Mean Aggregation Research Articles

2453. Wastewater-Based Surveillance of Vancomycin-Resistant Enterococci in Hospitals

Abstract Background Vancomycin-resistant Enterococcus (VRE) is an important nosocomial infection that may increase patient morbidity, mortality, and healthcare costs. We have adapted wastewater-based surveillance (WBS) as a novel tool to comprehensively and inclusively monitor the burden of VRE in tertiary acute care hospitals. Herein, we demonstrate our ability to detect, quantify and track VRE dynamically over time across a range of scales. Methods Wastewater (WW) was collected from three hospitals in Calgary, AB: Rockyview General Hospital (RGH; 615 beds), Peter Lougheed Centre (PLC; 517 beds), and Foothills Medical Centre (FMC via three independent sites – A, B, C; 1100 beds). Three WW treatment plants (WWTPs; BBW, PCW, and FCW) serving the entire City of Calgary were sampled as community controls. DNA was extracted from WW pellets obtained following centrifugation. A multiplexed qPCR assay was adapted and used to quantify the abundances of the vanA and vanB resistance gene copies. Copy numbers were assessed as raw (copies per mL of WW processed) or normalized against three fecal biomarker genes: total bacterial 16S rRNA, human 18S rRNA, and Bacteroides HF183 16S rRNA. Differences between hospitals and controls were determined with Mann-Whitney tests (GraphPad Prism version 9.0). Results Samples from the hospitals and WWTPs in Calgary, AB collected over 12 weeks demonstrated that all hospitals had 100-1000X higher mean aggregate abundances of both vanA and vanB relative to community-based WWTPs when assessed as raw or normalized by each fecal biomarker (Figure 1; only total bacterial 16S rRNA is shown, p< 0.001, Mann-Whitney). Within one individual hospital (RGH), each of vanA and vanB abundances follows similar trends over a 12-week period, regardless of whether the values were reported as raw or normalized with the three different fecal biomarker genes (Figure 2; only vanA is shown).Figure 1.Mean aggregate copy numbers of vanA and vanB in wastewater measured by qPCR. VRE vanA and vanB gene abundances normalized as a ratio against total bacterial 16S rRNA copies demonstrate significantly higher abundances of both vanA and vanB in hospitals than the WWTP community controls.Figure 2.Comparison of vanA gene copies assessed as raw versus normalized as measured by qPCR from Rockyview General Hospital (RGH). VRE vanA gene abundances in Rockyview General Hospital (RGH) WW assessed as raw and normalized as a ratio against three different fecal biomarkers (total bacterial 16S rRNA, human 18S rRNA and Bacteroides HF183 16S rRNA) demonstrated similar trends in vanA gene abundances over time. Conclusion WBS is a unique real-time tool that can be adapted to monitor the abundance of VRE across a range of scales. This tool has the potential to augment antimicrobial stewardship and infection prevention and control programs to better understand the contributing factors to selection and colonization. Disclosures All Authors: No reported disclosures

Towards reliable IoT communication and robust security: investigating trusted schemes in the internet of medical things using blockchain

The Internet of Things (IoT) is evolving in various sectors such as industries, healthcare, smart homes, and societies. Billions and trillions of IoT devices are used in e-health systems, known as the Internet of Medical Things (IoMT), to improve communication processes in the network. Scientists and researchers have proposed various methods and schemes to ensure automatic monitoring, communication, diagnosis, and even operating on patients at a distance. Several researchers have proposed security schemes and approaches to identify the legitimacy of intelligent systems involved in maintaining records in the network. However, existing schemes have their own performance issues, including delay, storage efficiency, costs, and others. This paper proposes trusted schemes that combine mean and subjective logic aggregation methods to compute the trust of each communicating device in the network. Additionally, the network maintains a blockchain of legitimate devices to oversee the trusted devices in the network. The proposed mechanism is further verified and analyzed using various security metrics, such as reliability, trust, delay, beliefs, and disbeliefs, in comparison to existing schemes.

