Search Results (18)

Drought severely threatens wheat production. Under drought conditions, root system architecture (DRSA)-related traits in common wheat significantly affect wheat production. In China, Zhoumai16 is a high-yield winter wheat variety in the Huang-Huai wheat region. It is suitable for high-fertilizer and high-water cultivation and has moderate drought tolerance. DK171 is a newly developed high-yield and stress-tolerant variety, with higher drought tolerance. Thus, identifying genetic loci associated with DRSA-related traits from DK171 and developing available molecular markers are of great importance for enhancing wheat stress tolerance breeding. In this study, DRSA-related traits, including the total root dry weight (DDRW), total root length (DTRL), total root area (DTRA), and the number of root tips (DNRT) under drought stress, were assessed using the hydroponic system in Zhoumai16/DK171 recombinant inbred lines (RIL) population. A total of five quantitative trait loci (QTL) for DRSA-related traits were identified, e.g., QDDRW.daas-1BL, QDTRS.daas-4AL, QDNRT.daas-4DS, QDTRL.daas-3AL, and QDDRW.daas-5D, and explained 6.1% to 18.9% of the phenotypic variances, respectively. Among these, QDTRS.daas-4AL and QDTRL.daas-3AL were consistent with previous reports, whereas the QDDRW.daas-1BL, QDNRT.daas-4DS, and QDDRW.daas-5D are novel. The favorable alleles of QDTRS.daas-4AL and QDNRT.daas-4DS were inherited from Zhoumai16, whereas the favorable alleles for QDDRW.daas-1BL, QDTRL.daas-3AL, and QDDRW.daas-5D were contributed by DK171. Furthermore, five kompetitive allele-specific PCR (KASP) markers, Kasp_1BL_DTRS (QDDRW.daas-1BL), Kasp_3AL_DTRS (QDTRL.daas-3AL), Kasp_4A_DTRS (QDTRA.daas-4A), Kasp_5D_DDRW (QDDRW.daas-5D), and Kasp_4D_DNRT (QDNRT.daas-4D), were developed and validated in a diverse panel with 108 wheat varieties mainly from China. Additionally, eight candidate genes related to plant hormone regulation, ABC transporters, and calcium-dependent lipid-binding domain proteins were identified. This study offers new loci, candidate genes, and available KASP markers for wheat drought tolerance breeding and facilitating progress in developing drought-tolerant wheat cultivars. Full article

Lipinski’s Rule of Five and Ghose filter are empirical guidelines for evaluating the drug-likeness of a compound, suggesting that orally active drugs typically fall within specific ranges for molecular descriptors such as hydrogen bond donors and acceptors, weight, and lipophilicity. We revisit these practices and offer a more analytical perspective using the Dominance-based Rough Set Approach (DRSA). By analyzing representative samples of drug and non-drug molecules and focusing on the same molecular descriptors, we derived decision rules capable of distinguishing between these two classes systematically and reproducibly. This way, we reduced human bias and enabled efficient knowledge extraction from available data. The performance of the DRSA model was rigorously validated against traditional rules and available machine learning (ML) approaches, showing a significant improvement over empirical rules while achieving comparable predictive accuracy to more complex ML methods. Our rules remain simple and interpretable while being characterized by high sensitivity and specificity. Full article

The handling of missing attribute values remains a challenging and problematic issue in data analysis. Imputation techniques are key procedures used to deal with missing attribute values. However, although these methods are widely used, they cause data bias. Rough set theory, a unique mathematical tool for decision making under uncertainty, overcomes this problem by properly adjusting the relationships. Rough sets are often preferred in both classification and sorting problems. The aim of sorting problems is to sort the objects in the decision table (DT) from best to worst and/or to select the best one. For this purpose, it is necessary to obtain a pairwise comparison table (PCT) from the DT. However, in the presence of missing values, the transformation from DT to PCT is not feasible because there are no ranking methods in the literature for sorting problems based on rough sets. To address this limitation, this paper presents a way to transform from DT to PCT and introduces a generalization of the relation belonging to the “do not care” type of missing values in the dominance-based rough set approach (DRSA) to the decision support tool jRank. We also adapted the DomLem algorithm to enable it to work in PCT with missing values. We applied our method step by step to a decision table with 11 objects and investigated the effect of missing values. The experimental results showed that our proposed approach captures the semantics of ‘do not care’ type missing values. Full article

