bluuebunny/crossref_metadata_2025 huggingface dataset:
СИНЕРГЕТИЧЕСКИЙ АНАЛИЗ ИННОВАЦИЙ И ИННОВАЦИОННОЙ ДЕЯТЕЛЬНОСТИ
Authors: Ли Екатерина
Published: April 2024

В статье рассматривается проблема инноваций в преломлении синергетического метода и анализируются понятия синергия (синергизм) и эмерджентность. Под эмерджентностью и синергией понимается появление новых качеств, ранее не свойственных системе. То есть, новое качество, которое не свойственно было частям по отдельности, имеющее качество новизны. Инновации представляют собой сложные, самоорганизующиеся системы, в которую включены элементы, системы, сети. Целью данной статьи является анализ проблемы инноваций и инновационной деятельности с позиции синергетики, то есть системы, имеющей сложную структуру, множество элементов, подсистем. Научные исследования проблемы инноваций и синергетики разработаны в мировой философско-методологической и научной литературе представителями различных областей наук и апробированы в научных статьях и монографиях. Для достижения научной цели и решения поставленных задач были использованы следующие методы: диалектический, синергетический, аналитический, методы, системный подход. В результате проведенного философского анализа, показано, что инновации и инновационная деятельность являются сложной, открытой, самоорганизующейся системой, которой характерны: устойчивость и динамичность, саморазвитие, эмерджентность, необратимость и др. Также проведен сравнительный анализ понятий эмерджентность, синергия, синергизм, синергетический эффект.
TECHNOLOGY FOR DEVELOPING COMMUNICATIVE COMPETENCE OF STUDENTS IN HIGHER TECHNICAL EDUCATION FIELDS (IN THE EXAMPLE OF ENGLISH LANGUAGE)
Authors: Nazarov Rustam
Published: April 2024

This article provides a lot of information about the technology for developing students' communicative competence in higher technical education (in the example of English). It is considered that the development of technology should be carried out through foreign experiences.
MILLIY KURASH SPORT TURINING FUNKSIYALARI VA MAQSADGA ERISHISHDAGI VOSITALARI
Authors: Ustaev Abdurazzoq
Published: April 2024

Ushbu maqolada muallif tomonidan  xozirgi kunda milliy sport turi hisoblanmish kurashning funksiyalari va vositalari haqida fiklar yuritilgan. Kurashning millatimiz uchun o’rni va ahamiyati shuningdek, u bilan shug’ullangan sportchining yuqori natijalarga erishishi aytib o’tilgan. Milliy kurashdagi sport mashg‘uloti vositalari yani tabiatning tabiiy kuchlari, gigiyenik omillar, jismoniy mashqlarning tasnifi ochib berilgan.
OILADAGI ZO’RAVONLIKNING O’SMIR XULQIGA PSIXOLOGIK TA’SIRI
Authors: Atoyeva Gulshoda
Published: April 2024

Maqolada o’smir xulq-atvoriga oilaviy omillar, xususan, oilaviy zo’ravonlikning ta’sir etuvchi omili tadqiq etilgan. Oilaviy zo’ravonlik turlari, ko’rinishlari, sabablari psixologik tahlil etilgan. Ularni bartaraf etish yo’llari ishlab chilgan.
MAKTABGACHA TA’LIMDA BOLALARNI IJTIMOIY HIMOYA QILISHGA INNOVATSION YONDOSHUVNING DOLZARBLIGI
Authors: Xadjamuratova Matlyuba
Published: April 2024

Maqolada maktabgacha ta’limda bolalarni ijtimoiy himoya qilishga davlatning innovatsion yondoshuvning dolzarbligi, barkamol avlod tarbiyasida maktabgacha ta’limning o‘rni masalalari yoritilgan.
Pengaruh Citra dan Tata Ruang Cafe dalam Peningkatan Jumlah Pelanggan pada Caf E Ej33 di Kota Tarakan
Authors: Hidayat Nurul , Benny Rkhan Meily , Yoshica Izzaurajz Imelda
Published: April 2024

The rapid development of the coffee business in Indonesia, particularly in major and minor cities, has attracted many entrepreneurs to open coffee shops. Customer impressions inside cafes are influenced by spatial arrangement, cafe image, and other factors such as products, services, and physical environment. Modern cafes offer a variety of food and beverages while creating a comfortable environment. The cafe's spatial arrangement plays a significant role in creating comfort and attraction that affect customer choices. This research focuses on Cafe EJ33 in Tarakan, North Kalimantan, which has a unique concept with spacious indoor and outdoor areas, including a VIP space. The study employs a survey method with 100 respondents to explore whether the cafe's image and spatial arrangement influence customer preferences at Cafe EJ33. The analysis results indicate a positive influence of the cafe's image on customer preferences, suggesting that Cafe EJ33 is perceived by respondents as a place that provides a positive experience and image. The study also validates the questionnaire and measures the reliability of the variables used in this research, with results supporting validity and reliability..
Pengaruh CAR, NPL, ROA, LDR, dan Ukuran Perusahaan terhadap Harga Saham pada Perbankan Konvensional di BEI (2020–2021)
Authors: Rohma Septiawati Rohma Septiawati , Yanti Yanti , Asih Asih
Published: April 2024

