Emerging Science Journal (ESJ)
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Modeling Plasmonics and Electronics in Semiconducting Graphene Nanostrips
Get PDFIn recent decades, both academia and industry have shown noteworthy interest in investigating the semiconducting properties of graphene. Nevertheless, the lack of a suitable bandgap in graphene has restricted its practical applications in the current semiconductor industry. To overcome this limitation, graphene micro/nano-strips have been actively explored. The focus of the present study centers on modeling the electronic and plasmonic characteristics of graphene strips with varying widths: 2.7, 100, 135 nm, and 4 m. This analysis is conducted at ultralow energies (0.3 eV, or ~73 THz). We employ conventional density functional computations to estimate the Fermi velocity of graphene, refining the results via the GW approximation. Utilizing the accurate Fermi velocity, we employ a semi-analytical model to explore the ground state and plasmon properties (frequency and dispersion) of these graphene strips. Notably, this approach effectively replicates the density of states observed in narrow experimental graphene nano-strips (2.7 nm) grown on Ge(001) and, similarly, reproduces the plasmon spectrum found in synthesized graphene microstrips (4 μm) on Si/SiO2. Interestingly, our study also offers insights into the potential application of this approach in comprehending the plasmon frequency and plasmon dispersion of graphene nano-strips (~135 nm) acquired through liquid-phase exfoliation. The outcomes of this investigation present compelling evidence that the properties of graphene-based strips can be customized to fulfill specific requirements and applications. These findings hold significant promise for advancing graphene-based technologies, bridging the gap between fundamental research and tangible applications. Doi: 10.28991/ESJ-2023-07-05-01 Full Text: PD
Factors Affecting Technological Readiness and Acceptance of Induction Stoves: A Pilot Project
Get PDFIn 2022, through the state electricity company, the Indonesian government launched a pilot experiment to cut imports of liquefied petroleum gas by giving program packages to 1,000 families in five districts in Surakarta. Objectives: Using the technology readiness and acceptance model (TRAM), this study examined the elements influencing the readiness and acceptability of the induction stove program in Surakarta. Method/Analysis: The empirical findings from a 389-respondent survey showed that the program's public acceptance was supported by favorable technological preparedness, including elements like innovation and optimism. Findings: Perceived use, enjoyment, usefulness, cost level, and confirmation were all factors that affected participants' happiness and willingness to continue using induction stoves and participating in the program. Interestingly, acceptability, general contentment, and the willingness to use induction stoves were not always affected by issues like discomfort and insecurity. Additionally, this research emphasized how crucial the social context is for successfully implementing a program and embracing new technologies. Novelty:This is the first study that concurrently identifies, assesses, and analyzes the integration of factors impacting technology readiness and acceptance (TRAM) into the community's intention to continue participating in the induction stove conversion program. These empirical results offer practical guidance for stakeholders in induction stove conversion projects, particularly in developing nations, and also add to a theoretical understanding of TRAM factors. Doi: 10.28991/ESJ-2023-07-06-04 Full Text: PD
Sustainable Growth of Greenhouses: Investigating Key Enablers and Impacts
Get PDFThe 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: PD
Developing a Linked Open Data Platform for Folktales in the Greater Mekong Subregion
Get PDFThis research paper presents the development of a linked open data (LOD) platform that aims to organize and facilitate access to valuable knowledge about folktales and ethnic groups in the Greater Mekong Subregion countries. The study's methodology involved the creation of a linked open data platform, structuring folktales' knowledge, and evaluating its performance through expert assessment. The LOD platform was constructed through Google OpenRefine to establish connections with external data sources, and the RDF files (N-Triples) were deployed on Fuseki Server (Apache Jena) to serve as the SPARQL endpoint for querying the linked open data. The Pubby web app was chosen for further development to provide a user-friendly interface, which customized with the Bootstrap framework, featuring an intuitive homepage and a search box function for simplified data retrieval. For the expert evaluation, the study confirmed that the platform performs a high suitability in terms of congruence, reliability, integrity, understandability, collaboration, accessibility, and connectedness. The developed LOD platform exhibits significant potential for expanding its application to various content domains, offering a valuable resource for accessing and exploring the rich cultural heritage of folktales in the Greater Mekong Subregion countries. Doi: 10.28991/ESJ-2023-07-06-06 Full Text: PD
The Effect of COVID-19 on Family Support for Home-Schooling in Urban Areas
Get PDFThe objective of the study is to clarify that the family participated in COVID-19 as a home-schooling family. The sample was selected according to the geographical distribution of schools in the eastern region of Khartoum. Data was collected through a questionnaire and interviews. The most important result is that there is an increasing interest from parents to support home-schooling despite the challenges of continuing learning, such as the widening digital divide in technology, previous experience, and poor network connectivity. This research paper focused on the point of view of parents in the city of Khartoum on supporting home-schooling according to social variables related to both mother and father, and the paper concluded that parents of students in private schools are more supportive of home education. Also, most of the parents of students who support home-schooling have reached their university level of education and post-university and belong to the youth age group. Although parents emphasize the importance of home-schooling during the COVID-19 infection, there are challenges associated with providing home-schooling for their children. Doi: 10.28991/ESJ-2023-07-05-021 Full Text: PD
A Unified Power-Delay Model for GDI Library Cell Created Using New Mux Based Signal Connectivity Algorithm
