Search Results (53)

The work aims to leverage computer vision and artificial intelligence technologies to quantify key components in food distribution services. Specifically, it focuses on dish counting, content identification, and portion size estimation in a dining hall setting. An RGB camera is employed to capture the tray delivery process in a self-service restaurant, providing test images for plate counting and content identification algorithm comparison, using standard evaluation metrics. The approach utilized the YOLO architecture, a widely recognized deep learning model for object detection and computer vision. The model is trained on labeled image data, and its performance is assessed using a precision–recall curve at a confidence threshold of 0.5, achieving a mean average precision (mAP) of 0.873, indicating robust overall performance. The weight estimation procedure combines computer vision techniques to measure food volume using both RGB and depth cameras. Subsequently, density models specific to each food type are applied to estimate the detected food weight. The estimation model’s parameters are calibrated through experiments that generate volume-to-weight conversion tables for different food items. Validation of the system was conducted using rice and chicken, yielding error margins of 5.07% and 3.75%, respectively, demonstrating the feasibility and accuracy of the proposed method. Full article

The impact of sporting events on local economies and their spatial distribution is a topic of active policy debate. This study adds to the discussion by examining granular cellphone location data to assess the spillover effects of Major League Baseball (MLB) games in a major US city. Focusing on the 2019 season, we explore granular geospatial patterns in mobility and consumer spending on game days versus non-game days in the Saint Louis region. Through density-based clustering and hotspot analysis, we uncover distinct spatiotemporal signatures and variations in visitor affluence across different teams. This study uses features like game day characteristics, location data (latitude and longitude), business types, and spending data. A significant finding is that specific spatial clusters of economic activity are formed around the stadium, particularly on game days, with multiple clusters identified. These clusters reveal a marked increase in spending at businesses such as restaurants, bars, and liquor stores, with revenue surges of up to 38% in certain areas. We identified a significant change in spending patterns in the local economy during games, with results varying greatly across teams. Notably, the XGBoost model performs best, achieving a test R² of 0.80. The framework presented enhances the literature at the intersection of urban economics, sports analytics, and spatial modeling while providing data-driven actionable insights for businesses and policymakers. Full article

This study examines the impact of spatial characteristics on gendered retail consumption in Seoul, South Korea, providing empirical evidence for gender-sensitive urban planning. Gender-sensitive urban planning integrates gender perspectives into all stages of urban development, aiming to address the diverse needs and experiences of all genders spatially. While existing research has predominantly focused on gender differences in labor participation, this study shifts the focus to retail consumption, which is a critical aspect of daily life. Our research analyzes the spatial attributes of urban spaces at the neighborhood scale and their influence on aggregated retail consumption by gender. The aggregated retail sales by census output area (jipgyegu) represent the aggregated retail consumption. Utilizing spatial regression methods, this study identifies significant spatial autocorrelations and clustering patterns in retail sales data. The findings reveal that traditional markets, less-developed commercial areas, and specific retail sector (retailing, medical, and educational services) densities positively impact SMW (subtraction of men’s retail sales from women’s retail sales), while city center areas, developed commercial districts, special tourism zones, and specific retail sector (restaurants and entertainment) densities have negative impacts. Full article

This study explores the spatial and temporal evolution of Paomo restaurants in Xi’an’s central urban area from 2012 to 2023, utilizing point-of-interest (POI) data and advanced analytical techniques. Employing methods such as nearest neighbor distance, kernel density estimation, and band set statistical analysis, we have systematically analyzed the shifting patterns and critical factors influencing the distribution of these restaurants. The study’s findings reveal a significant outward expansion from the city center to the periphery, marked by substantial spatial agglomeration and the emergence of multiple distinct cores. This distribution shift is largely driven by urban policy initiatives and market dynamics. Key factors identified include accessibility to transportation, residential living standards, and the availability of commercial services. These factors highlight the substantial impact of urban spatial reorganization and socioeconomic factors on the specialty catering sector. The insights from this study provide valuable implications for urban planning and policy-making, suggesting targeted strategies for the spatial arrangement of specialty catering services to enhance urban layouts and promote sustainable economic development. Full article

