Modelling a Response of Complex-Phase Steel at High Strain Rates.

In this article, a response of the complex-phase high-strength steel SZBS800 was modelled by considering the strain-rate influence. The material's response was first measured with a series of standard tensile tests at lower strain rates. Higher strain rates were achieved using the unconventional test of shooting the ball into flat specimens. A viscoplastic formulation of the Cowper-Symonds material model was applied to consider the strain-rate effects. The parameters SIGY, p, and C of the material model were estimated using a step-wise procedure. First, rough estimates of the three parameters were obtained from the tensile tests using the grid search method. Then, the parameters p and C were fine-tuned using the reverse engineering approach. With the help of explicit dynamic simulations and all the experimental data, a multi-criteria cost function was defined and applied to obtain a smooth response function for the parameters p and C. Its optimum was determined by a real-valued genetic algorithm. The optimal values of the estimated parameters model the material response well, although a domain of optimum candidates spans two orders of magnitude for the parameter p and a few orders of magnitude for the parameter C.

Water rights trading planning and its application in water resources management: A water-ecology-food nexus perspective.

Nexus approach provides an effective perspective for implementing synergetic management of water resources. In this study, an interval two-stage chance-constrained water rights trading planning model under water-ecology-food nexus perspective (ITCWR-WEF) is proposed to analyze the interaction between water trading and water-ecology-food (WEF) nexus, which fills in the water resources management gaps from a novel nexus perspective. ITCWR-WEF incorporates hydrological simulation with soil and water assessment tool (SWAT), water rights configuration with interval two-stage chance-constrained programming (ITCP), and multi-criterion analysis with Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). The developed ITCWR-WEF is applied to a real case of Daguhe watershed, which has characteristics of water scarcity, food producing areas and fragile ecosystem. Initial water rights allocation is addressed before the trading. Mechanisms analysis is designed to reveal mutual effect of water rights trading and WEF nexus. Optimal water management scenario is identified through multi-criterion analysis. Results reveal that the mechanism of water rights trading with WEF nexus under low constraint-violation risk level of water availability and environment capacity is recommended to promote the rational water resources allocation to balance the economic goals, water environment and water supply security, as well as ecological and food water demand guarantees.

Multi-Criterion Sampling Matting Algorithm via Gaussian Process.

Natural image matting is an essential technique for image processing that enables various applications, such as image synthesis, video editing, and target tracking. However, the existing image matting methods may fail to produce satisfactory results when computing resources are limited. Sampling-based methods can reduce the dimensionality of the decision space and, therefore, reduce computational resources by employing different sampling strategies. While these approaches reduce computational consumption, they may miss an optimal pixel pair when the number of available high-quality pixel pairs is limited. To address this shortcoming, we propose a novel multi-criterion sampling strategy that avoids missing high-quality pixel pairs by incorporating multi-range pixel pair sampling and a high-quality sample selection method. This strategy is employed to develop a multi-criterion matting algorithm via Gaussian process, which searches for the optimal pixel pair by using the Gaussian process fitting model instead of solving the original pixel pair objective function. The experimental results demonstrate that our proposed algorithm outperformed other methods, even with 1% computing resources, and achieved alpha matte results comparable to those yielded by the state-of-the-art optimization algorithms.

Systematic review of MCDM approach applied to the medical case studies of COVID-19: trends, bibliographic analysis, challenges, motivations, recommendations, and future directions.

