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<![CDATA[The Impact of Drug Abuse and Delinquency on Educational Environment Security ]]>
<![CDATA[Vera V. Orlova]]>;
<![CDATA[Larisa V. Shevchenko]]>
<![CDATA[ Emerging Science Journal Vol 7, No 5 (2023): October ]]>
Publisher : <![CDATA[Ital Publication ]]>
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DOI: 10.28991/ESJ-2023-07-05-018
<![CDATA[Creating and maintaining a secure and supportive educational environment is essential for the success and well-being of university students. This study investigates the interplay between drug abuse, delinquency, sociocultural factors, and the security of the educational environment. Drawing upon a sample of 356 students from the Federal State-Funded Institution of Higher Education—Tomsk State University of Control Systems and Radioelectronics (TUSUR), we employed a partial least squares structural equation modeling (PLS-SEM) approach to analyze the data. The findings indicate that sociocultural security significantly influences students' behavioral intentions, with a confirmed negative impact on the intention to commit delinquency (β = -0.461, p < 0.05). Additionally, student well-being demonstrates a significant negative relationship with the intention to use drugs (β = -0.583, p < 0.01) and the intention to commit delinquency (β = -0.714, p < 0.001). However, the impact of sociocultural security and well-being on the intention to use drugs was not confirmed (β = -0.731, p > 0.05). Furthermore, the study reveals that students' behavioral intentions significantly affect the security of the educational environment. The intention to use drugs and the intention to commit delinquency negatively impact the security of the educational environment (β = -0.635, p > 0.05; β = -0.660, p < 0.05, respectively). These findings contribute to the understanding of the complex dynamics that shape the educational environment in universities. The study highlights the importance of promoting sociocultural security and fostering student well-being to prevent negative behavioral intentions and maintain a secure learning environment. Doi: 10.28991/ESJ-2023-07-05-018 Full Text: PDF]]>
<![CDATA[Administrative Empowerment Impact on Enhancing the Leadership Skills for Modern Environment of Judiciary ]]>
<![CDATA[Ahmed Abo Eisheh]]>;
<![CDATA[Moza Al-Ghaithi]]>;
<![CDATA[Faris M. AL-Oqla]]>
<![CDATA[ Emerging Science Journal Vol 7, No 5 (2023): October ]]>
Publisher : <![CDATA[Ital Publication ]]>
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DOI: 10.28991/ESJ-2023-07-05-013
<![CDATA[Administrative empowerment aims to enhance teamwork spirit and expand the scope of supervision by giving workers the necessary powers and ability to make decisions towards sustainable management and decentralization. This study aims to determine the effect of the administrative empowerment context on developing the leadership skills of the second-level leaders in the Council of Administrative Affairs for the Judiciary in the Sultanate of Oman towards a more sustainable modern environment in this sector. Analysis of variances (ANOVA) method was utilized to investigate the effect of several related factors, including delegation of authority, task forces, effective communication, and training. Results have revealed that the current level of administrative empowerment was found medium in the council. However, the level of leadership skills of the second-level leaders was found to be high, but with insignificant utilization. It was also found that there were statistically significant effects for all the dimensions of administrative empowerment on developing the leadership skills of the second-level leaders. The novel results of the impact of delegation of authority on the environment of the judiciary in Oman have several consequences in the field. It can practically change the delegation of authority in the council to enhance administrative empowerment and the skill development of second-level leaders in the judiciary sector. Doi: 10.28991/ESJ-2023-07-05-013 Full Text: PDF]]>
<![CDATA[Optimization of the Extrusion Process in the Production of Compound Feeds for Dairy Cows ]]>
<![CDATA[Rabiga Kassymbek]]>;
<![CDATA[Auyelbek Iztayev]]>;
<![CDATA[Tahir Balevi]]>;
<![CDATA[Urishbay Chomanov]]>;
<![CDATA[Gulzhan Zhumaliyeva]]>;
<![CDATA[Assiya Shoman]]>
<![CDATA[ Emerging Science Journal Vol 7, No 5 (2023): October ]]>
Publisher : <![CDATA[Ital Publication ]]>
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DOI: 10.28991/ESJ-2023-07-05-08
