This work provides a complete methodology for adopting well-established AI methods (predictive analytics, LLM agents, forecasting) into Microsoft Dynamics 365 Customer Relationship Management (CRM) for agricultural lending. While not claiming that the algorithms are novel, this work contributes a pragmatic approach to implementing these algorithms that specifically address the regulatory, seasonal, and operational characteristics of agricultural finance, as regulated by the Farm Credit System. It focuses on the real-life constraints and constraints within the regulated financial services industry, and measurable impacts that occurred. The paper provides a domain-oriented application of specific existing AI-CRM integration, with credible statistical testing including an external validation on USDA datasets and benchmarking across peer Farm Credit institutions, as well as cross-institutional analysis. By taking a reasonably conservative duration of 18 months, the Farm Credit institutions noted a statistically significant impact (operational efficiencies of the lending institution to assess member interests) where average case resolution time reduced by 28% (67.2h to 48.4h), and lead conversions improved by 35% (25.9% to 35.0%). Each methodology of implementation also included a series of validations in compliance with regulatory oversight in financial institutions that started to build data governance, model performance compliance through a proactive risk definition, and compliance standards suitable for their institution, and within regulatory standards by regulations. Beyond statistical significance (paired tests, $p <0.001$), practical impact was quantified using absolute and relative changes and bootstrap confidence intervals. The article provides the agricultural lending industry an applied methodology to adopt AI for stakeholder innovation while ensuring they are adept in their enterprise risk management requirement, and still target measurable business outcomes. Given a conservative potential implementation timetable (i.e., 18 months) and validation methodology protocols developed to ensure complete data and model validation, this approach is scalable for agricultural lending implementation and would be a useful instrument across all 72 Farm Credit System institutions.

The diffusion of contactless payment technologies has become a critical component of digital transformation strategies aimed at enhancing SME competitiveness in developing economies. Among these technologies, Quick Response (QR) Code Payment offers a low-cost and infrastructure-light solution, yet its adoption among SMEs remains uneven. This study investigates the determinants of QR Code Payment adoption and its subsequent effects on SMEs’ sustainability performance. Anchored in the Technology Acceptance Model (TAM) and the Resource-Based View (RBV), the proposed framework incorporates perceived usefulness, perceived ease of use, digital literacy, QR Code Payment adoption, and sustainability performance as core constructs. Integrating TAM and RBV is essential because belief-based perceptions translate into actual adoption only when supported by adequate organizational resources and capabilities, making adoption decisions the product of an interaction between what users believe and what the firm is able to execute. Survey data from 326 SMEs in the Special Region of Yogyakarta, Indonesia, were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results demonstrate that perceived usefulness and digital literacy significantly drive QR Code Payment adoption, whereas perceived ease of use does not, suggesting that performance-oriented beliefs and capability endowments outweigh perceptions of simplicity in shaping adoption behavior. Furthermore, QR Code Payment adoption positively influences economic, social, and environmental aspects of sustainability performance. These findings highlight the strategic value of digital payment integration for advancing SME sustainability and underscore the need to strengthen digital capabilities to accelerate technological uptake. The study extends the literature by jointly applying TAM and RBV to elucidate how belief structures and firm-level capabilities interact to shape adoption outcomes and their performance implications within resource-constrained contexts. For ecosystem coordinators, aligning merchant education with simple analytics dashboards can help SMEs turn payment data into insights—underscoring the need for policy support from government, financial institutions, and payment providers to ensure QR payment adoption translates into real performance gains.

Mediterranean cities are increasingly affected by climate change, energy inequality and environmental pollution. Rising temperatures, the effects of the urban heat island (UHI) and growing energy demand exacerbate socio-economic inequalities and energy poverty. To address these challenges, integrated strategies are needed, that combine effective energy system management with robust urban planning adapted to local climatic conditions. This manuscript aims to present the pilot actions implemented in the EnerCmed Project, an EU-funded project that aims to promote the sustainability in port cities by taking action in urban areas near the port, with the aim of enhancing and supporting their development. The project adopt a combined approach in which a renewable energy community (REC) or a self-consumption scheme (SCS) will be implemented through the installation of renewable energy production using Photovoltaic plant (PV) systems, to collectively generate, manage and share the energy produced from renewable sources. The key paradigm of the project consists in the development of the REC/SCS coupled with a nature-based solution (NBS) tailored to the area concerned, that increase the social acceptability and reduce UHI. This approach is oriented towards the reduction of dependence on fossil fuels, acting as a catalyst for a society in which energy autonomy and socio-economic well-being become central, thanks to actions that mitigate energy poverty and support social inclusion. This manuscript highlights the preliminary results of the EnerCmed project, demonstrating how the simultaneous implementation of REC/SCS and NBS contributes to improving energy efficiency, climate adaptation, economic and social inclusion in line with the objectives of the EU Green Deal.

