Background In this study, we aimed to evaluate the impact of the COVID-19 epidemic on the workload and mental health of Iranian medical staff using the General Health Questionnaire (GHQ-12) and NASA -Task Load Index (NASA-TLX) Questionnaire between March and April 2020, respectively. Methods The present cross-sectional study was conducted from March 5th to April 5th, 2020. To evaluate the workload and mental health of participants NASA-TLX and GHQ-12 online questionnaires were distributed. Data were entered into software SPSS (Version 23) and T-test, ANOVA, Regression methods were used for data analysis. Results Health workers who encountered COVID- 19 patients, were subjected to more task load compared to those who had no contact with COVID- 19 patients at the workplace (p <  0.001). In terms of the subscale score of NASA-TLX, nurses had more scores in mental pressure, physical pressure, time pressure (temporal), and frustration compared to the other jobs (p <  0.05). Moreover, nurses had significantly more workload compared to the other jobs. Conclusions Type of job, the shift of work, educational level, and facing COVID-19 affected the score of NASA-TLX. NASA-TLX scores were higher in nursing compared to the scores of other health staff groups. The results of this study indicate that the scores of NASA-TLX and GHQ-12 among staff who had contact with COVID-19 patients were significantly higher than those who did not face COVID-19 patients. We suggested that a comprehensive assistance should be provided to support the well-being of healthcare workers especially nurses and healthcare workers who treated COVID-19 patients.

International audienceThe AVANTSSAR Platform is an integrated toolset for the formal specification and automated validation of trust and security of service-oriented architectures and other applications in the Internet of Services. The platform supports application-level specification languages (such as BPMN and our custom languages) and features three validation backends (CL-AtSe, OFMC, and SATMC), which provide a range of complementary automated reasoning techniques (including service orchestration, compositional reasoning, model checking, and abstract interpretation). We have applied the platform to a large number of industrial case studies, collected into the AVANTSSAR Library of validated problem cases. In doing so, we unveiled a number of problems and vulnerabilities in deployed services. These include, most notably, a serious flaw in the SAML-based Single Sign-On for Google Apps (now corrected by Google as a result of our findings). We also report on the migration of the platform to industry

Multi-party contract signing protocols specify how a number of signers can cooperate in achieving a fully signed contract, even in the presence of dishonest signers. This problem has been studied in different settings, yielding solutions of varying complexity. Here we assume presence of a trusted third party that will be contacted only in case of a conflict, asynchronous communication, and a totally ordered protocol. Our goal is to develop a lower bound on the number of messages in such a protocol. Using the notion of abort chaining, which is a specific type of attack on the fairness of signing protocols, we derive the lower bound α 2 + 1, with α > 2 being the number of involved signers. In order to achieve this lower bound, we relate the problem of developing fair signing protocols to the open combinatorial problem of finding shortest permutation sequences. This relation also indicates a way to construct signing protocols which are shorter than current state-ofthe-art protocols. We illustrate this by presenting the shortest three-party fair contract signing protocol.

Background: In this study, we aimed to evaluate the impact of the COVID-19 epidemic on the workload and mental health of Iranian medical staff using the General Health Questionnaire (GHQ-12) and NASA -Task Load Index (NASA-TLX) Questionnaire between March and April 2020, respectively.Methods: The present cross-sectional study was conducted from March 5th to April 5th, 2020. To evaluate the workload and mental health of participants NASA-TLX and GHQ-12 online questionnaires were distributed. Data were entered into software SPSS (Version 23) and T-test, ANOVA, Regression methods were used for data analysis.Results: Health workers who encountered COVID- 19 patients, were subjected to more task load compared to those who had no contact with COVID- 19 patients at the workplace (p<0.001). In terms of the subscale score of NASA-TLX, nurses had more scores in mental pressure, physical pressure, time pressure(temporal), and frustration compared to the other jobs (p<0.05). Moreover, nurses had significantly more workload compared to the other jobs.Conclusions: Type of job, the shift of work, educational level, and facing COVID-19 affected the score of NASA-TLX. NASA-TLX scores were higher in nursing compared to the scores of other health staff groups. The results of this study indicate that the scores of NASA-TLX and GHQ-12 among staff who had contact with COVID-19 patients were significantly higher than those who did not face COVID-19 patients. We suggested that a comprehensive assistance should be provided to support the well-being of healthcare workers especially nurses and healthcare workers who treated COVID-19 patients.

Abstract-We propose a light-weight, yet effective, technique for fuzz-testing security protocols. Our technique is modular, it exercises (stateful) protocol implementations in depth, and handles encrypted traffic. We use a concrete implementation of the protocol to generate valid inputs, and mutate the inputs using a set of fuzz operators. A dynamic memory analysis tool monitors the execution as an oracle to detect the vulnerabilities exposed by fuzz-testing. We provide the fuzzer with the necessary keys and cryptographic algorithms in order to properly mutate encrypted messages. We present a case study on two widely used, mature implementations of the Internet Key Exchange (IKE) protocol and report on two new vulnerabilities discovered by our fuzz-testing tool. We also compare the effectiveness of our technique to two existing model-based fuzz-testing tools for IKE.