<![CDATA[The paper proposes a way of enriching the composition of mixed fodder by combining two promising technologies with known effects: inclusion of germinated grain in the composition and extrusion. Crude and digestible protein, fiber, fat, and mineral elements Ca, K, Na, and P were studied. Inclusion of sprouted grain into mixed fodder and subsequent extrusion contribute to improvements in taste qualities, increased edibility, and assimilation of mixed fodder, as well as nutritive value. Taking into account the obtained knowledge about the influence of temperature regime and content of germinated triticale grain in mixed fodder production, it is advisable to continue research to adapt the technology and develop formulations for different types of animals and farm birds. The purpose of the study was to optimize the process of extrusion of sprouted triticale grain in order to reduce energy consumption and obtain high-quality extrudates. To achieve this goal, the following tasks were set: to analyze the regime factors affecting the fat content based on the optimization of technological modes of extrusion. To optimize the technology of extrusion of triticale grain of the Kozha variety, the fat content was chosen as the target function. Optimization of the technology of extrusion of triticale grain of the Kozha variety was carried out by the method of nonlinear programming. The following optimal technological modes of grain extrusion were obtained: The content of sprouted triticale grain is 15%, and the extrusion temperature is 140°C. With these optimal grain processing modes, the target function was 1.12%. The practical significance of the technology of the production of compound feeds with the use of extrusion in order to improve the quality and increase the shelf life. Doi: 10.28991/ESJ-2023-07-05-08 Full Text: PDF]]>
<![CDATA[Learning Curves Prediction for a Transformers-Based Model ]]>
<![CDATA[Francisco Cruz]]>;
<![CDATA[Mauro Castelli]]>
<![CDATA[ Emerging Science Journal Vol 7, No 5 (2023): October ]]>
Publisher : <![CDATA[Ital Publication ]]>
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DOI: 10.28991/ESJ-2023-07-05-03
<![CDATA[One of the main challenges when training or fine-tuning a machine learning model concerns the number of observations necessary to achieve satisfactory performance. While, in general, more training observations result in a better-performing model, collecting more data can be time-consuming, expensive, or even impossible. For this reason, investigating the relationship between the dataset's size and the performance of a machine learning model is fundamental to deciding, with a certain likelihood, the minimum number of observations that are necessary to ensure a satisfactory-performing model is obtained as a result of the training process. The learning curve represents the relationship between the dataset’s size and the performance of the model and is especially useful when choosing a model for a specific task or planning the annotation work of a dataset. Thus, the purpose of this paper is to find the functions that best fit the learning curves of a Transformers-based model (LayoutLM) when fine-tuned to extract information from invoices. Two new datasets of invoices are made available for such a task. Combined with a third dataset already available online, 22 sub-datasets are defined, and their learning curves are plotted based on cross-validation results. The functions are fit using a non-linear least squares technique. The results show that both a bi-asymptotic and a Morgan-Mercer-Flodin function fit the learning curves extremely well. Also, an empirical relation is presented to predict the learning curve from a single parameter that may be easily obtained in the early stage of the annotation process. Doi: 10.28991/ESJ-2023-07-05-03 Full Text: PDF]]>
<![CDATA[A Two-Nearest Wireless Access Point-Based Fingerprint Clustering Algorithm for Improved Indoor Wireless Localization ]]>
<![CDATA[Abdulmalik Shehu Yaro]]>;
<![CDATA[Filip Malý]]>;
<![CDATA[Karel Malý]]>
<![CDATA[ Emerging Science Journal Vol 7, No 5 (2023): October ]]>
Publisher : <![CDATA[Ital Publication ]]>
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DOI: 10.28991/ESJ-2023-07-05-019
<![CDATA[Fingerprint database clustering is one of the methods used to reduce localization time and improve localization accuracy in a fingerprint-based localization system. However, optimal selection of initial hyperparameters, higher computation complexity, and interpretation difficulty are among the performance-limiting factors of these clustering algorithms. This paper aims to improve localization time and accuracy by proposing a clustering algorithm that is extremely efficient and accurate at clustering fingerprint databases without requiring the selection of optimal initial hyperparameters, is computationally light, and is easily interpreted. The two closest wireless access points (APs) to the reference location where the fingerprint is generated, as well as the labels of the two APs in vector form, are used by the proposed algorithm to cluster fingerprints. The simulation result shows that the proposed clustering algorithm has a localization time that is at least 45% faster and a localization accuracy that is at least 25% higher than the k-means, fuzzy c-means, and lightweight maximum received signal strength clustering algorithms. The findings of this paper further demonstrate the real-time applicability of the proposed clustering algorithm in the context of indoor wireless localization, as low localization time and higher localization accuracy are the main objectives of any localization system. Doi: 10.28991/ESJ-2023-07-05-019 Full Text: PDF]]>
