Periodicals of Engineering and Natural Sciences (PEN - International University of Sarajevo)
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Happiness and organizational commitment in the workers of the fishing sector of the city of Chimbote - 2023
Get PDFIn this study, the relationship between happiness and organizational commitment in workers in the fishing sector of the City of Chimbote was investigated. A correlational quantitative approach and a non-experimental cross-sectional research design were used to determine the association between these two con-structs and to verify if there is a significant relationship between them. To collect data, a survey was applied to a sample of 342 workers in the fishing sector. Two measurement instruments were used to assess happi-ness and organizational commitment. The participants provided information about their level of subjective happiness and their degree of commitment to the organization in which they worked. The results revealed a significant positive correlation between happiness and organizational commitment. These findings support the idea that happiness in the workplace can have a positive impact on employee engagement with the or-ganization. These results are consistent with previous research that has also found a positive relationship between happiness and organizational commitment. This suggests that promoting happiness at work can be beneficial in fostering employee commitment to the organization. However, it is important to note that this study has its limitations. It focused on a specific industry and a particular geographic location, so the results may not be generalizable to other industries or locations. In addition, a self-report measure of happiness was used, which may be subject to bias and limitations. In future research, it would be useful to explore these relationships in different work contexts and consider using more objective measures or various sources to assess happiness. This would help to obtain a more complete and generalizable understanding of how happi-ness at work is related to organizational commitment
A comparative study between shrinkage methods (ridge-lasso) using simulation
Get PDFThe general linear model is widely used in many scientific fields, especially biological ones. The Ordinary Least Squares (OLS) estimators for the coefficients of the general linear model are characterized by good specifications symbolized by the acronym BLUE (Best Linear Unbiased Estimator), provided that the basic assumptions for building the model under study are met. The failure to achieve one of the basic assumptions or hypotheses required to build the model can lead to the emergence of estimators with low bias and high variance, which results in poor performance in both prediction and explanation of the model in question. The hypothesis that there are no multiple linear relationships between the explanatory variables is consid-ered one of the leading hypotheses on which the model is based. Thus, the emergence of this problem leads to misleading results and high (Wide) confidence limits for the estimators associated with those variables due to problems characterizing the model. Shrinkage methods are considered one of the most effective and preferable ways to eliminate the multicollinearity problem. These methods are based on addressing the mul-ticollinearity problems by reducing the variance of estimators in the model. Ridge and Lasso methods repre-sent the most and most common of these methods of shrinkage. The simulation was carried out for different sample sizes (40, 120, 200) and some variables (P=30, 60) in the first and second experiments arbitrarily and at the level of low, medium, and high correlation coefficients (0.2, 0.5, 0.8). When (p=30, 60) Lasso method has the smallest (MSE) than the Ridge method. The Lasso method proved its efficiency by obtaining the least MSE. Optimal Penalty parameter (λ) chosen from Cross-Validation through minimizing (MSE) of prediction. We see a rapid increase for (MSE) for both (Ridge-Lasso) where the top axis indicates the num-ber of model variables, and when the correlation between variables increases and sample size too, we can see the (MSE) values increase in the Ridge method than the Lasso method. A ridge method gives greater efficiency when the sample size is more significant than variables (p<n), but the Ridge method cannot shrink coefficients to precisely zero. So, the elasticity of ridge coefficients decreases, but variance increases bias, also (MSE) first remains relatively constant and then increases fast.
