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Background: We aimed to identify the factors contributing to human error in hospital emergency departments using scientific methods. Methods: We used the Fuzzy Analytical Network Process (FANP) and Success Likelihood Index Method (SLIM) to investigate human reliability in 54 hospital emergency departments in 15 provinces of Iran from 2021 to 2022. Results: The study classified 17 general factors affecting human errors in hospital emergency departments. Organizational (0.349), occupational (0.330), and personal factors (0.320) had the most significant impact on human error. Based on a matrix of paired comparisons for nine emergency tasks using the probability of success index method, "checking test results and diagnosis" had the highest probability of error when referring patients to intensive care or discharge. Although the study prioritized patients, there was still a cumulative probability of human error before disease diagnosis at 0.01332, highlighting the need for further training to minimize these risks. Conclusion: The FANP and SLIM were effective in identifying the factors contributing to human error in hospital emergency departments. Doctors and nurses working in these departments require more knowledge, experience, and responsibility to avoid errors. By identifying factors influencing the occurrence of human error and finding solutions to reduce risks, hospitals can improve the quality of their care and prevent errors.
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As an abrupt epidemic occurs, healthcare systems are shocked by the surge in the number of susceptible patients' demands, and decision-makers mostly rely on their frame of reference for urgent decision-making. Many reports have declared the COVID-19 impediments to trading and global economic growth. This study aims to provide a mathematical model to support pharmaceutical supply chain planning during the COVID-19 epidemic. Additionally, it aims to offer new insights into hospital supply chain problems by unifying cold and non-cold chains and considering a wide range of pharmaceuticals and vaccines. This approach is unprecedented and includes an analysis of various pharmaceutical features such as temperature, shelf life, priority, and clustering. To propose a model for planning the pharmaceutical supply chains, a mixed-integer linear programming (MILP) model is used for a four-echelon supply chain design. This model aims to minimize the costs involved in the pharmaceutical supply chain by maintaining an acceptable service level. Also, this paper considers uncertainty as an intrinsic part of the problem and addresses it through the wait-and-see method. Furthermore, an unexplored unsupervised learning method in the realm of supply chain planning has been used to cluster the pharmaceuticals and the vaccines and its merits and drawbacks are proposed. A case of Tehran hospitals with real data has been used to show the model's capabilities, as well. Based on the obtained results, the proposed approach is able to reach the optimum service level in the COVID conditions while maintaining a reduced cost. The experiment illustrates that the hospitals' adjacency and emergency orders alleviated the service level significantly. The proposed MILP model has proven to be efficient in providing a practical intuition for decision-makers. The clustering technique reduced the size of the problem and the time required to solve the model considerably.
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Recently, manufacturing firms and logistics service providers have been encouraged to deploy the most recent features of Information Technology (IT) to prevail in the competitive circumstances of manufacturing industries. Industry 4.0 and Cloud manufacturing (CMfg), accompanied by a service-oriented architecture model, have been regarded as renowned approaches to enable and facilitate the transition of conventional manufacturing business models into more efficient and productive ones. Furthermore, there is an aptness among the manufacturing and logistics businesses as service providers to synergize and cut down the investment and operational costs via sharing logistics fleet and production facilities in the form of outsourcing and consequently increase their profitability. Therefore, due to the Everything as a Service (XaaS) paradigm, efficient service composition is known to be a remarkable issue in the cloud manufacturing paradigm. This issue is challenging due to the service composition problem's large size and complicated computational characteristics. This paper has focused on the considerable number of continually received service requests, which must be prioritized and handled in the minimum possible time while fulfilling the Quality of Service (QoS) parameters. Considering the NP-hard nature and dynamicity of the allocation problem in the Cloud composition problem, heuristic and metaheuristic solving approaches are strongly preferred to obtain optimal or nearly optimal solutions. This study has presented an innovative, time-efficient approach for mutual manufacturing and logistical service composition with the QoS considerations. The method presented in this paper is highly competent in solving large-scale service composition problems time-efficiently while satisfying the optimality gap. A sample dataset has been synthesized to evaluate the outcomes of the developed model compared to earlier research studies. The results show the proposed algorithm can be applied to fulfill the dynamic behavior of manufacturing and logistics service composition due to its efficiency in solving time. The paper has embedded the relation of task and logistic services for cloud service composition in solving algorithm and enhanced the efficiency of resulted matched services. Moreover, considering the possibility of arrival of new services and demands into cloud, the proposed algorithm adapts the service composition algorithm.
