Abel José Rodríguez-Yparraguirre

Introduction: Industrial exoskeletons with integrated sensors are emerging as a technology for monitoring biomechanical exposure and supporting the prevention of work-related musculoskeletal disorders in industrial environments. Methods: This systematic review followed the PRISMA 2020 guidelines, retrieving 106 studies published between 2021 and 2026 from the Scopus and Web of Science databases. VOSviewer version 1.6.20 and R‐Studio version 2026.01.2+418 were used for bibliometric and descriptive analyses. The review provides a qualitative synthesis supported by descriptive quantitative reporting of the extracted outcomes. Results: The evidence indicated a technological shift from passive devices toward intelligent exoskeletons integrating surface electromyography, inertial measurement units, machine vision, physiological sensors, and artificial intelligence. Individual studies reported maximum reductions of 68.2% in muscle activity, 34% in spinal compression, and up to 60% in muscle fatigue, alongside productivity improvements of up to 59.5%. Machine learning approaches achieved an ROC of 0.985, automated assessment accuracies of 98.5%, and posture estimation with an ICC of 0.967. However, these values were not pooled estimates and varied across study designs, sensor configurations, tasks, and assessment protocols. Discussion: The evidence remains constrained by small sample sizes, sensor drift, interoperability limitations, and insufficient long‐term validation in real‐world industrial environments. Sensor‐integrated exoskeletons show potential for ergonomic monitoring and risk reduction, but their effectiveness and industrial generalizability require further validation through standardized, interoperable, and longitudinal field studies.

Capsicum seed waste is an underutilized agro-industrial biomass with considerable potential for the recovery of oils, proteins, phenolic compounds, capsaicinoids, peptides, and other value-added fractions. This study evaluates advanced separation technologies for valorization in a cascade biorefinery, with an emphasis on extraction yield, selectivity, product functionality, sustainability, and technological scalability. A systematic literature search was conducted in Scopus and Web of Science, and the retrieved records were merged and processed using R (version 4.6.1) in RStudio (version 2026.01.2+418) Study selection and presentation were aligned with the PRISMA protocol, resulting in 82 studies included for thematic analysis. The evidence indicates that ultrasound-assisted, enzymatic, aqueous oleosome, pressure-based, membrane, and chromatographic approaches can improve the recovery and selective separation of valuable seed-derived fractions. The reported experimental results include an oleosome recovery of 66.96% by weight, with a potential recovery approaching 90% by weight, and a 73.5% increase in extraction recovery after enzyme-assisted processing. Protein modification and fractionation have also demonstrated opportunities for the development of functional ingredients, bioactive fractions, and biomaterials. However, limited pilot-scale validation, process integration, standardization, techno-economic feasibility, and life-cycle assessments constrain industrial implementation. Future research must prioritize scalable, closed-loop cascade strategies to minimize energy use, solvent consumption, and environmental footprint while maximizing biomass conversion.

Introduction: The transition to Industry 5.0 is transforming manufacturing through the integration of smart technologies, while simultaneously increasing the need to protect workers’ health and reduce occupational risks. This systematic review synthesized cross-sectoral evidence on human-centered Industry 5.0 technologies for assessing and reducing occupational risks, with a focus on ergonomic outcomes, worker-related factors, and the conditions influencing their implementation and transferability. Methods: A systematic literature search was conducted in Scopus and Web of Science following the PRISMA 2020 framework and predefined eligibility criteria based on PECO. Included studies were analyzed according to technology type, occupational risk addressed, ergonomic dimension, assessment method, reported outcomes, implementation conditions, and implications for occupational health and safety, yielding a total of 78 studies that met the inclusion criteria. Results: The reported results from individual studies included ergonomic assessment accuracies exceeding 95%, fatigue reductions of more than 50% in selected applications, and cycle-time reductions of up to 28.4%, along with improvements in task allocation, workload monitoring, and production efficiency. These values should not be interpreted as overall effects of Industry 5.0 technologies, given the heterogeneity of technologies, experimental designs, and assessment methods. Discussion: The evidence was limited by laboratory validation, a lack of longitudinal research, heterogeneous assessment methods, and insufficient real-world implementation in specific industrial sectors.

