An integrated fuzzy logic and web-based framework for active protocol support

Knowledge acquisition for the design of clinical decision support systems can be facilitated when clinical practice guidelines serve as a knowledge source. We describe application of the Guideline Elements Model (GEM) in the design of a decision support system to promote smoking cessation. Following selection of relevant recommendations and markup of knowledge components with the GEM Cutter editor, the Extractor stylesheet was used to create a list of decision variables and actions for further processing. Decision variables and actions that reflect similar concepts were consolidated. Action types were identified. Extracting the critical concepts from the narrative text facilitates clarification of necessary content. The guideline-centric approach promotes accurate translation of guideline knowledge.

PT. Jayatama Selaras is a manufacturer of plastic packaging in Indonesia. The problem faced by the company is the delay in the arrival of raw materials and the quality of raw materials that are not in accordance with the specifications provided by the company, so the process of returning or re-ordering needs to be carried out to new suppliers, which increases the time needed to procure raw materials. In addition, there is no integrated system in data storage that is related to the process of procuring raw materials, such as supplier data, and raw material data. The purpose of this study is to design a decision support system in providing an objective selection of alternative suppliers for the Purchasing Department of PT. Jayatama Selaras. The design of a decision support system starts with analyzing the current system using the PIECES framework. Next is creating a database using MySQL based on data management components. The next system design is designing component management models. The model used in the design of decision support systems at PT. Jayatama Selaras is AHP and TOPSIS. The last stage is designing components of dialogue management or user interfaces using the Visual Basic programming language. The results of the decision support system design show that the system can determine the best alternative suppliers, for PVC and LDPE raw materials, for LDPE raw materials to produce alternative sequences of suppliers PT. Indo Thai Trading, PT. Itochu Indonesia, PT. Tempo Nagadi Trading, PT. Kharisma Karya Plast, PT. Akino Indonesia Trading, PT. Panca Budi Pratama and PT. Unipack Plasindo. In addition, a decision support system designed to produce a database of existing suppliers and facilitate the ordering of raw materials to suppliers. The design of this decision support system is addressed to the purchasing department, production department, quality control department, and accounting department.

On the design of group decision support systems

Naturalistic decision making (NDM) describes how people make decisions with time pressure and other complexities in familiar and meaningful environments. The complexities of NDM, although essential for a thorough understanding of experts, have limited the use of NDM in decision support system (DSS) design and decision analysis. There have been attempts to computationally model NDM theories; however, to do so has required significant programming skill, resulting in opaque models while still leaving significant portions of the theories unrepresented. To provide a path toward simple, representative, mathematical models of NDM, we present a general mathematical form that can model most of the components of the judgment and decision-making strategies from the fast-and-frugal heuristics (FFH) program. As a case study, the NDM quick test process was transformed into representative FFH components and then into mathematical form. The results show that the quick test process is similar to the FFH strategy tallying, which itself is a type of fast-and-frugal tree. Although the mathematical form does not capture all the cognitive processes at work, it does provide a simple, representative form for use in DSS design and decision analysis. Moreover, the model’s basis in the extensive human-subjects, mathematical, and computational analyses completed by the FFH program provides another method for integrating FFH and NDM by providing (a) a framework for generating constructive questions about how NDM theories account for FFH components; (b) a basis for prescriptive NDM decision support tools that are easy to communicate, understand, and apply; and (c) a method for approximating experience, expertise, and time pressure.

A web-based GIS Decision Support System for managing and planning USDA's Conservation Reserve Program (CRP)

