No description available.

Private cars are responsible for 15% of carbon emissions in the European Union. Ride hailing services like taxis could serve the door-to-door mobility demand of private car users with fewer overall vehicles. If the service combines multiple user trips, it might even reduce the distance driven compared to private cars which becomes ecologically sustainable. Such ride sharing services are particularly sustainable when many users share one vehicle. But connecting the trips of all users yields many small detours. These detours reduce if some users walk a short distance to a neighboring stop. When multiple stops are combined, vehicles drive to fewer stops. Such stop pooling promises to make ride sharing even more sustainable. Some ride sharing services already integrate short user walks into their system. But the effects of stop pooling on ride sharing systems are yet to be understood. Methods from theoretical physics like mean-field theory and agent-based modeling enable a systemic analysis of complex ride sharing systems. This thesis demonstrates that ride sharing may be more sustainable when users accept short walks. With stop pooling, users wait shorter for vehicles and drive shorter because of more direct vehicle routes. In consequence, the user travel time decreases on average despite additional walk time at constant fleet size. Put differently, stop pooling allows to reduce the fleet size at constant travel time. This also reduces the distance driven by all vehicles that is proportional to the fleet size when sufficient users share one vehicle. This result is robust in a data-driven model using taxi trip data from Manhattan (New York City, USA) with fluctuating demand over the day. At constant fleet size the travel time fluctuates with the demand and might deviate a lot from the expected average travel time. Such unreliable travel times might deter users from ride sharing. However, stop pooling reduces the travel time, the more the higher the travel time without walking. Consequently, stop pooling also reduces the fluctuations in the travel time. This effect is particularly large when adapting the maximum allowed walk distance to the current demand. In adaptive stop pooling users walk further at higher demand. Then, the travel time in ride sharing is more reliable when users accept short walks. All in all, this thesis contributes to the fundamental understanding of the collective dynamics of ride sharing and the effect of stop pooling at a systemic level while also explaining underlying mechanisms. The results suggest that ride sharing providers and users benefit from integrating adaptive stop pooling into the service. Based on the results, a framework can be established that roughly adjusts fleet size to demand to ensure that the ride sharing service operates sustainably. Even if this fleet size remains constant throughout the day, adaptive stop pooling keeps the travel time reliable.:1. Introduction 1 1.1. Private Cars are Unsustainable . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2. Potentially More Sustainable Ride Sharing Faces Detours . . . . . . . . . . . . . 2 1.3. Less Detours in Ride Sharing with Walking to Pooled Stops . . . . . . . . . . . . 4 1.4. Physics Methods Help Understanding Ride Sharing . . . . . . . . . . . . . . . . . 5 1.5. Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2. Fundamentals - A Physics Perspective on Ride Sharing 7 2.1. State of Research on Ride Sharing . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.1.1. Ride Sharing Systems are Complex . . . . . . . . . . . . . . . . . . . . . . 8 2.1.2. Measuring Efficiency and Sustainability of Ride Sharing Services . . . . . 8 2.1.3. Ride Sharing might be More Sustainable when Users Accept Short Walks 10 2.1.4. Data-Driven Analysis Yields more Detailed Results . . . . . . . . . . . . . 11 2.1.5. Open Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.2. Theoretical Physics Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.2.1. What is a Complex System? . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.2.2. Mean-Field Theory Simplifies Complex Systems . . . . . . . . . . . . . . 13 2.2.3. Model Complex Systems Based on Agents, not on Equations . . . . . . . 14 2.2.4. Methods from Statistical Physics to Evaluate Multi-Agent Simulations . . 14 2.2.5. Model Street Networks Using Graph Theory . . . . . . . . . . . . . . . . 