Sri Lanka is a famous tourist destination thus; ensuring food safety has garnered importance in public health. Chinese style fried rice are popular among consumers and the majority of them belong to the low-income category. Hence, the aim of this descriptive cross-sectional study was to detect food hygienic quality of Chinese style fried rice available in hotels, restaurants and take away outlets in the Colombo city.. Using systematic random and cluster sampling technique 25 eating houses were selected in the Colombo city limits in which prepared and available for sale. Two hundred Chinese style fried rice samples were collected comprising 35%of vegetable, 29 % of chicken, 18% of seafood, 9% egg, 5%of beef and 3.5% of mixed variety respectively. Aerobic Plate Count was carried out using USFDA Manual of Food Quality Control 1992. Detection and enumeration of coliforms, faecal coliforms and E.coli were carried out using SLS516:part3:1982 standard. In the present study Aerobic Plate counts of 39.50% samples were > 105 cfu/g. Furthermore, 24.50% of the total fried rice samples (200) tested contained ≥ 1.100 Coliforms/g of rice followed by 16% contained ≥ 1.100 Faecal Coliforms/g of rice. Alarmingly, E.coli was detected in 39.0 % of the fried rice samples tested and the highest percentages of varieties contaminated were egg, vegetable and mixed. The services of PHIs who render their services on food safety are strongly recommended to improve the microbial quality of ready-to-eat food available for sale in food establishments of the Colombo city
Purpose - This study delves into the relationship between value co-creation and business performance in travel agencies. Furthermore, the study examines the mediating role of artificial intelligence (AI) marketing strategies in travel agencies. Methodology – The study used questionnaires to collect primary data from the respondents, which were subsequently analyzed using the Smart-PLS software. Data collection focused on individuals employed in travel agencies within the Republic of Serbia, aiming to empirically test the study’s hypotheses. Findings – The findings highlight the importance of value co-creation in achieving superior business performance. They also suggest that implementing artificial intelligence marketing strategies positively correlates with the business performance of travel agencies in the Republic of Serbia. Finally, the findings illustrate a significantly positive relationship between AI-based marketing strategies, value co-creation, and business performance of travel agencies in the Republic of Serbia. Implications – Artificial intelligence has become a key topic for tourism organizations. A marketing strategy based on artificial intelligence, combined with feedback from service users, is likely to enhance the performance of service organizations.
Hospitality industry. Hotels, clubs, restaurants, etc. Food service
Quantifying post-consumer food waste in institutional dining settings is essential for supporting data-driven sustainability strategies. This study presents a cost-effective computer vision framework that estimates plate-level food waste by utilizing semantic segmentation of RGB images taken before and after meal consumption across five Iranian dishes. Four fully supervised models (U-Net, U-Net++, and their lightweight variants) were trained using a capped dynamic inverse-frequency loss and AdamW optimizer, then evaluated through a comprehensive set of metrics, including Pixel Accuracy, Dice, IoU, and a custom-defined Distributional Pixel Agreement (DPA) metric tailored to the task. All models achieved satisfying performance, and for each food type, at least one model approached or surpassed 90% DPA, demonstrating strong alignment in pixel-wise proportion estimates. Lighter models with reduced parameter counts offered faster inference, achieving real-time throughput on an NVIDIA T4 GPU. Further analysis showed superior segmentation performance for dry and more rigid components (e.g., rice and fries), while more complex, fragmented, or viscous dishes, such as stews, showed reduced performance, specifically post-consumption. Despite limitations such as reliance on 2D imaging, constrained food variety, and manual data collection, the proposed framework is pioneering and represents a scalable, contactless solution for continuous monitoring of food consumption. This research lays foundational groundwork for automated, real-time waste tracking systems in large-scale food service environments and offers actionable insights and outlines feasible future directions for dining hall management and policymakers aiming to reduce institutional food waste.
