Passenger Mutations in More Than 2,500 Cancer Genomes: Overall Molecular Functional Impact and Consequences.

The dichotomous model of "drivers" and "passengers" in cancer posits that only a few mutations in a tumor strongly affect its progression, with the remaining ones being inconsequential. Here, we leveraged the comprehensive variant dataset from the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) project to demonstrate that-in addition to the dichotomy of high- and low-impact variants-there is a third group of medium-impact putative passengers. Moreover, we also found that molecular impact correlates with subclonal architecture (i.e., early versus late mutations), and different signatures encode for mutations with divergent impact. Furthermore, we adapted an additive-effects model from complex-trait studies to show that the aggregated effect of putative passengers, including undetected weak drivers, provides significant additional power (∼12% additive variance) for predicting cancerous phenotypes, beyond PCAWG-identified driver mutations. Finally, this framework allowed us to estimate the frequency of potential weak-driver mutations in PCAWG samples lacking any well-characterized driver alterations.

Feeling unwell of passenger travel by small vehicles and associated risk factors in the North Shewa Zone, Oromiya, Ethiopia.

The current study investigated how and why sociocultural structures, situational conditions, and personal behavioural factors cause passengers to feel ill when travelling by minibuses, drawing on ideas from the social construction theory of illness. A significant objective was to investigate associated risk variables that influence passengers' feelings of illness related to the social environment, addressing their beliefs, meanings, practices, and behaviours. A survey method was used to obtain data from 384 passengers for the study. The results of logistic regression indicated that feeling ill when travelling by minibuses differed from passenger to passenger; then, they had their own set of practical and emotional challenges that had no known medical reason. Compared with male and older passengers, female and younger passengers were more likely to feel ill. Furthermore, stress and role-set effects increased passengers' experiences of feeling ill more than did passengers who had no stress prior to the trip and who had only one role. Additionally, passengers who travelled intermittently, utilized suppression techniques to lessen travel discomfort, and fastened seat belts were less likely to experience symptoms of illness. Passengers who travelled on unsafe roads and used alcohol before travel, on the other hand, were more likely to feel ill than those who travelled on safer roads and did not use alcohol before the trip. The findings suggest that passengers should be aware of predisposing conditions that result in illness, be able to rest before travelling, and use all suppressive methods to reduce or prevent illness while travelling by small buses.

COVID-19 control measures unexpectedly increased the duration of stay at High Speed Rail stations during the first community outbreak in Taiwan.

During the COVID-19 pandemic, Taiwan has implemented strict border controls and community spread prevention measures. As part of these efforts, the government also implemented measures for public transportation. In Taiwan, there are two primary public transportation systems: Taiwan Railways (TR) is commonly utilized for local travel, while the Taiwan High-Speed Rail (THSR) is preferred for business trips and long-distance journeys due to its higher speed. In this study, we examined the impact of these disease prevention measures on the number of passengers and duration of stay in two major public transportation systems during the first community outbreak from April 29th to May 29th, 2021. Using data from a local telecommunications company, our study observed an expected decrease in the number of passengers after the cancellation of non-reserved seats at both TR and THSR stations across all 19 cities in the main island of Taiwan. Surprisingly, however, the duration of stay in some of the cities unexpectedly increased, especially at THSR stations. This unanticipated rise in the duration of stay has the potential to elevate contact probability among passengers and, consequently, the transmission rate. Our analysis shows that intervention policies may result in unforeseen outcomes, highlighting the crucial role of human mobility data as a real-time reference for policymakers. It enables them to monitor the impact of disease prevention measures and facilitates informed, data-driven decision-making.

Thermal sensation prediction model for high-speed train occupants based on skin temperatures and skin wettedness.