Dual Hesitant q-Rung Orthopair Fuzzy Interaction Partitioned Bonferroni Mean Operators and Their Applications

The purpose of this paper is to introduce interaction partitioned Bonferroni mean operators under dual hesitant q-rung orthopair fuzzy environment. Motivated by the idea of q-rung orthopair fuzzy interaction operational laws, partitioned Bonferroni mean, and dual hesitant q-rung orthopair fuzzy sets, for dual hesitant q-rung orthopair fuzzy numbers, we present dual hesitant q-rung orthopair fuzzy interaction operational rules and propose several dual hesitant q-rung orthopair fuzzy interaction partitioned Bonferroni mean aggregation operators, including the interaction partitioned Bonferroni mean operator for dual hesitant q-rung orthopair fuzzy numbers, the weighted interaction partitioned Bonferroni mean operator for dual hesitant q-rung orthopair fuzzy numbers, the interaction partitioned geometric Bonferroni mean operator for dual hesitant q-rung orthopair fuzzy numbers, and the weighted interaction partitioned geometric Bonferroni mean operator for dual hesitant q-rung orthopair fuzzy numbers. Moreover, some properties and special cases associated with these proposed operators are also analyzed. For dual hesitant q-rung orthopair fuzzy numbers, based on the proposed operators, a multicriteria group decision-making method is proposed. Finally, an example for missile purchase problem is illustrated to demonstrate the superiority and feasibility by comparing with other existing multicriteria group decision-making methods.

Classification of epileptic EEG signals with the utilization of Bonferroni mean based fuzzy pattern tree

The Electroencephalogram (EEG) is an essential tool used to detect and investigate multiple neurological disorders within the human brain. However, examination and visualization of any such abnormalities in the brain through inspecting these EEG signals is a time-consuming and repetitive task for a neurologist. Hence, there is a need to develop a model that can automatically classify brain disorders based on the EEG signals. In this study, a novel fuzzy classification model is proposed for classifying the EEG signals. First, the discrete wavelet transform (DWT) technique is employed to decompose the signal into time–frequency sub-bands. Then, by utilizing these sub-bands, seven meaningful statistical features are extracted effectively for additional investigation purposes. Then, the retrieved features are provided to the fuzzy pattern tree classifier to obtain meaningful inferences from the data. Here, a distinctive structure of a fuzzy pattern tree is proposed by utilizing the interrelationship handling aggregation operators. More precisely, we utilize the Bonferroni mean (BM) aggregation operator for the enlargement or expansion of the tree while considering the concatenation between the parent and the slave tree. To the best of our knowledge, this is the first work that presents such an interrelationship handling fuzzy logic-based classifier model for EEG signal classification. The proposed algorithm is evaluated using two publicly available datasets, namely the Bonn University (Bonn) epileptic datasets and the Temple University Hospital (TUH) EEG subcorpus of abnormal (TUAB) and epilepsy (TUEP) datasets. This study considers different frames of the binary problem (i.e., healthy vs. seizure detection) and multi-class problem (i.e., healthy vs. seizure free vs. seizure) over the Bonn dataset and achieves more than 99% accuracy over the binary model by testing over all possible combinations of the available experimental dataset and 97.8% accuracy in multi-class model. In the case of TUAB and TUEP categories, the model achieves an accuracy of 88% in predicting normal to abnormal activities and 86% in detecting epilepsy and non-epilepsy within the signals. The outcomes derived from the proposed model outperform some existing studies available in the literature for EEG signal classification. Also, the model is efficient as it considers the uncertainty and association among the features while framing the classifier model.

Multi-attribute decision-making based on dual probabilistic interval-valued intuitionistic hesitant fuzzy weighted heronian mean aggregation operator

