This research addresses the rapid advancements and exponential growth in academic research on the use of augmented reality (AR) and the metaverse in e-commerce. Through comprehensive bibliometric analysis, the research evaluates the performance of publications and citation metrics, uncovers influential works and collaborative networks, and explores thematic trends and researcher sentiments in this domain. Data from Scopus was analyzed using tools such as R, RStudio, BiblioShiny, VOSviewer, Tableau, and Python. In addition, sentiment analysis was conducted via Hugging Face’s DistilBERT model. The research findings highlight key themes, including the integration of AR and the metaverse in retail, online shopping, and mobile commerce, emphasizing the role of immersive technologies in transforming consumer experiences. This study identifies emerging trends and gaps, providing a roadmap for future research and strategic implementation. Sentiment analysis reveals a balanced outlook among researchers, with both enthusiasm for technological advancements and concerns over implementation challenges. The research offers valuable insights for researchers, publications, and the e-commerce industry, guiding informed decision-making, fostering innovation, and enhancing consumer experiences in the evolving landscape of AR and the metaverse in e-commerce.
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.
Atmospheric turbulence induces severe blurring and geometric distortions in facial imagery, critically compromising the performance of downstream tasks. To overcome this challenge, a lightweight conditional diffusion model was proposed for the restoration of single-frame turbulence-degraded facial images. Super-resolution techniques were integrated with the diffusion model, and high-frequency information was incorporated as a conditional constraint to enhance structural recovery and achieve high-fidelity generation. A simplified U-Net architecture was employed within the diffusion model to reduce computational complexity while maintaining high restoration quality. Comprehensive comparative evaluations and restoration experiments across multiple scenarios demonstrate that the proposed method produces results with reduced perceptual and distributional discrepancies from ground-truth images, while also exhibiting superior inference efficiency compared to existing approaches. The presented approach not only offers a practical solution for enhancing facial imagery in turbulent environments but also establishes a promising paradigm for applying efficient diffusion models to ill-posed image restoration problems, with potential applicability to other domains such as medical and astronomical imaging.
The construction and real estate industry has been held responsible for nearly 40% of global CO2 emissions, a key focus for gathering efforts to combat climate change. Timber, a sustainable and carbon-storing building material, unravels significant potential to decarbonize the sector by replacing carbon-intensive materials such as steel and concrete. However, the full potential of timber remains underutilized, owing to a lack of knowledge, transparency, and investment opportunities in the forestry and timber industries. This paper addressed this gap by developing a comprehensive framework for investors to evaluate listed companies in the timber construction sector, based on their sustainability and financial performance. Specifically, the study sought to answer: How can investors effectively channel capital into the carbon storage capacity of timber, and what approaches are both sustainable and economically viable for timber investments? To achieve this, this paper examined how investors could invest in the CO2 storage capacity of timber, with a particular focus on the creation of Environmental, Social, and Governance (ESG) Timber Score to evaluate the sustainability of listed companies in the sector. By integrating sustainability and financial performance metrics, this study provided a robust framework that enabled investors to assess both the economic and environmental aspects of their investments. The findings revealed investment opportunities in both traditional markets (North America and Europe) and emerging markets (Asia and Africa). The current study emphasizes that investment decisions, if probable, should be tailored to individual preferences to achieve different levels of sustainability and financial goals.
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.
This paper investigated how trade openness and income inequality jointly shaped carbon outcomes using a panel of 94 countries from 1966 to 2015. On average, greater openness and lower inequality are associated with reduced CO2 emissions; however, their interaction is proved to be positive, suggesting that while trade openness could contribute to lower carbon emissions in relatively equal societies, its benefits diminished and even reversed under high inequality. In addition, heterogeneity analyses revealed stronger elasticities in non-high-income and high-openness subsamples, a statistically significant inequality threshold and effects that intensify at upper CO2 quantiles. Therefore, policy packages that pair trade facilitation with inequality compression and clean-technology diffusion are likely to be most effective, particularly where inequality and openness are already high. Future research should extend the analysis to consumption-based emissions, sectoral pathways, and institutional moderators to refine the trade-inequality-carbon nexus and its implications for environmental sustainability.
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.
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.
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