Ranking Results Research Articles

Fermatean fuzzy distance and Sugeno–Weber operators-based SPC-MARCOS approach for sustainable supplier evaluation in the healthcare supply chain

The present work proposes a new decision support tool for assessing the sustainable suppliers in the healthcare supply chain. For this purpose, the classical Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) model is integrated with the Sugeno–Weber weighted averaging operators, modified symmetry point of criterion (SPC) model, rank sum (RS) tool and Fermatean fuzzy sets (FFSs), and named as the ‘FF-SPC-RS-MARCOS’ framework. The developed model firstly determines the decision experts’ weights through RS model. Second, novel Sugeno–Weber weighted operators are introduced to combine the experts’ opinions. Third, a unified weighting procedure is presented based on the combination of modified SPC approach for objective weight and RS method for subjective weight of attributes. To this aim, a novel distance measure is introduced for FFSs and further applied to compute the distance between aggregated Fermatean fuzzy numbers and symmetry point value of an attribute in the modified SPC approach. Further, a hybrid FF-SPC-RS-MARCOS approach is proposed to tackle the decision-making problems on FFSs setting. To elucidate the efficacy of the developed method, it is applied to a case study of sustainable supplier selection problem in the healthcare supply chain. The paper further conducts sensitivity investigation and comparison with existent approaches to test the stability and robustness of the ranking outcomes. This study shows how the proposed MARCOS method in combination with SPC and RS models can be used to prioritize the alternative suppliers in the healthcare supply chain. The introduced work provides a new methodology, which can help the practitioners and academics to evaluate suppliers with uncertain information and can also be employed to other areas facing similar types of decision-making problems.

A detailed analysis of game statistics of professional tennis players: An inferential and machine learning approach.

Tennis, a widely enjoyed sport, motivates athletes and coaches to optimize training for competitive success. This retrospective predictive study examines anthropometric features and statistics of 1990 tennis players in the 2022 season, using 20,040 data points retrospectively obtained from the ATP official source after the end of the season. These data were cross-verified with information from other sources before categorisation to address any discrepancies. Employing various analytical methods, the results emphasize the strategic importance of tournament participation and gameplay for financial gains and higher rankings. Prize money analysis reveals a significant disparity favoring top players. Multivariate Analysis of Variance highlights the need to consider multiple variables for understanding ATP rankings. Multinomial Logistic Regression identifies age, height, and specific service-related metrics as key determinants, with older and taller players more likely to secure top positions. Neural Network models exhibit potential in predicting ATP Rank outcomes, particularly for ATP Rank (500). Our results argue for the use of Artificial Intelligence (AI), specifically Neural Networks, in handling complex interactions and emphasize that AI is a supportive tool in decision-making, requiring careful consideration by experienced individuals. In summary, this study enhances our understanding of ATP ranking factors, providing actionable insights for coaches, players, and stakeholders in the tennis community.

Different minimally invasive surgical methods to hysterectomy for benign gynecological disease: A systematic review and network meta-analysis.

This network meta-analysis aimed to compare the perioperative efficacy of various minimally invasive hysterectomy procedures for treating benign gynecological diseases and to assess whether vaginal natural orifice transluminal endoscopic hysterectomy (VNOTEH), a recently emerging procedure, is inferior to traditional laparoscopy. We searched PubMed, Cochrane Library, Embase, China National Knowledge Infrastructure (CNKI), China Biology Medicine disc (CBM), Wanfang Data, and China VIP Database from inception to August 2022 and updated in June 2023. We included randomized controlled trials (RCTs) comparing different minimally invasive hysterectomy techniques in patients with benign gynecological conditions. The intervention measures included nine minimally invasive hysterectomies. The two researchers used the Cochrane risk-of-bias assessment tool for study appraisal. All statistical analyses and drawings were performed using STATA 17.0 and R 4.4.1. A network meta-analysis (NMA) was conducted to compare the effectiveness of minimally invasive hysterectomy and rank its relative impact probabilistically. A total of 78 RCTs involving 7640 patients and nine minimally invasive hysterectomy methods with 16 intervention combinations were included in this study. Among these, 2, 63, and 13 studies were deemed to have a low, medium, and high risk of bias, respectively. Based on the Surface Under the Cumulative Ranking (SUCRA) probability ranking results of NMA, laparoendoscopic single-site surgery-laparoscopic-assisted vaginal hysterectomy (LESS-LAVH) demonstrated superior outcomes in terms of complications, infections, and 24-h postoperative pain scores. LAVH exhibited better performance in injuries and hospital stays, total laparoscopic hysterectomy showed the least blood loss, and vaginal hysterectomy had the shortest operation time. LESS-LAVH and LAVH are recommended options, if feasible. Meanwhile, VNOTEH can achieve comparable results to traditional laparoscopy but requires careful attention to the risk of injury and infection. Future research should aim to broaden the search scope by including high-quality, large-scale, multicenter RCTs.

