Conference Agenda

The treatment of wastewater in Lucanas, Ayacucho, employed indigenous consortia of Chlorella vulgaris and Bacillus sp. Initial analyses showed thermotolerant coliform levels exceeding established limits. After 4 days, the treatment achieved a 73% reduction compared to 62.8% without treatment. Samples from nearby water bodies were collected to isolate and characterize indigenous microorganisms, which were applied in a laboratory-scale experimental system with hydraulic retention times of 24 and 48 hours. The results showed an average coliform reduction of 65.56% at 24 hours and 71.77% at 48 hours, with statistically significant differences compared to untreated controls. The combined use of Chlorella vulgaris (3,820,000 cells/mL) and Bacillus sp. proved to be an effective, economical, and adaptable solution for contexts in the Peruvian highlands.

Wastewater from primary processing of hydrobiological resources, characterized by its high pollutant load, represents an environmental and sanitary risk. Although coagulation-flocculation with chemical agents has traditionally been the method used, its use is associated with negative adverse impacts, which has prompted the search for more sustainable natural alternatives. The purpose of this research was to evaluate the effect of a natural coagulant (chitosan), in comparison with aluminum sulfate, a chemical coagulant, in the reduction of turbidity and its adverse effects in fishery effluents. For this purpose, wastewater samples were collected from an Artisanal Fishing Landing Site (DPA) and coagulation-flocculation tests were performed using the jar test method, applying different doses of coagulant (5, 12.5 and 25 mg/L) and adjusting specific times for fast mixing, slow mixing and sedimentation. The results indicated that aluminum sulfate achieved the highest turbidity reduction (74.01 %) with an optimum dose of 20 mg/L, while chitosan, with a lower dose (12.5 mg/L), achieved a comparable reduction (61.72 %). Furthermore, chitosan had a lower influence on the pH of the treated water (0.03 variation) and a lower sludge generation (1.70 mL/L) in contrast to aluminum sulfate (pH variation of 0.17 and 5.50 mL/L of sludge produced). These findings show that chitosan, in addition to being a natural and sustainable alternative, has a performance comparable to that of aluminum sulfate in reducing turbidity in fishery effluents.

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor symptoms such as resting tremors, which can significantly impact patients' quality of life. The ELENA Project aims to develop an Internet of Things (IoT)-based system for monitoring motor symptoms in individuals with PD. To achieve this, it is essential to use precise and calibrated measurement sensors. This study analyzes the accuracy of the MPU6050 sensor, a low-cost accelerometer and gyroscope, through a process of calibration, experimental testing, and validation.

A six-position calibration was implemented to correct the sensor offset and improve measurement reliability. Subsequently, tests were conducted using a uniaxial vibration table, where the sensor's response to controlled frequencies and amplitudes was evaluated. Additionally, the results obtained were compared with those of an Apple Watch, a commercially available device, to assess the accuracy of the MPU6050 in tremor detection.

The results showed that the MPU6050, after calibration, was capable of accurately recording simulated tremors in the laboratory, with an average error of less than 1% in most measurements. This confirms the importance of an appropriate calibration process to ensure reliable measurements in tremor monitoring systems for PD patients. Finally, the experimental validation suggests that the MPU6050 can be used as a low-cost alternative within the ELENA Project, facilitating the detection and remote monitoring of motor symptoms in individuals with Parkinson’s disease.

In this work, the electromagnetic and mechanical behavior of four configurations for a brushless direct current motor (BLDC) was analyzed to determine which one represents the most suitable option for its implementation in a ventricular assist device with a central shaftless axial impeller. For this, a numerical simulation of computational electromagnetism based on the Finite Element Method is performed. From these simulations, the initial electrical angles where the torque is maximum, the distribution of the magnetic flux density, and the torque as a function of time for each configuration of the BLDC motor were obtained. Finally, it was found that for the physical and geometric parameters used, a 10-pole and 12-slot BLDC motor represents the most viable option for the ventricular assist device.

Glaucoma represents one of the leading causes of irreversible blindness worldwide, impacting millions, particularly in advanced age groups. This condition is characterized by a gradual increase in intraocular pressure (IOP), which progres sively damages the optic nerve. Due to its asymptomatic nature in early stages, many patients remain unaware of their condition until significant and irreversible damage has occurred. Early detection is crucial for intervention before further progression of the damage. This research presents a process for early glaucoma detection using Artificial Intelligence (AI) techniques. The review of scientific databases identifies a comprehensive 7-stage process that includes: image acquisition and preprocessing, segmentation of relevant regions, feature extraction, model training, validation and testing of the AI model, and diagnosis and monitoring using ocular images from specialized databases and various imaging devices. This integrated approach is detailed with scientific evidence, along with specific contributions at individual stages to provide a complete overview of this research field. This work may serve as a reference on the current state and future challenges in glaucoma detection using AI techniques.