Passenger thermal comfort in high-speed train (HST) carriages presents unique challenges due to factors such as extensive operational areas, longer travel durations, larger spaces, and higher passenger capacities. This study aims to propose a new prediction model to better understand and address thermal comfort in HST carriages. The proposed prediction model incorporates skin wettedness, vertical skin temperature difference (ΔTd), and skin temperature as parameters to predict the thermal sensation vote (TSV) of HST passengers. The experiments were conducted with 65 subjects, evenly distributed throughout the HST compartment. Thermal environmental conditions and physiological signals were measured to capture the subjects' thermal responses. The study also investigated regional and overall thermal sensations experienced by the subjects. Results revealed significant regional differences in skin temperature between upper and lower body parts. By analyzing data from 45 subjects, We analyzed the effect of 25 variables on TSV by partial least squares (PLS), from which we singled out 3 key factors. And the optimal multiple regression equation was derived to predict the TSV of HST occupants. Validation with an additional 20 subjects demonstrated a strong linear correlation (0.965) between the actual TSV and the predicted values, confirming the feasibility and accuracy of the developed prediction model. By integrating skin wettedness and ΔTd with skin temperature, the model provides a comprehensive approach to predicting thermal comfort in HST environments. This research contributes to advancing thermal comfort analysis in HST and offers valuable insights for optimizing HST system design and operation to meet passengers' comfort requirements.

Enhancing thermal comfort prediction in high-speed trains through machine learning and physiological signals integration.

Heating, Ventilation, and Air Conditioning (HVAC) systems in high-speed trains (HST) are responsible for consuming approximately 70% of non-operational energy sources, yet they frequently fail to ensure provide adequate thermal comfort for the majority of passengers. Recent advancements in portable wearable sensors have opened up new possibilities for real-time detection of occupant thermal comfort status and timely feedback to the HVAC system. However, since occupant thermal comfort is subjective and cannot be directly measured, it is generally inferred from thermal environment parameters or physiological signals of occupants within the HST compartment. This paper presents a field test conducted to assess the thermal comfort of occupants within HST compartments. Leveraging physiological signals, including skin temperature, galvanic skin reaction, heart rate, and ambient temperature, we propose a Predicted Thermal Comfort (PTC) model for HST cabin occupants and establish an intelligent regulation model for the HVAC system. Nine input factors, comprising physiological signals, individual physiological characteristics, compartment seating, and ambient temperature, were formulated for the PTS model. In order to obtain an efficient and accurate PTC prediction model for HST cabin occupants, we compared the accuracy of different subsets of features trained by Machine Learning (ML) models of Random Forest, Decision Tree, Vector Machine and K-neighbourhood. We divided all the predicted feature values into four subsets, and did hyperparameter optimisation for each ML model. The HST compartment occupant PTC prediction model trained by Random Forest model obtained 90.4% Accuracy (F1 macro = 0.889). Subsequent sensitivity analyses of the best predictive models were then performed using SHapley Additive explanation (SHAP) and data-based sensitivity analysis (DSA) methods. The development of a more accurate and operationally efficient thermal comfort prediction model for HST occupants allows for precise and detailed feedback to the HVAC system. Consequently, the HVAC system can make the most appropriate and effective air supply adjustments, leading to improved satisfaction rates for HST occupant thermal comfort and the avoidance of energy wastage caused by inaccurate and untimely predictive feedback.

Station-Level Effects of the COVID-19 Pandemic on Subway Ridership in the Seoul Metropolitan Area.

This paper investigates the station-level impacts of the coronavirus disease (COVID-19) pandemic on subway ridership in the Seoul Metropolitan Area. Spatial econometric models are constructed to examine the association between ridership reduction caused by the pandemic and station-level characteristics during the pandemic years 2020 and 2021. The results reveal unequal effects on station-level ridership, based on the pandemic waves, the demographics, and the economic features of pedestrian catchment areas. First, the subway system was severely disrupted by the pandemic, with significant decreases in ridership-by about 27% for each of the pandemic years-compared with the pre-pandemic year (2019). Second, the ridership reduction was sensitive to the three waves in 2020 and responded accordingly; however, it became less sensitive to the waves in 2021, indicating that subway usage was less responsive to pandemic waves during the second year of the pandemic. Third, pedestrian catchment areas with higher numbers of younger residents (in their 20s) and older residents (65 years and older), those with more businesses requiring face-to-face interactions with consumers, and stations located in the employment centers were hit the hardest in ridership reduction caused by the pandemic.