COMPARATOR REGRESSION ANALYSIS IN CONDITIONS OF FUZZY INITIAL DATA

Abstract. The canonical task of regression analysis consists in studying the problem of estimation and mathematical description of the influence caused by one or more independent variables (predictors, explanatory variables) on the dependent (explained) variable. The subject of comparator identification problem is a specific modification of the standard regression analysis problem in the case when, for some reason, it is not possible to measure values of the dependent, explained variable. However, the problem of reconstructing a regression polynomial can be solved if it is possible to qualitatively compare the results of experiments and rank them, for example, in the order of increasing. Taking into account the results of ranking the values of the explained variable in different experiments, a corresponding system of inequalities is formed. The purpose of the research consists in developing a method for mathematical processing of the resulting system of inequalities for analytical assessment of the level of influence of explanatory variables on the explained variable. The method for solving the problem is based on transforming a system of inequalities into a system of linear algebraic equations. A significant drawback of the known approach to solving this problem consists in the fact that the resulting system of linear algebraic equations is an underdetermined one (the number of unknowns in this system exceeds the number of equations). At the same time, the system has an infinite number of solutions, among which there may be an uncontrollable number of unacceptable ones. The exhaustive search method for finding acceptable solutions is futile. In this regard, the problem of developing a correct method of comparator identification remains relevant. Another difficulty that accompanies the actual procedure of comparator identification when solving practical problems consists in the fact that measurements of values of the controlled parameters are not accurate. The uncertainty that arises in the conditions of a small sample of initial data can be removed using the tools of fuzzy mathematics. It is assumed that, based on preliminary research, a mathematical description of the corresponding membership function can be obtained for each of the controlled parameters. As a result, effective approaches to solving emerging problems have been proposed, based on the use of developed optimization procedures in the conditions of a small sample of fuzzy initial data.

A novel multi-criteria decision making method to evaluate green innovation ecosystem resilience

Green innovation ecosystem resilience evaluation is crucial for ensuring sustainable development in dynamic environments. By comprehensively assessing resilience, it helps identify risks and facilitate effective responses, thus promoting sustainable development. To effectively evaluate the resilience of green innovation ecosystems, this paper proposes a model based on the interval-valued intuitionistic fuzzy multi-criteria decision-making (MCDM) method, designed to support decision-makers (DMs) in resilience evaluation. In this model, we improve the Criteria Importance Through Intercriteria Correlation (CRITIC) method by introducing Spearman's correlation coefficient to more accurately determine the objective weights of various indicators based on their interrelationships. Furthermore, we propose an Interval-Valued Intuitionistic Fuzzy Hybrid Average Aggregation (IVIFHAA) operator, which considers both the subjective and objective weight order for aggregating expert evaluation results. Additionally, a scoring function is introduced to generate ranked evaluation results. We also establish a resilience evaluation criteria system covering interconnectivity, sustainability, and diversity within green innovation ecosystems to assist DMs in resilience evaluation. Finally, to validate this method, we apply it to assess the resilience of regional green innovation ecosystems and compare it with other methods. The results demonstrate that the improved operator effectively reduces information loss and improves evaluation efficiency, while the improved CRITIC method handles outliers during the evaluation process.

Efficacy and safety of different oral anticoagulants for stroke prevention in older patients with atrial fibrillation: A network meta-analysis.

