bluuebunny/crossref_metadata_2025 huggingface dataset:
Artificial Intelligence for Healthcare and Diagnostics
Authors: Desai Urvi
Published: November 2024

Abstract: AI models are being applied to several components of healthcare including diagnostics, drug development, personalized healthcare plans and so on. The large datasets which rapidly rise in complexity and volume form part of big data, provide the potential for ML models to make significant progress in diagnostics, medical image interpretation and treatment recommendations. The introduction of AI across the various departments of the healthcare industry has already raised efficiency and dropped prices. Covid 19’s exponential growth rate completely overwhelmed the healthcare sector's capabilities. AI and robotics became paramount in boosting efforts to detect, identify the risk of and monitor the virus, and develop and deliver the vaccine throughout the planet. The systems that did well generally had large datasets, one modality and did well in testing with foreign data. Currently, low efficiency and high costs make research difficult. With big data being produced at astronomical rates, we should theoretically be making constant discoveries and advancements. However, only part of this data is currently being processed and studied. Attempts are being made to resolve this gap by leveraging AI tools
Dielectric and Emissivity Behavior of Soil of Rehand River of Surajpur Chhattisgarh at X-Band Microwave Frequency
Authors: Dewangan Shailesh Kumar , Yadav Kusum , Srivastava Dr. S. K.
Published: November 2024

Abstract: Microwaves are a form of electromagnetic radiation consisting of electric and magnetic fields oriented at right angles to one another. They operate within a frequency range of approximately 0.3 GHz to 300 GHz, corresponding to wavelengths from 1 meter to 1 millimeter. This paper explores a theoretical model for estimating the emissivity and dielectric constant of soil samples as they relate to moisture content. The emissivity of these soil samples was measured at X-Band frequencies for both vertical and horizontal polarizations. The results indicate that emissivity is higher for vertical polarization than for horizontal polarization. Additionally, the dielectric constant shows a gradual increase with rising moisture content.
Federated Learning-Based Authentication and Trust Scoring System for Cloud IoT Security
Authors: Rani Jyoti , Gupta Rushan , Singh Sameer Kumar , Sood Manik , Chauhan Kuldeep Kumar
Published: November 2024

Abstract: A number of IoT-related security challenges are faced due to the introduction of IoT devices in almost all sections, including smart homes and industrial applications. New security solutions will be required in IoT networks to tackle issues pertaining to privacy, scalability, and efficiency with solid robustness against potential vulnerabilities. Even though some occasions these are effective centralized security frameworks suffer from significant limitations, including single points of failure, increased latency, and risks associated with data centralization [1][2]. These problems worsen as IoT networks grow in size and complexity. The below is the Federated Learning-Based Authentication and Trust Scoring System proposal toward overcoming these challenges while keeping in view the benefits offered by FL. It enables IoT devices to jointly train a global model for anomaly detection without raw data sharing [3]. This happens in a distributed manner so that data privacy is ensured, with scalability and network performance improving [4]. With this, there is a dynamic trust scoring mechanism that evaluates each device's reliability in accordance with the behavioral patterns and history of interactions [5][6]. When combining FL with the trust scoring mechanism, the proposed solution would allow an IoT network to be autonomously able to identify anomalies and manage security. This system has shown practical applicability in such experiments in both the smart home and industrial environments to enhance IoT security, retain scalability, and maintain high privacy standards [7]. The adaptability of the system coupled with federated learning makes it apt for a variety of IoT ecosystems [8]
An Exploration of Extractive method and Abstractive Method of Text Summarization with Various Approaches, Techniques and Datasets
Authors: Mankar Prafull S. , Manwar Avinash B.
Published: November 2024

Abstract: In today’s era, an enormous amount of data available which is in complex form such as Social media content, Images, Audio, Video and Text data form. The mechanism is very much needed to provide these types of data in simple and easy to understandable form. In this paper, importance is given on text summarization of large amount of textual data of documents must be specified in concise and understandable form. Automatic Text Summarization is the technique which is used to represent most significant concepts in precise and comprehensible form from original or source document to target document providing greater flexibility and convenience. Since the advancement in emerging technologies leads to the challenges in research field which is leading towards to need arises to analyze and should be resolved over the time period. Generally, there are two approaches for automatic text summarization: Extractive and Abstractive. The process of extractive based text summarization can be divided into two phases: pre-processing and processing. Automatic text summarization process model can be divided into three steps in the preprocessing step, source text interpretation to representation, source text representation transform to summary text representation with an algorithm. Finally, summary text generation from summary representation. In this paper discussion were made about some of the extractive based and abstractive based methods of text summarization for single documents. Various approaches of text summarization, methods needed for it along with tools and techniques, metrics needed to measure quality of generated text summary applications have been discussed in detail. This survey paper highlights the main advancement in the field of Automatic Text Summarization and provides the key methods with their benefits and limitations. Gives an overview of the past, present and future direction with certain solution to the challenges arises in text summary generation process with extractive and abstractive methods.
ORGANISATIONAL SUPPORT AND TEACHERS' ENGAGEMENT FOR PROFESSIONAL DEVELOPMENT IN SECONDARY SCHOOLS OF NEPAL
Authors: Tamang Sushma , Mishra Anjay Kumar , Ghosh Abhijit , Bhaumik Amiya
Published: November 2024

