One of the biggest problems of water with high concentrations of calcium is its susceptibility to causing scaling in industrial equipment (boilers, heat exchangers, pipes, reverse osmosis membranes, storage tanks, etc.). The purpose of this study was to evaluate a recently built filter press (EC) type electrocoagulation reactor and investigate the efficiency of water hardness removal. The electrocoagulation (EC) reactor has been evaluated in batch mode using electrodes of aluminum (Al) and connected to a direct current power supply in a monopolar way. To evaluate the performance of the reactor, a synthetic solution with a concentration similar to that of brackish water was used. A factorial design was applied to investigate the influence of the electrical potential applied to the electrocoagulation cell at the levels of 3, 5, 7, and 9 V, and initial calcium hardness of 540.2 and 914.60 mg/L CaCO₃ at room temperature in 60 treatment minutes. The results revealed that the electrical potential applied to the electrocoagulation cell was the most significant factor in hardness removal, within the experimental ranges studied. The results showed that electrocoagulation at an electric potential applied at 9 volts and an initial concentration of 7400 mg/L allowed a higher hardness removal efficiency (25.83%). the pH of the solution increased throughout the process. The energy consumption ranged between 4.43 and 42 kW.h/m³ depending on the conditions of the factors. It has been shown that during the treatment process a layer of dense and compact calcium carbonate precipitate is formed on the surface of the cathode.

The use of cell phones has increased worldwide in the past few decades, particularly in children and adolescents. Using these electronic devices provides personal benefits. Communicating through cell phones was a very important factor in the socioeconomic progress of developed countries. However, it is beyond doubt that its indiscriminate use can bring up certain psychiatric disorders or cause some disorder in a person, within the phobic group of anxiety disorders called nomophobia; basically associated with anxiety, nervousness, discomfort, and distress when contact with the smartphone is lost, mainly in the youngest users. This research proposal aims to identify symptoms that have not yet been detected by unceasing cell phone use, considering that in Peru there are few studies of human health engineering and the physical mental health. For that reason, we sought to identify the symptomatic factors of nomophobia presented by students at the National University of Engineering and its interference with their academic life. To accomplish this study, we designed a questionnaire according to our reality with the use of focus groups techniques when the test was taken in class. Three symptomatic factors of nomophobia were identified: feelings of anxiety, compulsive smartphone use, and feelings of anxiety and panic. The study included a representative sample of 461 students in different years of study engineering (21% women, 79% men, over 17 years of age). Finally, given the widespread adoption of smartphones and their integration into educational environments, the results of this study can help educators understand students’ inclination to use their smartphones at all times.

Over the years, various models have been developed in the stages of the mining process that have allowed predicting and enhancing results, but it is the breakage, the variable that connects all the activities of the mining process from the point of view of costs (drilling, blasting, loading, hauling, crushing and grinding). To improve this process, we have designed and developed a computational model based on an Artificial Neural Network (ANN), the same that was built using the most representative variables such as the properties of explosives, the geomechanical parameters of the rock mass, and the design parameters of drill-blasting. For the training and validation of the model, we have taken the data from a copper mine as reference located in the north of Chile. The ANN architecture was of the supervised type containing: an input layer, a hidden layer with 13 neurons and an output layer that includes the sigmoid activation function with symmetrical properties for optimal model convergence. The ANN model was fed-back in its learning with training data until it becomes perfected, and due to the experimental results obtained, it is a valid prediction option that can be used in future blasting of ore deposits with similar characteristics using the same representative variables considered. Therefore, it constitutes a valid alternative for predicting rock breakage, given that it has been experimentally validated, with moderately reliable results, providing higher correlation coefficients than traditional models used, and with the additional advantage that an ANN model provides, due to its ability to learn and recognize collected data patterns. In this way, using this computer model we can obtain satisfactory results that allow us to predict breakage in similar scenarios, providing an alternative for evaluating the costs that this entails as a contribution to the work.

The presence of cadmium (Cd) in cocoa crops is currently a serious problem for farmers and producers in various regions of South America. Because its exports of cocoa and derivatives to European markets are threatened by possible signs of contamination in cocoa beans for export. Territories with a low organic component predated and exploited by illegal logging, burning and the intensity of unsustainable land use is common in large Amazonian areas in countries of the region. These factors were incorporated in statistical analysis in order to relate them to the contents of Cd in soil, leaves and beans in the study areas located in Peru. Such as the Campo Verde-Honoria-Tournavista corridor (Ucayali Region and Huanuco Region). Cadmium concentrations were determined using an atomic absorption spectrophotometer. As a consequence of this study, we determined and concluded that the observed difference in distribution of Cd contents by sectors can be explained by previous land use and age of cocoa crop. Indeed, the average content of Cd in soil in all cocoa growing areas is higher than the standard established by the Peruvian Ministry of the Environment (MINAM). However, when the measurements obtained in previously predated and exploited sectors are not considered, the Hotelling’s $T^2$ simultaneous 90% confidence interval contains the value of the Peruvian standard 1.4 mg/kg. Therefore, with this information we prepare a geochemical Cd map in soils for the study area, which will help cocoa producers to identify areas that exceed the allowed Cd values. In this way, we can carry out in the future a mitigation plan for areas with Cd problems, which allows to reduce their content with major challenges to sustainable agriculture and rural development.

Currently there are many supervised learning methods and evaluation models for the detection of diseases in plants, this initial study presents a model for the detection of phytophthora palmivora on the cocoa pod, which has been developed with the ResNet18 model, reaching 83% prediction accuracy, using 1596 images in total for training and testing. In addition, with the same model, other images were trained to predict the image of cocoa compared to other fruits similar to cocoa, obtaining a prediction accuracy of 96%. Due to the good results obtained in this work, we believe that this tool can help cocoa farmers to identify phytophthora and to obtain healthy cocoa crops.

This study proposes a fuzzy control strategy embedded in a Siemens IoT2040 gateway developed for removing inorganic arsenic from synthetic underground water in a treatment plant prototype. The prototype is used to dose a constant flow of Fe(VI) to maintain an oxide-reduction potential to guarantee the oxidation of arsenite into arsenate, while the fuzzy logic embedded in the IoT control manages the addition of Fe(III) to achieve a proper pH adjustment and efficient arsenate removal. The tests used synthetic Bangladesh groundwater enriched with 200 µg/L of arsenite and 200 µg/L of arsenate. The results revealed that the plant prototype yielded an effective treatment of the water. Arsenate was decreased to an average value of 6.66 µg/L and, the arsenite concentration decreased to 1.01 µg/L or less. These values were lower than the limit of 10 µg/L deemed by the World Health Organization as safe for human consumption.