Rodrigo Duarte-Casar, Marlene Rojas-Le-Fort, Jaime González-Amagua
et al.
Este estudio explora el significado culinario y cultural de las empanadas en Ecuador, analizando su diversidad regional, su evolución histórica y su papel socioeconómico. Mediante una revisión narrativa de la literatura académica y de datos de búsqueda en Google Trends, se identifican hallazgos clave: las empanadas ecuatorianas presentan variaciones geográficas notables, desde las de verde y yuca en la Costa y la Amazonía hasta las de viento y morocho en la Sierra, lo que refleja la riqueza agroecológica del país. Aunque generan menor interés en las búsquedas que en otros países latinoamericanos, mantienen una fuerte relevancia cultural, siendo protagonistas de festividades tradicionales y demostrando ser un alimento de consuelo en tiempos de crisis. La investigación revela importantes vacíos académicos en el tema, lo que subraya la necesidad de estudios nutricionales y antropológicos más profundos. Los resultados cuestionan concepciones homogéneas de la gastronomía nacional y posicionan a las empanadas como artefactos glocales que fusionan técnicas indígenas con influencias coloniales. El trabajo concluye proponiendo lineamientos para su valorización como patrimonio cultural, que incluyen su integración en rutas turísticas y programas de preservación
Food processing and manufacture, Nutrition. Foods and food supply
Storytelling has become an increasingly important aspect of hospitality and tourism that can have several positive impacts on visitors. This article argues for an understanding of narrative structure in the guest experience as well as a reconceptualisation of the visitor’s journey as being one of a character traversing a storyworld. It also suggests that narrative implementation should become deeper and more experimental, with structures and organisational types in place in attractions. The article presents 20 cases of narrative in global hospitality and tourism contexts that illustrate these principles.
Hospitality industry. Hotels, clubs, restaurants, etc. Food service
Hassan Kazemi Tehrani, Jun Cai, Abbas Yekanlou
et al.
Accurate food intake monitoring is crucial for maintaining a healthy diet and preventing nutrition-related diseases. With the diverse range of foods consumed across various cultures, classic food classification models have limitations due to their reliance on fixed-sized food datasets. Studies show that people consume only a small range of foods across the existing ones, each consuming a unique set of foods. Existing class-incremental models have low accuracy for the new classes and lack personalization. This paper introduces a personalized, class-incremental food classification model designed to overcome these challenges and improve the performance of food intake monitoring systems. Our approach adapts itself to the new array of food classes, maintaining applicability and accuracy, both for new and existing classes by using personalization. Our model's primary focus is personalization, which improves classification accuracy by prioritizing a subset of foods based on an individual's eating habits, including meal frequency, times, and locations. A modified version of DSN is utilized to expand on the appearance of new food classes. Additionally, we propose a comprehensive framework that integrates this model into a food intake monitoring system. This system analyzes meal images provided by users, makes use of a smart scale to estimate food weight, utilizes a nutrient content database to calculate the amount of each macro-nutrient, and creates a dietary user profile through a mobile application. Finally, experimental evaluations on two new benchmark datasets FOOD101-Personal and VFN-Personal, personalized versions of well-known datasets for food classification, are conducted to demonstrate the effectiveness of our model in improving the classification accuracy of both new and existing classes, addressing the limitations of both conventional and class-incremental food classification models.
This study presents a comprehensive Bayesian analysis of hotel booking cancellations using PyMC, comparing three model specifications of increasing complexity. We investigate how lead time, special requests, and parking requirements affect cancellation probability, and explore interaction effects with hotel type. Using MCMC sampling (NUTS algorithm) on 5,000 booking records, we find strong evidence that longer lead times increase cancellation probability (posterior mean: 0.600, 95\% HDI: [0.532, 0.661]), while special requests (posterior mean: -0.642) and parking (posterior mean: -3.879) significantly reduce cancellation risk. Model comparison via WAIC reveals that the full interaction model provides the best predictive performance, suggesting that the effects of booking characteristics vary systematically between city and resort hotels. This Bayesian approach enables full uncertainty quantification and provides actionable insights for revenue management.
