Terms Of Set Research Articles

FCM-CSMOTE: Fuzzy C-Means Center-SMOTE

Imbalanced class distributions in machine learning, where the minority class is often under-represented, pose a substantial challenge. Synthetic Minority Over-sampling Technique (SMOTE) has been widely employed to address this issue by generating synthetic minority samples through interpolation. Despite its popularity, SMOTE exhibits certain drawbacks caused by the implementation of random interpolation samples. In this paper, we introduce a new data level technique for oversampling, called Fuzzy C-Means Center-SMOTE (FCM-CSMOTE), which generates synthetic samples in each cluster using its center considered as the memory of the main data components. We demonstrate that the proposed selective strategy has a very low probability to generate noise. The experimental results demonstrate that the proposed method performs better than the state-of-the-art approaches on 21 real unbalanced data sets (regular and large size data set) in terms of several metrics, including Geometric Mean (GM), F-Measure (FM), Area Under the Curve (AUC), and Accuracy.

Research on social ecological evaluation of the spatial form of old urban blocks based on dual probabilistic linguistic term sets

Under the rapid process of urbanization, many early renovated urban villages have also encountered many problems. Due to the rapid development of urban construction and the continuous changes in spatial functions, early renovated urban villages have already encountered problems such as unreasonable commercial distribution, lack of parking spaces, reduced commercial vitality, and commercial activities crowding out affecting the normal lives of villagers. There is a serious contradiction between the need for development and the quality of life of villagers. Due to the fixed nature of architectural space, only by fully understanding the essential morphological characteristics of the space can we find the optimal solutions for different space usage functions, and obtain the matching of the optimal solutions in the existing space requirements. The social ecological evaluation of the spatial form of old urban blocks is a multi-attribute group decision making (MAGDM). Recently, the grey relational analysis (GRA) and CRITIC method has been used to cope with MAGDM issues. The dual probabilistic linguistic term sets (DPLTSs) are used as a tool for characterizing uncertain information during the social ecological evaluation of the spatial form of old urban blocks. In this manuscript, the dual probabilistic linguistic GRA (DPL-GRA) method is built to solve the MAGDM under DPLTSs. The CRITIC method is used to obtain the attributes weights. In the end, a numerical case study for social ecological evaluation of the spatial form of old urban blocks is given to validate the proposed method.

Projection-based TOPSIS method to MAGDM in dual probabilistic linguistic information environment

The dual probabilistic linguistic term sets (DPLTSs) are more effective than PLTSs in solving the problem of multi-attribute group decision-making (MAGDM). In this paper, an improved TOPSIS method is developed combining the TOPSIS method and projection measure of DPLTS to supplement the existing research. Firstly, considering the mathematical characteristics of DPLTS, this paper defines the concepts of the module, cosine function, and projection of DPLTS, and then proves the mathematical properties of the cosine function. Secondly, considering the uncertainty of decision-making problems, the weight-solving models are established respectively under the condition that the weight information is completely unknown and partially known. Furthermore, a novel DPLPrj-TOPSIS approach is established based on the projection measure proposed. It involves integrating experts’ DPLTS evaluations, normalizing different DPLTSs, calculating alternatives’ relative closeness and score, etc. Secondly, the proposed method’s feasibility is demonstrated through a case study that entails selecting network promotion plans for food manufacturers. Finally, the proposed method’s effectiveness and validity are verified by comparing and analyzing it with the traditional TOPSIS method based on a distance measure and other existing decision methods.

Lean Manufacturing Assessment: Dimensional Analysis with Hesitant Fuzzy Linguistic Term Sets

Lean Manufacturing has become, in recent years, one of the most important philosophies for improving production and organizational systems. The literature shows that Hesitant Fuzzy Linguistic Terms Sets (HFLTSs) are highly capable of manipulating the uncertainty that the judgments made by evaluators carry and that they are subject to their perception, especially when used in combination with multicriteria decision making (MCDM) for the measurement of indicators in this type of system, as well as their general performance. However, it is still of interest to researchers to develop techniques and instruments that facilitate the measurement of the results obtained after applying this philosophy in organizations. This article proposes a model for the evaluation of the Lean Manufacturing performance through the Analytic Hierarchy Process (AHP) and Dimensional Analysis (DA) using HFLTSs. The results obtained show that the proposed model is a solid tool for the evaluation of Lean Manufacturing systems from a different perspective and that it can be integrated into the issuance of evaluations in a better way by considering human subjectivity. At the same time, it offers a strategy to create priorities in the action plans that Lean system managers propose after evaluating. However, it is important to apply the proposed model to multiple organizations and analyze the results obtained to maximize its benefits.