Probabilistic interval-valued intuitionistic hesitant fuzzy sets (PIVIHFSs) can well describe the evaluation information of decision-makers (DMs) in multi-attribute decision-making (MADM) problems. However, PIVIHFSs only depict the situation where both membership and non-membership information occur with equal probability while ignoring the situations of non-equal possibility due to DMs’ subjective preferences. In this paper, we develop dual probabilistic interval-valued intuitionistic hesitant fuzzy sets (DPIVIHFSs) concept based on the truncated normal distribution. The DPIVIHFSs overcome the shortcomings of PIVIHFSs and are more interpretable. Then, the operations and ranking method of DPIVIHFSs are introduced. Furthermore, we study MADM methods in dual probabilistic interval-valued intuitionistic hesitant fuzzy environments by aggregation operators (AOs). We propose a series of AOs including the DPIVIHF heronian mean (DPIVIHFHM) operator and the DPIVIHF weighted heronian mean (DPIVIHFWHM) operator. The basic properties of the presented are discussed and proved. Finally, a novel method for solving the MADM problem is proposed based on the DPIVIHFWHM operator and a numerical example of express company selection strategy is used to illustrate the effectiveness of the method. The proposed method in this article can capture more fuzzy and uncertain information when solving MADM problems and have a wider application range.

Medium-to-long term sustainability of a health systems intervention to improve service readiness and quality of non-communicable disease (NCD) patient care and experience at primary care settings in Uganda

BackgroundWith the double burden of rising chronic non-communicable diseases (NCDs) and persistent infectious diseases facing sub-Saharan Africa, integrated health service delivery strategies among resource-poor countries are needed.Our study explored the post-trial sustainability of a health system intervention to improve NCD care, introduced during a cluster randomised trial between 2013 and 2016 in Uganda, focusing on hypertension (HT) and type-2 diabetes mellitus (DM) services.In 2020, 19 of 38 primary care health facilities (HFs) that constituted the trial’s original intervention arm until 2016 and 3 of 6 referral HFs that also received the intervention then, were evaluated on i) their facility performance (FPS) through health worker knowledge, and service availability and readiness (SAR), and ii) the quality-of-patient-care-and-experience (QoCE) received.MethodsCross-sectional data from the original trial (2016) and our study (2020) were compared. FPS included a clinical knowledge test with 222 health workers: 131 (2016) and 91 (2020) and a five-element SAR assessment of all 22 HFs. QoCE assessment was performed among 420 patients: 88 (2016) and 332 (2020). Using a pair-matched approach, FPS and QoCE summary scores were compared. Linear and random effects Tobit regression models were also analysed.ResultsThe mean aggregate facility performance (FPS) in 2020 was lower than in 2016: 70.2 (95%CI = 66.0–74.5) vs. 74.8 (95%CI = 71.3–78.3) respectively, with no significant difference (p = 0.18). Mean scores declined in 4 of 5 SAR elements.Overall FPS was negatively affected by rural or urban HF location relative to peri-urban HFs (p < 0.01). FPS was not independently predicted but patient club functionality showed weak association (p = 0.09).QoCE declined slightly to 8.7 (95%CI = 8.4–91) in 2020 vs 9.5 (95%CI = 9.1–9.9) in 2016 (p = 0.02) while the proportion of patients receiving adequate quality care also declined slightly to 88.2% from 98.5% respectively, with no statistical difference (p = 0.20). Only the parent district weakly predicted QoCE (p = 0.05).ConclusionsFour years after the end of research-related support, overall facility performance had declined as expected because of the interrupted supplies and a decline in regular supervision. However, both service availability and readiness and quality of HT/DM care were surprisingly well preserved.Sustainability of an NCD intervention in similar settings may remain achievable despite the funding instability following a trial’s end but organisational measures to prepare for the post-trial phase should be taken early on in the intervention process.

InterOpt: Improved gene expression quantification in qPCR experiments using weighted aggregation of reference genes

qPCR is still the gold standard for gene expression quantification. However, its accuracy is highly dependent on the normalization procedure. The conventional method involves using the geometric mean of multiple study-specific reference genes (RGs) expression for cross-sample normalization. While research on selecting stably expressed RGs is extensive, scant literature exists regarding the optimal approach for aggregating multiple RGs into a unified RG. In this paper, we introduce a family of scale-invariant functions as an alternative to the geometric mean aggregation. Our candidate method (weighted geometric mean minimizing standard deviation) demonstrated significantly better results compared to other proposed methods. We provide theoretical and experimental support for this finding using real data from solid tumors and liquid biopsies. Moreover, the closed form and regression-based solution enable efficient computation and straightforward adoption on various platforms. All the proposed methods have been implemented within an easy-to-use R package with graphics processing unit (GPU) acceleration.