Bioprotection of Transportation and Facilities from SARS-CoV-2 (COVID-19).

The recent COVID-19 pandemic has led to a nearly world-wide shelter-in-place strategy. This raises several natural concerns about the safe relaxing of current restrictions. This article focuses on the design and operation of heating ventilation and air conditioning (HVAC) systems in the context of transportation. Do HVAC systems have a role in limiting viral spread? During shelter-in-place, can the HVAC system in a dwelling or a vehicle help limit spread of the virus? After the shelter-in-place strategy ends, can typical workplace and transportation HVAC systems limit spread of the virus? This article directly addresses these and other questions. In addition, it also summarizes simplifying assumptions needed to make meaningful predictions. This article derives new results using transform methods first given in Ginsberg and Bui. These new results describe viral spread through an HVAC system and estimate the aggregate dose of virus inhaled by an uninfected building or vehicle occupant when an infected occupant is present within the same building or vehicle. Central to these results is the derivation of a quantity called the "protection factor"-a term-of-art borrowed from the design of gas masks. Older results that rely on numerical approximations to these differential equations have long been lab validated. This article gives the exact solutions in fixed infrastructure for the first time. These solutions, therefore, retain the same lab validation of the older methods of approximation. Further, these exact solutions yield valuable insights into HVAC systems used in transportation.

The prevalence of seat belt use among drivers and passengers: a systematic review and meta-analysis.

BACKGROUND: Seat belts might save people's lives in car accidents by preventing severe collision damage and keeping passengers safe from critical injuries. This meta-analysis was performed to assess the prevalence of seat belt use among drivers and passengers. METHODS: The databases of PubMed, Web of Science (WOS), and Google Scholar were searched from the beginning of 2000 to late December 2020 to identify studies that investigated the prevalence of seat belt use among drivers and passengers. The pooled prevalence was calculated using a random-effects model. The STATA-v14 software was used to perform data analysis. RESULTS: Sixty-eight studies that met the inclusion criteria and were suitable for this meta-analysis were identified. The pooled prevalence of seat belt use was 43.94% (95% CI: 42.23-45.73) among drivers, 38.47% (95% CI: 34.89-42.42) among front-seat passengers, and 15.32% (95% CI: 12.33-19.03) among rear-seat passengers. The lowest seat belt use among drivers and passengers was observed in Asia, the Middle East, and Africa, while the highest use was reported in Europe and America. Moreover, the prevalence of seat belt use was higher among women drivers [51.47% (95% CI: 48.62-54.48)] than men drivers [38.27% (95% CI: 34.98-41.87)] (P < 0.001). Furthermore, the highest prevalence of seat belt use was seen among drivers (68.9%) and front-seat passengers (50.5%) of sports utility vehicles (SUVs); in contrast, the lowest prevalence was observed among drivers and passengers of public vehicles such as buses, minibuses, and taxis. CONCLUSIONS: In general, the prevalence of seat belt use was not high among drivers and was even lower among passengers. Moreover, drivers and passengers in Asia, the Middle East, and Africa had the lowest prevalence of seat belt usage. Additionally, drivers and passengers of public transportation (buses, minibuses, and taxis) had a lower rate of seat belt use, especially among men. Therefore, effective interventional programs to improve seat belt use should be designed and implemented, particularly among these at-risk populations in Asia, the Middle East, and Africa.

Skip-Stop Strategy Patterns optimization to enhance mass transit operation under physical distancing policy due to COVID-19 pandemic outbreak.

