The issue of urban land management in the world in general and in Africa in particular has been exacerbated by the liberalization of land practices and the commodification of land, which has led to an increase in corrupt practices within land institutions in all cities. A mixed methodology was employed, combining a comparative case study of secondary towns with a quantitative survey of 559 landowners in the towns of Bohicon and Sokodé. In-depth interviews were conducted with 31 informants, who were surveyed on the land acquisition process, the individual determinants influencing corrupt practices, and the institutions most involved in these practices. The findings revealed that the acquisition of a formal title conferring property rights in both cities necessitates the completion of several steps. Corrupt practices are present at almost every stage of the transaction. The application of logistic regression models to the independent variables indicates that age and profession are highly significant in the sociodemographic characteristics of those most susceptible to engaging in these practices. Formal land administration institutions are the most involved in these types of everyday corruption. These practices are ultimately linked to people’s life paths and cannot therefore be combated without psychosociological education and the promotion of ethical behavior among all stakeholders, particularly among those who demand services.

In today’s fast-moving, disrupted business environment, supply chain risk management is crucial. More critically, Industry 4.0 has conferred competitive advantages on supply chains through the integration of digital technologies into manufacturing and logistics, but it also implies several challenges and opportunities regarding the management of these risks. This paper looks at some ways emerging technologies, especially Artificial Intelligence (AI), help address pressing concerns about the management of risk and sustainability in logistics and supply chains. The study, using a systemic literature review (SLR) backed by a mapping study based on the Scopus database, reveals the main themes and gaps of prior studies. The findings indicate that AI can substantially enhance resilience through early risk identification, optimizing operations, enriching decision-making, and ensuring transparency throughout the value chain. The key message from the study is to bring out what technology contributes to rendering supply chains resilient against today’s uncertainties.

This research presents a novel approach utilizing a self-enhanced chimp optimization algorithm (COA) for feature selection in crowdfunding success prediction models, which offers significant improvements over existing methods. By focusing on reducing feature redundancy and improving prediction accuracy, this study introduces an innovative technique that enhances the efficiency of machine learning models used in crowdfunding. The results from this study could have a meaningful impact on how crowdfunding campaigns are designed and evaluated, offering new strategies for creators and investors to increase the likelihood of campaign success in a rapidly evolving digital funding landscape.

Among contemporary computational techniques, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are favoured because of their capacity to tackle non-linear modelling and complex stochastic datasets. Nondeterministic models involve some computational intricacies when deciphering real-life problems but always yield better outcomes. For the first time, this study utilized the ANN and ANFIS models for modelling power generation/electric power output (EPO) from databases generated in a combined cycle power plant (CCPP). The study presents a comparative study between ANNs and ANFIS to estimate the power output generation of a combined cycle power plant in Turkey. The inputs of the ANN and ANFIS models are ambient temperature (AT), ambient pressure (AP), relative humidity (RH), and exhaust vacuum (V), correlated with electric power output. Several models were developed to achieve the best architecture as the number of hidden neurons varied for the ANNs, while the training process was conducted for the ANFIS model. A comparison of the developed hybrid models was completed using statistical criteria such as the coefficient of determination (R²), mean average error (MAE), and average absolute deviation (AAD). The R² of 0.945, MAE of 3.001%, and AAD of 3.722% for the ANN model were compared to those of R² of 0.9499, MAE of 2.843% and AAD of 2.842% for the ANFIS model. Even though both ANN and ANFIS are relevant in estimating and predicting power production, the ANFIS model exhibits higher superiority compared to the ANN model in accurately estimating the EPO of the CCPP located in Turkey and its environment.

Localization is globally accepted as the strategy towards attaining the Sustainable Development Goals (SDGs). In this article, we put forth the South Indian state of Kerala as a true executor of the localization of SDGs owing to her foundational framework of decentralized governance. We attempt to understand how the course of decentralization acts as a development trajectory and how it has paved the way for the effective assimilation of localization principles post-2015 by reviewing the state documents based on the framework propounded by the United Nations. We theorize that the well-established decentralization mechanism, with delegated institutions and functions thereof, encompasses overlapping mandates with the SDGs. Further, through the tools of development plan formulation, good governance, and community participation at decentralized levels, Kerala could easily adapt to localization, concocting output through innovative measures of convergence, monitoring, and incentivization carried out through the pre-existing platforms and processes. The article proves that constant and concerted efforts undertaken by Kerala through her meticulous and action-oriented decentralized system aided the localization of SDGs and provides an answer to the remarkable feat that the state has achieved through the consecutive four times achievements in the state scores of SDG India Index.