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The most challenging aspect of hospital design is the creation of an environment that heals rather than the one acting as a barrier to healing. Much has not been done in the aspect of ascertaining the level of impact “indoor environmental quality (IEQ)” has on building occupants in healthcare facilities. Therefore, this study aims to investigate the impact of IEQ on patients' health and well-being.

The study investigates the hypothesis that four IEQ parameters (thermal quality, acoustic quality, lighting quality and indoor air quality [IAQ]) influence patients' overall satisfaction with the performance of hospital wards. Questionnaire responses were sought from the patients as the main occupants of hospital ward buildings. A proposed weighted structural model for IEQ establishing the relationship between IEQ parameters, patients' overall satisfaction and patients' health outcome was analyzed using structural equation modeling (SEM).

The most influential IEQ parameters on patients' overall satisfaction with IEQ in hospital wards are thermal quality, IAQ and lighting quality. The findings from this study revealed that the parameters of influence on patients' overall satisfaction and health outcomes vary with hospital ward orientation and design configuration.

This study has explored the need for the integration of all factors of IEQ at the building design stage towards providing a hospital environmental setting that reflects occupants' requirements and expectations and also promotes patient healing processes. This should be the focus of architects and healthcare managers and providers.

Since the development of the COVID-19 vaccinations, questions surrounding race have been prominent in the literature on vaccine uptake. Early in the vaccine rollout, public health officials were concerned with the relatively lower rates of uptake among certain racial/ethnic minority groups. We suggest that this may also be patterned by racial/ethnic residential segregation, which previous work has demonstrated to be an important factor for both health and access to health care.

In this study, we examine county-level vaccination rates, racial/ethnic composition, and residential segregation across the U.S. We compile data from several sources, including the American Community Survey (ACS) and Centers for Disease Control (CDC) measured at the county level.

We find that just looking at the associations between racial/ethnic composition and vaccination rates, both percent Black and percent White are significant and negative, meaning that higher percentages of these groups in a county are associated with lower vaccination rates, whereas the opposite is the case for percent Latino. When we factor in segregation, as measured by the index of dissimilarity, the patterns change somewhat. Dissimilarity itself was not significant in the models across all groups, but when interacted with race/ethnic composition, it moderates the association. For both percent Black and percent White, the interaction with the Black-White dissimilarity index is significant and negative, meaning that it deepens the negative association between composition and the vaccination rate.

The analysis is only limited to county-level measures of racial/ethnic composition and vaccination rates, so we are unable to see at the individual-level who is getting vaccinated.

We find that segregation moderates the association between racial/ethnic composition and vaccination rates, suggesting that local race relations in a county helps contextualize the compositional effects of race/ethnicity.

The study uses machine learning techniques to cluster regional retail egg prices after 2000 in China. Furthermore, it combines machine learning results with econometric models to study determinants of cluster affiliation. Eggs are an inexpensiv, nutritious and sustainable animal food. Contextually, China is the largest country in the world in terms of both egg production and consumption. Regional clustering can help governments to imporve the precision of price policies and help producers make better investment decisions. The results are purely driven by data.

The study introduces dynamic time warping (DTW) algorithm which takes into account time series properties to analyze provincial egg prices in China. The results are compared with several other algorithms, such as TADPole. DTW is superior, though it is computationally expensive. After the clustering, a multinomial logit model is run to study the determinants of cluster affiliation.

The study identified three clusters. The first cluster including 12 provinces and the second cluster including 2 provinces are the main egg production provinces and their neighboring provinces in China. The third cluster is mainly egg importing regions. Clusters 1 and 2 have higher price volatility. The authors confirm that due to transaction costs, the importing areas may have less price volatility.

The machine learning techniques could help governments make more precise policies and help producers make better investment decisions.

This is the first paper to use machine learning techniques to cluster food prices. It also combines machine learning and econometric models to better study price dynamics.

The purpose of this paper is to investigate the aircraft pushback operations to predict and manage human errors, particularly those associated with the complex team work of carrying out the pushback operation. This should improve air ramp operations reliability.

The study applied the human reliability assessment “Systematic Human Error Reduction and Prediction Approach” that involved a total of 60 semi-structured interviews with practicing experts. Past ramp accident reports were also reviewed to provide more in-depth insights to the problem.

Some of the key performance reliability-degrading errors identified relate to some frequent critical technical inabilities within the team of headset operator and tug driver, as well as the vulnerable intra-team communications. Several best practices were similarly identified.

Based on its findings, this study proposes a new technological concept that can help enhancing safety of aircraft pushback operations. This should enhance reliability of aircraft ground handling and improve aircraft availability. It also provided a generic methodological approach to improve safety-critical operations within high-risk industries.

This study responses to the increasing trend in ramp accidents worldwide.

The research conducted to date in this area is still quite limited compared to that of flight and aircraft maintenance safety. The relevant existing studies focus more on ramp safety holistically, and do not go into the details of how safety and reliability of a ramp operation can be improved. The current paper aims at filling this gap.