Search Results (33)

The 1.5-degree lifestyles aim to reduce household carbon footprints across six areas (food, housing, mobility, consumer goods, leisure, and services), as identified by IGES, relying on public efforts. This study seeks to understand Japanese university students’ perceptions of 1.5-degree lifestyles, their preferred actions for implementation at the individual, family, and community levels, and the top three enablers and barriers they face. Using a questionnaire based on the KIDA (knowledge, interest, desire, action) framework, which aligns with IGES’s six sectors, data from 244 responses collected via snowball sampling were analyzed. Results reveal low awareness of 1.5-degree lifestyles among Japanese university students, along with a moderate desire to learn more. Gender differences were significant, with females showing higher awareness, desire, and action compared to males. Three common barriers identified include challenges with work–life balance, economic concerns, and gaps between knowledge and action. Recommendations emphasize increasing youth awareness and encouraging action on 1.5-degree lifestyles through early environmental education, youth-focused communication (such as social media), and securing strong political support to help address practical challenges. Full article

This study aimed to evaluate the effects of the COVID-19 pandemic on food service operations in a Brazilian tourist area. It is a longitudinal observational study. Data collection was performed through telephone interviews with owners or managers of 54 food service establishments concerning the pre-pandemic period (T0) and two time points during the pandemic (T1 and T2). Findings revealed a reduction in the number of establishments providing on-site service at T1 compared to T0, followed by an increase at T2. A reduction in operating hours (56.7%) and profits (100%) affected more establishments at T1 than at T2 (26.7% and 76.7%, respectively) (p < 0.05). At T2, with the resumption of in-person activities, there was a significant decrease in the use of sales channels such as telephone (p < 0.001), messaging applications (p = 0.0012), and websites/apps/social media (p = 0.009) compared to T0 and T1. The pandemic also caused an increase in mask use by employees (p < 0.01) and the availability of hand sanitizer dispensers at establishments (p < 0.01) during T1 and T2. At T2, there was also an increase in the supply of disposable cutlery, cups (p = 0.02), and gloves for customers (p = 0.015) compared to the pre-pandemic period. Full article

This study presents a computational analysis of industry-specific advertising message strategies through the theoretical lens of the FCB (Foote, Cone & Belding) grid framework. Leveraging the AiSAC (AI Analysis System for Ad Creation) system developed by the Korea Broadcast Advertising Corporation (KOBACO), we analyzed 27,000 Korean advertisements across five major industries using advanced machine learning techniques. Through Latent Dirichlet Allocation topic modeling with a coherence score of 0.78, we identified five distinct message strategies: emotional appeal, product features, visual techniques, setting and objects, and entertainment and promotion. Our computational analysis revealed that each industry exhibits a unique “message strategy fingerprint” that significantly discriminates between categories, with discriminant analysis achieving 62.7% classification accuracy. Time-series analysis using recurrent neural networks demonstrated a significant evolution in strategy preferences, with emotional appeal increasing by 44.3% over the study period (2015–2024). By mapping these empirical findings onto the FCB grid, the present study validated that industry positioning within the grid’s quadrants aligns with theoretical expectations: high-involvement/think (IT and Telecom), high-involvement/feel (Public Institutions), low-involvement/think (Food and Household Goods), and low-involvement/feel (Services). This study contributes to media science by demonstrating how computational methods can empirically validate the established theoretical frameworks in advertising, providing a data-driven approach to understanding message strategy patterns across industries. Full article

Background/Objectives: This study examined factors influencing health behaviours among 13–17-year-olds in Surrey, focusing on rising obesity rates and socioeconomic disparities using a whole systems approach to capture both the stakeholders’ voice and the young people’s voices. Methods: The research involved two components: a survey of youth service providers (e.g., teachers, youth workers; n = 35) and focus groups with adolescents (n = 27). Results: The survey revealed challenges faced by stakeholders, including insufficient training, environmental factors (e.g., schools, social media, food systems), and limited support from parents and healthcare professionals. The focus groups identified two key themes: (1) domains of care, for example diet and how availability and cost of food affects food choices, and (2) barriers and solutions, addressing financial, structural, and emotional obstacles to and facilitators of a healthy lifestyle. Transcending these themes was the key role of health inequalities linked to income, geography, and gender. Conclusions: This study underscores the complexity of adolescent health behaviours and calls for a multi-level, coordinated approach to address inequalities and foster supportive environments for healthier choices. Full article