This study uses fuzzy–rough analysis to investigate the influence of Environmental, Social, and Governance (ESG) ratings, along with critical financial and growth ratios, on the stock returns of blue-chip companies in Taiwan. The growing importance of ESG factors in investment decisions underscores the need to understand their impact on stock performance. By integrating the fuzzy–rough set theory, which accommodates uncertainty and imprecision in data, we analyze the complex relationships between ESG ratings, traditional financial metrics (such as ROE, return on equity), and stock returns. Our findings provide insights into how ESG considerations, alongside financial indicators, drive the returns of Taiwan’s blue-chip stocks. Three public-listed companies were evaluated using this approach, and the results are consistent with the actual stock performance. This research contributes to the field by offering a robust methodological approach to assess the nuanced effects of ESG factors on financial performance, thus aiding investors and management teams in making informed decisions. Full article

This research introduces a rule-based decision-making model to investigate corporate governance, which has garnered increasing attention within financial markets. However, the existing corporate governance model developed by the Security and Future Institute of Taiwan employs numerous indicators to assess listed stocks. The ultimate ranking hinges on the number of indicators a company meets, assuming independent relationships between these indicators, thereby failing to reveal contextual connections among them. This study proposes a hybrid rough set approach based on multiple rules induced from a decision table, aiming to overcome these constraints. Additionally, four sample companies from Taiwan undergo evaluation using this rule-based model, demonstrating consistent rankings with the official outcome. Moreover, the proposed approach offers a practical application for guiding improvement planning, providing a basis for determining improvement priorities. This research introduces a rule-based decision model comprising ten rules, revealing contextual relationships between indicators through if–then decision rules. This study, exemplified through a specific case, also provides insights into utilizing this model to strengthen corporate governance by identifying strategic improvement priorities. Full article

Because India has one of the world’s fastest-growing economies, the Indian banking sector is essential to the country’s reform. The approval of home loans to customers is one of the crucial tasks carried out by Indian banks. The risk of loan repayment outside of the agreed-upon time frame can be reduced by accurately estimating the customer’s loan need. The majority of earlier studies on the development of banking lacked a methodical approach to analyze qualitative data. Even though the traditional multivariate statistically based factor analysis approach is a great way to categories data in qualitative analysis, the technique cannot be used without any statistical presumptions and additional information about the data. This study handles the banking attributes related to home loans using the Dominance-based Rough Set Approach (D-RSA). In order to categorize the customer’s attributes, this study suggests using a preference-based “if … then” decision rule. This rule can aid decision makers in understanding the risk factors associated with loan factors for a financial organization. Full article

Understanding the reasons behind the decisions of complex intelligent systems is crucial in many domains, especially in healthcare. Local explanation models analyse a decision on a single instance, by using the responses of the system to the points in its neighbourhood to build a surrogate model. This work makes a comparative analysis of the local explanations provided by two rule-based explanation methods on RETIPROGRAM, a system based on a fuzzy random forest that analyses the health record of a diabetic person to assess his/her degree of risk of developing diabetic retinopathy. The analysed explanation methods are C-LORE-F (a variant of LORE that builds a decision tree) and DRSA (a method based on rough sets that builds a set of rules). The explored methods gave good results in several metrics, although there is room for improvement in the generation of counterfactual examples. Full article

