Ranking Order Research Articles

Factors affecting birth weight and stillbirth in sows housed in a tropical environment.

This study analysed data from a commercial swine herd in Thailand equipped with a free-farrowing housing system, comprising 17,196 piglets from 1318 litters, to explore the impact of sow and litter characteristics on the piglet birth weight and the incidence of stillbirth. The piglets were classified into four groups based on the total number of piglets born per litter (TB): ≤9 (n = 1434), 10-12 (n = 3232), 13-15 (n = 6537) and ≥16 (n = 5993). Sows were classified into four groups based on parity number: 1, 2-4, 5-7 and ≥8. The piglets were categorized into quartiles based on their birth order ranking: Q1 (n = 4786), Q2 (n = 4143), Q3 (n = 3808) and Q4 (n = 4456). Piglet birth weight was individually measured before colostrum ingestion. On average, TB, the number of live-born piglets and the incidence of stillbirth were 13.1 ± 3.7, 11.5 ± 3.8 and 6.3%, respectively. Among these litters, 26.6% had TB numbers ≥16. The average piglet birth weight was 1.37 ± 0.36 kg, with 18.3% of piglets weighing ≤1.0 kg at birth. Piglet birth weight was influenced by birth order ranking, as Q4 piglets were found to be heavier than piglets born in Q1-Q3 (p < .001). Moreover, the percentage of piglets with a birth weight of ≤1.0 kg increased from 5.9% in litters with TB of ≤9-25.3% in litters with TB of ≥16 (p < .001). Additionally, primiparous sows had lower piglet birth weights compared to sows with parity numbers 2-4, 5-7 and ≥8 (p < .001). Piglets born in the fourth quartile (Q4) had a higher risk of stillbirth compared to those born in the first (Q1), second (Q2) and third (Q3) quartiles (12.5% vs. 2.2%, 4.1% and 6.6%, respectively, p < .001). The incidence of stillbirth in litters with TB ≥16 was also higher than that in litters with TB ≤9 and 10-12 (p < .05). Furthermore, sows with parity numbers ≥8 had a higher incidence of stillbirth (9.7%) compared to primiparous sows (4.0%, p < .001), sows with 2-4 parity (5.2%, p < .001) and sows with 5-7 parity (7.6%, p = .003). In summary, a high incidence of stillbirth was found in piglets born in the last quartile of litters, in litters with >16 piglets and for sows with parity numbers ≥8. Piglets born in the last quartile of litters were heavier than those born in the first to third quartiles.

A perspective study for the assessment of field robots in agriculture: An enhanced fuzzy MADM approach

The industrial revolution led in the creation of robots capable of performing complicated tasks in a range of industries. Robotic agrifarming is an innovative smart farming technology that can improve food safety and crop yield and address the global labour crisis. Agribots are used to undertake field tasks ranging from seeding through harvesting, which improve soil quality and guarantee long-term growth. As a result, the development of a decision-support system for determining the most appropriate robot for an agricultural operation is necessary. This study develops a T2IF-based integrated decision system for evaluating the situation at hand. The performance of five field robots is assessed based on technical, economic, social, political, and environmental aspects using the integrated BWM-MULTIMOORA methodology. The advantage of the developed ranking technique is that it follows Borda’s rule for prioritizing the alternatives rather than dominance theory, where both the utility and the subordinate ranking order of methods are considered. In addition, a comparative and sensitivity investigation is carried out to examine the stability of the results.

OR27-06 Identification And Functional Analyses Of Therapeutic Targets For Testicular Germ Cell Tumors

