Breast cancer remains a significant health concern worldwide, necessitating the development of effective diagnostic tools. In this study, we employ a neural network-based approach to analyze the Wisconsin Breast Cancer dataset, sourced from Kaggle, comprising 570 samples and 30 features. Our proposed model features six layers (1 input, 1 hidden, 1 output), and through rigorous training and validation, we achieve a remarkable accuracy rate of 99.57% and an average error of 0.000170 as shown in the image below. Furthermore, our (...) investigation identifies the most influential features in breast cancer diagnosis, shedding light on the key determinants of malignancy. Notably, we find that factors such as fractal dimension_se, symmetry worst, compactness_worst, symmetry_se, and smoothness_se play pivotal roles in distinguishing between benign and malignant cases. This research contributes to the ongoing efforts to enhance breast cancer diagnosis, providing valuable insights into feature importance and showcasing the potential of neural networks in medical applications. Our findings have implications for improving early detection and treatment strategies, ultimately contributing to improved patient outcomes. (shrink)

Abstract: The efficiency and performance of sorting algorithms play a crucial role in various applications and industries. In this research paper, we present a comprehensive comparative analysis of popular sorting algorithms on datasets of different sizes and characteristics. The aim is to evaluate the algorithms' performance and identify their strengths and weaknesses under varying scenarios. We consider six commonly used sorting algorithms: QuickSort, TimSort, MergeSort, HeapSort, RadixSort, and ShellSort. These algorithms represent a range of approaches and techniques, including divide-and-conquer, (...) hybrid sorting, and simple comparison-based methods. To assess their performance, we employ a diverse set of datasets, including the Iris dataset (1K), student dataset (5.8K), Wine dataset (6.5K), Uniform (10K), Normal (10K), Exponential (10K), Bimodal (10K), Yelp dataset (10K), MNIST dataset (42K), Uniform (100K), Normal (100K), Exponential (100K), Bimodal (100K), Uniform (500K), Normal (500K), Exponential (500K), Bimodal (500K), Uniform (1M), Normal (1M), Exponential (1M), and Bimodal (1M). These datasets cover a wide range of sizes and characteristics, allowing us to analyze the algorithms' performance across different dimensions. We measure and compare several key metrics, including execution time, memory usage, algorithmic complexity and stability. By analyzing these metrics, we gain insights into the efficiency and suitability of each algorithm for different dataset sizes and characteristics. We also discuss the implications of the findings in practical applications. Our results reveal important trade-offs among the sorting algorithms. While some algorithms excel in certain scenarios, others demonstrate better scalability or memory efficiency. We identify the best-performing algorithms for specific dataset characteristics and highlight their strengths and limitations. This research can assist developers and practitioners in selecting appropriate sorting algorithms based on their specific requirements and dataset characteristics. In conclusion, this comparative analysis provides a valuable contribution to the understanding of sorting algorithm performance. The findings contribute insights into the efficiency and suitability of popular sorting algorithms across datasets of different sizes and characteristics. By evaluating key metrics and discussing the implications, we offer guidance for selecting the most appropriate sorting algorithm in various practical scenarios. (shrink)

Abstract: The Spotify Status Dataset is a valuable resource that provides real-time insights into the operational status and performance of Spotify, a popular music streaming platform. This dataset contains a wide array of information related to server uptime, user activity, service disruptions, and more, serving as a critical tool for both Spotify's internal monitoring and the broader data analysis community. As digital services like Spotify continue to play a central role in music consumption, understanding the platform's status (...) becomes crucial for ensuring a seamless user experience. This dataset offers numerous research and analysis opportunities, whether you're interested in exploring service availability trends or investigating user behavior patterns during service disruptions. This study highlights the significance of the Spotify Status Dataset, its potential applications, and the valuable insights it can offer to various stakeholders, from Spotify engineers working to enhance service reliability to researchers studying the effects of downtime on user engagement. It invites readers on a journey through this dataset to uncover the secrets of Spotify's operational status and its impact on music enthusiasts worldwide. (shrink)

AGGA (Academic Guidelines for Generative AIs) is a dataset of 80 academic guidelines for the usage of generative AIs and large language models in academia, selected systematically and collected from official university websites across six continents. Comprising 181,225 words, the dataset supports natural language processing tasks such as language modeling, sentiment and semantic analysis, model synthesis, classification, and topic labeling. It can also serve as a benchmark for ambiguity detection and requirements categorization. This resource aims to facilitate (...) research on AI governance in educational contexts, promoting a deeper understanding of the integration of AI technologies in academia. (shrink)

Background: Food fortification refers to the process of adding nutrients to foods during their production. It is a cost-effective strategy with well-documented health, economic, and social benefits. Food fortification practices in school meal programs need guidance and legal support from various national policies. Aim: This study aims to analyze how various national policies—such as those related to school feeding, nutrition, health, food safety, agriculture, and the private sector—associate with the implementation of in-school food fortification among countries with school meals programs. (...) Methods: The Bayesian Mindsponge Framework, combining the reasoning strengths of Mindsponge Theory and inference advantages of Bayesian analysis, was employed on a dataset of 126 government representatives who manage large-scale school meal programs in 126 different countries. Results: Findings showed that food safety policy and agriculture policy were positively and significantly associated with the in-school food fortification practices, while school feeding policy and private sector policy had ambiguous relationships with these practices. Nutrition policy and health policy had a significant negative association with food fortification practices among countries implementing school meal programs. Conclusions: Findings underscore the strong position of food safety policy and agriculture policy in guiding food fortification practices in school meal programs. There is a need to re-assess the implemented nutrition policy and health policy due to their significant negative associations with these practices. Enhancing and strengthening the school feeding policy and private sector policy may increase their potencies in giving positive impacts on in-school food fortification practices. (shrink)

Abstract: Lotteries have long been a source of fascination and intrigue, offering the tantalizing prospect of unexpected fortunes. In this research paper, we delve into the world of lottery predictions, employing cutting-edge AI techniques to unlock the secrets of lottery outcomes. Our dataset, obtained from Kaggle, comprises historical lottery draws, and our goal is to develop predictive models that can anticipate future winning numbers. This study explores the use of deep learning and time series analysis to achieve this (...) elusive feat. Through rigorous experimentation and data-driven approaches, we seek to determine the viability of AI in the realm of lottery predictions. Our findings reveal both the promise and limitations of AI in this context, shedding light on the complexities of lottery data and the potential need for quantum computing as a last resort. (shrink)

