•Fuzzy best-worst method is proposed to solve the issues under fuzzy environment.•A consistency ratio for fuzzy best-worst method is proposed for verification.•The results indicate the fuzzy ...best-worst method outperforms best-worst method.•The fuzzy best-worst method has a higher comparison consistency.
Considering the vagueness frequently representing in decision data due to the lack of complete information and the ambiguity arising from the qualitative judgment of decision-makers, the crisp values of criteria may be inadequate to model the real-life multi-criteria decision-making (MCDM) issues. In this paper, the latest MCDM method, namely best-worst method (BWM) was extended to the fuzzy environment. The reference comparisons for the best criterion and for the worst criterion were described by linguistic terms of decision-makers, which can be expressed in triangular fuzzy numbers. Then, the graded mean integration representation (GMIR) method was employed to calculate the weights of criteria and alternatives with respect to different criteria under fuzzy environment. According to the concept of BWM, the nonlinearly constrained optimization problem was built for determining the fuzzy weights of criteria and alternatives with respect to different criteria. The fuzzy ranking scores of alternatives can be derived from the fuzzy weights of alternatives with respect to different criteria multiplied by fuzzy weights of the corresponding criteria, and then the crisp ranking score of alternatives can be calculated by employing GMIR method for optimal alternative selection. Meanwhile, the consistency ratio was proposed for fuzzy BWM to check the reliability of fuzzy preference comparisons. Three case studies were performed to illustrate the effectiveness and feasibility of the proposed fuzzy BWM. The results indicate the proposed fuzzy BWM can not only obtain reasonable preference ranking for alternatives but also has higher comparison consistency than the BWM.
Abstract
For the average trajectory consistency test, the density is good, which leads to the problem of poor consistency. This paper proposes to use the Mahalanobis distance method to process the ...test data of two kinds of ammunition. First, each pair of values in a set of tests is obtained through experiments. The obtained data is processed by the Mahalanobis distance method for relative distance processing. Then the mean and variance of the distance data of the two ammunition are calculated. In the simulation test, the data of tests which are processed by the Mahalanobis distance method. The consistency of the two ammunition is checked according to the trajectory consistency test method. The test data using the Mahalanobis distance method can better than the data of the two ammunition in the consistency. The test data processed by the Mahalanobis distance method can improve the situation of good density and poor consistency in the consistency test, and provide technical support for the subsequent average trajectory consistency test.
To investigate the consistency between the Reflux Symptom Score-12 (RSS-12) and Reflux Symptom Index (RSI) in Chinese people.
Patients with symptoms of LPR from the outpatient ...otorhinolaryngology-head and neck surgery clinic were included. All included patients completed the RSS-12 and RSI. The patient with RSS-12>11 or RSI>13 suggested possible LPR. For the patients with RSI >13 or RSS-12>11, they were treated using diet recommendations and were prescribed a twice-daily pantoprazole for 12 weeks. The consistency between the RSS-12 and RSI was compared with the weighted Cohen's kappa statistic.
A total of 258 patients were included. The mean scores for RSS-12 and RSI were 13.21±17.31 and 12.86±6.15, respectively. The positive rate of LPR was 17.44% based on the RSI, and 24.42% based on the RSS-12. The kappa value between the RSS-12 and RSI was 0.736 (P < 0.001). Following 12 weeks of treatment, there was a significant reduction in both RSI and RSS-12. Based on the RSI, 73% of patients had a good treatment response, whereas according to the RSS-12, 85% of patients had a good treatment response.
There is a good consistency between RSS-12 and RSI, meaning that the RSS-12 is a feasible LPR initial screening tool. The RSS-12 provides a more comprehensive evaluation of reflux symptoms and treatment effect than RSI in patients with LPR.
With efficient appearance learning models, discriminative correlation filter (DCF) has been proven to be very successful in recent video object tracking benchmarks and competitions. However, the ...existing DCF paradigm suffers from two major issues, i.e., spatial boundary effect and temporal filter degradation. To mitigate these challenges, we propose a new DCF-based tracking method. The key innovations of the proposed method include adaptive spatial feature selection and temporal consistent constraints, with which the new tracker enables joint spatial-temporal filter learning in a lower dimensional discriminative manifold. More specifically, we apply structured spatial sparsity constraints to multi-channel filters. Consequently, the process of learning spatial filters can be approximated by the lasso regularization. To encourage temporal consistency, the filter model is restricted to lie around its historical value and updated locally to preserve the global structure in the manifold. Last, a unified optimization framework is proposed to jointly select temporal consistency preserving spatial features and learn discriminative filters with the augmented Lagrangian method. Qualitative and quantitative evaluations have been conducted on a number of well-known benchmarking datasets such as OTB2013, OTB50, OTB100, Temple-Colour, UAV123, and VOT2018. The experimental results demonstrate the superiority of the proposed method over the state-of-the-art approaches.