The carbon tax, one of Indonesia’s climate change mitigation instruments for achieving the Nationally Determined Contribution (NDC) target, has been continuously delayed since 2022. A carbon tax is applied to carbon-based products, particularly those derived from the oil and gas sector. The oil and gas sector aims to achieve a targeted production increase of 1 million barrels of oil and 12 Billion Cubic Feet (BCF) of gas by 2030, as mandated by the Indonesian government. However, the rise of the production target may lead to a rise in carbon emissions, contradicting the country's emission reduction commitments. This study aims to explore the perspectives of the government and the oil and gas industry regarding the urgency and readiness of carbon tax implementation in Indonesia’s oil and gas sector, as well as assessing alternative policies for emission reduction. Using a qualitative methodology, semi-structured interviews were conducted to obtain primary data. The result indicates the urgency of implementing a carbon tax in Indonesia’s oil and gas sector to reduce carbon emissions, support energy transition, and achieve Net Zero Emission (NZE), but it is outweighed by both government and industry unreadiness. The Regulatory Framework aspect primarily influences the government's unreadiness, as the absence of a carbon tax roadmap as a technical implementation guideline, combined with ongoing fuel subsidies, contributes to policy incoherence. On the other hand, the Production Sharing Contract (PSC), as the Regulatory Framework of the oil and gas sector, has the potential to be amended once the carbon tax is implemented. The findings provide an overview of the government’s considerations contributing to the years-long delay in implementation and enrich the government's viewpoint on developing a carbon tax policy, considering the industry's perspective and readiness factors.

Mass Rapid Transit (MRT) systems play a critical role in promoting sustainable development, particularly in megacities. This study assesses the quality of boarding/alighting facilities along with accessibility of MRT, as integrated system components which is vital for maintaining a safe, efficient and user-friendly transit system, in the context of built-up cities. A robust questionnaire form is designed using 29 selected variables derived from pilot survey which was administerd to 1,397 respondents across nine operational stations of MRT in Dhaka, a developing megacity of Southeast Asia. Using the collected data, Gini Index and ANOVA are employed for variable prioritization. Machine Learning Algorithms, i.e., Random Forest (RF) Classifier, Support Vector Machine (SVM) and Classification and Regression Trees (CART), are compared to assess predictive performance where RF demonstrated better performance based on accuracy. Additionally, feature selection identified critical factors related to MRT trip performance, such as switching cost comparison, feeder service cost, inclusive service performance, customer loyalty, lighting near stations, overall comfort, security. This study, further, incorporates two most crucial factor, switching cost comparison and feeder service cost to a hybrid function, assessing system components and user transferability, utilizing a novel matrix-based approach. The study’s conclusions provide insights into boarding/alighting facility and accessibility as system components incorporating hybrid cost function (HCF) to enhance the efficiency of MRT services in built-up cities across the world.

Functional plate is one of the most typical materials used for strengthening of reinforced concrete (RC) structures. This article focuses on using functional plates internally to improve the flexural response of RC beams. For this purpose, experimental and numerical investigations on the flexural behavior and ductility of steel-plated RC beams were conducted. Nine RC beams were cast and cured for 28 days. The steel plates were located at the tension side of the RC beams to investigate their effect on the flexural performance of the tested beams. To achieve the research objective, three configurations of the shape of steel plates were proposed, flat, curved, and rounded. The results demonstrate that using embedded steel plates is effective and significantly enhanced the flexural performance of concrete beams. The strengthening delayed the first cracking appearance and increasing of ultimate load up to 45% compared to the reference beam. Further, there was an improvement in ductility and stiffness behaviours by 202% and 46%, respectively, particularly for beams with constrained flat steel plates, which exhibited the highest performance gain. The experimental and finite element (FE) results showed a good agreement in terms of cracking behavior and with approximately 6% maximum ultimate load difference.

As part of the study of the possibilities of using hydrogen as an alternative energy source, in particular in aspects of its underground storage, it is necessary to evaluate its interaction with host rocks. This article describes the initial results of experimental studies on carbonate rocks, specifically clayey limestones, when injecting hydrogen together with methane under given reservoir conditions typical for underground gas storage facilities, paying special attention to the assessment of changes in the pore space. The paper compares the method of computed tomography, which analyzes discrepancies in the attenuation of X-ray radiation by various rock components, and nuclear magnetic resonance relaxation, based on the phenomenon of resonant absorption of electromagnetic field energy by matter caused by nuclear paramagnetism. As a result of the interpretation of the analysis, it was shown that the overall and effective porosity remain stable as the values decreased for the tested samples by 0.1%, which indicates that hydrogen does not significantly affect the reservoir properties. An important result was the assessment of clay porosity, according to nuclear magnetic resonance relaxation calculations, its value increased by 2 times (from 0.15% to 0.28%) after exposure in the hydrogen-methane mixture, indicating the need to control the state of the overlapping clay strata and their integrity. These initial studies can be used in oil and gas field practice.