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Abstract Pandemics like COVID-19 have illuminated the significant disparities in the performance of national healthcare systems (NHCSs) during rapidly evolving crises. The challenge of comparing NHCS performance has been a difficult topic in the literature. To address this gap, our study introduces a bi-criteria longitudinal algorithm that merges fuzzy clustering with Data Envelopment Analysis (DEA). This new approach provides a comprehensive and dynamic assessment of NHCS performance and efficiency during the early phase of the pandemic. By categorizing each NHCS as an efficient performer, inefficient performer, efficient underperformer, or inefficient underperformer, our analysis vividly represents performance dynamics, clearly identifying the top and bottom performers within each cluster of countries. Our methodology offers valuable insights for performance evaluation and benchmarking, with significant implications for enhancing pandemic response strategies. The study’s findings are discussed from theoretical and practical perspectives, offering guidance for future health system assessments and policy-making.

IntroductionExcessive alcohol consumption negatively impacts physical and psychiatric health, lifestyle, and societal interactions. Chronic alcohol abuse alters brain structure, leading to alcohol use disorder (AUD), a condition requiring early diagnosis for effective management. Current diagnostic methods, primarily reliant on subjective questionnaires, could benefit from objective measures.MethodThe study proposes a novel EEG-based classification approach, focusing on effective connectivity (EC) derived from resting-state EEG signals in combination with support vector machine (SVM) algorithms. EC estimation is performed using the partial directed coherence (PDC) technique. The analysis is conducted on an EEG dataset comprising 35 individuals with AUD and 35 healthy controls (HCs). The methodology evaluates the efficacy of connectivity features in distinguishing between AUD and HC and subsequently develops and assesses an EEG classification technique using EC matrices and SVM.ResultThe proposed methodology demonstrated promising performance, achieving a peak accuracy of 94.5% and an area under the curve (AUC) of 0.988, specifically using frequency bands 29, 36, 45, 46, and 52. Additionally, feature reduction techniques applied to the PDC adjacency matrices in the gamma band further improved classification outcomes. The SVM-based classification achieved an accuracy of 96.37 ± 0.45%, showcasing enhanced performance through the utilization of reduced PDC adjacency matrices.DiscussionThese results highlight the potential of the developed algorithm as a robust diagnostic tool for AUD detection, enhancing precision beyond subjective methods. Incorporating EC features derived from EEG signals can inform tailored treatment strategies, contributing to improved management of AUD.

Accurate estimation of porosity and permeability distribution is vital for reservoir characterization, particularly challenging in areas with limited well data. This study introduces a new approach to permeability estimation by implementing feature selection analysis and using elastic properties extracted from simultaneous seismic inversion method. Shapley Additive Explanations (SHAP) analysis was implemented to study the association between elastic properties, porosity, and permeability, while Recursive Feature Elimination with Cross-Validation (RFECV) determined the optimal feature configuration. Together, they enhance model interpretability and optimize predictive performance of machine learning models. Several ensemble-based models, including Extreme Gradient Boosting (XGBoost), Light Gradient Boosting (LightBoost), Categorical Gradient Boosting (CatBoost), Bagging Regressor, Random Forest and Stacking, were evaluated for predictive performance, along with Multi-Layered Perceptron Neural Network algorithms. Additionally, Tree-structured Parzen Estimator-Bayesian optimization method was utilized to optimize the machine learning model's hyperparameters and improve accuracy. The results show that although some elastic properties lack direct correlation with permeability, they still contribute to its prediction. Bulk density and Quality factors of S-wave (SQs) display a close relationship with permeability. Moreover, combining porosity with elastic properties significantly improves model accuracy compared to using either feature independently. The LightBoost model achieved the highest accuracy (R2 = 0.97, RMSLE = 0.012) when porosity is integrated with the elastic properties, outperforming all other models. In contrast, XGBoost model performed better (R2 = 0.87, RMSLE = 0.195) using only elastic properties as features. This research highlights a robust method for predicting permeability distribution using elastic properties, which can significantly enhance the efficiency of reservoir assessment. By enabling accurate permeability predictions with minimal well data, this approach facilitates the rapid identification of high-potential zones, potentially replacing traditional geophysical methods.