In this study, hydrothermal co-liquefaction of restaurant waste for biocrude production was conducted. The feedstock was resembled using the organic fraction of restaurant waste and low-density polyethylene, polypropylene, polystyrene, and polyethylene terephthalate, four plastic types commonly present in municipal solid waste. Using design of experiment and a face-centered central composite design, three factors (feedstock plastic fraction, temperature, time) were varied at three levels each: feedstock plastic fraction (0, 0.25, 0.5), temperature (290 °C, 330 °C, 370 °C), and reaction time (0 min, 30 min, 60 min). The literature reports positive synergistic interactions in hydrothermal co-liquefaction of biomass and plastics; however, in this work, only negative synergistic interactions could be observed. A reason could be the high thermal stability of produced fatty acids that give little room for interactions with plastics. At the same time, mass might transfer to other product phases. Full article

Conventional spatiotemporal methods take frequentist or density-based approaches to map event clusters over time. While these methods discern hotspots of varying continuity in space and time, their findings overlook locations of routine occurrences where the geographic context may contribute to the regularity of event occurrences. Hence, this research aims to recognize the routine occurrences of point events and relate site characteristics and situation dynamics around these locations to explain the regular occurrences. We developed an algorithm, Location Analytics of Routine Occurrences (LARO), to determine an appropriate temporal unit based on event periodicity, seek locations of routine occurrences, and geographically contextualize these locations through spatial association mining. We demonstrated LARO in a case study with over 250,000 reported traffic accidents from 2010 to 2018 in Dallas, Texas, United States. LARO identified three distinctive locations, each exhibiting varying frequencies of traffic accidents at each weekly hour. The findings indicated that locations with routine traffic accidents are surrounded by high densities of stores, restaurants, entertainment, and businesses. The timing of traffic accidents showed a strong relationship with human activities around these points of interest. Besides the LARO algorithm, this study contributes to the understanding of previously overlooked periodicity in traffic accidents, emphasizing the association between periodic human activities and the occurrence of street-level traffic accidents. The proposed LARO algorithm is applicable to occurrences of point-based events, such as crime incidents or animal sightings. Full article

The digitization of consumption, led by information and communications technology (ICT), has reshaped the urban commercial spatial structure (UCSS) of restaurants and retailers. However, the impacts of ICT on UCSS and location selection remain unclear. In this study, based on on-demand food delivery data and real-time traffic data, we used two types of machine learning algorithms, random forest regression (RFR) and the density-based spatial clustering of applications with noise (DBSCAN), to study the spatial distribution patterns, driving factors, and new geographical location phenomena of ‘brick-and-click’ (B&C) stores in Xinjiekou’s central business district (CBD) in Nanjing, China. The results show that the UCSS in the CBD is being decentralized, but the degree of influence is related to the business type. Additionally, the scale of demand and the distance from core commercial nodes greatly affect the scales of B&C stores. Moreover, the agglomeration of high-sales B&C stores seems to indicate a micro-location advantage, characterized by the concentration of delivery riders, which is usually located in the commercial hinterland with dense traffic. This makes stores situated in traditionally advantageous locations more attractive for online sales. Thus, ICT enhances the Matthew effect in business competition. These findings deepen our understanding of urban digital planning management and business systems. Full article

In this research, 3356 alcohol-related traffic crashes were obtained from blood-alcohol test reports in Tianjin, China. Population density, intersection density, road density, and alcohol outlet densities, including retail density, entertainment density, restaurant density, company density, hotel density, and residential density, were extracted from 2114 traffic analysis zones (TAZs). After a spatial autocorrelation test, the multiple linear regression model (MLR), geographically weighted Poisson regression model (GWPR), and semi-parametric geographically weighted Poisson regression model (SGWPR) were utilized to explore the spatial effects of the aforementioned variables on drunk-driving crash density. The result shows that the SGWPR model based on the adaptive Gaussian function had the smallest AICc value and the best-fitting accuracy. The residential density and the intersection density are global variables, and the others are local variables that have different influences in different regions. Furthermore, we found that the influence of local variables in the economic–technological development area shows significantly different characteristics compared with other districts. Thus, a comprehensive consideration of spatial heterogeneity would be able to improve the effectiveness of the programs formulated to decrease drunk driving crashes. Full article