The satisfaction of requirements and preferences of shippers is critical to enable the practicability of solutions that are derived from intermodal transportation routing problems. This study aims to propose a decision process to help shippers participate better in routing decisions. First, we considered shippers’ requests on transportation cost, timeliness, reliability, and flexibility to construct a multi-objective optimization model. Then, to solve the interactive optimization method that was proposed, NSGA-III was applied to obtain the Pareto front and dominance-based rough set approach to model the preference information. Finally, a case study was conducted and an expert was invited as decision-maker to demonstrate the applicability of the proposed model and the effectiveness of the interactive method for shippers. The results are expected to provide shippers with more rational transportation schemes and insights for the sustainable development of intermodal transportation. Full article

This study proposes an alternate data extraction method that combines three well-known feature selection methods for handling large and problematic datasets: the correlation-based feature selection (CFS), best first search (BFS), and dominance-based rough set approach (DRSA) methods. This study aims to enhance the classifier’s performance in decision analysis by eliminating uncorrelated and inconsistent data values. The proposed method, named CFS-DRSA, comprises several phases executed in sequence, with the main phases incorporating two crucial feature extraction tasks. Data reduction is first, which implements a CFS method with a BFS algorithm. Secondly, a data selection process applies a DRSA to generate the optimized dataset. Therefore, this study aims to solve the computational time complexity and increase the classification accuracy. Several datasets with various characteristics and volumes were used in the experimental process to evaluate the proposed method’s credibility. The method’s performance was validated using standard evaluation measures and benchmarked with other established methods such as deep learning (DL). Overall, the proposed work proved that it could assist the classifier in returning a significant result, with an accuracy rate of 82.1% for the neural network (NN) classifier, compared to the support vector machine (SVM), which returned 66.5% and 49.96% for DL. The one-way analysis of variance (ANOVA) statistical result indicates that the proposed method is an alternative extraction tool for those with difficulties acquiring expensive big data analysis tools and those who are new to the data analysis field. Full article

The DRSA method (dominance-based rough set approach) was used to create decision-making rules based on the results of physical examination and additional laboratory tests in the differential diagnosis of Kawasaki disease (KD), infectious mononucleosis and S. pyogenes pharyngitis in children. The study was conducted retrospectively. The search was based on the ICD-10 (International Classification of Diseases) codes of final diagnosis. Demographic and laboratory data from one Polish hospital (Poznan) were collected. Traditional statistical methods and the DRSA method were applied in data analysis. The algorithm formed 45 decision rules recognizing KD. The rules with the highest sensitivity (number of false negatives equals zero) were based on the presence of conjunctivitis and CRP (C-reactive Protein) ≥ 40.1 mg/L, thrombocytosis and ESR (Erythrocyte Sedimentation Rate) ≥ 77 mm/h; fair general condition and fever ≥ 5 days and rash; fair general condition and fever ≥ 5 days and conjunctivitis; fever ≥ 5 days and rash and CRP ≥ 7.05 mg/L. The DRSA analysis may be helpful in diagnosing KD at an early stage of the disease. It can be used even with a small amount of clinical or laboratory data. Full article

This paper presents the results of structure–activity relationship (SAR) studies of 140 3,3’-(α,ω-dioxaalkan)bis(1-alkylimidazolium) chlorides. In the SAR analysis, the dominance-based rough set approach (DRSA) was used. For analyzed compounds, minimum inhibitory concentration (MIC) against strains of Staphylococcus aureus and Pseudomonas aeruginosa was determined. In order to perform the SAR analysis, a tabular information system was formed, in which tested compounds were described by means of condition attributes, characterizing the structure (substructure parameters and molecular descriptors) and their surface properties, and a decision attribute, classifying compounds with respect to values of MIC. DRSA allows to induce decision rules from data describing the compounds in terms of condition and decision attributes, and to rank condition attributes with respect to relevance using a Bayesian confirmation measure. Decision rules present the most important relationships between structure and surface properties of the compounds on one hand, and their antibacterial activity on the other hand. They also indicate directions of synthesizing more efficient antibacterial compounds. Moreover, the analysis showed differences in the application of various parameters for Gram-positive and Gram-negative strains, respectively. Full article