Novel intuitionistic fuzzy Aczel Alsina Hamy mean operators and their applications in the assessment of construction material

Aggregation operators (AOs) are utilized to overcome the effects of attributes under some specific degree of weight in the decision-making (DM) process. The AOs have a large capacity to deal with uncertain and unpredictable information in multi-attribute decision-making (MADM) problems. The Hamy mean (HM) aggregation tools are well-known aggregation models, which are utilized to define correlation among different input arguments adequately. The intuitionistic fuzzy (IF) sets (IFS) can express unpredictable and vague information. The Aczel Alsina aggregation expressions are extensions of triangular norms. Recently, Aczel Alsina aggregation tools attained a lot of attentions from numerous research scholars. By inspiring the robustness and reliability of Aczel Alsina aggregation tools, we expose some appropriate operations of Aczel Alsina expressions under consideration of IF information. In this manuscript, we developed an intuitionistic fuzzy Aczel Alsina HM (IFAAHM) and an intuitionistic fuzzy Aczel Alsina weighted HM (IFAAWHM) operator. We also expressed the theory of Dual HM (DHM) tools and established a series of new approaches including intuitionistic fuzzy Aczel Alsina Dual HM (IFAADHM) and intuitionistic fuzzy Aczel Alsina weighted Dual HM (IFAAWDHM) operators. Some reliable characteristics and special cases of our derived approaches are also presented. The authors solved an application of a MADM technique under consideration of our derived approaches. To check the reliability and dependency of our derived mythologies, we gave an experimental case study to evaluate a desirable construction material based on some specific criteria of different Alternatives. To see the advantages and compatibility of our derived approaches, by comparing the results of existing approaches with the results of currently discussed AOs.

Multi-attribute group decision-making based on intuitionistic fuzzy aggregation operators defined by weighted geometric means

AbstractThis paper proposes a multi-attribute group decision-making methodology that takes advantage of a new weighted geometric mean aggregation operator on intuitionistic fuzzy numbers (IFNs). To this purpose, first, we define the intuitionistic fuzzy direct weighted geometric operator on IFNs, then we prove that it is a representable intuitionistic aggregation operator, and afterwards, we compare it with other aggregation operators motivated by the geometric mean. We use two proxies for the quantitative comparison of performances, namely the average of the Euclidean distances to the IFNs and the sum of squared error inspired by the k-means clustering algorithm.

Q-rung orthopair normal fuzzy Maclaurin symmetric mean aggregation operators based on Schweizer-Skla operations

In order to be able to make a good decision, we need to evaluate the uncertainty information of multiple attributes of different alternatives that obey the normal distribution, and the interrelationship among multiple attributes should be considered in the process of evaluating. This paper aims to propose a new multiple attribute decision-making (MADM) method, which uses a new aggregation operator to evaluate the uncertainty information that obey normal distribution comprehensively. We firstly extended Schweizer-Sklar (SS) t-norm (TN) and t-conorm (TCN) to q-rung orthopair normal fuzzy number (q-RONFN) and defined the Schweizer-Skla operational laws of q-rung orthopair normal fuzzy set (q-RONFs). Secondly, we developed q-rung orthopair normal fuzzy Maclaurin symmetric mean aggregation operators based on SS operations considering that the Maclaurin symmetric mean operator can reflect the interrelationship among multiple input variables. Furthermore, we discussed some desirable properties of the above operators, such as monotonicity, commutativity, and idempotency. Lastly, we proposed a novel MADM method based on developed aggregation operators. A numerical example on enterprise partner selection is given to testify the effectiveness of the developed method. The results of analysis indicated that our proposed aggregation operators have stronger information aggregation ability and are more general and flexible for MADM problems.