Various oral anticoagulants have been used for stroke prevention in older patients with atrial fibrillation (AF). However, the optimal anticoagulants for stroke prevention has not yet been developed. We performed a systematic review and network meta-analysis to determine the optimal instructions. We searched for randomized controlled trials (RCTs) from PubMed, Embase, and the Cochrane Library without restriction for publication date or language at January 2024. Any RCTs that compared the effectiveness of a direct oral anticoagulant and a vitamin K antagonist (VKA) for stroke prevention in older patients with AF were included in this network meta-analysis. The Bayesian network meta-analysis used a random effects model and surface under the cumulative ranking curve analysis to rank results. All analyses were done using R software with gemtc package, with statistical significance set at P < .05. We included 7 RCTs (79,003 patients) comparing 8 different instructions including Apixaban 5 mg, Dabigatran 110 mg, Dabigatran 150 mg, Edoxaban 30 mg, Edoxaban 60 mg, Rivaroxaban 15 mg, Rivaroxaban 20 mg, and VKA. Apixaban 5 mg, Dabigatran 110 mg, and Dabigatran 150 mg was more effective than the VKA for reducing stroke or systemic embolism risks, and the difference was statistically significant (P < .05). Apixaban 5 mg, Dabigatran 110 mg, Dabigatran 150 mg, Edoxaban 30 mg, and Edoxaban 60 mg was associated with a reduction of the intracranial hemorrhage rate than the VKA (P < .05). The surface under the cumulative ranking curve shows that Dabigatran 110 mg ranked first for reducing stroke or systemic embolism risks. Edoxaban 60 mg ranked first for major bleeding. Dabigatran 110 mg ranked first for intracranial hemorrhage. Apixaban 5 mg ranked first for all bleeding events. Direct oral anticoagulants were found to have lower rates of thromboembolic events compared to VKAs in older patients with AF. Apixaban 5 mg, Dabigatran 110 mg, Dabigatran 150 mg, Edoxaban 30 mg, and Edoxaban 60 mg were also associated with a reduction of intracranial hemorrhage than VKA.

HUBUNGAN POLA KONSUMSI MAKANAN CEPAT SAJI DENGAN STATUS GIZI PADA REMAJA DI SMP N 1 NGAWEN

Data from RISKESDAS 2018 Indonesian adolescents aged 15-20 years, found that adolescents consume instant noodles or other instant foods with a prevalence of more than 1 time per day 11.2%, more than 1 to 6 times per week 67.6%, and less than 3 times per month 21.2%. This can cause health problems Objective: This study aims to determine the relationship of fast food consumption patterns with nutritional status in adolescents at SMP N 1 Ngawen. Research method: This research is a type of quantitative correlation. With a cross sectional approach. Sampling using simple random sampling obtained 89 respondents. The instrument used ffq questionnaire and tools using scales and microtoice. Data were analysed using spearman rank. Results: Characteristics of respondents in this study the majority of 14 years (36.0%), female gender 44 (49.4%), male 45 (50.6%), the majority of patterns 45 (50.6%) and consumption of the majority of nutritional status 57 (64.0%). Spearman rank results obtained a value of 0.772 (&gt; 0.05). Conclusion: There is no relationship between fast food consumption patterns and nutritional status in adolescents at SMP N 1 Ngawen.

A large-scale supplier evaluation approach for circular economy in the presence of circular criteria interactions and weight consistency

Circular economy (CE) is an economic model in which we achieve sustainable development through resource recovery, reuse, and remanufacturing. Large-scale supplier evaluation is crucial for ensuring the sustainability of the CE supply chain and efficient utilization of resources. Especially, criterion interaction and consistency of criterion weights are two key factors of large suppliers’ evaluation in CE. However, current research often overlooks these key factors, this study proposes a large-scale supplier evaluation approach in CE to address these issues. The novel aspects of the proposed approach come with considering the interaction between circular and non-circular criteria, as well as eliminating the impact of large-scale complex information on the consistency of criterion weights. Firstly, large-scale hesitant evaluation information is fused by evidence reasoning, and interaction rules between circular and non-circular criteria are established. Then, the interaction weights of the criteria are obtained based on the interaction of circular criteria, and the comprehensive criteria weights are determined by an improved best-worst method. Next, the decision makers’ weights are generated by the utility value of the circular criterion. Finally, the results of numerical example and analysis show that 86% of the supplier ranking results are stable, and the consistency of the proposed approach is 0.9757, indicating that the proposed approach exhibits high validity and reliability. It emphasizes the significance of the supplier’s circular ability in CE and reduces the interference of large-scale complex information on the consistency of evaluation results.