The visual qualities of the urban environment influence people’s perception and reaction to their surroundings; hence the visual quality of the urban environment affects people’s mental states and can have detrimental societal effects. Therefore, people’s perception and understanding of the urban environment are necessary. This study used a deep learning-based approach to address the relationship between effective spatial criteria and people’s visual perception, as well as spatial modeling and preparing a potential map of people’s visual perception in urban environments. Dependent data on people’s visual perception of Tehran, Iran, was gathered through a questionnaire that contained information about 663 people, 517 pleasant places, and 146 unpleasant places. The independent data consisted of distances to industrial areas, public transport stations, recreational attractions, primary streets, secondary streets, local passages, billboards, restaurants, shopping malls, dilapidated areas, cemeteries, religious places, traffic volume, population density, night light, air quality index (AQI), and normalized difference vegetation index (NDVI). The convolutional neural network (CNN) algorithm created the potential map. The potential visual perception map was evaluated using the receiver operating characteristic (ROC) curve and area under the curve (AUC), with the estimates of AUC of 0.877 and 0.823 for pleasant and unpleasant visuals, respectively. The maps obtained using the CNN algorithm showed that northern, northwest, central, eastern, and some southern areas of the city are potent in pleasant sight, and southeast, some central, and southern regions had unpleasant sight potential. The OneR method results demonstrated that distance to local passages, population density, and traffic volume is most important for pleasant and unpleasant sights. Full article

In this paper, the effectiveness of transplanted (either created or restored) seagrass meadows as a coastal protection measure is assessed through a five-step methodology. The analysis is focused on a stretch of the Catalan coast (NW Mediterranean) which is a fetch-limited environment. The results show that even considering conservative values for the meadow parameters (plant diameter, meadow density and canopy height), significant reductions of the annual average wave heights reaching the beach may be obtained, reducing flooding and erosion risks. Therefore, the investment in the conservation and restauration of seagrass meadows for protecting coastal areas from erosion and flooding is a measure that must be considered, due to the multiple benefits that they provide including ecosystem services. In addition, the proposed methodology may be a useful tool for coastal managers to help them in the design of seagrass meadows for coastal protection. Full article

Walnut shells can be used as fuels in power plants directly or as biochars obtained by pyrolysis or torrefaction. They are an example of clean waste biomass which shows a low ash content and a high Net Calorific Value, making them excellent for energy recovery in industrial and non-industrial applications, such as in bakeries, restaurants, and homes. Their main inconvenience is their low bulk density. Densification is a possible solution that reduces the costs of transportation, handling, and storage. In this work, after the characterization of the walnut shells, briquettes were obtained using a hydraulic piston press briquette machine under different conditions to find the best quality without the need for previous grinding for pelletizing. This method features easy operation and maintenance, and the briquette shape could be adapted as desired. The quality of the briquettes was measured through their density and durability. After fixing a compaction pressure to obtain acceptable briquettes, the factors affecting their quality were studied: operating temperature, moisture content, and the presence of small amounts of walnuts. Good quality briquettes were obtained with a compaction pressure of 66 MPa, with densities around 1040 kg/m³, and durabilities higher than 94% when the process was carried at 140 °C. The greatest increase in durability was observed between briquettes obtained at room temperature and those obtained at 80 °C. The presence of small amounts of walnuts, common after the shelling process, improved the durability. Although water is necessary, briquettes obtained from biomass with only 1% of moisture showed better durabilities. Therefore, walnut shells are suitable for obtaining good quality briquettes according to the specifications of solid biofuels established in the standards, without the need for any pretreatment. Full article

Catering and urban elements have a strong spatial association. The spatial clustering and dispersal patterns of catering can effectively influence cities’ economic and socio-spatial reconfiguration. This research first introduced the concept of the ARTR (the attractiveness of residential areas to restaurants) and measured its value as well as its spatial and temporal evolutionary patterns using global and local colocation quotients. The DBSCAN algorithm and spatial hot-spot analysis were used to analyze their spatial evolution patterns. On this basis, a multiscale geographically weighted regression (MGWR) model was used to analyze the scale of and spatial variation in the drivers. The results show that (1) Nanjing’s ARTR is at a low level, with the most significant decline in ARTR occurring from 2005 to 2020 for MRs and HRs, while LRs did not significantly respond to urban regeneration. (2) The spatial layout of the ARTR in Nanjing has gradually evolved from a circular structure to a semi-enclosed structure, and the circular structure has continued to expand outward. At the same time, the ARTR for different levels of catering shows a diverse distribution in the margins. (3) Urban expansion and regeneration have led to increasingly negative effects of the clustering level, commercial competition, economic level and neighborhood newness, while the density of the road network has been more stable. (4) The road network density has consistently remained a global influence. Commercial diversity has changed from a local factor to a global factor, while economic and locational factors have strongly spatially non-smooth relationships with the ARTR. The results of this study can provide a basis for a harmonious relationship between catering and residential areas in the context of urban expansion and regeneration. Full article