When COVID-19 spread in China in December 2019, thousands of studies have focused on this pandemic. Each presents a unique perspective that reflects the pandemic's main scientific disciplines. For example, social scientists are concerned with reducing the psychological impact on the human mental state especially during lockdown periods. Computer scientists focus on establishing fast and accurate computerized tools to assist in diagnosing, preventing, and recovering from the disease. Medical scientists and doctors, or the frontliners, are the main heroes who received, treated, and worked with the millions of cases at the expense of their own health. Some of them have continued to work even at the expense of their lives. All these studies enforce the multidisciplinary work where scientists from different academic disciplines (social, environmental, technological, etc.) join forces to produce research for beneficial outcomes during the crisis. One of the many branches is computer science along with its various technologies, including artificial intelligence, Internet of Things, big data, decision support systems (DSS), and many more. Among the most notable DSS utilization is those related to multicriterion decision making (MCDM), which is applied in various applications and across many contexts, including business, social, technological and medical. Owing to its importance in developing proper decision regimens and prevention strategies with precise judgment, it is deemed a noteworthy topic of extensive exploration, especially in the context of COVID-19-related medical applications. The present study is a comprehensive review of COVID-19-related medical case studies with MCDM using a systematic review protocol. PRISMA methodology is utilized to obtain a final set of (n = 35) articles from four major scientific databases (ScienceDirect, IEEE Xplore, Scopus, and Web of Science). The final set of articles is categorized into taxonomy comprising five groups: (1) diagnosis (n = 6), (2) safety (n = 11), (3) hospital (n = 8), (4) treatment (n = 4), and (5) review (n = 3). A bibliographic analysis is also presented on the basis of annual scientific production, country scientific production, co-occurrence, and co-authorship. A comprehensive discussion is also presented to discuss the main challenges, motivations, and recommendations in using MCDM research in COVID-19-related medial case studies. Lastly, we identify critical research gaps with their corresponding solutions and detailed methodologies to serve as a guide for future directions. In conclusion, MCDM can be utilized in the medical field effectively to optimize the resources and make the best choices particularly during pandemics and natural disasters.

Life cycle impact of concrete incorporating nylon waste and demolition waste.

The large consumption of natural resources by the construction industry and resultant pollution have inspired the necessity to investigate the potential of eco-friendly materials, such as recycled aggregates and recycled fibers. In this study, the effect of different percentages of recycled coarse aggregate (RCA) and nylon waste fibers (NWFs) was investigated on engineering performance and performance-related carbon emissions of high-performance concrete (HPC). Engineering performance indices include compressive strength (CS), splitting tensile strength (STS), water absorption (WA), and chloride ion penetration (CIP). The environmental impact of designed mixes was evaluated using a cradle-to-gate life cycle assessment approach on the HPC mixes. The results showed that the incorporation of 0.25-0.5% yielded maximum STS for all percentages of RCA. The use of NWF helped overcome the negative impact of RCA on the STS of HPC. The use of the 0.1-0.25% volume of NWF was beneficial to the permeability-related durability indicators of HPC. CS-related emissions were minimum for concrete mixes incorporating 0.1-0.25% NWF with 0% and 50% substitution levels of RCA. While STS-related emissions were lowest for HPC incorporating 0.5% NWF with 50% and 100% substitution levels of RCA.

[Screening and Control Measures for New Priority Pollutants in Surface Water of Tianjin].

Emerging pollutants are frequently detected in surface water, threatening the regional aquatic ecosystem and human health. Due to their complex types and large differences in risk and toxicity, research based on the comprehensive assessment of the pollution characteristics to determine the new priority pollutants remains incomplete. This study established a multi-criterion scoring method targeting 41 emerging pollutants with the goal of protecting aquatic organisms and human health, using five key indicators including environmental exposure level, persistence, bioaccumulation, ecological risk, and health risk of pollutants. The emerging pollutants were screened and identified in the surface water of Tianjin. The priority levels of different congeners were divided, and the ecological and health risks of pollutants in the priority control list were evaluated. The results showed that 41 emerging pollutants were generally detected in the study area. The average concentration of sulfonamide antibiotics (SAs) and other drugs (Others) were the largest, with 200.04 ng·L-1 and 176.30 ng·L-1, respectively, followed by perfluorinated compounds (PFASs, 57.98 ng·L-1). In terms of pollutant categories, high-priority emerging pollutants were dominated by PFASs, accounting for 50%. Medium-priority emerging pollutants were still dominated by PFASs (26.32%), but the proportion was lower. Low-priority emerging pollutants were dominated by SAs, accounting for 31.25%. Perfluorooctane sulfonic acid (PFOS), perfluorooctanoic acid (PFOA), carbamazepine (CBZ), caffeine (CAF), perfluorohexyl sulfonic acid (PFHxA), and clarithromycin (CLA) were defined as a priority control list. Compared with other pollutants, PFOS and PFOA had higher scores in persistence, bioaccumulation, and health risk, whereas CBZ and CAF had higher scores in ecological risk. The average risk quotient (RQ) of CAF was 4.8, which indicated a relatively high ecological risk. Health risk indicated that the potential risk caused by PFOA (average hazard quotient was 0.018) cannot be ignored. For the priority control list, because of the high removal rate of CAF in sewage treatment plants, the construction of a pipe network and riverside belt with high vegetation coverage should be strengthened to reduce the impact of untreated sewage discharge and rainfall runoff. Pollutants with low removal rates in sewage treatment plants should be treated with combined technologies to improve the removal efficiency. In addition, industry substitution work should be accelerated to reduce emissions of PFOS, PFOA, and PFHxA.