A coronavirus pandemic is forcing people to be "at home" all over the world. In a life of hardly ever going out, we would have realized how the food we eat affects our bodies. What can we do to know our food more and control it better? To give us a clue, we are trying to build a World Food Atlas (WFA) that collects all the knowledge about food in the world. In this paper, we present two of our trials. The first is the Food Knowledge Graph (FKG), which is a graphical representation of knowledge about food and ingredient relationships derived from recipes and food nutrition data. The second is the FoodLog Athl and the RecipeLog that are applications for collecting people's detailed records about food habit. We also discuss several problems that we try to solve to build the WFA by integrating these two ideas.
Matthias Kaiser, Agnese Cretella, Cordula Scherer
et al.
We explore the challenges and opportunities of transitioning towards sustainable food systems through the lens of democratic food governance fostering inclusive and systemic transformation. Drawing on concepts of wicked problems and systems thinking, we propose a theory of change represented as a 'turtle model' that embraces the diversity of citizens' values and knowledge to highlight multiple avenues of transformation. As quadruple helix innovation and governance hubs, cities can be hotspots for food system transformations. We illustrate this for Dublin, Ireland, where local citizens' value-based food identities were galvanized to activate ecological awareness and promote sustainable seafood consumption. Within this democratic food governance framework, approaches such as open science, transdisciplinarity, and citizen engagement are fit-for-purpose to engage diverse food actors from government, industry, academia, and civil society in shared dialogue and action to transform food systems.
Robotic food scooping is a critical manipulation skill for food preparation and service robots. However, existing robot learning algorithms, especially learn-from-demonstration methods, still struggle to handle diverse and dynamic food states, which often results in spillage and reduced reliability. In this work, we introduce GRITS: A Spillage-Aware Guided Diffusion Policy for Robot Food Scooping Tasks. This framework leverages guided diffusion policy to minimize food spillage during scooping and to ensure reliable transfer of food items from the initial to the target location. Specifically, we design a spillage predictor that estimates the probability of spillage given current observation and action rollout. The predictor is trained on a simulated dataset with food spillage scenarios, constructed from four primitive shapes (spheres, cubes, cones, and cylinders) with varied physical properties such as mass, friction, and particle size. At inference time, the predictor serves as a differentiable guidance signal, steering the diffusion sampling process toward safer trajectories while preserving task success. We validate GRITS on a real-world robotic food scooping platform. GRITS is trained on six food categories and evaluated on ten unseen categories with different shapes and quantities. GRITS achieves an 82% task success rate and a 4% spillage rate, reducing spillage by over 40% compared to baselines without guidance, thereby demonstrating its effectiveness. More details are available on our project website: https://hcis-lab.github.io/GRITS/.
Women's sports research has experienced growing interest from sports marketers to better understand how and why individuals visit stadiums to support their teams. The stadium’s physical environment which is regarded as the “sportscape”, purported to have a significant influence on spectator revisit behaviour has also been greatly researched in developed markets. This study examines the spectator’s perceptions of distinctive variables of designated South African professional women's soccer stadium sportscape and their satisfaction leading to willingness to return for future matches, based on the Stimulus-Organism-Response (SOR) framework from an emerging markets’ perspective. A quantitative research approach was employed using an online self-administered structured questionnaire, applying a non-probability sampling technique with study participants from Gauteng province. Regression analysis revealed significant predictive associations between stadium sportscape attributes (seat comfort, stadium accessibility, facility aesthetics) and spectator satisfaction towards their willingness to return to the stadiums. Results also suggest that the respondent’s propensity to return is heightened when their expectations are satisfactorily met. The study contributes useful insights to sports marketing literature and educates sports and stadium facility managers about the importance of spectator perceptions on sportscape attributes that tend to influence their facility revisitation behaviour.
Hospitality industry. Hotels, clubs, restaurants, etc. Food service, Business
Oscar Blanco-Novoa, Tiago M Fernandez-Carames, Paula Fraga-Lamas
et al.
