera coordinate system, by processing images of illuminated 4-points in the beacon with known non-coplanar geometry. Estimation of the beacon’s 6-DOF position and attitude (XYZ, RPY) in a coordinate system attached to the camera is achieved with higher accuracy compared to standard localization methods based on mapping or inertial navigation. Image acquisition, feature extraction, and state estimation are executed using hardware resources (camera and CPU) of the host robot, resulting in a low-mass, low-power, computationally efficient sensing architecture suitable for embedded and resource-constrained systems. As case study, SVGS and UVGS-2 have been deployed as real-time guidance, navigation, and motion control sensor through integration with NASA’s Astrobee free-flying robot aboard the International Space Station (ISS), supporting autonomous proximity operations and formation flight. Sensor fusion based on the use of existing localization pipelines improves robustness against line-of-sight interruptions and illumination disturbances, and improves accuracy compared to the native Astrobee localization system (AstroLoc). Other case studies have demonstrated high-precision positioning performance in autonomous UAV landing experiments, with reliable target tracking over extended ranges and low angular estimation error, outperforming existing infrared-based landing approaches.

So far, space exploration has attracted increasing scientific interest due to the growth of missions promoted by private investment, such as SpaceX, Boeing, Blue Origin, and the recent attention generated by astronomical phenomena such as 3I/ATLAS. However, access to space experimentation remains limited and expensive. For this reason, new approaches to simulate space conditions on Earth are being developed to broaden research opportunities bio-inspired by plant responses to phototropism and geotropism. In this context, Betta Aerospace has continued the development of a microgravity simulation system consisting of a 3-axis clinostat powered by a single motor, continuous external electrical supply, and, in this project, a continuous external liquid supply. The proposed pioneer system was designed as a flexible platform manufactured through reinforced 3D printing, with an approximate size of 30 cm, an estimated payload of 30 kg, and a 24 V supply. Its main goal is to study the effects of simulated microgravity on aquatic organisms while enabling longer observation times in a controlled freshwater environment. Candidate biological samples include Ulva lactuca, Pyropia, Spirulina/Arthrospira, and Chlorella. Preliminary motion tests confirmed continuous operation at 10 rpm. In addition, a simplified static finite element analysis under a 294 N load yielded a maximum von Mises stress of 5.45 × 10⁷ Pa and a maximum displacement of 1.73 mm.

User Interface (UI) and User Experience (UX) design play a critical role in shaping human interaction with digital systems, particularly in professional environments where accuracy, safety, and efficiency are essential. Poor visual design increases cognitive load and the likelihood of human error, whereas ergonomically informed interfaces can substantially improve task performance. This systematic literature review analyzes 20 peer-reviewed studies published between 2020 and 2024 to examine how visual ergonomics embedded in UI/UX design contributes to error reduction across industrial and professional contexts. The reviewed studies report measurable improvements when ergonomic principles are applied, including reductions in operational errors ranging from approximately 30% to 70%, improvements in task completion time between 20% and 60%, and increased user accuracy and satisfaction in safety-critical and high-workload environments. The findings indicate that visual hierarchy, modular layouts, adaptive components, and real-time feedback are consistently associated with improved performance outcomes. Moreover, task complexity, user expertise, and working conditions were identified as moderating factors influencing ergonomic demands. Overall, the review demonstrates that visual ergonomics should be treated not merely as a usability enhancement but as a strategic design approach for minimizing human error and supporting reliable human–machine interaction in complex digital environments.