The unprecedented production and sharing of data, opinions, and comments among people on social media and the Internet in general has highlighted sentiment analysis (SA) as a key machine learning approach in scientific and market research. Sentiment analysis can extract sentiments and opinions from user-generated text, providing useful evidence for new product decision-making and effective customer relationship management. However, there are concerns about existing standard sentiment analysis tools regarding the generation of inaccurate sentiment classification results. The objective of this paper is to determine the efficiency of off-the-shelf sentiment analysis APIs in recognizing low-resource languages, such as Greek. Specifically, we examined whether sentiment analysis performed on 300 online ordering customer reviews using the Meaning Cloud web-based tool produced meaningful results with high accuracy. According to the results of this study, we found low agreement between the web-based and the actual raters in the food delivery services related data. However, the low accuracy of the results highlights the need for specialized sentiment analysis tools capable of recognizing only one low-resource language. Finally, the results highlight the necessity of developing specialized lexicons tailored not only to a specific language but also to a particular field, such as a specific type of restaurant or shop. Full article

Climate change presents significant challenges to agriculture in developing nations, affecting farmers’ livelihoods and food security. In Ethiopia, agriculture is crucial to the economy and the well-being of millions. This study focuses on analyzing the determinants that affect smallholder farmers’ adoption of climate-smart agriculture (CSA) technologies in the North Wello administrative zone, northern Ethiopia. Through multivariate and ordered probit econometric models, data from 411 rural household heads were analyzed. Results reveal the synergy among climate-smart agricultural practices in rainfed farming systems, essential for crafting a comprehensive CSA package within an agroecological framework. The multivariate probit estimation results show that education, membership in local organizations, farm size, tropical livestock unit (TLU), irrigated land ownership, plot number, steep slope, farmland distance to home, and access to a local media source are positive determinants for the decision to use the type and several climate-smart agricultural practices. Lack of credit, large family size, distance from extension services, and proximity to the nearest market were all associated with lower adoption of all CSA technologies. The findings suggest that blanket recommendations for climate-smart agricultural technologies for smallholder farmers can be avoided. The complementarities that exist between CSA technologies may require further investigation into how such complementarities have benefits in terms of improving land productivity and food security and reducing climate-related risks for smallholder farmers in Ethiopia and other contexts. Moreover, by emphasizing an agroecological framework, the study promotes environmentally sustainable and socially equitable agricultural practices that are economically viable, contributing to broader environmental sustainability and development goals. Full article

Shiga-toxin-producing Escherichia coli (STEC) causes a wide spectrum of diseases including hemorrhagic colitis and hemolytic uremic syndrome (HUS). The current Food Safety Inspection Service (FSIS) testing methods for STEC use the Food and Drug Administration (FDA) Bacteriological Analytical Manual (BAM) protocol, which includes enrichment, cell plating, and genomic sequencing and takes time to complete, thus delaying diagnosis and treatment. We wanted to develop a rapid, sensitive, and potentially portable assay that can identify STEC by detecting Shiga toxin (Stx) using the CANARY (Cellular Analysis and Notification of Antigen Risks and Yields) B-cell based biosensor technology. Five potential biosensor cell lines were evaluated for their ability to detect Stx2. The results using the best biosensor cell line (T5) indicated that this biosensor was stable after reconstitution with assay buffer covered in foil at 4 °C for up to 10 days with an estimated limit of detection (LOD) of ≈0.1–0.2 ng/mL for days up to day 5 and ≈0.4 ng/mL on day 10. The assay detected a broad range of Stx2 subtypes, including Stx2a, Stx2b, Stx2c, Stx2d, and Stx2g but did not cross-react with closely related Stx1, abrin, or ricin. Additionally, this assay was able to detect Stx2 in culture supernatants of STEC grown in media with mitomycin C at 8 and 24 h post-inoculation. These results indicate that the STEC CANARY biosensor developed in this study is sensitive, reproducible, specific, rapid (≈3 min), and may be applicable for surveillance of the environment and food to protect public health. Full article