Instant food delivery has become one of the most popular web services worldwide due to its convenience in daily life. A fundamental challenge is accurately predicting courier routes to optimize task dispatch and improve delivery efficiency. This enhances satisfaction for couriers and users and increases platform profitability. The current heuristic prediction method uses only limited human-selected task features and ignores couriers preferences, causing suboptimal results. Additionally, existing learning-based methods do not fully capture the diverse factors influencing courier decisions or the complex relationships among them. To address this, we propose a Multi-Relational Graph-based Route Prediction (MRGRP) method that models fine-grained correlations among tasks affecting courier decisions for accurate prediction. We encode spatial and temporal proximity, along with pickup-delivery relationships, into a multi-relational graph and design a GraphFormer architecture to capture these complex connections. We also introduce a route decoder that leverages courier information and dynamic distance and time contexts for prediction, using existing route solutions as references to improve outcomes. Experiments show our model achieves state-of-the-art route prediction on offline data from cities of various sizes. Deployed on the Meituan Turing platform, it surpasses the current heuristic algorithm, reaching a high route prediction accuracy of 0.819, essential for courier and user satisfaction in instant food delivery.
Julian Legler, Sebastian Werner, Maria C. Borges
et al.
Microservice architectures have become the dominant paradigm for cloud-native systems, offering flexibility and scalability. However, this shift has also led to increased demand for cloud resources, contributing to higher energy consumption and carbon emissions. While existing research has focused on measuring fine-grained energy usage of CPU and memory at the container level, or on system-wide assessments, these approaches often overlook the energy impact of cross-container service interactions, especially those involving network and storage for auxiliary services such as observability and system monitoring. To address this gap, we introduce a service-level energy model that captures the distributed nature of microservice execution across containers. Our model is supported by an experimentation tool that accounts for energy consumption not just in CPU and memory, but also in network and storage components. We validate our approach through extensive experimentation with diverse experiment configurations of auxiliary services for a popular open-source cloud-native microservice application. Results show that omitting network and storage can lead to an underestimation of auxiliary service energy use by up to 63%, highlighting the need for more comprehensive energy assessments in the design of energy-efficient microservice architectures.
There is increasing acknowledgement - including from the UK government - of the benefit of employing deliberative processes (deliberative fora, citizens' juries, etc.). Evidence suggests that the qualitative reporting of deliberative fora are often unclear or imprecise. If this is the case, their value to policymakers could be diminished. In this study we develop numerical methods of deliberative processes to document people's preferences, as a complement to qualitative analysis. Data are taken from the Food Conversation, a nationwide public consultation on reformations of the food system comprising 345 members of the general public. Each participant attended 5 workshops, each with differing stimuli covering subtopics of the food system. In each workshop, individuals twice reported responsibility, from 0-10, for changing the food system for 5 stakeholders (governments, the food industry, supermarkets, farmers, individuals). Analyses examined individuals' perceptions of food system change responsibility. Governments were most responsible and farmers least so. We assessed variation by workshop content, and by demographics. Reported responsibility changed most for individuals, and changed least for the food industry. We devise a model to document a reversion effect, where shifts in perceptions on responsibility that occurred during workshops waned over time; this was strongest among those who intended to vote (rather than not to). These results can support qualitative analyses and inform food system policy development. These methods are readily adopted for any such deliberative process, allowing for statistical evaluation of whether they can induce opinion change.
The COVID-19 pandemic badly affected the global economies and the tourism and hospitality sectors. However, Swat is an area that heavily relies on tourism, and the pandemic has caused devastating economic losses, business closure, and unemployment. However, it left the hospitality industry in extraordinarily challenging situations due to the change in consumer behavior. This study aimed to investigate the impacts of COVID-19 on Swat's hospitality sector, including employment, revenue generation, and operational challenges to hotels and restaurants in the pre-and post-pandemic era. This study was conducted through qualitative research design through in-depth interviews and Focus Group Discussions (FGDs) with the owners and employees of hotels and restaurants in Swat. The data were analyzed using Braun and Clark's six-step thematic Analysis model, and key themes were identified. The research findings reveal the destructive impact of the COVID-19 pandemic on Swat's tourism and hospitality sector, the reduction in tourists, the number of employment opportunities and the reduced income of the owners. The hospitality sector of Swat is deeply impacted by COVID-19, which impacts business, employment, and consumer behavior. After the closure of several hotels and restaurants that were at a loss because of no tourists, the unemployment ratio has increased significantly. People were worried about their health and preferred staying home, which shifted to online services. The lack of expertise of restaurant owners in online services worsened the economic sufferings of the hospitality industry. The sector's recovery is critically dependent on the sector's ability to adapt to changed consumer expectations and technological improvement. However, recovery support will require financial relief measures, skill development programs and incentives for digital transformation. We suggest tourism-build resilience by promoting domestic tourism and diversifying offerings and funds for tourism-specific emergencies. Joint efforts from government and private stakeholders must revive Swat's hospitality industry.