The principles of the Industry 4.0 are guiding manufacturing companies towards more automated and computerized factories. Such principles are also applied in shipbuilding, which usually involves numerous complex processes whose automation will improve its efficiency and performance. Navantia, a company that has been building ships for 300 years, is modernizing its shipyards according to the Industry 4.0 principles with the help of the latest technologies. Augmented Reality (AR), which when utilized in an industrial environment is called Industrial AR (IAR), is one of such technologies, since it can be applied in numerous situations in order to provide useful and attractive interfaces that allow shipyard operators to obtain information on their tasks and to interact with certain elements that surround them. This article first reviews the state of the art on IAR applications for shipbuilding and smart manufacturing. Then, the most relevant IAR hardware and software tools are detailed, as well as the main use cases for the application of IAR in a shipyard. Next, it is described Navantia's IAR system, which is based on a fog-computing architecture. Such a system is evaluated when making use of three IAR devices (a smartphone, a tablet and a pair of smart glasses), two AR SDKs (ARToolKit and Vuforia) and multiple IAR markers, with the objective of determining their performance in a shipyard workshop and inside a ship under construction. The results obtained show remarkable performance differences among the different IAR tools and the impact of factors like lighting, pointing out the best combinations of markers, hardware and software to be used depending on the characteristics of the shipyard scenario.
The Industry 4.0 revolution has been made possible via AI-based applications (e.g., for automation and maintenance) deployed on the serverless edge (aka fog) computing platforms at the industrial sites -- where the data is generated. Nevertheless, fulfilling the fault-intolerant and real-time constraints of Industry 4.0 applications on resource-limited fog systems in remote industrial sites (e.g., offshore oil fields) that are uncertain, disaster-prone, and have no cloud access is challenging. It is this challenge that our research aims at addressing. We consider the inelastic nature of the fog systems, software architecture of the industrial applications (micro-service-based versus monolithic), and scarcity of human experts in remote sites. To enable cloud-like elasticity, our approach is to dynamically and seamlessly (i.e., without human intervention) federate nearby fog systems. Then, we develop serverless resource allocation solutions that are cognizant of the applications' software architecture, their latency requirements, and distributed nature of the underlying infrastructure. We propose methods to seamlessly and optimally partition micro-service-based application across the federated fog. Our experimental evaluation express that not only the elasticity is overcome in a serverless manner, but also our developed application partitioning method can serve around 20% more tasks on-time than the existing methods in the literature.
The main purpose of the present research is to examine the relationships between the level of tourists’ connectedness to nature (CTN) and their landscape preferences (LP). For this purpose, the study first measures the construct validity and reliability of the CTN scale developed by Mayer and Frantz (2004) to confirm its appropriateness for different cultures and the originally one-dimensional structure of the scale is tested. Second, the LPs of domestic and foreign tourists are determined from six different landscape pictures and a relationship is sought between the tourists’ CTN levels and LP. The primary data were gathered through surveys of foreign and domestic tourists staying in Antalya. The relationships between the CTN and LP were determined by multinomial logistic regression analysis (MLRA). According to the findings, as the level of tourists’ CTN increases, their preference levels for shopping malls and historical places also increase. It has also been determined that as the CTN increases, preferences for beaches increases, too. Women who prefer shopping malls and historical sites are less likely than men to prefer wildlife. Also, as the preference for historical sites increases, the CTN level increases, too. The results of the study will be beneficial for planners to manage the landscape in destinations and to use the resources effectively.
Quizá la comida chilena por antonomasia es la empanada. Aun así, su imagen no está exenta de controversias, siendo la principal la presencia de pasas en el relleno. Se revisó la historia de la empanada chilena para entender la pertinencia y función de las pasas en el relleno, y así determinar si son parte de una tradición gastronómica ancestral o una moda asociada a gustos. Para ello se realizó una revisión bibliográfica no estructurada que abarca desde las primeras referencias documentadas hasta la segunda década del siglo XXI. Complementariamente, se realizó una comparación de ingredientes y del proceso de producción de tres recetas representativas; una del siglo XIX, una del siglo XX, y una receta reciente. Se concluyó que las pasas son parte importante en la receta de las empanadas de horno para ocasiones especiales, pero no lo son para las de consumo diario. Por lo tanto, su uso es discrecional y puede depender tanto del gusto del comensal como la ocasión para la que se prepara el producto, mismo que se determinó está en constante evolución.
