Decision-making Techniques Research Articles

Analysis and multi-objective evolutionary optimization of Solar-Biogas hybrid system operated cascade Kalina organic Rankine cycle for sustainable cooling and green hydrogen production

A novel cascade system of Kalina and Organic Rankine cycle for cooling and green hydrogen production was proposed. The cascade system is designed to utilizes the heat effectively with heat recovery components for sustainable cooling and green hydrogen. The proposed system consists of a solar-biogas hybrid heat source, Kalina and ORC power generation system, dual evaporator vapor compression cooling system and solid oxide electrolyser. The system's performance is evaluated based on the availability of low-temperature heat sources (90 to 150 °C) and medium-grade heat sources (190–280 °C). It is assessed for its capability to provide cooling only or in combination with hydrogen production, and their efficient utilization is compared from energetic, exergetic, environmental, and economic perspectives. To optimize this innovative system, a multi-objective evolutionary approach is employed, considering operating conditions as constraints and incorporating performance metrics for all criteria as objectives. Utilizing multi-objective decision-making techniques, diverse optimal points are pinpointed within the trade-off space, enabling real-time adaptive responses to changes in weather, energy demands, and biogas availability. The net present value analysis demonstrates that, assuming a hydrogen selling price of $6 per kilogram and cooling costs of $4 per KW the payback periods are calculated to be 2 years for the ORC-VCRS cycle and 3 years for the integrated Kalina topping with bottoming ORC cycle. The multi-objective optimization results indicate that the optimum operating conditions for the cascade system involve boiler, absorber, and condenser temperatures set at 196 °C, 110 °C, and 25 °C, respectively. Under these conditions, the system achieves an energy utilization ratio of 0.76, an exergy efficiency of 21.56 %, and a total cost of $58,677.

Prioritizing the antecedents of employer branding using fuzzy AHP: an extent analysis approach

Purpose One of the most decisive objectives of human resource management (HRM), essential for business performance, is to attract, maintain and engage a qualified workforce. This study aims to identify and prioritize the antecedents of employer branding, which are considered extremely important by potential applicants when they choose to join a particular employer. Design/methodology/approach The authors have adopted a popular multicriteria decision-making technique fuzzy analytical hierarchy process, to prioritize the identified antecedents. The modified Delphi techniques with the 22 experts have been conducted to validate the identified antecedents. Structured questionnaires were discussed, and their reliability was assessed using the expert’s opinion. Findings Results of this study indicate that potential applicants consider career advancement opportunities as the most important enabler that persuades them to join a particular employer. Future employees are also motivated to join if they are offered attractive compensation packages, which are distinct from competitors. Furthermore, corporate social responsibility is evolving as an antecedent of employer branding as potential candidates are attracted to employers who are socially responsible. Practical implications Employer branding can serve as a strategic HRM technique for any business seeking to attract, recruit, retain and involve outstanding employees, as the success of the organization is determined by how its image is built, communicated and promoted. This study provides insights for HR managers and practitioners who can think of developing an effective employer brand communication that offers a distinct and in-imitable image and reputation as an employer of choice. Originality/value This study is unique, as it offers meaningful visions to HR practitioners and experts for designing employer branding strategies for attracting potential applicants to join their organizations.

Multicriteria analysis for quantifying sustainability of developed load bearing lightweight geopolymer

In geopolymers, several factors such as the limited availability of amorphous aluminosilicate material, the need for a high dosage of activator, the requirement for hot curing conditions, and the activation by pressure pose challenges in considering geopolymer as a sustainable load-bearing material. Therefore, this experimental and analytical work aimed to address the aforementioned issues. The experimental work was carried out in four distinct series, where fly ash (FA) was combined with sugarcane bagasse ash (SCBA), slag (GGBS), and lime. The alkaline to precursor (A/P) ratio and curing conditions were varied. Initially, the compression casting approach was employed to reduce the dosage of activator. But later on, lime was added to prevent such a technique, and the alkaline dosage was reduced due to occupying effect. Thereafter, three key performance indices of sustainability were quantified: load-bearing lightweight (Dp), environment (Ep), and economic (Cp). These indices were determined by normalizing density, CO2 emissions, and cost by compressive strength, respectively. It was observed that FA-lime-based mortar is a sustainable geopolymer. It is ambiently cured and has the least alkaline activator, high strength, minimal CO2 emissions, and low cost. It was also compared to conventional construction materials such as fired clay brick and concrete, and promising results were obtained. Sustainable quantification was conducted analytically by extracting seven alternatives from four series and analyzing a 7 × 7 decision matrix using the multicriteria decision-making technique. The FA-lime-based specimen was selected experimentally and verified analytically, along with a prioritized list of all specimens.

