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: 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 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 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.

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 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 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 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 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 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: Energy consumption in cloud datacenters is an alarming issue in recent times. Although handful of researches have been conducted in this domain during virtual machine placement in cloud milieu, efficient techniques are still scarce. Hence, we have worked on a novel approach to propose a hybrid metaheuristic technique combining the salp swarm optimization and emperor penguins colony algorithm, i.e. SSEPC to place the virtual machines in the most suitable datacenters as well as servers in a cloud environment, while optimizing the energy consumption. The method we propose has been compared with certain relevant hybrid algorithms in this direction like Sine Cosine Algorithm and Salp Swarm Algorithm (SCA-SSA), Genetic Algorithm and Tabu-search Algorithm (GATA), and Order Exchange & Migration algorithm and Ant Colony System algorithm (OEMACS) to test its efficacy. It is found that proposed SSEPC is consuming 4.4%, 8.2%, and 16.6% less energy as compared to its counterparts GATA, OEMACS, and SCA-SSA respectively.

Abstract: An emergency is a situation that poses an immediate risk to health, life, property, or environment. Most emergencies require urgent intervention to prevent a worsening of the situation. So, it is always better to predict the emergency before its happening and to take action for optimizing the loss. In this work, we tried to predict the flood by analysing the month-wise rainfall index of a particular area. First, we tried to find the months which have more contributions towards predicting the flood. For this, we used Particle Swarm Optimization (PSO) as feature selection technique and then applied classification algorithms such as J48 and Random Forest (RF). The experimentation was done for both without and with feature selection on the considered dataset. The results obtained without feature selection indicate that 70.34% and 78.81% of data are correctly classified and with feature selection 66.10% and 76.27% respectively with J48 and RF classifiers. Then we removed the class attribute from the dataset to see the effect of results when the class is not available and we applied K-means and Density Based clustering techniques on the same dataset. It was observed from the results that K-means with manhattan distance approach and Density Based clustering without feature selection classifies accurately 72.03% and 72.88% of data respectively. Similarly, when K-means and Density Based clustering were used with feature selection, it was found that K-means and Distanced Based clustering result in correct classification of 70.03% and 68.64% of data. We had also compared the model building time for both classification and clustering techniques using without and with feature selection. It was noticed that although the accuracy percentage was decreased with feature selection in both the cases. However, the model building time was reduced by 29%, 50%, 78%, and 60% in case of j48, RF, K-means, and Density Based techniques respectively.

Abstract This paper presents a model that uses bio-inspired optimization algorithms to control of the mutual coupling (MC) in Multiple-Input Multiple-Output (MIMO) systems. The method promotes an increase in the channel throughput and a reduction in the distance among the array elements, searching values less than a half wavelength. A microstrip antenna design, at the frequency of 26 GHz, is used as a prototype for MIMO system testing and performance analysis. Genetic Algorithm (GA) is used to optimize, to determine the antenna physical parameters and to improve its performance. The mutual impedance concept is used to model the MC. The particle swarm optimization (PSO) algorithm is used to optimize the channel throughput and to reduce the distance between the elements. Simulation results show that it is possible to obtain a MIMO system performance improvement of 11.1% in channel throughput, the microstrip width is reduced by 14.5% and the distance among the elements can be reduced by 23.7%, considering the MC. bioinspired bio inspired MC (MC MultipleInput Multiple Input MultipleOutput Output (MIMO systems wavelength design 2 GHz analysis GA (GA PSO (PSO 111 11 1 11.1 145 14 5 14.5 237 23 7 23.7% 11. 14. 23.7 23.

Resumen: Actualmente, varios problemas de optimización del mundo real han sido modelados matemáticamente. El proceso de modelado considera la mayor cantidad de información posible para proporcionar resultados válidos, y el modelo obtenido comúnmente se resuelve computacionalmente. Sin embargo, a medida que aumenta la información, también aumenta la complejidad. En consecuencia, se necesita una mayor capacidad computacional para resolver problemas complejos y escalables. Como resultado, se han desarrollado algoritmos meta-heurísticos para resolver problemas complejos de optimización. Estos algoritmos se usan comúnmente para dos o más dimensiones en las que están involucradas operaciones vectoriales y matriciales. Por lo tanto, es útil realizar procesos paralelos que reduzcan el tiempo de ejecución para solucionar este problema. Actualmente, las unidades centrales de procesamiento (CPU, por sus siglas en inglés) multinúcleo logran resolver fácilmente pequeños problemas con cálculos paralelos. Sin embargo, la unidad de procesamiento de gráficos (GPU, por sus siglas en inglés) mejora el rendimiento porque integra una cantidad de núcleos más importante que la CPU. Es muy útil para resolver problemas utilizando varios procesos en paralelo. Las operaciones matriciales, el problema del vendedor y el problema de planificación de expansión de la transmisión (TEP) han sido seleccionados para implementarse utilizando la GPU para verificar la contribución del procesador al rendimiento de los cálculos científicos. En los resultados, la GPU ayudó a resolver el "problema del vendedor" porque se analizaron más soluciones o partículas candidatas en menos tiempo. Debido a estos resultados, se asumió que habría un mejor rendimiento resolviendo el problema TEP utilizando la GPU y analizando un número mayor de topologías candidatas en menos tiempo. Sin embargo, este no fue el caso; según los resultados, el uso de la GPU lleva más tiempo al analizar más partículas.

Abstract: Currently, several real-world optimization problems have been mathematically modeled. The modeling process considers as much information as possible to provide valid results, and the obtained model is commonly computationally solved. However, as information increases, complexity also increases. Consequently, a larger computational capacity is needed to solve complex and scalable problems. As a result, meta-heuristic algorithms have been developed to solve complex optimization problems. These algorithms are commonly used for two or more dimensions in which vector and matrix operations are involved. Therefore, it is helpful to carry out parallel processes that reduce the runtime to solve this problem. Currently, multi-core central processing units (CPUs) manage to solve small problems with parallel calculations easily. However, the Graphics Processing Unit (GPU) improves performance because it integrates a more significant number of cores than the CPU. It is very useful for solving problems using several processes in parallel. The matrix operations, the Travelling Salesman Problem (TSP), and the electric transmission expansion planning (TEP) problem have been implemented using the GPU to verify the processor's contribution to the performance of scientific calculations. In the results, the GPU helped solve the TSP. Because more solutions or candidate particles were analyzed in less time. Because of these results, it was assumed that there would be a better performance in solving the TEP problem by using the GPU and analyzing a more significant number of candidate topologies in less time. However, this was not the case; according to the results, the use of the GPU takes longer when analyzing more particles.

Abstract: Mobile cloud computing is currently an encouraging field in the cyber-physical world. It is an amalgamation of mobile computing and cloud computing. Computational offloading is one feature in the mobile cloud application that offloads the task to the cloud server, processes it, and gets the results back on the mobile device. During offload, the job needs to be queued on the cloud servers and allocated to the virtual machines. Task scheduling is an important step where the mobile task is assigned to the servers and processed somehow. In the overall offloading process, energy conservation is a significant concern. The scheduling problem involves mapping the offloaded task to the cloud server while satisfying the energy and time constraints. This paper proposes a hybrid scheduling scheme based on Gaussian-based multi-objective particle swarm optimization(GMOPSO) and bacterial foraging optimization(BFO). This scheme performs better when compared to other variants of PSO in terms of makespan and energy efficiency.