Abstract Disclosure: K. Horie: None. S. Kitayama: None. K. Ikeda: None. T. Suzuki: None. S. Kawakami: None. S. Inoue: None. Testicular germ cell tumor (TGCT) is the most common cancer among adolescent and young adult males. While cisplatin is a key drug that efficiently reduces TGCT growth, the acquisition of drug resistance is often life-threatening. To elucidate molecular mechanisms underlying the pathophysiology of TGCT, we established patient-derived TGCT spheroid cultures from nonseminomatous tumors, in which cancer stem cell population can be preferentially enriched. The TGCT spheroid cultures can be applied to the generation of in vivo xenograft models, which successfully recapitulated the pathological features of patient tumors with microenvironment. Transcriptomic analysis revealed that hypoxia-related genes were upregulated in TGCT spheroid cultures, and HIF1α inhibitor 2-methoxyestradiol could substantially repress the in vivo tumor formation. We further generated cisplatin-resistant TGCT models from embryonic cancer patient-derived spheroids and cell line, and found that transcription-related gene high mobility group nucleosome binding domain 5 (HMGN5) and meiosis-related gene testis-expressed 11 (TEX11) were remarkably upregulated in cisplatin-resistant TGCT cells compared with their parental cells. Either HMGN5 or TEX11 knockdown could reduce the viability of cisplatin-resistant TGCT cells in the presence of cisplatin. Of note, TEX11 knockdown increased cleaved PARP1 protein expression and the percentage of double-strand break marker γH2AX-positive cells in cisplatin-resistant TGCT cells. We demonstrated that siRNA-mediated TEX11 inhibition is a promising therapeutic option for cisplatin-resistant TGCT as we showed the efficacy in in vivo xenograft models generated from cisplatin-resistant patient-derived TGCT spheroids. Furthermore, chromatin immunoprecipitation sequencing for histone H3K27 acetylation status in the TGCT spheroid cultures revealed that the enrichment of “super-enhancers”, identified by the Rank Ordering of Super-enhancers algorithm [http://younglab.wi.mit.edu/super_enhancer_code.html], was well observed at chromosome 12p, where copy number gains are often observed in the tumors, and genes in the vicinity of super-enhancers identified in patient-derived TGCT models can be potential diagnostic and therapeutic targets for the advanced disease. Overall, the present study provides useful insights into the biology and chemoresistance of TGCT. Presentation: Saturday, June 17, 2023

Material identification and heavy metal characteristics of plastic packaging bags used in Chinese express delivery

With the rapid development of China’s express delivery services, environmental concerns have increased owing to the use of plastic packaging bags (PPBs) which have a heavy metal (HM) content due to the incorporation of scrap plastics in the production process. We sourced a variety of PPBs from different express delivery parcels, identified the materials in the samples using Fourier-transform infrared spectrometry, and conducted HM analysis using inductively coupled plasma mass spectrometry. The results demonstrated that the main material type in the PPBs was polyethylene. The results showed that the potential health concerns were posed by the four HMs (nickel [Ni], copper [Cu], zinc [Zn], and arsenic [As]) presented in the PPB samples. The mean concentrations of HMs in the PPBs were ranked as follows (presented in unit mg kg−1): Zn (120.42 ± 85.15)&amp;gt; Cu (45.21 ± 56.55) &amp;gt; lead [Pb] (6.43 ± 6.57) &amp;gt; Cr (6.03 ± 6.82) &amp;gt; Ni (2.13 ± 2.14) &amp;gt; As (0.19 ± 0.15) &amp;gt; mercury [Hg] (0.17 ± 0.71) &amp;gt; cadmium [Cd] (0.14 ± 0.20). HM content varied according to sample type, with a ranking order of Rm &amp;gt; Pm &amp;gt; Bm, corresponding to the degree of environmental and health risk. For Rm samples, high levels of low-toxicity HMs, such as Zn and Cu were detected, with respective maximum values of 365.9 and 184.2 mg kg−1; furthermore, the levels of high-toxicity HMs (i.e., Cd and Hg) exceeded the standard values set by the express delivery sector (0.5 mg kg−1). Thus, more attention should be paid to the effective management of PPBs for polyethylene-based Rm types, such as the limitation of Zn, Cu, Cd, and Hg. This study provides baseline data regarding HM-incorporating PPBs for stakeholders and is expected to support the formulation of relevant products for use in greener packaging policy and, thus, contributes to the re-assessment of China’s “plastic ban” policy.