AGGA (Academic Guidelines for Generative AIs) is a dataset of 80 academic guidelines for the usage of generative AIs and large language models in academia, selected systematically and collected from official university websites across six continents. Comprising 181,225 words, the dataset supports natural language processing tasks such as language modeling, sentiment and semantic analysis, model synthesis, classification, and topic labeling. It can also serve as a benchmark for ambiguity detection and requirements categorization. This resource aims to facilitate (...) research on AI governance in educational contexts, promoting a deeper understanding of the integration of AI technologies in academia. (shrink)

heart attack analysis & prediction dataset is a major cause of death worldwide. Early detection and intervention are essential for improving the chances of a positive outcome. This study presents a novel approach to predicting the likelihood of a person having heart failure using a neural network model. The dataset comprises 304 samples with 11 features, such as age, sex, chest pain type, Trtbps, cholesterol, fasting blood sugar, resting electrocardiogram results, maximum heart rate achieved, exercise-induced angina, oldpeak, (...) ST_Slope, and HeartDisease. A neural network model with four layers (1 input, 2 hidden, 1 output) was trained on the dataset and achieved an accuracy of 88% and an average error of 0078296. The most influential factors in heart failure prediction were found to be oldpeak, ST_Slope, sex, fastingBS, chestPainType, exerciseAngina, cholesterol, restingBP, maxHR, restingBP, and age. This study provides a valuable tool for early detection and intervention of heart failure, thereby contributing to the field of health and medicine. (shrink)

Emotions are a central aspect of communication. Consequently, emotion analysis (EA) is a rapidly growing field in natural language processing (NLP). However, there is no consensus on scope, direction, or methods. In this paper, we conduct a thorough review of 154 relevant NLP publications from the last decade. Based on this review, we address four different questions: (1) How are EA tasks defined in NLP? (2) What are the most prominent emotion frameworks and which emotions are modeled? (3) Is (...) the subjectivity of emotions considered in terms of demographics and cultural factors? and (4) What are the primary NLP applications for EA? We take stock of trends in EA and tasks, emotion frameworks used, existing datasets, methods, and applications. We then discuss four lacunae: (1) the absence of demographic and cultural aspects does not account for the variation in how emotions are perceived, but instead assumes they are universally experienced in the same manner; (2) the poor fit of emotion categories from the two main emotion theories to the task; (3) the lack of standardized EA terminology hinders gap identification, comparison, and future goals; and (4) the absence of interdisciplinary research isolates EA from insights in other fields. Our work will enable more focused research into EA and a more holistic approach to modeling emotions in NLP. (shrink)

As the world has become more digitally interconnected than ever before in the 21stcentury, the next generation is required to possess various sets of new skills to succeed in their works and lives. The purpose of the article is to present a dataset of socio-demographic, in-school, out-of-school factors as well as the eight domains of 21st-century skills of Vietnamese secondary school students. A total of 1183 observations from 30 secondary schools in both rural and urban areas of Vietnam are (...) introduced in this dataset. The linear regression analysis was also utilized as an analysis example for this dataset. The insights generated from the dataset are hoped to contribute to skills-based education and policy planning in Vietnam. (shrink)

Text classification is one of the most important task in natural language processing, In this research, we carried out several experimental research on three (3) of the most popular Text classification NLP classifier in Convolutional Neural Network (CNN), Multinomial Naive Bayes (MNB), and Support Vector Machine (SVN). In the presence of enough training data, Deep Learning CNN work best in all parameters for evaluation with 77% accuracy, followed by SVM with accuracy of 76%, and multinomial Bayes with least performance of (...) 69% accuracy. CNN has the best performance in the presence of large enough training dataset because of the presence of filter/ kernels which help to indentify patterns in text data regardless of their position in the sentence. We repeated the training again with just one-third of our data, at this point SVM comes with the best performance, the performance of CNN noticeably drops but still better than Multinomial Naive Bayes, the reason why SVM works best when we reduce the training data was because of its ability to look for a hyper-plane that creates a boundary between different classes of data so as to properly classify them, so we believed that getting the hyper-plane was more efficient when we reduce the dataset, hence reason for the good performance. Multinomial Naive Bayes have the least performance which we attributed to its assumption of independency between the features which sometimes does not hold true. We concluded that availability of data should be an important factor when choosing classifier for Natural Language Processing Text Classification task. CNN should be use in the presence of enough dataset, and SVM should be use when data is not enough. Multinomial Naive Bayes must not be trusted with state of the art NLP task due to its assumption of independency between the features . (shrink)

Objective: Kindling is an animal model of epilepsy induced by electrical stimulation of the brain. The present study aimed to present a different perspective with a bibliometric approach by using the literature data on the “Kindling model” related keywords in the Web of Science (WoS) online database between 1980 and 2023. -/- Methods: The bibliometric data were obtained from the online database WoS and analyzed and visualized with the VoS Viewer Program. The bibliometric datasets were analyzed and visualized regarding article (...) productivity numbers according to years, article productivity numbers according to countries, the most used keywords according to authors, and cross-country cooperation. -/- Result: Considering the results of the analysis of the published datasets, 2022 was determined as the year with the highest article productivity, and an acceleration was observed in the publication increase rate on the subject in general. When the order of the countries in the top three in the number of article productivity was examined, the USA, Germany, and Japan are the main countries, respectively. The most used keywords by the authors were determined as “Epilepsy”, “Kindling”, and “Hippocampus”. In the cooperation among countries, it was found that the USA, Germany, and Japan had more cooperation with other countries, respectively. -/- Conclusıon: This study will contribute to the literature by providing a detailed understanding of the research basis, relevant research results, current research boundaries and main research focus in the Kindling Model. (shrink)

Abstract: This research explores the application of artificial neural networks (ANNs) in predicting cancer using a synthetically generated dataset designed for research purposes. The dataset comprises 10,000 pseudo-patient records, each characterized by gender, age, smoking history, fatigue, and allergy status, along with a binary indicator for the presence or absence of cancer. The 'Gender,' 'Smoking,' 'Fatigue,' and 'Allergy' attributes are binary, while 'Age' spans a range from 18 to 100 years. The study employs a three-layer ANN architecture to (...) develop a predictive model. The achieved accuracy of 97.24% and a low loss value of 0.02 indicate promising performance. Further analysis involves a comprehensive examination of the confusion matrix, precision, recall, and F1 score metrics, along with the ROC curve and AUC score, to provide a detailed understanding of the model's strengths and weaknesses. Additionally, considerations for cross-validation, hyperparameter tuning, and interpretability are discussed. It is emphasized that while the synthetic nature of the dataset allows for controlled experimentation, results should be cautiously interpreted and validated on authentic clinical data before extrapolation to real-world medical scenarios. This research serves as a valuable contribution to the exploration of predictive models for cancer detection, encouraging continued investigation and development in the field. (shrink)