The assessment of a company's performance can be reflected in its stock value. In early 2020, particularly on March 2, the impact of the COVID-19 pandemic was felt in Indonesia, leading to a decline in stock prices on the Indonesia Stock Exchange (IDX). This research aims to examine the simultaneous and partial effects of Capital Adequacy Ratio (CAR), Non-Performing Loans (NPL), Return On Asset (ROA), Loan to Deposit Ratio (LDR), and Company Size on stock prices in conventional banking companies listed on the IDX during the period of 2020–2021. The research method employed is quantitative, utilizing secondary data from 40 banking companies that meet the criteria out of a total of 45 listed on the IDX. The results of the study indicate that partially, CAR has a positive effect on stock prices, while NPL, ROA, LDR, and Company Size have a negative impact. Simultaneously, CAR, NPL, ROA, LDR, and Company Size collectively have a positive influence on stock prices.
Forecasting Tingkat Kepuasan Siswa Terhadap Proses Pembelajaran Menggunakan Metode K-Nearest Neighbor
Authors: Elmayati Elmayati , Handayani Diah Fitria , Wijaya Harma Oktafia Lingga , Aktavera Beni
Published: April 2024

Education is a crucial aspect in the formation of society and individual development. Student satisfaction is an important indicator that reflects the effectiveness of the learning process. In this context, this research proposes applying the K-Nearest Neighbor (KNN) method as a predictive tool to identify factors influencing student satisfaction. Historical data on student satisfaction is collected and analyzed to build a prediction model using KNN. This research aims to increase the effectiveness of the learning process by understanding the factors contributing to student satisfaction. Through personalizing learning experiences, identifying causes of dissatisfaction, and developing innovative strategies, KNN predictions can provide deep insights into educational institutions. It is hoped that the results of these predictions can be used to increase student retention, efficiency in academic management, and transparency in the educational environment. By integrating artificial intelligence into evaluating student satisfaction, this research contributes to developing more adaptive and responsive educational strategies. In conclusion, predicting student satisfaction using KNN is an essential basis for creating a learning environment that has a positive and sustainable impact on student development in the modern educational era. This data was collected by distributing questionnaires to students with a sample size of 160 data. So it is known that there are 112 training data and 48 testing data. Then, from applying the K-Nearest Neighbor method, the value of K=12 is known. So, the test results using the Python programming language with a data division of 70%:30% produce an accuracy value of 80%, a precision value of 79%, a recall of 100%, and an F1-Score of 88%.
Development of the Earthquake Anxiety Scale: Validity and Reliability Study
Authors: Gül Yavuz Ercan
Published: April 2024

In this study, it was aimed to develop a new, valid and reliable measurement tool to measure general earthquake anxiety (EAS). For this purpose, firstly, the literature on earthquake was reviewed and an item pool was created. Then, two separate samples were taken for EFA and CFA. In order to determine the factor structure of the measurement tool, EFA was applied on the data obtained from the first sample group and a structure with 2 factors and 13 items was reached. In order to test the accuracy of this structure, data were collected from a different sample group and CFA was applied on these data. The results revealed that the scale has evidence of construct validity, discriminant validity and internal consistency reliability. In addition, earthquake anxiety scale items can be used to assess anxiety levels in different age groups.
Home Grown School Feeding Programme: A Survey of Implementation in Ube Schools in North Central Nigeria
Authors: Pinga Mahmud , Okpe Okpe Joseph
Published: April 2024

Providing school meals is not just vital in nourishing the children, but also keeps them stronger and healthier as well as sustains the desire for quality education. The study investigated the implementation of Home-Grown School Feeding Programme in UBE schools in North Central Nigeria. Three research questions guided the study, and three hypotheses were tested. Descriptive survey design was adopted for the study. The population of the study comprised 169 respondents; (49 state officials and 120 local government desk officers from the 120 local government areas) in six states and FCT Abuja. The Census sampling technique was used to collect data from all the 169 respondents. Implementation of Home-Grown School Feeding Programme Questionnaire (IHGSFPQ) and Key Informant Interview were developed by the researchers and used for data collection. The instruments were validated by three experts in the Faculties of Education in Benue State University, Makurdi and Nasarawa State University, Keffi and the questionnaire was subjected to a pilot testing and analyzed using Cronbach Alpha to determine the internal consistency and reliability co-efficient which yielded 0.85. The data collected were analyzed using Mean and Standard Deviation to answer the research questions while Chi-square test of goodness-of-fit was used to test the hypotheses at 0.05 level of significance. To collaborate the findings from the questionnaire, the qualitative data were analyzed using simple percentages, tables, and bar charts. The study’s findings revealed that the amount of funds provided for Home-Grown School Feeding Programme is significant for its implementation in UBE schools in North Central Nigeria. The finding also revealed a significant number of food vendors recruited to implement the Home-Grown School Feeding Programme in UBE schools. Furthermore, the finding indicated that there is a significant level of monitoring and evaluation of the success of the Home-Grown School Feeding Programme in UBE schools. It was recommended among others that stakeholders including government agencies and international donors should prioritise increasing funding for the HGSFP to ensure sufficient provision of nutritious meals and necessary resources for its effective implementation.
Assessment of Rural Banditry and Socio-Economic Implication in Niger State: A Focus on Shiroro Local Government Area Niger State, 2015-2021
Authors: Izom Yahuza Danjuma , Iliyasu Mansur
Published: April 2024