Video Semantic Segmentation Network with Low Latency Based on Deep Learning

Recently, new advances in deep learning algorithms have yielded some fascinating results in the field of computer vision technology. As a result, it can now perform activities that formerly required the use of human vision and the brain. Classification, object identification, and semantic segmentation have all seen substantial advancements in deep learning architecture in the last few years. For still images and movies, there has been a major advancement in the field of semantic segmentation. In practical uses like autonomous vehicles, segmenting semantic video continues to be difficult due to high-performance standards, the high cost of convolutional neural networks (CNNs), and the significant need for low latency. An effective machine-learning environment will be developed to meet the performance and latency challenges outlined above. The use of deep learning architectures like SegNet and FlowNet2.0 on the CamVid dataset enables this environment to conduct pixel-wise semantic segmentation of video properties while maintaining low latency. As a result, it is ideally suited for real-world applications since it takes advantage of both SegNet and FlowNet topologies. The decision network determines whether an image frame should be processed by a segmentation network or an optical flow network based on the expected confidence score. In conjunction with adaptive scheduling of the key frame approach, this technique for decision-making can help to speed up the procedure. Using the ResNet50 SegNet model, a mean Intersection on Union (IoU) of "54.27 percent" and an average frame per second of "19.57" were observed. Aside from decision network and adaptive key frame sequencing, it was discovered that FlowNet2.0 increased the frames processed per second9(fps) to "30.19" on GPU with a mean IoU of "47.65%". Because the GPU was utilized "47.65%" of the time, this resulted. There has been an increase in the speed of the Video semantic segmentation network without sacrificing quality, as demonstrated by this improvement in performance.

Ranking Competencies of Managers with the Combined Method of Dimtel and Network Analysis (Case study: Public Hospitals of Yazd Province)

Background: The success of any organization depends on the expertise and competence of human resources, especially its managers. Competence is defined as the knowledge and skills necessary to perform a job. The current study aimed to rank the competencies of managers based on the competence model of Dolewis and Higgs (2005), which was carried out using multi-criteria decision-making techniques (combined method of Dimtel and network analysis) in public hospitals of Yazd province.
 Methods: This inductive approach research was carried out quantitatively during 2021 to 2022 in public hospitals of Yazd province. In this study, multi-criteria decision-making techniques were used including the combined method of Dimtel and network analysis. The statistical population was a group of human resources experts and senior managers of public hospitals in Yazd province. Sampling was done first by judgment and then by snowball method and through the introduction of experts and based on the possibility of access to them, and 15 experts were selected as a sample. After extracting the data from the experts' questionnaires, Dimtel method was used to evaluate the relationships between the indicators.
 Results: In the current study, according to the experts’ opinions, the competence model of Doleviz and Higgs (2005) was considered, which included three managerial, personal, and intellectual dimensions. The dimension of managerial competence included components of success, empowerment, development, engaging and effective communication, resource management. The dimension of intellectual competence included components of judgment and critical analysis, perspective and foresight of strategic views. The dimension of personal competence included components of self-awareness and emotional resilience, intuitive understanding, interpersonal sensitivity, influence, motivation, and conscience. Based on the results of the super matrix of network analysis, the weights of competence in order of priority included managerial, intellectual, and personal competence. In managerial competence, the component of resource management, in personal competence, the influence component, and in intellectual competence, the components of foresight and perspective were more important.
 Conclusion: In order to be more successful in achieving future goals and perspective, hospital managers should strive to strengthen their competencies, since improving the competence of managers helps them in effective use of resources, better response to clients, and achieving goals, in accordance with the accreditation standards of public hospitals in Yazd province