Obstructive sleep apnea (OSA) is a very common condition in patients with temporomandibular disorders (TMD). However, there is little evidence of the connection between them. The aim of this systematic review (SR) was to assess the association between obstructive sleep apnea (OSA) and temporomandibular disorders (TMD) in the adult population. TMD should be diagnosed using Research Diagnostic Criteria (RDC/TMD) or Diagnostic Criteria (DC/TMD) and OSA using polysomnography (PSG) and/or validated questionnaires (QV). All studies were compared to healthy controls and there were no restrictions on language, gender or time of publication. Case-control, cross-sectional and cohort studies of the association between TMD and OSA were searched up to November 2022 in the EMBASE, LILACS, LIVIVO, PubMed/MEDLINE, Scopus, Web of Science, Google Scholar, Open Gray and Pro Quest databases. Risk of bias was assessed using the Joanna Briggs Institute Critical Assessment Checklists. An association meta-analysis was performed using the Review Manager software, version 5.4. The effect measure included the odds ratio (odds ratio) in dichotomous variables and a 95% confidence interval (CI). Certainty of evidence was determined by analyzing groups using the Grading of Recommendations Assessment, Development and Evaluation (GRADE). This study is registered in PROSPERO, under number CRD42021240373. Of the 1024 articles received, seven met the inclusion criteria for the SR and six were included in the quantitative analysis (one study was excluded due to insufficient data), with all articles found to be at low risk of bias. The odds ratio (OR) of OSA in patients with TMD showed a positive association (OR=2.61; 95% CI=2.31,2.95 ) in the quantitative analysis covering all studies. Two additional meta-analyses were performed to group studies by OSA diagnostic method. Studies using the OSA diagnostic method by PSG and VQ (OR= 2.74; 95%CI= 2.11,3.57 ) and studies using only VQ (OR = 2.55; 95% CI = 2.22,2.92). Another sensitivity MA without the larger population study, still showed a positive association of OSA in patients with TMD (OR= 2.48; CI= 95% 1.93,3.18). Analysis of evidence certainly was moderate. Patients with TMD had a statistically significant association with OSA regardless of the OSA diagnostic method (PSG and/or VQ). Against this background, OSA screening should be a routine part of the diagnosis of patients with TMD. However, due to the different OSA assessment methods used and the small number of studies included, it is necessary to include a larger number of studies with PSG in order to better investigate this association.

Obstructive sleep apnea (OSA) is a very common condition in patients with temporomandibular disorders (TMD). However, there is little evidence of a connection between them. The aim of this systematic review and meta-analysis is to assess the association between OSA and TMD in adult population. Case-control, cross-sectional and cohort studies on the association between TMD and OSA were searched in the EMBASE, LILACS, LIVIVO, PubMed/MEDLINE, Scopus, Web of Science, Google Scholar, Open Grey and Pro Quest databases. TMD should be assessed using Research Diagnostic Criteria (RDC/TMD) or Diagnostic Criteria (DC/TMD) and OSA using polysomnography (PSG) and/or a validated questionnaire. The risk of bias was evaluated using the Joanna Briggs Institute Critical Assessment Checklists; and an association meta-analysis was performed. The effect measure included the odds ratio (OR) in dichotomous variables and a 95% confidence interval (CI). Certainty of evidence was determined by analysing groups using the Grading of Recommendations Assessment, Development and Evaluation (GRADE). Out of the 1024 articles screened, 7 met the inclusion criteria for the qualitative synthesis, and 6 for quantitative analysis. All articles were classified at low risk of bias. A positive association with OSA was found in patients with TMD (OR = 2.61; 95% CI = 2.31, 2.95). A significant association was also found irrespective to the OSA diagnostic methods applied (for studies using PSG + validated questionnaires: OR = 2.74; 95% CI = 2.11, 3.57; for studies using validated questionnaires only: OR = 2.55; 95% CI = 2.22, 2.92). GRADE was moderate. Patients with TMD presented a significant association with OSA regardless of the OSA diagnostic method (PSG and/or validated questionnaires). OSA screening should be part of the TMD examination routine. Furthermore, due to the different OSA assessment methods used and the small number of studies included, there is a need to include a larger number of studies using PSG to better elucidate this association.

Soft set theory was first introduced by D. A. Molodtsov [D. Molodtsov, Soft set theory first results, Computers & Mathematics with Applications, 37(4–5) (1999) 19–31] in 1999 as an effective approach to deal with uncertainty. Over time, fuzzy soft set theory has evolved significantly, integrating concepts from both fuzzy set theory and soft set theory to enhance decision-making processes. Acharjee and Medhi [S. Acharjee and S. Medhi, Logical connectives of fuzzy soft set theory, New Mathematics and Natural Computation, 21(1) (2025) 339–352] introduced several advanced concepts related to logical connectives in fuzzy soft set theory. Building on Molodtsov’s foundational work, this paper explores new developments in fuzzy soft logic, extending the findings of Acharjee and Medhi. Specifically, we introduce the concepts of [Formula: see text]-fuzzy soft implication and [Formula: see text]-fuzzy soft bi-implication, along with their related results within the framework of fuzzy soft logic.