Banditry in Northern Nigeria, notably pervasive in Zamfara, Katsina, Sokoto, Niger, Kaduna, Kebbi, Abuja, and Kogi states, has evolved into a dire daily occurrence, primarily attributed to Fulani herders wielding sophisticated weapons. This issue has led to extensive displacement, loss of lives, and shattered communities, exacerbated by a lackadaisical government response, effectively turning criminal acts into lucrative enterprises. The socio-economic fabric of affected areas has been profoundly torn, with thousands displaced, lives lost, and families shattered. This violence has escalated into conflicts between herders and farmers, disrupting agricultural activities and posing a threat to food security. Focusing on Shiroro Local Government Area in Niger State, the study investigates banditry’s socio-economic effects between 2015 and 2021. It highlights poverty’s role in fuelling armed banditry and its substantial impact on socio-economic activities, as indicated by 64.5% of respondents strongly agreeing on poverty’s influence. Statistical analyses support a significant relationship between poverty and banditry, as well as economic challenges and banditry attacks. The findings underscore the urgent need for comprehensive interventions addressing poverty, unemployment, and governance failures to curb rural armed banditry and foster socio-economic stability. Based on the findings, this study recommends: equipping security agencies, supporting youth in farming, intelligence gathering, and community engagement to combat this menace and restore peace to affected areas.
Gendered Disinformation of Female Politicians on Social Media in Kenya: A Case of Migori Republican Council Facebook Page
Authors: Otieno Millicent Awuor
Published: April 2024

The increase in information communication technologies (ICTs) has undoubtedly facilitated socio-economic and political progress, granting individuals a platform for expression and engagement. However, this increased connectivity has also given rise to disturbing phenomena such as stalking, abuse, intimidation, and humiliation. While the internet has provided a space for both men and women to voice their opinions, gendered disinformation on social media poses a significant threat to women’s rights across various domains, including politics, social interactions, and psychological well-being. Migori Republican Council (MRC) is the largest Facebook page that provides a forum for debates on the Migori County’s political, social and economic issues. Through a case study and analysis of content of the Facebook page collected through web scraping using rapid miner during the political campaigns for the Kenya’s 2022 General elections in the period spanning January to July 2022.The study revealed a landscape dominated by political debates and campaigns for both male and female politicians. However, campaigns targeting female politicians were marred by gendered slurs, focusing on their physical appearance, marital status, and roles as mothers. This form of gendered disinformation undermines women’s credibility and perpetuates harmful stereotypes, hindering their political participation and representation. To address the negative impact of gendered disinformation, the study advocates for public awareness campaigns to sensitize individuals, bloggers, and Facebook page administrators about the risks associated with harmful content on social media. Additionally, administrators should prioritize the formulation and enforcement of community guidelines that uphold human dignity and prohibit gender-based harassment. Gender training programs for bloggers and the public are essential to empower individuals to recognize and combat gendered disinformation effectively. The bloggers, Facebook administrators and the public should be equipped with digital media and information literacy competencies. Furthermore, the study recommends the development of lexicons for local languages spoken in Migori County to facilitate the identification and elimination of gendered disinformation. This localized approach acknowledges the cultural context and linguistic nuances inherent in combating online disinformation and ensures broader inclusivity in addressing gender-based issues on social media platforms.
Peer Influence, Family Influence, Academic Difficulties and Gender Differences as Agents of Career Indecisiveness of Senior High School Students in Ghana
Authors: Boye Joy Olive
Published: April 2024

This research aimed at finding out how peer influence, family influence, and academic difficulties affect the career decision-making process among senior high school students in Ghana. Employing a descriptive survey design, the study involved 450 students selected through multi-stage sampling. Data collection utilized a Likert-type scale questionnaire. Analysis and presentation of findings relied on Descriptive Statistics. Results indicated that peer influence, family influence, and academic difficulties collectively played significant roles in causing career indecisiveness among senior high school students in Ghana. Females were affected more by the agents of career indecisiveness. The study recommends that school counsellors remain attentive to peer influence, family influence, and academic difficulties, offering appropriate guidance and counselling to aid informed and effective career decision-making among senior high school students. Gender specific characteristics and behaviour must be critically explored in catering for the diverse career counselling needs of gender groups.
THE DISTANCE EDUCATION, BETWEEN PLANNING AND THE AUDACITY, DURING THE COVID-19 PANDEMIC- MOROCCO MODEL –
Authors: EL GHOUAT HANANE
Published: April 2024