Prioritizing the attributes of sustainable banking performance

PurposeGrowing concern over sustainability adoption has presented an array of challenges to businesses. While vital to an economy's success, banking is not immune to societal, environmental and economic consequences of business practices. The study has examined the sustainable performance of banking institutions on the suggested multidimensional framework comprising economic, environmental, social, governance and financial dimensions and 52 sustainability indicators. The study benchmarks the significant performance indicators of leading banks indispensable to sustainable banking performance. The findings attempt to address research questions concerning the extent of sustainable banking performance, ranking the sustainability dimensions and indicators and standardizing sustainability adoption metrics.Design/methodology/approachTo determine the responsiveness of the banking industry to sustainability dimensions, content analysis was conducted using NVivo software for the year 2021–2022. Furthermore, a hybrid multicriteria decision-making (MCDM) approach is used by integrating entropy, the technique for order preference by similarity to ideal solution (TOPSIS) and VlseKriterijumska Optimizacija KOmpromisno Resenje (VIKOR) to provide relative weights to performance indicators and prioritize banks based on their sustainable performance. Sensitivity analysis is used to ensure the robustness of results.FindingsIn the context of the Indian banking industry, the pattern of sustainability reporting is inconsistent and concentrated on addressing environmental and social concerns. The results of the entropy methodology prioritized “Environmental” sustainability over other selected dimensions while “Financial” dimension has been assigned the least priority in the ranking order. The significant sustainable performance indicators delineated in this study should be used as standards to ensure the accountability and credibility of the sustainable banking industry. Additionally, the research findings will provide valuable inputs to policymakers and regulators to assure better contribution of the banking sector in meeting sustainability goals.Originality/valueConsidering the paucity of studies on sustainable banking performance, this study makes two significant contributions to the literature. First, the suggested multidimensional disclosure model integrating financial and nonfinancial indicators would facilitate banking institutions in addressing the five aspects of sustainability. As one of the first studies in the context of the Indian banking industry, the findings would pave the way for better diffusion of sustainability practices. Second, the inclusion of MCDM techniques prioritizes the significance of sustainability indicators and benchmarks the performance of leading banks to achieve better profits and more substantial growth.

Optimal estimators of the population mean of a skewed distribution using auxiliary variables in median ranked-set sampling

ABSTRACT In an asymmetric population, individuals are concentrated toward one tail of the distribution. An estimator of the population mean in this asymmetric case is constructed on the basis of median ranked-set sampling, that is, the population is divided into subsets of equal size and the intersections of these sets depend on the chosen order of ranking according to a known auxiliary variable. Ranking individuals according to this auxiliary variable should approximate their ranking with respect to the unknown variable of interest. This procedure is a cost-effective way of selecting the sample when the variable of interest is unknown. To do this, the auxiliary variable must be at least weakly correlated with the variable of interest. The proposed estimator extends that constructed with extreme ranked-set sampling, whose principle is to divide the population into subsets whose intersections depend on the extreme values of the auxiliary variable. The mean square error of the estimator is expressed analytically. A simulation allows for comparing the proposed estimator with estimators based on simple random sampling and with those based on sampling sets of extreme values. A simulation shows that when the response variable is correlated with both auxiliary variables, even if these correlations are weak, around 0.5 in absolute value, then the mean square error of the proposed estimator is at least 175% lower than the mean square error of estimators based either on simple random or on extreme ranked-set samplings. A first application focuses on household incomes in the Iranian provinces of Fars and Khuzestan in 2022, first with the single gross income, which is the total income that an individual or household earns before tax as auxiliary variable and then with the two auxiliary variables of total gross household income and wages paid year-round to heads of households through the banking network. In this application, the mean square error of the proposed estimator with median ranked-set sampling is at least 60% lower than that obtained with simple random and extreme ranked-set samplings. In the application of the physical preparation score with runners’ track records as an auxiliary variable concerning 160 Iranian athletes in 2022 with sample sizes of 6, 8, 10, 25, and 30, the mean square error of the proposed estimator with median ranked-set sampling is at least 50% lower than that obtained with simple random and extreme ranked-set samplings. In the third application of the COVID-19 mean mortality rate in 2022 in the USA, Iran, Turkey, and Germany, with sample sizes of 6, 8, 10, 25, and 30, estimations of the mean mortality rate are based on new cases. In each of the four countries, the mean square error of the proposed estimator under median ranked-set sampling is at least 60% lower than that obtained with simple random and extreme ranked-set samplings.