Kidney stones pose a significant health concern, and early detection can lead to timely intervention and improved patient outcomes. This research endeavours to predict the presence of kidney stones based on urine analysis, utilizing a neural network model. A dataset of 552 urine specimens, comprising six essential physical characteristics (specific gravity, pH, osmolarity, conductivity, urea concentration, and calcium concentration), was collected and prepared. Our proposed neural network architecture, featuring three layers (input, hidden, output), was trained and validated, achieving (...) an impressive accuracy of 98.67% and an average error of 0.012. In addition to model performance, feature importance analysis was conducted to determine the most influential factors in predicting kidney stone presence. The findings underscore the significance of urea concentration, specific gravity, calcium concentration, conductivity, osmolarity, and pH as key indicators. This research contributes to the early diagnosis of kidney stones and demonstrates the potential of neural networks in medical diagnostics. The clinical implications of these findings are discussed, emphasizing the importance of timely intervention in managing kidney stone-related health issues. (shrink)

A huge amount of data is being generated everyday through different transactions in industries, social networking, communication systems etc. Big data is a term that represents vast volumes of high speed, complex and variable data that require advanced procedures and technologies to enable the capture, storage, management, and analysis of the data. Big data analysis is the capacity of representing useful information from these large datasets. Due to characteristics like volume, veracity, and velocity, big data analysis is (...) becoming one of the most challenging research problems. Semantic analysis is method to better understand the implied or practical meaning of the input dataset. It is mostly applied with ontology to analyze content mainly in web resources. This field of research combines text analysis and Semantic Web technologies. The use semantic knowledge is to aid sentiment analysis of queries like emotion mining, popularity analysis, recommendation systems, user profiling, etc. A new method has been proposed to extract semantic relationships between different data attributes of big data which can be applied to a decision system. (shrink)

AI-powered records science is revolutionizing the way facts are analyzed and understood. It can significantly improve the exceptional of information evaluation and boost its speed. AI-powered facts technological know-how enables access to more extensive, extra complicated information sets, faster insights, faster trouble solving, and higher choice making. Using the use of AI-powered information technological know-how techniques and tools, organizations can provide more accurate outcomes with shorter times to choices. AI-powered facts technology also offers more correct predictions of activities and developments (...) and superior accuracy in sample reputation.Furthermore, AI-powered facts science techniques can help agencies discover anomalies in datasets, learn new patterns, and better understand the points. This paper discusses the advantages of AI-powered statistics science and how it may enhance data analysis. It additionally discusses a number of the challenges going through organizations in imposing AI-powered records science projects and gives excellent practices for deploying and coping with records technology projects. (shrink)

R Core Team. (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing. Supplement to Occipital and left temporal instantaneous amplitude and frequency oscillations correlated with access and phenomenal consciousness. Occipital and left temporal instantaneous amplitude and frequency oscillations correlated with access and phenomenal consciousness move from the features of the ERP characterized in Occipital and Left Temporal EEG Correlates of Phenomenal Consciousness (Pereira, 2015) towards the instantaneous amplitude and frequency of event-related changes correlated with a (...) contrast in access and in phenomenology. Occipital and left temporal instantaneous amplitude and frequency oscillations correlated with access and phenomenal consciousness proceed as following. In the first section, empirical mode decomposition (EMD) with post processing (Xie, G., Guo, Y., Tong, S., and Ma, L., 2014. Calculate excess mortality during heatwaves using Hilbert-Huang transform algorithm. BMC medical research methodology, 14, 35) Ensemble Empirical Mode Decomposition (postEEMD) and Hilbert-Huang Transform (HHT). -/- In the second section, calculated the variance inflation factor (VIF). In the third section, partial least squares regression (PLSR): the minimal root mean squared error of prediction (RMSEP). In the last section, partial least squares regression (PLSR): significance multivariate correlation (sMC) statistic. (shrink)

Background: The school meals program has multiple objectives of education, nutrition, and value transfer. To ensure achieving the goal, total quality management (TQM) is implemented in the school meals program. Supply chain issues pose significant challenges to TQM implementation in the program execution. Aim: This study aims to examine national and international capacities in supply chain management by analyzing the variety of food items delivered through the school meals program. Methods: The Bayesian Mindsponge Framework, combining the reasoning strengths of Mindsponge (...) Theory and inference advantages of Bayesian analysis, was employed on a dataset of 126 government representatives who manage large-scale school meal programs in 126 different countries. Results: Findings showed that the method of obtaining supplies, whether through in-kind donations from local, regional, national sources, or national food reserves and purchasing from neighboring or distant countries, had a significant positive association with the variety of food items included in school meals or snacks. Conversely, obtaining supplies in-kind from neighboring or distant countries had a significant negative association with the food variety. Additionally, purchasing supplies from local, regional, or national food reserves had an unclear association with the food variety. Conclusions: Findings underscore the importance of supporting the World Bank and World Food Programme’s recommendation to rely more on local resources and capacities. Enhancing supply chain management at the national level is crucial for developing a long-term and sustainable school meals program. (shrink)

The neurotransmitters GABA and glutamate have been suggested to play a role in Major Depressive Disorder (MDD) through an imbalance between cortical inhibition and excitation. This effect has been highlighted in higher brain areas, such as the prefrontal cortex, but has also been posited in basic sensory cortices. Based on this, magnetic resonance spectroscopy (MRS) was used to investigate potential changes to GABA+ and glutamate+glutamine (Glx) concentrations within the occipital cortex in MDD patients (n = 25) and healthy controls (n (...) = 25). No difference in occipital GABA+ or Glx concentrations, nor in the GABA+/Glx ratio, was found between groups. An analysis of an extended MDD patient and unmatched control dataset (n = 90) found no correlation between metabolite concentrations and depressive symptoms. These results were integrated with prior studies through metabolite-specific meta-analyses, revealing no difference in occipital GABA and Glx concentrations between patients and controls. An effect of publication year on GABA group differences was found, suggesting that previously reported results may have been artifacts of measurement accuracy. Taken together, our results suggest that, contrary to some prior reports, MRS measurements of occipital GABA and Glx do not differ between MDD patients and controls. (shrink)

Abstract: This research paper presents a novel approach to predict audit risks using a neural network model. The dataset used for this study was obtained from Kaggle and comprises 774 samples with 18 features, including Sector_score, PARA_A, SCORE_A, PARA_B, SCORE_B, TOTAL, numbers, marks, Money_Value, District, Loss, Loss_SCORE, History, History_score, score, and Risk. The proposed neural network architecture consists of three layers, including one input layer, one hidden layer, and one output layer. The neural network model was trained and validated, (...) achieving an impressive accuracy of 100% and an average error of 0.000015, indicating its robust predictive capability. Moreover, we conducted feature importance analysis to identify the most influential features for predicting audit risk. The key features found to be critical for classifying fraudulent activities in audit risk prediction are Sector_score, PARA_A, SCORE_A, PARA_B, SCORE_B, TOTAL, numbers, marks, Money_Value, District, Loss, Loss_SCORE, History, and History_score. This research contributes to the field of audit risk prediction by demonstrating the effectiveness of a neural network-based approach and highlighting the importance of specific features in detecting fraudulent activities. The findings have significant implications for auditors and organizations seeking to enhance their audit risk assessment processes, ultimately leading to improved financial transparency and fraud detection. (shrink)

