Counterterrorism resource allocation during a pandemic: The effects of dynamic target valuations when facing a strategic terrorist.

The outbreak of pandemics such as COVID-19 can result in cascading effects for global systemic risk. To combat an ongoing pandemic, governmental resources are largely allocated toward supporting the health of the public and economy. This shift in attention can lead to security vulnerabilities which are exploited by terrorists. In view of this, counterterrorism during a pandemic is of critical interest to the safety and well-being of the global society. Most notably, the population flows among potential targets are likely to change in conjunction with the trend of the health crisis, which leads to fluctuations in target valuations. In this situation, a new challenge for the defender is to optimally allocate his/her resources among targets that have changing valuations, where his/her intention is to minimize the expected losses from potential terrorist attacks. In order to deal with this challenge, in this paper, we first develop a defender-attacker game in sequential form, where the target valuations can change as a result of the pandemic. Then we analyze the effects of a pandemic on counterterrorism resource allocation from the perspective of dynamic target valuations. Finally, we provide some examples to display the theoretical results, and present a case study to illustrate the usability of our proposed model during a pandemic.

Food Safety Awareness and Opinions in China: A Social Network Analysis Approach.

Over recent years, food safety has garnered widespread attention and concern from society. Concurrently, social media sites and online forums have become popular platforms to disseminate news, share opinions, and connect with one's social network. In this research, we focus on the intersection of food safety and online social networking by utilizing natural language processing techniques and social network analysis to study public opinions related to food safety. Using real data collected from a popular Chinese question-and-answer platform, we first identify hot topics related to food safety, and then analyze the emotional state of users in each community (i.e., users communicating about the same topic) to understand the public's sentiment related to different food safety topics. We proceed by forming semantic networks to analyze the characteristics of food safety opinion networks. Our results show that Internet users form modular communities, each with differences in topics of concern and emotional states of community users. Users focus on a wide range of topics, showing that overall, food safety awareness is increasing. This paper provides novel insights that can help interested stakeholders monitor the discussions and opinions related to food safety.

Monitoring Misinformation on Twitter During Crisis Events: A Machine Learning Approach.

Social media has been increasingly utilized to spread breaking news and risk communications during disasters of all magnitudes. Unfortunately, due to the unmoderated nature of social media platforms such as Twitter, rumors and misinformation are able to propagate widely. Given this, a surfeit of research has studied false rumor diffusion on Twitter, especially during natural disasters. Within this domain, studies have also focused on the misinformation control efforts from government organizations and other major agencies. A prodigious gap in research exists in studying the monitoring of misinformation on social media platforms in times of disasters and other crisis events. Such studies would offer organizations and agencies new tools and ideologies to monitor misinformation on platforms such as Twitter, and make informed decisions on whether or not to use their resources in order to debunk. In this work, we fill the research gap by developing a machine learning framework to predict the veracity of tweets that are spread during crisis events. The tweets are tracked based on the veracity of their content as either true, false, or neutral. We conduct four separate studies, and the results suggest that our framework is capable of tracking multiple cases of misinformation simultaneously, with F 1 $F_1$ scores exceeding 87%. In the case of tracking a single case of misinformation, our framework reaches an F 1 $F_1$ score of 83%. We collect and drive the algorithms with 15,952 misinformation-related tweets from the Boston Marathon bombing (2013), Manchester Arena bombing (2017), Hurricane Harvey (2017), Hurricane Irma (2017), and the Hawaii ballistic missile false alert (2018). This article provides novel insights on how to efficiently monitor misinformation that is spread during disasters.

Defensive Resource Allocation: The Roles of Forecast Information and Risk Control.

In defensive resource allocation problems, the defender usually collects some forecast information about the attacker. However, the forecast information may be incorrect, which means that there could be a risk associated with the defender using it in their decision making. In this article, we propose a forecast and risk control (FRC) framework to manage the risk in defensive resource allocation with forecast information. In the FRC framework, we introduce a new measure of risk and three types of defense plans: riskless defense plan, risky defense plan, and risk-control defense plan. Several desirable properties based on the concepts of reward and penalty show that the risk-control defense plan is a general form to support defensive resource allocation. Subsequently, we study a specific defensive allocation problem with forecast information and develop an optimization model that considers the forecast information and the defender's risk tolerance level in order to obtain the risk-control defense plan with maximum reward. Furthermore, we provide some numerical analysis to illustrate the effects of forecast information and risk tolerance level on the risk-control defense plan. Finally, a numerical case study is presented to demonstrate the usability of a risk-control defense plan.

An Analysis of Public Opinions Regarding Take-Away Food Safety: A 2015-2018 Case Study on Sina Weibo.

Take-away food (also referred to as "take-out" food in different regions of the world) is a very convenient and popular dining choice for millions of people. In this article, we collect online textual data regarding "take-away food safety" from Sina Weibo between 2015 and 2018 using the Octopus Collector. After the posts from Sina Weibo were preprocessed, users' emotions and opinions were analyzed using natural language processing. To our knowledge, little work has studied public opinions regarding take-away food safety. This paper fills this gap by using latent Dirichlet allocation (LDA) and k-means to extract and cluster topics from the posts, allowing for the users' emotions and related opinions to be mined and analyzed. The results of this research are as follows: (1) data analysis showed that the degree of topics have increased over the years, and there are a variety of topics about take-away food safety; (2) emotional analysis showed that 93.8% of the posts were positive; and (3) topic analysis showed that the topic of public discussion is diverse and rich. Our analysis of public opinion on take-away food safety generates insights for government and industry stakeholders to promote the healthy and vigorous development of the food industry.

The effect of surrogate procedure volume on bariatric surgery outcomes: do common laparoscopic general surgery procedures matter?

BACKGROUND: A growing body of evidence shows that experience and acquired skills from surrogate surgical procedures may be transferrable to a specific index operation. It is unclear whether this applies to bariatric surgery. This study aims to determine whether there is a surrogate volume effect of common laparoscopic general surgery procedures on all-cause bariatric surgical morbidity. METHODS: This was a population-based study of all patients aged ≥ 18 who received a bariatric procedure in Ontario from 2008 to 2015. The main outcome of interest was all-cause morbidity during the index admission. All-cause morbidity included any documented complication which extended length of stay by 24 h or required reoperation. Bariatric cases included laparoscopic Roux-en-Y gastric bypass, sleeve gastrectomy, and biliopancreatic diversion with duodenal switch. Non-bariatric cases included three common laparoscopic general surgery procedures. RESULTS: 13,836 bariatric procedures were performed by 29 surgeons at nine centers of excellence. A reduction in all-cause morbidity was seen when bariatric surgeons exceeded 75 cases annually (OR 0.82, 95% CI 0.69-0.98, P = 0.023), with further reduction in increasing bariatric volume. However, the volume of non-bariatric surgeries did not significantly affect bariatric all-cause morbidity rates amongst bariatric surgeons, even when exceeding 100 cases (OR 0.84, 95% CI 0.61-1.12, P = 0.222). CONCLUSIONS: The present study suggests that experience and skills acquired in performing non-bariatric laparoscopic general surgery does not appear to affect all-cause morbidity in bariatric surgery. Therefore, only a surgeon's bariatric procedure volume should considered be a quality marker for outcomes after bariatric surgery.