Milan Ivkov, Srđan Milošević, Nemanja Dimić
et al.
Purpose – Scientific publications regarding business tourism, especially those which incorporate elements of sustainable development, still remain scarce in the existing literature. Therefore, the aim of this paper is to examine attitudes of local tourist organizations, travel agencies, catering establishments, chambers of commerce and other relevant stakeholders towards the sustainable development of conference and congress tourism (CCT) in Vojvodina, Serbia. Methodology – In order to investigate the attitudes of stakeholders (n=174), the adapted sustainable tourism attitude scale (SUS-TAS) was used in this paper. Moreover, the responses of the directors (managers) of local tourism organizations to several open-ended questions, which make an integral part of the survey, are also presented. Findings – The research instrument proved to be reliable. Based on other applied analyses (t-test and ANOVA), the existence of numerous statistically significant differences in the responses in relation to gender, job position, sector of work, age and education were identified. Implications – Apart from theoretical contribution, this paper reveals what aspects of CCT are in stakeholders’ main focus and what seems to be out of their radar. This should help defining necessary actions for further improvements of CCT. Lastly, study limitations and future research guidelines are discussed.
Hospitality industry. Hotels, clubs, restaurants, etc. Food service
Stephen Obadinma, Faiza Khan Khattak, Shirley Wang
et al.
Building Agent Assistants that can help improve customer service support requires inputs from industry users and their customers, as well as knowledge about state-of-the-art Natural Language Processing (NLP) technology. We combine expertise from academia and industry to bridge the gap and build task/domain-specific Neural Agent Assistants (NAA) with three high-level components for: (1) Intent Identification, (2) Context Retrieval, and (3) Response Generation. In this paper, we outline the pipeline of the NAA's core system and also present three case studies in which three industry partners successfully adapt the framework to find solutions to their unique challenges. Our findings suggest that a collaborative process is instrumental in spurring the development of emerging NLP models for Conversational AI tasks in industry. The full reference implementation code and results are available at \url{https://github.com/VectorInstitute/NAA}
Sophie Luisa Bolm, Wichard Zwaal, Macmillion Braz Fernandes
Hospitality and service workers commonly work under psychological and physical pressure with long working hours, resulting in high levels of occupational stress that affect their overall well-being and job satisfaction. This study investigates the effects of a mindfulness intervention on occupational stress and job satisfaction of hospitality and service workers. A total of 14 professionals participated in the study. They integrated a 15 to 30-minute audio mindfulness session into their daily work routine for fifteen days. A quasi-experimental pretest-intervention-posttest design was used. To measure the effects over the intervention period, a paired samples t-test was conducted. When data were not normally distributed, the Wilcoxon rank-sum test was performed to assess changes. After the intervention, participants showed significantly higher values in general mindfulness and job satisfaction and significantly lower scores in occupational stress. The present study shows that even low-cost, self-directed mindfulness training has a beneficial impact with significant work- and health-related relevance. Based on these findings, managers in the hospitality industry are recommended to invest in mindfulness training and integrate it into their human resources strategy.