The teacher professional development program in Nepal is an ambitious initiative designed to support teachers' professional growth. Organizational support and teacher engagement are of the utmost importance to ensure professional development. This study signifies the role of organizational support in enabling professional development and engaging teachers in teacher growth and development. This study aims to propose and rest an integrative model that views organizational supoort practices as a synergistic component that influences the sustainbility of teacher engagement. Survey-based cross-sectional data were collected from secondary schools in Kathmandu Valley using a structured questionnaire to teachers for this non-experimental correlational research design. Likert scales were used to express the perception-based data quantitatively and analyzed using regression models. This study confirms that organizational support procedures strongly influence professional development, highlighting organizational support as a moderator between teacher engagement and professional development. Organizational support also contributes to improving organizational performance. These practices are standard in Nepal schools but at a moderate level. However, schools are run for the benefit of one person only, so schools must be institutionalized with standard norms and values of participatory practices.
REGRESSION AND CLASSIFICATION MODELS FOR HUMAN AGE PREDICTION
Authors: Patil Bhuvaneshwari , Rumma Shivanand S. , Hangarge Mallikarjun
Published: November 2024

This study aims to enhance automated age prediction from facial images, a task with significant potential in security, law enforcement, and Human-Computer Interaction (HCI). While age estimation has seen progress, it remains a challenging problem due to the diverse factors influencing facial aging, such as genetics, environment, lifestyle, and facial expressions. These variations result in individuals of the same chronological age looking markedly different. Most existing age estimation methods rely on computationally intensive pre-trained models, often treated as "black boxes" with predefined input sizes and limited flexibility. To address these limitations, we propose using Convolutional Neural Networks (CNNs) for age prediction. Our approach combines classification and regression techniques to predict age more accurately. We applied our model to publicly available datasets, including FGNET, Adience, APPA-REAL, UTKFace, and All-Age-Face, encompassing images from constrained and unconstrained environments. The proposed CNN model was evaluated against existing pre-trained models, demonstrating comparable performance in age prediction tasks. Both classification and regression results underscored the model's accuracy, offering additional benefits in reduced computational complexity, increased flexibility, and adaptability. This study introduces a CNN-based approach as a viable alternative to pre-trained models for automated age prediction. It offers competitive accuracy while addressing critical limitations of current models, such as computational demands and lack of flexibility, thus contributing a more efficient solution for age estimation tasks in various real-world applications.
GROSS ALPHA/BETA ACTIVITY DETERMINATIONIN DRINKING WATER PROFICIENCY TESTS
Authors: Cfarku Florinda , Berdufi Irma
Published: November 2024

Water intended for drinking purposes has to be analyzed first for gross alpha/beta activity according to national and international standards and recommendations. According to Albanian legislation, Article 6, the gross alpha/beta radioactivity concentration in water, should be below the level of 0.1 Bq/L and 1 Bq/L respectively for human consumption. Our laboratory participated in an interlaboratory comparison organized by IAEA Terrestrial Environmental Laboratory under suggestion of ALMERA members. Proficiency Test among environmental radioactivity monitoring laboratories for the determination of gross alpha/beta activity concentration in drinking water and contaminated surfaces (2018, 2020). Independent standard methods were used for the reference value determination. Each sample was pretreated on site with nitric acid until reaching a pH level below 2. By creating relatively non-polar surfaces, this procedure avoids a loss of radionuclide fractions due to absorption into the walls of the containers.The Gross alpha/beta activity measurements were done using gas-flow proportional counter (GPC) method. The total dissolved solids should not exceed a surface density of 5 mg/cm2 for gross alpha determination and 10 mg/cm2 for gross beta determination The performance of participating laboratories was evaluated with respect to the reference values using relative deviations. The results presented from our laboratory in both inter comparisons (IAEA-TEL-2018-03) and (IAEA-TEL-2020-03) were all acceptable. The laboratory for the measurement of gross alpha/beta radioactivity and our Institute as a part of Tirana University is in the process of accreditation, so far the participation in the inter comparisons exercise is very important.
COMPARATIVE ANALYSIS OF STATE-OF-THE-ART CLASSIFIERS FOR PARKINSON'S DISEASE DIAGNOSIS
Authors: Hani Ahmed Alaa , Sallow Amira Bibo , Ahmad Hawar Bahzad , Abdulrahman Saman Mohammed , Asaad Renas Rajab , Zeebaree Subhi R. M. , Majeed Dilovan Asaad
Published: November 2024

