To analyze the effect of an increase in the quantity or quality of public investment on growth, this paper extends the World Bank’s Long-Term Growth Model (LTGM), by separating the total capital stock into public and private portions, with the former adjusted for its quality. The paper presents the LTGM public capital extension and accompanying freely downloadable Excel-based tool. It also constructs a new infrastructure efficiency index, by combining quality indicators for power, roads, and water as a cardinal measure of the quality of public capital in each country. In the model, public investment generates a larger boost to growth if existing stocks of public capital are low, or if public capital is particularly important in the production function. Through the lens of the model and utilizing newly-collated cross-country data, the paper presents three stylized facts and some related policy implications. First, the measured public capital stock is roughly constant as a share of gross domestic product (GDP) across income groups, which implies that the returns to new public investment, and its effect on growth, are roughly constant across development levels. Second, developing countries are relatively short of private capital, which means that private investment provides the largest boost to growth in low-income countries. Third, low-income countries have the lowest quality of public capital and the lowest efficient public capital stock as a share of GDP. Although this does not affect the returns to public investment, it means that improving the efficiency of public investment has a sizable effect on growth in low-income countries. Quantitatively, a permanent 1 ppt GDP increase in public investment boosts growth by around 0.1–0.2 ppts over the following few years (depending on the parameters), with the effect declining over time.

The Organic Rankine Cycle (ORC) is an electricity generation system that uses organic fluid instead of water in the low temperature range. The Organic Rankine cycle using zeotropic working fluids has wide application potential. In this study, data mining (DM) model is used for performance analysis of organic Rankine cycle (ORC) using zeotropik working fluids R417A and R422D. Various DM models, including Linear Regression (LR), Multi-Layer Perceptron (MLP), M5 Rules, M5 Model Tree, Random Committee (RC), and Decision Tree (DT) models are used. The MLP model emerged as the most effective approach for predicting the thermal efficiency of both R417A and R422D. The MLP’s predicted results closely matched the actual results obtained from the thermodynamic model using Genetron software. The Root Mean Square Error (RMSE) for the thermal efficiency was exceptionally low, at 0.0002 for R417A and 0.0003 for R422D. Additionally, the R-squared (R²) values for thermal efficiency were very high, reaching 0.9999 for R417A and R422D. The findings demonstrate the effectiveness of the DM model for complex tasks like estimating ORC thermal efficiency. This approach empowers engineers with the ability to predict thermal efficiency in organic Rankine systems with high accuracy, speed, and ease.

Plum (Prunus domestica) is a seasonal nutraceutical fruit rich in many functional food nutrients such as vitamin C, antioxidants, total phenolic content, and minerals. Recently, researchers have focused on improvised technologies for the retention of bioactive compounds during the processing of perishable fruits; plum is one of these fruits. This study looked at how the percentage of moisture content and percentage of acidity were affected by conventional drying and osmotic dehydration. Total phenolic content (mg GA/100 g of plum), total anthocyanin content (mg/100 g), and vitamin C (mg/100 g) Conventional drying of fruit was carried out at 80.0 ℃ for 5 h. At various temperatures (45.0 ℃, 50.0 ℃, and 55.0 ℃) and hypertonic solution concentrations (65.0 B, 70.0 B, and 75.0 B), the whole fruit was osmotically dehydrated. It was observed that the osmotically treated fruit retains more nutrients than conventionally dried fruit. The total phenolic content of fruit significantly increased with the increase in process temperature. However, vitamin C and total anthocyanin content of the fruit decreased significantly with process temperature, and hypertonic solution concentration was observed. Hence, it was concluded that osmodehydration could be employed for nutrient retention in plum fruit over conventional drying. This process needs to be further refined, improvised, and optimised for plum processing.

The purpose of this study is to investigate customer satisfaction with quality of service known as SERVQUAL improvement or service quality competitiveness in emerging markets. Using Indonesian government medical care as an example the author examines the satisfaction of patients. Information and data were collected through a survey of 399 BPJS users in Indonesia. All data were analyzed using Smart PLS. This study demonstrates that there is a negative value associated with the five-dimensional gap. As a result, the care provided to BPJS patients is below par. Specifically, the sensitivity dimension has the largest disparity at 0.15, while the physical evidence dimension has the smallest at 0.49. In order to raise the level of service provided, it may be necessary to take direct measures or examine tangible evidence. This study develops the relationship between different quality service models. There appears to be a substantial increase in the body of literature in the area of service quality, allowing for constant updates and the incorporation of the lessons learned from the experiences of the departed. These revised guidelines are intended to aid SERVQUAL study participants. The study gives practical support to academics and practitioners in directing service quality improvement through the use of data collected from large-scale surveys of patients and medical professionals as doctors in Indonesia.

To gain a deep understanding of maintenance and repair planning, investigate the weak points of the distribution network, and discover unusual events, it is necessary to trace the shutdowns that occurred in the network. Many incidents happened due to the failure of thermal equipment in schools. On the other hand, the most important task of electricity distribution companies is to provide reliable and stable electricity, which minimal blackouts and standard voltage should accompany. This research uses seasonal time series and artificial neural network approaches to provide models to predict the failure rate of one of the equipment used in two areas covered by the greater Tehran electricity distribution company. These data were extracted weekly from April 2019 to March 2021 from the ENOX incident registration software. For this purpose, after pre-processing the data, the appropriate final model was presented with the help of Minitab and MATLAB software. Also, average air temperature, rainfall, and wind speed were selected as input variables for the neural network. The mean square error has been used to evaluate the proposed models’ error rate. The results show that the time series models performed better than the multi-layer perceptron neural network in predicting the failure rate of the target equipment and can be used to predict future periods.

Based on the population change data of 2005–2009, 2010–2014, 2015–2019 and 2005–2019, the shrinking cities in Northeast China are determined to analyze their spatial distribution pattern. And the influencing factors and effects of shrinking cities in Northeast China are explored by using multiple linear regression method and random forest regression method. The results show that: 1) In space, the shrinking cities in Northeast China are mainly distributed in the “land edge” areas represented by Changbai Mountain, Sanjiang Plain, Xiaoxing’an Mountain and Daxing’an Mountain. In terms of time, the contraction center shows an obvious trend of moving northward, while the opposite expansion center shows a trend of moving southward, and the shrinking cities gather further; 2) in the study of influencing factors, the results of multiple linear regression and random forest regression show that socio-economic factors play a major role in the formation of shrinking cities; 3) the precision of random forest regression is higher than that of multiple linear regression. The results show that per capita GDP has the greatest impact on the contraction intensity, followed by the unemployment rate, science and education expenses and the average wage of on-the-job workers. Among the four influencing factors, only the unemployment rate promotes the contraction, and the other three influencing factors inhibit the formation of shrinking cities to various degrees.