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At least 19 records

Assessment of risk for asthma initiation and cancer and heart disease deaths among patrons and servers due to secondhand smoke exposure in restaurants and bars

Background: Despite efforts to reduce exposure to secondhand smoke (SHS), only 5% of the world’s population enjoy smoke-free restaurants and bars. Methods: Lifetime excess risk (LER) of cancer death, ischaemic heart disease (IHD) death and asthma initiation among non-smoking restaurant and bar servers and patrons in Minnesota and the US were estimated using weighted field measurements of SHS constituents in Minnesota, existing data on tobacco use and multiple dose-response models. Results: A continuous approach estimated a LER of lung cancer death (LCD) of 18×10 –6 (95% CI 13 to 23×10 –6 ) for patrons visiting only designated nonsmoking sections, 80×10 –6 (95% CI 66 to 95×10 –6 ) for patrons visiting only smoking venues/sections and 802×10 –6 (95% CI 658 to 936×10 –6 ) for servers in smoking-permitted venues. An attributable-risk (exposed/ non-exposed) approach estimated a similar LER of LCD, a LER of IHD death about 10 –2 for non-smokers with average SHS exposure from all sources and a LER of asthma initiation about 5% for servers with SHS exposure at work only. These risks correspond to 214 LCDs and 3001 IHD deaths among the general nonsmoking population and 1420 new asthma cases among non-smoking servers in the US each year due to SHS exposure in restaurants and bars alone. Conclusions: Health risks for patrons and servers from SHS exposure in restaurants and bars alone are well above the acceptable level. Restaurants and bars should be a priority for governments’ effort to create smoke-free environments and should not be exempt from smoking bans.

54 ENVIRONMENTAL SCIENCES↗

Partner with a Third-Party Delivery Service or Not? A Prediction-and-Decision Tool for Restaurants Facing Takeout Demand Surges During a Pandemic

Amidst the COVID-19 pandemic, restaurants become more reliant on no-contact pick-up or delivery ways for serving customers. As a result, they need to make tactical planning decisions such as whether to partner with online platforms, to form their own delivery team, or both. In this paper, we develop an integrated prediction-decision model to analyze the profit of combining the two approaches and to decide the needed number of drivers under stochastic demand. We first use the susceptible-infected-recovered (SIR) model to forecast future infected cases in a given region and then construct an autoregressive-moving-average (ARMA) regression model to predict food-ordering demand. Using predicted demand samples, we formulate a stochastic integer program to optimize food delivery plans. We conduct numerical studies using COVID-19 data and food-ordering demand data collected from local restaurants in Nuevo Leon, Mexico, from April to October 2020, to show results for helping restaurants build contingency plans under rapid market changes. Our method can be used under unexpected demand surges, various infection/vaccination status, and demand patterns. Here, our results show that a restaurant can benefit from partnering with third-party delivery platforms when (i) the subscription fee is low, (ii) customers can flexibly decide whether to order from platforms or from restaurants directly, (iii) customers require more efficient delivery, (iv) average delivery distance is long, or (v) demand variance is high.

97 MATHEMATICS AND COMPUTING↗

Feasibility and operating costs of an air cycle for CCHP in a fast food restaurant

This work considers the possibilities of an air-based Brayton cycle to provide the power, heating and cooling needs of fast-food restaurants. A model of the cycle based on conventional turbomachinery loss coefficients is formulated. The heating, cooling and power capabilities of the cycle are extracted from simulation results. Power and thermal loads for restaurants in Knoxville, TN and in International Falls, MN, are considered. It is found that the cycle can meet the loads by setting speed and mass flow-rate apportionment between the power and cooling functional sections. The associated energy costs appear elevated when compared to the cost of operating individual components or a more conventional, absorption-based CHP system. Lastly, a first-order estimate of capital investments is provided. Suggestions for future work whereby the operational costs could be reduced are given in the conclusions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Solar heating and hot water system installed at Shoney's Restaurant, North Little Rock, Arkansas

A solar heating system designed to supply a major portion of the space and water heating requirements for a restaurant is described. The restaurant has a floor space of approximately 4,650 square feet and requires approximate 1500 gallons of hot water daily. The solar energy system consists of 1,428 square feet of Chamberlain flat plate liquid collector subsystem, and a 1500 gallon storage subsystem circulating hot water producing 321 x 10 to the 6th power Btu/Yr (specified) building heating and hot water heating.

