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This work introduces a systematic review of state-of-the-art studies about enhancing the blood banks systems based on modern and smart technologies in line with the healthcare sector development, such as artificial intelligence, the Internet of things, blockchain, cloud computing, as well as machine learning, novel Multiple criteria decision-making (MCDM) method, and a fuzzy stochastic programming approach, all used to cover issues in the blood banks. Then, taxonomy has been built to group articles according to their motivations, objectives, and challenges. The search has been conducted in four major databases: IEEE Xplore, Science Direct, Springer, and PubMed. The final sets were included 54. Most articles (22 out of 54) about blood bank systems: computer and mobile-based blood bank systems(13), blood donor searching systems(5), and Internet of Things-based blood banks(4). Reviews (12 out of 54) have discussed the importance of smart technologies in blood donations. Studies (15 out of 54) have included various analyses about inventory issues with blood/platelet supply chain networks problems in disasters. Meanwhile, few articles (5) have presented devices to detect blood groups and measure viscosity in an easy, costeffective way. This work also has provided an overview of the recent contributions in blood banks mechanisms from 2017 to 2021 facilitating the donation in difficult situations, monitoring blood inventory. Besides, has highlighted the weaknesses in medical care techniques in developing countries suffering from disasters, diseases, and wars. On the other hand, how some limitations in such applications need to take more seriously ex: security, and privacy, older people may face problems in operating, as well as internet and power connectivity which are the main worries. We anticipate that this systematic review can help other researchers grasp the available concepts and gaps so that they can subsequently add to this type of research. [ABSTRACT FROM AUTHOR]

Blood is a significant part of the human body because it is essential for life. Nowadays, should make use of modern technologies to use in all fields like medical areas, especially the blood banks, which is a big challenge in poor countries. Despite many applications being provided via the internet, they lack privacy, authenticity, and reliability, and some need to purchase. Since the Mobile technology is widely spread and many people have their own mobile devices. In addition, the managing of blood bags starting from the donation step requires careful planning and control. So, the aim study is to create a trusted, effective way to connect donors and patients directly in time. A Mobile-Based Blood Bank Management System has been offered as a mechanism for finding the nearest suitable donors and provides an easy way to manage information and monitor the blood inventory, which helps reduce the number of people who die due to expired blood. This Application was developed through Android Studio and delivered the result on a Real-time basis. [ABSTRACT FROM AUTHOR]

This study evaluated the benchmarking process of active queue management (AQM) methods, which consider a multicriteria decision‐making (MCDM) problem using multidimensional criteria. Academic studies have benchmarked the AQM methods using MCDM techniques. However, these studies have used existing MCDM techniques, which face considerable theoretical challenges. The latest MCDM method called fuzzy decision by opinion score (FDOSM) was published in the Journal of Applied Soft Computing in 2020 to address the theoretical challenges of the existing MCDM methods. However, FDOSM continues to encounter serious issues. That is, it exclusively depends on the direct aggregation MCDM approach based on arithmetic mean (AM) operator. However, performing other operators (i.e., geometric mean, harmonic mean, and root mean square), in addition to applying other MCDM approaches (i.e., distance measurement and compromise rank), may result in different ranking results. Hence, this study mainly proposes an extension of FDOSM through the following aspects: (1) application of different aggregation techniques in the direct aggregation MCDM approach, (2) discussion of the effectiveness of each type on the final AQM benchmarking, and (3) use of varying MCDM approaches on FDOSM to reach the optimum result when benchmarking the AQM methods. The current research methodology is based on two sequential phases. The first phase provides the decision matrix used in benchmarking the AQM methods. The decision matrix was constructed based on the AQM evaluation criteria and a list of AQM methods. The second phase presents two stages, namely, data transformation unit and data processing. Findings of the AQM benchmarking are as follows. (1) In the individual FDOSM, two main configurations are recommended when using the AQM benchmarking: direct aggregation MCDM approach with AM operator and compromise rank approach. Benchmarking results of both configurations based on six decision makers are nearly similar, with the AQM BLUE method being ranked the best. The exception is for the results of the compromise rank approach based on the third decision maker, which revealed that the AQM ERED method is the best. (2) Results of the group FDOSM showed a relatively similar order for the AQM methods in both configurations, with the AQM BLUE method being the best. (3) Lastly, significant differences were found among the groups' scores, thereby indicating the validity of the FDOSM‐based AQM benchmarking results. [ABSTRACT FROM AUTHOR]

A new time related method of analysing the sinoaortic baroreceptor heart rate reflex, which determines reflex latency as well as sensitivity, was used to compare the results obtained with a phenylephrine ramp method (P) with those obtained using the whole of phase IV of Valsalva (V1) and using the phase IV systolic blood pressure overshoot alone (V2). Twenty five subjects with large ranges of age and resting blood pressures were studied. Each performed two standardised Valsalva manoeuvres and received three bolus injections of phenylephrine sufficient to cause transient pressor responses of 20-30 mmHg. Mean sensitivity values with P (6.2(3.5) ms·mmHg−1) were greater than those with V1 (4.6(2.3) ms·mmHg−1, p<0.001) and less than V2 (7.8(4.0) ms-mmHg−1, p<0.001). However, linear regression analysis showed a correlation of P with V1 (r=0.76, p<0.0001) and with V2 (r=0.80, p<0.0001). Reflex latency with P (1084(427) ms) was less than V1 (2416(423) ms, p<0.0001) and V2 (1504(441) ms, p<0.0005). Reflex sensitivity results obtained using phase IV of Valsalva's manoeuvre are proportionately related to phenylephrine results, but large errors were introduced into the absolute values obtained when relatively small changes were made to the method of analysis. [ABSTRACT FROM PUBLISHER]

Introduction: The vaccine distribution for the COVID-19 is a multicriteria decision-making (MCDM) problem based on three issues, namely, identification of different distribution criteria, importance criteria and data variation. Thus, the Pythagorean fuzzy decision by opinion score method (PFDOSM) for prioritising vaccine recipients is the correct approach because it utilises the most powerful MCDM ranking method. However, PFDOSM weighs the criteria values of each alternative implicitly, which is limited to explicitly weighting each criterion. In view of solving this theoretical issue, the fuzzy-weighted zero-inconsistency (FWZIC) can be used as a powerful weighting MCDM method to provide explicit weights for a criteria set with zero inconstancy. However, FWZIC is based on the triangular fuzzy number that is limited in solving the vagueness related to the aforementioned theoretical issues., Objectives: This research presents a novel homogeneous Pythagorean fuzzy framework for distributing the COVID-19 vaccine dose by integrating a new formulation of the PFWZIC and PFDOSM methods., Methods: The methodology is divided into two phases. Firstly, an augmented dataset was generated that included 300 recipients based on five COVID-19 vaccine distribution criteria (i.e., vaccine recipient memberships, chronic disease conditions, age, geographic location severity and disabilities). Then, a decision matrix was constructed on the basis of an intersection of the 'recipients list' and 'COVID-19 distribution criteria'. Then, the MCDM methods were integrated. An extended PFWZIC was developed, followed by the development of PFDOSM., Results: (1) PFWZIC effectively weighted the vaccine distribution criteria. (2) The PFDOSM-based group prioritisation was considered in the final distribution result. (3) The prioritisation ranks of the vaccine recipients were subject to a systematic ranking that is supported by high correlation results over nine scenarios of the changing criteria weights values., Conclusion: The findings of this study are expected to ensuring equitable protection against COVID-19 and thus help accelerate vaccine progress worldwide., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Authors.)