After the widespread impact of the COVID-19 pandemic, all public transport, including urban rail transit, inevitably adopted a vigorous physical-distancing policy to prevent the disease from spreading among passengers. Adoption of this measure resulted in a substantial reduction in train service capability and required control of the risk contact exposure duration. Thus, this paper proposes the Skip-Stop Strategy Patterns (3S-P) decision-support model to incorporate social distancing constraints in train operations. The 3S-P model is a two-stage, multi-objective optimization model for scheduling train skip-stop patterns to satisfy the study's two main objectives of minimizing the average passenger travel time and unserved passengers. In the proposed model, the first optimization identifies the optimal train skip-stop patterns, while the second assigns these patterns to establish an hourly train schedule. The paper's case study uses data from the Bangkok Mass Transit System (BTS) SkyTrain Silom Line in Bangkok, Thailand and considers the 0.5, 1, 1.5, and 2 m social distancing schemes. The results reveal that the optimal train skip-stop patterns are superior to the all-stop alternative with, on average, a 13.4% faster travel time at the same level of unserved passengers. Furthermore, the non-dominated schedules from the second optimization decrease the numbers of unserved passengers given equal average passenger travel times.

Effect of Passenger Encumbrance and Mobility Aid Use on Dwell Time Variability in Low-floor Transit Vehicles.

Public transit serves users with a broad range of physical capabilities and design needs. However information about the operational effects of diverse users interacting with the transit system is scarce. This paper examined the occurrence and effects of boarding and alighting passengers with mobility aids (wheelchairs, scooters, walkers and canes), or with large items (carts, strollers, bicycles, or carrying an infant) on bus stop dwell time in a fixed-route bus service. On-board video data from low-floor public transit buses serving Ann Arbor, Michigan were used from 199 bus stops with at least one passenger boarding or alighting with a mobility aid or encumbered with a large item, and an additional 1642 bus stops without any mobility aids or encumbrances. A sequence of linear regression models examined the relationship between dwell time and the addition of variables representing passengers with mobility aids and encumbrances, and use of the on-vehicle access ramp, beyond explanatory variables typically used in dwell time analysis. Accounting for passengers boarding/alighting with mobility aids and encumbrances (p < 0.001) and use of the access ramp (p < 0.001) increased the variance explanation of a dwell time model based on boarding passengers by fare payment, alighting passengers by door use, and passenger load from 46% to 56%. Results indicate distinct patterns in the durations for boarding and alighting by passengers with vs. without mobility aids and encumbrances, and when a ramp is used by wheeled mobility users vs. ambulatory passengers with walking aids. The findings suggest that accounting for the presence of passengers with mobility aids or encumbrances and ramp use in dwell time analyses could help transit operators make their service operationally more efficient and inclusive for all passengers and encourage more use of fixed-route transit among individuals with disabilities.

Estimation of left behind subway passengers through archived data and video image processing.

Crowding is one of the most common problems for public transportation systems worldwide, and extreme crowding can lead to passengers being left behind when they are unable to board the first arriving bus or train. This paper combines existing data sources with an emerging technology for object detection to estimate the number of passengers that are left behind on subway platforms. The methodology proposed in this study has been developed and applied to the subway in Boston, Massachusetts. Trains are not currently equipped with automated passenger counters, and farecard data is only collected on entry to the system. An analysis of crowding from inferred origin-destination data was used to identify stations with high likelihood of passengers being left behind during peak hours. Results from North Station during afternoon peak hours are presented here. Image processing and object detection software was used to count the number of passengers that were left behind on station platforms from surveillance video feeds. Automatically counted passengers and train operations data were used to develop logistic regression models that were calibrated to manual counts of left behind passengers on a typical weekday with normal operating conditions. The models were validated against manual counts of left behind passengers on a separate day with normal operations. The results show that by fusing passenger counts from video with train operations data, the number of passengers left behind during a day's rush period can be estimated within 10 % of their actual number.

Seatbelt use and risk of major injuries sustained by vehicle occupants during motor-vehicle crashes: a systematic review and meta-analysis of cohort studies.