Multiple-unit pellet systems (MUPS) offer many advantages over conventional solid dosage forms both for the manufacturers and patients. Coated pellets can be efficiently compressed into MUPS in classic tableting process and enable controlled release of active pharmaceutical ingredient (APIs). For patients MUPS are divisible without affecting drug release and convenient to swallow. However, maintaining API release profile during the compression process can be a challenge. The aim of this work was to explore and discover relationships between data describing: composition, properties, process parameters (condition attributes) and quality (decision attribute, expressed as similarity factor f₂) of MUPS containing pellets with verapamil hydrochloride as API, by applying a dominance-based rough ret approach (DRSA) mathematical data mining technique. DRSA generated decision rules representing cause–effect relationships between condition attributes and decision attribute. Similar API release profiles from pellets before and after tableting can be ensured by proper polymer coating (Eudragit^® NE, absence of ethyl cellulose), compression force higher than 6 kN, microcrystalline cellulose (Avicel^® 102) as excipient and tablet hardness ≥42.4 N. DRSA can be useful for analysis of complex technological data. Decision rules with high values of confirmation measures can help technologist in optimal formulation development. Full article

Competency for ESD (education for sustainable development) of university teachers is the key to the sustainable development of higher education. Developing teachers’ competency for ESD poses a new challenge for Chinese universities. Ideally, a new faculty development system should be established. However, this strategy is not feasible because of cost concerns, and improving competency for ESD by using the existing teacher competency training system is preferred. Therefore, this paper employed a specific data mining method to create a relationship model between the existing teacher competency system and the ESD competency according to teacher evaluation data. The developed model was used to analyze the teachers’ core competencies that are critical to ESD competency and derive decision rules for formulating improvement strategies. The teacher competency model was applied to the International Scholarly Exchange Curriculum (ISEC) program implemented at regional universities in China to conduct an empirical case study. The results of this study are anticipated to enable the formulation of an effective and appropriate strategy for improving teachers’ competency for ESD in the existing teacher development system. Full article

In Western societies, the unfamiliarity with insect-based food is a hindrance for consumption and market development. This may depend on neophobia and reactions of disgust, individual characteristics and socio-cultural background, and risk-perceptions for health and production technologies. In addition, in many European countries, the sale of insects for human consumption is still illegal, although European Union (EU) and the European Food Safety Authority (EFSA) are developing regulatory frameworks and environmental and quality standards. This research aims to advance the knowledge on entomophagy, providing insights to improve consumer acceptance in Italy. This is done by carrying out the characterization of a sample of consumers according to their willingness to taste several types of insect-based food and taking into account the connections among the consumers’ features. Thus, the dominance-based rough set approach is applied using the data collected from 310 Italian consumers. This approach provided 206 certain decision rules characterizing the consumers into five groups, showing the consumers’ features determining their specific classification. Although many Italian consumers are willing to accept only insects in the form of feed stuffs or supplements, this choice is a first step towards entomophagy. Conversely, young Italian people are a niche market, but they can play a role in changing trends. Full article

In recent years, users have increasingly focused on the privacy of social networking sites (SNS); users have reduced their self-disclosure intention. To attract users, SNS rely on active platforms that collect accurate user information, even though that information is supposed to be private. SNS marketers must understand the key elements for sustainable operation. This study aims to understand the influence of motivation (extrinsic and intrinsic) and self-disclosure on SNS through soft computing theories. First, based on a survey of 1108 users of SNS, this study used a dominance-based rough set approach to determine decision rules for self-disclosure intention on SNS. In addition, based on 11 social networking industry experts’ perspectives, this study validated the influence between the motivation attributes by using Decision-Making Trial and Evaluation Laboratory (DEMATEL). In this paper, the decision rules of users’ self-disclosure preference are presented, and the influences between motivation attributes are graphically depicted as a flow network graph. These findings can assist in addressing real-world decision problems, and can aid SNS marketers in anticipating, evaluating, and acting in accord with the self-disclosure motivations of SNS users. In this paper, practical and research implications are offered. Full article