Sequential Selection for Minimizing the Variance with Application to Crystallization Experiments

For many crystal-based products (e.g., pharmaceuticals, energy storage), the size uniformity is not only a key quality attribute, but sometimes also an indicator of other attributes such as solid purity. This article proposes a sequential selection approach to find a proper experimental setting that leads to high uniformity, or equivalently, small variance for crystal sizes, from the advanced slug flow reaction crystallization process of a model crystal, called manganese oxalate hydrate. The proposed sequential selection approach contains a Bayesian adaptive method to incorporate new uniformity measurements in each step and two design acquisition functions to improve the selection of the most promising experimental setting in terms of minimizing the variance. We study the performance of the proposed approach through multiple synthetic numerical studies, as well as a case study based on data from slug flow crystallization experiments. Throughout these studies, the proposed approach shows competitive performance in identifying the best experimental setting.

Multidimensional Multi-Connected Fuzzy Interval-Logic Controller

The key features and the principle of operation of a multidimensional fuzzy interval-logic controller with interconnected adjustable parameters developed by the authors are considered. The scheme of interpretation of continuous physical quantities by an equivalent set of terms and the basic version of the block diagram of the regulator are presented. The main elements of the logical output block are described, including the block of production rules, which includes a predefined database of values of control actions on control objects in the form of ordinal numbers of terms of output continuous quantities. The advantages and features of setting up the mutual connections between the input and output continuous values of the regulator are considered, which is expressed in determining the functional dependencies between them. The set of parameters possessed by the terms of input continuous quantities in relation to specific output continuous quantities of the regulator is described. The methods of defuzzification of the values of output continuous quantities within the boundaries of their terms are shown. The possibility of combining input and output continuous quantities into groups when setting up mutual connections between them is described. The mechanism of determining groups of interrelated continuous quantities is illustrated, the total number of which is determined by forming a matrix of relationships, according to the author’s scheme. The algorithm of sequential processing of the contents of the cells of the matrix of relationships, consisting of three steps, is considered. Expressions are given for calculating the basic parameters of a multidimensional fuzzy interval-logic controller with interrelated adjustable parameters. The results of a computational experiment to reduce the system of production rules of the regulator in determining the relationships between input and output continuous quantities are presented.

Effectiveness of an adaptive, multifaceted intervention to enhance care for patients with complex multimorbidity in general practice: protocol for a pragmatic cluster randomised controlled trial (the MM600 trial)

IntroductionPatients with complex multimorbidity face a high treatment burden and frequently have low quality of life. General practice is the key organisational setting in terms of offering people with complex...

Use of psychoacoustic spectrum warping, decision template fusion, and neighborhood component analysis in newborn cry diagnostic systems.

Dealing with newborns' health is a delicate matter since they cannot express needs, and crying does not reflect their condition. Although newborn cries have been studied for various purposes, there is no prior research on distinguishing a certain pathology from other pathologies so far. Here, an unsophisticated framework is proposed for the study of septic newborns amid a collective of other pathologies. The cry was analyzed with music inspired and speech processing inspired features. Furthermore, neighborhood component analysis (NCA) feature selection was employed with two goals: (i) Exploring how the elements of each feature set contributed to classification outcome; (ii) investigating to what extent the feature space could be compacted. The attained results showed success of both experiments introduced in this study, with 88.66% for the decision template fusion (DTF) technique and a consistent enhancement in comparison to all feature sets in terms of accuracy and 86.22% for the NCA feature selection method by drastically downsizing the feature space from 86 elements to only 6 elements. The achieved results showed great potential for identifying a certain pathology from other pathologies that may have similar effects on the cry patterns as well as proving the success of the proposed framework.

