Abstract The present research work was carried out in Zaruma´s underground workings, a city located in the province of El Oro in the southwest of Ecuador. Zaruma is a city that has been exploited since the 1500's, the urban center sits on a swarm of veins enriched with gold, the antitechnical exploitation has caused the appearance of sinkholes on the surface of the city, with the emergency that occurred in December 2021, partial remediation was carried out corresponding to the filling of the galleries and construction of plugs in order to avoid collapses in the urban area, for the execution of the remediation work required manpower, workers who entered the s underground galleries in which carbon monoxide (CO) was detected, the objective was evaluated the health effects based on the levels of exposure to carbon monoxide. The research methodology is a non-experimental, cross-sectional, descriptive study, with a sample of fifteen workers operating inside the mine, carbon monoxide measurements were taken in the mine workings and symptomatology interviews were applied to the workers taking aspects of age and time of exposure. The results describe that the operators presented symptoms: headaches, nausea, fatigue, weakness; the severity of the symptoms is a function of time and CO concentration. It´s concluded that exposure to CO in high concentrations for a period of time causes intoxication, with headache being the most frequent symptom.

Resumen El presente trabajo de investigación se desarrolló en las galerías subterráneas de Zaruma, ciudad ubicada en la Provincia de El Oro al Sur Oeste del Ecuador. Zaruma es una ciudad que ha sido explotada desde los años 1500, el casco urbano se asienta sobre un enjambre de vetas enriquecidas con oro, la explotación antitécnica ha provocado la aparición de hundimientos en la superficie de la ciudad, con la emergencia sucedida en diciembre del 2021, se realizó la remediación parcial que corresponde al relleno de la galerías y construcción de tapones con la finalidad de evitar colapsos en el casco urbano, para la ejecución de las obras de remediación se requirió de mano de obra, operarios quienes ingresaron a las labores subterráneas en las que se detectaron monóxido de carbono (CO), el objetivo evaluar los efectos en la salud en relación a los niveles de exposición a monóxido de carbono. Se ha aplicado una metodología de investigación no experimental, de corte transversal, descriptivo, con una muestra de quince trabajadores operativos interior mina, se realizó mediciones de monóxido de carbono en las labores mineras y se aplicó entrevistas de sintomatología a los operarios tomando aspectos de edad y tiempo de exposición. Los resultados describen que los operarios presentaron síntomas: cefaleas, náuseas, fatiga, debilidad, la severidad de los síntomas está en función del tiempo y concentración de CO. Se concluye que la exposición a CO en concentraciones altas por un periodo de tiempo causa intoxicación, siendo la cefalea el síntoma que se presenta como el más frecuente.

Abstract This paper presents the design, implementation, and validation of a deposition system for a ferromagnetic pheromone. The project originates from the necessity of a bio-inspired communication method, like pheromones, on a swarm of mobile robots. This was desired to achieve a collective behavior due to the interaction of several robots. The designed system was able to deposit pheromone drops of 6mm in diameter, with a 1.5% error and a magnetization time of 30 minutes, by using a Robotino robot.

Resumen En este artículo se muestra el diseño, la implementación y validación de un sistema de deposición de una feromona ferromagnética. El trabajo surge de la necesidad de implementar un método de comunicación bio inspirado, como lo son las feromonas, en un enjambre de robots móviles. Esto para lograr un comportamiento colectivo coordinado por medio de la interacción entre diversos robots. El sistema diseñado logra la deposición de gotas de feromona de 6mm de diámetro con un error del 1.5% y con un tiempo de magnetización de hasta 30 minutos, a través de un robot denominado Robotino.

Abstract: A road constitutes a material mediation between local communities and the State. The constitution of this nexus entails a swarm of possibilities whose realization results from the type of relationship established between the actors involved. This article highlights the complex web of actions that underlie the construction, use, maintenance, and repair of marginal routes that link, in this case, Pewenche communities with their regional environment. Through ethnographic observation, we accompanied them on their journey while interviewing users and officials of the Q 699 route. The results allow us to visualize the road as something alive, whose changing condition is consistent with the processes experienced locally. The analysis highlights that the parties the State and the Pewenche communities manage to preserve their purposes in terms of maintaining levels of relative autonomy and a spasmodic presence. However, it is concluded that the environment assumes the cost of increasing environmental degradation a growing degradation caused by companies that turn out to be made invisible by arrhythmic governance.