Restaurant wastewater contains a high concentration of O&G, up to 3434 mg/L. This study aims to (a) assess the efficiency of EC combined with US methods for O&G removal in restaurant wastewater, (b) identify the optimum condition for COD degradation using EC treatment via response surface methodology (RSM), and (c) determine the morphological surface of the aluminium (Al) electrode before and after EC treatment. The wastewater samples were collected from the Lembaran cafeteria at the Universiti Sains Malaysia (USM). The efficiency of EC, US, and US-EC, combined methods for O&G removal, was investigated using a batch reactor (pH 7). The interelectrode distance (ID, 2–6 cm), electrolysis time (T, 15–35 min), and current density (CD, 40–80 A/m²) were analysed, followed by RSM. The response variables were O&G (1000 mg/L) and chemical oxygen demand (COD low range, 1000 mg/L). The central composite design (CCD) with a quadratic model was used to appraise the effects and interactions of these parameters. The morphological surface of the electrode used was observed via scanning electron microscopy (SEM). The optimum removal efficiencies obtained were 95.4% (O&G) and 75.9% (COD) (ID: 2.4 cm, T: 30.5 min, and CD: 53.2 A/m²). The regression line fitted the data (R² O&G: 0.9838, and R² COD: 0.9558). The SEM images revealed that the use of US was useful in minimising cavitation on the electrode surface, which could lower the EC treatment efficacy. The US-EC combined technique is highly recommended for O&G removal from the food industry’s wastewater. Full article

The hedonic pricing model posits that the price of a given product or service is an aggregate of several distinct characteristics that define it. In the context of tourism, this approach can be used to assess the prices of accommodations in a given area or market. This study explores the main determinants of tourism prices in the Brașov area of central Romania, a county that ranked as the second most visited area in Romania in 2021 based on the number of arrivals according to the ordinary least squares regression model on a sample of 398 accommodation units of different types. The main finding of this research paper is that prices are strongly influenced by luxury amenities and that prices tend to rise in lower-population-density areas, which we associate with rural areas. As previously noted in the literature, monetary values can be assigned to incremental changes in the independent variables, with the most significant changes deriving from the inclusion of spa, sauna and restaurant services in the tourism offer of accommodation units. Full article

The cauldron stoves used in restaurants and canteens usually adopt the combustion mode of blast diffusion. Low combustion efficiency leads to low thermal efficiency and high CO and NOx emissions. To address these problems, a 52 kW fully premixed stove with porous media is designed, and the heat transfer characteristics of the stove are analyzed by theoretical analysis and numerical simulation. The results show that under the rated power, the thermal efficiency of the stove reaches 68.55%, which is more than twice the thermal efficiency of the traditional blast diffusion stove. Among them, the radiant heat efficiency of the stove reaches 47.16%; thus, radiation heat transfer has become an important way of heat transfer of the porous media stove. Moreover, increasing the diameter and emissivity of porous media will increase the radiant thermal efficiency of the stove, but it will significantly reduce the flame temperature. In addition, the influence of the diameter is greater than the emissivity. The increase of the thickness of porous media can significantly improve the preheating temperature of the premixed gas, thus improving the ignition performance of the stove. Additionally, the stove has an appropriate thickness (approximately 3 mm), which not only ensures the preheating temperature but also does not easily allow for breakage and damage of porous media. Increasing the pore density or reducing the porosity of porous media can enhance the ignition performance of the stove. Moreover, the results of numerical simulation verify the theoretical results to a certain extent and shows that there is an optimal flue position as well. Full article