Suchergebnisse

Intro -- Preface -- Contents -- 1 Machine Learning Application in Food Safety, Production, and Quality -- 1.1 Introduction -- 1.2 An Introduction to Food Supply Chain -- 1.2.1 Food Safety -- 1.2.1.1 Foodborne Illness -- 1.2.1.2 Foodborne Disease Outbreaks -- 1.2.2 Food Spoilage and Quality -- 1.2.2.1 Food Authenticity -- 1.2.2.2 Food Post-harvesting -- 1.2.3 Food Production Process -- 1.2.3.1 Food Harvesting -- 1.2.3.2 Food Packaging -- 1.2.3.3 Food Traceability -- 1.2.3.4 Food Distribution -- 1.2.3.5 Food Storage -- 1.3 An Introduction to Machine Learning -- 1.3.1 Machine Learning Applications in Food Safety -- 1.3.2 Machine Learning Applications in Food Quality -- 1.3.3 Machine Learning Applications in Food Production -- 1.4 Conclusion -- References -- 2 Foodborne Bacterial PathogenBig Data - Genomic Analysis -- 2.1 Introduction -- 2.2 Whole Genome Sequencing -- 2.2.1 WGS in Source Attribution -- 2.2.2 WGS in Disease Surveillance -- 2.2.3 Antimicrobial Resistance, Virulence Potential, and Risk Analysis -- 2.2.4 WGS Technologies -- 2.2.4.1 First-Generation Sequencing: Sanger Shotgun Approach -- 2.2.4.2 Second-Generation Sequencing: The Massively Parallel Approach -- 2.2.4.3 Third-Generation Sequencing: The Long-Read Approach -- 2.3 Bioinformatics: Algorithms and Databases -- 2.4 Future Opportunities and Challenges for WGS -- 2.4.1 Predicting Emerging Treats -- 2.4.2 Low- and Middle-Income Countries -- 2.4.3 Culture-Independent Diagnostic Tests -- 2.4.3.1 Metagenomic Sequencing -- References -- 3 Foodborne Viral Pathogen Big Data: Genomic Analysis -- 3.1 Introduction -- 3.1.1 Norovirus -- 3.1.2 HAV -- 3.1.3 HEV -- 3.1.4 SARS-CoV-2 -- 3.1.5 WGS -- 3.2 Applications -- 3.2.1 Surveillance and Source Attribution -- 3.2.2 Analysis of Variants and Viral Evolution -- 3.2.3 Predictive Analytics -- 3.3 Conclusion and Future Perspectives -- References.

During the COVID-19 pandemic, health and political leaders have attempted to update citizens using Twitter. Here, we examined the difference between environments that social media has provided for male/female or health/political leaders to interact with people during the COVID-19 pandemic. The comparison was made based on the content of posts and public responses to those posts as well as user-level and post-level metrics. Our findings suggest that although health officers and female leaders generated more contents on Twitter, political leaders and male authorities were more active in building networks. Offensive language was used more frequently toward males than females and toward political leaders than health leaders. The public also used more appreciation keywords toward health leaders than politicians, while more judgmental and economy-related keywords were used toward politicians. Overall, depending on the gender and position of leaders, Twitter provided them with different environments to communicate and manage the pandemic.

To foster trust on social media during a crisis, messages should implement key guiding principles, including call to action, clarity, conversational tone, compassion and empathy, correction of misinformation, and transparency. This study describes how crisis actors used guiding principles in COVID-19 tweets, and how the use of these guiding principles relates to tweet engagement. Original, English language tweets from 10 federal level government, politician, and public health Twitter accounts were collected between 11 March 2020 and 25 January 2021 (n = 6053). A 60% random sample was taken (n = 3633), and the tweets were analyzed for guiding principles. A tweet engagement score was calculated for each tweet and logistic regression analyses were conducted to model the relationship between guiding principles and tweet engagement. Overall, the use of guiding principles was low and inconsistent. Tweets that were written with compassion and empathy, or conversational tone were associated with greater odds of having higher tweet engagement. Across all guiding principles, tweets from politicians and public health were associated with greater odds of having higher tweet engagement. Using a combination of guiding principles was associated with greater odds of having higher tweet engagement. Crisis actors should consistently use relevant guiding principles in crisis communication messages to improve message engagement.

AbstractDigital technologies are being developed and adopted across the agro‐food system, from farm to fork. Within decision‐making spaces, however, little attention is being paid to political factors arising from such technological developments. This review draws from critical social sciences to examine emerging technologies and big data systems in agriculture and assesses some key issues arising in the field. We begin with an introduction and review of the so‐called 'digital revolution' and then briefly outline how political economy is effective for understanding major challenges for governing technologies and data systems in agriculture. These challenges include: (1) data ownership and control, (2) the production of technologies and data development, and (3) data security. We then use literature and examples to consider the extent to which the political and economic landscape can be shifted to support greater equity in agriculture, while reflecting on structural challenges and limits. In doing so, we emphasise that while there are significant systemic tensions between digital ag‐tech development and agroecological approaches, we do not see them as mutually exclusive per se. This article intends to provide decision‐makers, practitioners and scholars from a wide range of disciplines with a timely assessment of agro‐food digitalisation that attends to political economic factors. In doing so, this article contributes to policy and decision‐making discussions, which, from our perspective, continue to be rather technocentric in nature while paying little attention to how digital technologies can support agroecological systems specifically.