Public-private partnerships (PPPs) were established in Brazil at the beginning of this century, following a global trend of using these partnerships to stimulate investment in infrastructures, particularly in a framework of restrictive budgetary and fiscal conditions. Despite their growing importance and the expectation of an expanding role in the future, not much is known about the actual facts on the ground. The objective of this paper is to be a first step in the direction of filling this information gap by providing important stylized facts about the universe of PPPs in Brazil: the quantitative evolution of PPP adoptions; the characterization of the geographical distribution of PPPs by government level (federal, state, district, and municipal); the characterization of the PPP intervention areas, including the total value of contracts and the modalities of PPP concession (sponsored and administrative). This objective is rendered possible by the development of a new database that covers the entire process of PPP contracting from 2005 to 2022, including the opening of public consultation procedures, the publication of the official notice, and the signing of contracts, as well as multiple thematic, financial, jurisdictional, and regional indicators. In turn, we see the establishment of these stylized facts as a necessary first step in the direction of understanding the factors that may determine or condition their adoption. In general, having a clear picture of the universe of the PPPs in Brazil is fundamental as their use and their role are expected to significantly increase in the future as the country pursues a path of improved economic activity and well-being of the population.

Based on the research on 31 provincial-level administrative regions at the end of 2022, we used the geographic concentration index, geographic imbalance index, SPSS and ARCGIS spatial analysis techniques to study the spatial distribution, distribution factor correlation, and accessibility of national 5A-level scenic spots. The research results show that the overall distribution of my country's 5A-level scenic spots is unbalanced, with a low degree of concentration, showing a pattern of denseness in the east and sparseness in the west, with large inter-provincial differences. The density of traffic highways is positively correlated with the distribution density of 5A-level scenic spots. The traffic lines in the central and eastern regions are dense, and there are a large number of 5A-level scenic spots, especially the Beijing-Tianjin-Hebei region, the Yangtze River Delta region, and the middle and lower reaches of the Yangtze River and Yellow River. Therefore, the spatial distribution of China's 5A-level tourist attractions is mainly affected by the interaction of economic, transportation and social factors, among which GDP, transportation network and attraction of scenic spots are the most critical factors. These research results can provide a reference for optimizing the spatial layout of China's scenic resources and promoting regional socio-economic development.

This research introduces a novel framework integrating stochastic finite element analysis (FEA) with advanced circular statistical methods to optimize heat pump efficiency under material uncertainties. The proposed methodologies and optimization focus on balancing the mean efficiency and variability by adjusting the concentration parameter of the Von Mises distribution, which models directional variability in thermal conductivity. The study highlights the superiority of the Von Mises distribution in achieving more consistent and efficient thermal performance compared to the uniform distribution. We also conducted a sensitivity analysis of the parameters for further insights. The results show that optimal tuning of the concentration parameter can significantly reduce efficiency variability while maintaining a mean efficiency above the desired threshold. This demonstrates the importance of considering both stochastic effects and directional consistency in thermal systems, providing robust and reliable design strategies.

Census 2022 of Saudi Arabia was released recently, with 12 years of intercensal interval. Although it appeared provisional having no reports similar to the 2010 census, efforts to analyze, interpret, disseminate, and discuss were essential for building structures and systems at par with demographic trends and patterns. An analysis was carried out with this census data compared to 2004 and 2010 to track population change—demographic pace, trends, and patterns—over the two decades. Data from all three censuses were analyzed with conventional demographic techniques. A reduction in growth was observed with a declining percentage of the childhood population but with an expanding percentage of the adults (working age) indicating a demographic dividend resulted, mostly, from fertility decline. An aging trend established by the previous censuses was lost, recently: the constriction of the pyramid of 2010 was changed to a different shape. Not only the percentage distribution trends were uneven but also the age-based indices. Thus, these trends revealed a demographic difference to an extent, that demands standardized reports, uniform procedures for the data collection and compilation, and geographic distribution equations. The increasing concentration in urban centers of major administrative areas—Al-Riyadh, Makkah Al-Mokarramah, and the Eastern Region demand redistribution policies. Self-contained townships appear as a strategic option in population redistribution, guaranteeing quality standards and lifestyle.

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.