BACKGROUND: In 2004, a World Health Report on road safety called for enforcement of measures such as seatbelt use, effective at minimizing morbidity and mortality caused by road traffic accidents. However, injuries caused by seatbelt use have also been described. Over a decade after publication of the World Health Report on road safety, this study sought to investigate the relationship between seatbelt use and major injuries in belted compared to unbelted passengers. METHODS: Cohort studies published in English language from 2005 to 2018 were retrieved from seven databases. Critical appraisal of studies was carried out using the Scottish Intercollegiate Guidelines Network (SIGN) checklist. Pooled risk of major injuries was assessed using the random effects meta-analytic model. Heterogeneity was quantified using I-squared and Tau-squared statistics. Funnel plots and Egger's test were used to investigate publication bias. This review is registered in PROSPERO (CRD42015020309). RESULTS: Eleven studies, all carried out in developed countries were included. Overall, the risk of any major injury was significantly lower in belted passengers compared to unbelted passengers (RR 0.47; 95%CI, 0.29 to 0.80; I2 = 99.7; P = 0.000). When analysed by crash types, belt use significantly reduced the risk of any injury (RR 0.35; 95%CI, 0.24 to 0.52). Seatbelt use reduces the risk of facial injuries (RR = 0.56, 95% CI = 0.37 to 0.84), abdominal injuries (RR = 0.87; 95% CI = 0.78 to 0.98) and, spinal injuries (RR = 0.56, 95% CI = 0.37 to 0.84). However, we found no statistically significant difference in risk of head injuries (RR = 0.49; 95% CI = 0.22 to 1.08), neck injuries (RR = 0.69: 95%CI 0.07 to 6.44), thoracic injuries (RR 0.96, 95%CI, 0.74 to 1.24), upper limb injuries (RR = 1.05, 95%CI 0.83 to 1.34) and lower limb injuries (RR = 0.77, 95%CI 0.58 to 1.04) between belted and non-belted passengers. CONCLUSION: In sum, the risk of most major road traffic injuries is lower in seatbelt users. Findings were inconclusive regarding seatbelt use and susceptibility to thoracic, head and neck injuries during road traffic accidents. Awareness should be raised about the dangers of inadequate seatbelt use. Future research should aim to assess the effects of seatbelt use on major injuries by crash type.

On-road emissions of CO, CO2 and NOX from four wheeler and emission estimates for Delhi.

This study presents the emission factor of gaseous pollutants (CO, CO2, and NOX) from on-road tailpipe measurement of 14 passenger cars of different types of fuel and vintage. The trolley equipped with stainless steel duct, vane probe velocity meter, flue gas analyzer, Nondispersive infra red (NDIR) CO2 analyzer, temperature, and relative humidity (RH) sensors was connected to the vehicle using a towing system. Lower CO and higher NOX emissions were observed from new diesel cars (post 2010) compared to old cars (post 2005), which implied that new technological advancement in diesel fueled passenger cars to reduce CO emission is a successful venture, however, the use of turbo charger in diesel cars to achieve high temperature combustion might have resulted in increased NOX emissions. Based on the measured emission factors (g/kg), and fuel consumption (kg), the average and 95% confidence interval (CI) bound estimates of CO, CO2, and NOX from four wheeler (4W) in Delhi for the year 2012 were 15.7 (1.4-37.1) , 6234 (386-12,252) , and 30.4 (0.0-103) Gg/year, respectively. The contribution of diesel, gasoline and compressed natural gas (CNG) to total CO, CO2 and NOX emissions were 7:84:9, 50:48:2 and 58:41:1 respectively. The present work indicated that the age and the maintenance of vehicle both are important factors in emission assessment therefore, more systematic repetitive measurements covering wide range of vehicles of different age groups, engine capacity, and maintenance level is needed for refining the emission factors with CI.

"Good Passengers and Not Good Passengers:" Adolescent Drivers' Perceptions About Inattention and Peer Passengers.

PURPOSE: The purpose of this qualitative focus group elicitation research study was to explore teen driver perceptions of peer passengers and driver inattention. DESIGN & METHODS: We utilized focus groups for data collection and content analysis to analyze the data, both of which were guided by the theory of planned behavior. We conducted 7 focus groups with 30 teens, ages 16-18, licensed for ≤1year to examine attitudes, perceived behavioral control, and norms related to driving inattention and peer passengers. RESULTS: The sample was 50% male, mean age 17.39 (SD 0.52) with mean length of licensure 173.7days (SD 109.2). Three themes emerged: 1) "Good and not good" passengers; 2) Passengers and technology as harmful and helpful; and 3) The driver is in charge. CONCLUSIONS: While passengers can be a source of distraction, our participants also identified passenger behaviors that reduced risk, such as assistance with technology and guidance for directions. PRACTICAL IMPLICATIONS: An understanding of teens' perceptions of peer passengers can contribute to the development of effective interventions targeting teen driver inattention. Nurses are well-positioned to contribute to these teen crash prevention efforts.