Prehospital Use of Waveform Capnography in Intubated Neonates

Introduction Routine continuous monitoring of endotracheal tube placement with waveform capnography is considered standard of care in the prehospital setting. However, maintaining this standard in neonatal patients remains a challenge due to low tidal volumes that do not tolerate the additional dead space ETCO2 attachments add. Additionally, continuous ETCO2 can increase the risk of ETT dislodgement or kinking because of the weight and size of the capnography attachments relative to the patient and tube size. We hypothesize that there is a gap in care of intubated neonates when compared to adults in the prehospital setting in terms of continuous monitoring of ETT placement. Methods Data were obtained from a single air medical agency. Through a retrospective chart review, records of intubated neonates (<28 days), children (≥28 days–12 years), adolescents (13–18 years), and adults (aged ≥18 years) were analyzed. Records were available from 11/21/13–1/21/22. The number of intubation attempts, whether an intubation was successful, and the use of capnography were recorded in RedCap. Statistical analysis was performed in Microsoft Excel via Chi Square Goodness of Fit Tests. Results During the study period, 674 intubation attempts were identified, and 28 charts were excluded due to missing patient age. Continuous waveform ETCO2 monitoring was used on 62%, 94%, 95%, and 97% of successfully intubated neonates, children, adolescents, and adults, respectively. There was a statistically significant difference between use of continuous waveform capnography in adults and neonates (p-value = 0.013). There was also a statistically significant difference between use of continuous waveform capnography in intubated neonates, children, and adolescents (p-value = 0.049). Conclusion Continuous ETCO2 monitoring is underutilized in intubated neonates compared to children, adolescents, and adults in the prehospital setting in this study population. This suggests a gap in the standard of care provided to neonates. Additional studies are needed to determine if these results are consistent around the industry and if there is a higher rate of undetected tube displacement in neonates who are transported without waveform capnography.

Approach to Choosing Communication Strategies of Social Projects Based on Indistinct Liguistic Method

Communication strategies of promoting goods, services and brands form an integral part of marketing strategies. Resolving the problem of choosing communication strategy is based on expert opinion of one or several specialists, whose position can be taken by professionals in promotion of commercial and social goods. The goal is to choose the best alternative of conducting communication campaigns based on its potential effectiveness. The article uses mathematic tools of fluctuating set of indistinct linguistic terms in order to resolve the problem of choosing communication strategy for social projects. On this basis the task was formulated for decision-making concerning the choice of marketing strategy in conditions of uncertainty and an example was provided for choosing communication strategy of promoting a social project. As an example of using these tools the inter-regional literature camping ‘War and Peace’ was studied, where a multitude of 3 alternatives was formed, expert opinions in the form of linguistic expressions were obtained and the multitude of alternatives was regulated on the basis of non-dominant strategy. Findings of the research show possibility to use indistinct linguistic computing model for decision-making in marketing.

A novel Multi-Criteria Decision Making framework based on Evidential Reasoning dealing with missing information from online reviews

Online reviews are a valuable tool for Multi-Criteria Decision Making (MCDM) because they provide insightful information on several aspects. However, not all reviews yield complete information on all the necessary criteria for the MCDM problem. In practice, users might not effectively analyze all of them due to a lack of knowledge or time constraints. Despite this shortcoming, these online reviews still offer useful information that would be lost if neglected. Therefore, this paper proposes a framework that offers a new approach to using online reviews in MCDM and effectively addresses missing information on a given criterion or criteria. This new approach first establishes an information processing mechanism to transform online reviews into Probabilistic Linguistic Term Sets (PLTSs) with interval uncertainty. Afterward, a weight determination model is defined to calculate the criteria weights, which are given as interval values to reflect the uncertainty of the criteria and consider information from online reviews to better capture the opinions of reviewers. Next, the Evidential Reasoning (ER) algorithm is extended to the PLTSs with interval uncertainty environment to fuse the evaluation information and generate the interval-valued expected utilities of the alternatives by considering the uncertainty related to the missing information. Finally, a case study is conducted to illustrate the implementation process of the proposed framework, and a comparative analysis is carried out to evaluate its performance.