Parkinson's disease (PD) presents a growing global health challenge, with early detection being crucial for effective management and treatment. This study seeks to develop an innovative machine learning (ML) framework for the early detection of PD by integrating advanced techniques for data preprocessing, dimensionality reduction, feature selection, and ensemble classification, aiming to significantly improve detection accuracy and timeliness. The research employs a robust ML pipeline, beginning with data preprocessing using mean imputation, standardization, min-max scaling, and SMOTE (Synthetic Minority Over-sampling Technique) to handle imbalanced data. Dimensionality reduction is achieved through Principal Component Analysis (PCA), while feature selection is performed using SelectKBest coupled with the ANOVA F-test to identify the most relevant features. Four ensemble methods—Random Forest, Gradient Boosting, XGBoost, and Support Vector Machine (SVM)—are evaluated for classification. Among the classifiers tested, the Gradient Boosting model stands out with an impressive accuracy of 0.9487, demonstrating its superior performance in PD detection. Integrating multiple preprocessing, dimensionality reduction, and feature selection techniques proves essential in optimizing model performance, highlighting the importance of a multifaceted approach in handling complex datasets. This research introduces a comprehensive ML framework that combines multiple advanced techniques in a streamlined process, significantly improving the early detection of Parkinson's disease. Ensemble methods, combined with strategic feature selection and data balancing techniques, offer a novel approach that could be applied to other neurodegenerative disorders, expanding its potential impact beyond PD detection.
DOES COVID-19 AFFECT SHARIAH COMPLIANT STOCK? EVIDENCE FROM SELECTED OIC COUNTRIES
Authors: Shaikh Zakir Hossen , Irfan Mohammad , Nomran Naji Mansour , Abey Joji
Published: November 2024

This study aims to examine the movements of Islamic stock markets in ten selected OIC (Organization of Islamic Cooperation) countries in relation to Covid-19 cases, providing a comprehensive analysis of market behavior during the pandemic. The countries—Saudi Arabia, Pakistan, Bangladesh, Turkey, Indonesia, Oman, Qatar, UAE, Kuwait, and Bahrain—were chosen based on their large Muslim populations. Data was collected over a one-year period from January 1, 2020, to January 31, 2021, analyzing the relationship between Covid-19 cases and Islamic stock market indices. The study employed co-integration tests to identify long-term relationships and the Vector Error Correction Model (VECM) to explore short-run dynamics. The co-integration test results show a significant long-run relationship between Covid-19 cases and Islamic stock markets in most of the selected OIC countries. Specifically, the Shariah indices in Pakistan, Bangladesh, Turkey, Qatar, UAE, Kuwait, and Bahrain have a positive and significant relationship with Covid-19 cases. Conversely, Saudi Arabia, Indonesia, and Oman exhibit a negative long-term relationship with Covid-19 cases, suggesting a different market response. These results suggest that countries with diversified economies, particularly those relying on natural resources such as oil and agriculture, were more resilient during the pandemic. This study provides novel insights into the unique responses of Islamic stock markets in OIC countries during the pandemic, highlighting regional differences in market behavior and recovery. It suggests that despite the global economic downturn, OIC countries present attractive investment opportunities, particularly due to their swift recovery and resource-based economies, offering a robust portfolio for investors during crises.
SCALE FOR PRE-SERVICE SCIENCE TEACHERS’ KNOWLEDGE IN DESIGNING INCLUSIVE LEARNING: CONSTRUCTION, VALIDATION, AND RELIABILITY
Authors: Suprihatiningrum Jamil , Wijayanti Astuti , Salam Firman Walidi , Hidayah Muhammad Aditya
Published: November 2024

Scale development is essential in testing constructs and variables in social research, including education. Scales that meet the rules of validity and reliability are developed using standardized procedures. This research focuses on scale production to create reliable and valid construct measures to assess teacher knowledge in designing inclusive learning, using three frameworks, namely Technological Pedagogical and Content Knowledge, Backward Design, and Universal Design for Learning. Research on this issue involves measuring organizational and psychological constructs, which present unique challenges because they are generally unobservable, very abstract, often complex, and may consist of several different components. As a result of this complexity, developing this scale was challenging. The production of this scale followed Boateng et al., and the field test involved 259 pre-service teachers. The Rasch Rating Scale Model analysis was used to test the scale and produced 18 items that have high reliability (0.96). This scale can be used nationally to assess teachers' ability to implement inclusive education. The scale also applies as a teacher self-assessment tool for developing their competence.
ENHANCING COMPETITIVENESS OF PERUVIAN TEXTILE MSES THROUGH QUALITY MANAGEMENT: A FOCUS ON LEADERSHIP, TRAINING, AND CONTINUOUS IMPROVEMENT
Authors: Munive Marco Antonio Ledesma
Published: November 2024

