In fuzzy rough sets, the traditional approach primarily employs fuzzy rough approximation operators and fuzzy neighborhood operators to solve attribute reduction problems. However, measures constructed by these operators fail to capture the correlation between attributes, whereas the generalized Shapley value(SHV), as a non-additive measure, takes into account the correlation between attributes from a global perspective. Based on the global attribute correlation problems, SHV for fuzzy neighborhoods utilizing pseudo-overlap functions are proposed to deal with the problem of attribute reduction. Firstly, in the fuzzy β covering approximation spaces( β -FCASs), based on pseudo-overlap functions and their corresponding residual implications(( I _PO, PO )), ( I _PO, PO )-fuzzy β neighborhood operators ((I _PO,PO)−β−FNoperators) and four pairs of ( I _PO, PO ) fuzzy β neighborhood measures(( I _PO, PO )-fuzzy β−NMs) based on these operators are constructed, thereby extending the representational capacity of fuzzy covering-based rough sets. Secondly, four pairs of SHVs utilize on ( I _PO, PO )-fuzzy β−NMs are introduced to evaluate attribute significance from a global perspective. And a new method of attribute reduction of fuzzy β covering information decision tables( β -FCIDTs) based on SHV is proposed. Thirdly, four pairs of Choquet integrals(CHIs) based on SHV are constructed. On this basis, a method for addressing the attribute reduction of β -FCIDT is proposed by considering the correlation of global attributes, and the proposed method is used to deal with the classification problem specifically. Finally, the validity and practicality of the proposed methods are confirmed through the use of several public datasets.

H. pylori infection is prevalent in Pakistan, affecting nearly 81% of the population. Many high-quality clinical practice guidelines (CPGs) have been developed in high-income countries. Low- and middle-income countries (LMICs) face challenges in developing de novo CPGs. Modifying pre-existing guidelines through the GRADE-ADOLOPMENT process is a practical approach to addressing this issue. We aimed to create a contextually relevant and comprehensive CPG for H. pylori management in Pakistan. The selected source guidelines underwent a thorough literature review and evaluation aligned with the GRADE approach. Recommendations were categorized as "Adopt," "Adapt," or "Exclude" after assessment by expert gastroenterologists. Adopted recommendations were included as-is or with minor changes. Excluded recommendations were removed, while adapted recommendations underwent significant alterations through the GRADE-ADOLOPMENT process. The GRADEPro application and Evidence to Decision tables aided the consensus process. The final meeting resulted in unanimous agreement on the CPG. The source guidelines included 33 recommendations: 29 were adopted, and three were excluded. One recommendation required adaptation: "non-endoscopic testing for H. pylori infection is an option for patients under 60 with uninvestigated dyspepsia and no alarm features." According to the adapted version, patients under the age of 50 years with uninvestigated dyspepsia should undergo non-endoscopic testing. The creation of this CPG will equip general physicians with effective management strategies. Our study presents a CPG for H. pylori infection treatment in Pakistan, recommending endoscopic testing for patients aged over 50 years. Further research is needed to investigate the efficacy of early screening for uninvestigated dyspepsia in Pakistan. Our approach can help improve patient care globally, especially in LMICs.

The European Medicines Agency (EMA) guideline Declaration of Storage Conditions in the Product Information of Medicinal Products (CPMP/QWP/609/96/Rev.2) was last revised in 2007. Since then, advances in stability science, increased climatic variability, and evolving regulatory expectations have highlighted limitations in the framework, particularly the use of vague descriptors such as "room temperature" or "no special storage conditions." This work reassesses the scientific and regulatory basis for storage statements, integrating current understanding of mean kinetic temperature (MKT), climate-related risks, and developments in ICH and WHO guidance, and proposes a harmonized revision (Rev.3). A structured analysis of regulatory and scientific principles was conducted, incorporating MKT, climate-related risks, and developments in ICH and WHO guidance. These elements informed a revised framework and model storage statements, including a decision table linking stability outcomes to label statements. The proposed update introduces explicit temperature-based statements, strengthens the linkage between stability data and label wording, clarifies requirements for moisture- and light-sensitive products, expands guidance for sterile and reconstituted preparations, and provides contingency instructions for temperature excursions. A decision table mapping stability outcomes to label statements is included to support regulatory implementation. This revision provides an updated synthesis of EMA, ICH, and WHO requirements, translating regulatory principles into clear storage statements and contingency guidance. It offers a more precise framework reducing ambiguity, supports justification of storage conditions through stability data and MKT-informed risk assessment, aiming to improve understanding among patients and healthcare professionals, strengthening real-world product quality and contributing to future EMA guideline development. Identifies scientific and regulatory limitations in current "no special storage conditions" terminology; Links ICH stability guidelines and climatic variability to precise storage statements; Proposes harmonised core label phrases for room temperature, fridge, and freezer; Introduces contingency labelling for climate-related and cold-chain excursions.

