Sustainability in road construction projects is hindered by the extensive use of non-renewable materials, high greenhouse gas emissions, risk cost, and significant disruption to the local community. Sustainability involves economic, environmental, and social aspects (triple bottom line). However, establishing metrics to evaluate economic, environmental, and social impacts is challenging because of the different nature of these dimensions and the shortage of accepted indicators. This paper developed a comprehensive method considering all three dimensions of sustainable development: economic, environmental, and social burdens. Initially, the economic, environmental, and social impact category indicators were assessed using the Life cycle approach. After that, the Analytic Hierarchy Process (AHP) method and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) were utilized to prioritize the alternatives according to the acquired weightings and sustainable indicators. The steps of the AHP method involve forming a hierarchy, determining priorities, calculating weighting factors, examining the consistency of these assessments, and then determining global priorities/weightings. The TOPSIS method is conducted by building a normalized decision matrix, constructing the weighted normalized decision matrix, evaluating the positive and negative solutions, determining the separation measures, and calculating the relative closeness to the ideal solution. The selected alternative performs the highest Relative Closeness to the Ideal Solution. Lastly, a case study was undertaken to validate the proposed method. In three alternatives in the case study (Cement Concrete, Dense-Graded Polymer Asphalt Concrete, and Dense-Graded Asphalt Concrete), option 3 showed the most sustainable performance due to its highest Relative Closeness to the Ideal Solution. Integrating AHP and TOPSIS methods combines both strengths, including AHP’s structured approach for determining criteria weights through pairwise comparisons and TOPSIS’s ability to rank choices based on their proximity to an ideal solution.

In this paper, the characteristic behavior of the disc consisting of thermoplastic composite CF/PA6 material was considered. Analysis was made by taking into account the usage areas of the materials and referring to certain temperatures between 30 ℃ and 150 ℃. Composite materials are lightweight; they show high strength. For these reasons, they are preferred in technology, especially in the aircraft and aerospace industry. With this study, the radial and tangential stresses determined within a certain temperature The temperatures were determined and compared with previous studies in the literature. According to the results obtained, it is believed that the thermoplastic composite CF/PA6 disc design can be used in engineering.

A salinity gradient solar pond (SGSP) is a large and deep artificial basin of layered brine, that collects and stores simultaneous solar energy for use in various applications. Experimental and theoretical studies have been launched to understand the thermal behavior of SGSPs, under different operating conditions. This article then traces the history of SGSPs, from their natural discovery to their current artificial applications and the progress of studies and research, according to their chronological sequence, in terms of determining their physical and dynamic aspects, their operation, management, and maintenance. It has extensively covered the theoretical and experimental studies, as well as the direct and laboratory applications of this technology, especially the most famous and influential in this field, classified according to the aspect covered by the study, with a comparison between the different results obtained. In addition, it highlighted the latest methods to improve the performance of an SGSP and facilitate its operation, such as the use of a magnetic field and the adoption of remote data acquisition, with the aim of expanding research and enhancing the benefit of this technology.

The human brain has been described as a complex system. Its study by means of neurophysiological signals has revealed the presence of linear and nonlinear interactions. In this context, entropy metrics have been used to uncover brain behavior in the presence and absence of neurological disturbances. Entropy mapping is of great interest for the study of progressive neurodegenerative diseases such as Alzheimer’s disease. The aim of this study was to characterize the dynamics of brain oscillations in such disease by means of entropy and amplitude of low frequency oscillations from Bold signals of the default network and the executive control network in Alzheimer’s patients and healthy individuals, using a database extracted from the Open Access Imaging Studies series. The results revealed higher discriminative power of entropy by permutations compared to low-frequency fluctuation amplitude and fractional amplitude of low-frequency fluctuations. Increased entropy by permutations was obtained in regions of the default network and the executive control network in patients. The posterior cingulate cortex and the precuneus showed differential characteristics when assessing entropy by permutations in both groups. There were no findings when correlating metrics with clinical scales. The results demonstrated that entropy by permutations allows characterizing brain function in Alzheimer’s patients, and also reveals information about nonlinear interactions complementary to the characteristics obtained by calculating the amplitude of low frequency oscillations.