Emerging Science Journal (ESJ)
Not a member yet
960 research outputs found
Sort by
A Digital Model of Full-Cycle Training Based on the Zettelkasten and Interval Repetition System
Get PDFThe study aims to propose and validate a new digital model based on the Zettelkasten and interval repetition system for comprehensive and full-cycle training of the students. The core idea is to enhance the learning experience and effectiveness of the given training course by enhancing the student's attention during the learning and long-term information retention. In pursuit of the aforementioned research aim, the study incorporates a quantitative research methodology by combining experiments and a survey. In particular, the effectiveness of the proposed model was assessed via an achievement assessment involving two groups of students and assessing their scores on the same test. This was followed by a metacognitive awareness survey of the two groups to investigate their perceived understanding and performance (with and without the use of a model). The proposed model was found to be effective in enhancing the learning experience and effectiveness of the students on the training course. The Zettelkasten facilitates the management of the student's attention, while the interval repetition system contributes to increased retention. The students that used this model in their learning and preparation scored better than their peers. Also, they reported a significantly higher understanding and awareness of their learning than their peers. The model can be incorporated into the learning process or the provision of training courses to the students. This study is the first to suggest the integration of Zettelkasten and the interval repetition system into one learning model for the students. The article proposes a practical model that can be incorporated by teachers to improve the learning effectiveness of their students. This article has some limitations as well that must be acknowledged. Doi: 10.28991/ESJ-2023-SIED2-01 Full Text: PD
The Role of Leadership in Digital Learning Organizations
Get PDFThe study aims to examine the relationship and interaction between learning organization culture and the factors influencing digital transformation (leadership style, training, digital readiness, and trust), as well as identify factors that significantly influence learning organization functioning by exploring the extension of a grounded theory framework. The survey was conducted using an online questionnaire. The survey population was composed of managers of Eastern European manufacturing companies who were reached through the Orbis database. The survey yielded 618 evaluable responses (n = 618). The PLS-SEM method was used because the structural model is complex, with many constructs (some of which are formatively measured) and model relationships. Leadership behavior and a supportive management style inspire the development and training of employees, through which the level of readiness for digitalization and Industry 4.0 technologies can be increased. Training in these skills will increase confidence in digitalization technologies. Leadership support also influences digital trust and employee response to the use of digital technologies, as does participation in training, which directly supports digitalization and I4.0 readiness. The results of the research not only support previous research findings but also complement them by focusing specifically on the impact on the learning organization in the context of digitalization. This study provides evidence that leadership that is supportive of the learning organization's culture plays a key role. Overall, leadership is a dominant influence in the digital transformation of organizations and in shaping the learning organization culture this requires, but all the relationships represented in the model have a significant positive relationship. Doi: 10.28991/ESJ-2023-SIED2-09 Full Text: PD
A Two-Nearest Wireless Access Point-Based Fingerprint Clustering Algorithm for Improved Indoor Wireless Localization
Get PDFFingerprint 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: PD
Learning Curves Prediction for a Transformers-Based Model
Get PDFOne 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: PD
Immobilization of Aspergillus fumigatus α-Amylase via Adsorption onto Bentonite/Chitosan for Stability Enhancement
Get PDFStability enhancement attempted in this study demonstrated that significant improvement in the stability of the α-amylase isolated from Aspergillus fumigatus was achieved by immobilizing the enzyme on a bentonite/chitosan hybrid matrix using the adsorption method. Centrifugation was used to isolate the α-amylase, which was then refined using (NH4)2SO4 salt precipitation and dialysis. The purity of the α-amylase improved 19.40 times when compared to that of the crude extract. The optimal temperature for free α-amylase is 50ËšC, while the optimum temperature for α-amylase/bentonite/chitosan is 60ËšC. The KM value of α-amylase/bentonite/chitosan was 1.69 ± 0.08 mg mL-1 substrate and the Vmax value was 52.32 ± 3.29 µmol mL-1 min-1, whereas for free α-amylase, the KM value of 2.56 ± 0.09 mg mL-1 substrate and the Vmax value of 3.78 ± 0.09 µmol mL-1 min-1 were obtained. The ΔGi value of free α-amylase is 102.68 ± 0.30 kJ mol-1 and the t½ is 21.23 ± 0.23 min, whereas the ΔGi value of α-amylase/bentonite/chitosan is 104.43 ± 0.00 kJ mol-1 and the t½ is 94.29 ± 0.91 min. The higher values of ΔGi and t½demonstrated that α-amylase/bentonite/chitosan has better stability than that of free α-amylase. Another important finding is that α-amylase /bentonite/chitosan was able to retain their activity as high as 47.61 ± 0.53% after six recycles, indicating that the enzyme has the potential to be used in industry. Doi: 10.28991/ESJ-2023-07-05-023 Full Text: PD
A Data Science Maturity Model Applied to Students' Modeling
Get PDFMaturity models define a series of levels, each representing an increased complexity in information systems. Data Science appears in the Business Intelligence (BI) and Business Analytics (BA) literature. This work applies the _IABE maturity model, which includes two additional levels: Data Engineering (DE) at the bottom and Business Experimentation (BE) at the top. This study uses the _IABE model for students' modeling in the ModEst project. For this purpose, the Public Administration organism is the Directorate-General for Statistics of Education and Science (DGEEC) of the Portuguese Education Ministry. DGEEC provided vast data on two million students per year in the Portuguese school system, from pre-scholar to doctoral programs. This work presents the comprehensible _IABE maturity model to extract new knowledge from the DGEEC dataset. The method applied is _IABE, where after the DE level, wh-questions are formulated and answered with the most appropriate techniques at each maturity level. This work's novelty is applying the maturity model _IABE to a unique dataset for the first time. Wh-questions are stated at the BI level using data summarization; at the BA level, predictive models are performed, and counterfactual approaches are presented at the BE level. Doi: 10.28991/ESJ-2023-07-06-08 Full Text: PD
