Conference Agenda

This study evaluates the geotechnical properties of soils in the Huacariz sector (Cajamarca, Peru) and the stability of a natural slope in the Santa Apolonia hill, aiming to determine its suitability for infrastructure development. Standard soil classification tests were conducted, including natural moisture content, grain size distribution, specific gravity, plasticity limits, and field density. The results allowed classifying the predominant soil as silty sand (SM), non-plastic, with frictional and draining behavior, favorable for shallow foundations and controlled fills. The average natural moisture content (17%) indicates adequate volumetric stability under natural conditions. Slope stability was analyzed using limit equilibrium methods (Bishop, Spencer, and Janbu) in Slide software. The obtained safety factors (1.13–1.07) are below the recommended minimum for permanent slopes (FS ≥ 1.50), indicating a limiting stability condition and high susceptibility to failure due to moisture variation or additional loads. Stabilization measures are therefore required to ensure geotechnical safety. Additionally, an experimental section of the study evaluated the incorporation of igneous rock as partial replacement of fine aggregate in 300 kg/cm² concrete. Mechanical performance improved, with a 10.87% increase in compressive strength (15% replacement) and a 22.03% improvement in flexural strength (10% replacement). Statistical analysis confirmed the significance of these improvements. In conclusion, the area presents soils with adequate performance for foundations; however, the slope requires geotechnical reinforcement. Moreover, igneous rock is confirmed as a viable alternative to enhance concrete resistance for local infrastructure.

In many industrial warehouses, floor-level storage restricts the use of available cubic space and tends to prolong outbound operations. This study reports an applied case in an industrial battery warehouse in Peru, where a vertical storage system using five-level metal racks was implemented to increase capacity and reduce dispatch time. A quasi-experimental before–after design without a control group was used, comparing 12 weeks prior to the intervention with 12 weeks after implementation. Approximately 180 dispatches were recorded for each condition, and for inferential analysis, weekly averages were paired by product family, order volume, and shift (n = 12 pairs). The main performance indicators were installed capacity (available storage positions for unit loads) and dispatch time per unit load, measured through time studies and operational records. After rack installation and warehouse layout reorganization, capacity increased from 125 to 200 unit loads (+60%), and average dispatch time decreased from 50 to 15 minutes (–70%), with statistically significant improvement in dispatch time (paired t-test, p < 0.001). Total investment was US$ 4,434, and the estimated annual labor-hour savings reached US$ 1,817, resulting in an approximate payback period of 2.44 years. Overall, the results show that vertical racking, even with a moderate investment, can tangibly improve space utilization and warehouse operational performance.

This study evaluated the effect of the solid-to-solvent ratio on the ultrasound-assisted extraction of total saponins from quinoa husk (Chenopodium quinoa Willd.), with the aim of identifying an efficient operating range for further optimization studies. Saponin quantification was performed using the vanillin-sulfuric acid spectrophotometric method, employing commercial saponin from Quillaja saponaria as a standard. Calibration curves with high linearity (R² > 0.996) and adequate reproducibility were obtained.

Screening assays were carried out under fixed extraction conditions (40% v/v ethanol, 60 °C, 50% ultrasonic amplitude, and a 30 s ON–30 s OFF pulse cycle), evaluating five solid-to-solvent ratio levels (0.01–0.10 g of quinoa husk/mL solution). The results showed a significant increase in the total saponin content of the extract with increasing solid-to-solvent ratio, reaching concentrations of up to 20.27 mg total saponins/mL of extract. A reduction in the progressive increase in yield was observed at treatments above 0.075 g/mL, attributed to physical limitations of the system. Based on these results, an optimal operating range was established between 0.050 and 0.100 g of quinoa husk/mL of solution, which provides a technical basis for the subsequent application of a Box-Behnken design for multivariable process optimization. This study contributes relevant experimental criteria for the sustainable valorization of quinoa husk using ultrasound-assisted extraction technologies.