Modeling Driving Performance Using In-Vehicle Speech Data From a Naturalistic Driving Study.

OBJECTIVE: We aimed to (a) describe the development and application of an automated approach for processing in-vehicle speech data from a naturalistic driving study (NDS), (b) examine the influence of child passenger presence on driving performance, and (c) model this relationship using in-vehicle speech data. BACKGROUND: Parent drivers frequently engage in child-related secondary behaviors, but the impact on driving performance is unknown. Applying automated speech-processing techniques to NDS audio data would facilitate the analysis of in-vehicle driver-child interactions and their influence on driving performance. METHOD: Speech activity detection and speaker diarization algorithms were applied to audio data from a Melbourne-based NDS involving 42 families. Multilevel models were developed to evaluate the effect of speech activity and the presence of child passengers on driving performance. RESULTS: Speech activity was significantly associated with velocity and steering angle variability. Child passenger presence alone was not associated with changes in driving performance. However, speech activity in the presence of two child passengers was associated with the most variability in driving performance. CONCLUSION: The effects of in-vehicle speech on driving performance in the presence of child passengers appear to be heterogeneous, and multiple factors may need to be considered in evaluating their impact. This goal can potentially be achieved within large-scale NDS through the automated processing of observational data, including speech. APPLICATION: Speech-processing algorithms enable new perspectives on driving performance to be gained from existing NDS data, and variables that were once labor-intensive to process can be readily utilized in future research.

Contact tracing for influenza A(H1N1)pdm09 virus-infected passenger on international flight.

In April 2009, influenza A(H1N1)pdm09 virus infection was confirmed in a person who had been symptomatic while traveling on a commercial flight from Mexico to the United Kingdom. Retrospective public health investigation and contact tracing led to the identification of 8 additional confirmed cases among passengers and community contacts of passengers.

Norwegian airline passengers are not more afraid of flying after the terror act of September 11. The flight anxiety, however, is significantly attributed to acts of terrorism.

The aim of this paper is to study: (1) the prevalence of flight anxiety among Norwegian airline passengers; (2) situations that may be of concern during flights and situations not related to flying; (3) whether passengers feel more afraid after the terror act of September 11, 2001; and (4) whether passengers were more afraid in 2002 than in 1986.A questionnaire was distributed during domestic flights in Norway in 1986 and 2002. To asses flight anxiety, a six point scale was used, from 0 = not afraid at all, to 5 = always very afraid, and sometimes avoid flying because of that. A 10-cm visual analogue scale (VAS) was used to measure the degree of anxiety. There were 50.8% who were not afraid at all. There were 12 women (5.2%) and one man (0.4%) with flight phobia. However, 22 (4.5%) had cancelled flights because of anxiety during the last two years. Situations that caused most concern during flights were turbulence and fear of terrorism and highjacking. After September 11, 48% were not more afraid, 38% a little more, 10% moderately, 3% rather much and 2% very much. The passengers, however, were not more afraid of flying in 2002 than in 1986. About 3% of Norwegian airline passengers have a flight phobia. Women are significantly more concerned than men. The impact of the terror act September 11, 2001 was rather moderate. The level of flight anxiety among Norwegian airline passengers was not significantly different in 2002 and 1986.

The adoption of self-service check-in kiosks among commercial airline passengers.