Application of a Hybrid Multi-Criterion Decision-Making Model for Evaluation and Improvement of Nurses' Job Satisfaction.

Background: The global shortage and turnover of nurses is a current challenge. Past studies have shown that nurse job satisfaction may ameliorate nurse shortage. Although there are many studies on the criteria influencing nurses' job satisfaction, few have examined the causal relationships and weight of each criterion from a systematic perspective. Objective: Identify the key criteria and causal relationships that affect nurses' job satisfaction, and help nurse leaders identify high-weight, high-impact dimensions and contextualize them for improvement. Methods: The study developed a hybrid multi-criterion decision-making model, which incorporated the McCloskey/Mueller satisfaction 13-item scale (MMSS-13), and the Decision-Making Trial and Evaluation Laboratory and the Importance-Performance Analysis methods the model was used to analyze key factors of nurse satisfaction and their interrelationships based on the experience of 15 clinical nurse specialists. Results: In MMSS-13's dimension level, "satisfaction with work conditions and supervisor support" (C5) had the highest impact, and "satisfaction with salary and benefits" (C1) had the highest weight. In criteria level, "salary" (C11), "flexibility in scheduling time off" (C24), "maternity leave time" (C31), "opportunities for social contact after work" (C41), and "your head nurse or facility manager" (C51) had high influence under their corresponding dimensions. The "benefits package" (C13) was the top criterion with the highest impact on MMSS-13. Conclusions: This study assessed nurses' job satisfaction from a multidimensional perspective and revealed the causal relationships between the dimensions. It refined the assessment of nurse job satisfaction to help nurse leaders better assess nurse job satisfaction and make strategic improvements. The study found that compensation and benefits had the highest weight in nurses' job satisfaction. Meanwhile, support for family responsibilities and working conditions, and support from supervisors were the cause dimensions of job satisfaction. Among the more detailed criteria, salary, benefits package, maternity leave time, and leadership had a greater impact on nurses' job satisfaction. Nurse leaders should start with these dimensions to achieve efficient improvement of nurses' job satisfaction.

[Clinical comprehensive evaluation of Reyanning Mixture in treatment of acute upper respiratory tract infection (heat-toxin attacking lung syndrome)].