Resumen: El camino constituye una mediación material entre las comunidades locales y el Estado. La constitución de este nexo entraña un enjambre de posibilidades cuya realización es el fruto del tipo de relación que se establece entre los actores involucrados. Este artículo procura poner de relieve la compleja trama de acciones que subyace a la construcción, el uso, la mantención y la reparación de rutas marginales que vinculan, en este caso, a comunidades Pewenche con su entorno regional. Para ello, mediante la observación etnográfica, se acompaña en su desplazamiento a residentes locales y se entrevista a usuarios y funcionarios de la ruta Q 699. Los resultados permiten visualizar el camino como algo vivo, cuya condición cambiante se condice con los procesos vividos localmente. El análisis pone de relieve que las partes -el Estado y las comunidades Pewenchelogran preservar sus propósitos en términos de mantener niveles de relativa autonomía y de una presencia espasmódica. No obstante, se concluye que el costo de tales adecuaciones lo asume el entorno en términos de una creciente degradación provocada por empresas que resultan ser invisibilizadas por una gobernanza arrítmica.

Abstract: In actuality, the financial investment in Enterprises of the world is common. This investment is performed using internet platforms and value markets. This can generate a loss for many investors due to the uncertainty of the future financial health of the Enterprise. A comparison for the prediction of financial health based on algorithms of machine learning, particularly Support Vector Machine (SVM), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), and Adaptive Neuro-Fuzzy Networks (ANFIS) is presented. The database of the Taiwan Economic Journal from 1999 to 2009 is used, with 95 financial ratios of enterprises financially healthiest and with bankruptcy problems. In the ANN, the epoch number, numbers of neurons, activation functions in each layer, loss function, and learning rate are tested; also, an architecture of a Convolutional Neural Network (CNN) is implemented. In SVM, the experiments are performed using different kernels, polynomial, RBF, linear. Besides, the size of C, size of gamma, and size of the polynomial are varied. In KNN, experiments with different numbers of neighbors, types of weight, and values of P are realized. In ANFIS, experiments with variants of the numbers of fuzzy rules, quantity and type of membership functions, number of epochs, and input dimensions are performed. Optimization using Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) of the three models with the best results are performed; the optimization is based on the search for the best hyperparameters that would provide a higher accuracy. The neural network models presented the best average for all the proposed tests.

Abstract: As the world becomes more digitalized, the potential for attacks increases, therefore, effective techniques for intrusion detection on network are needed. In this study, the authors propose a two steps approach. First, the Correlation-based Features Selection as a feature evaluator based on Particle Swarm Optimization is used to select the relevant features. This evaluator is compared with other evaluators. Second, the Random Forest algorithm is used to classify attacks in a network. A comparative study is also performed conducted with different classifiers such as Naïve Bayes, Stochastic Gradient Descent, Deep Learning, k-Nearest Neighbors and Support Vector Machine. Experiments were conducted on the NSL-KDD database and the results show an efficiency of 98.78% for binary classification. The performance results obtained show that the proposed technique performs better than other competing techniques.

Abstract: Recurrent Neural Networks have proven to provide good results in time series prediction. In this paper, an ensemble recurrent neural network, for prediction is used. Euro/Mexican pesos and Euro/Dollar series are utilized to develop a prediction model. The design of this consists of an ensemble recurrent neural network and the optimization of its structure is achieved with the bird swarm algorithm and Particle Swarm Optimization. The outputs of the networks are integrated with type-1 and type-2 fuzzy systems. These fuzzy systems are of the Mamdani type. The tests were realized with the designed method and a good result was obtained in the application to time series, as well as a comparison of the two optimization algorithms was made.

ABSTRACT Current corn sowing equipment shows relatively low levels of automation and intelligent response. Issues such as wheel slipping and chain jumping occur in seeders driven by wheels and chains, leading to a decrease in the seeding qualification rate. To solve the problems of wheel slipping and low control strategy accuracy, an electric control sowing system has been designed. This system uses radar to collect the locomotive speed and automatically adjusts the motor speed. To improve the control accuracy of the electric control system, a dual closed-loop control method is employed, and the proportional-integral-derivative (PID) controller parameters are optimized by combining the particle swarm optimization (PSO) algorithm and the fuzzy algorithm. Control accuracy tests and field tests are conducted for both the traditional PID control system and the fuzzy PID control system based on the PSO algorithm. The average error of this system is 0.622%, which is 1.6% lower than that for the traditional method. Field tests show that in operation, the sowing system achieves the average seeding qualification index of 93.99%, which is 3.19% higher than that for the traditional PID control method. The fuzzy PID control system shows improved sowing effectiveness compared to the traditional PID control method. response chains rate designed closedloop closed loop employed proportionalintegralderivative proportional integral derivative (PID (PSO 0622 0 622 0.622% 16 1 6 1.6 operation 9399 93 99 93.99% 319 3 19 3.19 062 62 0.622 1. 939 9 93.99 31 3.1 06 0.62 93.9 3. 0.6 93. 0.