Multi-Attribute Three-Way Decision Approach Based on Ideal Solutions under Interval-Valued Fuzzy Soft Environment

The interval-valued fuzzy soft set (IVFSS) model, which combines the benefits of the soft set model with the interval-valued fuzzy set (IVFS) model, is a growing and effective mathematical tool for processing hazy data. In detail, this model is characterized by symmetry, which has the lower and upper membership degree. The study of decision-making based on IVFSS has picked up more steam recently. However, existing multi-attribute decision-making (MADM) methods can only sort alternative schemes, but are not able to classify them, which is detrimental to decision-makers’ efficient decision-making. In this paper, we propose a multi-attribute three-way decision-making (MATWDM) algorithm based on ideal solutions for IVFSS. MATWDM is extended to the IVFSS environment by incorporating the concept of the ideal solution, offering a more adaptable and comprehensive approach for addressing uncertain MADM issues. The method not only obtains the ranking results of the alternatives, but also divides them into acceptance domain, rejection domain, and delayed-decision domain, which makes the decision results more reasonable and effective, facilitating decision-makers to make better decisions. We apply the proposed three-way decision algorithm to two practical cases as diverse as mine emergency decision and Homestay selection decision. Additionally, the effectiveness and viability of the suggested method are confirmed by experimental findings.

Build your own all-virtual multidisciplinary kidney stone clinic.

With the dramatic rise of telemedicine in the post coronavirus disease 2019 (COVID-19) pandemic, our objective was to develop a totally virtual multidisciplinary kidney stone clinic and assess patient satisfaction of this format. The virtual multidisciplinary stone clinic began July 2021 and continued monthly. Prior to the beginning of each clinic, providers from the urology, nephrology, and dietitian teams meet virtually to discuss the patients. Patients would then log into WebEx virtual platform and providers would subsequently log into the patient's virtual room, to review radiology, laboratory results, and dietary logs then provide counseling. Patients were then sent a survey via electronic mail regarding their experience. A 5-point Likert scale was used for responses ranging from strongly disagree to strongly agree. Scores were averaged to rank results. A total of 122 patients were sent surveys, and a total of 31 surveys were completed. Sixty-one percent of patients strongly agree and 13% agree that they felt comfortable using the virtual platform. When asked if they prefer using the virtual platform for their visit, 70% of patients agreed or strongly agreed and only 16% of patients disagreed or strongly disagreed. In regards to potential advantages of a virtual visit, the Likert scores were averaged and ranked from most to least important with improved timeliness (3.7) and ease of scheduling into day (3.6) the highest rated advantages. Most patients did not find any concerns using the virtual platform, however the ability to see the provider in-person and connecting personally was of highest concern with an average Likert score of 2.3. Overall, 83% of patients agreed or strongly agreed that the multidisciplinary stone clinic satisfied their kidney stone related questions regarding treatment and prevention. A virtual multidisciplinary kidney stone clinic can be implemented with high patient satisfaction scores and help overcome the limitations of physical clinic space and provider schedule coordination. There are few disadvantages to using the platform.

Acupuncture and Related Therapies for Endometriosis: A Network Meta-Analysis of Randomized Controlled Trials.

Acupuncture and related therapies are effective and safe ways to relieve the pain, and improve the health and quality of life in women with endometriosis-related pain. However, it is still unclear which treatment is the most effective. Our study aims to summarize the evidence and determine the most effective and safe method to treat the endometriosis. We searched PubMed, EMBASE, Cochrane Library, and Web of science, China Biology Medicine, China National Knowledge Infrastructure, Wan fang Data, Chinese Scientific Journal Database and conducted manual searches of relevant papers, summarized randomized clinical trials of acupuncture-related therapies for endometriosis from database inception to 21 April 2024. After independent literature screening and data extraction that pain VAS was selected as the primary outcome measure. The quality evaluation was conducted by Review Manager 5.4. Perform network meta-analysis (NMA) used Stata 15.0 software. Forty-two eligible trials involving six acupuncture-related interventions and 3,635 participants were included in this NMA. Pairwise meta-analyses show that combination therapy was more efficacious than western medicine and Chinese herb medicine for pain VAS scores, serum CA125 level and response rate results. The NMA estimates indicated that: for pain VAS scores, acupuncture (SMD: -2.33; 95% CI: -4.37, -0.29) and combination therapy (SMD: 1.79 95% CI: 1.21, 2.41) were superior to western medicine. For serum CA125 level, acupoint application (SMD: -11.33 95% CI: -20.28, -2.97) and combination therapy (SMD: 6.20; 95% CI: 1.60, 10.75) were associated with better efficacy when considered alongside western medicine. For response rate, combination therapy (SMD: 0.20; 95% CI: 0.14, 0.29) and auricular therapy (SMD: 8.01; 95% CI: 2.08, 45.37) were more efficacious than western medicine. The comprehensive ranking results show that acupoint catgut embedding was the best performing at lowering pain VAS scores, acupoint application was identified as the most effective in reducing serum CA125 level, and auricular therapy was ranked first in improving the response rate. Acupoint catgut embedding, auricular therapy, acupoint application and combination therapy may be the best solutions for the treatment of endometriosis. Additional trials are needed to develop higher-quality evidence and optimal regimens.