Food is generally prepared and vacuum-sealed in a water bath, then heated to a precise temperature and circulated in a sous vide machine. Due to its affordability and ease of use, this cooking method is becoming increasingly popular in homes and food service businesses. However, suggestions from manufacturers and chefs for long-term, low-temperature sous vide cooking raise questions about food safety in the media. In this study, heat treatment with different times and wild thyme essential oil (EO) in sous vide-processed rabbit longissimus dorsi muscle were found to inactivate Salmonella enterica. The rabbit meat samples were vacuum-packed in control groups, in the second group the rabbit meat samples were injected with S. enterica, and in the third group were meat samples infected with S. enterica with Thymus serpylum EO additive. The vacuum-packed samples were cooked sous vide for the prescribed time at 55, 60, and 65 °C. At 5, 15, 30, and 60 min, the quantities of S. enterica, total bacterial counts, and coliform bacteria were measured in groups of sous vide rabbit meat. Microbiological analyses of rabbit meat samples on days 1 and 7 were evaluated. In this study, total viable counts, coliforms bacteria, and number of Salmonella spp. were identified. After incubation, isolates from different groups of microorganisms were identified by the mass spectrometry technique. For each day measured, the test group exposed to a temperature of 55 °C for 5 min had a greater number of total microbiota. The most isolated microorganisms by MALDI-TOF MS Biotyper from the control and treated groups were Lactococcus garvieae and in the treated groups also S. enterica. Based on our analysis of sous vide rabbit meat samples, we discovered that adding 1% of thyme essential oil to the mixture reduced the amount of Salmonella cells and increased the overall and coliform bacterial counts. The microbiological quality of sous vide rabbit meat that was kept for seven days was positively impacted by the addition of thyme essential oil. Full article

Healthy and sufficient crop and food production are very much essential for everyone as the population is increasing globally. The production of crops affects the economy of a country to a great extent. In agriculture, observing the soil, weather, and water availability and, based on these factors, selecting an appropriate crop, finding the availability of seeds, analysing crop demand in the market, and having knowledge of crop cultivation are important. At present, many advancements have been made in recent times, starting from crop selection to crop cutting. Mainly, the roles of the Internet of Things, cloud computing, and machine learning tools help a farmer to analyse and make better decisions in each stage of cultivation. Once suitable crop seeds are chosen, the farmer shall proceed with seeding, monitoring crop growth, disease detection, finding the ripening stage of the crop, and then crop cutting. The main objective is to provide a continuous support system to a farmer so that he can obtain regular inputs about his field and crop. Additionally, he should be able to make proper decisions at each stage of farming. Artificial intelligence, machine learning, the cloud, sensors, and other automated devices shall be included in the decision support system so that it will provide the right information within a short time span. By using the support system, a farmer will be able to take decisive measures without fully depending on the local agriculture offices. We have proposed an IoT-enabled soil nutrient classification and crop recommendation (IoTSNA-CR) model to recommend crops. The model helps to minimise the use of fertilisers in soil so as to maximise productivity. The proposed model consists of phases, such as data collection using IoT sensors from cultivation lands, storing this real-time data into cloud memory services, accessing this cloud data using an Android application, and then pre-processing and periodic analysis of it using different learning techniques. A sensory system was prepared with optimised cost that contains different sensors, such as a soil temperature sensor, a soil moisture sensor, a water level indicator, a pH sensor, a GPS sensor, and a colour sensor, along with an Arduino UNO board. This sensory system allowed us to collect moisture, temperature, water level, soil NPK colour values, date, time, longitude, and latitude. The studies have revealed that the Agrinex NPK soil testing tablets should be applied to a soil sample, and then the soil colour can be sensed using an LDR colour sensor to predict the phosphorus (P), nitrogen (N), and potassium (K) values. These collected data together were stored in Firebase cloud storage media. Then, an Android application was developed to fetch and analyse the data from the Firebase cloud service from time to time by a farmer. In this study, a novel approach was identified via the hybridisation of algorithms. We have developed an algorithm using a multi-class support vector machine with a directed acyclic graph and optimised it using the fruit fly optimisation method (MSVM-DAG-FFO). The highest accuracy rate of this algorithm is 0.973, compared to 0.932 for SVM, 0.922 for SVM kernel, and 0.914 for decision tree. It has been observed that the overall performance of the proposed algorithm in terms of accuracy, recall, precision, and F-Score is high compared to other methods. The IoTSNA-CR device allows the farmer to maintain his field soil information easily in the cloud service using his own mobile with minimum knowledge. Additionally, it reduces the expenditure to balance the soil minerals and increases productivity. Full article

