In the United States, several studies have looked at the association between automobile ownership and sociodemographic factors and built environment qualities, but few have looked at household travel characteristics. Their interactions and nonlinear linkages are frequently overlooked in existing studies. Utilizing the 2017 US National Household Travel Survey, the authors employed an extreme gradient boosting tree model to evaluate the nonlinear and interaction impacts of household travel characteristics and built environment factors on vehicle ownership in three states of the United States (California, Missouri, and Kansas) that are different in population size. To develop these models, three main XGBT parameters, including the number of trees, maximal depth, and minimum rows, were optimized using a grid search technique. In California, the predictability of vehicle ownership was driven by household travel characteristics (cumulative importance: 0.62). Predictions for vehicle ownership in Missouri and Kansas were dominantly influenced by sociodemographic factors (cumulative importance: 0.53 and 0.55, respectively). In all states, the authors found that the number of drivers in a household plays a vital role in the vehicle ownership decisions of households. Regarding the built environment attributes, deficiencies in cycling infrastructure were the most prominent attribute in predicting household vehicle ownership in California. This variable, however, has threshold connections with vehicle ownership, but the magnitude of these relationships is small. The outcomes imply that improving the condition of cycling infrastructure will help reduce the number of vehicles. In addition, incentives that encourage the households’ drivers not to buy new vehicles are helpful. The outcomes of this study might aid policymakers in developing policies that encourage sustainable vehicle ownership in the United States.

A competitive advantage for the molten carbonate fuel cell (MCFC)-driven energy processes is their fuel versatility. On the other hand, since MCFC consumes carbon dioxide, it can reduce the amount of carbon dioxide emissions compared with other fuel cells. Accordingly, the current paper develops and evaluates the thermodynamic, exergoeconomic and environmental analyses of a new hybrid energy process (HEP) based on a reformer MCFC stack and a biomass gasification process. A homogeneous ignition engine (HIE) and a heat recovery system are two other main units embedded in the developed HEP. In the developed cycle, MCFC and HIE produce electric energy, for which the required fuel is supplied through the obtained syngas from the biomass gasification process and the anode off-gas, respectively. The heat recovery system also leads to the establishment of the thermal balance of the energy cycle by recovering the thermal energies of the MCFC and HIE. The aim of the present work is to develop the multi-aspect evaluation of the proposed energy system toward a fruitful implementation feasibility analysis. The outcomes indicated that the rates of electrical and thermal powers obtained from the planned HEP were equal to nearly 1112 and 367 kW, respectively. Further, the calculated efficiencies for electric, gross power and total energy terms were equal to 51.5%, 55.7% and 69.9%, respectively. The developed HEP had 48% less carbon dioxide emissions compared with a petroleum-fired power plant, 15.8% less compared with a natural gas-fired plant and approximately 63% lower compared with a coal-fired power plant.

Background: The rapid integration of artificial intelligence (AI) into workplace systems is transforming job design and employee experiences. While AI promises efficiency gains, it also creates a paradox by simultaneously reducing workload and increasing psychological strain, leading to divergent effects on burnout. Objective: This study systematically reviews evidence on the relationship between AI exposure and employee burnout and develops the AI as Meta-System (AIMS) framework. Methods: Following PRISMA 2020, four bibliographic databases and Google Scholar were searched from inception to 31 March 2026. Forty-three peer-reviewed primary empirical studies were included: 20 measured burnout or an established burnout dimension, and 23 examined mechanisms, moderators, proxy outcomes, measurement, or implementation conditions. Methodological quality was appraised using the Mixed Methods Appraisal Tool at the criterion level. Results: Findings varied according to the functional role of AI and the study design. Assistive AI was generally associated with lower burnout or exhaustion in randomized and pre–post healthcare studies, whereas monitoring and algorithmic-control exposures were associated with greater burnout or related psychosocial strain in observational studies. Perceptual AI exposure produced direct, indirect, and null associations through pathways involving job stress, job insecurity, work–family interference, perceived organizational support, and organizational commitment. Three randomized studies provided the strongest evidence for assistive interventions. Evidence for nonlinear effects was limited to supporting proxy outcomes and did not directly establish an inverted U-shaped AI–burnout relationship. Conclusion: Artificial intelligence is not inherently harmful or beneficial; its associations with burnout depend on its functional role, implementation, and employee appraisal. The AIMS framework integrates these pathways and identifies propositions requiring longitudinal and experimental testing.