This study examines the impact of quality management on the competitiveness of micro and small enterprises (MSEs) in the Peruvian textile clusters, a sector that is a significant driver of local economic activity. Unlike previous studies, this research provides a comprehensive analysis of the combined influence of leadership, employee training, customer orientation, continuous improvement, and benchmarking on MSE competitiveness. Utilizing a robust quantitative research design, data were collected from 347 participants through a structured survey. The findings reveal a high level of engagement in quality management practices among the surveyed MSEs, with the identified factors significantly contributing to improved productivity, customer satisfaction, and market presence. The originality of this study lies in its in-depth exploration of these quality management dimensions within the specific context of Peruvian textile clusters, offering new insights into their role in driving competitiveness. The implications are particularly relevant for business owners, managers, and policymakers seeking to enhance the sustainability and competitive advantage of textile MSEs in a highly dynamic market environment.
DEVELOPMENT AND IMPLEMENTATION OF A FIVE-TIER DIAGNOSTIC TEST TO IDENTIFY STUDENT MISCONCEPTIONS ON FRACTIONS: A SIGNIFICANT STEP TOWARDS IMPROVING MATHEMATICS EDUCATION
Authors: Herliana Herliana , Maison Maison , Syaiful Syaiful
Published: November 2024

Misconceptions in fractions, a significant challenge in mathematics education, occur when students' conceptions do not align with scientific conceptions, yet they firmly believe in the correctness of their conceptions. These deeply rooted misconceptions are difficult to eliminate and not easily identified with conventional instruments. Therefore, this study aims to develop a valid and reliable five-tier diagnostic test instrument to identify students' misconceptions about fractions effectively. The design of this development research involves qualitative and quantitative methods using the 4D model (define, design, develop, disseminate). Sixty-eight seventh-grade students were involved in quantitative instrument quality testing; data were analyzed using SPSS. The final result of the development of this instrument was 12 questions declared valid and reliable. Furthermore, the instrument was applied to 30 seventh-grade students in different schools to identify their misconceptions about fractions. Based on the analysis of the combination of students' answers, it was obtained that 36.4% of students understood the concept, 8.9% were in the false positive category, 7.2% were false negative, 9.7% lacked knowledge, and 37.8% had misconceptions. It was found that the most dominant description of misconceptions (33%), namely, students assume that the smaller the denominator of a fraction, the smaller the value of the fraction. Then, it was also found that students' thinking, in general, causes misconceptions to occur. The practical implication of the results of this study is that teachers can use the instrument and identify misconceptions to develop more effective teaching methods, thereby improving mathematics education by helping students avoid and anticipate misconceptions.
QUALITY OF LIFE: THE SPIRITUALITY AND SPIRITUAL WELLBEING OF ELDERLIES IN MALAYSIA
Authors: Yahya Fatimah Binti , Hassan Hafizah Che , Panduragan Santhna Letchmi , Mat Samsiah
Published: November 2024

With an increasing aging population worldwide, there is a need for greater focus on the spirituality of older people, to better support them and lead them to an enhancement of their inner peace. This study used a qualitative with exploratory sequential design approach (with semi-structured interviews) in exploring spirituality with older people in community of Federal Territory of Kuala Lumpur, which is related to a sense of personal belief and connection that helped them in their daily lives. The research involved a rigorous exploratory process. The fifteen (15) participants provided information about their rich experiences and their connections in four domains: (1) Faith, (2) Belief, (3) Practice and (4) Emotion. Results: The reliability of the dimensions was all high as the Cronbach’s Alpha coefficients were above 0.90. Therefore, this spirituality measurement tool can be considered highly reliable. Conclusion: This paper has developed as well as validate the spirituality measurement tool. It was used to construct a profile of elderly in Malaysia, and it may be applied to profile elderly in other countries.
SELF-EFFICACY REDUCES RELAPSE IN DRUG ABUSE WITH COGNITIVE BEHAVIOUR THERAPY
Authors: Susanti Meri , Dania Ira Aini , Ibarra Florante
Published: November 2024

Maladaptive substance abuse (NAPZA) can lead to clinically significant disorders. According to the National Narcotics Agency (BNN) states that around 90 percent of former drug addicts who undergo the rehabilitation process experience a relapse and return to abusing drugs. The research results show that cognitive Behaviour Therapy (CBT) is an effective intervention method in treating drug abuse. This research aims to test the effectiveness of cognitive behavior therapy to increase self-efficacy to reduce the risk of relapse in drug abusers. The study used a pre-posttest experimental method with a control group. Research respondents were selected using a purposive sampling technique with a sample size of 30 people. Both groups were given the Drug Abstinence Self-Efficacy Scale (DASES) measurement to see the effectiveness of applying cognitive behavior therapy in increasing self-efficacy in addicts who have participated in the rehabilitation program. The change in self-efficacy in both groups from medium to high was 66.7% after the intervention. A comparison between the two groups was made before the intervention, where the significance value was p = 0.399, and after the intervention, there was a change in the score between the two groups to 0.001. There was a significant difference between the two groups before and after the intervention, with a value of p = 0.001. Meanwhile, there was no substantial change in the control group in p=0.177. CBT is effective in increasing abstinence self-efficacy in drug addicts as an effective therapy to reduce the risk of relapse in drug addicts in rehabilitation programs
IDENTIFYING RISK FACTORS FOR STUNTING AMONG UNDER-FIVE INDONESIAN CHILDREN
Authors: Ayu Mayang Sari , Rahmadhani Mayasari , Pangestuti Dewi , Ibarra Florante
Published: November 2024