A comprehensive reappraisal of long-term aerosol characteristics, trends, and variability in Asia

Abstract. Changes in aerosol loadings and properties are important for the understanding of atmospheric environment and climate change. This study investigates the characteristics and long-term trends of aerosols of different sizes and types in Asia from 2000 to 2020 by considering multi-source aerosol data and novel analysis methods and perspectives, all of which provides the groundwork for promoting the acquisition of new discoveries that are different from the past. The geometric mean aggregation method is applied, and serial autocorrelation is considered to avoid the overestimation of trend significance. Among regions in Asia, high values of aerosol optical depth (AOD) are mainly concentrated in East Asia (EA) and South Asia (SA) and are closely related to the population density. The AOD in EA showed the most significant negative trend, with a value of -5.28×10-4 per year, mainly owing to decreases in organic carbon (OC), black carbon (BC), and dust aerosols. It is also worth noting that this observed large-scale decrease in OC and BC is a unique and significant phenomenon to region of EA and mainly around China. In contrast, the aerosol concentrations in SA generally show a positive trend, with an increased value of AOD of 1.25 × 10−3 per year. This increase is mainly due to large emissions of fine-mode aerosols, such as OC and sulfate aerosol. Additionally, the high aerosol loading in northern SA has a lower AOD variability when compared with that of East China Plain, revealing a relatively more persistent air pollution situation. Over the whole region of Asia, the characteristics of the percentage changes in different types of AOD are seen in the increases in BC (6.23 %) and OC (17.09 %) AOD, together with a decrease in dust (−5.51 %), sulfate (−3.07 %), and sea salt (−9.80 %) AOD. Except for anthropogenic emissions, the large increase in the percentage of OC is also due to wildfires found in northern Asia in the summer, whereas the differently sized AOD only shows slight changes in Asia in that small-sized AOD decreases (−3.34 %) and that the total AOD did not show a significant change. This suggests that, from a trend perspective, decreases in aerosol in recent years have mostly been offsetting earlier increases in the anthropogenic emissions over Asia. To summarize, the above findings analyse the comprehensive characteristics of aerosol distributions and reappraise the long-term trends of different aerosol parameters, which will greatly enhance the understanding of regional and global aerosol environment and climatology, in addition to filling the gaps and breaking through the limitations of past knowledge.

A correlation study between mean platelet volume and platelet aggregation study in acute coronary syndrome patients.

Platelets play an important role in cardiovascular disease mainly in the development of acute thrombotic events. Elevated platelet indices have been proposed as a risk factor for acute coronary syndrome (ACS). It remains uncertain whether increased platelet indices are the result or the cause of ACS. This study aimed to correlate mean platelet volume (MPV) and platelet aggregation response to know the functional status of platelets based on their size. A total of 50 patients with ST-segment elevation ACS (STE-ACS) or non-ST-segment elevation ACS (NSTE-ACS) were included and their MPV was measured and platelet aggregometry was performed. Patients were divided into two groups, patients with MPV ≤9.1 fl as group 1 and those with MPV >9.1 fl as group 2. The mean maximum platelet aggregation response (MMPAR) with ADP, Collagen, and Epinephrine, of both the groups, were compared. MMPAR to ADP, Collagen, and Epinephrine in group 1 was 74.47%, 66.13%, and 72.9%, respectively, and in group 2, 72.94%, 59.97%, and 72.43%, respectively. There was no statistically significant difference in the MMPAR to ADP, Collagen, and Epinephrine among the two groups. Increased MPV does not indicate the platelets are hyperreactive. An increase in MPV may be because of the increased release of immature platelets from bone marrow as there is increased consumption of platelets at the site of thrombus formation in ACS.

Poster 152: Detrimental Effects of Chlorhexidine on Articular Cartilage Viability, Matrix, and Mechanics