Today the world of social media has immersed in the world of education and influencing the world of the students' activities. It is inevitable that students in many levels of education have been using social media in their life either as students or as interactants in the certain community. Therefore, it is interesting to see the existence of social media among the students. In this study, the researchers highlight the use of social media among the students in the university. This research applied a qualitative research design taking 60 students of English in the universities in Makassar. The data of this research were collected by employing a survey of Google Form and analyzed descriptively and qualitatively. The result of the research shows that the English students in the universities in Makassar gave their positive perceptions about the use of social media. The informants have been using social media in their daily life and also nowadays has applied the social media as a tool of learning English. Some advantages and strategies in using social media in learning English were displayed in this study. Findings from this study will become important input for students themselves in the university context and also as beneficial information among the university practitioners in order to make use the social media in the university not only as communication tool but also as learning and teaching tools.

Abstract Over the past few years, the use of machine learning has gained considerable momentum in many industries, including exploration seismic. While supervised machine learning is increasingly being used in seismic data analysis, some obstacles hinder its widespread application. Seismic facies classification—a crucial aspect in this field—particularly faces challenges such as the selection of appropriate input attributes. Plethora of seismic attributes have been created over the years, and new ones are still coming out. Yet, several have been deemed redundant or geologically meaningless. In the context of machine learning, it is crucial to avoid these redundant and irrelevant attributes as they can result in overfitting, building unnecessary complex models, and prolonging computational time. The current study incorporates an attribute selection approach to seismic facies classification and evaluates the importance of several available seismic attributes. Two datasets from the AN Field and the Dangerous Grounds region offshore Malaysia were utilized. Several attribute selection techniques were evaluated, with most of them yielding perfect attribute subsets for the AN dataset. However, only the wrapper and embedded methods could produce optimal subsets for the more complex Dangerous Grounds dataset. In both datasets, distinguishing the targeted seismic facies was mainly dependent on amplitude, spectral, and gray-level co-occurrence matrix attributes. Furthermore, spectral magnitude components played a significant role in classifying the facies of the Dangerous Grounds broadband data. The study demonstrated the importance of attribute selection, established a workflow, and identified significant attributes that could enhance seismic facies classification in Malaysian basins and similar geologic settings.

This study aims to figure out the difference in perceptions of female and male students of gender equity in education by determining quantitative and qualitative approaches. To measuring the three-domain of gender equity in education (1) equity of access, (2) equity of opportunity in the learning process, and (3) equity of opportunity in educational achievement. We used questionnaires and distributed them to 111 students which are categorized into two genders; 59 female students, and 52 male students. Subsequently, depth interviews were carried out with 10 male students who were randomly chosen to investigate why male students did not accept fully gender equity in education. The independent t-test analysis was utilized to pointed out empirical facts. There was a significant difference in perception between female students and male students of gender equity in education. Gender equity have been coveted by many communities of society including the government. It is supposed to include gender equity material in the curriculum so that stakeholders and all school elements have awareness and are proactive in promoting gender equity in education.

Physics learning in universities utilized the Moodle-based e-learning media as an online learning platform. However, the effectiveness of remediating misconception using online media has not been widely researched. Therefore, this study was set to determine the level of misconception percentage reduction through the use of narrative feedback, the e-learning modules, and realistic video. The study was a quantitative approach with a quasi-experimental method involving 281 students who were taking basic physics courses in the Department of Physics, Chemistry, and Biology Education. The data collection used a three-tier diagnostic test based on e-learning at the beginning of the activity and after the treatment (posttest). The results of the data analysis with descriptive statistics show that the most significant treatment in reducing misconception percentage on the topic of free-fall motion was in the following order: narrative feedback, e-learning modules and realistic video. The misconception percentage reduction in the sub-concept of accelerated free-fall was effective for all types of the treatments.

