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Network-on-Chip (NoC) is a key component in chip multiprocessors (CMPs) as it supports communication between many cores. NoC is a network-based communication subsystem on an integrated circuit, most typically between modules in a system on a chip (SoC). Designing a reliable NoC against failures that can prevent failure using some measures or preventing error or system failure while failure happens and proper performance became a significant concern. For a reliable design against failures, first, the system should be analysed to discover the critical points. Hence, in this research, it is tried first to investigate the scale of fault tolerance effect on the mechanism in the router on the network by injecting simulated errors, and then these errors are prevented. As the major novelty, the authors implemented a router on a synchronised network and calculated the network buffering fault tolerance by injecting error in the buffer. Specifically, a new method for improving fault tolerance is proposed, which uses the existing resources efficiently. So, it does not impose any overhead on hardware and improves the error tolerance scale. The authors also evaluate it from different perspectives to show its superior performance.

Introduction: The pandemic of COVID-19 has several implications for patients with chronic stress-sensitive diseases such as alopecia areata (AA). On the other hand, the vulnerability of AA patients using immunosuppressives to a more severe infection is in the shadow of ambiguity. This teledermatology study aimed to evaluate the course and outcome of AA in patients during this challenging period. Methods: Patients with AA who had previously received systemic therapy included in this study. Information about demographic data, AA history, characteristics, and treatments, hair loss progression, Corona Disease Anxiety Scale (CDAS), adherence to protective measures against the COVID-19, possible infection, and its features obtained via a telephone call. Results: A total of 57 patients participated. The majority (84.2%) of the participants had mild anxiety assessed by CDAS. Two patients (3.5%) had got infected with COVID-19. Twenty-one (36.8%) participants experienced hair loss progression. Hair loss progression correlated with drug dose reduction (OR: 46.09, 95% CI 5.48–387.14, p < 0.001) although it did not influence by the level of anxiety evaluated by the CDAS (p > 0.05). Conclusion: The anxiety perceived by severe AA patients about COVID-19 is mild; however, many experience hair loss progressions owing to their drug dose reduction.

Background/aim: Acute unintentional and deliberate poisoning by medications and chemicals is a frequent emergency, especially in Iran. This study aimed to evaluate the frequency and character of skin findings occurring in patients with acute intentional and aunintentional poisoning. Materials and methods: This prospective observational study was performed at the Loghman Hakim Hospital Poison Center over a period of 6 months from April 2016 to September 2016. Data including patient demographics, cause of poisoning, and level of consciousness were collected. Pediatric patients (under the age of 13) and patients who died in the first hours of admission were excluded from the study. Results: The most common cause of toxicity-related admission in our patients was methadone overdose. The most common skin finding in these patients was xerosis. According to our results, there was an association between tramadol poisoning and self-induced lesions. Shin hyperpigmentation was found to be significantly more frequent in patients with lead poisoning. Conclusion: Further study is recommended to shed light on the possible association of drug poisoning and skin lesions.

The creation of small and cheap sensors promoted the emergence of large scale sensor networks. Sensor networks allow monitoring a variety of physical phenomena, like weather conditions (temperature, humidity, atmospheric pressure ...), traffic levels on highways or rooms occupancy in public buildings. Some of the sensors produce large volume of data such as weather temperature. These data should be stored somewhere for user queries. In this paper two known sensor data storage methods that store data semantically has been compared and it has been shown that storing data in ontology form consumes more energy so the lifetime of sensor network would decreases. The reason we choose them is that they are useful and popular.