Objectives: Chlorhexidine gluconate (CHG) is commonly used in antiseptic irrigation solutions for bacterial decontamination during orthopaedic surgery. Although the chondrotoxicity of CHG to articular cartilage has been reported, CHG irrigation is utilized by some surgeons to limit surgical site infection and to salvage grafts after accidental contamination during joint surgery. The full extent of CHG irrigation-related chondrotoxicity, as well as its effects on articular cartilage extracellular matrix and mechanical properties, are unknown. The purpose of this study was to determine the in vitro effects of a single one-minute CHG exposure on the viability, biochemical content, and mechanics of native articular cartilage explants. Methods: Articular cartilage explants (n=6 per group) were harvested from the femoral condyles of the porcine stifle and sectioned at the tidemark. Explants were bathed in Irrisept® chlorhexidine solution (0.05% CHG in sterile water) at varying concentrations (0% control, 0.01% CHG, and 0.05% CHG) to stimulate surgical irrigation for 1 minute, followed by complete phosphate-buffered saline wash and culture. After seven days of culture in chondrogenic medium, the explants were analyzed for viability (live/dead assay); collagen content (hydroxyproline assay); glycosaminoglycan (GAG) content (dimethyl methylene blue assay); and compressive mechanical testing (creep indentation testing). Statistical analyses were performed using a one-way ANOVA with post hoc Tukey test. Results: Seven days after CHG exposure, a dose-dependent decrease in mean chondrocyte viability was observed in the CHG groups (p&lt;0.001), with &lt;3% viability observed in the 0.05% CHG group (Figure 1). There was no significant difference in mean collagen content per wet weight among groups. Conversely, 0.05% CHG exposure led to a decrease in mean GAG per wet weight compared to negative control (p=0.029) and 0.01% CHG (p=0.046) (Figure 2). These changes in GAG content corresponded to observed changes in the compressive mechanical properties of the explants. A dose-dependent decrease in mean aggregate modulus and shear modulus was observed after CHG exposure (p&lt;0.029), indicating weakening of the cartilage matrix and increased deformation in response to compressive loads (Figure 3). Conclusions: One-minute CHG exposure to articular cartilage explants led to dose-dependent decreases in chondrocyte viability, GAG content, and compressive mechanical properties. These detrimental effects were observed for both 0.05% and 0.01% CHG concentrations. There is a paucity of literature examining the effects of CHG on articular cartilage matrix and mechanics, and the findings of this study suggest a lack of matrix maintenance following cell death, raising concern for risk of mechanical failure of the cartilage tissue. Clinicians should be judicious regarding the use of CHG irrigation at these concentrations in the presence of native articular cartilage.

Graph Ordering Attention Networks

Graph Neural Networks (GNNs) have been successfully used in many problems involving graph-structured data, achieving state-of-the-art performance. GNNs typically employ a message-passing scheme, in which every node aggregates information from its neighbors using a permutation-invariant aggregation function. Standard well-examined choices such as the mean or sum aggregation functions have limited capabilities, as they are not able to capture interactions among neighbors. In this work, we formalize these interactions using an information-theoretic framework that notably includes synergistic information. Driven by this definition, we introduce the Graph Ordering Attention (GOAT) layer, a novel GNN component that captures interactions between nodes in a neighborhood. This is achieved by learning local node orderings via an attention mechanism and processing the ordered representations using a recurrent neural network aggregator. This design allows us to make use of a permutation-sensitive aggregator while maintaining the permutation-equivariance of the proposed GOAT layer. The GOAT model demonstrates its increased performance in modeling graph metrics that capture complex information, such as the betweenness centrality and the effective size of a node. In practical use-cases, its superior modeling capability is confirmed through its success in several real-world node classification benchmarks.

Morphological evolution of desiccation cracking in purple soil under the influence of aggregate size distribution

Desiccation cracking in purple soil subjected to rainfall-evaporation cycles is a common natural phenomenon, in the Three Gorges Reservoir area of China, that detrimentally affects the hydraulic and mechanical properties of soils. The significant spatial variability in the aggregate size distribution (ASD) of purple soil in this area leads to variations in crack morphology. In this study, we experimentally investigated the effects of ASD on morphological evolution of cracks and conducted a regression analysis based on the ASD indices of purple soil. The dynamic geometrical, topological, and statistical characteristics of the crack morphology were analysed using digital image processing and morphological algorithms. The results indicated that the process of crack evolution in purple soil mainly contained three stages, two intersection shapes, and three network forms. The crack morphology in soil with a larger initial mean aggregate size was more irregular and discontinuous. We proposed a cracking evolution function that depicts the crack geometry and topology considering the gravimetric water content as a variable, which proved to be a good fit for modeling the dynamic morphological characteristics of desiccation cracks. The distribution of crack length, crack area, and clod area were well described by the ExpDec function, whereas the crack width revealed a bimodal Gaussian distribution, with primary and secondary peaks of 1–1.5 and 2–3.5 mm, respectively. Moreover, Multiple linear regression models between the crack geometric parameters and ASD indices were established, which indicated that the ASD in the micro-aggregate state determines the final morphological characteristics of cracks, with the content of clay-sized aggregates having the most significant effect. The tensile stress of aggregates in the unsaturated state decreases with increasing radius, based on the microscopic mechanical analysis. These results could provides a theoretical basis for predicting the macroscopic cracking behavior of purple soil under various ASD conditions.