Enhancing Global Health System Resilience and Sustainability Post-COVID-19: A Grounded Theory Approach
Get PDFBased on a grounded theory approach to healthcare professionals' experience, this study conducted a qualitative assessment of new opportunities and provided a conceptual justification of forward-looking trends of global health system efficiency improvement and resilience toward universal health in the post-COVID-19 era. The data were collected through semi-structured interviews, while theoretical saturation was reached after 20 interviews with international experts in Global Health System Resilience (psychologists, nurses, and community health consultants). The data were analyzed using four coding stages: initial, focused, axial, and theoretical. The results showed that to achieve global health system resilience toward universal health and safety coverage in the post-COVID-19 era, it is required to develop a strategy including the support of healthcare professionals, which allows coping with the emotional and psychological impact of the pandemic. Moreover, opportunities for professional development and collaboration can enhance their resilience and adaptability during and after crises. The findings suggest factors of global health system resilience ranked using the AHP method. The present research provides a valuable insight into the significance of telemedicine, remote care, and ensuring the safety of the healthcare industry and patients as the most crucial factors for enhancing health system sustainability. This article provides new policy recommendations to ensure the long-term effectiveness and sustainability of health systems. Doi: 10.28991/ESJ-2023-07-06-011 Full Text: PD
Fear and Group Defense Effect of a Holling Type IV Predator-Prey System Intraspecific Competition
Get PDFField and experimental data on aquatic ecosystem species show the effect of fear on changing prey demographics. The fear effect has an impact on aquatic ecosystems, such as species migration to settled areas. In this paper, the type of research described is a literature study. The cost effect assigned to the reproductive system of the prey population and the predation function response are given as Holling Type IV for research purposes to model the fear effect. Some research novelties, the equilibrium points are all shown in the population dynamics system model with an analysis of positive equilibrium. Positive and biologically realistic equilibrium points were analyzed using the Routh-Hurwitz criterion which is mathematically a local asymptotically stable. A pair of imaginary eigenvalues with a negative real part can increase population growth. An equilibrium region showing equilibrium for several parameters such as extinction, no predators, and two populations coexisting in a sustainable manner. The correlation and fluctuation of fear and fear cost were investigated to obtain a better model. The results of the numerical simulations show that the prey population becomes more daring to fight or fighting power with significant prey growth rates or high predator mortality rates. Doi: 10.28991/ESJ-2023-07-02-06 Full Text: PD
Exploring the Asymmetric Effect of Internal and External Economic Factors on Poverty: A Fresh Insight from Nonlinear Autoregressive Distributive Lag Model
Get PDFObjective: This study examines the asymmetric impact of both internal (military, education, and health expenditures) and external (trade opening and foreign direct investment) factors that contribute to poverty reduction. Methodology: To find an asymmetric relationship between the proposed variables, we used a non-linear ARDL co-integration approach for the period ranging from 1981-2019. Findings: The findings of the study confirm the asymmetric impact of internal (education, military, health expenditures, quality of governance) and external (foreign direct investment, openness) factors on poverty. The finding confirms that ignoring nonlinear or asymmetric properties of macroeconomic variables may mislead inferences. This study has policy implications for government officials to reduce poverty. Novelty: theeconomic theory of poverty is studied from different perspectives by using internal and external factors that have direct and indirect effects on poverty. Furthermore, for in-depth analysis, a nonlinear approach is used to determine which factor has a strong contribution to eliminating poverty. Doi: 10.28991/ESJ-2023-07-03-07 Full Text: PD
Reaction of Carbon Dioxide Gas Absorption with Suspension of Calcium Hydroxide in Slurry Reactor
Get PDFChemical phenomena involving three phases (solid, liquid, and gas) are often found in the industry. Carbonate (CaCO3) is widely used in industries as a powder-making material in the cosmetic industry, a pigment in the paint industry, and filler in the paper and rubber industry. This research aim to study the ordering process carbonate deposits (CaCO3) from the absorption process of CO2 gas with Ca(OH)2 suspension. The absorption reaction of CO2 gas with Ca(OH)2 suspension was carried out in a stirred slurry tank reactor. Initially, the reactor containing water was heated to a certain temperature, then Ca(OH)2 was added to the reactor. Furthermore, CO2 gas with a certain flow rate and temperature (according to the reactor temperature) is flown with the help of a gas distributor. Samples were taken every 1 min until the concentration of Ca(OH)2 could not be detected (completely reacted). The variables in this study were: stirrer rotation speed (5.66711.067 rps), CO2 gas flow rate (34.0127–60.5503 c/s), and temperature (30–50°C). The mass transfer coefficient and the reaction rate coefficient were determined by minimizing Sum of Squares of Errors (SSE). This experimental process follows a dynamic regime. A dimensionless number relationship for the gas-liquid mass transfer for the value range is Re1 = 18928.76-38217.20, Sh = 0.07928 Reg0.4383 Rel0.4399 Sc0.6415 with an error of 5.19%. The dimensionless number relationship for solid-liquid mass transfer is Sh = 0.0001179 Reg0.4674 Rel0.5403 Sc1.444 with an error of 7.31%. The relationship between the reaction rate constant and the temperature in the 30-50 °C range can be approximated by the Arrhenius equation, namely kr = 1771000 e-2321.4/T cm3/mgmol/s with an error of 3.63%. Doi: 10.28991/ESJ-2023-07-02-02 Full Text: PD