Assessment of human health hazard associated with heavy metal accumulation in popular freshwater, coastal and marine fishes from south-west region, Bangladesh

An analysis was conducted on both freshwater, coastal and marine fish species to evaluate the concentrations of heavy metals, with the aim of assessing their levels and examining the potential health risks for humans linked to the consumption of contaminated fish. This study estimate concentrations of Cr, Fe, Cu, As, Cd and Pb in 60 individuals belonging to 20 species (10 species for freshwater and another 10 for coastal and marine fishes) by using Atomic Absorption Spectrophotometer. Metal concentrations of Cr, Fe, and Pb in freshwater fishes and Cr, Fe, As, and Pb in marine fishes were exceeded the maximum allowable concentration (MAC). The Estimated Daily Intake (EDI), Average Pollution Load Index (APLI), Target Hazard Quotient (THQ), Hazard Index (HI) and Target Cancer Risk (TCR) of heavy metals were determined for the assessment of human health risk. Ranking order of the values of EDI for freshwater fishes, coastal and marine fishes were Cd > Fe > Pb > Cr > Cu > As and Cd > Fe > Pb > Cr > As > Cu. Highest APLI value of 8.14 (Puntius ticto) that is seriously polluted and 3.003 observed in Otolichthoides pama in freshwater and marine fishes, respectively. THQPb and THQAs for all the fish species were exceed the safe limit (THQ>1) suggesting potential health risk to consumers. The hazard index for both the fish samples were exceeded the USEPA (United States Environmental Protection Agency) permitted risk level (HI > 1). The target carcinogenic risk value for Cr and As were crossed the USEPA standard limit (TCR> 1E-04), which denotes that continuous consumption of studied fishes may cause health risk to the consumers. On the other hand, sensitivity analysis of freshwater, coastal and marine fishes indicates that all the metal concentrations were responsible factor for health risk.

An intuitionistic fuzzy-based model for performance evaluation of EcoPorts

The EcoPort performance level serves as a basic indicator to determine the environmental status of a port and its achievement of certification standards. The European Sea Ports Organization has defined ten essential EcoPort criteria that contribute significantly to the assessment of EcoPort performance. The primary motivation for this study is to determine the importance of the criteria from the perspective of academics and chief officers, as well as to evaluate the EcoPort performance of six port authorities that comply with three basic certification standards for environmental management systems. The research methodology and application are conducted in four phases. In the first phase, experts, criteria, and alternative ports are identified. In the second phase, the importance levels of experts are determined using neutrosophic sets, while the criteria are weighted using the intuitionistic fuzzy weighting averaging operator. In the third phase, the alternative ranking order method accounting for two-step normalization (AROMAN) based on the intuitionistic fuzzy sets is introduced to evaluate the EcoPort performance of the ports. In the fourth phase, the results are supported by sensitivity analysis scenarios. The research results identified air pollution as the most critical criterion, while the Valencia Port Authority secured the highest rank in terms of EcoPort performance. Ultimately, this research contributes to the literature by developing and applying the new IF-AROMAN method for EcoPort performance evaluation.

Application of Artificial Neural Networks to Predict Genotypic Values of Soybean Derived from Wide and Restricted Crosses for Relative Maturity Groups

The primary objective of soybean-breeding programs is to develop cultivars that offer both high grain yield and a maturity cycle tailored to the specific soil and climatic conditions of their cultivation. Therefore, predicting the genetic value is essential for selecting and advancing promising genotypes. Among the various analytical approaches available, deep machine learning emerges as a promising choice due to its capability to predict the genetic component of phenotypes assessed under field conditions, thereby enhancing the precision of breeding decisions. This study aimed to determine the efficiency of artificial neural networks (ANNs) in predicting the genetic values of soybean genotypes belonging to populations derived from crosses between parents of different relative maturity groups (RMGs). We characterized populations with broad and restricted genetic bases for RMG traits. Data from three soybean populations, evaluated over three different agricultural years, were used. Genetic values were predicted using the multilayer perceptron (MLP) artificial neural network and compared to those obtained using the best unbiased linear prediction from variance components using restricted maximum likelihood (RR-BLUP). The MLP neural network efficiently predicted genetic values for the relative maturity group trait for genotypes belonging to populations of broad and restricted crosses, with an R2 of 0.999 and root-mean-square error (RMSE) of 0.241, and for grain yield, there was an R2 of 0.999 and an RMSE of 0.076. While the percentage of coincident superior genotypes remained relatively consistent, a significant difference was observed in their ranking order. The genetic gain with selection estimated using MLP was higher by 30–110% compared to RR-BLUP for the relative maturity group trait and 90–500% for grain yield. Artificial neural networks (ANNs) showed higher efficiency than RR-BLUP in predicting the genetic values of the soybean population. Local selection at intermediate latitudes is conducive to developing lines adaptable for regions at higher and lower latitudes.

