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This study investigates the role of green technology implementation (GTI) based on artificial intelligence (AI) at the household level to achieve carbon neutrality by addressing gaps in the existing research. The research focuses on understanding how education on green consumption preferences, green invention and emission impacts can optimally influence AI-based GTI decisions. Through behavioural analysis at the household level, this study quantifies the effects of education and preferences on emissions and proposes subsidies as accelerators for carbon-neutral transitions. Furthermore, the study employs regression analysis and simulation-based optimisation, which are then validated against prior methodologies, with a focus on Punjab, Pakistan. Utilising a simple random sampling technique, approximately 1000 households were surveyed to represent the province's diverse demographics comprehensively. Findings reveal that higher education levels correlate with less enthusiasm for AI-based GTI. Simulations measured optimal subsidy levels by striking a balance between encouraging green behaviour and technological adoption. By integrating diverse factors and AI-based GTI optimisation, this study defines important thresholds for education and subsidies, thus highlighting their pivotal role in advancing AI-based green technologies and sustainable household practices. This research significantly enhances the understanding of the complex relationship between AI-based GTI decisions and educational influences, thereby contributing to the advancement of environmental sustainability.

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Studying individual ecological niches within the oral cavity is a logical first step to understanding the distribution of antimicrobial resistance genes (ARGs); however, it is not representative of the whole oral resistome. The aim of our systematic review was to provide a map of the oral resistome by reviewing the composition of individual niches. A total of 580 papers were retrieved from a search of all English language publications investigating the presence of oral ARGs in five electronic databases between January 2015 and August 2023. Fifteen studies [10 PCR and 5 next-generation sequencing (NGS)] were included in this review. The heterogeneity of methods precluded meta-analysis. ARGs are present throughout the oral cavity with 158 unique ARGs identified across 6 locations - supra and sub-gingival biofilm, mucosa, oropharynx, root canal system (RCS) and saliva. The supragingival biofilm had the highest resistome richness, while the RCS had the least. Tetracycline was the dominant antimicrobial resistance (AMR) class found. Three core genes were identified - tet(M), tet(O) and ermB.This review highlights the necessity of NGS studies to comprehensively characterize the oral resistome in its entirety. This is the logical foundation for future 'omics studies to truly understand the scope of the resistome and its contribution to AMR.

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This study addresses critical gaps in supply chain management (SCM) by integrating emission-risk minimization (ERM), green purchasing (GP), and profit maximization (PM). The research focuses on the optimal behaviors of manufacturers, agents, and retailers within the SCM framework to achieve carbon neutrality and reduce carbon dioxide emissions (CO2e). This study considers Guangdong province, China, a region facing challenges in optimizing energy systems and meeting CO2e reduction targets. Simulation-based optimization techniques within mathematical models are employed. A design of experiment (DOE) method was used to explore the dynamics of key variables in the SCM environment. Results reveal optimal behaviors for manufacturers, agents, and retailers, demonstrating the ideal values for profit and pricing decisions. Manufacturers optimize production quantity, achieving CO2e reduction and PM through ERM. Agents exhibit a strong commitment to GP practices, enhancing PM and carbon-neutral goals. Retailers get more PM than manufacturers and agents, contributing to a clean environment. Interestingly, retailers make contributions to the clean environment without considering ERM and GP in SCM. The study contributes novel insights by addressing the identified gap in SCM research, emphasizing the joint consideration of ERM, GP, and PM. This research assists manufacturers, agents, and retailers in terms of PM for economic objectives. It cleans the environment through carbon-neutral SCM in society.

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As carbon emissions are one of the major problems of an emission-generating firm (EGF), there is a dire need to reduce them, and so this study fills this gap in the literature by considering the optimal behavior of environmental policies. This research develops policy measures under a duopoly game model such as carbon emission tax (CET) and subsidy on green investment (SGI). Additionally, we formulate a simulation model to measure the optimal behavior of CET, SGI, and production quantity to maximize profit and carbon emission reduction. The findings indicate that when green investment decreases, the EGFs are better able to pay CET and the government is also capable of providing SGI. Moreover, a lower unit production cost reduces more carbon emissions versus a higher unit production cost. In this way, the government receives revenue due to CET implementation and an EGF obtains revenue due to SGI. Both parties gain benefits at the same time and play important roles in cutting carbon emissions to make the environment clean. This study helps governments in finding their own optimal CET and SGI. An optimal SGI assists decision-makers at reducing carbon emissions and targeting profit maximization.

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Carbon emissions are one of the major constraints considered under a Cap-and-Trade (C-and-T) system, regarding the implementation of green technologies in the operations of emissions-generating companies. Green technology implementation, based on optimal pricing decisions, has become an inevitability due to rising carbon emissions. We studied the profit-maximizing behavior of a firm considering whether to implement of green technology due to subsidies offered on emission-reduction rates. In order to achieve the desired results, we used a simulation-based model and developed a conceptual model for the verification of functions. When the product price was high, the firm achieved a high profit, which was the main focus of the firm. The firm thus had sufficient resources to implement green technology. However, when the product price was low, the firm could achieve its goal of profit maximization, but did so without implementing green technology. To solve this problem, we studied government involvement in the market to incentivize emissions reduction and to benefit the firm. We decided to model emissions-reduction policy to encourage the implementation of green technology and support firm profits. We found that subsidies enabled a firm to maximize its profits while ensuring green technology implementation, while the firm would not have adopted green technology without subsidies or mandates. This study should help decision makers understand pricing strategies in the maximization of the profit. Additionally, this study helps demonstrate that the government plays an important role in monopolized markets by reducing negative externalities.

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RATIONALE AND OBJECTIVES: The conceptualization of acetabular fractures can present a daunting challenge to radiology residents. 3D models have been shown to aid in the spatial perception of complicated anatomy and may help residents grasp the elaborate classification systems for these anatomically complex fractures. Prior studies have explored the utility of 3D printed models for surgical planning in various settings. To our knowledge, no study has evaluated their efficacy in radiology resident training. MATERIALS AND METHODS: Following IRB approval, 22 radiology residents were randomized and stratified by Post Graduate Year into two groups of 11 residents. Both groups received separate identical presentations on the 5 most common acetabular fractures given by a musculoskeletal trained radiologist. Residents in the experimental group received 3D printed models of the five most common fracture types with which to interact during the presentation, while the control group did not. Both groups received a pretest and a follow up posttest three weeks later. RESULTS: A Wilcoxon rank sum test was performed to determine if statistically significant differences between the pretest and posttest scores of the experimental and control groups existed. There was no statistically significant difference in scores on the pre-test, which confirmed successful randomization. There was a statistically significant difference (P = 0.02) on the posttest scores between the experimental and control groups. CONCLUSION: 3D printed models promise as an effective educational tool for resident learning with respect to acetabular fractures, improving short-term understanding of complex anatomy and classification systems.

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Intrahospital transportation of critically ill patients is associated with significant complications. In order to reduce overall risk to the patient, such transports should well organized, efficient, and accompanied by the proper monitoring, equipment, and personnel. Protocols and guidelines for patient transfers should be utilized universally across all healthcare facilities. Care delivered during transport and at the site of diagnostic testing or procedure should be equivalent to the level of care provided in the originating environment. Here we review the most common problems encountered during transport in the hospital setting, including various associated adverse outcomes. Our objective is to make medical practitioners, nurses, and ancillary health care personnel more aware of the potential for various complications that may occur during patient movement from the intensive care unit to other locations within a healthcare facility, focusing on risk reduction and preventive strategies.