A hybrid approach based on dual hesitant q-rung orthopair fuzzy Frank power partitioned Heronian mean aggregation operators for estimating sustainable urban transport solutions

Transportation systems are a key part of sustainable development, and they need to be carefully evaluated to show that they have a strong impact on the target area’s social, environmental, and economic sustainability. For this reason, involving the developed decision support systems helps to shed light on the users’ demand and provide unblemished policy decisions considering the existing situation. The “q-rung orthopair fuzzy set (q-ROFS)” is a generalization of the “intuitionistic fuzzy sets (IFSs)” and “Pythagorean fuzzy sets (PFSs)” that expresses vague and uncertain data more efficiently. In the interim, the notion of “dual hesitant q-rung orthopair fuzzy set (DHq-ROFS)” is presented to account for human hesitancy, which may be more applicable to genuine “multicriteria group decision-making (MCGDM)” situations. The main goal of this study is to address MCGDM problems using Heronian mean (HM) and DHq-ROF data. The first step is to introduce the Frank t-norm and t-conorm-based DHq-ROF HM (DHq-ROFFHM) operator. DHq-ROFFHM’s features are next described in depth. In addition, the DHq-ROF Frank weighted HM (DHq-ROFFWHM) operator is presented, which takes into account different degrees of liking for input arguments. The DHq-ROF Frank weighted power partitioned HM model is then used to come up with a way to solve models in MCGDM problems where individual arguments are grouped together and have relationships with each other. A final example shows how the established model can be implemented and how well it works.

Assessing the sustainability and scalability of a diabetes eHealth innovation: a mixed-methods study

BackgroundTo date, little is known about the sustainability and scalability of MyDiabetesPlan, an eHealth innovation designed to facilitate shared decision-making within diabetes care. To avoid the possibility of its short-lived implementation and promote wider adoption so as to promote patient-centred diabetes care, it is critical to understand MyDiabetesPlan’s sustainability and scalability in order to ensure its long-term impact at a greater scale. We sought to identify the sustainability and scalability potential of MyDiabetesPlan and its limiting factors.MethodsUsing a concurrent triangulation mixed-methods approach, data were collected from 20 individuals involved in the development and implementation of MyDiabetesPlan. The National Health Services Sustainability Model (NHSSM) and the Innovation Scalability Self-administered Questionnaire (ISSaQ) were administered using a ‘think-aloud’ approach and subsequently, short semi-structured interviews were conducted. Mean aggregate scores and stakeholder-specific scores were generated for the NHSSM and ISSaQ, to quantitatively determine facilitating and limiting factors to sustainability and scalability. Content analysis occurred iteratively with qualitative data, to examine commonalities and differences with the quantitative findings.ResultsThe top facilitating factor to sustaining MyDiabetesPlan was “Staff involvement and training to sustain the process.”, whereas the top limiting factors were: “Adaptability of Improved Process”, “Senior Leadership Engagement” and “Infrastructure for Sustainability”. The top three facilitating factors for scale-up were “Acceptability”, “Development with Theory” and “Consistency with Policy Directives.” Conversely, the top three limiting factors were “Financial and Human Resources”, “Achievable Adoption” and “Broad Reach”. Qualitative findings corroborated the limiting/facilitating factors identified.ConclusionsAddressing staff involvement throughout the dynamic care contexts, and resource constraints impacting scale-up can enhance the sustainability and scalability of MyDiabetesPlan. As such, future plans will focus on garnering organizational leadership buy-in and support, which may address the resource constraints associated with sustainability and scalability and improve the capacity for adequate staff involvement. eHealth researchers will be able to prioritize these limiting factors from the outset of their tool development to purposefully optimize its sustainability and scalability performance.