<![CDATA[Sustainable Growth of Greenhouses: Investigating Key Enablers and Impacts ]]>
<![CDATA[Akmal Durmanov]]>;
<![CDATA[Nodira Saidaxmedova]]>;
<![CDATA[Murodjon Mamatkulov]]>;
<![CDATA[Khavakhon Rakhimova]]>;
<![CDATA[Nazimjon Askarov]]>;
<![CDATA[Shakhnoza Khamrayeva]]>;
<![CDATA[Abdukholik Mukhtorov]]>;
<![CDATA[Shakhida Khodjimukhamedova]]>;
<![CDATA[Talantbek Madumarov]]>;
<![CDATA[Khurshida Kurbanova]]>
<![CDATA[ Emerging Science Journal Vol 7, No 5 (2023): October ]]>
Publisher : <![CDATA[Ital Publication ]]>
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DOI: 10.28991/ESJ-2023-07-05-014
<![CDATA[The main objective of this study was to identify the factors influencing greenhouse development in Uzbekistan. Supported by the literature, the conceptual model of the study hypothesized that economic viability, supportive infrastructure services, enablers, and competition impacts positively affect greenhouse development. Therefore, a questionnaire was administered among 200 individuals working in greenhouses across the Tashkent, Syrdarya, Jizzakh, and Bukhara regions. Quantitative empirical evidence using structural equation modeling revealed that enablers and competition impacts have a significant positive influence on greenhouse development. However, economic viability and supportive infrastructure services did not have a direct impact, although they indirectly contributed to the overall growth and functioning of the greenhouse industry. The study provides theoretical contributions by identifying key factors influencing greenhouse development and offers practical recommendations for policymakers and stakeholders to foster an enabling environment, manage competition effectively, enhance supportive infrastructure, promote international collaboration and investment, encourage research and development, and strengthen market linkages. This research contributes to the understanding of greenhouse development in Uzbekistan and provides insights for evidence-based decision-making and strategic planning in the industry. Doi: 10.28991/ESJ-2023-07-05-014 Full Text: PDF]]>
<![CDATA[The Magic of Brand Experience: A Value Co-creation Perspective of Brand Equity on Short-form Video Platforms ]]>
<![CDATA[Athapol Ruangkanjanases]]>;
<![CDATA[Asif Khan]]>;
<![CDATA[Ornlatcha Sivarak]]>;
<![CDATA[Untung Rahardja]]>;
<![CDATA[Shih-Wen Chien]]>;
<![CDATA[Shih-Chih Chen]]>
<![CDATA[ Emerging Science Journal Vol 7, No 5 (2023): October ]]>
Publisher : <![CDATA[Ital Publication ]]>
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DOI: 10.28991/ESJ-2023-07-05-09
<![CDATA[Many companies are utilizing short-form video platforms to enhance the value co-creation between brands and consumers and ultimately create brand equity. However, there is a research gap in the present literature regarding the significance of consumers' participation in short-form video platforms affecting corporate brand equity. The present study aims to address this research gap by incorporating value co-creation theory to investigate the role and mechanism of value co-creation generated by consumer participation in short-form video platforms, i.e., corporate-initiated value co-creation and customer-initiated value co-creation, on corporate brand equity. The current study used a survey methodology to collect 481 samples from TikTok users using a convenience sampling method. The present study used structural equation modeling on the collected data. The findings show that corporate-initiated value co-creation can positively influence corporate brand equity, in which brand experience plays a partially mediating role. Customer-initiated value co-creation can also promote brand equity, whose role is also partially mediated by brand experience. This study theoretically contributes to the value co-creation concept and its theoretical association with brand equity and brand experience. This research also offers practical implications for practitioners regarding the utilization and enhancement of brand experience to improve clients' engagement in value co-creation activities. Doi: 10.28991/ESJ-2023-07-05-09 Full Text: PDF]]>
<![CDATA[IRS-BAG-Integrated Radius-SMOTE Algorithm with Bagging Ensemble Learning Model for Imbalanced Data Set Classification ]]>
<![CDATA[Lilis Yuningsih]]>;
<![CDATA[Gede Angga Pradipta]]>;
<![CDATA[Dadang Hermawan]]>;
<![CDATA[Putu Desiana Wulaning Ayu]]>;
<![CDATA[Dandy Pramana Hostiadi]]>;
<![CDATA[Roy Rudolf Huizen]]>
<![CDATA[ Emerging Science Journal Vol 7, No 5 (2023): October ]]>
Publisher : <![CDATA[Ital Publication ]]>
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DOI: 10.28991/ESJ-2023-07-05-04