Application Of Probabilistic Linguistic Term Sets and Prospect Theory in Multi-Attribute Decision Making: Aviation Industry Evaluation

With the increasing complexity and diversification of current decision objectives, partial data absence in evaluating certain decision criteria is common. Therefore, this paper introduces a fuzzy evaluation method using probabilistic linguistic term sets and integrates it with prospect theory. This integration allows reflecting decision-makers' preferences while addressing the issue of data absence for evaluation. Additionally, the paper employs the entropy-weighted Analytic Hierarchy Process (AHP) to evaluate other criteria, and all criteria are evaluated using the TOPSIS method, forming a comprehensive evaluation methodology. Finally, the proposed methodology is applied to assess the aviation industry level in eastern coastal provinces represented by Zhejiang Province. Finally, the proposed methodology is applied to assess the aviation industry level in eastern coastal provinces represented by Zhejiang.

Power allocation for enhancing energy efficiency in unmanned aerial vehicle networks

SummaryThe non‐orthogonal multiple access (NOMA) wireless networks are a robust multi‐access mechanism that serves multiple clients accessing the same resource block simultaneously. The fifth‐generation wireless networks offer huge efficiency in spectrum utilization, which can be exploited to deploy NOMA for LOS communication of unmanned aerial vehicles (UAVs). Previous research has extensively analyzed various aspects of NOMA‐UAV communication systems, including user access fairness, coverage, maximizing system capacity, and total energy efficiency. However, only a few researchers have focused on maximizing the EE of NOMA‐UAV wireless networks with user quality of service (QoS) constraints. This paper proposes a fuzzy logic‐based crossover‐based coati optimization algorithm for maximizing the energy efficiency of NOMA‐UAV, along with user scheduling. The main objective of this model is to offer a solution to the joint energy efficiency and user QoS scheduling problem. The fuzzy decision‐making strategy optimizes the energy efficiency (EE) of NOMA‐UAV by selecting appropriate power and time resources. Additionally, the crossover‐based coati optimization algorithm transforms the subchannel allocation problem into a two‐sided matching procedure. The efficiency of the proposed algorithm is evaluated concerning overall residual energy, number of remaining nodes, and time consumption. The experimental outcomes demonstrate the capability of the proposed model in optimizing the energy efficiency of the NOMA‐UAV network by identifying the optimal resource set in terms of time and power while satisfying the clients' QoS.

Dkk1 as a Prognostic Marker for Neoadjuvant Chemotherapy Response in Breast Cancer Patients.

To investigate the role of Dkk1 as a predictor of response to NACT in BC patients. This retrospective monocentric study included 145 women who had undergone NACT followed by breast surgery. Dkk1 protein expression was assessed using immunohistochemistry staining in core needle biopsies and mammary carcinoma specimens. Dkk1 levels were lower in treated BC tumours than in untreated tumours. The outcomes of 68 matched pre- and post-therapy tissues showed that Dkk1 levels in mammary carcinoma tissues were significantly predicted by levels in core needle biopsies and that Dkk1 expression was reduced in 83% of cases. Smaller cT stage, positive Her2 expression, and decreased Dkk1-IRS in core needle biopsy tissues were all independent predictors of regression grade (R4), according to Sinn. However, the percentage of Dkk1 expression differences prior to and following NACT had no effect on PFS or OS. In this study, we demonstrated for the first time that Dkk1 could be identified as an independent predictor of NACT response in BC patients, particularly those with TNBC. Further research with a multicentric expanded (pre-/post-therapy) sample set and better-defined populations in terms of molecular subtypes, therapy modality, and long-term follow-up is recommended to obtain more solid evidence.

