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The study aims to devise a comprehensive evaluation model (CEM) for evaluating spatial equity in the layout of elderly service facilities (ESFs) to address the inequity in the layout of ESFs within city center communities characterized by limited land resources and a dense elderly population.

The CEM incorporates a suite of analytical tools, including accessibility assessment, Lorenz curve and Gini coefficient evaluations and spatial autocorrelation analysis. Utilizing this model, the study scrutinized the distributional equity of three distinct categories of ESFs in the city center of Xi’an and proposed targeted optimization strategies.

The findings reveal that (1) there are disparities in ESFs’ accessibility among different categories and communities, manifesting a distinct center (high) and periphery (low) distribution pattern; (2) there exists inequality in ESFs distribution, with nearly 50% of older adults accessing only 18% of elderly services, and these inequalities are more pronounced in urban areas with lower accessibility, and (3) approximately 14.7% of communities experience a supply-demand disequilibrium, with demand surpassing supply as a predominant issue in the ongoing development of ESFs.

The CEM formulated in this study offers policymakers, urban planners and service providers a scientific foundation and guidance for decision-making or policy amendment by promptly assessing and pinpointing areas of spatial inequity in ESFs and identifying deficiencies in their development.

Given the growing emphasis among scholars, practitioners and policymakers on financial sustainability, this study aims to explore the applicability of machine learning techniques in predicting the financial performance of microfinance institutions (MFIs).

This study gathered 9,059 firm-year observations spanning from 2003 to 2018 from the World Bank's Mix Market database. To predict the financial performance of MFIs, the authors applied a range of machine learning regression approaches to both training and testing data sets. These included linear regression, partial least squares, linear regression with stepwise selection, elastic net, random forest, quantile random forest, Bayesian ridge regression, K-Nearest Neighbors and support vector regression. All models were implemented using Python.

The findings revealed the random forest model as the most suitable choice, outperforming the other models considered. The effectiveness of the random forest model varied depending on specific scenarios, particularly the balance between training and testing data set proportions. More importantly, the results identified operational self-sufficiency as the most critical factor influencing the financial performance of MFIs.

This study leveraged machine learning on a well-defined data set to identify the factors predicting the financial performance of MFIs. These insights offer valuable guidance for MFIs aiming to predict their long-term financial sustainability. Investors and donors can also use these findings to make informed decisions when selecting their potential recipients. Furthermore, practitioners and policymakers can use these findings to identify potential financial performance vulnerabilities.

This study stands out by using a global data set to investigate the best model for predicting the financial performance of MFIs, a relatively scarce subject in the existing microfinance literature. Moreover, it uses advanced machine learning techniques to gain a deeper understanding of the factors affecting the financial performance of MFIs.

This study aims to investigate the impact of temperature and fiber volume fraction on the mechanical properties of 3D-printed composites of continuous glass fiber reinforced onyx.

Continuous glass fiber reinforced onyx (carbon-filled nylon) 3D-Printed composites have been designed and tested at 40°C, 60°C and 80°C for fiber volume fractions ranging from 13%, 20%, 27%, 33% and 40%.

The results of three-point bending tests have shown that at higher temperatures, i.e. greater than the room temperature the 3D-Printed onyx loses its mechanical properties as obvious for thermoplastic composites. However, the inclusion of high temperature glass fibers has improved the mechanical properties of the onyx polymer and its resistance to deformation at higher temperatures. At all temperatures, the increase in fiber fraction increases the yield strength and decreases the elongation of the composite in the strain region below the yield point. At Vf >0.27 the elongation in samples seems less affected by the fiber content. The comparison of the specimen with different fiber volume fractions (Vf) shows that the elongation of the samples at Vf = 0.4, the samples’ response to the applied load has become independent of the temperature above 40°C.

The experimental and numerically calculated results are well matched, showing the accuracy in the methodology of designing the fiber reinforced onyx composites.