A new correlation-based measure on Fermatean fuzzy applied on multi-criteria decision making for electric vehicle selection

Purpose:Since the pollution from the transportation sector has become intolerable in urban areas, many authorities are beginning to ban vehicles powered by fossil fuels. In this scenario, electric vehicle (EV) mobility is the only viable option for the transportation sector. Despite the fact that EVs offer a number of advantages over fuel-powered vehicles, it can be challenging for a purchaser to select a model due to its technical features and their lack of knowledge about EVs. This article aims to assist purchasers of EV by constructing an MCDM problem with multiple features as criteria and EV models as alternatives. Design/methodology/approach:The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method ranks alternatives to a multi-criteria decision-making (MCDM) problem based on the distance between the positive and negative ideal solutions. Even though the TOPSIS methodology has several fuzzy enhancements, it is difficult to select an appropriate distance measure in an uncertain context. To replace the distance measure, we introduced an improved correlation coefficient measure in fermatean fuzzy contexts by considering the hesitancy function of fermatean fuzzy sets (FFS). First, we establish the proposed correlation coefficient by defining its main characteristics, including the weighted correlation coefficient, type I and type II closeness measures, and the weighted index coefficient. Then, the TOPSIS method is extended using the proposed correlation measure in a fermatean fuzzy environment. There is an algorithm that presents the proposed fermatean fuzzy TOPSIS (FF-TOPSIS) method. The proposed approach is used to determine the ranking order of the EVs. To assist the decision-maker, the sensitivity of the proposed technique is investigated by varying its parameters. Finally, the consistency of the proposed FF-TOPSIS is evaluated by comparing it to existing crisp as well as fuzzy MCDM methods. Findings:By implementing the FF-TOPSIS approach, it has been determined that the fifth EV model is the most favorable option, while the first model is deemed the least desirable choice. The primary factors to evaluate an EV are its range and its price, rather than battery life, storage, and charging time. Originality:This article presents a novel correlation coefficient for FFSs, which incorporates the hesitancy function into the calculation for the first time. The correlation-based FF-TOPSIS method is a novel extension of the TOPSIS technique in fermatean fuzzy systems.

A new look at the left-handed advantage in baseball

ABSTRACT Using 120 years of Major League baseball data, we re-examine the apparent and widely presumed performance advantage of left-handers. We verify the previously known phenomenon that left-handers hit better than right-handers, but we show this advantage exists only because they face pitchers with the opposite hand far more often; they are not innately more talented. We find, however, that left-handed pitchers are indeed better than right-handed pitchers, and we show the perhaps unusual result that a player’s throwing hand plays a role in the quality of his batting. We further show that switch-hitting is largely ineffective, and we explore a symmetry puzzle in which batters of a given hand facing pitchers of a given hand do not bat similarly to batters of the other hand facing pitchers of the other hand. Taken together, we show that the handedness advantage in baseball is about more than the batting hand. Three handedness factors matter: the matchup advantage, the pitcher quality, and the natural handedness of the batter. Of the 40,320 possible rank orderings of the eight combinations of these three handedness factors, our theory predicts one specific order and finds that it indeed holds across the 1.8 million matchups of 15,000 players.

P08.08.A EXPLORATION OF SURVIVORSHIP ISSUES AND QUALITY OF LIFE IN A COHORT OF IDH MUTATED HIGH GRADE GLIOMA 5 YEARS FOLLOWING DEFINITIVE TREATMENT