20 3. Model for Ride Sharing with Walking to Pooled Stops 25 3.1. Ride Sharing Combines Trips with Similar Directions . . . . . . . . . . . . . . . . 25 3.2. Stop Pooling with Dynamic Stop Locations Maintains Flexibility . . . . . . . . . 26 3.3. Simple Algorithm Assigns Users by Reducing Bus Detour . . . . . . . . . . . . . 28 3.3.1. Standard Ride Sharing Algorithm . . . . . . . . . . . . . . . . . . . . . . 28 3.3.2. Stop Pooling Algorithm at Similar Speed . . . . . . . . . . . . . . . . . . 29 3.4. Basic Setting in Continuous Space . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.4.1. Uniform Request Distribution . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.4.2. Heterogeneous Request Distribution . . . . . . . . . . . . . . . . . . . . . 32 3.5. Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.5.1. Relative Distance Driven Measures Ecological Sustainability . . . . . . . . 33 3.5.2. Measure Service Quality by Average User Travel Time . . . . . . . . . . . 34 3.5.3. Further Observables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.5.4. Bisection Method to Find Minimal Travel Time with Small Effort . . . . 36 3.6. Model Extensions Yield More Detailed Results . . . . . . . . . . . . . . . . . . . 37 3.6.1. Fine-Grained Street Network Enables Short Walk Distances . . . . . . . . 38 iii Contents 3.6.2. Data-Driven Demand is Heterogeneous . . . . . . . . . . . . . . . . . . . . 39 3.6.3. Explicit Stop Times Ensure Penalty For Each Stop . . . . . . . . . . . . . 41 3.6.4. Imbalanced Demand Requires Rebalancing of Buses . . . . . . . . . . . . 42 3.6.5. More Detailed Assignment Algorithm Uses Constraints . . . . . . . . . . 43 4. Quantifying Sustainability of Ride Sharing 45 4.1. Two Mechanisms Influence Ride Sharing Sustainability . . . . . . . . . . . . . . . 46 4.1.1. Pickup Detours Increase Distance Driven . . . . . . . . . . . . . . . . . . 46 4.1.2. Trip Overlap Reduces Distance Driven . . . . . . . . . . . . . . . . . . . . 47 4.2. Distance Driven Reduces with Bus Occupancy . . . . . . . . . . . . . . . . . . . 48 4.3. Ride Sharing is more Sustainable than Private Cars for Sufficient Load . . . . . . 50 4.4. Result is Robust for more Complex Models . . . . . . . . . . . . . . . . . . . . . 52 4.5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 5. Ride Sharing Sustainability with Stop Pooling 55 5.1. Ride Sharing Trades Sustainability for Travel Time . . . . . . . . . . . . . . . . . 57 5.2. Stop Pooling is more Sustainable at Same Travel Time . . . . . . . . . . . . . . . 58 5.2.1. Roughly Constant Distance Driven Despite Saved Stops . . . . . . . . . . 58 5.2.2. Stop Pooling Reduces Travel Time . . . . . . . . . . . . . . . . . . . . . . 59 5.2.3. Stop Pooling Breaks The Trade-off Between Sustainability And Travel Time 60 5.3. Higher Stop Pooling Effect for High Loads . . . . . . . . . . . . . . . . . . . . . . 61 5.3.1. Stop Pooling Limits Growth of Best Travel Time . . . . . . . . . . . . . . 62 5.3.2. Stop Pooling Breaks Trade-off for Sufficient Load . . . . . . . . . . . . . . 63 5.4. Robust Effect for Simple Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 5.5. Robust Effect with More Detailed Model . . . . . . . . . . . . . . . . . . . . . . . 66 5.5.1. Load Quantifies Stop Pooling Sustainability . . . . . . . . . . . . . . . . . 67 5.5.2. Already 1.2 Minutes Walk Time might Save 1 Minute Travel Time . . . . 68 5.5.3. Robust Result for Different Parameters . . . . . . . . . . . . . . . . . . . 69 5.6. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 6. Ride Sharing Reliability with Stop Pooling 71 6.1. Unreliable Standard Ride Sharing with Fluctuating Demand . . . . . . . . . . . . 72 6.2. More Reliable Stop Pooling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 6.3. Robust Effect of Stop Pooling with Limited User Delay . . . . . . . . . . . . . . 77 6.4. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 6.5. Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 7. Discussion 81 7.1. Results and Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 7.1.1. When is Ride Sharing More Sustainable than Private Cars? . . . . . . . . 81 7.1.2. How Does Stop Pooling Influence Sustainability of Ride Sharing? . . . . . 82 7.1.3. How Does Stop Pooling Influence Reliability of Ride Sharing? . . . . . . . 