The Politecnica Salesiana University in Ecuador is an institution of higher education and Christian inspiration with Catholic character and a Salesian. The university is characterized by its opportunities for youth, especially from the poor sectors.
This study evaluated the activity concentrations of 226Ra in cassava pulp, peel, and derived starch cultivated in three production sectors of Chone, Ecuador, and assessed soil-to-plant transfer and associated dietary exposure. Results show pronounced sector-dependent variability in radionuclide distribution, with consistently lower 226Ra levels in starch relative to raw plant fractions. Estimated ingestion doses for both children and adults were negligible and well below international reference values. These findings provide the first multi-matrix baseline for 226Ra transfer in cassava under tropical Ecuadorian conditions and support site-specific radiological risk assessments.
Facial recognition systems have experienced rapid progress with the integration of deep learning architectures, especially convolutional neural networks (CNNs). Among the most prominent approaches, ArcFace and FaceNet are recognized for their high accuracy and robustness in identity verification tasks. However, their comparative performance under real-world, uncontrolled conditions where factors such as illumination, resolution, and occlusion vary remains insufficiently studied. This research presents a systematic comparison between ArcFace and FaceNet using the Analytic Hierarchy Process (AHP) as a decision-support framework. The study employs three publicly available datasets—Labeled Faces in the Wild (LFW), CASIA-WebFace, and Celeb-DF—to evaluate performance in terms of accuracy, precision, recall, F1-score, and equal error rate (EER). Controlled experiments simulate degraded conditions, including low resolution, poor lighting, and partial occlusion. Results reveal that ArcFace consistently outperforms FaceNet in accuracy and F1-score across challenging scenarios, while FaceNet achieves faster inference and a lower false positive rate in some instances. These insights offer practical guidance for selecting optimal models for security, forensic, and identity verification applications.
Neural network quantization has become established as a key strategy for transitioning medical imaging models from research environments to clinical devices and resource-constrained edge platforms; however, the available evidence remains fragmented and focused on highly heterogeneous use cases. This study presents a systematic review of 72 studies on quantization applied to medical images, following PRISMA guidelines, with the aim of characterizing the relationship among quantization technique, network architecture, imaging modality, and execution environment, as well as their impact on latency, memory footprint, and clinical deployment. Based on a structured variable matrix, we analyze—through tailored visualizations—usage patterns of Post-Training Quantization (PTQ), Quantization-Aware Training (QAT), mixed precision, and binary/low-bit schemes across frameworks such as PyTorch V 2.6.0, TensorFlow 2.19.0, and TensorFlow Lite, executed on server-class GPUs, edge/embedded devices, and specialized hardware. The results reveal a strong concentration of evidence in PyTorch/TensorFlow pipelines using INT8 or mixed precision on GPUs and edge platforms, contrasted with limited attention to PACS/RIS interoperability, model lifecycle management, energy consumption, cost, and regulatory traceability. We conclude that, although quantization can approximate real-time performance and reduce memory footprint, its clinical adoption remains constrained by integration challenges, model governance requirements, and the maturity of the hardware–software ecosystem.
Climate change constitutes a major contemporary challenge for society. The transition to sustainable energy systems is essential for mitigating its adverse impacts. Renewable resources are critical for reducing greenhouse gas emissions and fostering climate resilience. This study introduces an iterative multivariable method for optimal sizing of the generation mix in distributed renewable resources. The objective is to minimize energy grid interchange. This represents an initial step toward achieving grid disconnection through the synergy of photovoltaic, wind, biogas, and hydropower sources, while considering each technology’s technical constraints. The methodology includes a pre-design stage for renewable plants, resource allocation across various scenarios, and optimization of generation mix sizing. Case study results for a rural village in eastern Spain demonstrate the feasibility of achieving grid disconnection. The proposed method offers a straightforward, replicable tool for energy system planning, suitable for isolated areas common in developing countries and regions with unreliable grid connectivity.
The optimal deployment of Low-Power Wide-Area Networks (LPWANs) such as LoRaWAN in complex urban environments remains an NP-Hard Set Covering Problem. Traditional network planning often relies on 2D mathematical grids that ignore physical RF barriers, leading to topographic shadowing and single points of failure. This research proposes the Native 3D Memetic Spatially Aware Genetic Algorithm (3D-M-SAGA), an optimization framework that operates over a Morphological Digital Twin. By fusing OpenStreetMap (OSM) vector topologies with NASA SRTM elevation data and autonomous urban clutter classification, the framework evaluates physical constraints—including ITU-R knife-edge diffraction and dielectric absorption—directly within the evolutionary loop. To counteract the epistatic variance inherent to standard genetic algorithms, the 3D-M-SAGA integrates a vectorized memetic “Smart Repair” operator driven by heuristic attraction and repulsion forces. Formulated as a multi-objective optimization problem balancing Capital Expenditure (CAPEX) and topological Quality of Service (QoS) through K-coverage, the framework is evaluated using a 36-scenario parametric grid search and a 50-iteration Monte Carlo benchmark. Results show that the 3D-M-SAGA tightly bounds stochastic CAPEX variance (σ=±0.51 gateways) while reducing single-point-of-failure network fragility (K=1) by up to 20%, guaranteeing fault tolerance (K≥2) without over-provisioning civic infrastructure.