Social media usage for news, feedback on services, and even shopping is increasing. Hotel services, food cleanliness and staff behavior are also discussed online. Hotels are reviewed by the public via comments on their websites and social media accounts. This assists potential customers before they book the services of a hotel, but it also creates an opportunity for abuse. Scammers leave deceptive reviews regarding services they never received, or inject fake promotions or fake feedback to lower the ranking of competitors. These malicious attacks will only increase in the future and will become a serious problem not only for merchants but also for hotel customers. To rectify the problem, many artificial intelligence–based studies have performed discourse analysis on reviews to validate their genuineness. However, it is still a challenge to find a precise, robust, and deployable automated solution to perform discourse analysis. A credibility check via discourse analysis would help create a safer social media environment. The proposed study is conducted to perform discourse analysis on fake and real reviews automatically. It uses a dataset of real hotel reviews, containing both positive and negative reviews. Under investigation is the hypothesis that strong, fact-based, realistic words are used in truthful reviews, whereas deceptive reviews lack coherent, structural context. Therefore, frequency weight–based and semantically aware features were used in the proposed study, and a comparative analysis was performed. The semantically aware features have shown strength against the current study hypothesis. Further, holdout and k-fold methods were applied for validation of the proposed methods. The final results indicate that semantically aware features inspire more confidence to detect deception in text. Full article

The present work is the first detailed study of sub-assistant agricultural officers (SAAOs), who are key players in delivering agriculture extension services in Bangladesh. We determined the status of information and communication technology (ICT) usage, knowledge gaps, approaches and tools for information delivery, barriers and obstacles to ICT usage, requirements for adoption, and possible solutions for efficient agriculture extension advisory services. We surveyed key respondents (SAAOs, n = 117) from nine sub-districts under the six administrative districts of Bangladesh with a semi-structured questionnaire. We found that 73.2% of all extension officers had basic knowledge of ICT. We observed that the most effective tool for information dissemination was field visits (90.90%, Moulvibazar district) and the most frequent extension approach was training and workshops (77.31%, Dhaka district). The best sources for information collection were broadcast media and social media. While delivering information, difficulty was found due to technical obstacles in Gazipur district, Rajshahi district, Sylhet district, and Dhaka district among 60% to 70% of SAAOs. However, farmers’ ignorance was reported in both Feni district and Moulvibazar district (36.36%). A shortage of computers in Gazipur district (56.25%) was an essential barrier to ICT usage for extension services. A major challenge in using ICT applications in Dhaka district was inadequate training support (64.51%). Mobile devices, internet connectivity, updated ICT applications, and a farmers’ database were the most important supports needed for the SAAO’s skilled advisory activities. In conclusion, our results and recommendations will help to redesign policies to improve infrastructure and allocate funding for capacity and skill development and ICT-based innovations in this sector to achieve sustainable extension and advisory services and attain food security in Bangladesh. Full article