Abstract BACKGROUND Approximately 2000 people are diagnosed with brain cancer in Australia each year. Compared to glioblastoma patients, the IDH-mutated high-grade glioma (IDHmHGG) is a more favourable diagnosis; better prognosis and survival outcomes. Coupled with improved treatments, the IDHmHGG cohort is living longer which means treatment side effects (eg. fatigue, cognitive deficits, physical limitations etc.) can significantly impact their quality of life in the survivorship years. Hence there is a need to identify and determine the survivorship issues most pertinent for the IDHmHGG cohort. MATERIAL AND METHODS A mixed- METHODS cross-sectional study was conducted to investigate the relationship between socio-demographic, clinical factors and quality of life in the IDHmHGG cohort. Qualitative data collection consisted of a neuro-oncology clinician focus group (n=11) regarding survivorship issues and was contrasted with semi-structured patient interviews (n=27). Quantitative data collection (n=41) involved socio-demographic questions, the Functional Assessment of Cancer Therapy - Brain (FACT-Br) tool and The Hospital Anxiety and Depression Scale (HADS). RESULTS The results indicate that marital status, employment status, and treatment complications are strongly associated with quality of life in IDHmHGG patients. Being single and unemployed is associated with lower quality of life (p&amp;lt;0.05). Similarly, our findings show that psychological distress, specifically the presence of anxiety and depressive symptoms, is a major factor that can negatively impact the QoL in an IDHmHGG cohort. The order of ranking between clinician and patient identified issues only matched for physical limitations (ranked 3rd). Although not matched in ranking, both groups identified cognitive function, employment, social function, isolation and mental health as commonly experienced in the survivorship years. CONCLUSION This study highlights the importance of considering socio-demographic and clinical factors in the care and support of brain cancer patients. The most important finding of this study was that being single and unemployed negatively predicted QoL. Additionally, it is notable that our results show a discrepancy between clinician identified issues and patient identified issues. This finding suggests that in order to deliver patient-centred and meaningful care, there is a need to incorporate a more patient focused perspective. Future survivorship models of care for the IDHmHGG cohort should be co-designed with patients.

Life Cycle Assessment of a novel functionally integrated e-axle compared with powertrains for electric and conventional passenger cars

Road transport significantly contributes to climate change and air pollution. Efforts to reduce transport sector emissions include deploying battery electric vehicles and designing their powertrains for improved performance. The European H2020 funded Functionally Integrated E-axle Ready for Mass Market Third GENeration Electric Vehicles (FITGEN) developed a novel functionally integrated e-axle (the FITGEN e-axle) for electric vehicles. This paper presents the environmental performance of the FITGEN e-axle. Using the Life Cycle Assessment (LCA) methodology, the study compares the FITGEN e-axle to the 2018 State-of-the-Art (SotA) e-drive, besides diesel and petrol-fuelled powertrains. The FITGEN powertrain reduces climate impacts by 10 % and energy consumption by 17 %, compared with the 2018 SotA e-drive due to the efficiency improvements and components integration. It also outperforms the 2018 SotA e-drive in several other impact categories, such as human toxicity (4–10 %), land use (19 %), and mineral depletion (8 %). However, the FITGEN powertrain only outperforms diesel and petrol powertrains in climate change and fossil resource scarcity impact categories. These findings imply that more efforts are required to improve the environmental profile of electric powertrains. Metal mining and production, especially for copper and aluminium, are critical for toxicity impacts. The sensitivity analysis demonstrates the robustness of the results, with no significant shift in their ranking order. The following aspects should be considered to improve the performance of electric powertrains from a life cycle perspective: improvement of components efficiency, reduced use of electronics and component integration, and use of low-carbon energy mix from their metal mining sites to production and use.

A fuzzy TOPSIS model for selecting digital technologies in circular supply chains

Several digital technologies are available to facilitate the transition toward a circular supply chain infrastructure. Small-Medium Enterprises (SMEs) should assess their readiness and measure their performance to select the most appropriate digital technology. This study explores how well-established digital technologies such as Cyber-Physical Systems (CPS), the Internet of Things (IoT), Cloud Manufacturing (CM), and Big Data Analytics (BDA) impact circular supply chain infrastructure in SMEs. Questionnaires have been distributed to collect employees’ preferences concerning the circular supply chain management criteria (profit, innovation, sustainability, and optimization). The responses have been organized into three clusters using Principal Component Analysis (PCA). A fuzzy Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) technique is adopted to evaluate these technologies since it constitutes a reliable managerial tool when vagueness impacts the smooth operation of the supply chain. Results indicate the ranking order of the investigated digital technologies (CPS>IoT>CM>BDA) as well as the circular benefits and the supply chain attributes imparted upon implementing these technologies. Such benefits and attributes are provided to assess the impact of these digital technologies on a circular economy. Lastly, the perspective of the selection process affected by other factors, such as the enterprise’s extroversion level and its internal structure, are discussed.

