Suchergebnisse

Purpose: A smart contract represents an agreement between parties that exploits a blockchain as the enforcing medium, limiting or avoiding the need of a legal enforcement (e.g., a court). The purpose of this paper is the review advantage and challenges of smart contracts on Oil, Gas & Petrochemical Industry.
Methodology: This paper does a systematic review to discuss the application prospects of blockchain technology in the oil, gas & Petrochemical industry.
Findings: The results show that the smart contract would be effective in Oil, Gas & Petrochemical industry.
Originality/Value: Finally, we note that, despite its significant potential, smart contracting is still a developing technology and has several open challenges associated with its implementation, such as privacy concerns, risk of cyber-attacks (such as hacking) and the energy required for computation and blockchain deployment of the contracts. So far, smart contract applications in energy systems have been mostly focused on research, proof-of-concept and demonstration projects (such as P2P demonstration projects run in a local community or microgrid).

Data is the new oil. From the beginning of the 21st century, data is similar to what oil was in the 18th century, an immensely untapped valuable asset. This paper reviews recent advances in the field of optimization under uncertainty via a modern data lens and highlights key research challenges and the promise of data-driven optimization that organically integrates machine learning and mathematical programming for decision-making under uncertainty. A brief review of classical mathematical programming techniques for hedging against uncertainty is first presented, along with their wide spectrum of applications in Process Systems Engineering. we provide an introduction to the topic of uncertainty in machine learning as well as an overview of attempts so far at handling uncertainty in general and formalizing this distinction in particular. In line with the statistical tradition, uncertainty has long been perceived as almost synonymous with standard probability and probabilistic predictions. Yet, due to the steadily increasing relevance of machine learning for practical applications and related issues such as safety requirements, new problems, and challenges have recently been identified by machine learning scholars, and these problems may call for new methodological developments.

Underwater Wireless Sensor Network (UWSN) is a useful technology that can be used in Underwater Surveillance System (USS). USSs are mostly used in military purposes for detecting underwater military activities. One of the most important issues in USS is mission reliability or survivability. Due to harsh underwater environment and mission critical nature of military applications, it is important to measure survivability of USS. Underwater sensor node failures can be detrimental for USS. To improve survivability in USS, we propose a fault-tolerant underwater sensor node model. To the best of our knowledge, this is the first fault-tolerant underwater sensor node model in USS that evaluates survivability of an USS. ÂWe develop Markov models for characterizing USS survivability and MTTF (Mean Time to Failure) to facilitate USS. Performance evaluation results show the effectiveness of proposed model.

Abstract
Aflatoxin is a toxic metabolite produced mainly by Aspergillus spp. which may occur in dairy products because of biotransformation. In this work, a systematic and meta-analysis approach has been considered on the topic of aflatoxin M1 (AFM1) content in dairy Iranian products. Based on the literature review, AFM1 was the most common aflatoxin contamination in dairy product. Additionally, studies revealed that higher levels of AFM1 were produced during cold seasons includes winter and autumn. Although, immunochemical technique (ELISA) was the frequent and rapid test, thin-layer chromatography (TLC) and chromatographic methods (HPLC) were commonly used as confirmative techniques to determine the level of aflatoxin. Meta-analyzing of the results showed that AFM1 can be found in the dairy products with overall prevalence percentage of 63.53 (95% confidence interval [CI]: 56.28–70.78) and 54.05 (95% CI: 43.09–65.02) based on the sample type and production process, respectively. The higher prevalence percentage of AFM1 of 73.96 (95% CI: 60.27–87.66) and 69.91 (95% CI: 62.00–78.83) was found in yoghurt and industrial production type of samples, respectively. In general, 17.8% of cheese, 14% of yogurt, 12.63% of kashk, and 2.1% of doogh contained AFM1 in concentrations exceeding the permitted level of standards. Totally, results showed that 88.89% of dairy products were contaminated by AFM1 exceeding from standard limits.

Abstract Background Road traffic injuries (RTIs) are a major public health problem, especially in low- and middle-income countries. Among middle-income countries, Iran has one of the highest mortality rates from RTIs. Action is critical to combat this major public health problem. Stakeholders involved in RTI control are of key importance and their perceptions of barriers and facilitators are a vital source of knowledge. The aim of this study was to explore barriers to the prevention of RTIs and provide appropriate suggestions for prevention, based on the perceptions of stakeholders, victims and road-users as regards RTIs. Methods Thirty-eight semi-structured interviews were conducted with informants in the field of RTI prevention including: police officers; public health professionals; experts from the road administrators; representatives from the General Governor, the car industry, firefighters; experts from Emergency Medical Service and the Red Crescent; and some motorcyclists and car drivers as well as victims of RTIs. A qualitative approach using grounded theory method was employed to analyze the material gathered. Results The core variable was identified as "The lack of a system approach to road-user safety". The following barriers in relation to RTI prevention were identified as: human factors; transportation system; and organizational coordination. Suggestions for improvement included education (for the general public and targeted group training), more effective legislation, more rigorous law enforcement, improved engineering in road infrastructure, and an integrated organization to supervise and coordinate preventive activities. Conclusion The major barriers identified in this study were human factors and efforts to change human behaviour were suggested by means of public education campaigns and stricter law enforcement. However, the lack of a system approach to RTI prevention was also an important concern. There is an urgent need for both an integrated system to coordinate RTI activities and prevention and a major change in stakeholders' attitudes towards RTI prevention. The focus of all activities should take place on road users' safety.