Preserving roads involves regularly evaluating government policy through advanced assessments using vehicles with specialized capabilities and high-resolution scanning technology. However, the cost is often not affordable due to a limited budget. Road surface surveys are highly expected to use low-cost tools and methods capable of being carried out comprehensively. This research aims to create a road damage detection application system by identifying and qualifying precisely the type of damage that occurs using a single CNN to detect objects in real time. Especially for the type of pothole, further analysis is to measure the volume or dimensions of the hole with a LiDAR smartphone. The study area is 38 province’s representative area in Indonesia. This research resulted in the iRodd (intelligent-road damage detection) for detection and classification per type of road damage in real-time object detection. Especially for the type of pothole damage, further analysis is carried out to obtain a damage volume calculation model and 3D visualization. The resulting iRodd model contributes in terms of completion (analyzing the parameters needed to be related to the road damage detection process), accuracy (precision), reliability (the level of reliability has high precision and is still within the limits of cost-effective), correct prediction (four-fifths of all positive objects that should be identified), efficient (object detection models strike a good balance between being able to recognize objects with high precision and being able to capture most objects that would otherwise be detected-high sensitivity), meanwhile, in the calculation of pothole volume, where the precision level is established according to the volume error value, comparing the derived data to the reference data with an average error of 5.35% with an RMSE value of 6.47 mm. The advanced iRodd model with LiDAR smartphone devices can present visualization and precision in efficiently calculating the volume of asphalt damage (potholes).

This paper presents an assessment approach to fostering socioeconomic re-development and resilience in Iraqi regions emerging from the destruction and instability, in the aftermath of the war conflict in Iraq. Focusing on the intricate interplay of logistics infrastructure and economic recovery, the present study proposes a novel framework that integrates general resilience insights, data analytics, infrastructure systems, and decision support from Data Envelopment Analysis (DEA). We draw inspiration also from historical cases on “creative destruction” or “Blessing in Disguise” (BiD) phenomena, like the post-WWII reconstruction of Rotterdam, so as to develop the notion of stepwise or cascadic prosilience, analyzing how innovative logistics systems may in various stages contribute to economic rejuvenation. Our approach recognizes the multifaceted nature of regional resilience capacity, encompassing both static (conserving resources, rerouting, etc.) and dynamic (accelerating recovery through innovative strategies) dimensions. The logistics aspect spans both the supply side (new infrastructure, ICT facilities) and the demand side (changing transportation flows and product demands), culminating in an integrated perspective for sustainable growth of Iraqi regions. In our study, we explore several forward-looking strategic future options (scenarios) for recovery and reconstruction policy factors in the context of regional development in Iraq, regarding them as crucial strategic elements for effective post-conflict rebuilding and regeneration. Given that such assets and infrastructures typically extend beyond a single city or area, their geographic scope is broader, calling for a multi-region approach. By leveraging the extended DEA approach by an incorporation of a super-efficiency (SE) DEA approach so as to better discriminate among efficient Decision-Making Units (DMUs)—in this case, regions in Iraq—our research aims to present actionable and effective insights for infrastructure investment strategies at regional-governorate scale in Iraq, that optimize efficiency, sustainability and resilience. This approach may ultimately foster prosperous and stable post-conflict regional economies that display—by means of a cascadic change—a new balanced prosilient future.

Increasing the environmental friendliness of production systems is largely dependent on the effective organization of waste logistics within a single enterprise or a system of interconnected market participants. The purpose of this article is to develop and test a methodology for evaluating a data-based waste logistics model, followed by solutions to reduce the level of waste in production. The methodology is based on the principle of balance between the generation and beneficial use of waste. The information base is data from mandatory state reporting, which determines the applicability of the methodology at the level of enterprises and management departments. The methodology is presented step by step, indicating data processing algorithms, their convolution into waste turnover efficiency coefficients, classification of coefficient values and subsequent interpretation, typology of waste logistics models with access to targeted solutions to improve the environmental sustainability of production. The practical implementation results of the proposed approach are presented using the production example of chemical products. Plastics production in primary forms has been determined, characterized by the interorganizational use of waste and the return of waste to the production cycle. Production of finished plastic products, characterized by a priority for the sale of waste to other enterprises. The proposed methodology can be used by enterprises to diagnose existing models for organizing waste circulation and design their own economically feasible model of waste processing and disposal.

This research delves into the urgent requirement for innovative agricultural methodologies amid growing concerns over sustainable development and food security. By employing machine learning strategies, particularly focusing on non-parametric learning algorithms, we explore the assessment of soil suitability for agricultural use under conditions of drought stress. Through the detailed examination of varied datasets, which include parameters like soil toxicity, terrain characteristics, and quality scores, our study offers new insights into the complexities of predicting soil suitability for crops. Our findings underline the effectiveness of various machine learning models, with the decision tree approach standing out for its accuracy, despite the need for comprehensive data gathering. Moreover, the research emphasizes the promise of merging machine learning techniques with conventional practices in soil science, paving the way for novel contributions to agricultural studies and practical implementations.

Through a comparative investigation of the function of socialist realism in the drama and law of Kenya, Nigeria, and South Africa, this research investigates the decolonization of neo-colonial hegemonies in Africa. Using the drama and legal systems of Kenya, Nigeria, and South Africa as comparative case studies, the research explores how African societies can challenge and demolish oppressive systems of domination sustained by colonial legacies and contemporary neo-colonial forces. Relying on the Socialist Realism and Critical Postcolonial theoretical frameworks which both support literary and artistic genre that encourages social and political transformation, the research deploys the case study analysis, comparative literature analysis and focused group discussion methods. Data obtained are subjected to content and thematic analysis. The study emphasizes how important the relationship between the legal and artistic worlds is to the fight against neo-colonialism. It further reveals the transformational potential of socialist realism as a catalyst for social change by looking at themes of resistance, social justice, and the amplifying of disadvantaged voices in drama and legal discourse. The research contributes to ongoing discussions about de-neo-colonization through this comparative case study, and emphasizes the role socialist realism plays in overthrowing neo-colonial hegemonies. The study sheds light on the distinct difficulties and opportunities these nations—and indeed, all of Africa—face in their pursuit of decolonial justice by examining the experiences of Kenya, Nigeria, and South Africa.

In the process of seeking sustainable development, enterprises have chosen international business strategy. The purpose of this study is to examine the relationship between the degree of internationalization of Chinese listed firms and financial reporting quality, as well as whether audit committees can moderate the impact of enterprise internationalization on financial reporting quality. The empirical analysis results of Chinese listed manufacturing firms from 2014 to 2018 show that: the degree of corporate internationalization has a significant U-shaped relationship with earnings management. This new finding solves the problem that scholars have inconsistent views on the internationalization of enterprises and the quality of financial reporting. The study also found that audit committees with experience working in accounting firms can inhibit firm earnings management behavior in the early stage of internationalization; audit committees with experience working overseas can inhibit firm earnings management behavior in the later stage of internationalization; the higher the remuneration of audit committee experts, the more it can inhibit firm earnings management behavior in the early stage of internationalization. In the later stage of internationalization, the higher the remuneration of audit committee experts, it helps the earnings management behavior of firms. This provides new evidence on the functioning of the audit committee’s role; however, the independence of the audit committee and the proportion of financial experts do not have a significant effect on the inhibition of earnings management.