Inequity in infrastructure distribution and social injustice’s effects on Ethiopia’s efforts to build a democratic society are examined in this essay. By ensuring fair access to infrastructure, justice, and economic opportunity, those who strive for social justice aim to redistribute resources in order to increase the well-being of individuals, communities, and the nine regional states. The effects that social inequity and injustice of access to infrastructure have on Ethiopia’s efforts to develop a democratic society were the focus of the study. Time series analysis using principal component analysis (PCA) and composite infrastructure index (CII), as well as structural equation modeling–partial least squares (SEM-PLS), were necessary to investigate this issue scientifically. This study also used in-depth interviews and focus group discussions to support the quantitative approach. The research study finds that public infrastructure investments have failed or have been disrupted, negatively impacting state- and nation-building processes of Ethiopia. The findings of this research also offer theories of coordination, equity, and infrastructure equity that would enable equitable infrastructure access as a just and significant component of nation-building processes using democratic federalism. Furthermore, this contributes to both knowledge and methodology. As a result, indigenous state capability is required to assure infrastructure equity and social justice, as well as to implement the state-nation nested set of policies that should almost always be a precondition for effective state- and nation-building processes across Ethiopia’s regional states.

In this study, the influence of sewage sludge ash (SSA) waste particle contents on the mechanical properties and interlaminar fracture toughness for mode I and mode II delamination of S-glass fiber-reinforced epoxy composites was investigated. Composite laminate specimens for tensile, flexural double-cantilever beam (DCB), and end-notched fracture (ENF) tests were prepared and tested according to ASTM standards with 5, 10, 15, and 20 wt% SSA-filled S-glass/epoxy composites. Property improvement reasons were explained based on optical and scanning electron microscopy. The highest improvement in tensile and flexural strength was obtained with a 10 wt% content of SSA. The highest mode I and mode II interlaminar fracture toughness’s were obtained with 15 wt% content of SSA. The mode I and mode II interlaminar fracture toughness improved by 33% and 63.6%, respectively, compared to the composite without SSA.

One-dimensional unsteady theoretical models of three different photovoltaic module installation modes are established. Through MATLAB modeling and simulation, the influence of photovoltaic modules on roof heat transfer in different layout modes is compared. Comparing with ordinary roof, the shading effect of photovoltaic roof in summer and heat preservation effect in winter was analyzed. The results show that the PV roof layout with ventilation channel is better in summer. The proof layout with closed flow channel is better in winter.

The wide distribution of the common beech (Fagus sylvatica) in Europe reveals its great adaptation to diverse conditions of temperature and humidity. This interesting aspect explains the context of the main objective of this work: to carry out a dendroclimatic analysis of the species Fagus sylvatica in the Polaciones valley (Cantabria), an area of transition with environmental conditions from a characteristic Atlantic type to more Mediterranean, at the southern limit of its growth. The methodology developed is based on the analysis of 25 local chronologies of growth rings sampled at different altitudes along the valley, generating a reference chronology for the study area. Subsequently, the patterns of growth and response to climatic variations are estimated through the response and correlation function, and the most significant monthly variables in the annual growth of the species are obtained. Finally, these are introduced into a Geographic Information System (GIS) where they are cartographically modeled in the altitudinal gradient through multivariate analysis, taking into account the different geographic and topographic variables that influence the zonal variability of the species response. The results of the analyses and cartographic models show which variables are most determinant in the annual growth of the species and the distribution of its climatic response according to the variables considered.

Cartography includes two major tasks: map making and map application, which is inextricably linked to artificial intelligence technology. The cartographic expert system experienced the intelligent expression of symbolism. After the spatial optimization decision of behaviorism intelligent expression, cartography faces the combination of deep learning under connectionism to improve the intelligent level of cartography. This paper discusses three problems about the proposition of “deep learning + cartography”. One is the consistency between the deep learning method and the map space problem solving strategy, based on gradient descent, local correlation, feature reduction and non-linear nature that answer the feasibility of the combination of “deep learning + cartography”; the second is to analyze the challenges faced by the combination of cartography from its unique disciplinary characteristics and technical environment, involving the non-standard organization of map data, professional requirements for sample establishment, the integration of geometric and geographical features, as well as the inherent spatial scale of the map; thirdly, the entry points and specific methods for integrating map making and map application into deep learning are discussed respectively.