Capsicum seed waste is an underutilized agro-industrial biomass with considerable po-tential for the recovery of oils, proteins, phenolic compounds, capsaicinoids, peptides, and other value-added fractions. This study evaluates advanced separation technolo-gies for valorization in a cascade biorefinery, with an emphasis on extraction yield, se-lectivity, product functionality, sustainability, and technological scalability. A system-atic literature search was conducted in Scopus and Web of Science, and the retrieved records were merged and processed using RStudio. Study selection and presentation were aligned with the PRISMA protocol, resulting in 82 studies included for thematic analysis. The evidence indicates that ultrasound-assisted, enzymatic, aqueous oleo-some, pressure-based, membrane, and chromatographic approaches can improve the recovery and selective separation of valuable seed-derived fractions. The reported ex-perimental results include an oleosome recovery of 66.96% by weight, with a potential recovery approaching 90% by weight, and a 73.5% increase in extraction recovery after enzyme-assisted processing. Protein modification and fractionation have also demon-strated opportunities for the development of functional ingredients, bioactive frac-tions, and biomaterials. However, limited pilot-scale validation, process integration, standardization, techno-economic feasibility, and life-cycle assessments constrain in-dustrial implementation. Future research must prioritize scalable, closed-loop cascade strategies to minimize energy use, solvent consumption, and environmental footprint while maximizing biomass conversion.

This study reports the psychometric-multivariate characterization of the sensory space of a Smallanthus sonchifolius fructose syrup obtained via enzymatic hydrolysis, addressing the need for robust evaluation of novel functional sweeteners. A 12-member selected analytical panel (selected from 38 candidates; 31.6% retention) evaluated the matrix using a 16-item, four-dimensional psychometric instrument. Content validity was pre-established by expert consensus (global Aiken’s V = 0.824, 95% CI [0.798, 0.852]), and panel reliability was confirmed through Friedman’s test across three replicates (p > 0.05 for D2, D3, D4). Results from Principal Component Analysis (PCA) explained 90.17% of the total variance, identifying two primary axes: somatic-emotional integration (PC1: 64.15%) and contextual-cognitive differentiation (PC2: 26.02%). Hierarchical Cluster Analysis (HCA) segmented three distinct perceptual archetypes within the panel (k = 3), with the “Temporally Driven” segment (C3) emerging as the dominant profile (50%). The findings demonstrate that yacon syrup perception is characterized by statistical score homogeneity across oral body-mapping and experiential familiarity dimensions. This psychometric approach provides a high-fidelity map of the sensory experience, offering a reproducible baseline for the quality control and standardization of functional yacon-derived products.

Musculoskeletal disorders represent one of the most frequent occupational health problems in labour-intensive industries, particularly in fish processing, where repetitive tasks and prolonged postures are common. The objective was to determine the level of ergonomic risk by applying the Rapid Entire Body Assessment (REBA) method and based on the results, to formulate recommendations aimed at preventing musculoskeletal disorders and improving preventive management within the organization. The assessment included 30 workers distributed across three operational workstations, where the overall average REBA score was 8.60 ± 1.65 (range: 6–12), indicating a predominantly high level of ergonomic risk. In categorical terms, 60.0% of the workers were classified as high risk, 13.3% as very high risk, and 26.7% as medium risk, while none reached negligible or low risk levels. Significant differences were observed between workstations (Kruskal–Wallis H = 16.72, p < 0.001, ε² = 0.545), with the nobbing stage exhibiting the highest biomechanical load (mean REBA = 10.38 ± 1.06). It is concluded that ergonomic risk is structurally integrated into the operational design of the evaluated production system; therefore, ergonomic interventions focused on redesigning workstations, adjusting height, and configuring tasks are recommended to reduce biomechanical exposure and strengthen the organization’s preventive occupational safety framework.

Pitahaya (Hylocereus spp.) production is increasingly affected by climatic factors, as well as by phytopathogens and abiotic stress, leading to delays in agronomic interventions and reduced productivity. The objective was to design, implement, and validate a multimodal system (CARYPAR) that enables early disease detection and agile decision-making, characterized by low latency and reduced dependence on cloud connectivity. The methodology integrates climate reanalysis from NASA POWER, biophysical remote sensing variables derived from Sentinel-1/2, and proximal computer vision captured via mobile devices using a late fusion architecture and an optimized convolutional neural network, EfficientNet-V2B0, which discriminates between optimal and pathological conditions in vegetative tissues and fruit. The results of the experimental validation carried out in 160 georeferenced units achieved an overall accuracy of 80.0% and an F1 score of 0.8645 for Bad Fruit. The McNemar test and the operational agreement with agro-industrial experts yielded a Cohen’s Kappa index of κ = 0.6831, with an inference latency reduced to 22.00 ms. It is concluded that the multimodal integration of satellite bio-environmental data with edge computer vision achieves substantial agreement with agronomic expert judgment under heterogeneous field conditions (Cohen’s κ = 0.6831), supporting its role as a decision-support tool rather than a replacement for expert assessment. Therefore, its adoption can enhance real-time irrigation management and crop protection, while contributing to traceability and sustainable resource management in agricultural regions with limited connectivity.