Large group decision-making considering multiple classifications for participators: a method based on preference information on multiple elements of alternatives

In large group decision-making, participators with different knowledge structures, backgrounds, and other characteristics are unlikely to accurately evaluate alternatives. For this, it is necessary to decompose alternatives into several elements, and consider the participators’ preferences for elements of alternatives and the multiple classifications for participators according to their characteristics. However, related studies are still scarce. The objective of this paper is to propose a multi-elemental large group decision-making method, in which the desirable alternative(s) are selected from a set of feasible alternatives according to the preference information on multiple elements of alternatives provided by participators from multiple subgroups, and multiple classifications for participators are considered. In the method, according to the strict preference ordering of elements provided by participators, the percentage distributions on preferences of each subgroup concerning each element are firstly presented under each classification for participators. Secondly, the decision weight of each subgroup is determined by three factors, i.e., the consensus of preferences provided by each subgroup, the organizer’s preference for each subgroup, and the number of participators in each subgroup. Then, the comprehensive preference concerning each element is determined by combing the preference information from multiple subgroups and the decision weights of multiple subgroups, the overall preference vector can be obtained under each classification, and the virtual alternatives are determined by normalizing the overall preference vector. Further, considering multiple classifications for participators, the overall dominant degrees of alternatives can be obtained by calculating the similarity degrees between each virtual alternative and each alternative, thus the ranking order of alternatives can be obtained based on the overall dominant degrees of alternatives. Finally, an example is given to confirm the feasibility of the proposed method. The results of the sensitivity and comparative analyses show that the proposed method is applicable and effective. The proposed method can further enrich and improve the theory and approach of large group decision-making with multiple elements considering multiple classifications for participators.

OPTIMIZING THE SPATIAL RESOLUTION OF MAMMOGRAPHY IMAGING QUALITY USING A CIRS-016A LINE PAIR GAUGE AND THE TAGUCHI METHODOLOGY

This study optimized spatial resolution of mammography imaging quality using a CIRS-016A commercial line gauge and the Taguchi methodology. The line gauge with a precise line pair from 5[Formula: see text]lp/mm to 20[Formula: see text]lp/mm was placed on top of triangular PMMA plates to simulate the female breast undergoing mammography. Five factors: target/filter, kVp, mAs, PMMA plate thickness, and compression force, were organized into 18 groups according to the Taguchi L[Formula: see text] orthogonal array. Tactically, the 18 various combinations of factors could provide similar confidence levels, as those following the full factorial combination in reality. Seven experienced radiology experts judged the 18 imaging qualities based on contrast, sharpness, and spatial resolution. Then the signal-to-noise ratio was calculated according to the “the larger, the better” ranking order. The optimal preset of mammography was verified from the unique fish bone plot and the follow-up analysis of variance (ANOVA) test. The optimal combination of factors was as follows: Rh/Ag as target/filter, 32[Formula: see text]kVp, 36[Formula: see text]mAs, a 45[Formula: see text]mm thick PMMA plate, and a 13[Formula: see text]daN compression force in routine diagnosis. The concurrent resolution of 6[Formula: see text]lp/mm or about a 0.09[Formula: see text]mm minimum detectable difference (MDD) was superior to 5[Formula: see text]lp/mm of the conventional preset or combinations of factors of either highest Avg or lowest std. Compared to other studies with various facilities, this was the finest resolution among the routine X-ray, cardiac X-ray or computed tomography (CT), and computed tomography angiography (CTA).

University’s recruitment process using Fermatean fuzzy Einstein prioritized aggregation operators

In comparison to intuitionistic fuzzy set (IFS) and Pythagorean fuzzy set (PFS), the Fermatean Fuzzy Set (FFS) is more efficacious in dealing ambiguous and imprecise data when making decisions. In this paper, we propose unique operations on Fermatean fuzzy information based on prioritized attributes, as well as Einstein’s operations based on adjusting the priority of characteristics in the Fermatean fuzzy environment. We use Einstein’s operations with prioritized attributes to propose new operations on Fermatean fuzzy numbers (FFNs), and then introduce basic aspects of these operations. Motivated by Einstein operations on FFNs, we develop Fermatean fuzzy Einstein prioritized arithmetic and geometric aggregation operators (AOs). In the first place, the concepts of a Fermatean fuzzy Einstein prioritized average (FFEPA), Fermatean fuzzy Einstein prioritized weighted average (FFEPWA), and Fermatean fuzzy Einstein prioritized ordered weighted average (FFEPOWA)-operators are introduced. Then, Fermatean fuzzy Einstein prioritized geometric (FFEPG) operator, Fermatean fuzzy Einstein prioritized weighted geometric (FFEPWG) operator, Fermatean fuzzy Einstein prioritized ordered weighted geometric (FFEPOWG) operator, and Fermatean fuzzy Einstein hybrid geometric (FFEHG) operator are given. We also go through some of the key characteristics of these operators. Moreover, using these operators, we establish algorithm for addressing a multiple attribute decision-making issue using Fermatean fuzzy data and attribute prioritizing. The case of university faculty selection is taken as a scenario to analyze and demonstrate the applicability of our suggested model. In addition, a comparison of the proposed and current operators is conducted, and the impact of attribute priority on the ranking order of alternatives is explored.