Cloud computing is a model of distributed systems. This system allows users to access virtual resources including the processing power, storage, applications, etc. Storage as a Service (SaaS) is one ...of the cloud computing services. Cloud storage systems provide this service for the end-users, and deliver data availability and durability as well as global accessibility throughout the Internet. High data availability and scalability are very crucial criteria for the end-users in cloud storage systems. To achieve them, we need replication in these systems. However, the replication brings about asynchronization of data among replicas in different cloud data-centers. It reduces the performance of the cloud storage systems as well. Therefore, replication is one of the crucial challenges in cloud storage systems. These systems need to ensure that the data are synchronized among different replicas by implementing consistency policies. In this paper, we present the Strict Timed Causal Consistency (STCC) as a hybrid consistency model which can be considered as an extension to the cloud computing. This consistency model has two components: client-side, and server-side. At the client-side, this model supports monotonic read, monotonic write, read your write, and write follow read consistencies. At the server-side, it supports the Timed Causal Consistency (TCC) as well. Additionally, it is stronger than the client-centric and is more flexible than the data-centric approaches. In spite of partition tolerance, our proposed method guarantees the consistency and satisfies data availability. Cassandra is a NoSQL database with high scalability and availability. Cassandra comes with multiple consistency levels as a service such as ONE, ALL, QUORUM, etc. We have examined the proposed approach with respect to different consistency levels of Cassandra and Causal Consistency (CC). Yahoo Cloud Serving Benchmark (YCSB) consists of a number of workloads which are used to evaluate our proposed method. We have executed different workloads on the Cassandra clusters and with respect to which we have made a comparison between the performance of our proposed method and the four other different consistency levels in Cassandra. The experimental results based on the comparison between the proposed method and ONE, ALL, QUORUM, as well as the CC consistencies, on a Cassandra cluster with 24 nodes, testify that on average our approach has reduced the stale read rate by 24% on workload-A, and on workload-B by 25%. Also, the system throughput with respect to workload-A has increased by more than 20%. Besides, when we applied our proposed STCC on workload-B the system throughput increased by almost 35%.
•Cloud storage systems provide the storage service for the end-users.•Cloud storage systems need to ensure that data is synchronized among the replicas.•Strict timed causal consistency as a hybrid consistency model.•This model supports session and timed causal at the client and server side.•Cassandra is an NOSQL database with high scalability and availability.
Behavioral variation among individuals is ascribed to the species' biology and the life history of each one. Many study areas consider individual differences, acknowledging their significant impact ...on fitness. Although boldness remains the most extensively studied behavioral dimension of individual differences in animals, ongoing debates persist regarding the evaluation of behavioral consistency over time and between contexts, as well as the determination of which features are crucial for delineating profiles. In this study, we investigated which behavioral traits explain the profiles of shyness and boldness and assessed their temporal and contextual consistency. For this, we divided zebrafish into bold and shy profiles by applying an emergence test (black-to-white entrance) three consecutive times with the same population of fish. The two groups formed (bold and shy) went through five different behavioural tests: novel tank, open field, black and white preference, aggressiveness, and sociability, which were employed twice, with an interval of 30 days. Bold animals showed less anxiety-like behaviour and higher aggressiveness compared to shy animals, and this pattern remained consistent over time for the two contexts. This suite of related behavious were considered the main factors to classify zebrafish into bold and shy profiles. In addition, the consistency appeared to be context dependent. The differences noted in the behavioural profiles allowed us to understand aspects of behavioural syndromes and how individuals behave when facing environmental challenges in different situations.
•Aggressiveness was a primary factor distinguishing bold from shy profiles.•Anxiety-like behavior was the second predictor used to classify bold and shy profiles.•Bold individuals consistently exhibited behaviors of aggression and risk-taking over time.•Shy individuals consistently exhibited behaviors of anxiety and low aggression over time.
In this paper, we present a new method for group decision making using incomplete fuzzy preference relations based on the additive consistency and the order consistency with consistency degrees to ...overcome the drawbacks of Lee’s method 15, where Lee’s method cannot obtain the correct preference order of alternatives in some situations. First, we estimate unknown preference values of incomplete fuzzy preference relations based on the additive consistency. Then, we construct modified consistency matrices of experts which satisfy the additive consistency and the order consistency simultaneously. We also prove some properties of the constructed modified consistency matrices. Finally, based on the constructed modified consistency matrices of experts, we present a new method for group decision making. The proposed method provides us with a useful way for group decision making using incomplete fuzzy preference relations based on the additive consistency and the order consistency with consistency degrees.
The training of a feature extraction network typically requires abundant manually annotated training samples, making this a time-consuming and costly process. Accordingly, we propose an effective ...self-supervised learning-based tracker in a deep correlation framework (named: self-SDCT). Motivated by the forward-backward tracking consistency of a robust tracker, we propose a multi-cycle consistency loss as self-supervised information for learning feature extraction network from adjacent video frames. At the training stage, we generate pseudo-labels of consecutive video frames by forward-backward prediction under a Siamese correlation tracking framework and utilize the proposed multi-cycle consistency loss to learn a feature extraction network. Furthermore, we propose a similarity dropout strategy to enable some low-quality training sample pairs to be dropped and also adopt a cycle trajectory consistency loss in each sample pair to improve the training loss function. At the tracking stage, we employ the pre-trained feature extraction network to extract features and utilize a Siamese correlation tracking framework to locate the target using forward tracking alone. Extensive experimental results indicate that the proposed self-supervised deep correlation tracker (self-SDCT) achieves competitive tracking performance contrasted to state-of-the-art supervised and unsupervised tracking methods on standard evaluation benchmarks.
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