The prevalence of child stunting in Indonesia in 2023 at the national level from 24.5% to 21.6%. Despite declining, the figure is still relatively high, as 28 out of 100 babies suffer from stunting. The study aims to predict stunting risk factors in children under the age of 5. They use bivariate and multivariate logistic regression to identify significant factors associated with stunting risk. Stunting prevalence in Batu Bara District of North Sumatra Province was 18.35% out of 28.716 children under 5 years of age (967 children very short and 3,944 young children). The study was conducted on 100 households with brief and concise children living in rural areas. The results of the study showed maternal education (OR 5.27; 95% CI 1.04-5.62); low maternal nutritional status during pregnancy (OR 2.57, 95% CI 1.22-5.89); formula milk delivery less than 6 months (OR 3.47; 95% CI 1.40-8.28); low birth weight of less than 2500 grams (OR 4.94; 95% CI 1.30-18.80); lack of access to drinkable water (or 3.14; 95% CI 1.29-7.65); low family wealth index (OR 0.13; 95% CI 0.05-0.32). Environmental and household factors have a significant relationship with stunting risk control. Multilateral efforts from the level of family empowerment, increasing the role of health services, and improving nutritional status are access to public health status.
VALIDITY OF ENGAGEMENT INSTRUMENT DURING ONLINE LEARNING IN MATHEMATICS EDUCATION
Authors: Hidayat Riyan , Imami Muh Khairul Wajedi , Liu Sibo , Qudratuddarsi Hilman , Mohd Saad Mohd Rashid
Published: November 2024

The current study aimed to assess the validity of the engagement instrument during online learning in mathematics education. This study used a survey research design as its approach. The current research participants were 203 Generation Z students in West Nusa Tenggara Barat, Indonesia. Convenience sampling techniques were used to assess who had completed the online survey. Three procedures were used to analyze the data in this research: exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and Rasch analysis. EFA revealed that the online engagement instrument had two sub-components: behavioral engagement and emotional engagement. At the same time, the CFA results showed that the model fit indices established the first- and second-order model's two-factor structure. Finally, the results showed that the online engagement instrument’s item and person reliability were good. The findings indicate a potential for enhancement even though the Rasch analysis largely supported the results of EFA and CFA. The current research’s novelty is that it provides a valid and reliable instrument to assess student`s engagement during online learning in a mathematical education context. Using the current instrument can ensure the accuracy, reliability, and credibility of research on student engagement during online learning in mathematics education.
EVALUATING THE IMPACT OF AI–POWERED TUTORS MATHGPT AND FLEXI 2.0 IN ENHANCING CALCULUS LEARNING
Authors: Alvarez Joel I.
Published: November 2024

This study evaluates the effectiveness of AI-driven technologies, Flexi 2.0 and MathGPT, in enhancing personalized learning and advanced cognitive abilities among pre-service mathematics educators participating in Calculus I. Participants were allocated to a control group receiving conventional training and an experimental group utilizing AI technology, applying a quantitative experimental design. Data were gathered utilizing validated evaluations, surveys, and interviews. The results indicated that students employing AI tutors showed significant improvements in problem-solving and personalized learning compared to the control group. Concerns arose regarding potential over-reliance on AI, underscoring the need for rigorous criteria to guarantee that students engage actively in learning rather than passively receiving AI-generated responses. The findings suggest that educators should create activities requiring students to critically evaluate AI-generated responses to promote independent thinking. The findings highlight the potential of AI in mathematics education while stressing the importance of ethical considerations and teacher oversight. It requires both students and staff training to adapt to improvements in AI inside education. The study reveals that while AI can improve learning outcomes, careful implementation is essential for its effective and responsible use in educational settings.
SENTIMENT ANALYSIS OF PUBLIC OPINION ON PRESIDENTIAL ADVISORY APPOINTMENTS USING NAIVE BAYES CLASSIFICATION
Authors: Negara Edi Surya , Syaputra Rezki , Erlansyah Deni , Andryani Ria , Saksono Prihambodo Hendro , Aditya Ferdi , Agam Padel Mohammad
Published: November 2024