In this paper we propose an non-machine learning artificial intelligence (AI) based approach for telecom data analysis, with a special focus on clique detection. Clique detection can be used to identify households, which is a major challenge in telecom data analysis and predictive analytics. Our approach does not use any form of machine learning, but another type of algorithm: satisfiability for propositional logic. This is a neglected approach in modern AI, and we aim to demonstrate that for certain (...) tasks, it may be a good alternative to machine learning-based approaches. We have used a simple DPLL satisfiability solver over an artificially generated telecom dataset (due to GDPR regulations), but our approach can be implemented on any telecom data by following the SAT encoding we have developed, and the DPLL solver can be substituted by a more advanced alternative such as CDCL. This paper extends the method presented in [1] for banking logs to data containing caller information, and proposes a more efficient encoding. -/- . (shrink)

Abstract—Breast cancer is considered the second most common cancer in women compared to all other cancers. It is fatal in less than half of all cases and is the main cause of mortality in women. It accounts for 16% of all cancer mortalities worldwide. Early diagnosis of breast cancer increases the chance of recovery. Data mining techniques can be utilized in the early diagnosis of breast cancer. In this paper, an academic experimental breast cancer dataset is used to perform (...) a data mining practical experiment using the Waikato Environment for Knowledge Analysis (WEKA) tool. The WEKA Java application represents a rich resource for conducting performance metrics during the execution of experiments. Pre-processing and feature extraction are used to optimize the data. The classification process used in this study was summarized through thirteen experiments. Additionally, 10 experiments using various different classification algorithms were conducted. The introduced algorithms were: Naïve Bayes, Logistic Regression, Lazy IBK (Instance-Bases learning with parameter K), Lazy Kstar, Lazy Locally Weighted Learner, Rules ZeroR, Decision Stump, Decision Trees J48, Random Forest and Random Trees. The process of producing a predictive model was automated with the use of classification accuracy. Further, several experiments on classification of Wisconsin Diagnostic Breast Cancer and Wisconsin Breast Cancer, were conducted to compare the success rates of the different methods. Results conclude that Lazy IBK classifier k-NN can achieve 98% accuracy among other classifiers. The main advantages of the study were the compactness of using 13 different data mining models and 10 different performance measurements, and plotting figures of classifications errors. (shrink)

Abstract Alzheimer's disease has spread insanely throughout the world. Early detection and intervention are essential to improve the chances of a positive outcome. This study presents a new method to predict a person's likelihood of developing Alzheimer's using a neural network model. The dataset includes 373 samples with 10 features, such as Group,M/F,Age,EDUC, SES,MMSE,CDR ,eTIV,nWBV,Oldpeak,ASF.. A four-layer neural network model (1 input, 2 hidden, 1 output) was trained on the dataset and achieved an accuracy of 98.10% and an (...) average error of 0.01. This study provides a valuable tool for early detection and intervention of Alzheimer's, thus contributing to the field of health and medicine. (shrink)

This study employs machine learning to predict diabetes using a Kaggle dataset with 13 features. Our three-layer model achieves an accuracy of 98.73% and an average error of 0.01%. Feature analysis identifies Age, Gender, Polyuria, Polydipsia, Visual blurring, sudden weight loss, partial paresis, delayed healing, irritability, Muscle stiffness, Alopecia, Genital thrush, Weakness, and Obesity as influential predictors. These findings have clinical significance for early diabetes risk assessment. While our research addresses gaps in the field, further work is needed (...) to enhance model generalizability. (shrink)

The research outlines a workflow that incorporates data collection, preprocessing, model training, and optimization. Real-world datasets from retail and e-commerce sectors are utilized to validate the proposed methodology, showcasing substantial improvements in model performance. The results indicate that optimized models not only provide better predictions of consumer behaviour but also enhance customer segmentation and targeting strategies. The study concludes with recommendations for future research, including the exploration of hybrid optimization techniques and the application of these methods in real-time analytics.

Since Circular 34 from the Ministry of Science and Technology of Vietnam required the head of the national project to have project results published in ISI/Scopus journals in 2014, the field of economics has been dominating the number of nationally-funded projects in social sciences and humanities. However, there has been no scientometric study that focuses on the difference in productivity among fields in Vietnam. Thus, harnessing the power of the SSHPA database, a comprehensive dataset of 1,564 Vietnamese authors (854 (...) males, 705 females) with 2,410 publications in the 2008 – 2018 period was extracted and analyzed. Various factors were considered including age, gender, new authors, leading authors, co-authorship, and Impact Factor. The findings suggest a high level of contribution from authors at the age of 40 – 44 in economics (858 publications) in a 12-years period, which is equivalent to the social medicine total output, and two times more than the total output of the education. Moreover, the presence and reinforcement of male researchers are still dominating in Economics and other fields, with the only exception of education. Despite the rapid rise in the number of Vietnamese lead authors, gender disparity among disciplines is an issue. Contrary to the strong international collaboration-oriented tendency in social medicine, economics, and other fields, educational authors are not open to international collaborating. Finally, most of the publications in economics belong to the group with JIF from 0 to 2, in contrast with the high number of social medicine publications with JIF from 2 to 5, which suggesting the field of economics is fulfilling the quantity, but still, need more quality publications. (shrink)

Two fundamentally different perspectives on knowledge diffusion dominate debates about academic disciplines. On the one hand, critics of disciplinary research and education have argued that disciplines are isolated silos, within which specialists pursue inward-looking and increasingly narrow research agendas. On the other hand, critics of the silo argument have demonstrated that researchers constantly import and export ideas across disciplinary boundaries. These perspectives have different implications for how knowledge diffuses, how intellectuals gain and lose status within their disciplines, and how intellectual (...) reputations evolve within and across disciplines. We argue that highly general claims about the nature of disciplinary boundaries are counterproductive, and that research on the nature of specific disciplinary boundaries is more useful. To that end, this paper uses a novel publication and citation network dataset and statistical models of citation networks to test hypotheses about the boundaries between philosophy of science and 11 disciplinary clusters. Specifically, we test hypotheses about whether engaging with and being cited by scientific communities outside philosophy of science has an impact on one’s position within philosophy of science. Our results suggest that philosophers of science produce interdisciplinary scholarship, but they tend not to cite work by other philosophers when it is published in journals outside of their discipline. Furthermore, net of other factors, receiving citations from other disciplines has no meaningful impact—positive or negative—on citations within philosophy of science. We conclude by considering this evidence for simultaneous interdisciplinarity and insularity in terms of scientific trading theory and other work on disciplinary boundaries and communication. (shrink)