Entropy weighted WASPAS and MACBETH approaches for optimizing the performance of solar water heating system

In this work, multi-criteria decision-making (MCDM) techniques namely the weighted aggregated sum product assessment (WASPAS) method and the measuring attractiveness by a categorical based evaluation technique (MACBETH) are applied for predicting the optimum performance of a solar water heating system (SWHS) equipped with perforated delta-shaped obstacles. In total fifteen SWHS design alternatives based on the variation of pitch ratio (0.5, 1, 1.5, 2.0), angle of attack (15°, 30°, 45°), and Reynolds number (400, 800, 1200) were selected. The Nusselt number, friction factor, and thermohydraulic efficiency were fixed as the selection criteria in evaluating SWHS alternatives. By the entropy method, the importance of the identified criteria is quantified as weight, whereas the ranking of SWHS alternatives is determined by the WASPAS and MACBETH methods. The results show that the SWHS design alternative with a Reynolds number of 1200, pitch ratio of 1, and 45° angle of attack exhibits the optimal performance in selected criteria. Sensitivity analysis was used to demonstrate the robustness of the acquired ranking findings, which were further, validated using ten popular decision-making models.

A methodology for personnel selection in business development: An interval type 2-based fuzzy DEMATEL-ANP approach

The employee selection procedure holds significant importance for every company or organization involved in current economic activities. It is vital to underscore staff selection since it plays a crucial role in determining a firm's success. When a corporation selects a suitable applicant for a position and initiates the recruiting process, it might mark a critical point for the firm and substantially impact its functioning. In the present study, I utilized the trapezoidal interval type-2 (IT2) fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) - Analytic Network Process (ANP) methodology to conduct person selection. Additionally, statistically significant values for both the primary and secondary criteria related to staff recruitment were determined. The employment of square comparison matrices is essential for implementing the DEMATEL-ANP approach. The number of subcriteria for this application is equal to that of the alternative. The approach was successfully executed without facing any obstacles. The aim of this research was to investigate the viability of employing IT2 Fuzzy Systems to handle and control uncertainty efficiently. In addition, the implementation of sensitivity analysis plays a vital role in the context of multicriteria decision-making (MCDM) techniques.

A multi-agent digital twin–enabled decision support system for sustainable and resilient supplier management

Industry 4.0 is reshaping the manufacturing landscape, bringing about revolutionary changes in both product manufacturing and collaborative interactions among value chain constituents. Within the manufacturing value chain process, sustainable and resilient supplier management is recognized as a strategic endeavor that drives value creation, such as enhanced product quality, risk mitigation, and improved organizational reputation. Sustainable and resilient supplier management processes necessitate the integration of multiple criteria, efficient supplier coordination, and adaptability to evolving challenges. Nonetheless, conventional supplier management practices primarily rely on expert judgment, focus on the present situation, and lack a robust decision validation mechanism, leading to a limited capability to constitute proactive managerial decision-making under uncertainty. This paper introduces a digitally enabled decision support system for facilitating an intelligent and customized supplier management process in dynamic and complex environments. The system leverages stratified fuzzy decision-making techniques to account for the potential impacts of future events. Additionally, it integrates multi-agent digital twin technology to provide a reliable validation mechanism for assessing the effectiveness of supplier development strategies. The effectiveness of the proposed system was demonstrated through its successful implementation within a multinational solar water pump manufacturing company, highlighting its promising potential for practical applications.