This paper is an extrapolation of the Moroccan experience, related to the audacity of the distance education/learning pattern, during the Covid-19 crisis, which generally produced educational problems and educational learning disabilities, the reform of which requires great effort and time. We have tried, through our reading of the reality of this audacity, to take into consideration the views of the various stakeholders in this process, in response to the following implicit problematic question: Has the boldness of the distance education model in Morocco achieved its desired goals and established in the national educational vision? What is the extent of the harmony between theorizing and educators audacity in Moroccan education? This paper revealed the great gap between the organizational theoretical visions of educational work, and the strategies of his audacity, as there is a clear lack of harmony between the ambition of directed educational documents, and the frustrations of repeated boldness. Its generality is with haste and urgency, rather than the gradualness and readiness that directed educational documents call for. Invoking the modern educational view, which considers the learner the center of the learning process, it is necessary to adopt principles of learning agreed upon by those working in the field of education and psychology together, and can be applied through educational applications of educational technology, including: - The learner must learn on his own, that is, through self-learning. - Each learner has his own rate, and this is achieved by adopting the educational program. - The large and gradual amount of learning is achieved through programmed learning. - General Proficiency This is achieved by dealing with the Bloom Model of Learning and Proficiency. - Learning motivation, where the learner must be aware of his role as a manager of learning resources and be trained on a set of jobs.
Developing of NLP models by model based software developement
Authors: Krutilla Zsolt
Published: April 2024

In the case of software development, we can talk about several methodologies, such as the waterfall model, test-driven software development, agile software development, but the question rightly arises whether software development methods in the traditional sense are applicable to the development of natural language processing language models, especially from the aspect of AI and dictionary-based NLP models. Learning AI models is substantially different from the classical software development process. Whereas in classical software development, developers explicitly describe the operation and behavior to the computer, in AI model learning, the models themselves learn from the input data. This paper presents a possible solution that applies the agile Scrum methodology used in classical software development to the development of dictionary-based NLP models, and identify the agile development opportunities in case of the machine learning-based NLP models.
Study of LED Retrofit Lamps in HSPV Luminaires Based on Photometric Method for Road Lighting
Authors: Jamaludin Mohd Hanif , Wan Ismail Wan Zakiah , Mohd Husini Elina , Mohd Bahror Nor Ain
Published: April 2024

Energy reduction is a great challenge in road lighting applications. Replacing high-pressure sodium vapor (HPSV) with light-emitting diodes (LED) is a viable approach to reducing energy consumption. However, a total replacement can incur a significant capital cost. This study aims to investigate the effects on light distribution by replacing HPSV lamps with LED lamps in HPSV luminaires using Light Intensity Distribution (LID) curve measurement and Backlight, Uplight and Glare (BUG) rating evaluation to reduce the adoption costs. While LED lamps have high illumination rates, the structural differences from HPSV lamps can affect the LID curve and original lighting design. Therefore, it is crucial to study photometric dispersion after retrofitting light sources. Both lamps were installed into similar HPSV luminaires to assess photometric performance using goniophotometer measurements. The HPSV lamp outperforms the LED lamp in terms of luminous flux (11.13%) and light intensity (7.69%), whereas the LED lamp outperforms the HPSV lamp in terms of efficacy rating (68.67%) and wattage used (47.61%). The findings indicate that retrofit LED luminaires have an LOR of 46.77% lower than the HPSV luminaires. The light distribution pattern is maintained but reduced to 40 to 50% for the main usable light angles. The reduced performance is caused by the lamp structure, which occupies a large area inside the luminaire housing, obstructing proper light distribution. Although overall energy consumption is reduced, similar illumination levels cannot be maintained. These outcomes can assist authorities and manufacturers with alternative methods of reducing costs while maintaining lighting levels.
Optimisation of the Distribution System Reliability with Shielding and Grounding Design Under Various Soil Resistivities
Authors: Tang Jia-Wen , Wooi Chin-Leong , Tan Wen-Shan , Nabipour Afrouzi Hadi , Abdull Halim Hana , Md Arshad Syahrun Nizam
Published: April 2024

Lightning strikes can cause equipment damage and power outages, so the distribution system's reliability in withstanding lightning strikes is crucial. This research paper presents a model that aims to optimise the configuration of a lightning protection system (LPS) in the power distribution system and minimise the System Average Interruption Frequency Index (SAIFI), a measure of reliability, and the associated cost investment. The proposed lightning electromagnetic transient model considers LPS factors such as feeder shielding, grounding design, and soil types, which affect critical current, flashover rates, SAIFI, and cost. A metaheuristic algorithm, PSOGSA, is used to obtain the optimal solution. The paper's main contribution is exploring grounding schemes and soil resistivity's impact on SAIFI. Using 4 grounding rods arranged in a straight line under the soil with 10 Ωm resistivity reduces grounding resistance and decreases SAIFI from 3.783 int./yr (no LPS) to 0.146 int./yr. Unshielded LPS has no significant effect on critical current for soil resistivity. Four test cases with different cost investments are considered, and numerical simulations are conducted. Shielded LPSs are more sensitive to grounding topologies and soil resistivities, wherein higher investment, with 10 Ωm soil resistivity, SAIFI decreases the most by 73.34%. In contrast, SAIFIs for 1 kΩm and 10 kΩm soil resistivities show minor decreases compared to SAIFIs with no LPS. The study emphasises the importance of considering soil resistivity and investment cost when selecting the optimal LPS configuration for distribution systems, as well as the significance of LPS selection in reducing interruptions to customers.
An Improved Ensemble Machine Learning Approach for Diabetes Diagnosis
Authors: Mohammed Rashid Mohanad , Yaseen Omar Mahmood , Riyadh Saeed Rana , Alasaady Maher Talal
Published: April 2024