Interaction Power Bonferroni Mean Aggregation Operators Based on T-Spherical Fuzzy Information and Their Application in Multi-attribute Decision Making

Interaction Power Bonferroni Mean Aggregation Operators Based on T-Spherical Fuzzy Information and Their Application in Multi-attribute Decision Making

Association of remote, longitudinal patient-reported outcomes (PROs) and step counts with hospitalization or death among patients with advanced cancer undergoing chemotherapy: Secondary analysis of the PROStep randomized trial.

1581 Background: Patients with advanced cancer undergoing chemotherapy experience significant symptoms and declines in functional status, which are often associated with poor outcomes. Remote monitoring of PROs (symptoms) and step counts (functional status), may proactively identify patients at risk of hospitalization or death. Our objectives were to evaluate the association of 1) longitudinal PROs with step counts, and 2) PROs and step counts with hospitalization or death. Methods: The PROStep randomized trial (NCT04616768) enrolled 108 patients with advanced gastrointestinal (GI) or lung cancers undergoing cytotoxic chemotherapy at a large academic cancer center. Patients were randomized to weekly text-based monitoring of 8 PROs (each measured on a 0-4 scale) + continuous step count monitoring via Fitbit, vs usual care. This pre-planned secondary analysis included 57 of 75 patients randomized to the intervention who had PRO and step data. We analyzed associations between PROs and mean daily step count, and associations of PROs and step counts with the composite outcome of hospitalization or death, using bootstrapped generalized linear models to account for longitudinal data. Results: Among 57 evaluable patients, mean age was 57 years, 24 (42%) were female, 49 (86%) were White, 43 (75%) had advanced GI cancer, and 14 (25%) had advanced lung cancer. A 1-point weekly increase (on a 32-point scale) in aggregate PRO score was associated with 247 fewer mean daily steps (95% CI -277, -213, p &lt; 0.001). PROs most strongly associated with step count decline were patient-reported activity level (daily step change -892), nausea (-677), and constipation (-524). 25 patients (44%) were hospitalized (n = 21) or died (n = 8). A 1-point weekly increase in aggregate PRO score was associated with 20% greater odds of hospitalization or death (adjusted odds ratio [aOR] 1.2, 95% CI 1.1, 1.4, p = 0.008). PROs most strongly associated with hospitalization/death were pain (aOR 3.2), decreased activity (aOR 3.2), dypsnea (aOR 2.6) and sadness (aOR 2.1). A decrease in 1000 steps was associated with 16% greater odds of hospitalization or death (aOR 1.2, 95% CI 1.0, 1.3, p = 0.03). Compared to baseline, mean daily step count decreased 6.7%, 8.5%, 16.2% in the 3, 2, and 1 weeks prior to hospitalization/death. Mean aggregate PRO score increased by 11.0%, 25.3%, 36.4% in the 3, 2, and 1 weeks prior to hospitalization/death. Conclusions: In this secondary analysis of a randomized trial, increased PRO-based symptom burden was associated with lower daily step count. Higher symptom burden and decreased step count were independently associated with and predictably worsened close to hospitalization or death. Future interventions should leverage longitudinal PRO and step data to identify risk for poor outcomes. Clinical trial information: NCT04616768 .

Picture fuzzy soft Bonferroni mean aggregation operators and their applications

Due to more advanced features of the picture fuzzy soft set and valuable characteristics of aggregation operators, in this article, we present the notion of picture fuzzy soft Bonferroni mean aggregation operators and weighted picture fuzzy soft Bonferroni mean aggregation operators. Moreover, some basic properties of these introduced aggregation operators have been given. As cancer is one of the most rapidly increasing diseases globally. But due to different kinds of cancer diseases, it is very difficult to say which type of cancer disease is increasing rapidly. So to reduce this difficulty in the medical field, we have applied our work to medical diagnosis problems to ensure that fuzzy ideas can also help in the medical field. Also, an algorithm along with a descriptive example is established that conforms to the authenticity of initiated work. Furthermore, a comparative analysis of the introduced work has been given to show how this work is more efficient and dominant than other existing theories.