Assessing the Role of Information and Communication Technology (ICT) in Safeguarding the Environment through the Application of the MOORA Method

Information and Communication Technology (ICT) plays a vital role in bolstering endeavors aimed at safeguarding the environment, and the MOORA method offers a structured approach. ICT involves the use of digital tools and technologies to manage and transmit information, enabling real-time data collection, analysis, and communication. In environmental protection, ICT aids in various ways, such as monitoring air and water quality, tracking wildlife patterns, and managing waste disposal. The MOORA method is a decision-making technique that helps prioritize alternatives based on multiple conflicting criteria. In the context of environmental protection, the MOORA method assists in selecting the most effective ICT solutions. It evaluates various ICT options by considering multiple objectives, such as efficiency, cost-effectiveness, ecological impact, and scalability. MOORA computes ratios to compare alternatives against each criterion, enabling a comprehensive assessment. By assigning weights to the criteria, stakeholders can emphasize specific factors according to their importance. For instance, when choosing between different ICT systems for waste management, the MOORA method can quantify the ecological benefits of reduced emissions, energy savings, and waste reduction against factors like implementation costs and technological feasibility. This systematic evaluation ensures that the chosen ICT solution aligns with the overall goal of environmental protection while considering practical constraints. ICT leverages advanced technologies to bolster environmental protection, and the MOORA method provides a structured approach to assess and prioritize ICT solutions. This combined approach facilitates informed decision-making, leading to the adoption of efficient and sustainable technologies that contribute to a healthier planet. The Smart Grid System (A1), E-waste Recycling Program (A2), Air Quality Monitoring Network (A3), Water Pollution Detection Sensors (A4), Green Supply Chain Management Software (A5), and Virtual Environmental Education Platform (A6) are employed as alternative solutions. These alternatives are assessed based on their ability to achieve Reduction in Environmental Impact (C1), Enhancement of Efficiency (C2), Cost Efficiency (C3), and User-Friendliness (C4).The environmental production of E-waste Recycling Program is got first rank and Smart Grid System is got lowest rank.

Benchmarking Methodology of Banks Based on Financial Sustainability Using CRITIC and RAFSI Techniques

This study aims to determine the influence of financial risks on the financial sustainability of Iraq banks, in contrast considering the moderator role of financial expertise and the risk of directors’ riskiest directorships. Moreover, the investment attractiveness has also been accounted for specifying the mediating impact between the moderator variables and financial sustainability. Robust random effect model was employed to investigate the determinants of financial sustainability for the listed banks on the Iraqi stock trade and the Iraqi financial marketplace. In addition, multicriteria decision-making techniques were utilized to rank the banks from the best to worst based on financial sustainability. This study has examined 19 Iraqi banks over 14 years from 2007 to 2020 (266 observations), during the development of the Iraqi corporate governance cod. The findings indicate that financial risk has a negative influence on the financial sustainability of Iraq. Furthermore, the link between financial risks and financial sustainability is fully mediated by investment attractiveness. In this context, financial expertise and the relinquishing of directors’ riskiest directorships have an interactive role in the link among investment attractiveness and financial sustainability indicators. Multi-criteria decision making (MCDM) results revealed that the best bank in terms of financial sustainability is BTRI and the worst bank is BNOR. The findings enable managers to develop strategies to mitigate financial risk in phases and prepare for future unpredicted risks. Hence, the results provided an insight to practitioners regarding the best banks in terms of financial sustainability in order to invest and deal with them.

Vehicle Detection and Speed Estimation Using Semantic Segmentation with Low Latency

Computer vision researchers are actively studying the use of video in traffic monitoring. TrafficMonitor uses a stationary calibrated camera to automatically track and classify vehicles on roadways. In practical uses like autonomous vehicles, segmenting semantic video continues to be difficult due to high-performance standards, the high cost of convolutional neural networks (CNNs), and the significant need for low latency. An effective machine learning environment will be developed to meet the performance and latency challenges outlined above. The use of deep learning architectures like SegNet and Flownet2.0 on the CamVid dataset enables this environment to conduct pixel-wise semantic segmentation of video properties while maintaining low latency. In this work, we discuss some state-of-the-art ways to estimating the speed of vehicles, locating vehicles, and tracking objects. As a result, it is ideally suited for real-world applications since it takes advantage of both SegNet and Flownet topologies. The decision network determines whether an image frame should be processed by a segmentation network or an optical flow network based on the expected confidence score. In conjunction with adaptive scheduling of the key frame approach, this technique for decision-making can help to speed up the procedure. Using the ResNet50 SegNet model, a mean IoU of "54.27 per cent" and an average fps of "19.57" were observed. Aside from decision network and adaptive key frame sequencing, it was discovered that FlowNet2.0 increased the frames processed per second to "30.19" on GPU with such a mean IoU of "47.65%". Because the GPU was utilised "47.65%" of the time, this resulted. There has been an increase in the speed of the Video semantic segmentation network without sacrificing quality, as demonstrated by this improvement in performance.