This study systematically sorted out the evidence data of the safety, effectiveness, economy, innovation, suitability, accessibility, and characteristics of traditional Chinese medicine(TCM) of Reyanning Mixture in the treatment of acute upper respiratory tract infection(heat-toxin attacking lung syndrome) with the qualitative and quantitative evaluation methods adopted. Based on evidence-based medicine, epidemiology, clinical medicine, evidence-based pharmacy, pharmacoeconomics, and health technology evalua-tion(HTA), the clinical value of Reyanning Mixture was evaluated using multi-criteria decision analysis(MCDA) model, Chinese patent medicine clinical evidence, and value evaluation software(CSC v2.0). The SRS monitoring data, Meta-analysis, and other safety evidence showed that the main adverse reactions of Reyanning Mixture were nausea, diarrhea, and rash, and no serious adverse reactions were found. The pharmacovigilance system was sound, and the system was perfect. There was no recall, notification, or interview for unqualified products. Based on the existing research, the evidence was sufficient, and the risk was controllable. Hence, its safety was grade A. Meta-analysis showed that in the treatment of acute upper respiratory tract infection, Reyanning Mixture combined with Amoxicillin Capsules was better than Amoxicillin Capsules alone in shortening the complete fever relief time and improving the cure rate. Besides, it was superior to Shuanghuanglian Granules in shortening the complete fever relief time, cough relief time, nasal congestion relief time, and pharyngeal congestion relief time. The Meta-analysis was conducted based on AMSTAR standard, and its ove-rall quality was proved good. The evidence quality in GRADE system evaluation was medium and low. The quality of evidence was medium, and the clinical value was obvious. Hence, its effectiveness was grade A. The results of pharmacoeconomic research showed that compared with Amoxicillin Capsules, Reyanning Mixture alone or in combination with Amoxicillin Capsules had cost-effectiveness advantages in the treatment of acute upper respiratory tract infection, and the results were stable in sensitivity analysis. According to the CASP economic evaluation checklist, the research problems were clear and the results were reliable. As revealed by the comprehensive evaluation, the evidence quality was sufficient and the result was clear. Its economy was grade B. Reyanning Mixture had multiple therapeutic targets like anti-virus, anti-bacteria, antipyresis, and anti-cough, with good clinical innovation. There were many innovative initiatives in ensuring drug supply, especially at the grass roots, drug safety, and effectiveness, and also multiple innovative contributions to production technology, quality control, scientific and technological research and development, and enterprise management and marketing. Therefore, its innovation was grade B. The dosage form of Reyanning was mixture, which made it convenient for storage and transportation. The usage was easy to be mastered and accepted by doctors and nurses, exhibiting good suitability for clinicians, nurses, pharmacists, and patients who received this drug and basically meeting the needs of clinical medication. The suitability was grade B. The average daily cost of this drug was 8.082 yuan, and the price was low. The treatment cost accounted for a small proportion of the annual disposable income of urban and rural residents, indicating that it was affordable. Reyanning Mixture was available in 31 provinces, cities and autonomous regions, covering 6 910 hospitals. The allocation of hospitals at all levels was more than 50%. There was no shortage or supply restriction of medicinal material resources. The annual production capacity was sufficient to meet the supply demand, so its accessibility was grade A. Reyanning Mixture, sourced from "pneumonia Ⅲ", has been subjected to a real-world study of its clinical application, with 4 367 cases involved, and the characteristic of TCM was grade B. The comprehensive evaluation results demonstrated that the clinical value score of Reyanning Mixture in the treatment of acute upper respiratory tract infection(heat-toxin attacking lung syndrome) was 0.80, making it rated class A. According to the Guidelines for Management of Comprehensive Clinical Evaluation of Drugs(trial version 2021), it is recommended to convert it into the relevant policy results of basic clinical medication management according to the procedures.

Research on KBP model refining method using multi-criterion optimization technology / 中华放射肿瘤学杂志

Objective:Utilizing multi-criterion optimization (MCO) technology to improve plan design quality based on knowledge-based planning (KBP) model.Methods:Fifty-five patients with nasopharyngeal carcinoma (NPC) who had completed radiotherapy were selected, and fixed-field intensity-modulated radiotherapy (IMRT) technology was used in each case. Among them, 40 cases were randomly selected as training set 1. Then, IMRT plans in training set 1 were preprocessed by MCO technology to construct a new training set 2. With the initial training set 1 and the processed training set 2 as training samples, the traditional KBP model and the MCO-KBP model refined by MCO technology were trained, respectively. Among the remaining 15 cases, 5 cases were randomly selected as the validation set, and the remaining 10 cases were used as the test set. After verification, the test set was used to statistically analyze the plan quality of the initial manual plan and the automatic plan generated by the traditional KBP model and the MCO-KBP model.Results:The target dose (D 95%) of plans generated by the traditional KBP model and the MCO-KBP model met the clinical requirements. Conformity index (CI) and homogeneity index (HI) were almost the same ( P>0.05), and the doses of organ at risk (OAR) of the automatic plans generated by the MCO-KBP model were lower than those of the traditional KBP model. For example, compared with the traditional KBP model, the average D max of the brainstem in the automatic plans generated by the MCO-KBP model was lower by 2.13 Gy, the average D mean of the left parotid gland was lower by 1.39 Gy, the average D mean of the right parotid gland was lower by 1.59 Gy, and the average D max of the left optic nerve was lower by 1.42 Gy, the average D max of the right optic nerve was lower by 1.16 Gy, and the average D max of the pituitary gland was lower by 1.88 Gy. All of the above-mentioned dosimetry indexes were statistically significant. Conclusion:Compared with the traditional KBP model, the IMRT plans designed by the refined MCO-KBP model have obvious advantages in the protection of OAR, which proves the feasibility of utilizing MCO technology to improve the plan design quality of the KBP model.