 
Development of a monitoring system for COVID-19 monitoring in early stages
Get PDFCovid-19 is considered the most infectious virus today. Likewise, the struggle to mitigate the effects of the variants, the flexibility in some measures such as the use of face masks, the advancement of vaccination and prevention and self-care campaigns continue to be topics of research and of global interest. The world health authorities published that the disease was characterized by presenting the same symptoms as the flu along with a complex picture where in the most serious cases they lead to difficulty breathing due to pneumonia, sepsis and septic shock that can lead to death. Some systems implemented for taking body temperature such as thermographic cameras, digital thermometers, for the description of symptoms in the people they analyze at the time of carrying out the epidemiological fences are not enough, since they handle low precision, are taken in isolation, individually or randomly and is not suitable for characterizing interest groups. Then, establishing risk levels by measuring non-invasive variables can be considered inputs into prevention campaigns and a low-cost way of monitoring the community. This article shows the design of a non-invasive embedded device for the measurement of 5 priority variables for the detection of the risk of covid-19 infection. The proposed device was duly calibrated and synchronized for the acquisition of data from 594 people in the city of Bucaramanga, Colombia, who authorize the monitoring of the symptoms. The people must be in a state of rest to be able to acquire the data with great accuracy, in this way the data is entered into the system in charge of doing the monitoring analysis. Additionally, the implementation of an interface that allows the visualization of results, laying the foundations for the development of automatic learning techniques or models for the risk classification in future work
Analysis of the thermal sensation in single-family home from microclimatic monitoring: Case study Bucaramanga Colombia
Get PDFThe present work describes the experimentation of monitoring for the finding of the heat index and the indi-vidual thermal sensation, where the behavior of the real temperature in the exterior and interior of a conven-tional single-family house with a warm and temperate climate in Colombia was analyzed. The detailed mon-itoring campaign is carried out for 2880 hours, where the conditions of the interior area of the house and the local climatic conditions of the area are recorded, through the implementation of a thermohydrometers regis-tration system. The methodology for the calculation of the sensation of heat and thermal comfort was deter-mined under the adjusted equation of cooling power of Leonardo Hill and Morikofer-Davos, applied in the analyses of the Institute of Hydrology, Meteorology and Environmental Studies - IDEAM. The results showed a thermal sensation of dissatisfaction of 97.7%, because with the monitored temperature the thermal sensation is calculated yielding in 1382.4 hours with very hot, 1151.6 hours of hot and 280.8 hours of warmthermal sensation
Decision support system based on BWM for Analyzing success factors affecting the quality in the Iraqi construction projects
Get PDFCreating a balance between cost, time, and quality in construction projects is always expected. It is possible to have a project with excellent quality and minimal cost, but at the expense of time, or vice versa. The goal of this paper is to discover, evaluate and prioritize the factors that most influence the desired construction projects\u27 level of quality (success factors) in Iraq. Over a comprehensive review of literature, 11 potential quality-related factors were found to fall into the following five categories: client, contractor, design, mate-rials, and project related factors. These factors\u27 significance was determined using fuzzy Best Worst Method (BWM). Result shows the most three significant success factors influencing quality in the construction pro-jects were related to contractor, client, and designer. These factors were financial competence of contractor, technical capability of client, and designer suitable selection with weights (30.84%, 15.58%, and 10.05%) respectively. These results conclude that maximization of the success factors will guarantee that the building sector achieves its quality objectives
A new shrinkage method for higher dimensions regression model to remedy of multicollinearity problem
Get PDFThis research seeks to present new method of shrinking variables to select some basic variables from large data sets. This new shrinkage estimator is a modification of (Ridge and Adaptive Las-so) shrinkage regression method in the presence of the mixing parameter that was calculated in the Elastic-Net. The Proposed estimator is called (Improved Mixed Shrinkage Estimator (IM-SHE)) to handle the problem of multicollinearity. In practice, it is difficult to achieve the re-quired accuracy and efficiency when dealing with a big data set, especially in the case of multi-collinearity problem between the explanatory variables. By using Basic shrinkage methods (Las-so, Adaptive Lasso and Elastic Net) and comparing their results with the New shrinkage method (IMSH) was applied to a set of obesity -related data containing (52) variables for a sample of (112) observations. All shrinkage methods have also been compared for efficiency through Mean Square Error (MSE) criterion and Cross Validation Parameter (CVP). The results showed that the best shrinking parameter among the four methods (Lasso, Adaptive Lasso, Elastic Net and IMSH) was for the IMSH shrinkage method, as it corresponds to the lowest (MSE) based on the cross-validation parameter test (CVP). The new proposed method IMSH achieved the optimal shrinking parameter (λ = 0.6932827) according to the (CVP) test, that leads to have minimum value of mean square error (MSE) equal (0.2576002). The results showed when the value of the regularization parameter increases, the value of the shrinkage parameter decreases to become equal to zero, so the ideal number of variables after shrinkage is (p=6)