Abstract In recent years, there has been a growing interest in integrating distributed generation (DG) technologies into the distribution network (DN) to improve system efficiency, reduce carbon emissions, and increase the power system's reliability. However, the optimal placement of DG systems within the DN is a difficult task, since it is dependent on several variables, including load demand, renewable energy sources, and energy storage systems (ESS). In this context, demand response (DR) programs can play a vital role in enhancing the efficiency of DG systems, since they allow consumers to lower their energy usage during peak hours and move their demand to off-peak hours. DR and solar photovoltaic (SPV) systems are two prominent technologies that can play a substantial role in the power DN. In this paper, a bi-level particle swarm optimization (PSO) method is employed to determine the best allocation of DG in the coordination of DR. In the suggested methodology, the first level of optimization determines the optimal size and location of DG, and the second level of optimization determines the optimal power dispatch in the coordination of DR. The proposed method is implemented on the IEEE 33 bus system, and the results demonstrate that the power quality parameters have significantly improved. years (DG (DN emissions s reliability However task variables sources ESS. ESS . (ESS) context (DR offpeak off SPV (SPV paper bilevel bi PSO (PSO methodology 3 improved (ESS

ABSTRACT The concept of a swarm of drones assumes the presence of a wireless ad hoc network, in which drones are network nodes and exchanging information with each other. This article is devoted to studying the behavior of an ad hoc network in a transient mode and assessing the characteristics of updating local adjacency matrices (LAM), which allow network nodes to autonomously form packet transmission routes. Using a simulation model, a comparison was made of two multiple access methods to a common data transmission channel in the process of updating matrices: cyclic and random (slotted ALOHA). The simulation results made it possible to determine areas of their effective application: slotted ALOHA is advisable to use with relatively high probabilities of packet distortion (of the order of 0.1 and higher), a numerous nodes (more than 40), and low network connectivity, and cyclic access (CA) is effective at a low level of distortion. It is shown that the completion of the process of updating adjacency matrices (AM) can be judged by such indicators of the exchange of routing information as the achievement of a certain threshold of the number of transmissions and a decrease in the level of network traffic. other LAM, LAM , (LAM) routes model ALOHA. . ALOHA) application 01 0 1 0. higher, higher higher) more 40, 40 40) connectivity CA (CA AM (AM traffic (LAM 4

ABSTRACT This study investigates the issue of multi-objective mission planning for multi-payload satellite constellations via the non-dominated sorting carnivorous plant algorithm (NSCPA). Observation time windows are generated, and a constraint satisfaction model is established based on multiple regional targets, satellite orbits, and characteristics of the synthetic aperture radar (SAR) payload and optical payload. A task conflict detection and resolution method is proposed to handle the task assignment among multiple satellites. Based on the existing single objective-based CPAs, a modified multi-objective NSCPA is first developed for multi-objective planning optimization using the non-dominated sorting algorithm. The effectiveness and superiority of the NSCPA are verified by a series of simulation experiments and comparisons with the traditional non-dominated sorting genetic algorithms-II (NSGA-II) and particle swarm optimization (PSO). multiobjective multi objective multipayload nondominated non dominated NSCPA. . (NSCPA) generated targets orbits SAR (SAR satellites objectivebased CPAs algorithmsII algorithms II NSGAII NSGA (NSGA-II PSO. PSO (PSO) (NSCPA (PSO