Food processing and manufacture, Nutrition. Foods and food supply
Francisco Joaquim Barbosa Gonçalves , Carlos Manuel Martins da Costa
Barcelos is part of the UNESCO Creative Cities network in the category of Crafts and Folks Art, since 2017, however, according to a study by Gonçalves and Costa (2020), this fact was known by 5.1% of visitor’s respondents only, despite of being a territory of craftspeople and the birthplace of the “Rooster of Barcelos” (“Galo de Barcelos” in Portuguese language), one of the main symbols of the Portuguese tourism. This study aims at understanding how Creative Tourism might support the vitality of crafts, encourage a new generation of craftspeople, and contribute to the sustainable development of a territory being part of the UNESCO Creative Cities network. The data collection for this research was carried out through a “focus group” session (group interview), which is a qualitative research technique. It might be concluded that the development and implementation of a Creative Tourism project emerges as a priority and a tool for the sustainable development of the territory and crafts of Barcelos.
Keywords: Tourism; Creative Experiences; Crafts; UNESCO Creative Cities.
Hospitality industry. Hotels, clubs, restaurants, etc. Food service
The COVID-19 pandemic has significantly impacted the tourism and hospitality sector. Public policies such as travel restrictions and stay-at-home orders had significantly affected tourist activities and service businesses' operations and profitability. To this end, it is essential to develop an interpretable forecast model that supports managerial and organizational decision-making. We developed DemandNet, a novel deep learning framework for predicting time series data under the influence of the COVID-19 pandemic. The framework starts by selecting the top static and dynamic features embedded in the time series data. Then, it includes a nonlinear model which can provide interpretable insight into the previously seen data. Lastly, a prediction model is developed to leverage the above characteristics to make robust long-term forecasts. We evaluated the framework using daily hotel demand and revenue data from eight cities in the US. Our findings reveal that DemandNet outperforms the state-of-art models and can accurately predict the impact of the COVID-19 pandemic on hotel demand and revenues.
Food profiling is an essential step in any food monitoring system needed to prevent health risks and potential frauds in the food industry. Significant improvements in sequencing technologies are pushing food profiling to become the main computational bottleneck. State-of-the-art profilers are unfortunately too costly for food profiling. Our goal is to design a food profiler that solves the main limitations of existing profilers, namely (1) working on massive data structures and (2) incurring considerable data movement for a real-time monitoring system. To this end, we propose Demeter, the first platform-independent framework for food profiling. Demeter overcomes the first limitation through the use of hyperdimensional computing (HDC) and efficiently performs the accurate few-species classification required in food profiling. We overcome the second limitation by using an in-memory hardware accelerator for Demeter (named Acc-Demeter) based on memristor devices. Acc-Demeter actualizes several domain-specific optimizations and exploits the inherent characteristics of memristors to improve the overall performance and energy consumption of Acc-Demeter. We compare Demeter's accuracy with other industrial food profilers using detailed software modeling. We synthesize Acc-Demeter's required hardware using UMC's 65nm library by considering an accurate PCM model based on silicon-based prototypes. Our evaluations demonstrate that Acc-Demeter achieves a (1) throughput improvement of 192x and 724x and (2) memory reduction of 36x and 33x compared to Kraken2 and MetaCache (2 state-of-the-art profilers), respectively, on typical food-related databases. Demeter maintains an acceptable profiling accuracy (within 2% of existing tools) and incurs a very low area overhead.
Understanding the dynamics of food banks' demand from food insecurity is essential in optimizing operational costs and equitable distribution of food, especially when demand is uncertain. Hence, Gaussian Mixture Model (GMM) clustering is selected to extract patterns. The novelty is that GMM clustering is applied to identify the possible causes of food insecurity in a given region, understanding the characteristics and structure of the food assistance network in a particular region, and the clustering result is further utilized to explore the patterns of uncertain food demand behavior and its significant importance in inventory management and redistribution of surplus food thereby developing a two-stage hybrid food demand estimation model. Data obtained from a food bank network in Cleveland, Ohio, is used, and the clusters developed are studied and visualized. The results reveal that this proposed framework can make an in-depth identification of food accessibility and assistance patterns and provides better prediction accuracies of the leveraged statistical and machine learning algorithms by utilizing the GMM clustering results. Also, implementing the proposed framework for case studies based on different levels of planning led to practical results with remarkable ease and comfort intended for the respective planning team.