82 7.2. Limitations of the Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 7.2.1. Simple Algorithms for Ride Sharing and Stop Pooling . . . . . . . . . . . 82 7.2.2. Integrate Adaptive Stop Pooling into Virtual Bus Stops . . . . . . . . . . 83 7.2.3. Distance Driven as Estimator for Ecological Sustainability . . . . . . . . . 83 7.2.4. Deviations from Load Prediction . . . . . . . . . . . . . . . . . . . . . . . 84 7.2.5. Mean-Field Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 7.2.6. Further Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 7.3. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 A. Appendix 87 A.1. Manhattan Street Network Resembles Grid . . . . . . . . . . . . . . . . . . . . . 87 A.2. Computation Details of Bisection Method . . . . . . . . . . . . . . . . . . . . . . 88 A.3. Average Pickup Distance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 A.4. Robustness of Ride Sharing Sustainability . . . . . . . . . . . . . . . . . . . . . . 90 A.5. Stop Pooling Saves Stops . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 A.6. Stop Pooling Effectively Reduces Load . . . . . . . . . . . . . . . . . . . . . . . . 92 A.7. Example Breaking of Trade-off in Simple Model . . . . . . . . . . . . . . . . . . . 93 A.8. Transition in Best Walk Limit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 A.9. Maximal Trade-off Shift Increases with Load . . . . . . . . . . . . . . . . . . . . 95 A.10.Rebalancing Buses is more Important with Constraint . . . . . . . . . . . . . . . 97 A.11.Breaking of Trade-off in Complex Model . . . . . . . . . . . . . . . . . . . . . . . 98 A.12.More Stop Pooling at Destinations and High Demand . . . . . . . . . . . . . . . 99 A.13.Roughly Constant Wait and Drive Time in Adaptive Stop Pooling . . . . . . . . 100 A.14.Influence of Capacity Constraint . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 A.15.Walk Time of Rejected Users . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 Bibliography 101 Acknowledgment 116 Statement of Contributions 118

Trauma is a leading cause of death, and in potentially preventable trauma deaths insufficient bleeding control has often been noted. For bleeding-related injuries, providing aid quickly is crucial since people may die from haemorrhage within minutes. One group that has been identified as able to provide quick aid is bystanders and other people present at the scene of injury. In such settings, using bleeding control (b-con) equipment such as tourniquets is effective to help control bleeding. Thus, b-con kits including such equipment has been recommended to be placed in public areas. However, for publicly placed b-con kits to be effective people must be able to find the kits quickly. This leads to the aim of this thesis, which is to explore how b-con kits can be made easier to find for the public by using signage and b-con kit placement. Two studies were conducted to explore this topic. In the first study, signage for marking b-con kits was developed since no standardised or well-recognised signage for b-con kits existed. Three b-con kit signage designs were developed in accordance with existing standards and regulations for safety signage. Then, the three signage designs were evaluated by a survey. One design, depicting an injured arm, was found to be most effective for marking b-con kits. In the second study, the effect of signage use and b-con kit placement on time to find a b-con kit was examined by an experiment carried out in a virtual environment (VE) using virtual reality (VR). The study found that providing directional signage made people find the b-con kit faster. The study also found that placing the b-con kit at a reception (a central location) made people find the b-con kit quicker compared to when the b-con kit was placed between emergency exits (a more peripheral location). The study also examined if people would find the b-con kit faster when b-con signage was used for directional signage compared to when general first aid signage was used, but no significant difference was found. In addition, the VE was validated. The results of the validation showed that the findings in the study would likely transfer to real world settings. In conclusion, using signage and placing b-con kits strategically were found to be effective ways to aid the public in finding publicly placed b-con kits, which may help people provide possibly life-saving aid to haemorrhage victims.