Clinical comprehensive evaluation of Reyanning Mixture in treatment of acute upper respiratory tract infection (heat-toxin attacking lung syndrome) / 中国中药杂志

This study systematically sorted out the evidence data of the safety, effectiveness, economy, innovation, suitability, accessibility, and characteristics of traditional Chinese medicine(TCM) of Reyanning Mixture in the treatment of acute upper respiratory tract infection(heat-toxin attacking lung syndrome) with the qualitative and quantitative evaluation methods adopted. Based on evidence-based medicine, epidemiology, clinical medicine, evidence-based pharmacy, pharmacoeconomics, and health technology evalua-tion(HTA), the clinical value of Reyanning Mixture was evaluated using multi-criteria decision analysis(MCDA) model, Chinese patent medicine clinical evidence, and value evaluation software(CSC v2.0). The SRS monitoring data, Meta-analysis, and other safety evidence showed that the main adverse reactions of Reyanning Mixture were nausea, diarrhea, and rash, and no serious adverse reactions were found. The pharmacovigilance system was sound, and the system was perfect. There was no recall, notification, or interview for unqualified products. Based on the existing research, the evidence was sufficient, and the risk was controllable. Hence, its safety was grade A. Meta-analysis showed that in the treatment of acute upper respiratory tract infection, Reyanning Mixture combined with Amoxicillin Capsules was better than Amoxicillin Capsules alone in shortening the complete fever relief time and improving the cure rate. Besides, it was superior to Shuanghuanglian Granules in shortening the complete fever relief time, cough relief time, nasal congestion relief time, and pharyngeal congestion relief time. The Meta-analysis was conducted based on AMSTAR standard, and its ove-rall quality was proved good. The evidence quality in GRADE system evaluation was medium and low. The quality of evidence was medium, and the clinical value was obvious. Hence, its effectiveness was grade A. The results of pharmacoeconomic research showed that compared with Amoxicillin Capsules, Reyanning Mixture alone or in combination with Amoxicillin Capsules had cost-effectiveness advantages in the treatment of acute upper respiratory tract infection, and the results were stable in sensitivity analysis. According to the CASP economic evaluation checklist, the research problems were clear and the results were reliable. As revealed by the comprehensive evaluation, the evidence quality was sufficient and the result was clear. Its economy was grade B. Reyanning Mixture had multiple therapeutic targets like anti-virus, anti-bacteria, antipyresis, and anti-cough, with good clinical innovation. There were many innovative initiatives in ensuring drug supply, especially at the grass roots, drug safety, and effectiveness, and also multiple innovative contributions to production technology, quality control, scientific and technological research and development, and enterprise management and marketing. Therefore, its innovation was grade B. The dosage form of Reyanning was mixture, which made it convenient for storage and transportation. The usage was easy to be mastered and accepted by doctors and nurses, exhibiting good suitability for clinicians, nurses, pharmacists, and patients who received this drug and basically meeting the needs of clinical medication. The suitability was grade B. The average daily cost of this drug was 8.082 yuan, and the price was low. The treatment cost accounted for a small proportion of the annual disposable income of urban and rural residents, indicating that it was affordable. Reyanning Mixture was available in 31 provinces, cities and autonomous regions, covering 6 910 hospitals. The allocation of hospitals at all levels was more than 50%. There was no shortage or supply restriction of medicinal material resources. The annual production capacity was sufficient to meet the supply demand, so its accessibility was grade A. Reyanning Mixture, sourced from "pneumonia Ⅲ", has been subjected to a real-world study of its clinical application, with 4 367 cases involved, and the characteristic of TCM was grade B. The comprehensive evaluation results demonstrated that the clinical value score of Reyanning Mixture in the treatment of acute upper respiratory tract infection(heat-toxin attacking lung syndrome) was 0.80, making it rated class A. According to the Guidelines for Management of Comprehensive Clinical Evaluation of Drugs(trial version 2021), it is recommended to convert it into the relevant policy results of basic clinical medication management according to the procedures.

Multi-criterion Intelligent Decision Support system for COVID-19.