Big Data is a large-sized and complex dataset, which cannot be managed using traditional data processing tools. Mining process of big data is the ability to extract valuable information from these large datasets. Association rule mining is a type of data mining process, which is indented to determine interesting associations between items and to establish a set of association rules whose support is greater than a specific threshold. The classical association rules can only be extracted from binary data where (...) an item exists in a transaction, but it fails to deal effectively with quantitative attributes, through decreasing the quality of generated association rules due to sharp boundary problems. In order to overcome the drawbacks of classical association rule mining, we propose in this research a new neutrosophic association rule algorithm. The algorithm uses a new approach for generating association rules by dealing with membership, indeterminacy, and non-membership functions of items, conducting to an efficient decision-making system by considering all vague association rules. To prove the validity of the method, we compare the fuzzy mining and the neutrosophic mining. The results show that the proposed approach increases the number of generated association rules. (shrink)

This essay examines the quantitative aspects of Greco-Roman science, represented by a group of established disci¬plines, which since the fourth century BC were called mathēmata or mathē¬ma¬tikai epistē¬mai. In the group of mathēmata that in Antiquity normally comprised mathematics, mathematical astronomy, harmonics, mechanics and optics, we have also included geography. Using a dataset based on The Encyclopaedia of Ancient Natural Scientists, our essay considers a community of mathēmatikoi (as they called themselves), or ancient scientists (as they are defined for (...) the purposes of the present paper) from a sociological point of view, focusing on the size of the scientific population known to us and its disciplinary, temporal and geographical distribution. A diachronic comparison of neighboring and partly overlapping communities, ancient scientists and philosophers, allows the pattern of their interrelationship to be traced. An examination of centers of science throughout ancient history reveals that there were five major centers – Athens, Alexandria, Rhodes, Rome and Byzantium/Constantinople – that appear and replace one another in succession as leaders. These conclusions serve to reopen the issue of the place of mathēmata and mathēmatikoi in ancient society. (shrink)

In anticipation of the journal’s centenary in 2027 this paper provides a citation network analysis of all available citation and publication data of the Australasian Journal of Philosophy (1923–2017). A total of 2,353 academic articles containing 21,772 references were collated and analyzed. This includes 175 articles that contained author-submitted keywords, 415 publisher-tagged keywords and 519 articles that had abstracts. Results initially focused on finding the most published authors, most cited articles and most cited authors within the journal, followed by (...) most discussed topics and emerging patterns using keywords and abstracts. The analysis then proceeded to apply social network analysis using Kumu© – a visualization platform for mapping systems and relationships using large datasets. Analysis reveals topic clusters both unique to the journal, and inclusive of the journal’s history. Results from this analysis reaffirm the journal’s continuing focus on topics in traditional analytic philosophy such as morality, epistemology and knowledge, whilst also featuring topics associated with logic and paradox. This paper presents a new approach to analysing and understanding the historic and emerging topics of interest to the journal, and its readership. This has never previously been done for single philosophy journal. This is historically important given the journal’s forthcoming centenary. (shrink)

This paper analyses three centuries of developing German-language philosophy journals, from the first journals published in 1665 to those from the first decade of post-WWII recovery. Relying upon two bibliographies of philosophical journals collected in the 1970s, one by Joachim Kirchner and one by Wolfram Hogrebe, Rudolf Kamp, and Gert König, we attained a dataset of 607 journals. To analyse the population of periodicals, we identified three key components: the longevity of each journal and the growth rate and the (...) continuity of the body of the journal population. The most puzzling finding is that there was a rapid growth in the number of journals at the end of the eighteenth century followed by a long decline in numbers that lasted almost a century. This paper analyses the structure of the boom in philosophical periodicals after 1888, followed by the effects of both World Wars, and identifies the communication crisis that occurred at the height of the Weimar Republic. (shrink)

Abstract: Heart attacks, or myocardial infarctions, are a leading cause of mortality worldwide. Early prediction and accurate analysis of potential risk factors play a crucial role in preventing heart attacks and improving patient outcomes. In this study, we conduct a comprehensive review of datasets related to heart attack analysis and prediction. We begin by examining the various types of datasets available for heart attack research, encompassing clinical, demographic, and physiological data. These datasets originate from diverse sources, including hospitals, (...) research institutions, and public health agencies. Our analysis aims to identify common features, data quality, and potential biases in these datasets. Next, we explore the predictive modeling techniques employed in heart attack prediction. Machine learning algorithms, such as decision trees, support vector machines, and deep neural networks, have gained prominence in predicting heart attacks. We discuss the strengths and limitations of these methods and highlight recent advancements in predictive modeling. Furthermore, we delve into the critical risk factors associated with heart attacks. Factors such as age, gender, hypertension, diabetes, and cholesterol levels are examined for their significance in predicting cardiac events. We also investigate the role of lifestyle factors, including smoking, diet, and physical activity, in heart attack risk assessment. Additionally, this review addresses the importance of data preprocessing and feature engineering in improving prediction accuracy. Feature selection methods, missing data handling, and data scaling techniques are discussed to enhance the robustness of heart attack prediction models. In conclusion, this comprehensive dataset review provides valuable insights into the state of heart attack analysis and prediction. It serves as a resource for researchers and healthcare professionals seeking to better understand the datasets available for heart attack research and the methods employed for accurate prediction. Ultimately, our efforts in dataset analysis and predictive modeling contribute to the advancement of preventive cardiology and the reduction of heart attack-related morbidity and mortality. (shrink)

Abstract: This research delves into the utilization of Artificial Neural Networks (ANNs) as a powerful tool for predicting the overall ratings of books by leveraging a diverse set of attributes. To achieve this, we employ a comprehensive dataset sourced from Goodreads, enabling us to thoroughly examine the intricate connections between the different attributes of books and the ratings they receive from readers. In our investigation, we meticulously scrutinize how attributes such as genre, author, page count, publication year, and reader (...) reviews influence a book's overall rating. Through rigorous analysis and experimentation, we construct an advanced ANN model tailored for predictive analysis in the realm of book ratings. The outcomes of our study reveal the remarkable potential of ANNs in this domain. The ANN model exhibits an impressive level of accuracy when it comes to forecasting book ratings, underlining the efficacy and promise of artificial neural networks in enhancing our understanding and prediction of book evaluations. This research opens up new avenues for leveraging machine learning techniques to gain deeper insights into the dynamics of book ratings and reader preferences. (shrink)