Big data technology progress has led to improved precision of machine tool failure prediction. This paper utilises geometric data such as the structure and assembly relationships of CNC machine tools, combined with digital twin technology, to construct a multi-body kinematic/dynamic model. This enables simulation of the corresponding parts during machine tool operation, providing data support for predictive maintenance. Gray rough sets are introduced to optimise the BP neural network prediction algorithm. The predictive maintenance accuracy of CNC machine tools may be improved through the creation of an initial decision table, the use of grey relational analysis, and the processing of discrete information. The findings show that the BP neural-network prediction model based on grey rough sets has loss values of 0.099, 0.059 and 0.018 at three different iteration settings. In five different signal-to-noise ratio datasets, the loss function values are all less than 0.15, and the prediction accuracy exceeds 90%. This enables precise prediction and maintenance of CNC machine tool failures.

Nowadays, the modernization of education information is also an inevitable development path, and education management information systems have been widely used. The TQ and comprehensive evaluation of education and classrooms from the perspective of students and teachers. However, the traditional teaching assessment system has imperfect aspects. For example, the content of teaching assessment is too broad and the students' evaluation attitude is not objective, etc., which may cause the teaching assessment to lose its meaning. Using the methods of literature, mathematical statistics and questionnaire, this paper deeply studies the application of data mining theory and Image Feature Fusion technology. An application experiment of data mining based on Image Feature Fusion technology in teaching assessment is designed, established a decision table of key factors affecting teaching assessment, through the data preprocessing process and attribute reduction teaching method, the important factors affecting teaching evaluation are analyzed, preparation before class, etc. Finally, through the analysis of these factors, it is learned that students’ dissatisfaction with the current teaching assessment system accounts for 47.87%, and the dissatisfaction of teachers accounts for 31.28%. Therefore, teachers also should strive to improve their professional skills and teaching quality.

Relational games and repeated bayesian relational games with decision tables for influencer marketing

Software testing is an essential process to ensure the correctness, completeness, security, and quality of a product. It helps verify and validate whether the product meets customer requirements and functions as expected. Testing identifies and fixes bugs or defects to improve software quality and reliability. This paper explores the principles, methods, and different types of software testing. It highlights key strategies like verification (ensuring the product is built right) and validation (ensuring the product meets the intended needs). We also look at different testing stages, like unit testing, integration testing, and user acceptance testing are also considered. Testing techniques like white-box testing (focusing on the internal structure of the software) and black-box testing (focusing on outputs and user requirements) are discussed. Different test methods, such as boundary value analysis and decision tables, are also introduced to help find defects early and efficiently. The paper emphasizes the importance of early testing, systematic planning, and a structured approach to uncover defects and ensure the product's reliability. Ultimately, the goal of software testing is to create a defect-free product that fulfills customer expectations and performs well under various conditions.

Accurately predicting the ultimate strain of fiber-reinforced polymer (FRP)-confined concrete columns is essential for the widespread application of FRP in strengthening reinforced concrete (RC) columns. This study comprehensively investigates the performance of ensemble machine learning (ML) models in estimating the ultimate strain of FRP-confined concrete (FRP-CC) columns. A dataset of 547 test results of the ultimate strain of FRP-CC columns was collected from the literature for training and testing ML models. The four best single ML models were used to develop ensemble models employing voting, stacking and bagging techniques. The performance of the ensemble models was compared with 10 single ML and 11 empirical strain models. The study results revealed that the single ML models yielded good agreement between the estimated ultimate strain and the test results, with the best single ML models being the K-Star, k-Nearest Neighbor (k-NN) and Decision Table (DT) models. The three best ensemble models, a stacking-based ensemble model comprising K-Star, k-NN and DT models; a stacking-based ensemble model comprising K-Star and k-NN models and a voting-based ensemble model comprising K-Star and k-NN models, achieved higher estimation accuracy than the best single ML model in estimating the strain capacity of FRP-CC columns.