The phenomena of language use in the class still become the object of study ever since the languages are essential aspects in classroom interaction. It cannot be denied that in the classroom interaction, the communicative styles of the lecturers and students will be influenced by many aspects. One of them is the social status differences which lead to the occurrence of code-crossing in the class. For that purpose, the study in this paper is directed to explore the occurrence of code-crossing in the class and the factors influencing it. This research applied a qualitative research design taking two English classes and their students at one university in Makassar as the subject. The data of this research were collected by employing classroom observation and audio recording. The data were analyzed descriptively by adopting Discourse Analysis approach which relies on data recording, data transcription, data selection, and data interpretation. The result of the research shows that the lecturers and the students employed code-crossing in EFL classroom interaction which can be seen from the use of low and high code. This study also found that the use of that high and low code in the form of code-crossing of the lecturers and the students is influenced by the power of social status, age differences, the social distance or familiarity, and intimacy between the lecturers and the students. Findings from this study are worthy of reading for English language teaching practitioners in their effort to create effective classroom interaction.

The recent development in the field of science, technology and commerce has revived the significance of English language teaching and learning in Saudi Arabia. The Saudi educational policies are envisioned to configure the students' communicative competence in English to the demands of the specific professional disciplines as well as the global market. Engineering undergraduates studying at Saudi public universities rely on the Preparatory Year Programme (PYP) to meet their English language needs. However, it seems that no empirical study to date has been conducted to analyse the English language needs of the engineering undergraduates. This study aims at analysing the English for Specific Purposes (ESP) needs of engineering undergraduates studying at a Saudi university. It intends to identify what academic tasks they often carry out in English. The researcher adopted a mixed-methods approach by employing a questionnaire and interviews to collect data from 257 engineering undergraduates and 32 content-subject teachers. Quantitative data analysis was performed using SPSS, while qualitative data were analysed through NVivo software. The participants' perceptions of target tasks were compared and examined to see if there are significant differences between the groups based on the specialty of department or year of study. Overall, 27 tasks were identified as most frequently performed by undergraduates across all engineering departments at the university. The findings also revealed significant differences in the undergraduates' responses across all four levels of study. The findings suggest incorporating those tasks into the current ESP syllabus which are aligned to the immediate needs of undergraduates of engineering departments.

Living on Bangladesh’s riverine islands (Char lands) puts one at the disadvantage of underdevelopment. Hence, this study focused on the sociodemographic peculiarities and livelihood practices of farmers in the Char lands of Bangladesh. Convergent mixed methods research design was adopted to generate the required data. The quantitative data were analyzed using Statistix 10 software, while the qualitative data were analyzed by collecting themes relative to the study objectives and describing them accordingly. Results revealed that, the sociodemographic peculiarities of the respondents were low and vulnerable, being majority illiterate, having small farm size on which they predominately depend, poor housing, and a population dominated by ≥ 30 years old persons. The mean income (%) from on-farm livelihood practices was superior to that from off-farm livelihood practices, and a big proportion of the mean annual expenditure coverage was split between food and agricultural inputs/implements. Farm size contributes significantly to income, but, natural disaster, illiteracy, riverbank erosion, and lack of improved agriculture knowledge were the foremost livelihood problems in the Char lands. Consequently, the present study deduces that an all-inclusive masterplan is inevitable for sustainable development of the Bangladesh Char lands.

Data on formal sector employment in DKI Jakarta shows a significant and negative relationship between investment and employment. Now, industries are more capital-intensive and prefer to implement automation, IoT robotics, and artificial intelligence. The purpose of this study was to build a model of the relationship between character, work ability, organizational commitment, and organizational culture as they relate to the performance of formal sector employees. The research method was descriptive quantitative. The sampling technique was non-probability sampling. Samples were determined by accidental sampling and were collected from 151 respondents who were formal sector employees of DKI Jakarta. Structural Equation Model applied in data processing. The results of the research indicated that Character factor and workability have a significant direct influence on employee performance on formal sector employees in DKI Jakarta. Only the organizational commitment factor is not significantly correlated directly to performance. Indirect relationship analysis states that organizational culture through organizational commitment correlates with character and workability factors which ultimately have an impact on performance. The implication of this research shows that the creativity factor and environmentally friendly work behavior need to be improved by employees.