Effectiveness Evaluation Method of Constellation Satellite Communication System with Acceptable Consistency and Consensus Under Probability Hesitant Intuitionistic Fuzzy Preference Relationship

System effectiveness evaluation is an important part of constellation satellite communication system research, with applications in project verification and optimization as well as tactical and technical measurement argumentation. This paper presents a systematic and comprehensive effectiveness evaluation method for a constellation satellite communication system under a probabilistic hesitant intuitionistic fuzzy preference relationship (PHIFPR), aiming to better address the fuzziness and uncertainty in effectiveness evaluation. First, a proposed definition of PHIFPR describes the hesitancy of evaluators, provides hesitancy distribution information, and depicts the worst negative information and risk preferences in effectiveness evaluation. Then, we deduce the approximate consistency index of PHIFPR and establish a mathematical programming model to increase individual consistency when the approximate consistency index does not reach a predetermined level. In the sequel, a proposed group consensus index uses the PHIFPR-based Hausdorff distance to measure the closeness between evaluators' judgements. Afterwards, a consistency and consensus improvement model is designed to retain the original opinions of evaluators to make the consistency and consensus of PHIFPRs acceptable. Moreover, a goal programming model is established to gain the reliable scheme priority weights by regarding the approximate consistency condition of a PHIFPR as a fuzzy constraint. Finally, an experimental example is offered to highlight the practicability and feasibility of the proposed method, and some comparative analyses with other methods offer insights into the designed method.

Algebraic structures for pairwise comparison matrices: Consistency, social choices and Arrow’s theorem

We present the algebraic structures behind the approaches used to work with pairwise comparison matrices and, in general, the representation of preferences. We obtain a general definition of consistency and a universal decomposition in the space of PCMs, which allow us to define a consistency index. Also Arrow’s theorem, which is presented in a general form, is relevant. All the presented results can be seen in the main formulations of PCMs, i.e., multiplicative, additive and fuzzy approach, by the fact that each of them is a particular interpretation of the more general algebraic structure needed to deal with these theories.

Incorporating Glucose Variability into Glucose Forecasting Accuracy Assessment Using the New Glucose Variability Impact Index and the Prediction Consistency Index: An LSTM Case Example

Background: In this work, we developed glucose forecasting algorithms trained and evaluated on a large dataset of free-living people with type 1 diabetes (T1D) using closed-loop (CL) and sensor-augmented pump (SAP) therapies; and we demonstrate how glucose variability impacts accuracy. We introduce the glucose variability impact index (GVII) and the glucose prediction consistency index (GPCI) to assess the accuracy of prediction algorithms. Methods: A long-short-term-memory (LSTM) neural network was designed to predict glucose up to 60 minutes in the future using continuous glucose measurements and insulin data collected from 175 people with T1D (41,318 days) and evaluated on 75 people (11,333 days) from the Tidepool Big Data Donation Dataset. LSTM was compared with two naïve forecasting algorithms as well as Ridge linear regression and a random forest using root-mean-square error (RMSE). Parkes error grid quantified clinical accuracy. Regression analysis was used to derive the GVII and GPCI. Results: The LSTM had highest accuracy and best GVII and GPCI. RMSE for CL was 19.8 ± 3.2 and 33.2 ± 5.4 mg/dL for 30- and 60-minute prediction horizons, respectively. RMSE for SAP was 19.6 ± 3.8 and 33.1 ± 7.3 mg/dL for 30- and 60-minute prediction horizons, respectively; 99.6% and 97.6% of predictions were within zones A+B of the Parkes error grid at 30- and 60-minute prediction horizons, respectively. Glucose variability was strongly correlated with RMSE (R≥0.64, P < 0.001); GVII and GPCI demonstrated a means to compare algorithms across datasets with different glucose variability. Conclusions: The LSTM model was accurate on a large real-world free-living dataset. Glucose variability should be considered when assessing prediction accuracy using indices such as GVII and GPCI.

Comparison of The Fiber Characteristics of the Some Cotton Varieties Ginned by Using Sawgin and Rollergin

In this study carried out in 2015 under conditions of Kahramanmaraş province of Turkey, it was aimed at determining the effects of sawgin and rollergin methods on fiber quality in some cotton (Gossypium hirsutum L.) cultivars. In the study varieties of Lydia, Carisma, PG 2018, Flash, BA 440, BA 119 Maraş-92 and Erşan-92 were used as experimental materials. The trial was established according to factorial randomized block design with four replications. Seed cottons obtained from the trial were ginned in the rollergin and sawgin machines. In the obtained lint cotton samples, a number of fiber characteristics were determined by using HVI and AFIS fiber analysis devices. Ginnig outturn (38.6%), fiber length (30.21 mm), uniformity index (86.02%), fiber strength (31.76 g tex-1), spinning consistency index (SCI) (104.68) determined by using rollergin system were found higher than ones determined by using sawgin system (respectivelly 37.2%, 29.78 mm, 84.61%, 30.97 g tex-1, 94.50). Short fiber index (3.47%) and nep count (59.40 number g-1) obtained from rollergin system were found lower than ones obtained from sawgin system (respectivelly 4.38% and 119.34 number g-1). As a result it can be said that the rollergin method has positive effect on ginnig outturn, fiber length, uniformity index, fiber strength, spinning consistency index, short fiber index and nep count. When fiber length, fiber strength, spinning consistency index, nep size are considered together the best variety was Lydia cv. (respectivelly 30.87 mm, 32.56 g tex-1, 104.25, 675.63 μm). Ginning outturn, uniformity index, short fiber index, total particule number, dust particule number and trash count are considered together the best variety was Erşan-92 cv. (respectivelly 39.4%, 86.02%, 3.48%, 231.4 number g-1, 206.3 number g-1, 25.13 number g-1). For fiber fineness the best varieties were BA 119 and Maraş-92 cv. (respectivelly 4.78 mic. and 4.80 mic.).

Evaluating Results

This chapter explores the tools used to evaluate the quality and plausibility of results of phylogenetic analyses. The term “fit” has been widely used in the phylogenetic literature to indicate the degree to which data conform to (or are explained by) a cladogram. The most commonly used measure of fit applied to discrete character data is the consistency index, or ci. Meanwhile, measures of synapomorphy are less frequently reported than the consistency index. The chapter then considers the resolution of branches; multiple equally parsimonious cladograms; successive approximations weighing; and data decisiveness. It also differentiates between total evidence and consensus, before describing supertrees. Finally, the chapter looks at approaches for evaluating support or stability of phylogenetic results, including branch support, jackknifing, bootstrapping, randomization tests, and sensitivity analysis.