Two-stage stochastic multi-criteria group decision making for wind-photovoltaic-hydropower scheduling

Scheduling of wind-photovoltaic-hydropower systems is a complex multi-objective collaborative problem which requires multi-criteria decision making (MCDM) to assist in formulating the most-preferred scheduling scheme. There are uncertainties existing in criteria weights (CWs) due to the limitations of the knowledge level of decision makers and the subjectivity of preferences. This paper proposes a two-stage stochastic multi-criteria group decision making model considering the uncertainties in the decision making environment and the progression of the decision making process. For the prophase decision making (the first stage), the stochastic multi-criteria acceptability analysis (SMAA) theory and the MCDM model VIKOR are combined to construct a SMAA-VIKOR model for inverse weight space analysis on the situation that the initial CW information is completely unknown. In the second stage of decision making, the decision makers are allowed to express their fuzzy preferences based on the information provided in the first stage by coupling the intuitionistic fuzzy analytic hierarchy process (IFAHP) into the SMAA-VIKOR model to obtain the final scheme. The wind-photovoltaic-hydropower system in Yalong River Basin is selected as the case study. Results show that among the 12 alternative schemes, scheme A2 is ranked first with the probability of 0.69 in the first stage while the probability of taking the first ranking in the second stage is increased to 1. More explicit ranking results are obtained based on the results of the first stage, which effectively enhances the confidence of decision makers and improves decision making efficiency.

Developing a Risk Management Framework for Agricultural Water Systems Using Fuzzy Dynamic Bayesian Networks and Decision-Making Models

AbstractGiven the various natural and human-caused hazards that threaten the agricultural water distribution process from the main source to farms, establishing a framework to analyze these risks is crucial. This study aims to develop an intelligent risk management framework to help stakeholders devise long-term and sustainable solutions for managing agricultural water systems. First, we developed a Fuzzy Dynamic Bayesian Network (FDBN) model for multi-hazard risk assessment, taking into account the temporal causal interactions between parameters and incorporating fuzzy theory. Next, we defined several risk management scenarios across structural, non-structural, automated control, and integrated methods. These scenarios were implemented in the FDBN model to mitigate the risks associated with the system. Various economic, social, environmental, and technical criteria were considered, and scenarios were ranked using the WASPAS, TOPSIS, and MultiMoora methods. The Copeland approach was used to combine the ranking results. The results showed that automated scenarios, specifically Model Predictive Control (MPC) and Proportional-Integral (PI) controllers, could reduce the system's risk by 11.4% and 9.8%, respectively, and were ranked the highest. The findings of this study and the proposed framework can assist operators in the sustainable planning and management of water systems in light of anticipated threats.

Analysis and evaluation of factors affecting the motivation of construction workers

The characteristics of the construction works are the participation of many fields and many jobs with different requirements. Therefore, in construction projects, even when applying science, technology and automation, the construction workers also play an important role. The construction workers are the main force and directly perform the tasks of creating construction products. For desiring to improve the labor productivity in the construction industry, research on the construction workers is focused increasingly. This paper learns about the factors which affect the motivation of the construction workers. Analytical Hierarchy Process (AHP) method is used to analyze and evaluate the affecting factors the work motivation of the construction workers. The ranking results of the factors show that the factors which are from the employer, have a strong impact on their work motivation. These factors are such as salary, bonus and welfare, labor management method, work discipline, regular job guarantee. They are factors with high weight and making decisive to the work motivation of the construction workers. Meanwhile, factors which are from employees, have less impact on their work motivation. This result is consistent with current reality.