Profile of digital literacy of mathematics education students in online learning and its relationship with learning motivation
Get PDFThis study aims to determine the digital literacy profile of Mathematics Education students at Universitas Negeri Makassar in online learning and its relationship with learning motivation. The research was designed with a quantitative approach ex-post facto model with the main method of survey. The research was con-ducted on Mathematics Education students at Universitas Negeri Makassar. The result of the studies: (1) mathematics education students have a high level of digital literacy, (2) mathematics education students have a high level of learning motivation, and (3) the relationship between digital literacy and learning moti-vation has a linear relationship and strong correlation between digital literacy and learning motivation
Estimation and prediction of temperature in Iraq using the multi-layered neural network model
Get PDFThe forecasting using the multi-layered neural network model is one of the methods used recently in fore-casting, especially in climate forecasts for certain regions, because of its accuracy in forecasting, which sometimes reaches levels close to the real collected data. In this research, the daily temperatures in the cli-mate of Iraq were predicted, by taking data from the Iraqi Meteorological Authority by (228) observations, which represent the daily temperatures of Karbala Governorate in the year (2021), The results of the auto-correlation and partial autocorrelation showed that the daily temperature series of Karbala governorate is unstable, and this was confirmed by conducting the augmented Dickey Fuller test. The data was analyzed using the multi-layered neural network model in two stages, and it was later shown that the accuracy of estimation and prediction using the multi-layered neural network even if the time series is not stable, The results showed an indication of an rising increase in temperatures during the coming years. The researcher concluded that it is necessary to pay attention to the vegetation cover and to conduct many predictive studies of the climate using the multi-layered neural network.
 
Evaluation of shear behavior of prepared recycled concrete aggregate concrete deep beam
Get PDFIn this article, the shear behavior of a deep beam made of Recycled Aggregate Concrete (RAC) was analyzed. Rapid urbanization has presented a massive new activity that is necessary to meet the needs of the influx of people. Developments of all types, from housing to infrastructure, ne-cessitate considerable input from both natural and monetary resources. The purpose of this study is to compare the strength and loading capacity of RAC to that of Naturally Aggregate Concrete (NAC). The samples were evaluated at a controlled deformation rate of 2mm/minute in the "Ma-terial Testing Laboratory of the Department of Civil Engineering," where this investigation was conducted. The researcher has chosen two different sizes of coarse totals to use throughout this study: those measuring 5mm to 15mm (60.2%) and those measuring 15mm to 25mm (40.3%). In support of her claims, the researcher presents a variety of charts and datasets in the following research. There is an overall drop in strength in the recycled aggregate concrete samples. The load-deflection curves and the techniques are depicted by which the specimens failed. Shear re-quired beams\u27 experimental data and predicted values. This study reveals that compared to natu-ral aggregate concrete, recycled aggregate concrete has weaker compressive, flexural, and break-ing tensile strengths. The maximum load-bearing strength of longitudinally supported beams built of "recycled and natural aggregate concrete" is also not significantly different
Enhancing quality of service in IoT through deep learning techniques
Get PDFWhen evaluating an Internet of Things (IoT) platform, it is crucial to consider the quality of service (QoS) as a key criterion. With critical devices relying on IoT technology for both personal and business use, ensur-ing its security is paramount. However, the vast amount of data generated by IoT devices makes it challeng-ing to manage QoS using conventional techniques, particularly when attempting to extract valuable charac-teristics from the data. To address this issue, we propose a dynamic-progressive deep reinforcement learning (DPDRL) technique to enhance QoS in IoT. Our approach involves collecting and preprocessing data sam-ples before storing them in the IoT cloud and monitoring user access. We evaluate our framework using metrics such as packet loss, throughput, processing delay, and overall system data rate. Our results show that our developed framework achieved a maximum throughput of 94%, indicating its effectiveness in im-proving QoS. We believe that our deep learning optimization approach can be further utilized in the future to enhance QoS in IoT platforms