Social media platforms such as Twitter, Facebook, and YouTube have become significant channels for public discourse, where users freely express opinions, including negative sentiments and hate speech. To better understand public opinion, particularly in politically charged contexts, sentiment analysis can classify user comments as either positive or negative. This study aims to analyze public sentiment regarding the formation of a special advisory team for President Jokowi, using a sentiment classification approach. The study employed a Naïve Bayes classifier to analyze sentiment from 3,000 comments gathered from Twitter, Facebook, and YouTube. The dataset was divided into 80% training data (used to train the model with known sentiment) and 20% test data. The Naïve Bayes algorithm was chosen for its simplicity and effectiveness in handling large datasets in text classification tasks. The Naive Bayes classification on sentiment analysis of public opinion regarding the appointment of presidential advisors achieved an overall accuracy of 71% in classifying the test data. Negative sentiment was classified with an accuracy of 71%, while positive sentiment was classified with an accuracy of 70%. The results demonstrate that the Naïve Bayes classifier is a viable method for sentiment analysis in political discourse, although the model's performance indicates room for improvement. The novelty of this research lies in its focus on sentiment analysis of public opinion specifically related to presidential advisory appointments, an area not yet extensively explored in sentiment analysis studies. This study contributes to the field by providing insights into the public’s perception of political decisions using machine learning techniques. The implications for future research include refining classification methods for better accuracy and applying the model to other political or governmental topics.
URBAN FLOOD RESILIENCE: A MULTI-CRITERIA EVALUATION USING AHP AND TOPSIS
Authors: Kosova Robert , Hajrulla Shkelqim , Xhafaj Evgjeni , Kapçiu Rinela
Published: November 2024

Floods are increasingly recognized as one of the most destructive natural disasters, driven by urban expansion, climate change, and unregulated development. This is particularly true in developing countries, where rapid urbanization has increased impervious surfaces, amplifying flood risks in urban areas. This study focuses on Albania, evaluating urban resilience against floods through the lens of water-related disaster data. Using a multi-criteria decision analysis (MCDA) approach, specifically the Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), the research assesses flood vulnerability and resilience in rapidly urbanizing regions. Integrating AHP and TOPSIS with mixed methods introduces a novel approach, offering a comprehensive evaluation framework for flood risk management. The findings highlight critical vulnerabilities and suggest that targeted urban planning and disaster mitigation efforts can enhance resilience. Future research could incorporate climate projections and granular urban data, supporting a more adaptive flood management strategy.
VILLAGE APPARATUS PERFORMANCE: THE ROLE OF CULTURE, ORGANIZATION SUPPORT AND JOB SATISFACTION?
Authors: Sabil Muhammad Asnawi , Yanto Heri , Pramono Suwito Eko , Wijayati Nanik
Published: November 2024

The performance of village officials has always been a focal point for various parties because the foundation of urban and national progress lies in rural development. However, the performance of village officials in Indonesia is still not optimal. This study aims to investigate the roles of job satisfaction, organizational culture, and perceived organizational support as mediators and moderators in the relationship between competence and performance. The research involved 368 village officials across 19 regencies in West Java province, selected using the inverse root square method. Data collection was conducted through questionnaire distribution using area random sampling methods. Data analysis was performed using Warp PLS method. The findings indicate that job satisfaction successfully mediates the relationship between competence and performance. Organizational culture and perceived organizational support significantly moderate the influence of competence on performance. Village government bodies, village community empowerment agencies, and the Ministry of Villages need to formulate policies that support improving job satisfaction, foster a healthy organizational culture, and provide facilitative support, both in infrastructure and in developing hard and soft skills for village officials.
APPLIED MIXED KERNEL AND FOURIER SERIES MODELLING IN NONPARAMETRIC REGRESSION
Authors: I Wayan Sudiarsa Sudiarsa , Ni Putu Ayu Mirah Mariati Mirah , Ni Made Sukma Sanjiwani Sukma
Published: November 2024

There are three nonparametric regression approaches, namely, parametric, nonparametric and semi-parametric regression. Nonparametric regression allows the response variable to follow a different curve from one predictor variable to another. In paired data, the components of predictor variable and the response variable are assumed to follow unknown data patterns, so that they can be approximated by a kernel based regression model and Fourier series. The base components are approximated by kernel functions and Fourier series functions. Error is assumed to be normally distributed with zero mean and constant variance. The aim of this research is to make a kernel-based nonparametric regression model and Fourier series on poverty data in the Province of Bali. The research phase method begins with the introduction of the kernel based nonparametric regression model and the Fourier series. The next step is to study the estimation of the regression curve. The function estimation results are highly dependent on the bandwidth, smoothing and oscillation parameters. The resulting model gives a value of R2=0.6278, means that the used variables can explain the model by 62.78 percent. From the obtained modeling re-sults, the Open Unemployment Rate has a positive effect on the percentage of poor people in Bali.
Keywords: Nonparametric Regression; Modeling; Kernel; Fourier Series.
THE INFLUENCE OF MANAGERIAL TEAM E-PROJECTS ON STUDENTS’ INDEPENDENT ATTITUDES
Authors: Budiarti Retni S , Harlis Harlis , Siburian Jodion
Published: November 2024