The Situation of Cross-Border Transportation of Goods through the 2nd Friendship Bridge Savannakhet-Mukdahan

This study is about the situation of cross-border transportation of goods through the 2nd Friendship Bridge Savannakhet-Mukdahan. The purpose of the study is to 1) study the situation of development of transportation and logistics in the friendship bridge, 2) The condition of transporting goods through the friendship bridge, and 3) Difficulties of entrepreneurs using the 2nd friendship bridge service, the population represented in the study was the driver of a truck that passed through the Savannakhet-Mukdahan International Checkpoint, Specific types of imported goods a total of 138 people were identified. The statistics used for data analysis is descriptive statistics to find frequencies and percentages. The results of the study show that the world bank has determined the logistics performance index: LPI and published the results of the comparative efficiency ranking of 160 countries. In 2018, the Lao PDR was ranked 82nd in the world with a score of 2,8 and in which ranked 69th of 160 counties in terms of the ability to trach and trace consignments with an average score of 2,9. Laos can now export and distribute goods to Thailand with other countries in Asia and Europe: China, Hungary, Poland, Belgium, the Netherlands, Singapore, Japan, Italy and France using Road NO 12 and Route 9. This is an important strategic route due to the ability to transport goods to countries in the region, which is a wholesale market and a channel to import cheap machinery, equipment, or inputs, making locally produced goods cheaper, which will increase domestic competitiveness and increase domestic competitiveness. Exports and imports across the 2nd Friendship Bridge, a five-year period from 2016-2020, see a year-on-year trade deficit due to the need to use agricultural inputs to boost agricultural production and boost agricultural exports in the future. The problems in transportation of goods in practice, the country has not yet been able to take full advantage of cross-border transport via the 2nd Friendship Bridge. Due to climate change, which is a barrier to delayed transportation, the overall infrastructure is still incomplete as there are still some areas of the road that need to be constructed and improved, along with the problems and obstacles of transport rules and regulations, such as: Problems with one-stop inspection, difficulties in customs clearance procedures and convenience of transport officials.

Effect of Direct and Water-Mediated Interactions on the Identification of Hotspots in Biomolecular Complexes with Multiple Subsystems.

Identification of important residues in biochemical complexes is often a crucial step for many problems in molecular biology and biochemistry. A method is proposed to identify hotspots in biomolecular complexes based on a new metric, derived from networks representing molecular subunits (residues, bridging solvent molecules, ligands etc.) connected by interactions. A singular value decomposition of the weighted adjacency matrix is used to construct a scalar rank for each subunit that reflects its importance in the residue interaction network. This metric is called the singular value centrality. In addition, a new formalism is proposed to account for water-mediated interactions in the ranking of residues. Interactions for a residue network can be provided by various computational methods. In this work interactions are obtained from full quantum-mechanical calculations of protein-protein complexes using the fragment molecular orbital method. The ranking results are shown to be in good agreement with earlier computational and experimental studies. The developed method can be used to gain a deeper insight into the role of subunits in complex biomolecular systems.

Stability of Resistance of Maize to Ear Rots (Fusarium graminearum, F. verticillioides and Aspergillus flavus) and Their Resistance to Toxin Contamination and Conclusions for Variety Registration.

All major ear rots (F. graminearum, F. verticillioides, and Aspergillus flavus) and their toxins are present in maize of preharvest origin in Hungary. Resistance can be an important tool in reducing the infection and toxin contamination from these rots in maize. Previous results identified resistance differences in maize hybrids that were suitable for use in evaluating their risk from toxigenic fungi and their toxins. During the tests, two methodical improvements were achieved: the use of three isolates of the fungus secured and a more precise estimation of resistance to ear rots and their resistance to toxin accumulation or overproduction. The improvement in sampling and the tests of subsamples made the evaluation for the statistics much more exact. This way, we were able to reduce the Within value, providing a statistically more reliable method of evaluation. Earlier data had confirmed that toxin contamination could not be predicted well from visual ear rot severity data. Contradictory results for hybrid ranking were often identified between isolates. The resistance to disease and toxin contamination is not generally valid. The new suggested methodology compares the performance of hybrids in a large number of epidemic situations to identify adaptable hybrids that can respond to diverse conditions; therefore, the stability of resistance and toxin response is decisive information to evaluate risk analyses. The increased number of disease toxin data allowed for lower LSD 5% values for toxins, a much finer analysis of toxin overproduction and underproduction, and a wider database for stability analyses. This way, we obtained important additional separated information about resistance to accumulation of toxins and about maize resistance to these pathogens that is suitable to provide much more reliable testing than was possible until now. Globally, about 50-100 million metric tons can be saved by excluding susceptible hybrids from commercial production.