Warehouse inventory management increasingly relies on web-based applications for real-time stock monitoring amidst the complexity of the Indonesian supply chain. This study evaluates the functional suitability of a Web-Based Warehouse Inventory Application using black box testing according to the ISO/IEC 25010 standard. Using a descriptive qualitative approach, the study population includes 11 functional modules, with a purposive sample of 50 test cases that test authentication, master data, transactions, and reports. The instruments consist of structured test cases based on equivalence partitioning, boundary value analysis, decision table testing, and use case testing, analyzed through PASS/FAIL comparisons, defect severity classification according to ISO/IEC 29119, and thematic pattern identification. The results show an 85% success rate for core operations such as login validation, CRUD functions, and stock reporting, but critical failures were found in stock validation (allowing negative stock), dashboard access without login, lack of session timeout, and the transaction edit feature. In conclusion, although the basic functions meet operational needs, security and data integrity gaps require immediate remediation for production readiness.

The paper addresses the problem of formalization and automated processing of textual business rules in decision support systems, taking into account the existing data structure and the contextual constraints of the decision-making domain. The study proposes mathematical models covering three key stages: identifying the structural components of a textual business rule, generating a formal DMN decision table, and validating the syntax and semantics of the resulting model in accordance with logical consistency rules. The structural analysis model extracts conditions, actions, and dependencies while incorporating the available data schema; the DMN generation model maps textual constructs to decision table elements with respect to the context of the decision support system; the validation model detects logical inconsistencies, incompleteness, and coherence errors in the formalized model. A prototype implementation is presented, enabling experimental evaluation of the proposed models. The results demonstrate correct formalization, completeness of business rule transformation into the DMN table, and effective detection of structural, syntactic, and semantic errors in the generated decision model.

Edit distance is an algorithm to measure the difference between two strings, usually represented as the minimum number of editing operations required to transform one string into another. The edit distance algorithm involves complex dependencies and constraints, making state management and verification work tedious. This paper proposes a derivation and verification method that avoids directly handling dependencies and constraints by proving the equivalence between the edit distance algorithm and existing functional modeling. First, the derivation process of edit distance algorithm mainly includes 1) describing problem specifications, 2) inductively deducing recursive relations, 3) formally constructing loop invariants using the optimization theory (memorization technology and optimal decision table) and properties (optimal substructure property and subproblems overlapping property) of the edit distance algorithm, 4) generating the Minimalistic Imperative Programming Language (IMP) code based on the recursive relations. Second, the problem specification, loop invariants, and generated IMP code are input into Verification Condition Generator (VCG), which automatically generate five verification conditions, and then the correctness of edit distance algorithm is verified in the Isabelle/HOL theorem prover. The method utilizes formal technologies and theorem prover to complete the derivation and verification of the edit distance algorithm, and it can be applied to linear and nonlinear dynamic programming problems.

In this study, eight QSAR models were constructed to develop novel compounds as tyrosinase inhibitors. The decision tables, Bagging, and Random Committee methods showed the best predictive abilities (q2 ≥ 0.5) among the models. Based on these models, twelve new kojic acid derivatives were synthesized. Tyrosinase inhibition was determined using a spectrophotometric method with L-DOPA as the substrate. Molecular docking studies were conducted to provide insights into the tyrosinase-inhibiting mechanisms of the compounds. Cytotoxic effects on the B16F10 melanoma cell line were investigated using the SRB assay. A melanogenesis assay was also performed to detect the inhibition of melanin production. Compounds 4l, 4j, and 4b exhibited better tyrosinase inhibitory effects than the positive control, kojic acid (218.8 µM), with IC50 values of 138.1, 159.0, and 208.9 µM, respectively. Compound 4j showed the best anti-melanogenesis effect among the compounds tested. These findings demonstrate the potential of the compounds developed as novel tyrosinase inhibitors for clinical and cosmetic applications.