This study aims to evaluate the impact of the E-Project Managerial Team on fostering independence among Biology Education students at Universitas Jambi Faculty of Teacher Training and Education. The research investigates how managerial technology influences students' ability to manage tasks autonomously in an academic setting. Using a mixed-methods approach with an exploratory sequential design, the study combines quantitative data from questionnaires with qualitative insights. A random sampling method was applied to select participants from 143 students. Data analysis was conducted using IBM SPSS 24, employing descriptive statistics, normality, linearity, and regression tests to interpret the relationships between variables. The normality test returned a significance value of 0.614, while the linearity test showed a value of 0.482, confirming the appropriate distribution and relationships of the data. Results from the regression analysis (F value of 36.487, p = 0.001) revealed a statistically significant positive influence of the E-Project Managerial Team on students' independence. Descriptive analysis also indicated that while some students held neutral views, a considerable portion acknowledged improvements in their self-management and autonomy due to the team's involvement. This research uniquely integrates managerial technology with biology education, highlighting how digital project management tools can enhance student independence. It contributes to the growing literature on educational technology by offering innovative methods for promoting autonomy, time management, and individual skill development among students. This work provides practical insights for educators seeking to implement technological solutions to support self-directed learning.
KNOWLEDGE, PRACTICES, AND CONSTRAINTS AMONG BREASTFEEDING EMPLOYEES TOWARDS IMPLEMENTATION OF LACTATION AREA
Authors: Villarama Joseph A. , Fabros Bench G. , Angeles Cristina I. , Dela Cruz Lyan Mae Micah V. , Dela Fuente Abegail V. , Concepcion Emily A. , Antalan Jorri Anne Amethyst R. , Barcelita Karen Jean C.
Published: November 2024

Breastfeeding empowers women and children and working mothers who revolutionize the workplace globally, specifically in carrying mutual roles as employees delivering quality service to humanity and mothers nurturing their children. This study explored the knowledge, practices, and constraints among breastfeeding working mothers in Region III, Philippines, towards implementing lactation areas. A mixed-methods approach combined a quantitative survey with a qualitative semi-structured interview component. Findings showed a high prevalence of breastfeeding among working mothers, coupled with a good level of knowledge about breastfeeding practices. However, constraints such as lack of lactation areas in offices, heavy workload, and limited support were revealed, thus foregrounding the need for institutionalized policies and guidelines to establish lactation areas in offices, promote breastfeeding, and support working mothers and their welfare, anchored on sustainable development goals (SDGs). By addressing these issues, workplaces position a more conducive environment for breastfeeding mothers, contributing to improved maternal and infant health outcomes. Further, this research provides valuable insights for policymakers, employers, and healthcare providers in initiating more effective strategies to support breastfeeding working women. Through the documented findings and by providing concrete recommendations for policy and infrastructure, this research advances the shaping of a more breastfeeding-friendly environment, promotion of maternal and infant health, supporting SDG 3-good health and well-being, and enhancement of workplace productivity towards achieving SDG 8-decent work and economic growth and SDG 5-gender equality.
THE NEXUS BETWEEN TRADE OPENNESS AND ENVIRONMENTAL DEGRADATION : A VECM ANALYSIS
Authors: Wicaksana Tania , Widodo Wahyu
Published: November 2024

This research aimed to analyze the influence of trade openness, ICT, per capita income, and Human Development Index on environmental quality in Upper-Middle Income countries in the short and long term and how per capita income influenced environmental quality in the U-shaped EKC hypothesis in Upper-Middle Income countries over the 1990-2020 periods using the VECM Panel analysis. Several findings were revealed in this research. First, the coefficient of trade openness in the long-term and short-term on environmental quality was positive and statistically significant at the 1% level. Second, the relationship between ICT and environmental quality had a positive influence in the short term and a negative influence in the long term. Third, the relationship between per capita income and environmental quality positively influenced the long- and short-term. In these findings, an increase in per capita income increased carbon dioxide, which caused a decrease in environmental quality and emerged an inverted U relationship in the long- and short-term of the EKC hypothesis in UMI countries, with the turning point in the short-term being US$ 728,530 and in the long-term US$ 772,839. Finally, the Human Development Index in this research positively influences environmental quality in the short- and long-term. The outcomes of this study have considerable Policy implications regarding trade openness policy formulation to reduce environmental degradation, especially in Upper Middle-Income Countries.
A TRACER STUDY OF PHD IN EDUCATIONAL ADMINISTRATION GRADUATES: RECOMMENDATIONS TO STRENGTHEN THE PROGRAM
Authors: Pentang Jupeth T. , Bautista Ronalyn M. , Gamozo Mary Jane D.
Published: November 2024

This study aims to evaluate the effectiveness and relevance of a PhD in Educational Administration program at a State University in Western Philippines through a tracer study, offering insights into the graduates’ demographic and academic profiles, career alignment, and recommendations for improvement. The study employed a descriptive research design, collecting data from 19 non-randomly selected graduates based on availability. Data were gathered using an instrument based on guidelines from the Philippine Commission on Higher Education, with additional elements drawn from previous tracer studies. Analytical tools such as frequency counts, percentages, and arithmetic means were used to describe the findings. Results highlight that the PhD in Educational Administration program effectively serves mid-career professionals, attracting individuals from diverse backgrounds and helping them meet evolving leadership demands in educational administration. Graduates reported that the program equipped them with the necessary skills to excel in their current roles. However, they suggested several areas for improvement, including enhancing the program's accessibility and inclusivity. These recommendations are crucial to ensuring the program remains relevant, especially in preparing leaders capable of addressing modern challenges in education. This research provides new insights into the strategic role of tracer studies in shaping the future of educational leadership programs. It aligns with Sustainable Development Goal 4 (Quality Education) by proposing a continuous curriculum review, which ensures the program evolves alongside educational leadership trends. The study also underscores the importance of equipping faculty with current insights to strengthen the program's ability to prepare leaders for a dynamic educational landscape.
INSTRUCTIONAL LEADERSHIP AND TEACHER SELF-EFFICACY ON JOB SATISFACTION: THE MEDIATING EFFECT OF SCHOOL CLIMATE IN INDONESIAN ISLAMIC SENIOR HIGH SCHOOLS
Authors: Ridwan Ahmad , Hendra Robi , Guillén-Gámez Francisco D. , Fauzan Muhammad , Alqhatani Turki Mesfer , Yaqin Lalu Nurul , Setambah Mohd Afifi Bahurudin
Published: November 2024