How representative are participants in geriatric oncology clinical trials? The case of the 5C RCT in geriatric oncology: A cross-sectional comparison to a geriatric oncology clinic

IntroductionFrail older adults make up a substantial portion of the older adult population. However, frail patients are often excluded from randomized controlled trials. This underrepresentation restricts the extent to which trial findings can be generalized to this population. We compared a sample from the Canadian 5C Randomized Controlled Trial investigating comprehensive geriatric assessment (CGA) in the geriatric oncology setting in terms of frailty to patients referred to the Older Adults with Cancer Clinic (OACC) to determine if the trial sample was representative of the normal geriatric oncology practice. Materials and MethodsBaseline CGA data of 5C Trial participants seen at the Princess Margaret Cancer Centre (PM), were compared to data from OACC patients that were seen during the duration of the 5C trial (between April 2018 and April 2020) and that satisfied the 5C inclusion criteria. To assess the frailty of samples, sex, age, disease site, comorbidity level, medical optimization, social supports, functional status, falls risk, nutrition, cognition, and mood were compared between 5C participants and OACC patients using Fisher's exact and independent samples t-test. ResultsA sample of 115 5C participants and 205 OACC patients were included. The mean age of 5C participants and OACC patients was 75.4 and 81.6 years, respectively (p < 0.001). The distribution of disease sites was significantly different between the samples (p < 0.001) and OACC patients were also significantly more impaired compared to 5C participants in comorbidity (23.4% versus 10.4% high comorbidity) (p = 0.001), IADL dependence (55.1% versus 42.6%) (p = 0.036), impaired physical function (70.6% versus 31.3%) (p < 0.001), falls risk (67.8% versus 27%) (p < 0.001), impaired nutrition (55.6% versus 40.9%) (p = 0.014), and cognition (47.2% versus 10%) (p < 0.001). There were no differences in sex, medication optimization, poor social supports, and impaired mood between the samples. DiscussionThe 5C sample was less frail and younger than patients seen in the geriatric oncology clinic. Finding strategies to address barriers to the inclusion of frailer older adults is important to increase their representation in future trials to allow findings to be generalized to this vulnerable population.Trial Registration: Clinicaltrials.gov # NCT03154671

A linguistically asymmetric similarity decision model integrating item tendency for rating predictions

Neighbourhood-based collaborative filtering (CF) methods typically rely only on user rating information for similarity calculation, without considering linguistic concepts (terms) that reflect user fuzzy preferences. However, in real-world decision-making processes, users often prefer to express their preferences for items linguistically rather than numerically. Inspired by this, we propose a probabilistic linguistic term set–based item similarity method that transforms absolute ratings into linguistic terms to capture the degree of importance users place on explicit aspects and opinions. Furthermore, we take into account the positive impact of users’ preferred consistency towards items on similarity results and introduce a Bhattacharyya coefficient–based item tendency to adjust semantic similarities, enhancing the reliability of predictions. In addition, we account for the asymmetric relation between items when selecting appropriate neighbours to optimise rating predictions. The experiments on two benchmark data sets indicate that our method outperforms existing similarity methods across various evaluation metrics. Specifically, compared with the state-of-the-art method, intuitionistic fuzzy set–based hybrid similarity model (IFS-HSM), the proposed model improves the performance by at least 2.1% and 1.9%, respectively, within the metrics mean absolute error ( MAE) and F1. Moreover, our approach provides a new insight for measuring similarity between items from both qualitative and quantitative perspectives.

Large-scale consensus with dynamic trust and optimal reference in social network under incomplete probabilistic linguistic circumstance

Consensus plays a pivotal role in large-scale group decision-making, and the social network relationship significantly impacts the consensus reaching process (CRP). Consequently, there is considerable focus on achieving large-scale consensus within social network (LSC-SN). Typically, experts are used to expressing their evaluations with language that can be effectively characterized by probabilistic linguistic term set (PLTS). However, the existing distance measure of PLTS exhibits drawbacks, and PLTSs provided by experts might be incomplete. To address the two challenges, this research defines an improved distance measure and proposes an estimation method for incomplete PLTS from the perspectives of trust relationships, collaborative filtering, and confidence level. Subsequently, in the CRP, a hierarchical clustering algorithm based on the trust-similarity measure is developed to cluster experts who share mutual trust and similar evaluations. Then, a consensus model employing dynamic trust and optimal reference is introduced to achieve both intra-cluster and inter-cluster consensus simultaneously. Finally, to illustrate the feasibility and advantage of the proposed model, a numerical experiment and the comparative analyses are conducted.