The agricultural sector is undergoing a profound digital transformation driven by artificial intelligence, the Internet of Things, remote sensing, robotics, blockchain, and edge computing, which are being integrated into crop monitoring, irrigation management, disease detection, and supply chain transparency systems. This study employs systematic evidence mapping to characterize the applications of emerging digital technologies in sustainable agriculture; it delineates technological trajectories, areas of application, implementation gaps, and opportunities for improvement. Adhering to the PRISMA 2020 reporting protocol, 101 peer-reviewed articles indexed in Scopus and Web of Science (2020–2025) were identified, screened, and subjected to integrated thematic and bibliometric synthesis, using RStudio Version: 2026.01.1+403 and VOSviewer 1.6.20 for data mining on keywords and technological evolution patterns. Results show that deep learning and computer vision models achieved diagnostic accuracies of 90–99%, smart irrigation systems reduced water consumption by 10–30%, predictive yield models frequently reported R² values above 0.80, and greenhouse automation reduced energy consumption by approximately 20–30%. Blockchain-based architectures improved traceability and secure data transmission by 15–20%, while remote sensing integration enhanced spatial estimation accuracy up to R² = 0.92. The findings demonstrate a measurable transition toward data-driven, resource-efficient agricultural ecosystems supported by validated digital architectures. However, interoperability limitations, lack of standardized performance metrics, scalability challenges, and uneven geographical implementation—identified in nearly 40% of studies—highlight the need for harmonized evaluation frameworks, cross-platform integration standards, and long-term field validation to ensure sustainable and scalable digital transformation.

Musculoskeletal disorders represent one of the most frequent occupational health problems in labor-intensive industries, particularly in fish processing, where repetitive tasks and prolonged postures are common. The objective was to determine the level of ergonomic risk by applying the Rapid Entire Body Assessment (REBA) method and based on the results, to formulate recommendations aimed at preventing musculoskeletal disorders and improving preventive management within the organization. The assessment included 30 workers distributed across three operational workstations, where the overall average REBA score was 8.60 ± 1.65 (range: 6–12), indicating a predominantly high level of ergonomic risk. In categorical terms, 60.0% of the workers were classified as high risk, 13.3% as very high risk, and 26.7% as medium risk, while none reached negligible or low risk levels. Significant differences were observed between workstations (Kruskal-Wallis H = 16.72, p < 0.001, ε² = 0.545), with the nobbing stage exhibiting the highest biomechanical load (mean REBA = 10.38 ± 1.06). It is concluded that ergonomic risk is structurally integrated into the operational design of the evaluated production system; therefore, ergonomic interventions focused on redesigning workstations, adjusting height, and configuring tasks are recommended to reduce biomechanical exposure and strengthen the organization’s preventive occupational safety framework.

Sustainable agriculture is under increasing pressure due to climate variability, resource scarcity, and the need to reduce environmental impacts without compromising productivity. This study aimed to systematically analyze recent advances in emerging digital technologies applied to sustainable agriculture. The PRISMA protocol was applied to Scopus and Web of Science, considering publications from 2020 to 2025, which were analyzed using RStudio 4.5.10 and VOSviewer 1.6.20, resulting in 101 relevant articles. The findings indicate that multisensor monitoring and precision agriculture enable high-resolution characterization of soil–crop variability, supporting site-specific irrigation, fertilization, and phytosanitary management. Likewise, machine learning-based predictive models improve decision-making by forecasting yield, water stress, nutrient deficiencies, and disease outbreaks. In addition, edge computing and autonomous systems enhance operational efficiency and reduce labor dependency. Blockchain strengthens transparency and sustainability certification through secure traceability, while digital twins optimize management strategies through prior simulation. Despite these advances, limitations remain, including platform fragmentation, limited interoperability, uneven adoption among smallholders, and challenges in model generalization across heterogeneous agroecosystems. Therefore, further progress toward integrated and interoperable digital ecosystems is recommended.