Diabetes is recognized as one of the most detrimental diseases worldwide, characterized by elevated levels of blood glucose stemming from either insulin deficiency or decreased insulin efficacy. Early diagnosis of diabetes enables patients to initiate treatment promptly, thereby minimizing or eliminating the risk of severe complications. Although years of research in computational diagnosis have demonstrated that machine learning offers a robust methodology for predicting diabetes, existing models leave considerable room for improvement in terms of accuracy. This paper proposes an improved ensemble machine learning approach using multiple classifiers for diabetes diagnosis based on the Pima Indians Diabetes Dataset (PIDD). The proposed ensemble voting classifier amalgamates five machine learning algorithms: Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbor (KNN), Random Forests (RF), and XGBoost. We obtained the individual model accuracies and used the ensemble method to improve accuracy. The proposed approach uses a pre-processing stage of standardization and imputation and applies the Local Outlier Factor (LOF) to remove data anomalies. The model was evaluated using sensitivity, specificity, and accuracy criteria. With a reported accuracy of 81%, the proposed approach shows promise compared to prior classification techniques.
Cultivating Community Health and Well-being in Open Source: Mitigating Burnout and Prioritizing Mental Health
Authors: Raghunathan Savitha
Published: April 2024

Purpose: This whitepaper addresses the critical but often overlooked mental health challenges within open-source communities, including stress and burnout. The collaborative spirit of open-source communities has undoubtedly accelerated technological innovation, but the mental well-being of their contributors is frequently neglected. The objective is to uncover the root causes of these mental health issues and evaluate their impact, thereby underscoring the need for supportive and inclusive community practices.

Methodology: This paper uses the ‘Mixed methods’ research approach, analyzing data from industry surveys and extracting insights from personal narratives and experiences documented by contributors in blogs and forums. This comprehensive strategy allows for a detailed exploration of mental health within the open-source environment, facilitating the identification of key stress factors and mental health challenges associated with community participation.

Findings: The investigation reveals a direct link between the operational dynamics of open-source projects and the prevalence of mental health problems among participants. Key stressors include high expectations regarding workload, the predominantly volunteer nature of contributions, social isolation, and a lack of formal support structures. Additionally, the study highlights a significant lack of awareness of these issues and the availability of intervention strategies, further increasing the risk of burnout and stress among community members.

Unique Contribution to Theory, Practice, and Policy: This paper makes a novel contribution to understanding mental health in open-source communities by merging analytical data with qualitative insights from contributor narratives. It offers recommendations for a wide range of stakeholders, from contributors to leaders and policymakers, to cultivate a culture that supports mental well-being, diversity, and sustainability. This paper presents a comprehensive blueprint for nurturing healthier, more resilient open-source communities.
From Geo-political to Geo-economics: The significance of IMEC for India
Authors: Gauba Sucheta , Singh Juhi
Published: April 2024

On the side-lines of the G20 Leaders’ Summit, Leaders of India, European Union, France, Germany, Italy, Saudi Arabia, UAE and US announced an MOU on 9th September 2023 committing to work together to develop a new India-Middle East-Europe Economic Corridor (IMEC). The IMEC will comprise of two separate corridors, the east corridor connecting India to the Gulf and northern corridor connecting Gulf to Europe. The corridor will provide reliable and cost-effective cross-border ship to rail transit network to supplement existing maritime routes. It intends to increase efficiency, reduce costs, secure regional supply chains, increase trade accessibility, enhance economic cooperation, generate jobs and lower greenhouse gas emission, resulting in a transformative integration of Asia, Europe and the Middle East (West Asia). This project reflects emerging geopolitical trends as it offers an alternative to China’s Belt and Road Initiative (BRI). BRI is often perceived as a tool for exerting influence and creating debt traps in developing nations. The Indian government’s efforts to strengthen political and strategic ties with the UAE and Saudi Arabia can now be further solidified by creating enduring connectivity between India and the Arabian Peninsula. This initiative dispels the notion that India and the United States may collaborate in the Indo-Pacific but not in the Middle East. The project showcases how India and the U.S., along with Israel and the UAE, can partner in developing joint economic projects through forums like the I2U2. The mega connectivity project has the potential to reduce political tensions in the Arabian Peninsula and serve as “Infrastructure for Peace” by promoting intra-regional connectivity, according to the U.S. The corridor signifies Europe’s active involvement in infrastructure development in the region. The European Union’s support for this initiative positions it as a major stakeholder in integrating India with both the Arabian Peninsula and Europe. The success of IMEC could lead to further strategic collaborations.
Temperature Dependency of Photoelectronic Properties of Group III-V Arsenide Solar Cell
Authors: Al Humayun Md. Abdullah , Hossen Masum , Haider Md. Zamil , Yousif Bedir , Chughtai Muhammad Tajammal , Islam Muhammad , Khan Sheroz
Published: April 2024

