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.

This study investigates the escalating complexity and unpredictability of global supply chains, with a particular emphasis on resilience in the agricultural sector of Antioquia, Colombia. The aim of the study is to identify and analyze the dynamic capabilities, specifically flexibility and adaptability that significantly enhance resilience within agri-food supply chains. Given the sector’s vulnerability to external disruptions, such as climate change and economic volatility, a thorough understanding of these capabilities is imperative for the formulation of effective risk management strategies. This research is essential to provide empirical insights that can inform stakeholders on fortifying their supply chains, thereby contributing to enhanced competitiveness and sustainability. By presenting a comprehensive framework for evaluating dynamic capabilities, this study not only addresses existing gaps in the literature but also offers practical recommendations aimed at bolstering resilience in the agricultural sector.

This study explored the relationships between green market orientation and competitive advantage, with a particular focus on the mediating role of green sustainable innovation. The research utilized a structured questionnaire to gather data from managers involved in environmental protection and professionals working in the manufacturing sectors of computers, electronics, optical products, and electrical equipment. The survey targeted respondents from key regions in Saudi Arabia, including Riyadh, Qassim, and the Eastern Province, resulting in a total of 273 responses. The collected data were analyzed using structural equation modeling (SEM), a robust statistical technique that allows for the examination of complex relationships between variables. The findings confirmed a mediational model where green sustainable innovation—comprising both green product and green process innovation—served as a critical intermediary linking green market orientation to competitive advantage. Furthermore, the study validated direct effects of green market orientation on both green sustainable innovation and competitive advantage. These results emphasize the dual pathways through which green market orientation influences business performance. The research concludes by offering actionable insights for Saudi managers, highlighting strategies to maximize profitability and competitiveness through the adoption and implementation of green sustainable innovation practices.

Balancing broad learning outcomes in graduate programs with detailed classroom learning outcomes is increasingly crucial in education systems. This study employs a qualitative paradigm through a case study method to address the gap between learning outcomes at the graduate program level and those at the course level. Using the ESSENTIA CURRICULUM framework—a curriculum design methodology derived from software engineering practices—we propose an innovative and adaptable approach for aligning program-wide and course-specific learning outcomes. The ESSENTIA CURRICULUM, named for its focus on the “essence of the curriculum”, is applied to the ICT for Research course within the M.Sc. program in University Teaching at the University of Nariño. This framework fosters a consistent educational journey centered on learning achievements and demonstrates its effectiveness through a comprehensive self-assessment process and stakeholder feedback. The implications of this research are twofold: it highlights the potential of adopting interdisciplinary methodologies for curriculum design and provides a scalable and alternative strategy for harmonizing learning outcomes across diverse educational contexts. By bridging principles from software engineering into education, this novel approach offers new avenues for improving curriculum coherence and applicability.

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.

Urban mobility in Grand Lomé is affected by several negative externalities, including road congestion, insecurity and environmental pollution. Traffic jams cause considerable economic losses, estimated at more than 13,000 CFA francs per month for some public officials, and represent a financial drain of several million CFA francs per day on the Togolese economy. These challenges are accentuated by rapid urbanization and a dizzying increase in the number of vehicles, especially motorcycle taxis. These factors not only cause economic losses, but also to the deterioration of the quality of life of the inhabitants. On average, motorists lose up to 49.5 min per day in traffic jams, with fuel and time costs estimated at hundreds of thousands of CFA francs per year for each user of the main boulevards. Through an in-depth analysis of the impacts of these negative externalities on mobility and sustainable development, this study reveals that traffic congestion, combined with the lack of road infrastructure, generates considerable economic and environmental costs. These traffic jams also worsen air pollution, making the transport sector responsible for 80% of greenhouse gas emissions. These proposed solutions include: 1) The modernization of road infrastructure, culminating in the construction of new lanes entirely dedicated to public and non-motorized transport. 2) The regulation of motorcycle taxis, inspired by regional examples, to improve safety and efficiency. 3) The introduction of rapid transit systems, such as Bus Rapid Transit (BRT), to make travel more fluid. 4) The implementation of strict environmental standards and regular technical controls to reduce greenhouse gas emissions. These proposals aim to reduce social and economic costs, while promoting sustainable mobility and a better quality of life for residents.