Improving the picking efficiency of a cold warehouse to avoid temperature abuse

PurposeThis research investigates how to optimize storage location assignment to decrease the order picking time and the waiting time of orders in the staging area of low-temperature logistics centers, with the goal of reducing food loss caused by temperature abuse.Design/methodology/approachThe authors applied ABC clustering to the products in a simulated database of historical orders modeled after the actual order pattern of a large cold logistics company; then, the authors mined the association rules and calculated the sales volume correlation indices of the ordered products. Finally, the authors generated three different simulated order databases to compare order picking time and waiting time of orders in the staging area under eight different storage location assignment strategies.FindingsAll the eight proposed storage location assignment strategies significantly improve the order picking time (by up to 8%) and the waiting time of orders in the staging area (by up to 22%) compared with random placement.Research limitations/implicationsThe results of this research are based on a case study and simulated data, which implies that, if the best performing strategies are applied to different environments, the extent of the improvements may vary. Additionally, the authors only considered specific settings in terms of order picker routing, zoning and batching: other settings may lead to different results.Practical implicationsA storage location assignment strategy that adopts dispersion and takes into consideration ABC clustering and shipping frequency provides the best performance in minimizing order picker's travel distance, order picking time, and waiting time of orders in the staging area. Other strategies may be a better fit if the company's objectives differ.Originality/valuePrevious research on optimal storage location assignment rarely considered item association rules based on sales volume correlation. This study combines such rules with several storage planning strategies, ABC clustering, and two warehouse layouts; then, it evaluates their performance compared to the random placement, to find which one minimizes the order picking time and the order waiting time in the staging area, with a 30-min time limit to preserve the integrity of the cold chain. Order picking under these conditions was rarely studied before, because they may be irrelevant when dealing with temperature-insensitive items but become critical in cold warehouses to prevent temperature abuse.

Assouad-type dimensions of overlapping self-affine sets

We study the Assouad and quasi-Assoaud dimensions of dominated rectangular self-affine sets in the plane. In contrast to previous work on the dimension theory of self-affine sets, we assume that the sets satisfy certain separation conditions on the projection to the principal axis, but otherwise have arbitrary overlaps in the plane. We introduce and study regularity properties of a certain symbolic non-autonomous iterated function system corresponding to ``symbolic slices'' of the self-affine set. We then establish dimensional formulas for the self-affine sets in terms of the dimension of the projection along with the maximal dimension of slices orthogonal to the projection. Our results are new even in the case when the self-affine set satisfies the strong separation condition: in fact, as an application, we show that self-affine sets satisfying the strong separation condition can have distinct Assouad and quasi-Assouad dimensions, answering a question of the first named author.

Universal adjacency spectrum of the cozero-divisor graph and its complement on a finite commutative ring with unity

For a finite simple undirected graph [Formula: see text], the universal adjacency matrix [Formula: see text] is a linear combination of the adjacency matrix [Formula: see text], the degree diagonal matrix [Formula: see text], the identity matrix [Formula: see text] and the all-ones matrix [Formula: see text], that is [Formula: see text], where [Formula: see text] and [Formula: see text]. The cozero-divisor graph [Formula: see text] of a finite commutative ring [Formula: see text] with unity is a simple undirected graph with the set of all nonzero nonunits of [Formula: see text] as vertices and two vertices [Formula: see text] and [Formula: see text] are adjacent if and only if [Formula: see text] and [Formula: see text]. In this paper, we study structural properties of [Formula: see text] by defining an equivalence relation on its vertex set in terms of principal ideals of the ring [Formula: see text]. Then we obtain the universal adjacency eigenpairs of [Formula: see text] and its complement, and as a consequence one may obtain several spectra like the adjacency, Seidel, Laplacian, signless Laplacian, normalized Laplacian, generalized adjacency and convex linear combination of the adjacency and degree diagonal matrix of [Formula: see text] and [Formula: see text] in an unified way. Moreover, we get the universal adjacency eigenpairs of the cozero-divisor graph and its complement for a reduced ring and the ring of integers modulo [Formula: see text] in a simpler form.