This study explores the effect of temperature on different characteristics of Solar Cells (SC) composed of a structured III-V arsenide group. The temperature dependence of the SC characteristics was investigated numerically and by simulation. In both approaches, each characteristic was compared with a conventional Si SC. InAs showed superior stability and lower temperature sensitivity, as it has a negligible decrease of 0.098 eV in the energy bandgap, while the energy bandgaps of Si, AlAs, and GaAs are 0.129, 0.186, and 0.200 eV, respectively. Moreover, with a decay rate of 81.911 mV/°K, InAs exhibited the lowest temperature sensitivity in open-circuit voltage. InAs additionally demonstrated the least increase in degradation rate, while the SC power output is still a cause of concern. AlAs, Si, and GaAs had a total accumulative gradient change of 0.162, 0.136, and 0.034% in the degradation rate, respectively, while InAs showcased the highest stability by displaying a change of only 0.008%. A comparative analysis illustrated that among these III-V arsenide compounds, InAs had a rock-bottom sensitivity to temperature changes and better temperature stability in both numerical and simulation approaches.
Investigating the Response Variability of Statically Determined Sandwich Beams considering two Random Fields of Elastic Modulus
Authors: Ngoc Tien Dao , Hiep Tran The , Thanh Hoang Van , Thuan Nguyen Van
Published: April 2024

In this paper, the displacement variation in sandwich beams is determined by employing a semi-analytical approach. The classical displacement is calculated by integration using Mohr’s equation, although the integration is complicated due to the inclusion of random fields in the inertial moment term. Using the trapezoidal rule to compute these integrals, the random fields are discretized into random variables at the nodal point of the beam segments. Thus, the expected displacement, standard deviation, and coefficient of variation can be computed. To validate the results, the random fields are simulated using a previously described spectral method. The results of numerical examples were compared with the semi-analytical method and the Monte Carlo simulation demonstrating the high accuracy of the proposed method. The results also illustrate the influence of the parameters of the random fields of elastic modulus on the variability of displacement.
A System Dynamics Approach to Feedback Processes in Project Scheduling
Authors: Odedairo Babatunde Omoniyi , Alarjani Ali
Published: April 2024

Projects, as catalysts for proactive transformation, offer a temporary and adaptable framework that effectively handles complexities (or uncertainties) within a competitive corporate landscape. Hence, the use of an effective project management framework, such as Dynamic Project Scheduling (DPS), is a method to handle intricacies in order to accomplish organizational objectives. DPS refers to a triangle interaction involving baseline scheduling, schedule risk analysis, and project control while supporting schedule adjustment in response to changes and uncertainties. However, there is a lack of information regarding studies that have investigated the feedback mechanisms among DPS components. This study was designed to examine the counterintuitive relationships between these components using system dynamics. The quantities within the DPS system were identified and defined. A casual loop diagram was used to illustrate the interactions among these quantities. Subsequently, a Stock and Flow Diagram (SFD) was created to identify the inputs, states, and flow mechanisms within the DPS. Using the SFD, a system dynamics expression was generated which was then employed to compute the rate of change of the Budgeted Cost of Work Remaining (BCWR) for two projects at different time intervals. The results properly indicated the period of idleness during project execution. The use of BCWR rather than schedule variance provides a more effective visual representation for evaluating performance and tracking progress. The BCWR and planned value exhibit contrasting trends, highlighting the importance of earned value quantities in project control. The use of system dynamics in project management can enhance the planning and scheduling phase, allow project managers to monitor pertinent performance measures, and optimize project outcomes through informed decisions.
The Efficiency of Surface Impedance Technique in the Transverse Wave Approach for the EM-Modeling of Fractal-Like Tree Structure used in 5G Applications
Authors: Ayari Mohamed , Altowaijri Saleh
Published: April 2024

Fractal antenna technology is a promising approach for 5G applications because its complex nature offers optimization potential in terms of time and space trade-offs. However, the computational effort required to analyze such antennas is significant. This paper investigates the Advanced Transverse Wave Approach (ATWA), which utilizes the surface impedance technique to improve simulation efficiency. This study introduces and analyzes a fractal-like 5G tree structure, displaying improved computational accuracy and efficacy, as well as peak memory utilization compared to current works. The proposed approach demonstrates significant effectiveness in enhancing the performance of complex fractal antennas for 5G technology and shows promise for integration with cloud, fog, and edge computing environments. This integration could potentially optimize data processing and network efficiency in these advanced computing landscapes.
Ensemble Learning-based Algorithms for Traffic Flow Prediction in Smart Traffic Systems
Authors: Alkarim Anas Saleh , Al-Malaise Al-Ghamdi Abdullah S. , Ragab Mahmoud
Published: April 2024