ABSTRACT Traditional hydrological models have been widely used in hydrologic studies, providing credible representations of reality. This paper introduces a hybrid model that combines the traditional hydrological model Soil Moisture Accounting Procedure (SMAP) with the machine learning algorithm XGBoost. Applied to the Sobradinho watershed in Brazil, the hybrid model aims to produce more precise streamflow forecasts within a three-month horizon. This study employs rainfall forecasts from the North America Multi Model Ensemble (NMME) as inputs of the SMAP to produce streamflow forecasts. The study evaluates NMME forecasts, corrects bias using quantile mapping, and calibrates the SMAP model for the study region from 1984 to 2010 using Particle Swarm Optimization (PSO). Model evaluation covers the period from 2011 to 2022. An XGBoost model predicts SMAP residuals based on the past 12 months, and the hybrid model combines SMAP's streamflow forecast with XGBoost residuals. Notably, the hybrid model outperforms SMAP alone, showing improved correlation and Nash-Sutcliffe index values, especially during periods of lower streamflow. This research highlights the potential of integrating traditional hydrological models with machine learning for more accurate streamflow predictions. studies reality (SMAP Brazil threemonth three month horizon (NMME mapping 198 201 PSO. PSO . (PSO) 2022 1 months SMAPs s Notably alone NashSutcliffe Nash Sutcliffe values predictions 19 20 (PSO 202 2

RESUMO Os modelos hidrológicos tradicionais têm sido amplamente utilizados em estudos hidrológicos, fornecendo representações credíveis da realidade. Este artigo introduz um modelo híbrido que combina o modelo hidrológico tradicional Soil Moisture Accounting Procedure (SMAP) com o algoritmo de aprendizado de máquina XGBoost. Aplicado à bacia de Sobradinho no Brasil, o modelo híbrido tem como objetivo produzir previsões de vazão mais precisas em um horizonte de três meses. Este estudo utiliza previsões de chuvas do North America Multi Model Ensemble (NMME) como entradas do SMAP para produzir previsões de vazão. O estudo avalia as previsões do NMME, corrige viés usando mapeamento de quantis e calibra o modelo SMAP para a região de estudo de 1984 a 2010 usando a Otimização por Enxame de Partículas (PSO). A avaliação do modelo abrange o período de 2011 a 2022. Um modelo XGBoost prevê os resíduos do SMAP com base nos últimos 12 meses, e o modelo híbrido combina a previsão de vazão do SMAP com os resíduos do XGBoost. Notavelmente, o modelo híbrido supera o SMAP sozinho, mostrando melhor correlação e valores do índice Nash-Sutcliffe, especialmente durante períodos de menor vazão. Esta pesquisa destaca o potencial da integração de modelos hidrológicos tradicionais com aprendizado de máquina para previsões de vazão mais precisas. realidade (SMAP Brasil meses NMME (NMME 198 201 PSO. PSO . (PSO) 2022 1 Notavelmente sozinho NashSutcliffe, NashSutcliffe Nash Sutcliffe, Sutcliffe Nash-Sutcliffe 19 20 (PSO 202 2

ABSTRACT This paper introduces a new method for synthesizing guidance laws for anti-tank guided missiles (ATGM) to intercept maneuvering tank targets. It utilizes a nonlinear relative model in the two-dimensional horizontal plane and optimal error dynamics (OED) theory. The nonlinear relative model simplifies the problem of targeting a moving target into attacking a stationary target, making the guidance law synthesis task easier. The selection of OED allows for the design of a guidance command that ensures the zero effort miss (ZEM) error decreases to zero within a finite time, ensuring successful target interception. The paper also introduces an exponential decay weighting function of remaining time-to-go to optimize the distribution of command accelerations throughout the guidance process, thereby reducing initial command requirements and converging acceleration commands towards zero at the end time. The synthesized guidance laws are derived based on the nonlinear relative model and OED without making any small-angle linearization assumptions, allowing them to address various nonlinear scenarios. Numerical simulations illustrate the proposed guidance law’s performance. antitank anti ATGM (ATGM targets twodimensional two dimensional (OED theory easier ZEM (ZEM time interception timetogo go process smallangle small angle assumptions scenarios s performance