Lifestyle modifications, including calorie deficit diets targeting weight loss, are the first line treatment and prevention measures for the development and progression of the emerging cardiometabolic disease, nonalcoholic fatty liver disease (NAFLD), particularly early in the disease course when interventions may alter the natural history. However, the optimal composition of eating patterns to protect liver health is still under debate. High-quality diets with anti-inflammatory, antioxidant, and anti-obesity effects have the potential to play an important role in NAFLD occurrence; however, evidence is still limited. Dietary Approaches to Stop Hypertension (DASH) and a Mediterranean-style diet are two major healthy dietary patterns that have been extensively reported to prevent cardiometabolic outcomes such as hypertension (HTN) and cardiovascular disease (CVD), major concomitant diseases of NAFLD. A few indexes have been developed to measure adherence to both of these dietary patterns in observational studies. However, none of the DASH indexes have been updated with current evidence in more than a decade. Research suggests that greater reductions in cardiometabolic disorders are possible with more updated guidance. In addition to the impact of overall diet quality, the effects of specific foods on liver fat are still unknown, and some, such as eggs, are controversial. Until recently, eggs have been considered unhealthy due to concerns about dietary cholesterol intake, but evidence suggests these concerns may have been influenced by confounding. Eggs are rich in many important nutrients and bioactive compounds, including dietary choline and carotenoids, that may benefit cardiometabolic outcomes, including NAFLD. Choline deficiency has been shown to cause liver steatosis in preclinical studies, but little is known about the relations between egg consumption, egg-rich nutrients, and liver fat in humans. The objectives of this dissertation are to evaluate the association between adherence to different healthy dietary patterns, including a Mediterranean-style diet and the DASH diet, and NAFLD risk. To account for updated evidence, we also developed and tested the reliability and validity of a newly modified DASH (mDASH) eating pattern and evaluated its association with incident NAFLD risk as well as changes in liver fat over a 6-year follow-up period. Lastly, we evaluated the associations between eggs and selected egg-rich nutrients (i.e., choline, lutein, and zeaxanthin) and NAFLD risk and prevalence alone and in combination with other dietary habits. We used data from several cohorts in the Framingham Heart Study, including the Offspring, Third Generation, Omni Generation Cohort 1, and Omni Generation Cohort 2. Liver fat was assessed using a computed tomography (CT) scan using the average liver fat attenuation relative to a control phantom to create the liver phantom ratio (LPR) at two sequential CT scans (2002–2005 and 2008–2011) in the Offspring and Third Generation cohorts; measures were available at a single CT scan (2008–2011) in Omni cohorts. NAFLD was defined as a LPR ≤0.33 in the absence of heavy alcohol use (>14 drinks per week for females and >21 drinks per week for males). Prevalent NAFLD was determined on the follow-up CT scan, which all cohorts had available. Among those with no NAFLD at the first CT scan, incident NAFLD was determined on the follow-up CT scan. To determine changes in liver fat, we calculated the difference in LPR from one exam to the next by subtracting the 1st CT scan LPR measure from the 2nd CT LPR measure. Next, we calculated annualized changes by dividing LPR change values by the year difference between the 1st and 2nd CT scan dates for each participant. For the assessment of incident NAFLD and liver fat change, dietary data were derived from food frequency questionnaires (FFQs) administered as close as possible in time to the first liver fat measurement. For the analysis of prevalent liver fat, we used the FFQ as close as possible in time to the second liver fat measurement. The first specific aim of this dissertation is to evaluate whether adherence to the Mediterranean diet (MeDiet Index) or a DASH eating pattern (Fung DASH index) was associated with NAFLD risk. Modified Poisson regression models were used to compute incident NAFLD risk ratios (RR) and 95% confidence intervals (CI) associated with three score categories (low, moderate, and high) on each index adjusting for confounding in 1413 Framingham Offspring and Third Generation participants. Multivariable linear regression models were used to compute adjusted annualized means of liver fat change over a median of 6 years of median follow-up in 1691 participants. In the second specific aim, we aimed to evaluate the adherence to a mDASH eating pattern on NAFLD risk and changes in liver fat using a newly developed mDASH index. Firstly, we developed and evaluated the psychometric properties (reliability