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, nano-scale microstructural evolution in 6061-T6 alloy after laser shock processing (LSP) was studied. 6061-T6 alloy plate was subjected to multiple LSP. The LSP treated area was characterized by X-ray diffraction and the microstructure of the samples was analyzed by transmission electron microscopy. Focused Ion Beam (FIB) tools were used to prepare TEM samples in precise areas. It was found that even though aluminum had high stacking fault energy, LSP yielded to formation of ultrafine grains and deformation faults such as dislocation cells, stacking faults. The stacking fault probability (PSF) was obtained in LSP-treated alloy using X-Ray diffraction. Deformation induced stacking faults lead to the peak position shifts, broadening and asymmetry of diffraction. XRD analysis and TEM observations revealed significant densities of stacking faults in LSP-treated 6061-T6 alloy. And mechanical properties of LSP-treated alloy were also determined to understand the hardening behavior with high concentration of structural defects.
In order to improve the quality and efficiency of heat treatment in welds of power stations, this paper summarizes the current situation of 600 MW supercritical power plant welding site heat treatment and puts forward the improved methods and measures accordingly. The heat treatment of welding holes in the construction site Play a certain guiding role.
This research study was undertaken to complete a comparative study of the seminal work conducted by Anderson and Ruderman on procedural and distributive justice systems versus unionization. This research was conducted in 2023. The main focus of this research effort was to determine if current U.S. organizations were utilizing any form of justice system in protecting employees’ rights and providing processes that would prevent employees from having a desire to join a union for its protections. Parts of the original survey used by Anderson and Ruderman were used in this study to address the research questions and hypotheses posed for this study. A statistical analysis of the data was conducted, and the results indicated employees have a need for protection in their employment relationship. It is suggested that procedural and distributive justice systems be implemented as an alternative to unionization of employees to meet these employee protections.
The expanding adoption of artificial intelligence systems across high-impact sectors has catalyzed concerns regarding inherent biases and discrimination, leading to calls for greater transparency and accountability. Algorithm auditing has emerged as a pivotal method to assess fairness and mitigate risks in applied machine learning models. This systematic literature review comprehensively analyzes contemporary techniques for auditing the biases of black-box AI systems beyond traditional software testing approaches. An extensive search across technology, law, and social sciences publications identified 22 recent studies exemplifying innovations in quantitative benchmarking, model inspections, adversarial evaluations, and participatory engagements situated in applied contexts like clinical predictions, lending decisions, and employment screenings. A rigorous analytical lens spotlighted considerable limitations in current approaches, including predominant technical orientations divorced from lived realities, lack of transparent value deliberations, overwhelming reliance on one-shot assessments, scarce participation of affected communities, and limited corrective actions instituted in response to audits. At the same time, directions like subsidiarity analyses, human-cent
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