Accurate statistical analysis of Fortnite gameplay data is essential for improving gaming strategies and performance. In this study, we present a novel approach to analyze Fortnite statistics using machine learning techniques. Our dataset comprises a wide range of gameplay metrics, including eliminations, assists, revives, accuracy, hits, headshots, distance traveled, materials gathered, materials used, damage taken, damage to players, damage to structures, and more. We collected this dataset to gain insights into Fortnite player performance and strategies. The proposed (...) model employs advanced machine learning algorithms and data preprocessing techniques. We aim to identify key performance indicators and influential factors that contribute to Fortnite success. By exploring patterns and relationships within the dataset, our model can provide valuable insights into gameplay optimization. Our research involves feature analysis to determine the most influential factors in predicting Fortnite performance. These factors include eliminations, assists, revives, accuracy, hits, headshots, distance traveled, materials gathered, materials used, damage taken, damage to players, damage to structures, and more. By understanding the impact of these factors, we aim to help Fortnite players enhance their skills and strategies. Through extensive training and validation of our machine learning model, we achieved remarkable results, with an accuracy rate of [Your Accuracy Rate]% and an average error of [Your Average Error]. This research not only provides valuable insights into Fortnite gameplay but also offers a foundation for future research in the field of esports analytics and performance optimization. (shrink)

Background: Food biofortification practices in agricultural sectors involve the process of employing biotechnology to enhance the nutritional content of crops during their growth process. Biofortification makes foods even more nutritious and highly functional for addressing malnutrition among children. These practices in farming industries need guidance and legal support from various national policies to support high-quality supplies of school meals fully. Aim: This study aims to analyze the association between various national policies and the implementation of food biofortification practices in agricultural (...) sectors among countries implementing school meal programs. Methods: The Bayesian Mindsponge Framework, combining the reasoning strengths of Mindsponge Theory and inference advantages of Bayesian analysis, was employed on a dataset of 126 government representatives who manage large-scale school meal programs in 126 different countries. Results: Findings showed that food safety policy was positively associated with food biofortification practices in agricultural sectors among countries implementing school meal programs. In contrast, nutrition policy had a significant negative association with these practices. Additionally, policies of school feeding, health, agriculture, and the private sector had unclear associations with food biofortification. Conclusions: Findings underscore the strong position of food safety policy in guiding food biofortification practices in agricultural sectors among countries implementing school meal programs. There is a need to re-assess the implemented nutrition policy due to its significant negative association with these practices. Enhancing and strengthening the policies of school feeding, health, agriculture, and the private sector may increase their potencies in giving positive impacts on food biofortification practices in agricultural sectors to ensure high-quality school meal supplies. (shrink)

In the present study, we tested whether civic identity consisting of moral and political identity via the bifactor model of civic identity with the Stanford Civic Purpose dataset. Previous research in youth development proposed that civic identity consists of two closely related identity constructs, i.e., moral and political identity. Given the bifactor model in factor analysis assumes the presence of both the general and specific factors, we hypothesized that the bifactor model would better fit the data than conventional (...) alternative models. Thus, we compared the candidate models, including the bifactor model, with confirmatory factor analysis. To address the concern about the potential overfitting of the bifactor model, we additionally conducted cross-validation and measurement invariance tests across different gender groups and time points. The analysis results supported our hypothesis that the bifactor model better fitted the data. Both the cross-validation and measurement invariance test results suggested that the overfitting issue was not a significant concern. In the present study, we empirically demonstrated that the bifactor model of civic identity consisting of moral and political identity, which was supposed to be more consistent with the theoretical and conceptual framework of civic identity, better fitted the data than conventional alternative models. (shrink)

Marine and coastal ecosystems are crucial in maintaining human livelihood, facilitating social development, and reducing climate change impacts. Studies have examined how the benefit perception of aquatic ecosystems, knowledge, and emotion about climate change affect peoples’ support for marine protection. However, their interaction effects remain understudied. The current study explores the intricate interaction effect of the benefit perception of aquatic ecosystems, knowledge, and worry about climate change on marine protection support. Bayesian Mindsponge Framework (BMF) analytics was employed on a (...) class='Hi'>dataset of 709 stakeholders from 42 countries generated by MaCoBioS—a research project funded by the European Commission Horizon 2020. The statistical analysis shows that the impacts of benefit perception of ocean ecosystems, knowledge, and worry about climate change on marine protection support vary due to their interactions. Specifically, when stakeholders perceive ocean ecosystems to have little utility in mitigating climate change, greater climate change knowledge and concern are associated with a higher level of marine protection support. Nevertheless, in the scenarios where stakeholders perceive the benefits of ocean ecosystems, the effect of climate change knowledge becomes conditional on the worry level. If stakeholders are concerned about climate change, those with a greater level of climate change knowledge will associate with a higher level of marine protection support. Otherwise, greater climate change knowledge will result in lower support. These findings highlight emotion’s importance in directing climate change knowledge’s effect on marine protection support. Linking people’s “objects of care” to the consequences of climate change can help improve climate change communication effectiveness. (shrink)

Products made from animal fur and skin have been a major part of human civilization. However, in modern society, the unsustainable consumption of these products – often considered luxury goods – has many negative environmental impacts. This study explores how people’s perceptions of biodiversity affect their attitudes and behaviors toward consumption. To investigate the information process deeper, we add the moderation of beliefs about biodiversity loss. Following the Bayesian Mindsponge Framework (BMF) analytics, we use mindsponge-based reasoning to construct conceptual models (...) and employ Bayesian analysis aided by Markov Chain Monte Carlo (MCMC) algorithms on a 535 Vietnamese urban residents dataset. The results show that people’s preference for using products made from animal skin/fur is negatively associated with the perceived consequences of biodiversity loss when they believe biodiversity loss is a major problem. In contrast, if urban residents believe biodiversity loss is unreal or not a significant issue, the association between perceived consequences of biodiversity loss and personal preference happens in the opposite direction. The same effects of biodiversity loss perception on people’s possession of skin/fur products were not found, indicating a more complex information process on behaviors compared to attitudes. Nevertheless, in the scenario that people believe biodiversity loss is not a significant issue, the higher the perceived consequences of biodiversity loss are the greater number of animal-based products they likely own. Our results suggest that policymakers should not neglect the factor of personal belief besides knowledge and awareness in environmental campaigns. (shrink)