and validity) of the new mDASH index. To develop the index, we started with the components included in the original DASH eating pattern (fruits, vegetables, and low-fat dairy), and then proceeded to add components, one at a time, that have been suggested in the literature to protect against known DASH-related outcomes. The selection of the components included in the final mDASH index (total vegetables (including potatoes), total fruit, total dairy (including full-fat dairy), red and processed meat, whole grains, legumes, nuts and seeds, sugar-sweetened beverages, and fish) was based on two criteria: 1) factors found in previous literature to be associated with blood pressure and cardiovascular outcomes, and 2) the ability of possible components in the new mDASH to predict the risk of known DASH-related outcomes (i.e., HTN and atherosclerotic CVD (ASCVD)). Test-retest reliability analyses of the proposed mDASH index were performed with Pearson correlation coefficients over four sequential examination visits in 1339 Offspring Cohort individuals. To assess construct validity, we computed cross-sectional linear regression and correlation analyses between the proposed mDASH index against key DASH nutrients such as calcium, potassium, magnesium, and fiber in 2763 Offspring Cohort individuals. Next, we used Cox regression models to evaluate the predictive validity of the mDASH index, with one modification at a time, for incident HTN (n=1714) and then for ASCVD (n=2700) risks over 11 and 25 medians years of follow-up, respectively. Once the mDASH index was finalized, we used Cox models to determine whether our final mDASH index represents an improvement over three previously established DASH indexes (Fung, Dixon, and Günther) for predicting HTN and ASCVD in two Framingham cohorts, the Offspring and the younger Third Generation cohorts. Lastly, we aimed to evaluate the association between mDASH (vs. earlier DASH indexes) and the risk of incident NAFLD. Multivariable modified Poisson regression and general linear models were used to compute incident NAFLD RR (n=1406) and adjusted means of annualized liver fat change (n=1692) associated with the mDASH index and other prior DASH indexes in a combined sample from the Offspring and Third Generation cohorts. In the third specific aim, we evaluated the impact of egg and egg-rich nutrients (choline, lutein, and zeaxanthin) on NAFLD risk. Egg intake was classified into three categories based on the food frequency questionnaire categories (<1, 1, and ≥2 per week). Dietary choline was body weight adjusted based on the residual method while lutein and zeaxanthin were classified into tertiles, respectively. Multivariable logistic regression models were used to compute prevalent NAFLD odds ratios (OR) associated with egg intakes in 2644 participants from the Offspring, Third Generation, Omni 1, and Omni 2 cohorts. Multivariable modified Poisson regression and general linear models were used to compute NAFLD incident RR (n=1414) and adjusted means of annualized liver fat change (n=1690) associated with egg, choline, and lutein and zeaxanthin intakes in a combined sample from the Offspring and Third Generation cohorts which had repeated measures of liver fat. In our analyses related to the comparison of the DASH eating pattern with a Mediterranean-style diet (Aim 1, Chapter 2), in a combined sample from the Offspring and Third Generation cohorts 19% of participants (n=1413, mean age 51 years, 53% female) developed new onset of NAFLD during follow-up and liver fat increased for most participants. High score category (>28 vs. ≤23 scores) on the Fung DASH index were associated with a 40% lower risk of incident NAFLD (95% CI: 0.42–0.84) after adjusting for age, sex, alcohol intake, education status, current smoking status, multivitamin use, moderate-to-vigorous physical activity, and annualized waist-to-height ratio changes. These findings were stronger in women than men (RR for higher (vs. lower) DASH scores: 0.46, 95% CI: 0.26–0.84 in women; 0.69, 95% CI: 0.45–1.05 in men). DASH scores were also associated with statistically significantly less acquisition of liver fat over study follow-up (p-value<0.05). Further, the DASH diet was protective against NAFLD risk even among individuals with prevalent HTN or an elevated triglyceride:high density lipoprotein cholesterol (TG:HDL-C) ratio. We observed no association between adherence to a MeDiet index and NAFLD risk or liver fat change. The development and testing of the mDASH index are shown in Chapter 3. We found in the analyses for this second specific aim that there was strong evidence of test-retest reliability for the newly-developed mDASH index as supported by moderate to high correlations (r=0.59–0.71) in total mDASH scores between four sequential exams. The construct validity of the mDASH index was supported by its associations with selected nutrients in expected directions. Total mDASH scores were strongly positively