The Foodborne Outbreak Challenge—Using Experiential Learning to Foster Interdisciplinary Training Among Students on Foodborne Disease Outbreak Investigations

The Integrated Food Safety Centers of Excellence aim to develop novel learning methods to educate and train the future public health workforce to investigate foodborne outbreaks. The Foodborne Outbreak Challenge (FOC) was a one‐day event hosted by the Colorado Integrated Food Safety Center of Excellence at the Colorado School of Public Health. The FOC incorporated experiential, problem‐based, and interdisciplinary pedagogies from case studies, simulation exercises, and public health case competitions, to deliver a novel learning experience that met the training needs of a multidisciplinary foodborne outbreak response team with diverse skills sets. The event received positive feedback, and participants demonstrated knowledge gain. Event materials are available for other institutions to use.     

‘War and Peace’ at the 1980 Moscow and 2014 Sochi Olympics: The Role of Hard and Soft Power in Russian Identity

Abstract

Russia’s (and the USSR’s) use of sports mega-events (SMEs) makes this BRICS country (Brazil, Russia, India, China, and South Africa) not only an outlier among emerging states, but also among key SME hosts generally. Both the historic Moscow Olympics (1980) and the more recent hosting of the 2014 Sochi Winter Olympics provide evidence that Russia has, on both occasions, focussed on geopolitical priorities using hard power (military might) at the expense of soft power acquisition. Furthermore, both Olympics were, first and foremost, used to pursue domestic soft power goals. This, again, makes Russia an outlier in terms of the political use of SMEs by states in the literature on this subject. The 1980 Olympic Games, therefore, in terms of their potential to generate soft power and national unity, turned out to be a mis-used opportunity for Russia. To what extent is this specific to Russia and could the first Russian Olympics provide valuable insight into the modern-day hosting of SMEs by Russia (2014/2018)?     

Reducing prolonged sedentary time using a treadmill desk acutely improves cardiometabolic risk markers in male and female adults

This study evaluated the acute effects of interrupting prolonged sitting with an accumulated 2 h of light-intensity walking on postprandial cardiometabolic risk markers. In this randomised crossover trial, 24 participants (twelve males) aged 18–55 years took part in two, 6.5 h conditions: 1) prolonged sitting (SIT) and 2) sitting interrupted hourly with 20 min light-intensity treadmill desk walking at between 1.2–3.5 km/h^?1 (INT-SIT). Standardized meals were provided at 0 h and 3 h. Blood samples and blood pressure measures were taken hourly. Statistical analyses were completed using linear mixed models. Postprandial incremental area under the curve responses (mmol/L?6.5 h) for glucose (4.52 [3.47, 5.56] and 6.66 [5.62, 7.71] for INT-SIT and SIT, respectively) and triglycerides (1.96 [0.96, 2.96] and 2.71 [1.70, 3.71] for INT-SIT and SIT, respectively) were significantly lower in INT-SIT than SIT. Mean systolic and diastolic blood pressure responses were lower by 3% and 4%, respectively, in INT-SIT than SIT (P < 0.05). There was no significant condition x sex interaction effect for any outcomes (P > 0.05). These findings suggest that interrupting sitting with an accumulated 2 h of light-intensity walking acutely improves cardiometabolic risk levels in males and females compared with prolonged sitting.     

以SLA理论为指导,在化学学科中进行双语教学

SLA理论作为新兴的语言学说,指出了影响学习者的外在因素和内在因素,对学习者学习第二语言是有帮助的。本文根据对化学本科生学习外语的问卷调查,提出以SLA理论为指导,在化学部分学科中进行双语教学的教学策略和教学建议。     

A distance measure for automatic document classification by sequential analysis

This research has investigated the feasibility of using a distance measure, called the Bayesian distance, for automatic sequential document classification. It has been shown that by observing the variation of this distance measure as keywords are extracted sequentially from a document, the occurrence of noisy keywords may be detected. This property of the distance measure has been utilized to design a sequential classification algorithm which works in two phases. In the first phase keywords extracted from a document are partitioned into two groups—the good keyword group and the noisy keyword group. In the second phase these two groups of keywords are analyzed separately to assign primary and secondary classes to a document. The algorithm has been applied to several data bases of documents and very encouraging results have been obtained.