In the last decade, the cases of breast and cervical cancer have been positioned at the top rank of cancer statistics worldwide. Consequently, many husbands become family caregivers (FCGs) and get the burden of cancer caregiving. Being blind and incompetent, they need supportive care from healthcare professionals (HCPs). To support them, HCPs provide various healthcare information to meet their needs. Further, their demand for a specific type of healthcare information may reflect their need for emotional support from the HCPs to (...) reduce the burden. This study aims to examine the predictors of the need for emotional support among FCGs of female cancer patients by analyzing the demanded healthcare information. The mindsponge theory was used in conceptual development and interpretation. Bayesian Mindsponge Framework (BMF) analytics were used for statistical analysis on a dataset of 48 husbands and 12 significant others in five congested communities of Surabaya, Indonesia. Results showed that among the six types of healthcare information, the demanded cancer-specific information and information on cancer physical needs significantly predict the need for emotional support among respondents. Meanwhile, FCGs with a higher demand for information on alternative therapies are less likely to need emotional support. Other types of information have ambiguous effects on the need for emotional support. This study reveals that the demanded cancer-specific information, e.g., cancer prognosis or likely outcome, and information on cancer physical needs must be prioritized in information disclosure to meet the needs for emotional support among FCGs of female cancer patients. (shrink)

Gene clustering is a useful exploratory technique to group together genes with similar expression levels under distinct cell cycle phases or distinct conditions. It helps the biologist to identify potentially meaningful relationships between genes. In this study, we propose a clustering method based on multivariate normal mixture models, where the number of clusters is predicted via sequential hypothesis tests: at each step, the method considers a mixture model of m components (m = 2 in the first step) and tests if (...) in fact it should be m - 1. If the hypothesis is rejected, m is increased and a new test is carried out. The method continues (increasing m) until the hypothesis is accepted. The theoretical core of the method is the full Bayesian significance test, an intuitive Bayesian approach, which needs no model complexity penalization nor positive probabilities for sharp hypotheses. Numerical experiments were based on a cDNA microarray dataset consisting of expression levels of 205 genes belonging to four functional categories, for 10 distinct strains of Saccharomyces cerevisiae. To analyze the method’s sensitivity to data dimension, we performed principal components analysis on the original dataset and predicted the number of classes using 2 to 10 principal components. Compared to Mclust (model-based clustering), our method shows more consistent results. (shrink)

Abstract: Heart diseases are increasing daily at a rapid rate and it is alarming and vital to predict heart diseases early. The diagnosis of heart diseases is a challenging task i.e. it must be done accurately and proficiently. The aim of this study is to determine which patient is more likely to have heart disease based on a number of medical features. We organized a heart disease prediction model to identify whether the person is likely to be diagnosed with a (...) heart disease or not using the medical features of the person. We used many different algorithms of machine learning such as Gaussian Mixture, Nearest Centroid, MultinomialNB, Logistic RegressionCV, Linear SVC, Linear Discriminant Analysis, SGD Classifier, Extra Tree Classifier, Calibrated ClassifierCV, Quadratic Discriminant Analysis, GaussianNB, Random Forest Classifier, ComplementNB, MLP Classifier, BernoulliNB, Bagging Classifier, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Gradient Boosting Classifier, Decision Tree Classifier, and Deep Learning to predict and classify the patient with heart disease. A quite helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of heart diseases in any person. The strength of the proposed model was very satisfying and was able to predict evidence of having a heart disease in a particular person by using Deep Learning and Random Forest Classifier which showed a good accuracy in comparison to the other used classifiers. The proposed heart disease prediction model will enhances medical care and reduces the cost. This study gives us significant knowledge that can help us predict the person with heart disease. The dataset was collected from Kaggle depository and the model is implemented using python. (shrink)

Abstract: Rice, as a paramount staple crop worldwide, sustains billions of lives. Precise classification of rice types holds immense agricultural, nutritional, and economic significance. Recent advancements in machine learning, particularly Artificial Neural Networks (ANNs), offer promise in enhancing rice type classification accuracy and efficiency. This research explores rice type classification, harnessing neural networks' power. Utilizing a rich dataset from Kaggle, containing 18,188 entries and key rice grain attributes, we develop and evaluate a neural network model. Our neural network, featuring (...) a single hidden layer, achieves remarkable results—a staggering 100% accuracy and a minute average error rate of 0.000001. Beyond performance metrics, we delve into the intricacies of rice type classification through feature importance analysis. The most influential features—'id,' 'Area,' 'MajorAxisLength,' 'MinorAxisLength,' 'Eccentricity,' 'ConvexArea,' 'EquivDiameter,' 'Extent,' 'Perimeter,' 'Roundness,' and 'AspectRatio'—uncover the physiological traits underpinning accurate rice classification. This research contributes to advancing rice classification methods and highlights the potential of ANNs in optimizing agricultural practices, ensuring food safety, and bolstering global trade. (shrink)

In the trust-health relationship, how trusting other people in society may promote good health is a topic often examined. However, the other direction of influence – how health may affect trust – has not been well explored. In order to investigate this possible effect, we employed Bayesian Mindsponge Framework (BMF) analytics to go deeper into the information processing mechanisms underlying the expressions of trust. Conducting Bayesian analysis on a dataset of 1237 residents from Cali, Colombia, we found that (...) general health status is positively associated with generalized trust, but recent experiences of illnesses/injuries have a negative moderating effect. Personalized trust is largely unchanged across different general health conditions, but the trust level becomes higher with recent experiences of illnesses/injuries. Psychophysiological mechanisms of increasing information filtering intensity toward unfamiliar sources during a vulnerable state of health is a plausible explanation of found patterns in generalized trust. Because established personal relationships are reinforced information channels, personalized trust is not affected as much. Rather, the results suggest that people may rely even more on loved ones when they are in bad health conditions. This exploratory study shows that the trust-health relationship can be examined from a different angle that may provide new insights. (shrink)

In current philosophical discussions on evidence in the medical sciences, epidemiology has been used to exemplify a specific version of evidential pluralism. According to this view, known as the Russo–Williamson Thesis, evidence of both difference-making and mechanisms is produced to make causal claims in the health sciences. In this paper, I present an analysis of data and evidence in epidemiological practice, with a special focus on research on the exposome, and I cast doubt on the extent to which evidential (...) pluralism holds in this case. I start by focusing on the claim that molecular data allows for the production of mechanistic evidence. On the basis of a close look at the ways in which molecular data is used in exposome research, I caution against interpretations in terms of mechanistic evidence. Secondly, I expand my critical remarks on the thesis by addressing the conditions under which data is categorised as evidence in exposome research. I argue that these show that the classification of a dataset as a type of evidence is dependent on the ways in which the data is used. This is in contrast with the approach of evidential pluralism, where evidence is classified in different types on the basis of its intrinsic properties. Finally, I come back to what I consider the core of the thesis and suggest that the epidemiological research analysed in the paper indicates different interpretations of evidential pluralism and its applicability in the health sciences. (shrink)