Jollibee is one of the most widely known fast food in Filipino-based restaurants in the world. However, the COVID-19 pandemic has impacted restaurants across the world. The decrease in profit and dividend, and even closure of branches were evident. This study aimed to determine the relationships between Jollibee’s price, food quality, culture/social influence, and service quality through the SERVQUAL dimensions on customer satisfaction during the COVID-19 pandemic using the structural equation modeling (SEM) approach. A total of 303 respondents were recruited using a purposive sampling approach to answer an online survey through social media platforms. This study found that among the factors mentioned, responsiveness, reliability, and assurance dimensions yielded to be statistically insignificant to the service quality. Due to the normal attributes the staff and restaurant have, the different factors were deemed insignificant. At the same time, the service quality was observed to have the largest significant direct relationship with customer satisfaction, followed by the cultural influence, food quality, COVID-19 protocols, and pricing. It was also seen that cleanliness and appearance, empathetic staff, food quality, price, and proper implementation of COVID-19 protocol prevention would lead to high levels of satisfaction among customers in Jollibee fast-food restaurant. Moreover, cultural/social influence has played a big role seeing that the indicators represent the feeling of belongingness since childhood. This study is the first study that analyzed the factors affecting the customer satisfaction of Jollibee. Finally, this study could be used as a basis for fast-food companies and service-related industries to increase its performance by enhancing customer satisfaction worldwide. Full article

Social media data reveal patterns of knowledge, attitudes, and behaviours of users on a range of topics. This study analysed 4398 tweets gathered between 17 January 2022 and 3 February 2022 related to ecosystem services, using the keyword and hashtag “ecosystem services”. The Microsoft Excel plugin, NodeXL was used for social and semantic network analysis. The results reveal a loosely dense network in which information is conveyed slowly, with homogeneous, medium-sized subgroups typical of the community cluster structure. Citizens, NGOs, and governmental administrations emerged as the main gatekeepers of information in the network. Various semantic themes emerged such as the protection of natural capital for the sustainable production of ecosystem services; nature-based solutions to protect human structures and wellbeing against natural hazards; socio-ecological systems as the interaction between human beings and the environment; focus on specific services such as the storage of atmospheric CO₂ and the provision of food. In conclusion, the perception of social users of the role of ecosystem services can help policymakers and forest managers to outline and implement efficient forest management strategies and plans. Full article

As businesses dealt with an increasingly anxious public during the COVID-19 pandemic and were frequently tasked with enforcing various COVID-19 prevention policies such as mask mandates, workplace violence and harassment (WPV) emerged as an increasing important issue affecting worker safety and health. Publicly available media reports were searched for WPV events related to the COVID-19 pandemic that occurred during 1 March 2020, and 31 August 2021, using Google News aggregator services scans with data abstraction and verification. The search found 408 unique WPV events related to COVID-19. Almost two-thirds involved mask disputes. Over half (57%) of the 408 events occurred in retail (38%) and food service (19%). We also conducted a comparison of events identified in this search to a similar study of media reports between March 2020 to October 2020 that used multiple search engines to identify WPV events. Despite similar conclusions, a one-to-one comparison of relevant data from these studies found only modest overlap in the incidents identified, suggesting the need to make improvements to future efforts to extract data from media reports. Prevention resources such as training and education for workers may help industries de-escalate or prevent similar WPV events in the future. Full article

Social media connects individual users and corporate bodies on a self-service technology (SST) platform. The food and beverage industry has increasingly adopted the social media-based SST over other online and kiosk types of technologies for their service delivery. The present study sheds light on the dimensionality of service quality on the social media-based SST in the food service delivery context and went further to investigate the impact of the SST service quality on functional value, user satisfaction, and intention to reuse. The analytic results of 410 valid survey data found five salient dimensions (i.e., Functionality, Enjoyment, Assurance, Convenience, and Customization) constituting the service quality. The results also revealed that the perceived quality of social media-based food service is directly and positively associated with consumer’s satisfaction and perceived functional value and is indirectly associated with intention to reuse. The results provide practical suggestions regarding how to take advantage of using social media platforms for food and beverage professionals. Full article