Soil Macropore and Hydraulic Conductivity Dynamics of Different Land Uses in the Dry–Hot Valley Region of China

Soil macropores and hydraulic conductivity are important indexes used to describe soil hydrology. In the dry-hot valley region of Southwest China, with its dramatic seasonal dry–wet rhythm, soil properties and hydraulic conductivity can reflect unique dynamics as determined by the interaction between land use and the seasonal dry–wet cycle. In this study, the soil macropore characteristics and hydraulic conductivity of five land uses (traditional corn, plum orchard, pine forest, grassland, and abandoned cropland) in a dry–hot valley region were quantified using X-ray computed tomography (CT) and a mini disk infiltrometer in the rainy season (July) and dry season (November), respectively. The results showed that the soil macropore indexes (soil macroporosity, mean diameter of macropores, connectivity, hydraulic radius and compactness) in the rainy season were, on average, 1.26 times higher than those in the dry season. Correspondingly, the hydraulic conductivity of different land uses in the rainy season was significantly higher than those in the dry season (2.10 times higher, on average). Correlation analysis and principal component analysis (PCA) indicated that the hydraulic conductivity was mainly determined by soil macropore parameters rather than by general soil properties, such as organic matter (OM) and bulk density (BD). The hydraulic conductivity for the five land uses followed the order of PF &gt; GL &gt; TC &gt; PO &gt; AC in both the rainy and the dry seasons. This ranking order reflects the protective effect of vegetation in reducing raindrop splash and soil crust formation processes. The above results can help guide soil water conservation and vegetation restoration in the dry-hot valley region of Southwest China.

Recruitment of TikTok Live Hosts for Selling Clothes Using the MOORA Method

Mistakes in recruiting Hosts for the business of selling clothes on TikTok live streaming can have a negative impact on business owners, because Host failure can cause the products offered not to sell to potential customers. To overcome this problem, a decision support system is needed as a support tool for clothing selling entrepreneurs in recruiting TikTok Live Hosts that are relevant and can contribute to the development of their business. In this study the authors used Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) for the TikTok Live Host recruitment process with 5 (five) alternatives and 5 assessment criteria in subjective decision making. The results of this study indicate that the alternative that gets the first ranking position and is recommended to be accepted as a TikTok Live Host is A5 (Rina) with a value of 0.2752. As for the ranking order of other alternatives, namely A1 (Janses) with a value of 0.2738 at rank 2, A3 (Aisyah) with a value of 0.2582 at rank 3, A4 (Kylen) with a value of 0.2460 at rank 4, and A2 (Delima) with a value of 0.2190 at rank 5.

Laboratory learning objectives: ranking objectives across the cognitive, psychomotor and affective domains within engineering

ABSTRACT The literature on laboratory objectives in engineering education research is scattered and inconsistent. Systematic literature reviews identified the need for better understanding. This paper ranks the laboratory learning objectives across the cognitive, psychomotor and affective domains to improve scaffolding. It provides an opportunity for reflection, a pathway to confirm assessment alignment, and opens future research areas. To accomplish this, the Laboratory Learning Objectives Measurement (LLOM) instrument is used to survey 160 academics from around the world representing 18 engineering disciplines. The results suggest that the collective ranking order does represent a framework that can be used broadly. However, for greater alignment with consensus thinking, discipline rankings should be used. The cognitive domain was deemed the most important. These results provide the community’s opinion and may not necessarily be best practice, providing an opportunity for reflection.