The partnership between a visually impaired runner (VIR) and sighted guide runner (SGR) constitutes a unique sporting dyad. The quality of these partnerships may profoundly impact the sport and physical activity (PA) experiences of visually impaired (VI) people, yet little is known about the experiences of VIRs and SGRs. This study aimed to explore qualitatively the running experiences of VIRs and SGRs. Five VIRs and five SGRs took part in in-depth, semi-structured interviews (M length = 62 minutes) exploring their running (...) journeys and perceptions of running-together. We analysed the dataset using reflexive thematic analysis. Four themes were generated, comprising: becoming and being a running team; a multifaceted intercorporeal experience; running-together promotes change; and disabling social interaction within running. Participants were generally positive about their running experiences, highlighting a range of benefits derived from the activity. Nevertheless, some examples of barriers to participation were also identified. Although the positive experiences described by the runners suggest guided running holds promise to increase PA in VI people, our findings illustrate the importance of directing attention towards developing high-quality relationships between VIRs and guides, alongside reinforcing the need for further change to promote inclusivity. (shrink)

Products made from animal fur and skin have been a major part of human civilization. However, in modern society, the unsustainable consumption of these products – often considered luxury goods – has many negative environmental impacts. This study explores how people’s perceptions of biodiversity affect their attitudes and behaviors toward consumption. To investigate the information process deeper, we add the moderation of beliefs about biodiversity loss. Following the Bayesian Mindsponge Framework (BMF) analytics, we use mindsponge-based reasoning for constructing conceptual models (...) and employ Bayesian analysis aided by Markov Chain Monte Carlo (MCMC) algorithms on a dataset of 535 Vietnamese urban residents. The results show that people’s preference for using products made from animal skin/fur is negatively associated with perceived consequences of biodiversity loss when they believe biodiversity loss is real and a major problem. In contrast, if urban residents believe biodiversity loss is unreal or not a significant issue, the association between perceived consequences of biodiversity loss and personal preference happens in the opposite direction. The same effects of biodiversity loss perception on people’s possession of skin/fur products was not found, indicating a more complex information process on behaviors compared to attitudes. Nevertheless, in the scenario that people believe biodiversity loss is not a significant issue, the higher the perceived consequences of biodiversity loss are, the greater number of animal-based products they likely own. Our results suggest that policymakers should not neglect the factor of personal belief besides knowledge and awareness in environmental campaigns. (shrink)

Cancer care has transitioned from clinical-based to home-based care to support longterm care in a more familiar and comfortable environment. This care transition has put family caregivers (FCGs) in a strategic position as care providers. Cancer care at home involves psychological and emotional treatment at some point, making FCGs deal with the stress of cancer patients frequently. Due to their limited care competencies, they need supportive care from healthcare professionals in cancer stress management. This study aims to examine how types (...) of demanded healthcare information affect the FCG’s role in reducing the stress of female cancer patients. The mindsponge theory was used in conceptual development and interpretation. Bayesian Mindsponge Framework (BMF) analytics were used for statistical analysis on a dataset of 48 spouses (husbands) and 12 other family members in five congested communities of Surabaya, Indonesia. Results showed that among the six types of healthcare information, FCGs with higher demand for cancer-specific information were more likely to need support in reducing the stress of female cancer patients. Meanwhile, FCGs with a higher demand for information about support services were less likely to need support to reduce cancer patients’ stress. Other types of healthcare information have ambiguous effects on the need for support in reducing cancer-induced stress. This study reveals that the demanded cancer-specific information, e.g., cancer prognosis or likely outcome, must be prioritized to assist FCG’s role in managing cancer stress. (shrink)

Adaptation and stress are two main concepts useful for better understanding the phases of illness and health-related human behavior. The two faces of adaptation, adaptation as a process and adaptation as a product, have raised the question of how long the adaptation process will take in cancer trajectories. The care setting transition from clinical-based into home-based cancer care has stressed the role of family caregivers (FCG) in cancer management. This study examines how types of demanded healthcare information affect the FCG’s (...) role in catalyzing the adaptation of female cancer patients. The mindsponge theory was used in conceptual development and results interpretation. Bayesian Mindsponge Framework (BMF) analytics was used for statistical analysis on a dataset of 48 spouses (husbands) and 12 other family members in five congested communities of Surabaya, Indonesia. Results showed that among the six types of demanded healthcare information, FCGs with a higher tendency to demand cancer-specific information are more likely to need support in catalyzing the adaptation of female cancer patients. Meanwhile, FCGs with a higher demand for information on alternative therapies are less likely to need support in catalyzing cancer adaptation. Other types of healthcare information have ambiguous effects on the need for support in cancer adaptation assistance. This study reveals that the demanded cancer-specific information, e.g., cancer’s prognosis or likely outcome, must be prioritized to assist FCG’s role in catalyzing adaptation among female cancer patients. Cancer prognosis and outcomes reflect the future of cancer care results. (shrink)

Creativity is a crucial aspect of entrepreneurship. However, research on the information processing mechanism of creativity in relation to entrepreneurship is still very limited. To explore factors contributing to creativity performance among entrepreneurs in terms of information processing, we applied the Bayesian Mindsponge Framework. We used the Serendipity-Mindsponge-3D (SM3D) knowledge management theory to construct models and conducted Bayesian analysis on the most comprehensive and well-designed dataset of 3071 Vietnamese entrepreneurs up to date. We found that entrepreneurs who give (...) more time to their startup attempts are likely to have lower levels of creativity. Both factors of higher levels of knowledge within one’s discipline and better connections to out-of-discipline knowledge are positively associated with more creativity. While the effect of openmindedness on the relationship between within-discipline knowledge and creativity is unclear, openmindedness was found to have a positive moderating effect on the association between out-of-discipline knowledge and creativity. These findings support entrepreneurs in understanding the information processing mechanisms behind creativity for creating more effective knowledge management strategies. (shrink)

Climate change and environmental degradation are threatening the existence of humanity. The youth have the potential and capability to play a pivotal role in tackling these challenges. Therefore, the current study aims to examine how school and family environmental education can enhance environmental knowledge, willingness to take action, and pro-environmental behaviors among children and young people. The Bayesian Mindsponge Framework (BMF) analytics was utilized on a nationally representative dataset of 2069 Vietnamese primary, secondary, and high school students. The (...) class='Hi'>analysis results suggest that school and family environmental education is beneficial for improving students’ environmental knowledge and willingness to take environmental actions. Notably, the effect of school education is more substantial for cultivating environmental knowledge, whereas family education has a stronger impact on raising students’ willingness than school education. Students with higher levels of environmental knowledge are more likely to conduct pro-environmental behaviors only when they are willing to take environmental actions. If students are unwilling to act, higher environmental knowledge is negatively associated with the likelihood of pro-environmental behavior. Following these findings, we call for coordinated education efforts of schools and families to cultivate students’ eco-surplus culture. The education efforts should be implemented along with exposing students to environmental settings and encouraging them to read environmental books. (shrink)