Hospitality industry. Hotels, clubs, restaurants, etc. Food service
In 2016, the new Mauritius Code of Corporate Governance has made it mandatory for public interest entities to disclosure some environmental information in their annual reports. Five years following the entry into force of this new Code, this research aims at firstly assessing the level of environmental reporting by hotels listed on the stock exchange of Mauritius, secondly investigating the relationship between environmental reporting and financial performance of these hotels and lastly, assessing the impact of these hotels’ attributes on environmental reporting measured by their size, multinational characteristics and certification. Both qualitative and quantitative research methods will be adopted. In particular, data on each listed hotels’ environmental reporting and financial performance will be taken from their annual reports for the years 2017 to 2021 and some statistical tests will be performed on these data. The findings of this research will be of use for all organisations across the tourism and hospitality industry that intend to adopt environmental or other ESG measures. These research findings may also be of help to academics, policymakers, organisations and investors to promote eco-friendly behaviour and in turn, sustainable economic development.
Martijn Sparnaaij, Yufei Yuan, Winnie Daamen
et al.
The Covid-19 pandemic has had a large impact on the world. The virus spreads especially easily among people in indoor spaces such as restaurants. Hence, tools that can assess if and how different restaurant settings can impact the potential spread of the virus are of high value. The novel activity choice and scheduling model for restaurants, presented in this paper, is a key part of a larger model which combines pedestrian dynamics modelling and epidemiological models to achieve exactly this. This novel activity model can produce activity schedules for both customers and staff at a restaurant for a variety of restaurant settings. A key feature of the model is that it requires little user inputs to do so which is important as the intended users are restaurant owners. These owners have neither the time nor the expertise to deal with complex input. Tests show that the model can produce face-valid activity schedules for both staff and customers for a variety of restaurant settings. The tests also show that different restaurant settings lead to distinctly different contact patterns between people in a restaurant. As such, the model can provide valuable insights into how a restaurant setting relates to the risk of virus transmission. This is especially the case when it is combined with data about the virus. Hence, it shows the high relevance of pedestrian dynamics modelling in these pandemic times and especially the relevance of activity choice and scheduling models.
On the 27th of March 2020, South Africa entered hard lockdown (alert level 5) following the outbreak of the COVID-19 pandemic, with the banning of all physical activity outside a place of residence. As a result, official parkrun events were immediately suspended. In June 2020, the country moved to alert level 3, no longer curbing the use of public spaces for leisure, entertainment, and physical activity, albeit with restrictions. However, group
sports leisure, such as parkruns remained prohibited. Thus, parkrun, a highly successful global movement where individuals gather on Saturday mornings for a timed 2 or 5km run, jog, or walk, with family and friends, was severely affected by COVID-19 prevention measures. In mitigation, parkrun officials launched, in June 2020, the (not)parkrun to enable individuals to log (on the parkrun website) their own 5km activity, irrespective of time, day or route. In this regard, parkrun enabled parkrunners to bring the event ‘home’, that is, to informally claim public space and time for their physical leisure. By analysing participation figures and feedback posted on the national parkrun blog and social media pages, this research shows how the (not)parkrun enabled Gauteng parkrunners to ‘event-tualise’ their runs to counter act the de-eventualisation of the parkrun by lockdown regulations.
Hospitality industry. Hotels, clubs, restaurants, etc. Food service, Business
Telecom industry is significantly evolving all over the globe than ever. Mobile users number is increasing remarkably. Telecom operators are investing to get more users connected and to improve user experience, however, they are facing various challenges. Decrease of main revenue streams of voice calls, SMS (Short Message Service) and LDC (Long distance calls) with a significant increase in data traffic. In contrary, with free cost, OTT (Over the top) providers such as WhatsApp and Facebook communication services rendered over networks that built and owned by MNOs. Recently, OTT services gradually substituting the traditional MNOs` services and became ubiquitous with the help of the underlying data services provided by MNOs. The OTTs` services massive penetration into telecom industry is driving the MNOs to reconsider their strategies and revenue sources.