Get PDFThe challenges of innovative IC technology typically come with various new design constraints in terms of circuit implementation, behaviour, scaling, and an accurate power-delay model to evaluate the circuit's performance. The circuit realization technique using GDI is gaining popularity because of its power and transistor utilization factors. Considering the core advantage of the GDI technique, this research presents the creation of new GDI library cells implemented using the MUX-based algorithm and its delay-power model. This research defines two goals; the former goal depicts the proposal of GDI library cells with full swing using a MUX-based signal connectivity model, and the later presents the mathematical delay-power model for the proposed GDI library cells. The number of attributes defined in the delay and power model incorporates minimum variables without sacrificing precision. It calculates the delay for simple RC networks and combinational circuits with multiple paths. The power model is given using the node activity factor and the power factor related to the internal node capacitances, wiring, and gate capacitances of the driving and receiving GDI nodes. The experimental results of this study, which conform to the specifications of the sub-micron library supported for the SilTerra 130 nm 6-metal layer fabricated for the CMOS n-well process, demonstrate that the proposed GDI library is indeed superior in terms of delay-transistor and power utilisation to PTL and CMOS technology. The simulation results reveal that there is 55 to 65 % improvement in terms of power and delay factor with the existing CMOS and PTL logic. The proposed delay model demonstrates that GDI cells require less logical effort than CMOS technology. The proposed power model shows that the node activity factor of the proposed GDI cells lies between 0.1 and 0.2, while in CMOS, it is between 0.1 and 0.3. Doi: 10.28991/ESJ-2023-07-04-022 Full Text: PD
Impact of Continuing Education on Employee Productivity and Financial Performance of Banks
Get PDFObjectives: This research aims to measure the impact of continuing education on employee productivity and that of the latter on the financial performance of commercial banks in Kosovo. Methods: A quantitative approach was employed to achieve the research objectives and questions. The statistical population comprised 3636 employees working at commercial banks operating in Kosovo. We obtained data from the Central Bank of Kosovo (CBK). A sample of 360 employees was then determined using Slovin's formula to include the representative sample. Findings: The Ordinary Least-Squares (OLS) model demonstrated that continuing education affects employee productivity, and the latter affects the financial performance of commercial banks in Kosovo. The findings indicated that 40.2% of employee productivity is explained by continuing education, while 20.4% of financial performance is explained by employee productivity. Novelty/improvement:This research showed that commercial banks could receive feedback on the importance of employees' continuing education in increasing their productivity and, subsequently, the bank's financial performance. This can improve effectiveness and productivity at work and the organization's financial results, especially cost optimization and income generation. Doi: 10.28991/ESJ-2023-07-04-09 Full Text: PD
Architectural Model and Modified Long Range Wide Area Network (LoRaWAN) for Boat Traffic Monitoring and Transport Detection Systems in Shallow Waters
Get PDFMonitoring the movement of boats in shallow waters requires a real-time monitoring system. However, for small-size wooden boats, they are still monitored manually, and data is unavailable in real time, which makes it difficult to effectively monitor them. The integration of IoT platforms with the boat monitoring system is a challenging task, especially in the transport system. This paper has the objective of developing an architectural model of a modified LoRaWAN-based boat monitoring system that is connected to a GPS-based mobile device and base station. The proposed architectural model is an integration of Bluetooth Low Energy (BLE) and LoRaWAN networks, which are also tested in real time to solve the boat traffic monitoring issues. The field tests with parameters of signal transmission, location coordinates, and position of the boats are also presented. The analysis result shows the proposed model is suitable for waters with high noise levels, especially in shallow water and delta rivers. The signal noise can be reduced by extracting the real-time data. In addition, signal interference can be minimized. The performance of this system is also compared to the reference system in real conditions, which shows an adequate correlation result. This proof of concept forms an important basis for deploying it for large-scale applications and commercialization capabilities. Doi: 10.28991/ESJ-2023-07-04-011 Full Text: PD
The Partial L-Moment of the Four Kappa Distribution
Get PDFStatistical analysis of extreme events such as flood events is often carried out to predict large return period events. The behaviour of extreme events not only involves heavy-tailed distributions but also skewed distributions, similar to the four-parameter Kappa distribution (K4D). In general, this covers many extreme distributions such as the generalized logistic distribution (GLD), the generalized extreme value distribution (GEV), the generalized Pareto distribution (GPD), and so on. To utilize these distributions, we have to estimate parameters accurately. There are many parameter estimation methods, for example, Method of Moments, Maximum Likelihood Estimator, L-Moments, or partial L-Moments. Nowadays, no researchers have applied the partial L-Moments method to estimate the parameters of K4D. Therefore, the objective of this paper is to derive the partial L-Moments (PL-Moments) for K4D, namely the PL-Moments of the K4D in order to estimate hydrological extremes from censored data. The findings of this paper are formulas of parameter estimation for K4D based on the PL-Moments approach. We have derived the Partial Probability-Weighted Moments (PPWMs) of the K4D (β'r) and derive the estimation of parameters when separated by shape parameters (k,h) conditions i.e., case k>-1 and h>0, case k>-1 and h=0 and case -1<k<-1/h and h<0. Finally, we expect that the parameter estimate for K4D from this formula will help to make accurate forecasts. Doi: 10.28991/ESJ-2023-07-04-06 Full Text: PD
IRS-BAG-Integrated Radius-SMOTE Algorithm with Bagging Ensemble Learning Model for Imbalanced Data Set Classification
Get PDFImbalanced 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: PD