COVID-19 is a buzz word nowadays. The deadly virus that started in China has spread worldwide. The fundamental principle is "if the disease can travel faster information has to travel even faster". The sequence of events reveals the upheaval need to strengthen the ability of the early warning system, risk reduction, and management of national and global risks. Digital contact tracing apps like Aarogya setu (India) and Pan-European privacy preserving proximity tracing (German) has somehow helped but they are more effective in the initial stage and less relevant in the community spread phase. Thus, there is a need to devise a Decision Support System (DSS) based on machine learning algorithms. In this paper, we have attempted to propose an Additive Utility Assumption Approach for Criterion Comparison in Multi-criterion Intelligent Decision Support system for COVID-19. The dataset of Covid-19 has been taken from government link for validating the results. In this paper, an additive utility assumption-based approach for multi-criterion decision support system (MCDSS) with an accurate prediction of identified risk factors on certain well-defined input parameters is proposed and validated empirically using the standard SEIR model approach (Susceptible, Exposed, Infected and Recovered). The results includes comparative analysis in tabular form with already existing approaches to illustrate the potential of the proposed approach including the parameters such as Precision, Recall and F-Score. Other advanced parameters such as, MCC (Matthews Correlation Coefficient), ROC (Receiver Operating Characteristics) and PRC (Precision Recall) have also been considered for validation and the graphs are illustrated using Jupyter notebook. The statistical analysis of the most affected top eight states of India is undertaken effectively using the Weka software tool and IBM Cognos software to correctly predict the outbreak of pandemic situation due to Covid-19. Finally, the article has immense potential to contribute to the COVID-19 situation and may prove to be instrumental in propelling the research interest of researchers and providing some useful insights for the current pandemic situation.

Generalized Z-numbers with hesitant fuzzy linguistic information and its application to medicine selection for the patients with mild symptoms of the COVID-19.

Fuzzy set theory and a series of theories derived from it have been widely used to deal with uncertain phenomena in multi-criterion decision-making problems. However, few methods except the Z-number considered the reliability of information. In this paper, we propose a multi-criterion decision-making method based on the Dempster-Shafer (DS) theory and generalized Z-numbers. To do so, inspired by the concept of hesitant fuzzy linguistic term set, we extend the Z-number to a generalized form which is more in line with human expression habits. Afterwards, we make a bridge between the knowledge of Z-numbers and the DS evidence theory to integrate Z-valuations. The identification framework in the DS theory is used to describe the generalized Z-numbers to avoid ambiguity. Then, the knowledge of Z-numbers is used to derive the basic probability assignment of evidence and the synthetic rules in the DS theory are used to integrate evaluations. An illustrative example of medicine selection for the patients with mild symptoms of the COVID-19 is provided to show the effectiveness of the proposed method.

Food Footprint as a Measure of Sustainability for Grazing Dairy Farms.

Livestock productions require significant resources allocation in the form of land, water, energy, air, and capital. Meanwhile, owing to increase in the global demand for livestock products, it is wise to consider sustainable livestock practices. In the past few decades, footprints have emerged as indicators for sustainability assessment. In this study, we are introducing a new footprint measure to assess sustainability of a grazing dairy farm while considering carbon, water, energy, and economic impacts of milk production. To achieve this goal, a representative farm was developed based on grazing dairy practices surveys in the State of Michigan, USA. This information was incorporated into the Integrated Farm System Model (IFSM) to estimate the farm carbon, water, energy, and economic impacts and associated footprints for ten different regions in Michigan. A multi-criterion decision-making method called VIKOR was used to determine the overall impacts of the representative farms. This new measure is called the food footprint. Using this new indicator, the most sustainable milk production level (8618 kg/cow/year) was identified that is 19.4% higher than the average milk production (7215 kg/cow/year) in the area of interest. In addition, the most sustainable pasture composition was identified as 90% tall fescue with 10% white clover. The methodology introduced here can be adopted in other regions to improve sustainability by reducing water, energy, and environmental impacts of grazing dairy farms, while maximizing the farm profit and productions.

Based on Real Time Remote Health Monitoring Systems: A New Approach for Prioritization "Large Scales Data" Patients with Chronic Heart Diseases Using Body Sensors and Communication Technology.