The garment manufacturing sector operates under high production demands, where repetitive movements and sustained awkward postures increase workers’ exposure to musculoskeletal disorders (MSDs). Conventional ergonomic assessment methods such as Rapid Upper Limb Assessment (RULA) and Rapid Entire Body Assessment (REBA) rely on direct observation and extended evaluation periods, requiring trained personnel and potentially introducing subjectivity. This study proposes the implementation of a Convolutional Neural Network (CNN) to automate ergonomic risk assessment in garment operations. A dataset of 1,000 images capturing arm and forearm postures during sewing tasks was compiled and labeled according to the RULA method. Image preprocessing and annotation were conducted using Kinovea and Roboflow. The CNN model integrates a keypoint detection algorithm based on the COCO-Pose framework for posture estimation and classification. The proposed model achieved a mean Average Precision (mAP) of 96.40%, recall of 93.50%, and precision of 92.30%, demonstrating high reliability in ergonomic posture classification. The results indicate that the automated approach enhances evaluation accuracy, reduces observer subjectivity, and provides a scalable tool for improving workplace ergonomics and preventing MSDs in garment manufacturing environments.

Conventional polymer injection molding achieves high part quality and productivity; however, equipment and tooling costs frequently restrict its use in educational laboratories and early-stage research settings. This study introduces a requirement-driven conceptual design for a low-cost injection molding device that uses readily available filament-extrusion hardware to fabricate ASTM D638 Type I specimens. The methodology translates functional requirements into integrated subsystem decisions for the injection unit, mold unit, and structural frame, supported by first-order flow, thermal, and mechanical analyses. The design process establishes a feasible low-throughput operating window and addresses key risks through targeted solutions: guideline-based venting to minimize gas entrapment, conduction-focused mold heating with cartridge heaters and optional cooling channels for thermal regulation, and a stable clamping and alignment strategy for precise nozzle-to-mold positioning. Sprue and runner alternatives were assessed as coupled variables, with the constant-cylindrical sprue selected as the most robust option for maintaining quality consistency, mitigating defects, and maintaining dimensional stability under laboratory constraints. Material screening and preliminary sizing criteria identified aluminum tooling as suitable for the reference configuration. The resulting framework offers a practical pre-prototyping approach for low-volume comparative testing, educational use, and early laboratory validation, while excluding industrial high-throughput operation from its intended scope.

Industry 5.0 is transforming production planning by integrating human factors into decision-making. This paper addresses a multi-objective permutation flow shop scheduling problem with missing operations, where jobs may skip machines and processing times depend on worker-related factors such as skill level, age, learning, and forgetting. The model simultaneously minimizes makespan, total tardiness, and total earliness. To solve the problem, an NSGA-II-based evolutionary algorithm with permutation encoding was implemented. Computational experiments with 0%, 20%, and 40% missing operations show that greater route flexibility generally improves schedule quality, reducing makespan by 4.41% to 27.71% and total earliness by 18.11% to 77.53% in compromise solutions, while tardiness shows a more heterogeneous behavior. The results also indicate that missing operations reshape the trade-off structure among objectives rather than simply reducing problem size. Overall, the study provides a human-centered scheduling approach aligned with Industry 5.0 and relevant for production environments with workforce heterogeneity and route variability.

This study evaluates three underground blasting designs V-cut, Burn cut, and Parallel cut applied to fractured andesite, compact limestone, and hard granodiorite, aiming to determine the most efficient and safe blasting pattern. The research combined geomechanical characterization, 3D simulation using JKSimBlast, and field tests with vibration and gas monitoring.

Results show that the Burn cut, combined with Electronic Detonators (EED), achieved the best performance: average fragmentation D80 ≈ 18 cm, 30% reduction in overbreak, vibrations

<9 mm/s, and advance ≈ 3.0 m per blast, maintaining optimal energy consumption. Ventilation time was also reduced by 37% due to lower gas emissions.

The study concludes that the integration of digital modeling, geometric optimization, and electronic timing significantly improves efficiency, safety, and environmental control in underground blasting operations.

This paper analyzes the development of organizational resilience in a major Latin American oil refinery, documenting its transition from reactive to proactive resilience strategies over a ten-year period (2014–2025). Employing a qualitative case study that includes semi-structured interviews with five senior managers and comprehensive document analysis, we identify critical disruptions incurring costs of up to US$8 million daily and establish an empirically grounded methodology for fostering proactive resilience. Our findings indicate that systematic capture and integration of lessons learned reduced mean time to recovery from 168 hours to 24 hours (86% improvement), yielding an 859:1 return on investment. The research extends resilience theory by demonstrating how resource-constrained refineries in emerging economies can leverage organizational learning as a dynamic capability. The proposed six-stage framework provides practical guidance for practitioners seeking to enhance operational continuity in volatile environments, while contributing empirical evidence from a context underrepresented in current literature.