PurposeThe purpose of this study was to investigate restaurant clientele tipping behavior and its inspiration on foodservice empathy within two- and three-star hotels in Kisumu County, Kenya. This was with the objective of analyzing the tipping effect on restaurant food service quality with an emphasis on Parasuraman, Zeithmal and Barry's empathy as a key dimension of quality in accordance to the SERVQUAL model.Design/methodology/approachFor the purpose of this study, food service empathy was investigated in relation to the influence of tipping on restaurant food service quality. Further, a census of all the two and three star-rated hotels within Kisumu County was carried out. The study applied descriptive research design in the investigation on the tipping behavior and its inspiration on foodservice empathy. Moreover, simple random sampling was employed in the selection of clients since it yielded a sample that is representative of the population. Additionally, the study employed the use of questionnaires for collection of data, which were coded, analyzed and presented in frequencies, tables and graphs.FindingsThe study findings reveal that there is a significant relationship between rewards upon perception of service and food service empathy but failed to find a significant relation between incentives for improved future service as well as the social norms and foodservice empathy. However, in general, the study established a significant relationship between tipping and foodservice empathy in the sampled hotels in Kenya. Thus, in summary, at 95% confidence level, the study concluded that there is a significant relationship between tipping and foodservice empathy.Research limitations/implicationsThis study was restricted on two and three-star hotels within Kisumu County in Kenya with a sample size of 384 respondents, which would otherwise limit the degree to which the findings were applied. Consequently, the study sought to collect data from restaurant clients although the access and, therefore, direct interaction were denied by some of the hotels. Further, this study employed a survey approach in the collection of data from restaurant clients in two and three-star hotels. Accordingly, there was minimal local and regional research literature available on the study topic.Practical implicationsTipping in the context of the broad global service industry, including hospitality's restaurant food service, is as old as Roman times. However, tipping is practiced differently across the world with some countries practicing while other countries not practicing the act of tipping. For that reason, tipping is not regulated in some of the countries including Kenya and therefore the lack of policy. Nonetheless, tipping is perceived to be the genesis of food service failures as a result of discriminatory restaurant food service in addition to increasing costs of eating out. This study therefore sought to investigate restaurant tipping behavior and its inspiration on foodservice empathy. The study results might be applied in policy formulation in order to curb the negative effect of tipping on food service empathy.Originality/valueMinimal studies have been instituted and published in the area of tipping and service quality relationship with an emphasis on each of Parasuraman, Zeithmal and Berry's dimensions of quality. This research survey, therefore, sought to collect data from restaurant clients in two and three-star hotels within Kisumu County in Kenya and therefore investigated restaurant clientele tipping behavior and its inspiration on food service empathy.
This article validates a proposed Customer Relationship Management conceptual framework for the Mauritian Hotel Industry based on identified critical success factors. The research provided insights on how different factors need to be considered to ensure a smooth implementation of a Customer Relationship Management strategy and to address the causes of Customer Relationship Management project failures. Extant reviews of the literature on Customer Relationship Management theoretical frameworks, Customer Relationship Management causes of failure, and critical success factors for Customer Relationship Management success were conducted and
subsequently 33 items were identified. An exploratory factor analysis was done to validate the results of the survey followed by which a confirmatory factor analysis was conducted to confirm the appropriateness of the
proposed theoretical model, followed by a Structural Equation Model analysis conducted to validate the adequacy of the proposed model. The Structural Equation Model analysis confirmed the appropriateness of the proposed
framework and confirmed the exactitude of each Customer Relationship Management success factor included in the framework. The design of the new proposed conceptual framework intends to provide a novel approach of indepth knowledge on how to implement Customer Relationship Management in the Mauritian Hotel Industry and acts as a guideline for hotel managers to implement CRM successfully and to increase profitability
Hospitality industry. Hotels, clubs, restaurants, etc. Food service, Business