Spatial model of Dengue Hemorrhagic Fever (DHF) risk: scoping review

BackgroundCreating a spatial model of dengue fever risk is challenging duet to many interrelated factors that could affect dengue. Therefore, it is crucial to understand how these critical factors interact and to create reliable predictive models that can be used to mitigate and control the spread of dengue.MethodsThis scoping review aims to provide a comprehensive overview of the important predictors, and spatial modelling tools capable of producing Dengue Haemorrhagic Fever (DHF) risk maps. We conducted a methodical exploration utilizing diverse sources, i.e., PubMed, Scopus, Science Direct, and Google Scholar. The following data were extracted from articles published between January 2011 to August 2022: country, region, administrative level, type of scale, spatial model, dengue data use, and categories of predictors. Applying the eligibility criteria, 45 out of 1,349 articles were selected.ResultsA variety of models and techniques were used to identify DHF risk areas with an arrangement of various multiple-criteria decision-making, statistical, and machine learning technique. We found that there was no pattern of predictor use associated with particular approaches. Instead, a wide range of predictors was used to create the DHF risk maps. These predictors may include climatology factors (e.g., temperature, rainfall, humidity), epidemiological factors (population, demographics, socio-economic, previous DHF cases), environmental factors (land-use, elevation), and relevant factors.ConclusionsDHF risk spatial models are useful tools for detecting high-risk locations and driving proactive public health initiatives. Relying on geographical and environmental elements, these models ignored the impact of human behaviour and social dynamics. To improve the prediction accuracy, there is a need for a more comprehensive approach to understand DHF transmission dynamics.

Multi-criteria group decision making based on graph neural networks in Pythagorean fuzzy environment

Given that the majority of existing approaches for multi-criteria group decision making (MCGDM) rely solely on the preferences of decision makers (DMs) and fail to consider the various relationships between alternatives, this paper attempts to model the relevant relational structures using graphs and introduce the concept of graph neural networks (GNNs) in the context of group decision-making. By leveraging the powerful expressive capabilities of GNNs, the aim is to mine additional information pertinent to the decision-making process and screen out alternatives for the final decision. To begin, we provide a mapping of MCGDM to the graph domain and construct a corresponding relation graph among alternatives. Additionally, to deal with uncertain or vague information, we transform the group decision-making problem into a Pythagorean fuzzy environment and define a novel measure of entropy specifically designed for Pythagorean fuzzy sets (PFSs) in the entropy weight model to determine the weights of criteria. Simultaneously, we propose a new distance measure for PFSs, which is then applied to the extended Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method to rank alternatives. Furthermore, we develop a GNNs-based Pythagorean fuzzy MCGDM approach that incorporates the aforementioned techniques for group decision-making. Finally, to validate the effectiveness and superiority of this approach, we employ it to address a supplier selection issue. Compared with baseline group decision-making approaches, our approach can indeed capture the relationships among alternatives in complex group decision-making scenarios and outperforms the best-performing baseline by nearly 2.8% in terms of ranking accuracy.

