Sistema Regional de Información
en línea para Revistas Científicas de América Latina,
el Caribe, España y Portugal

 The VLE-196 area, located north of Block V Lamar in Maracaibo, Venezuela, encompasses Eocene reservoirs, especially the C-SUPERIOR and C-INFERIOR sands of the MISOA formation, with a particular focus on the C-4/C-5 sands of C-INFERIOR. In recent years, water production in the 32 active wells has experienced a gradual increase, generating technical-economic problems that threaten oil recovery. The study was divided into four phases. The first consisted of the static and dynamic characterization of the reservoir. Subsequently, a well review was performed for a comprehensive analysis, which provided a diagnosis based on well characteristics and similarities/differences with neighboring wells. The final proposal included actions aimed at minimizing water production, improving sweep efficiency, extending well life, and reducing water treatment and disposal costs. Two pressure zones were identified, one to the north as a low-pressure zone and one to the south as a highpressure zone. Wells in the high-pressure zone exhibited coning primarily in C-5, while those in the lowpressure zone exhibited adedimentation in C-4 flow units. Differences in fluid behavior between C-4 and C-5 sands suggest considering these reservoirs as separate entities in future studies. The research seeks to address the current challenges through a comprehensive approach, offering solutions tailored to individual well characteristics to optimize production and mitigate water-related problems.

During the machining process, the temperature of the workpiece material has a direct effect on both the final product quality and the cutting tool life. The temperature increase can negatively affect the surface finish of the workpiece and safety risks during operation. On the other hand, excessively low temperatures can lead to higher cutting force and heightened friction between the tool and the workpiece. Then, accurate cutting temperature prediction has great importance in the production planning of machined parts. The temperature increase in the machined material is mainly due to the strain rate and the amount of deformed material. Cutting parameters that are directly associated with these factors include cutting speed, depth, feed rate, and tool geometry. In this study, they have proposed five models that consider different combinations of these machining parameters in a selective manner. The modeling process was carried out using a multivariate linear regression technique. These models are straightforward to implement and are applicable to various ranges of machining for medium-carbon steel. The results obtained from this study are satisfactory and align well with the reference experimental observations, demonstrating approximations in the range of 83-91%.

 This study focuses on implementing an automated feeding system based on IoT technology in the shrimp farming sector in Ecuador. pH, temperature, turbidity, and salinity sensors are employed for continuous monitoring of water conditions, variables critical for the survival and development of shrimp. The ESP32 board is responsible for collecting, and processing data from these sensors, and transmitting them to IoT platforms such as Arduino Cloud and ThingSpeak for monitoring and control. The internal real-time clock of the ESP32 enables the programmed automatic operation of the feeder, facilitating precise and timely feeding adjustments. The results, validated at the Camachasa farm, have demonstrated the efficiency of the proposal, thereby solidifying IoT technology as an effective solution for the challenges faced by the Ecuadorian shrimp farming sector.

The precipitation behavior of Cu, affected by lead free tin-based solder is investigated as a function of cold deformation and artificial ageing using microhardness measurements, electrical resistivity, differential scanning calorimetry, reflectance behavior as well as microstructural observation. To compare preciously the aforementioned properties commercially pure Cu and two amounts Cu-Sn alloys has been taken under consideration. When Sn doped to Cu, it has been found that changes in such parameters as cold rolling and ageing contribute significantly to the physio-electrical qualities of pure Cu. Cold rolling shows the superior hardness for Sn doped alloys because of the dissimilar crystal orientation BCC of Sn precipitated within the FCC Cu matrix. That is why increase then electrical resistivity. Two processes, namely precipitation strengthening through supersaturated solid solution and recovery as well as recrystallization softening are observed for the alloys. Fine precipitates hinder the dislocation movement as a result improves the recrystallization temperature. The DSC study also confirms the improvement by showing the peaks at higher temperatures. Spectral reflectance study reveals that Sn doping provides lower percent reflectance whereas improve under ageing treatment due to formation of fine precipitates. Micrographs studies confirm that cold rolling expands the grains in its rolling direction and relatively thick grain boundaries are observe as the presence of different particles at the grain boundaries for minor added alloys. All alloys under ageing treatment at 400ºC for one hour attain equiaxed grain by reaching more or less recrystallized state.

The work produced activated carbon (CACCA) from the lignocellulosic biomass of pineapple peel and crown (CCA) by slow pyrolysis in a fixed bed reactor. Activation was carried out by water vapor at a temperature of 700 °C for a period of 30 min.. The objective was to remove the Congo red textile dye from the charcoal obtained. The biomass showed values for density 0.264 g/ mL, moisture 7.03%, volatilematter 80.74%, ash 6.33%, fixedcarbon 5.9%, carbon 43.43%, hydrogen 1.16%, oxygen 48, 19%, nitrogen 6.22%. The CACCA yield was 25.24%. The formation of a mesoporous structure of the activated cavão and pHpcz of the CACCA favored the adsorption process, which reached up to 94.94% efficiency in the removal of the Congo red dye.