This study examines passenger adoption of self-service check-in kiosks based on the technology acceptance model (TAM) and its extension. This study investigated the relationships between three independent variables (technology self-efficacy, need for human interaction, and perceived enjoyment) and two TAM's cognitive variables (perceived usefulness and ease of use). The analysis of these relationships is novel in aviation self-service technology research. Structural equation modeling (SEM) was employed to analyze 346 valid responses. The analysis reveals that enjoyment enhances the perception of self-service check-in kiosks as useful and easy to use, resulting in greater intention to use them. Moreover, passengers with higher confidence in their technological self-efficacy are more likely to perceive check-in kiosks as helpful. However, passengers may perceive kiosks as less useful when they require more human interaction. The results of this study contribute to the current knowledge on aviation self-service adoption. Additionally, managerial implications are offered to improve the future of airlines' passenger services.

Screening and early detection of communicable diseases on board cruise ships: An assessment of passengers' preferences on technical solutions.

BACKGROUND: Implementing technological solutions to screen for and detect early the most prevalent communicable diseases on cruise ships is contingent on, among others, willingness of passengers to accept use of such solutions. METHOD: We surveyed passenger preferences to record their willingness to accept technological solutions for screening and early detection of communicable diseases on cruise ships. Self-reported sociodemographic characteristics, use of technology and acceptance of solutions were recorded anonymously in paper format. Multiple logistic regression analyses investigated the association of demographic and other characteristics with willingness and barriers/concerns of passengers to endorse proposed solutions. RESULTS: Of a total of 1344 passengers on two successive cruises on board CELESTYAL OLYMPIA, 336 (1 every 4) participated in the survey. The vast majority of passengers (92.3 %, n = 310) agreed with at least one solution. Passengers showed lower levels of acceptance for more personalized solutions, such as use of wearable devices (45.5 %) and monitoring with cameras (64.0 %), whereas they were more receptive to less personally invasive solutions, such as integration of cabins with air purifiers (89.6 %) and air quality sensors (80.4 %). Age, self-employment status, educational level, and fear of contacting a communicable disease were significantly correlated with passengers' willingness to adopt proposed solutions. CONCLUSIONS: To successfully integrate screening and early detection technological solutions in cruise ships, it is imperative that targeted awareness and education interventions are implemented on passengers to strengthen understanding and acceptance of such solutions and assuage concerns around monitoring and handling of personal health data.

Injury Patterns in Vehicle Crashes: The Significance of Occupant Seating Position.

OBJECTIVES:  Investigating patterns among the outcomes of patients involved in motor vehicle accidents (MVAs) can provide information necessary to guide targeted interventions to improve road traffic safety. Our purpose is to identify any differences between passenger and driver injury severity and overall clinical course after MVAs. METHODS:  We performed a retrospective review and analysis of 3,693 patients involved in MVAs from 2016 to 2021. We divided the data into two groups, drivers and passengers, and compared the Injury Severity Score (ISS), Revised Trauma Score (RTS) on admission, days in the Intensive Care Unit (ICU), length of hospital stay (LOS), post Emergency Department (ED) disposition, discharge (DC) disposition, and signs of life on arrival (SOLA) to the ED. We compared mean ISS, New Injury Severity Score (NISS), RTS, length in ICU and LOS using a student's T-test and SOLA, post-ED and DC disposition using Chi-square analysis. RESULTS:  We did not find any statistically significant difference in ISS, RTS, days in ICU, LOS, or SOLA between the drivers and passengers. However, we did find a statistically significant difference in the post-ED (X2= 113.743, p=<0.0001) and DC disposition (X2=41.172, p=<0.0001) of drivers and passengers. After the ED and DC, more passengers were transferred to a higher level of care than expected, while the inverse was true for drivers. The number of drivers discharged to Skilled Nursing Facilities (SNFs) was also higher than expected, further contributing to the observed difference in DC disposition.  Conclusion: Our study found no statistically significant difference between driver and passenger injury severity, length of hospital stay, days in ICU, and SOLA after an MVA. The clinical courses of the two groups were found to be significantly different based on post-ED and DC disposition data. We identified limitations, such as a relatively small sample size and insufficient data on specific car seat locations for passengers, underscoring the need for a more nuanced exploration. Future research must broaden its scope to encompass diverse crash scenarios, vehicle design and safety technologies, seat belt dynamics, and age- and gender-specific vulnerabilities.