Dynamic Power Consumption in Base Transceiver Station in Urban and Rural Setting in Awka, Nigeria using Multi-Attribute Decision-Making Technique

The goal of this paper was to lower base transceiver station (BTS) dynamic power consumption. A multi-attribute decision-making technique was implemented that reflects the stochastic nature of cellular networks and guarantees cost-effective traffic generation and improved BTS power consumption performance. Every related candidate or attribute that is taken into consideration to help reduce power consumption is given a utility function, and the candidate with the highest utility value is chosen to meet the target. The Utility function that the Technique selects is the weighted sum of the normalized attributes. In contrast to the works of other authors, which lowered consumption by 20%, the developed dynamic power consumption model cut consumption to 25.14 percent. The two BTS of interest were measured, and the results showed that Mgbakwu BTS has two Peak (Busy) Hour periods: in the morning, 9.55Erlangs from 7.30am to 8.30am, and in the evening, 10.7Erlangs from 8.30pm to 9.30pm. The BTS is located in a rural/residential area. The BTS measurement and analysis for the UNIZIK temporary site revealed that it is located in a commercial/business setting with a morning peak (busy) hour period of 19.1 Erlangs between 10 and 11 a.m. 14.6 Erlangs is the afternoon peak hour, which runs from 3.30 to 4.30 p.m. The traffic generated in the BTS under various load situations is reflected in the relevant Power Consumption for both BTS.

Toward Smart Logistics: Hybrization of Intelligence Techniques of Machine Learning and Multi-Criteria Decision-Making in Logistics 5.0

With cutting-edge technology and data-driven insights, intelligent logistics has become a game-changing supply chain management strategy, helping maximize productivity and streamline processes. The main ideas of smart logistics are outlined in this study. It includes several factors that companies must consider while assessing and implementing intelligent logistics solutions. Real-time visibility, supply chain collaboration, inventory management, robotics and automation, supply chain integration, predictive analytics, scalability and flexibility, security and data privacy, and return on investment are some requirements. Using these criteria, organizations may use intelligent logistics to optimize inventory levels, boost customer happiness, save costs, simplify procedures, and enhance supply chain efficiency. In a dynamic and connected world, intelligent logistics solutions help businesses make data-driven choices, react fast to market needs, and adjust to changing business situations. We applied machine learning algorithms to analyze the supply chain dataset. We used two machine learning algorithms: a decision tree and a random forest. We compute the accuracy, precision, recall, and f1 score. The decision tree has the highest accuracy, with 91%. Then, we used the multi-criteria decision-making (MCDM) methodology to analyze the criteria of intelligent logistics. The AHP method is used to compute the weights of criteria.

Analysis of Three Generations of Recycled Concrete: an Approach Using LCA and Weighted Sum Model

This extensive study conducted a thorough life cycle assessment of a closed-loop of recycling concrete by considering six vital criteria encompassing technical aspects (short and long-term compressive strength), environmental concerns (impacts on human health, ecosystem quality, climate change, and resource usage), and economic considerations (cost). To comprehensively assess these criteria, one potent multi-criteria decision-making technique, namely the weighted sum model (WSM) expertly employed. Two diverse weighting methodologies were utilized, the equal performance method, and the entropy method, to optimize for a range of construction applications, with a particular focus on sustainable residential buildings and housing. The results derived from this study showed that reusing concrete as a third generation of recycled concrete with 20% fly ash and 12% silica fume not only reduced costs by approximately 15% but also significantly mitigated the overall environmental impact, achieving a remarkable reduction of up to 38.5%, While reaching superior strength to the first and second generations at 28, 90, and 180 days of testing.

Abstract A016: Reducing the growing disparity of colorectal cancer screening among underserved populations in rural health clinics: The PRIME-CRC trial

