Currently, important efforts are being made to improve governability and governance by combining the monopoly of state decisions with the collaboration of diverse actors in public practice. Based on the above, the purpose of this article is to analyze the evolution of conceptual approaches to both terms over the last 23 years, examining scientific production by author authors, journals, and countries. The methodology was based on a bibliometric analysis: First, the WoS and Scopus databases were searched. Subsequently, scientometric techniques and the Science Tree methodology were used to identify patterns, structures, and trends, to understand the progress and behavior of scientific production, and to measure the quantity and quality of research that has addressed these issues from different perspectives. This study examined governability and governance publications and their annual citations to assess their impact and analyzed the total output of both datasets to identify similarities and differences in governability and governance research. The findings reveal that the number of publications and citations in this field is increasing, with the United States being the most academically influential country and the journal Marine Policy being the most prominent in ranking. These data provide key information for decision-makers, researchers, and academics for future debate and discussion toward operationalizing the concepts at the practical level of action, management, and the functioning of government structures.

This study conducts a comparative analysis of various machine learning and deep learning models for predicting order quantities in supply chain tiers. The models employed include XGBoost, Random Forest, CNN-BiLSTM, Linear Regression, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), Bidirectional LSTM (BiLSTM), Bidirectional GRU (BiGRU), Conv1D-BiLSTM, Attention-LSTM, Transformer, and LSTM-CNN hybrid models. Experimental results show that the XGBoost, Random Forest, CNN-BiLSTM, and MLP models exhibit superior predictive performance. In particular, the XGBoost model demonstrates the best results across all performance metrics, attributed to its effective learning of complex data patterns and variable interactions. Although the KNN model also shows perfect predictions with zero error values, this indicates a need for further review of data processing procedures or model validation methods. Conversely, the BiLSTM, BiGRU, and Transformer models exhibit relatively lower performance. Models with moderate performance include Linear Regression, RNN, Conv1D-BiLSTM, Attention-LSTM, and the LSTM-CNN hybrid model, all displaying relatively higher errors and lower coefficients of determination (R²). As a result, tree-based models (XGBoost, Random Forest) and certain deep learning models like CNN-BiLSTM are found to be effective for predicting order quantities in supply chain tiers. In contrast, RNN-based models (BiLSTM, BiGRU) and the Transformer show relatively lower predictive power. Based on these results, we suggest that tree-based models and CNN-based deep learning models should be prioritized when selecting predictive models in practical applications.

In this paper, we assess the results of experiment with different machine learning algorithms for the data classification on the basis of accuracy, precision, recall and F1-Score metrics. We collected metrics like Accuracy, F1-Score, Precision, and Recall: From the Neural Network model, it produced the highest Accuracy of 0.129526 also highest F1-Score of 0.118785, showing that it has the correct balance of precision and recall ratio that can pick up important patterns from the dataset. Random Forest was not much behind with an accuracy of 0.128119 and highest precision score of 0.118553 knit a great ability for handling relations in large dataset but with slightly lower recall in comparison with Neural Network. This ranked the Decision Tree model at number three with a 0.111792, Accuracy Score while its Recall score showed it can predict true positives better than Support Vector Machine (SVM), although it predicts more of the positives than it actually is a majority of the times. SVM ranked fourth, with accuracy of 0.095465 and F1-Score of 0.067861, the figure showing difficulty in classification of associated classes. Finally, the K-Neighbors model took the 6th place, with the predetermined accuracy of 0.065531 and the unsatisfactory results with the precision and recall indicating the problems of this algorithm in classification. We found out that Neural Networks and Random Forests are the best algorithms for this classification task, while K-Neighbors is far much inferior than the other classifiers.

This study replicates and extends Corbett and Kirsch (2001) and Vastag (2004) using a new data set to investigate the drivers of ISO 14000 certification diffusions using decision tree analysis. The findings indicate that at the national level, ISO 14000 certification diffusions are influenced by factors other than ISO 9000 certification diffusions, such as the number of environmental treaties signed and ratified, industrial activities as a percentage of GDP, and GDP per capita, thus provides a range of managerial insights and enhances scholarly understanding of sustainability beyond the influence of ISO 9000. Future studies might extend the countries included in this study to see if the results are the same. Future research may include other factors like a country’s Environmental, Social, and Governance (ESG) indicators to better understand its commitment to sustainability, including environmental sustainability. The country’s culture may influence customers, investors, and other stakeholders’ knowledge and desire for sustainable practices and inspire firms to obtain ISO 14000 certifications. Since larger firms may seek ISO 14000 certification, future studies may evaluate the influence of the number of large firms in various countries as drivers of ISO certification diffusions.

Homelessness is a global social issue that has affected various nations around the world, including South Africa. The instances of homelessness began during the apartheid era in South Africa and have since risen to alarming levels in provinces such as Gauteng, Western Cape, and KwaZulu-Natal, as reported in the 2022 census. Despite the lack of comprehensive research on homelessness in South Africa, this study conducted a scoping review to evaluate research completed on homelessness from independence to 2020 in the country. The scoping review followed the Preferred Reporting Item for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and involved a systematic search of the Development Southern Africa and Urban Forum databases. A total of 72 research articles were identified, with 10 meeting the inclusion and exclusion criteria for the review, which were then analyzed using thematic analysis. The study identified several key themes, including homelessness as a reflection of patriarchal systems, gender-based conflicts leading to homelessness, proactive and reactive interventions by non-state actors for homeless individuals, and the quantitative focus of research on homelessness in South Africa from independence to the present day. The study presents the applicability of these findings to tackle homelessness in Papua New Guinea and recommends the use of mixed methods approaches to research homelessness in South Africa to gain a more comprehensive understanding of the various dimensions of homelessness in the country.

In recent years, the construction of Jiafeng (家风)has become an important research topic in the field of street-level governance. A systematic literature review method is used to review 504 journal articles sourced from China National Knowledge Infrastructure (CNKI). The research overview is presented from the perspectives of overall research characteristics, highly cited literature, theoretical foundations, and research methods. The research systematically elaborates on the results of literature analysis from the perspective of the connotation and extension of Jiafeng, the practical mechanisms and related suggestions for Jiafeng construction. The research has found that the practical mechanisms of Jiafeng construction includes institutional support mechanism, theoretical consolidation mechanism, collaborative mechanism, social education mechanism, application innovation mechanism, and efficiency evaluation mechanism. On the basis of constructing a framework for the study of Jiafeng, this article provides prospects for future research: consolidating the theoretical foundation of Jiafeng construction, defining the connotation and extension of Jiafeng, refining the practical mechanism of Jiafeng construction, enriching the research methods of Jiafeng and measuring tools for governance effectiveness.