Source record↗

Modelling Airborne Transmission and Ventilation Impacts of a COVID-19 Outbreak in a Restaurant in Guangzhou, China

Computational fluid dynamics (CFD) modelling was performed to simulate spatial and temporal airborne pathogen concentrations during an observed COVID-19 outbreak in a restaurant in Guangzhou, China. The reported seating configuration, overlap durations, room ventilation, layout, and dimensions were modelled in the CFD simulations to determine relative exposures and probabilities of infection. Results showed that the trends in the simulated probabilities of infection were consistent with the observed rates of infection at each of the tables surrounding the index patient. Alternative configurations that investigated different boundary conditions and ventilation conditions were also simulated. Increasing the fresh-air percentage to 10%, 50%, and 100% of the supply air reduced the accumulated pathogen mass in the room by an average of ~30%, ~70%, and ~80%, respectively, over 73 min. Overall, the probability of infection was reduced by ~10%, 40%, and 50%, respectively.

60 APPLIED LIFE SCIENCES↗

Solar heating for a restaurant--North Little Rock, Arkansas

Hot water consumption of large building affects solar-energy system design. Continual demand for hot water at restaurant makes storage less important than at other sites. Storage capacity of system installed in December 1979 equals estimated daily hot-water requirement. Report describes equipment specifications and modifications to existing building heating and hot water systems.

Source record↗

Restaurant Energy Use Benchmarking Guideline

A significant operational challenge for food service operators is defining energy use benchmark metrics to compare against the performance of individual stores. Without metrics, multiunit operators and managers have difficulty identifying which stores in their portfolios require extra attention to bring their energy performance in line with expectations. This report presents a method wherebymultiunit operators may use their own utility data to create suitable metrics for evaluating their operations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Feature Detection From LiDAR Point Cloud and Aerial Imagery Using Distance Dependent Chinese Restaurant Process [Slides]

It is possible to integrate data captured by UAS from multiple data modalities spatially. We can detect features by combining information across images and LiDAR point clouds. Performance issues exist with the current algorithms in place & could be improved. We hope to test it for at least 500 iterations. Post processing of data takes enormous efforts in case of data generated by proprietary software tools…We intend to develop a better framework for seamless integration.

97 MATHEMATICS AND COMPUTING↗

Healthy Buildings Guide for Small Businesses: A Quick Reference to Improve Indoor Air Quality (Restaurants Excluded)

Indoor air pollutant concentrations can be higher than those outdoors, putting occupants at risk. These pollutants come from a variety of sources: materials used to build and furnish indoor spaces, retail products and services, and occupants themselves. Ventilation, temperature, and humidity also affect indoor air quality. It is important to consider the documented benefits of healthy indoor environments at any time – not just during a public health crisis – and it is key to consider the indoor air quality where we do business as well as at home. Small businesses vary in size, services, products, ownership style, and building mechanical systems, yet all benefit from healthy indoor spaces for employees and customers. This guide provides steps, in conjunction with public health guidance, that small businesses can take to improve and maintain indoor air quality. Small businesses often have unique ownership structures – the building owner and business owner can be different parties – so this guide addresses both building and business owner action items. There are many ways to pursue healthy indoor air, including eliminating, minimizing, and/or controlling the contaminants we bring in, and removing or diluting any existing contaminants through ventilation, filtration, air cleaning, and/or disinfection. Best results are achieved through a comprehensive approach combining various strategies, including those discussed in this guide.

air filtration↗

A global analysis of cities’ geosocial temporal signatures for points of interest hours of operation

The temporal nature of humans interaction with Points of Interest (POIs) in cities can differ depending on place type and regional location. Times when many people are likely to visit restaurants (place type) in Italy, may differ from times when many people are likely to visit restaurants in Lebanon (i.e. regional differences). Geosocial data are a powerful resource to model these temporal differences in cities, as traditional methods used to study cross-cultural differences do not scale to a global level. As cities continue to grow in population and economic development, research identifying the social and geophysical (e.g., climate) factors that influence city function remains important and incomplete. Here, we take a quantitative approach, applying dynamic time warping and hierarchical clustering on temporal signatures to model geosocial temporal patterns for Retail and Restaurant Facebook POIs hours of operation for more than 100 cities in 90 countries around the world. Results show cities’ temporal patterns cluster to reflect the cultural region they represent. Furthermore, temporal patterns are influenced by a mix of social and geophysical factors. Trends in the data imply social factors influence unique drops in temporal signatures, and geophysical factors influence when daily temporal patterns start and finish.