Granular computing is essential for data mining and knowledge discovery because real-world data is overwhelmingly complex and incomplete. Given the constraints of single-scale tables, multi-scale decision tables have attracted significant research interest. In this paper, we propose a new data analysis model called the incomplete multi-scale probabilistic covering rough set from the perspective of granular computing. This model gains some fault-tolerant characteristics by introducing a pair of probability thresholds, as well as the ability to handle uncertain and imprecise information better. In view of this, we further construct a new type of decision table, namely the incomplete multi-scale probabilistic covering decision table. For both consistent and inconsistent incomplete multi-scale probabilistic covering decision tables, we propose corresponding optimal scale selection algorithms. Furthermore, we construct an object threshold calculation approach to improve the accuracy of model employing the relative loss function. We also developed a knowledge acquisition rule algorithm based on the three-way decision concept, thereby enhancing the applicability and universality of the decision rules. Finally, the effectiveness of the proposed model and methods is verified through example analysis.

Cavitation phenomenon in piston pumps not only causes vibration and noise but also leads to component damage. Conventional diagnostic methods suffer from low accuracy, while deep learning approaches lack interpretability. To address these limitations, this paper proposes an intelligent fault diagnosis method based on the rough set Attribute Weighted Convolutional Neural Network (RSAW-CNN). First, based on cavitation mechanisms and the mathematical model, the computational fluid dynamics model of the piston pump is established to simulate the failure condition. Subsequently, employing rough set theory, an original fault decision table is constructed, discretized, and subjected to attribute reduction. A weight matrix is generated according to the importance of each data channel in the classification decision and embedded into the input layer of the Convolutional Neural Network (CNN) to enhance the influence of key features. Decision rules are also extracted to provide interpretable decision support for fault diagnosis. Experimental results demonstrate that the proposed RSAW-CNN method achieves an average diagnostic accuracy of over 99.2%. Compared to the backpropagation neural network, residual neural network, CNN, and the CNN with squeeze-and-excitation networks, its average accuracy has improved by 15.87%, 10.83%, 7.48%, and 5.40%. The proposed method not only exhibits high diagnostic accuracy but also offers strong interpretability and reliability.

In clinical trials, the initial step typically involves assessing a new drug's toxicity profile, aiming to identify a tolerable dose level for subsequent studies. In phase I trials, the primary objective is to determine the maximum tolerated dose, defined as the highest dose associated with an acceptable level of toxicity. Numerous methods have been developed to guide dose escalation and de-escalation decisions during trial conduct. Among these approaches, the calibration-free odds (CFO) design has demonstrated superior operating characteristics and has emerged as one of the most effective approaches for dose finding. To facilitate the application of the CFO design in clinical trial practice, an R package and a Shiny app have been released. This study presents CFO decision tables in Excel files to further remove the barrier of applying the CFO design to real trials. Anyone involved in the trial conduct can implement the CFO design with no difficulties. During the trial, dose movement decisions can be made simply by referring to the cumulative data (including numbers of patients treated and observed toxicities) and the pregenerated decision tables, without any additional statistical calculation. This approach significantly enhances the usability of the CFO design and reduces the operational complexity associated with its implementation in clinical trials. The Excel CFO decision tables can be downloaded from CFO Shiny App.

This study's object is those business processes in the financial management system of business entities that are related to making substantiated management decisions based on aggregated financial data. The problem addressed is the lack of a comprehensive and formalized approach to the organization of decision-making support processes in the financial management system. A procedure that organizes financial decision-making has been devised by using digital technologies based on Business Intelligence (BI), which holistically combines business process modeling, the construction of reasoned conclusions, as well as the processing of structured data. The feasibility of using the BPM+ approach was clarified and a procedure for its implementation was devised. Based on the latter, the course of analytical business processes in the financial management system was modeled, based on the results of which reasoned conclusions are made and appropriate management decisions and measures could be developed. Modeling financial management tasks at the analysis stage in BPMN determines the order of implementation of business processes and the areas of responsibility for responsible persons involved in their implementation. Using an example of the process of analyzing the financial stability of the model built, the logic behind obtaining analytical conclusions using the Decision Model and Notation (DMN), which contains the Decision Requirements Diagram (DRD) and the decision table, has been formalized. Input data for forming a decision is proposed to be prepared in the environment of digital products. Given the relevance of using BI software in analytics, the example of applying MS PowerBI was applied to demonstrate the features of building and transforming an information model of financial data, which is the basis for designing interactive dashboards. The financial indicator monitoring panels constructed provide operational support for decision-making by financial managers