This paper presents a new approach to prioritize "Large-scale Data" of patients with chronic heart diseases by using body sensors and communication technology during disasters and peak seasons. An evaluation matrix is used for emergency evaluation and large-scale data scoring of patients with chronic heart diseases in telemedicine environment. However, one major problem in the emergency evaluation of these patients is establishing a reasonable threshold for patients with the most and least critical conditions. This threshold can be used to detect the highest and lowest priority levels when all the scores of patients are identical during disasters and peak seasons. A practical study was performed on 500 patients with chronic heart diseases and different symptoms, and their emergency levels were evaluated based on four main measurements: electrocardiogram, oxygen saturation sensor, blood pressure monitoring, and non-sensory measurement tool, namely, text frame. Data alignment was conducted for the raw data and decision-making matrix by converting each extracted feature into an integer. This integer represents their state in the triage level based on medical guidelines to determine the features from different sources in a platform. The patients were then scored based on a decision matrix by using multi-criteria decision-making techniques, namely, integrated multi-layer for analytic hierarchy process (MLAHP) and technique for order performance by similarity to ideal solution (TOPSIS). For subjective validation, cardiologists were consulted to confirm the ranking results. For objective validation, mean ± standard deviation was computed to check the accuracy of the systematic ranking. This study provides scenarios and checklist benchmarking to evaluate the proposed and existing prioritization methods. Experimental results revealed the following. (1) The integration of TOPSIS and MLAHP effectively and systematically solved the patient settings on triage and prioritization problems. (2) In subjective validation, the first five patients assigned to the doctors were the most urgent cases that required the highest priority, whereas the last five patients were the least urgent cases and were given the lowest priority. In objective validation, scores significantly differed between the groups, indicating that the ranking results were identical. (3) For the first, second, and third scenarios, the proposed method exhibited an advantage over the benchmark method with percentages of 40%, 60%, and 100%, respectively. In conclusion, patients with the most and least urgent cases received the highest and lowest priority levels, respectively.

A new approach to formulating and appraising drug policy: A multi-criterion decision analysis applied to alcohol and cannabis regulation.

BACKGROUND: Drug policy, whether for legal or illegal substances, is a controversial field that encompasses many complex issues. Policies can have effects on a myriad of outcomes and stakeholders differ in the outcomes they consider and value, while relevant knowledge on policy effects is dispersed across multiple research disciplines making integrated judgements difficult. METHODS: Experts on drug harms, addiction, criminology and drug policy were invited to a decision conference to develop a multi-criterion decision analysis (MCDA) model for appraising alternative regulatory regimes. Participants collectively defined regulatory regimes and identified outcome criteria reflecting ethical and normative concerns. For cannabis and alcohol separately, participants evaluated each regulatory regime on each criterion and weighted the criteria to provide summary scores for comparing different regimes. RESULTS: Four generic regulatory regimes were defined: absolute prohibition, decriminalisation, state control and free market. Participants also identified 27 relevant criteria which were organised into seven thematically related clusters. State control was the preferred regime for both alcohol and cannabis. The ranking of the regimes was robust to variations in the criterion-specific weights. CONCLUSION: The MCDA process allowed the participants to deconstruct complex drug policy issues into a set of simpler judgements that led to consensus about the results.

Enantiomeric separations by capillary electrophoresis: Theoretical method to determine optimum chiral selector concentration.

A method to optimize the ligand concentration [S] in the background electrolyte of capillary electrophoresis separations is presented. It is based on the use of a model which predicts apparent electrophoretic mobilities as a function of ligand concentration (expressed as p[S] = -log[S]). This model is employed to compose the expression of a recently proposed criterion to qualify separations in electrophoresis. Two strategies to find the optimum p[S], leading to the best separation of all compounds, are explained: 1.- a graphical method using a windows map depicting the single separation criteria between all possible combination of compounds by pairs, and 2.- an analytical method where an extended multicriterion optimization function is composed and optimum p[S] is found by mathematical maximization. The procedure is applied to a hard-to-separate model system: enantiomeric separations of racemic mixtures. 2-Hydroxypropyl-ß-cyclodextrin was chosen as a model ligand, and four pharmaceutical drugs as model analytes. In order to demonstrate the performance of the procedure, results of electrophoretic separations obtained at p[S] found as optimum are compared with separations obtained at p[S] values slightly higher and lower than the optimum.