Due to the tremendous growth of road traffic accidents, Intelligent Transportation Systems (ITSs) are becoming even more important. To prevent road traffic accidents in the long term, it is necessary to find new vehicle flow management techniques in order to optimize traffic flow. With the high growth of deep learning and machine learning, these methods are increasingly being used in ITSs. This research provides a novel conceptual ITS model that aims to predict vehicle movement through the collective learning usage to anticipate intersections. The proposed approach consists of three main stages: data collection through cameras and sensors, implementation of machine learning and deep learning algorithms, and result evaluation, utilizing the coefficient of determination (R-squared), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). To accomplish this, various machine learning and deep learning algorithms, such as Random Forest, LSTM, Linear Regression, and ensemble methods (bagging), were incorporated into the model. The findings revealed the enhancement due to the proposed method, which was observed through a significant performance improvement of 93.52%.
Predicting the Number of Software Faults using Deep Learning
Authors: Alkaberi Wahaj , Assiri Fatmah
Published: April 2024

The software testing phase requires considerable time, effort, and cost, particularly when there are many faults. Thus, developers focus on the evolution of Software Fault Prediction (SFP) to predict faulty units in advance, therefore, improving software quality significantly. Forecasting the number of faults in software units can efficiently direct software testing efforts. Previous studies have employed several machine learning models to determine whether a software unit is faulty. In this study, a new, simple deep neural network approach that can adapt to the type of input data was designed, utilizing Convolutional Neural Networks (CNNs) and Multi-Layer Perceptron (MLP), to predict the number of software faults. Twelve open-source software project datasets from the PROMISE repository were used for testing and validation. As data imbalance can negatively impact prediction accuracy, the new version of synthetic minority over-sampling technique (SMOTEND) was used to resolve data imbalance. In experimental results, a lower error rate was obtained for MLP, compared to CNN, reaching 0.195, indicating the accuracy of this prediction model. The proposed approach proved to be effective when compared with two of the best machine learning models in the field of prediction. The code will be available on GitHub.
Machine Learning-based Predictive Maintenance for Fault Detection in Rotating Machinery: A Case Study
Authors: Khalil Ardalan F. , Rostam Sarkawt
Published: April 2024

In the realm of industrial production, condition monitoring plays a pivotal role in ensuring the reliability and longevity of rotating machinery. Since most of the production facilities rely heavily on vibration analysis, it has become the cornerstone of condition monitoring practices. However, manual analysis of vibration signals is a time-consuming and expertise-intensive task, often requiring specialized domain knowledge. The current research addresses the aforementioned challenges by proposing a novel semi-automated diagnostics system. The approach leverages historical vibration data in the form of Fast Fourier Transform (FFT) spectrums. The system extracts energy features from the frequency domain by dividing the frequency range into a predefined number of bins and summing the energy values within each bin. Subsequently, each datapoint is labeled based on the corresponding machine condition, enabling the system to learn diagnostic patterns by employing machine learning models. This approach facilitates efficient and accurate diagnostics with minimal manual intervention. The resulting dataset effectively represents and provides an interpretable result. Support Vector Machines (SVM), and ensemble algorithms are utilized to diagnose the faults instantaneously and with minimal error rates. The proposed system is capable of providing early warnings and thus prevents further deterioration and unplanned downtimes. Experimental validation using real-world data demonstrates the system's efficacy, achieving an accuracy of over 90%.
Shoreline Changes and Sediment Transport along Nhat Le Coast, Vietnam
Authors: Cuong Vu Dinh , Hung Nguyen Thanh , Hoa Tran Dinh , Thanh Nguyen Tien
Published: April 2024

One of the most beautiful beaches in Northern Vietnam, Nhat Le, has recently experienced severe erosion as a result of the ensemble interaction of natural factors, such as tropical cyclones, extreme weather events, and human activities. Consequently, negative impacts on tourism and social and economic development have been recorded. This paper aims to provide a deep understanding of the changes in shoreline and longshore sediment transport at Nhat Le estuary based on two modules of LITDRIFT and LITLINE of the LITPACK software package combined with geospatial analysis. The rate of change statistics is calculated using the Digital Shoreline Analysis System (DSAS) from 30-year multi-temporal satellite data (1989-2019) for multiple historical shoreline positions. The Module of LITDRIFT is employed to estimate sediment transport and the shoreline position calculated from the LITLINE module. These data are then compared with measured topographic data and satellite images. Wave climate conditions are incorporated into the LITDRIFT module to identify the volume of sediment transport along the coast on seasonal and annual bases. The results illustrate that a mean erosion rate of about 2 m per year was observed in the southern sandspit of Nhat Le from 1989 to 2019. This rate reaches 4.5 m per year during 2009-2019.
Residual Attention Augmentation Graph Neural Network for Improved Node Classification
Authors: Abbas Muhammad Affan , Ali Waqar , Smarandache Florentin , Alshamrani Sultan S. , Raza Muhammad Ahsan , Alshehri Abdullah , Ali Mubashir
Published: April 2024

