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In this study, machine learning (ML) algorithms were employed to predict the shear capacity and behavior of DCSWs.

In this study, ML algorithms were employed to predict the shear capacity and behavior of DCSWs. Various ML techniques, including linear regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost) and artificial neural network (ANN), were utilized. The ML models were trained using a dataset of 462 numerical and experimental samples. Numerical models were generated and analyzed using the finite element (FE) software Abaqus. These models underwent push-over analysis, subjecting them to pure shear conditions by applying a target displacement solely to the top of the shear walls without interaction from a frame. The input data encompassed eight survey variables: geometric values and material types. The characterization of input FE data was randomly generated within a logical range for each variable. The training and testing phases employed 90 and 10% of the data, respectively. The trained models predicted two output targets: the shear capacity of DCSWs and the likelihood of buckling. Accurate predictions in these areas contribute to the efficient lateral enhancement of structures. An ensemble method was employed to enhance capacity prediction accuracy, incorporating select algorithms.

The proposed model achieved a remarkable 98% R-score for estimating shear strength and a corresponding 98% accuracy in predicting buckling occurrences. Among all the algorithms tested, XGBoost demonstrated the best performance.

In this study, for the first time, ML algorithms were employed to predict the shear capacity and behavior of DCSWs.

The purpose of this paper is to formulate a model for waqf financing of public goods and mixed public goods in Malaysia which constitute the country’s federal government expenditures. The model is built on the basis of understanding the concept of waqf, learning from waqf institutions of the past and present and addressing specific Malaysian waqf issues.

This study uses both primary and secondary data. The primary data originate from semi-structured interviews of waqf academicians from the Islamic economics and Islamic finance fields, waqf government officials and private sector institutions that are involved in waqf management. The secondary data come from the Malaysian Federal Constitution, law enactments, books, e-books, bulletins, journals, conference proceedings, government reports and websites.

By synthesizing the data, it is found that return from cash waqf investment in unit trust can be used to finance 11 items of federal government expenditures. The overall process can be managed by Yayasan Waqaf Malaysia through a collaboration with an Islamic unit trust firm.

This research shows how waqf can practically assist the Malaysian federal government in financing public goods and mixed public goods. It indirectly shows an alternative source of financing for these goods. Other economies can also learn and adapt from the model developed in this paper.

This paper attempts to revive the function of waqf as a provider of public goods and mixed public goods from Islamic history. Inadvertently, this paper also introduces waqf as a possible fiscal tool.

This paper aims to present recently published resources on information literacy and library instruction through an extensive annotated bibliography of publications covering all library types.

This paper annotates English-language periodical articles, monographs, dissertations and other materials on library instruction and information literacy published in 2017 in over 200 journals, magazines, books and other sources.

The paper provides a brief description for all 590 sources.

The information may be used by librarians and interested parties as a quick reference to literature on library instruction and information literacy.

Present study investigated the influence of female descendent entrepreneur's self-compassion on the perceived succession success of small-family businesses (S-FB) with the mediating mechanism of financial literacy.

The primary data was collected from 319 female descendent entrepreneurs who were designated as chairwomen, and managing director positions in their retails sector S-FBs. The purposive sampling technique was used to collect the data. The provided hypotheses are tested using the partial least square structural equation modeling (PLS-SEM) technique. This study followed multiple regression analyses to see the influence of self-compassion (mindfulness, self-isolation, self-judgment and over-identification) on financial literacy and perceived succession success.

The results reveal that female descendent entrepreneurs mindfulness and over-identification significantly increase but self-isolation decreases the likelihood of successful succession transition. Moreover, female descendent entrepreneur's financial literacy increases mindfulness and overidentification while it decreases self-isolation and improves the likelihood of succession success. However, financial literacy does not influence self-judgmental traits and perceived succession success.

This study highlights a vital issue, how the financial literacy of female descendent entrepreneurs manages their self-compassion and increases the likelihood of succession success. In addition, it covers a research gap and helps the S-FBs to improve their survival rate by focusing on the descendent entrepreneur's self-compassion and financial literacy.

This study contributes to the body of knowledge by emphasizing predictors that influence the successful succession transition to subsequent generations. This study determines the influence of self-compassion of female descendent entrepreneurs on perceived succession success and financial literacy as a mediator by using the self-control theory. The study can be useful to family business consultants, policymakers and family businesses.