Based Real Time Remote Health Monitoring Systems: A Review on Patients Prioritization and Related "Big Data" Using Body Sensors information and Communication Technology.

The growing worldwide population has increased the need for technologies, computerised software algorithms and smart devices that can monitor and assist patients anytime and anywhere and thus enable them to lead independent lives. The real-time remote monitoring of patients is an important issue in telemedicine. In the provision of healthcare services, patient prioritisation poses a significant challenge because of the complex decision-making process it involves when patients are considered 'big data'. To our knowledge, no study has highlighted the link between 'big data' characteristics and real-time remote healthcare monitoring in the patient prioritisation process, as well as the inherent challenges involved. Thus, we present comprehensive insights into the elements of big data characteristics according to the six 'Vs': volume, velocity, variety, veracity, value and variability. Each of these elements is presented and connected to a related part in the study of the connection between patient prioritisation and real-time remote healthcare monitoring systems. Then, we determine the weak points and recommend solutions as potential future work. This study makes the following contributions. (1) The link between big data characteristics and real-time remote healthcare monitoring in the patient prioritisation process is described. (2) The open issues and challenges for big data used in the patient prioritisation process are emphasised. (3) As a recommended solution, decision making using multiple criteria, such as vital signs and chief complaints, is utilised to prioritise the big data of patients with chronic diseases on the basis of the most urgent cases.

Don't Discount Societal Value in Cost-Effectiveness Comment on "Priority Setting for Universal Health Coverage: We Need Evidence-Informed Deliberative Processes, Not Just More Evidence on Cost-Effectiveness".

As healthcare resources become increasingly scarce due to growing demand and stagnating budgets, the need for effective priority setting and resource allocation will become ever more critical to providing sustainable care to patients. While societal values should certainly play a part in guiding these processes, the methodology used to capture these values need not necessarily be limited to multi-criterion decision analysis (MCDA)-based processes including 'evidence-informed deliberative processes.' However, if decision-makers intend to not only incorporates the values of the public they serve into decisions but have the decisions enacted as well, consideration should be given to more direct involvement of stakeholders. Based on the examples provided by Baltussen et al, MCDA-based processes like 'evidence-informed deliberative processes' could be one way of achieving this laudable goal.

Beyond Ecosystem Services: Valuing the Invaluable.

The ecosystem services framework (ESF) is advantageous and widely used for itemising and quantifying ways in which humans benefit from natural places. However, it suffers from two important problems: (i) incoherence of definitions and (ii) a narrow approach to valuation, inadequate to represent the full range of human motives for conservation and the diverse interests of different stakeholders. These shortcomings can lead to a range of problems including double-counting, blind spots and unintended consequences. In this opinion article, we propose an ecosystem valuing framework as a broader and more rigorous way to deliver the benefits currently sought from the ESF, without the conceptual problems.

Optimization of extraction technology process for Honghua Tongluo Prescription by an FAHP-based evaluation model / 中草药

Objective: To establish an optimization model for the extraction technology process of Honghua Tongluo Prescription (HTP) based on fuzzy analytic hierarchy process (FAHP). Methods: The evaluation indexes tree was established which included objective level, criterion level, and index level. The criterion level consisted of effective constituent, impurity control, behavioristics, histopathology, and immune response. The index level consisted the extraction efficiency of hydroxysafflor yellow A (HSYA) and icariin, the content of total substances, mechano-allodynia, and mechano-hyperalgesia, the ratios of decentered nucleolus in dorsal root ganglion, the content of SP in pelma skin with plantar P quality as the index layer. Weighting coefficient of all the indexes was decided by FAHP based calculation. HTP extraction process route was comprehensively evaluated. Results: The importance degrees (ω) of each criterion from high to low were: immune response (ω=0.245), histopathology (ω=0.23), effective constituent, behavioristics (ω=0.20), and impurity control (ω=0.125). The optimized extraction technology process was that Carthami Flos was better extracted by warm maceration with water at 60℃, Epimedii Herba was circulated by reflux extraction with water. Cinnamomi Ramulus was better treated by reflux extraction with 80% alcohol. Conclusion: An FAHP-based evaluation method with pharmacological indexes, effective constituent indexes, and impurity control as evaluation indexes, which could put formula Chinese materia medica principle and modern pharmaceutical engineering demands into consideration, is comprehensive and effective.