Graph Neural Networks (GNNs) have emerged as a powerful tool for node representation learning within graph structures. However, designing a robust GNN architecture for node classification remains a challenge. This study introduces an efficient and straightforward Residual Attention Augmentation GNN (RAA-GNN) model, which incorporates an attention mechanism with skip connections to discerningly weigh node features and overcome the over-smoothing problem of GNNs. Additionally, a novel MixUp data augmentation method was developed to improve model training. The proposed approach was rigorously evaluated on various node classification benchmarks, encompassing both social and citation networks. The proposed method outperformed state-of-the-art techniques by achieving up to 1% accuracy improvement. Furthermore, when applied to the novel Twitch social network dataset, the proposed model yielded remarkably promising results. These findings provide valuable insights for researchers and practitioners working with graph-structured data.
Deflection and Elastic Modulus Assessment of Subgrade in Flexible Pavement mixed with Waste Tire Scrap Material
Authors: Sarkar Sujoy , Biswas Sumit Kumar , Chakraborty Saibal
Published: April 2024

This study aims to assess the deflection and elastic modulus (Es) of subgrade in flexible pavements, focusing on a comparative analysis between pavements with clayey soil subgrade and subgrade modified with tire scrap. The research utilized Falling Weight Deflectometer (FWD) for measuring subgrade deflection, essential in evaluating pavement performance. The FWD applied a dynamic load to the pavement, with deflection measurements processed using the KGP-BACK software to calculate the Es of the pavement subgrade. This approach included assessing the Lower Layer Index (LLI) and Es of the subgrade. Findings revealed a notable reduction of 37.5% in deflection and 2.68 times increase in Es for the tire scrap modified subgrade pavement compared to the standard clayey soil subgrade pavement. These results demonstrate significant enhancements in pavement structure, underlining the potential of recycled materials in sustainable civil engineering practices.
Influence of Supplementary Oxide Layer on Solar Cell Performance
Authors: Oproescu Mihai , Schiopu Adriana-Gabriela , Calinescu Valentin Marian , Iana Vasile-Gabriel , Bizon Nicu , Sallah Mohammed
Published: April 2024

The increasing use of solar energy for electricity production has led to a directly proportional growth in the production of solar cells. Photovoltaic (PV) performance of silicon solar cells can be improved by using more efficient technologies, optimizing processes, and changing behavior in order to reduce operational costs and greenhouse gas emissions. In order to propose solutions for commercial solar cell production with better performance, this article presents an experimental assessment on Supplementary Oxide Layers (SOLs) that are deposited on the surface of a solar cell absorber layer. SOLs are typically used to improve the performance of solar cells by passivating surface defects, reducing recombination losses, and improving the electrical contact between the absorber layer and the metal electrodes. The obtained solar cells are tested under natural sunlight conditions, following a variable dynamic electronic charge profile. The experimental results along with the corresponding I-V and P-V curves, are assessed according to the process parameters, the lighting parameters, and the dynamic load scenario. SOLs have been shown to improve the Power Conversion Efficiency (PCE) of solar cells considerably. The proposed method for increasing the energy efficiency of solar cells can be applied to any type of commercial solar cell and it is easy to implement at the industrial or research level by controlling process parameters. The integration of the whole process, i.e. development of precursor solutions, deposition of thin films, and testing of electrical properties is another contribution of the current study, along with its interdisciplinary character, which involves materials science, electronics, and software programming.
Optimizing Unit Scheduling with Fuzzy Logic: A Strategic Approach for Efficient Power Network Operations
Authors: Marrouchi Sahbi , Ben Hessine Moez , Chebbi Souad
Published: April 2024

This study delves into addressing the challenge of resolving the Unit Commitment (UC) problem, which focuses on enhancing the efficiency of production units and devising their operational schedules to accommodate fluctuations in consumption spanning from a day to a month. Given the intricate, combinatorial, and nonlinear constraints associated with each production unit, this study advocates an optimization approach rooted in fuzzy logic. A Langrangian function was established to simplify the UCP and to transform the different inequality into a linear unconstrained problem. The choice of fuzzy inputs was established using the partial derivatives of a Lagrangian function as a function of the powers injected into each node of the electrical network. This combination of the Lagrangian function and the input of the fuzzy regulator made it possible to control the different constraints in the total production cost function and to improve the operating efficiency of the different production units. This method was effectively applied to a 14-bus IEEE power network encompassing 5 generating units, to address the UC problem by optimizing generator load capacity (LCG) and minimizing Incremental Losses (IL). The numerical processing of the fuzzy linguistic variables was implemented using Mamdani-type fuzzy rules. This strategy stands out for its robust exploratory capability, facilitating the identification of optimal solutions to reduce production costs while ensuring optimal planning of production units.