A Decision Support System for market-driven product positioning and design

The feature chart: A tool for communicating the analysis for a decision support system

Product development decisions represent challenging processes with various stakeholders from different disciplines and with different perspectives involved. Decisions can be highly complex due to the high amount of influencing factors that have to be considered simultaneously. Among other methods and tools, decision support systems (DSS) were developed to support decision-makers in handling complex decisions. One main disadvantage of DSS is the little adaptation to and integration of decision-maker needs and characteristics. Thus, current trends focus on the development of human- and decision-maker-centered DSS. This paper aims in providing a multilevel stepwise procedure for the modeling of decision-makers in order to enable a more effective decision support. The decision-maker modeling procedure is based on findings from literature research and results from research projects performed in cooperation with industry partners. The procedure serves as a guideline for generating diverse decision-maker models for different applications and for integrating human cognitive thinking procedures within the design and functionality of decision support methodologies and systems. The procedure represents an important design element of the human-centered decision support methodology currently being developed at the Institute of Product Development of the Technische Universität München (Germany).

A structural design of decision support systems for deteriorating repairable systems

Decision support systems enhanced by Artificial Intelligence (AI) are increasingly being used in high-stakes scenarios where errors or biased outcomes can have significant consequences. In this work, we explore the conditions under which AI-based decision support systems affect the decision accuracy of humans involved in face matching tasks. Previous work suggests that this largely depends on various factors, such as the specific nature of the task and how users perceive the quality of the decision support, among others. Hence, we conduct extensive experiments to examine how both task difficulty and the precision of the system influence human outcomes. Our results show a strong influence of task difficulty, which not only makes humans less precise but also less capable of determining whether the decision support system is yielding accurate suggestions or not. This has implications for the design of decision support systems, and calls for a careful examination of the context in which they are deployed and on how they are perceived by users.

A conceptual design for decision support systems (DSS) employing management science models is presented. The motivations for the research include a marked increase in the use of management science models in recent years and the limited success achieved to date in providing user-friendly model access to the decisionmaker, a capability which is essential to the basic notion of a decision support system. The design presented supports a “model management” approach to the problem, which is contrasted with the more common and traditional “meta-language” approach. Relevant software systems in the DSS include three physically and logically independent systems for data-base management, model-base management, and dialogue management, as well as a fourth coordinating entity, the problem processing system. Included are descriptions of the component parts that comprise the DSS conceptual model, discussions of associated personnel roles, operational considerations related to impiementability, and an illustrative example application.

Background. The digital transformation of dentistry involves not only the digitization of patient records but also the integration of intelligent Decision Support Systems (DSS), which can enhance diagnostic accuracy, standardize treatment protocols, and optimize clinician – patient interaction. With the rapid development of artificial intelligence (АІ) technologies, DSS are gaining increasing importance in both clinical and educational contexts within dentistry. Purpose – to systematize existing approaches to the classification, design, and implementation of DSS in dentistry, with a focus on their functional capabilities, architectural models, and preconditions for successful clinical deployment. Materials and Methods. The study is based on the analysis of peer-reviewed scientific literature published between 2020 and 2024 from Scopus, PubMed, and IEEE databases, as well as technical documentation of existing DSS solutions. Methods of systems analysis, descriptive classification, comparative evaluation, and synthesis of preconditions for implementation were applied. Two summary tables were developed: one on the advantages and limitations of different DSS types, and another on implementation preconditions. Results. Two primary classification axes of DSS were identified: functional (diagnostic, prognostic, therapeutic planning, administrative, educational, AI-based) and architectural (rule-based, case-based, AI-based, fuzzy logic, hybrid systems). Rule-based systems remain the most widely used due to their transparency and alignment with clinical protocols, while AI-driven systems demonstrate potential but require further validation and explainability. A set of key preconditions for effective implementation was formulated, including structured clinical data, interoperability with electronic health records, appropriate infrastructure, and clinical staff readiness. Conclusions. DSS has demonstrated high potential for improving the quality, safety, and personalization of dental care. Promising directions for future development include hybrid DSS models that combine rule-based and AI approaches, as well as the creation of unified standards for integration and legal compliance.