Abstract Introduction: Colorectal cancer (CRC), the third leading cause of cancer death in the United States, can be significantly reduced if it is detected early. Although overall CRC screening rates have increased significantly, disparities persist among low-income individuals, adults with low literacy and those living in rural areas. These groups all have screening completion rates below 50%. Purpose of Study: To assess the effectiveness of a patient-centered, shared decision making, health literacy strategy to promote longer-term adherence to colorectal cancer (CRC) screening in resource-limited, rural health clinics via colonoscopy or annual fecal immunochemical test (FIT). Methods Used: A two-arm randomized control trial implemented in 6 rural community clinics. Clinics reported CRC baseline screening rates of 17% to 38%. Eligible patients, age 45-75, who were not up to date on CRC screening were recruited. Clinic based RAs screened and consented patients. They collected baseline data and then recommended screening and gave brief literacy and culturally appropriate education using a pamphlet developed by the authors (4th grade level). The RA used shared decision-making techniques to aid patients in selecting the test they prefer and that met their needs – colonoscopy or FIT. All patients who selected the FIT kit were given simplified instructions (3rd grade level) and a demonstration of how to use it and those who chose colonoscopy were given simplified colonoscopy prep instructions and had a referral made to GI clinic. Patients were randomized into intervention (INT) arm or enhanced usual care (EUC) arm. Those in INT who selected the FIT (and had not returned it) at 4, 8 and 12 weeks received an automated follow-up call or text from a provider at their clinic using plain language and motivational messages encouraging patients to complete the FIT (colonoscopy INT patients received reminder calls prior to appointment to remind them about prepping for procedure and their appointment). Summary of Results: 734 patients have been enrolled to date: 361 in INT arm and 373 in EUC arm; 64.1% are African American, 60.3% women; 38% have limited literacy; 71.8% highest level of education completed was high school or less; only 5.6% have annual incomes > $50,000. 72.4% of patients had heard of tests to screen for CRC and 75.4% had a doctor or nurse recommend screening for CRC in the past. With regard to screening tests, 5.6% of patients have chosen a colonoscopy and 94.4% have selected a FIT. Screening to date in Year 1: 56.0% completed screening by FIT in INT arm versus 54.2% in EUC arm; only 20% have completed screening by colonoscopy to date. Conclusions: Simplified instructions accompanied by a face-to-face demonstration of FIT, use of “teach back” to confirm understanding with a follow-up automated text message or call if needed, facilitated completion rates of all patients, particularly those with limited literacy. This has not been effective to date with colonoscopy. Citation Format: Connie L. Arnold, Joseph Dean, James Morris, Elise Reed, Laura Curtis, Terry Davis. Reducing the growing disparity of colorectal cancer screening among underserved populations in rural health clinics: The PRIME-CRC trial [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr A016.

A hybrid emergency decision-making technique based on trapezoidal fuzzy best-worst method and zero-sum game

A hybrid emergency decision-making technique based on trapezoidal fuzzy best-worst method and zero-sum game

Strategic Reliability Centered Maintenance Decision Analysis Model Using Fuzzy and Analytic Hierarchy Process for Refinery Valves

Modern industries, particularly oil and gas, necessitate robust maintenance management and optimization for increased profits, plant availability, and maintenance cost reduction. The maintenance strategy is therefore crucial, especially under global economic pressures on equipment reliability. This study examines the present maintenance management practices for refinery valves, critical equipment whose failure can have severe consequences. Many existing models for selecting critical equipment in maintenance management face limitations due to inadequate decision analysis and exclusion of key criteria. This research proposes a strategy model, combining artificial intelligence and multi-criteria decision-making techniques like Fuzzy Logic and Analytical Hierarchy Process, to fill this gap and manage refinery valves' maintenance. Proven by case studies and interviews, the model improves safety, reliability, operational availability and enables operators to monitor their decision consequences. The model can convince asset managers to invest in maintenance initiatives.

Multiple-Criteria Decision-Making (MCDM) techniques to study the behavior of dendrimers using topological indices.

Topological indices provide a mathematical language for capturing molecular structure, symmetry, and predicting properties. Dendrimers are microscopic bilaterally symmetrical molecules with a well-defined homogeneous nanoparticles structure, often consisting of a symmetric center, inner shell, and outer shell. In this work, first we compute some degree-based topological indices of Porphyrin (DnPn),Poly (Propyl) Ether Imine(PETIM), Zinc porphyrin (DPZn), and Polyamidoamine (PAMAM) dendrimers. Then, we use multi-criteria decision making (MCDM) techniques to establish the weighted evaluation of dendrimer classes based on certain topological indices. For weighted analysis we correlate the properties of benzene derivatives with topological invariants. Finally, based on the multi-criteria decision making techniques namely TOPSIS, SAW and MOORA method, we have ranked the dendrimer structures based on their properties.