<![CDATA[Imbalanced learning problems are a challenge faced by classifiers when data samples have an unbalanced distribution among classes. The Synthetic Minority Over-Sampling Technique (SMOTE) is one of the most well-known data pre-processing methods. Problems that arise when oversampling with SMOTE are the phenomenon of noise, small disjunct samples, and overfitting due to a high imbalance ratio in a dataset. A high level of imbalance ratio and low variance conditions cause the results of synthetic data generation to be collected in narrow areas and conflicting regions among classes and make them susceptible to overfitting during the learning process by machine learning methods. Therefore, this research proposes a combination between Radius-SMOTE and Bagging Algorithm called the IRS-BAG Model. For each sub-sample generated by bootstrapping, oversampling was done using Radius SMOTE. Oversampling on the sub-sample was likely to overcome overfitting problems that might occur. Experiments were carried out by comparing the performance of the IRS-BAG model with various previous oversampling methods using the imbalanced public dataset. The experiment results using three different classifiers proved that all classifiers had gained a notable improvement when combined with the proposed IRS-BAG model compared with the previous state-of-the-art oversampling methods. Doi: 10.28991/ESJ-2023-07-05-04 Full Text: PDF]]>
<![CDATA[ARL Evaluation of a DEWMA Control Chart for Autocorrelated Data: A Case Study on Prices of Major Industrial Commodities ]]>
<![CDATA[Yadpirun Supharakonsakun]]>;
<![CDATA[Yupaporn Areepong]]>
<![CDATA[ Emerging Science Journal Vol 7, No 5 (2023): October ]]>
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DOI: 10.28991/ESJ-2023-07-05-020
<![CDATA[The double exponentially weighted moving average (DEWMA) control chart, an extension of the EWMA control chart, is a useful statistical process control tool for detecting small shift sizes in the mean of processes with either independent or autocorrelated observations. In this study, we derived explicit formulas to compute the average run length (ARL) for a moving average of order q (MA(q)) process with exponential white noise running on a DEWMA control chart and verified their accuracy by comparison with the numerical integral equation (NIE) method. The results for both were in good agreement with the actual ARL. To investigate the efficiency of the proposed procedure on the DEWMA control chart, a performance comparison between it and the standard and modified EWMA control charts was also conducted to determine which provided the smallest out-of-control ARL value for several scenarios involving MA(q) processes. It was found that the DEWMA control chart provided the lowest out-of-control ARL for all cases of varying the exponential smoothing parameter and shift size values. To illustrate the efficacy of the proposed methodology, the presented approach was applied to datasets of the prices of several major industrial commodities in Thailand. The findings show that the DEWMA procedure performed well in almost all of the scenarios tested. Doi: 10.28991/ESJ-2023-07-05-020 Full Text: PDF]]>
<![CDATA[Ocular Microbiota of Severe Meibomian Gland Dysfunction (Chronic Dry Eyes) after Intense Pulsed Light (IPL) ]]>
<![CDATA[Lampet Wongsaroj]]>;
<![CDATA[Krit Pongpirul]]>;
<![CDATA[Attawut Watthanathirakawi]]>;
<![CDATA[Nattawut Wanumkarng]]>;
<![CDATA[Anchana Iam-a-non]]>;
<![CDATA[Deborah Dean]]>;
<![CDATA[Naraporn Somboonna]]>
<![CDATA[ Emerging Science Journal Vol 7, No 5 (2023): October ]]>
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DOI: 10.28991/ESJ-2023-07-05-015
<![CDATA[Ocular IPL therapy has recently been widely used for MGD, especially for patients not showing improvement with traditional therapies (warm compresses and lid scrubs) to clean debris and reduce bacterial overgrowth. Insights on the ocular microbiome and quantitative microbiome in MGD after a course of IPL could provide useful data on bacterial community monitoring and associated mechanisms linked with IPL. Ocular swabs were obtained from a severe MGD patient and age-sex matched healthy for metagenomics, followed by 16S rRNA gene sequencing and qPCR. Of 10 samples, including left and right eyes (el, er) of severe MGD females before (Db) and after 2-4 IPLs (Da2, Da3, and Da4) and the matched non-MGD females (H), both of ~40 years Using 16S rRNA gene sequencing as microbiota and combined 16S rRNA gene qPCR as quantitative microbiota revealed significant disperse in the microbiome structures of Db compared with Da and H (HOMOVA, p<0.001). Bacterial Propionibacterium acnes and unclassified taxa in the family Propionibacteriaceae and order Actinomycetales represented the core Db microbiota and were reduced after 2-4 IPLs in Da, making the Da microbiome and clinical (mucocutaneous junction, corneal, and conjunctival fluorescein score) closer to H (NMDS with Pearson’s correlation, p<0.05). The recovery of the Da microbiome also allowed Da metabolic potentials to be closer to H. Our findings first demonstrated the ocular microbiome dysbiosis in severe MGD, dispersed from Da and H, in Thai subjects, correlated with bacterial quantity and clinical MGD, including the mucocutaneous junction process. The results additionally provided taxa representing Db vs. Da and H and preliminarily underlie the idea that IPL could improve dysbiosis in the MGD microbiome. Doi: 10.28991/ESJ-2023-07-05-015 Full Text: PDF]]>