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Cloud manufacturing is a new globalized manufacturing framework which has improved the performance of manufacturing systems. The service-oriented architecture as the main idea behind this framework means that all resources and capabilities are considered as services. The agents interact by way of service exchanging, which has been a part of service composition research topics. Service allocations to demanders in a cloud manufacturing system have a dynamic behavior. However, the current research studies on cloud-based service composition are mainly based on centralized global optimization models. Therefore, a distributed deployment and real-time synchronization platform, which enables the globalized collaboration in service composition, is required. This paper proposes a method of using blockchain to solve these issues. Each service composition is considered as a transaction in the blockchain concept. A block includes a set of service compositions and its validity is confirmed by a predefined consensus mechanism. In the suggested platform, the mining role in blockchain is interpreted as an endeavor for proposing the proper service composition in the manufacturing paradigm. The proposed platform has interesting capabilities as it can increase the response time using the blockchain technology and improve the overall optimality of supply-demand matching in cloud manufacturing. The efficiency of the proposed model was evaluated by investigating a service allocation problem in a cloud manufacturing system in four large scale problems. Each problem is examined in four centralized modes, two, three and four solvers in blockchain-based model. The simulation results indicate the high quality of the proposed solution. The proposed solution will lead to at least 15.14% and a maximum of 34.8 percent reduction in costs and 20 to 68.4 percent at the solving time of the problem. It is also observed that with increasing the number of solvers (especially in problems with larger dimensions) the solution speed increases sharply (more than 68% improvement in some problems), which indicates the positive effect of distribution on reducing the problem-solving time.
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BACKGROUND: Malnutrition is an important factor affecting hypertensive incidence. Since the unhealthiest nutritional behaviors are rooted in childhood attitudes and experiences, applying educational interventions to these age groups will be most useful in the formation of preventive nutritional behaviors. OBJECTIVE: To determine the predictive power of the TPB on hypertension in junior high-school students. METHODS: The present cross-sectional study was conducted on 160 junior high-school students in Kashmar, Iran in academic year commencing 2-13-2014, selected through random sampling. The participants completed a researcher-made questionnaire consisting of a demographic information form and a section to evaluate the constructs of the TPB. The data collected were analyzed in SPSS-16 using the correlation Wilcoxon statistics test, the one-way ANOVA and multiple linear regression analysis. RESULTS: The mean age of the students was 13.51. A total of 47% of the students had snacked on potato chips and cheese puffs, 45% had eaten high-fat foods and 51.2% had eaten cookies and chocolates within the past week. The variable of behavioral intention predicted 32% of the variations in preventive nutritional behaviors by itself. The Pearson product-moment correlation analysis found that hypertension preventive nutritional behaviors were significantly correlated with attitude (p<0.001, r=0.38), perceived behavioral control (p<0.001, r=0.38), and behavioral intention. Moreover, significant correlations were observed between subjective norms and attitude (p<0.028, r=0.173), perceived behavioral control and attitude (p<0.003, r=0.231), behavioral intention and attitude (p<0.001, r=0.370), behavioral intention and subjective norms (p<0.030, r=0.171) and between behavioral intention and perceived behavioral control (p<0.001, r=0.484). CONCLUSION: The examined students revealed an adequate adherence to preventive nutritional behaviors. Nutrition education interventions should be developed based on variables such as behavioral intention and its determinants, i.e. attitude, perceived behavioral control and subjective norms.
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Dust is an atmospheric phenomenon that causes adverse environmental effects. It is deemed to have harmful effects on health, economics, and climate. This study aimed to analyze the content published on the phenomenon of dust in the widely circulated newspapers in Iran. We investigated the content of all national and provincial newspapers that were published between July and August 2014. Data on the materials related to the dust phenomenon in the newspapers were categorized and coded. From a total of 510 newspaper issues, 143 articles were devoted to the dust phenomenon which 74.1% of them were published in provincial newspapers. Among the national newspapers, Hamshahri newspaper with 16 headlines and from the provincial newspapers; Karoon with 23 headlines published the highest number of articles on dust phenomenon. 45.5% of content on dust were printed on the first page of the newspapers. The most common approach to the type of content published in these newspapers was an interview. Moreover, we noticed that 28.7% of the content published in the newspapers was related to the health issue. The media plays an important role in the transmission of health information. Weaknesses in addressing the causes of dust occurrence and also in providing solutions for the dust control and prevention were noticeable in the content published in the newspaper. It seems necessary to take practical measures to disseminate relevant information to dust and also address the needs of the target audience community influenced by the dust phenomenon properly.