Autoencoders are unsupervised models which have been used for detecting anomalies in multi-sensor environments. A typical use includes training a predictive model with data from sensors operating under normal conditions and using the model to detect anomalies. Anomalies can come either from real changes in the environment (real drift) or from faulty sensory devices (virtual drift); however, the use of Autoencoders to distinguish between different anomalies has not yet been considered. To this end, we first propose the development of Bayesian Autoencoders to quantify epistemic and aleatoric uncertainties. We then test the Bayesian Autoencoder using a real-world industrial dataset for hydraulic condition monitoring. The system is injected with noise and drifts, and we have found the epistemic uncertainty to be less sensitive to sensor perturbations as compared to the reconstruction loss. By observing the reconstructed signals with the uncertainties, we gain interpretable insights, and these uncertainties offer a potential avenue for distinguishing real and virtual drifts.
We propose a novel conflict resolution framework for IoT services in multi-resident smart homes. The proposed framework employs a preference extraction model based on a temporal proximity strategy. We design a preference aggregation model using a matrix factorization-based approach (i.e., singular value decomposition). The concepts of current resident item matrix and ideal resident item matrix are introduced as key criteria to cater to the conflict resolution framework. Finally, a set of experiments on real-world datasets are conducted to show the effectiveness of the proposed approach.
In 2019, the Law on the Catering Industry was enacted in the Republic of Serbia. This law regulates the catering industry, which was the subject of the Law on Tourism before that. The subject of the catering industry is catering services, i.e. accommodation services, the services of preparation and serving food, drinks, and beverages, as well as the preparation and delivery of the food to the customers on some other place (other than the place where the catering business is located). The provider of these services is a caterer. A caterer is a commercial company, entrepreneur, while certain catering services may render a natural person. The catering services are to be provided in the catering facilities. Those are accommodation facilities (hotel, motel, camp, etc.), as well as facilities that (only) serve food, drinks, and beverages (bar, pizzeria, restaurant, etc.). The catering business may be conducted in the movable catering facilities, too. The catering business may be conducted throughout the entire year, seasonally and temporarily (fairs, trade fairs, markets, etc.). The catering facilities are assigned different categories, on the basis of the level of achieved quality standards. The paper particularly analyzes the catering services in the family household, in the countryside touristic households, as well as non-contractual obligations of the caterer.
Tourism in Cabo Verde is considered the engine of the country's economic growth, representing a significant
weight in GDP, being the largest source of foreign exchange and the main export sector for services in the country. The purpose of this study was to identify and measure the impact of the main determinants of the inbound international tourism inflow. To achieve this, a panel data approach was used. The annual panel's data set includes the number of arrivals from the nine (9) European generating countries, published by INE in the period 2000 - 2018, and several possible explanatory variables. Taking into account the changing structure of consumer preferences, a dynamic model is estimated using the GMM-DIFF estimator. The results suggest that the international demand for tourism in Cabo Verde depends strongly on the evolution of economic activity in each of the issuing countries and is strongly influenced by the previous demand. The results also show that external shocks, such as economic and financial crises, negatively impact the demand for tourism in Cabo Verde. As policy measures, the study recommends diversifying the promotion of tourism to different countries, in order to limit vulnerability to changing economic conditions in a single region or specific economic bloc.
Hospitality industry. Hotels, clubs, restaurants, etc. Food service, Business
Hotels offer a range of attribute-based services perceived to be wanted and gladly used by guests while staying at the hotel. That is, hotels at least think they have recognized the attributes of importance to their guests. Whether there is a desire for high-quality Wi-Fi, touchscreen technology, RFID or even tablet-controlled hotel room to satisfy the digital-savvy guests or small fridge, microwave and tea for families, hotels today find themselves into a position where online reviews represent one of the most valuable tools for getting insights into the factors that determine guests' experiences. By scraping the online reviews of 21 five-star hotels in North Macedonia on Booking.com, this paper investigates the attributes that are affecting guests' experience by analyzing the sets of online reviews using text mining. Research findings offer a more consistent understanding of the guest experience expressed in online reviews in terms of determining which amenities enhance guest satisfaction. The paper also illustrates how the methodological approach of text mining enables the use and visual interpretation of the data, and thus contributes to the studies in the field of hotel management.
Hospitality industry. Hotels, clubs, restaurants, etc. Food service