Artificial intelligence-based expert weighted quantum picture fuzzy rough sets and recommendation system for metaverse investment decision-making priorities

There should be some improvements to increase the performance of Metaverse investments. However, businesses need to focus on the most important actions to provide cost effectiveness in this process. In summary, a new study is needed in which a priority analysis is made for the performance indicators of Metaverse investments. Accordingly, this study aims to evaluate the main determinants of the performance of the metaverse investments. Within this context, a novel model is created that has four different stages. The first stage is related to the prioritizing the experts with artificial intelligence-based decision-making method. Secondly, missing evaluations are estimated by expert recommendation system. Thirdly, the criteria are weighted with Quantum picture fuzzy rough sets-based (QPFR) M-Step-wise Weight Assessment Ratio Analysis (SWARA). Finally, investment decision-making priorities are ranked by QPFR VIKOR (Vlse Kriterijumska Optimizacija Kompromisno Resenje). The main contribution of this study is the integration of the artificial intelligence methodology to the fuzzy decision-making approach for the purpose of computing the weights of the decision makers. Owing to this condition, the evaluations of these people are examined according to their qualifications. This situation has a positive contribution to make more effective evaluations. Organizational effectiveness is found to be the most important factor in improving the performance of metaverse investments. Similarly, it is also identified that it is important for businesses to ensure technological improvements in the development of Metaverse investments. On the other side, the ranking results indicate that regulatory framework is the most critical alternative in this regard.

A three-stage evaluation model for biomass co-firing combined with carbon capture and storage (in oil wells) retrofit in coal-fired power plants based on multi-criteria decision-making: An example from Hebei Province, China

Coal-fired power plants can be retrofitted by biomass co-firing combined with CO2 capture and storage, reducing carbon emissions significantly. Site selection is an important task for the entire project, but little research has been done on this. Therefore, this paper proposed a three-level evaluation model. Firstly, alternative power plants and carbon sequestration sites (set oil wells as an example) were screened by feasibility evaluations. Secondly, source-sink pairs were performed based on the shortest distance between power plants and oil wells. Finally, source-sink pairs were evaluated with suitability evaluation. Entropy weight method (EWM) and Analytic hierarchy process (AHP) were used to calculate objective and subjective weights, respectively. Lagrangian optimization method was used to combine the two weights. The ranking results of alternative source-sink pairs were obtained through Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) improved by combined weight. An example of in-service power plants in Hebei Province was conducted. Engineering geology and reservoir characteristics were the most important considerations. D5 (source: Langfang thermal power plant, sink: Bieguzhuang - Jing 11 oil well) was the best choice of the five given candidates. The results of the study provided an evaluation framework for governments and investors in retrofitting power plants.

High-temperature heat pump working fluid development based on molecular design and the group contribution method

High-temperature heat pumps are widely recognized as a critical approach for decarbonizing energy-intensive industries, and their performance is significantly influenced by the working fluids. However, conventional working fluid selection methods primarily focus on existing fluids, limiting the selection scale. Therefore, a group-contribution-based computer-aided molecular design method has been proposed to design and select working fluids for high-temperature heat pumps on a large scale, comprehensively considering environmental impact, safety, operating reliability and performance. A total of 9771 working fluids were generated by the computer-aided process based on the backtracking searching algorithm. Under different working conditions, 208, 232, 243, 228, and 208 working fluids have passed the multi-level screening process based on the group contribution method, respectively, with an overall passing rate below 2.49 %. The H(C)FOs among the top 50 working fluids, ranked by the coefficient of performance, were further analyzed. Statistically, 92.4 % and 98.73 % of the total top 50 working fluids had their Tbr and Tc values within the ranges of 0.640–0.675 and 432–480 K, respectively. Among the 18 HCFOs within the top 50 list, 83.3 % were non-existent. R1251zd and R1344zf demonstrated the best ranking results, while R1242zf, R1233yf, and R1233xf exhibited superior working condition flexibility. As an existing HCFO, R1251yd outperformed other working fluids. Of the 35 HFOs in the top 50 list, 88.57 % were non-existent, and R1381myz, R1371my and R1381mzz showed both exceptional performance and superior working condition adaptability. In conclusion, there remain promising prospects and a notable necessity for the new H(C)FO development, and guidance can be expected from this study in this regard.