ABSTRACT The climatic data used in NBR 16401-2024 is based on the ASHRAE Fundamentals 2017 manual, covering 52 Brazilian cities. For system sizing, it is crucial to consider variables such as dry bulb temperature, coincident dry bulb temperatures with maximum wet bulb temperatures, wet bulb temperature associated with maximum dry bulb temperature, and dew point temperature, all cataloged at three concentration levels. Choosing the appropriate temperature for calculating thermal load can be challenging, as it depends on the specific conditions of each project. This article presents a decision table that guides professionals on which type of temperature to use in different situations, thereby clarifying the guidelines of the standard. With this tool, it will be possible to optimize the sizing of air conditioning systems, ensuring energy efficiency and thermal comfort. By integrating this information and decision-making, engineers and designers will be better prepared to select the correct climatic data, promoting system effectiveness and user well-being. Thus, a proper understanding of climatic data and the application of the decision table become essential for the efficiency of air conditioning projects. Keywords: ASHRAE, RTSM, Cooling.

Design and application of MDSSP using the MLL3 distribution for pandemic mortality data

This paper compares the inverse transient analysis (ITA) in the frequency domain (FITA) with its counterpart in the time domain (TITA) for leak detection and calibration of looped water supply networks (WSNs). The leak detection results demonstrate that both FITA and TITA achieve 100% accuracy in predicting leak location and size for the case study. However, FITA exhibits superior computational efficiency, converging twice as fast as TITA. Regarding the calibration performance, TITA demonstrates higher precision in estimating pipe friction factors, with an overall average error of 0.019%. In comparison, FITA achieves an average error of 4.14%, due to the linearization of the friction loss formula. It is also noteworthy that the high sensitivity of FITA to leak parameters allows for reliable leak detection even without simultaneous calibration of the friction factor. In fact, the impacts of leaks and frictional losses are nearly separable in the frequency domain. Furthermore, the novel dominant frequencies (DFs) method provides direct leak detection through frequency response analysis, while the Decision Table Method (DTM) optimizes measurement site placement with reduced computational overhead. Uncertainty analysis confirms FITA’s robustness to friction factor variations within reasonable ranges, making it particularly suitable for large or complex WSNs where computational efficiency is paramount.

<h2>Abstract</h2><h3>Background</h3> Clinicians frequently face arch-length discrepancies but lack a unified, evidence-based workflow to prioritize space-gaining options before committing to extractions. <h3>Objective</h3> To synthesize current evidence into a practical, stepwise framework-SPEED-L (Stripping, Proclination, Expansion, Extraction, Distalization, and Leeway space) guides individualized orthodontic space management. <h3>Methods</h3> A narrative review was undertaken of peer-reviewed literature (2000-mid-2025) across PubMed, Scopus, Cochrane, and Google Scholar. Systematic reviews, meta-analyses, clinical trials, and comparative cohort studies addressing extraction and non-extraction strategies were screened and summarized into diagnostic guardrails, feasibility limits, and sequencing rules. PRISMA screening identified ∼780 records; 80 studies met inclusion for qualitative synthesis. <h3>Synthesis</h3> The framework links diagnostic variables-incisor inclination, soft-tissue profile (E-line), crowding severity, vertical pattern, periodontal biotype, and growth status-to the capacity and risks of each modality. Conservative options (IPR within enamel safety envelopes, controlled proclination with torque control, skeletal/dental expansion including MARPE, and planned distalization with TAD anchorage) can resolve mild–moderate discrepancies, while extractions remain indicated for severe crowding or protrusion. Leeway space preservation provides physiologic relief in the mixed dentition. Decision tables and guardrails help quantify achievable space and avoid exceeding alveolar limits. <h3>Conclusions and Clinical Significance</h3> SPEED-L operationalizes evidence into a decision compass that integrates modalities rather than treating them as siloed choices, encouraging minimally invasive, profile-aware plans and reserving extractions for clearly indicated scenarios. The framework supports transparent consent, digital simulation, and stable outcomes through tailored sequencing and retention.