Abstract Internet of Things (IoT)-assisted Wireless Sensor Networks (WSNs) integrate traditional WSNs with the expansive ecosystem of IoT devices. This integration enables sensor nodes (SNs) to connect to the internet, facilitating seamless data exchange, remote monitoring, and real-time control of physical environments. IoT-assisted WSNs are crucial in various fields, including industrial automation, smart cities, healthcare, and environmental monitoring. In these networks, sensor nodes near the base station (BS) are responsible for relaying data to nearby nodes and the BS itself, a process that consumes significant energy. This issue, known as the "hotspot problem," arises when certain nodes deplete their energy faster than others. Unequal clustering techniques address this challenge by distributing the energy load more effectively, allowing nodes with higher energy reserves to take on more tasks while conserving the energy of nodes with lower reserves. This study introduces the Tuna Swarm Algorithm-based Energy Efficient Unequal Clustering Approach (TSA-EEUCA) to enhance the performance of IoT-assisted WSNs. The proposed method aims to improve energy efficiency and extend network lifetime by organizing nodes into clusters of unequal sizes. The core of this approach is the Tuna Swarm Algorithm (TSA), inspired by the cooperative foraging behavior of tuna swarms. Unequal cluster formation and cluster head selection are determined by a fitness function that considers both energy levels and distance metrics. To validate the effectiveness of the proposed method, a series of simulations were conducted. The results showed that the proposed method outperforms existing techniques, offering a more efficient and longer-lasting solution for IoT-assisted WSNs. IoTassisted assisted (WSNs devices SNs (SNs internet exchange monitoring realtime real time environments fields automation cities healthcare networks (BS itself issue hotspot problem, problem others effectively Algorithmbased based TSAEEUCA TSA EEUCA (TSA-EEUCA sizes TSA, , (TSA) swarms metrics conducted longerlasting longer lasting (TSA

Abstract The Trust-Aware Aggregation Authentication Protocol for SIoT Networks is a security process intended for SIoT platforms. It concentrates on ensuring the reliability of communication and data aggregation between interrelated IoT devices. This protocol deploys authentication systems for verifying the identities of devices and integrates trust-aware mechanisms to estimate the trustworthiness of data exchanged from the social environment of SIoT. By establishing a trustworthy and secure communication structure, this protocol improves the entire integrity and security of SIoT networks, addressing potential vulnerabilities connected with social communications between IoT devices. Therefore, this study develops an enhanced Trust Management using Locally Weighted Salp Swarm Algorithm with Deep learning (ETM-LWSSADL) technique for SIoT Networks. The ETM-LWSSADL technique computes direct and indirect trust values and is assessed depending upon different weighing factors for maximizing the application performance and creating a secure data transmission process. During authentication process, when the SIoT device with total trust value (TTV)is not greater than the threshold value (THV) or authentication token is invalid, the gateways then disregard the node. Besides, bidirectional gated recurrent unit (BiGRU) model is applied to generate a THV on collected traffic data. Moreover, the ETM-LWSSADL technique exploits the LWSSA technique for optimum hyper parameter selection of the BiGRU algorithm. To highlight the enhanced performance of the ETM-LWSSADL methodology, an extensive range of simulations can be involved. TrustAware Aware platforms trustaware aware structure networks Therefore ETMLWSSADL ETM LWSSADL (ETM-LWSSADL TTVis TTV (THV invalid node Besides (BiGRU Moreover algorithm methodology involved

Abstract The precision agriculture discipline swiftly adopted blockchain as a key technology in numerous applications. From just smart farms to an internet of smart farms in precision farming, the Internet of Things (IoT) and blockchain is going to boost crop yield in precision agriculture. This proposed methodology presents a comprehensive approach for secure data sharing in precision agriculture. It integrates advanced techniques through multiple layers, including Data Collection, Data Preprocessing, Intelligent Analysis, Security, and Blockchain. IoT sensors collect data on soil moisture, temperature, humidity, crop health, and weather conditions. Preprocessing involves removing outliers, normalizing values, and extracting relevant features, then the required features are selected using the hybrid Sand Cat with Fire Hawk Algorithm (HSCFHA) which is the combination of standard Fire Hawk Optimization (FHO) and Sand Cat Swarm Optimization (SCSO). Deep learning models like Capsule Neural Network (CapsNet), Recurrent Neural Network (RNN), and Bi-directional Long Short-Term Memory (Bi-LSTM) networks provide valuable insights for prediction, classification, and anomaly detection. The blockchain layer establishes a decentralized and tamper-resistant ledger for transparent and immutable data transactions. Smart contracts automate and enforce data sharing rules. By incorporating optimized clustering, deep learning models, hybrid encryption, and blockchain technology, this framework empowers precision agriculture while ensuring secure and efficient data sharing with 98.31% of accuracy. applications farming (IoT layers Collection Analysis Security Blockchain moisture temperature humidity health conditions outliers values HSCFHA (HSCFHA FHO (FHO SCSO. SCSO . (SCSO) CapsNet, CapsNet , (CapsNet) RNN, RNN (RNN) Bidirectional Bi directional ShortTerm Short Term BiLSTM LSTM (Bi-LSTM prediction classification detection tamperresistant tamper resistant transactions rules clustering encryption 9831 98 31 98.31 accuracy (SCSO (CapsNet (RNN 983 9 3 98.3 98.