Direct methods for optimal tuning of the parameters of a PID-type controller are developed using the theory of LQR when the order of the plant model equation is known. This is achieved through two fundamental results. The first one defines a link between the weighting matrix of the LQR performance index with the desired temporal specifications of the closed-loop controlled response, expressed through a desired overshoot and settling time, as well as the tracking of a constant reference input with zero steady-state error. The second result establishes the connection between the state feedback solution of the LQR problem and the parameters of the PID-type controller. The design method is validated through simulation studies on a heat flow experiment, a coupled tank system, and a radar antenna.

Productivity in agriculture is a major driver of economic expansion. The fact that plant diseases are so frequent is one of the reasons why plant disease detection is so important in the agricultural industry. Plants suffer severe effects if proper care is not given in this area, which might affect the amount, quality, or productivity of the relevant products. For instance, both living and non-living organisms can cause various diseases in stone fruits and other crops. Early disease patterns and clusters can be identified using computer vision technologies. This work focuses on deep learning-based crop image segmentation research. Firstly, the fundamental concepts and features of deep learning-based crop leaf image segmentation are presented. The future development path is enlarged by outlining the state of the research and providing a summary of crop image segmentation techniques together with an analysis of their own drawbacks. Crop image segmentation based on deep learning has still faced challenges in research, despite recent remarkable advances in crop segmentation. For instance, there are not many crop images in the datasets, the resolution is modest, and the segmentation accuracy is not very great. The real-field criteria cannot be satisfied by the imprecise segmentation findings. With an eye towards the aforementioned issues, a thorough examination of the state-of-the-art deep learning-based crop image segmentation techniques is offered to assist researchers in resolving present issues.

This scholarly paper introduces an innovative and comprehensive ideology that aims to significantly expand the utility of Named Entity Recognition (NER) through the application of Transformers in various Natural Language Processing (NLP) tasks. One prominent task that necessitates attention is the intricate classification of emails into multiple labels, wherein each label can be associated with not just one but potentially multiple independent classes. Despite the existence of several research methodologies attempting to address numerous challenges in this domain, the industry continues to face a substantial hurdle when it comes to accurately categorizing multi-label texts like financial emails, which can encompass diverse categories such as Payment Information, Invoice Information, Disputes, and more. Considering these challenges, our proposed methodology serves as a breakthrough solution, demonstrating remarkable performance in the classification task across a wide range of datasets, including Financial Emails and Consumer Complaint Datasets. By leveraging the power of advanced Transformers, we have achieved an exceptional accuracy rate of 94% for full match of the multi-label classes, while the accuracy for partial match to individual classes soared to an impressive 97%. This achievement not only highlights the effectiveness of the proposed approach but also showcases its potential to enhance the efficiency and reliability of NER applications in practical settings.

In this work, extraction of high-utility data from massive datasets is one of the most well-known areas of research in data mining. The goal of high-utility itemset mining is to identify the inventory's most lucrative items that users tend to favor. An LSTM-based approach is suggested to determine what consumers buy most frequently. Based on these purchases, high-utility items that are expected to be in demand in the future are then identified from client buying patterns. The development of the design, which will be utilized to manage inventories going forward and identify each consumer's item set, is the main focus rather than just the price or amount of the item purchased. Additionally, related consumer groups can be put together and consumers of similar commodities can be located by expanding the use case of the model. Lastly, an empirical comparison of the algorithms examines the accuracy of the approach and the number of valuable item sets and consumers that have been discovered via the use of the algorithms. The LSTM-based approach is the most effective, as seen by its 98\% accuracy rate. It is especially useful for predicting future consumer purchases, identifying the most lucrative items, and properly managing inventories.

The goal of this study was to solve the radiative transfer equation in one dimension utilizing the Six-Flux Model technique (SFM-1D) to characterize, estimate, and optimize the radiant field in annular photocatalytic reactors by performing an energy balance in a cylinder's element. The local volumetric rate of photon absorption (LVRPA), a parameter required for the description of the photocatalytic reaction kinetics, was defined. This parameter was defined for two types of reactors with a constant-intensity radiant source located vertically at the center of the first reactor (annular-reactor R1) and outside around the second reactor (tubular-reactor R2). Simulations were made by taking the commercial TiO2 P25 as the catalyst model with its optical properties taken from other authors. The model was evaluated with Heyney-Greenstein (HG) and diffuse reflectance (DR) phase functions. For reactor R1, the LVRPA decreases from the reactor's inner wall to its outer wall while it decreases from the reactor wall to its center for reactor R2. The volumetric rate of photon absorption per unit of reactor length (VRPA/H) which gives a broader view of the radiation absorption inside the reactor was established. The originality of the present model makes it better than that found in the literature since it was derived from an energy balance and the other was only deduced from the LVRPA formulated on a slab geometry with the SFM approach. The VRPA found in this work differed from that found in the literature by approximately 13.78 % on reactor R1. For both reactors, the VRPA/H was found to increase exponentially with the increase of catalyst loading (Ccat) until reaching a value where no significant increase was observed. The optimum apparent thicknesses were about 4.8 and 10.6 for R1 and R2 respectively using Heyney-Greenstein phase function. The optimum radius for R2 was found in the range of 1-3 cm while for R2, the optimum reaction space thickness was found less than 3 cm and the dimensionless parameter , which was introduced for optimization purposes was found between 0.55 and 0.8.