associated with intakes of calcium, potassium, magnesium, and fiber (p-values<0.05). These associations were similar to or stronger than those observed with previous DASH indexes. The predictive validity of the new mDASH index was supported by the statistically significant 31% and 29% lower risks of incident HTN and ASCVD associated with score quintile 5 (vs. quintile 1) on the new index in the older Offspring Cohort. Similarly, strong inverse associations between the mDASH index and both HTN and ASCVD were also observed in the younger Third Generation Cohort. These associations were of a similar magnitude to those observed with the Fung index for both HTN and ASCVD but stronger than those seen with the Dixon or Günther indexes. In Chapter 3, we also examined the association between the new mDASH index and risk of incident NAFLD. We found that score tertile 3 (vs. tertile 1) on the mDASH index was associated with a 29% lower NAFLD risk (95% CI: 0.53–0.95) after adjusting for age, sex, energy, sodium intake, current smoking status, education level, multivitamin use, and alcohol intake. In addition, the highest scores on all DASH indexes (mDASH, Fung, Dixon, and Günther) were associated with substantially smaller annualized increases in liver fat over the study follow-up (p-value <0.05). In Chapter 4 of this dissertation, we first examined the association between egg consumption and prevalent and incident NAFLD. NAFLD prevalence was 29% among participants (n=2644) in a combined sample from the Offspring, Third Generation, Omni 1, and Omni 2 cohorts. In a sample from the Offspring and Third Generation cohorts with two measures of liver fat (n=1414), the cumulative incidence of NAFLD was 19% and liver fat was found to increase for most participants during follow-up. After adjusting for confounding by age, sex, energy, red meat and alcohol intakes, prevalent HTN, and body mass index (BMI), we found no association between egg intake and prevalent NAFLD (OR for ≥2 vs. <1 eggs per week (referent): 1.15, 95% CI: 0.92–1.45). Similarly, we observed no associations between egg intake and incident NAFLD risk (RR for ≥2 vs. <1 eggs per week (referent): 1.00, 95% CI: 0.77–1.30) or annualized change in liver fat. Our final specific aim in Chapter 4 was to examine the associations between selected egg-rich nutrients and NAFLD risk. Here, we found that dietary choline intake was strongly associated with a 31% lower risk of incident NAFLD (RR for highest vs. lowest tertile: 0.69, 95% CI: 0.51-0.94) after adjusting for age, sex, education level, and waist-to-height ratio. No associations were found between lutein and zeaxanthin intakes and NAFLD. In summary, the findings of this dissertation suggested that adherence to a DASH eating pattern is beneficially associated with a lower risk of incident NAFLD. Our new mDASH index supports other evidence suggesting that the DASH eating pattern should no longer be limited to the consumption of low-fat dairy and that total vegetable intake does not need to exclude white or sweet potatoes from the diet. The new mDASH is associated with a lower risk of traditional DASH-related outcomes, including HTN and ASCVD, as well as with the emerging cardiometabolic risk factor, NAFLD. Lastly, higher egg intake alone or combined with other eating patterns was not associated with NAFLD, while dietary choline intakes were inversely associated with NAFLD risk.

No description available.

No description available.

No description available.

No description available.

In this article, we analyse the factors influencing homeowners' plans to renovate their houses in the near future. We also examine their interest in the idea of a one-stop shop where a single actor provides services and all necessary information required for energy efficiency renovations. Our analysis is based on the content of an online survey questionnaire of 12194 homeowners in Sweden. Approximately 83% of the respondents intend to renovate in the near future, with around 55% opting for energy-efficient renovation of their dwelling. Environmental concerns, past energy-saving measures taken by the respondents, and education positively influenced their renovation plans. While 28% of respondents expressed their interest in a one-stop shop, work guarantee, environmental concerns and renovation plans were the main elements affecting interest in the one-stop shop.

This paper looks upon monsters as a medium; how concentration of fear can result in the demonisation of individuals. It explores how the use of craft, with a focus on stop motion animation, can be used as a tool to build empathy and help to heal fragmentations of society. Working with contemporary mythologies, crafted techniques are metaphorically related to the fragility of societal structures and collective narratives. Hand crafting references relics of consumer culture and explores variable autonomies over personal narrative, investigating imbalances of power.