99 GENERAL AND MISCELLANEOUS↗

The association between neighborhood obesogenic factors and prostate cancer risk and mortality: the Southern Community Cohort Study

Background: Prostate cancer is one of the leading causes of cancer-related mortality among men in the United States. We examined the role of neighborhood obesogenic attributes on prostate cancer risk and mortality in the Southern Community Cohort Study (SCCS). Methods: From the total of 34,166 SCCS male participants, 28,356 were included in the analysis. We assessed the relationship between neighborhood obesogenic factors [neighborhood socioeconomic status (nSES) and neighborhood obesogenic environment indices including the restaurant environment index, the retail food environment index, parks, recreational facilities, and businesses] and prostate cancer risk and mortality by controlling for individual-level factors using a multivariable Cox proportional hazards model. We further stratified prostate cancer risk analysis by race and body mass index (BMI). Results: Median follow-up time was 133 months [interquartile range (IQR): 103, 152], and the mean age was 51.62 (SD: ± 8.42) years. There were 1,524 (5.37%) prostate cancer diagnoses and 98 (6.43%) prostate cancer deaths during follow-up. Compared to participants residing in the wealthiest quintile, those residing in the poorest quintile had a higher risk of prostate cancer (aHR = 1.32, 95% CI 1.12–1.57, p = 0.001), particularly among non-obese men with a BMI < 30 (aHR = 1.46, 95% CI 1.07–1.98, p = 0.016). The restaurant environment index was associated with a higher prostate cancer risk in overweight (BMI ≥ 25) White men (aHR = 3.37, 95% CI 1.04–10.94, p = 0.043, quintile 1 vs. None). Obese Black individuals without any neighborhood recreational facilities had a 42% higher risk (aHR = 1.42, 95% CI 1.04–1.94, p = 0.026) compared to those with any access. Compared to residents in the wealthiest quintile and most walkable area, those residing within the poorest quintile (aHR = 3.43, 95% CI 1.54–7.64, p = 0.003) or the least walkable area (aHR = 3.45, 95% CI 1.22–9.78, p = 0.020) had a higher risk of prostate cancer death. Conclusion: Living in a lower-nSES area was associated with a higher prostate cancer risk, particularly among Black men. Restaurant and retail food environment indices were also associated with a higher prostate cancer risk, with stronger associations within overweight White individuals. Finally, residing in a low-SES neighborhood or the least walkable areas were associated with a higher risk of prostate cancer mortality.

60 APPLIED LIFE SCIENCES↗

Carl's Jr. Installs Demand-Based Defrost Controller

This case study describes CKE Restaurants, Inc’s efforts to cut energy waste by field testing an advanced demand defrost control system for walk-in coolers and freezers at Carl’s Jr. restaurants.

advanced demand defrost control system, walk-in co↗

Impact of the built, social, and food environment on long‐term weight loss within a behavioral weight loss intervention

Abstract Background Behavioral weight loss interventions can lead to an average weight loss of 5%–10% of initial body weight, however there is wide individual variability in treatment response. Although built, social, and community food environments can have potential direct and indirect influences on body weight (through their influence on physical activity and energy intake), these environmental factors are rarely considered as predictors of variation in weight loss. Objective Evaluate the association between built, social, and community food environments and changes in weight, moderate‐to‐vigorous physical activity (MVPA), and dietary intake among adults who completed an 18‐month behavioral weight loss intervention. Methods Participants included 93 adults (mean ± SD; 41.5 ± 8.3 years, 34.4 ± 4.2 kg/m 2 , 82% female, 75% white). Environmental variables included urbanicity, walkability, crime, Neighborhood Deprivation Index (includes 13 social economic status factors), and density of convenience stores, grocery stores, and limited‐service restaurants at the tract level. Linear regressions examined associations between environment and changes in body weight, waist circumference (WC), MVPA (SenseWear device), and dietary intake (3‐day diet records) from baseline to 18 months. Results Grocery store density was inversely associated with change in weight ( β = −0.95; p = 0.02; R 2 = 0.062) and WC ( β = −1.23; p < 0.01; R 2 = 0.109). Participants living in tracts with lower walkability demonstrated lower baseline MVPA and greater increases in MVPA versus participants with higher walkability (interaction p = 0.03). Participants living in tracts with the most deprivation demonstrated greater increases in average daily steps ( β = 2048.27; p = 0.02; R 2 = 0.039) versus participants with the least deprivation. Limited‐service restaurant density was associated with change in % protein intake ( β = 0.39; p = 0.046; R 2 = 0.051). Conclusion Environmental factors accounted for some of the variability (<11%) in response to a behavioral weight loss intervention. Grocery store density was positively associated with weight loss at 18 months. Additional studies and/or pooled analyses, encompassing greater environmental variation, are required to further evaluate whether environment contributes to weight loss variability.

Tewahade, Selam↗