This study explores the impact of instructional leadership and teacher self-efficacy on job satisfaction among teachers in Indonesian Islamic Senior High Schools (Madrasah Aliyah), focusing on the mediating role of school climate. Instructional leadership plays a crucial role in shaping a positive school environment, which can enhance teacher job satisfaction by providing necessary guidance, resources, and support. A mixed-method approach was employed, with data collected from 219 teachers using validated survey instruments. The Content Validity Index (CVI) was used to ensure instrument reliability. Data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess relationships between variables and the mediating effect of school climate. The results show that instructional leadership significantly impacts both school climate and teacher job satisfaction, with path coefficient values of .463 and .818, respectively. However, school climate did not significantly mediate the relationship between instructional leadership and job satisfaction. Teacher self-efficacy, while positively influencing school climate (p < .05), did not have a substantial direct effect on job satisfaction (ß = -0.058). This research provides fresh insights into the nuanced interplay between leadership, teacher self-efficacy, and job satisfaction in the context of Islamic schools. The findings highlight school climate's indirect yet pivotal role in educational leadership dynamics. By integrating these variables, the study offers practical recommendations for improving leadership strategies and enhancing teacher well-being in faith-based educational settings.
TRANSFORMATION OF DIGITAL INNOVATION IN EDUCATION IN THE POST-COVID ERA: AN EXPLORATION CENTERED ON DRONES AND VIRTUAL REALITY
Authors: Lu Jiaxin , Dawod Ahmad Yahya , Ying Fangli
Published: November 2024

The relevance of this study is underscored by the significant shifts in traditional methods of teaching software disciplines in the post-COVID era. The widespread adoption of remote learning and integrating innovative technologies have necessitated re-evaluating educational practices. This study aims to explore the impact of modern innovative technologies on the effectiveness of education and the quality of learning in the post-COVID period. The primary method employed in this study was a survey, through which questions related to the use of drones and virtual reality (VR) technologies in the educational process were posed to students and educators. The results obtained during this study indicate a high level of acceptance by modern students and their educators of the role of drones and VR technologies in the educational environment of contemporary educational institutions. Participants in the surveys positively assessed the transformation trends of innovative technologies in the educational environment, which applies to almost all groups of students and educators without exception, regardless of their specialties. Respondents highlighted multiple positive effects of integrating drones and VR technologies into the educational space of educational institutions, including a high level of comfort when using these technical devices, improvement in the perception of educational material, and enhancement of the quality of mastering software disciplines. Both educators and students positively view the emerging prospects of practical use of drones and VR technologies in educational institutions' educational space, particularly in the post-COVID period and beyond.
THE INFLUENCE OF GENERIC SKILLS ON GENDER DIFFERENCES IN SCIENTIFIC PROCESSING AND CRITICAL THINKING IN PHYSICS MOTION DYNAMICS
Authors: Darmaji Darmaji , Astalini Astalini , Kamid Kamid , Triani Elza
Published: November 2024

This study investigates the gender differences in generic skills, scientific process abilities, and critical thinking among high school students during physics learning, specifically within motion dynamics. The research aims to uncover the impact of generic skills on students' scientific processing and critical thinking abilities while examining how gender influences these cognitive domains. Employing a comparative quantitative research design, data were gathered from 180 students (90 male and 90 female) in Jambi City using observation sheets, test questions, and questionnaires. The sample was selected through simple random sampling, and statistical analyses were performed to examine the differences in learning outcomes between genders. Results revealed notable gender disparities in generic skills, scientific processing, and critical thinking, with male and female students demonstrating distinct proficiencies in physics learning. This underscores the necessity of adopting gender-sensitive educational approaches to foster equitable learning experiences. The uniqueness of this research lies in its detailed exploration of the interconnectedness between generic skills, scientific processing, and critical thinking within the framework of gender. Unlike prior studies, which often focus on isolated skills, this study integrates these core competencies to provide a more holistic understanding of how gender influences physics learning. The findings emphasize the need for inclusive curricula and gender-tailored instructional strategies that promote equal opportunities for all students in science education, particularly in physics, thus contributing to a more balanced and equitable academic environment.