Evaluation of Vehicle Handling Stability Based on Interval TOPSIS and Entropy Weights

The purpose of this study is to evaluate the handling stability of vehicle from the perspective of users using a multi-criteria decision-making approach with interval numbers. In order to achieve this purpose, the evaluation criteria easy for drivers to understand were constructed based on questionnaire, the interval weights of the criteria were calculated using the entropy weight method with interval numbers, and the interval TOPSIS method was used to evaluate the vehicle handling stability. As the improvement of the method, a new method to determine the crisp value of the interval weight was given, and the relative closeness index used for ranking alternatives was extended to a new additive ranking index. The proposed method was used to evaluate the handling stability of 12 vehicles, and the results may provide some basis for vehicle R & D and design.

An Improved Fuzzy TOPSIS Method with a New Ranking Index

Owing to vague concepts frequently represented in decision data, the crisp values are inadequate to model real-life situations. In this paper, the rating of each alternative and the weight of each criterion is described by linguistic terms which can be expressed in triangular fuzzy numbers. Next, we focus on fuzzy TOPSIS (FTOPSIS) method. We show that, however, the conventional FTOPSIS is interesting, but it suffers from some flaws. The shortcomings of classical FTOPSIS are shown and some solutions are given. Further, a new similarity index is proposed and then is illustrated using numerical examples. By treating the separations of an alternative from the fuzzy positive ideal solution (FPIS) and the fuzzy negative ideal solution (FNIS) as “cost” criterion and “benefit” criterion, respectively, we reduce the original fuzzy multiple criteria decision making (FMCDM) problem to a new one with two criteria. Illustrative examples are given to show the advantages of the proposed approach.

Diagnosis of lumbar degenerative disc disease by using Lp-spaces related to generalized interval-valued m-polar neutrosophic choquet integral Operator

Innovative and astonishing developments in the field of spine analysis can commence with this manuscript. The lumbar disks ([Formula: see text] to [Formula: see text]) are most commonly impaired by degeneration due to their long-standing degeneration and associated strain. We investigate the indications, purposes, risk factors, and therapies of lumbar degenerated disc disease (L-DDD). We assume that the degeneration of five discs creates many effects, making it difficult to differentiate between the different types of degenerated discs and their seriousness. Since the indeterminacy and falsity portions of science or clinical diagnosis are often ignored. Due to this complexity, the reliability of the patient’s progress report cannot be calculated, nor can the period of therapy be measured. The revolutionary concept of interval-valued m-polar neutrosophic Choquet integral aggregation operator (IVmPNCIAO) is proposed to eliminate these problems. We associate generalized interval-valued m-polar neutrosophic Choquet integral aggregation operator (GIVmPNCIAO) with the statistical formulation of [Formula: see text]-spaces and use it to identify the actual kind of degenerative disc in the lumbar spine. For the classification of interval-valued m-polar neutrosophic numbers (IVMPNNs), we set the ranking index and score function. These concepts are appropriate and necessary in order to better diagnose degeneration by associating it with mathematical modeling. We construct a pre-diagnosis map based on the fuzzy interval [0,1] to classify the types of degenerative discs. We develop an algorithm by using GIVmPNCIAO based on interval-valued m-polar neutrosophic sets (IVMPNNs) to identify the degenerative disc appropriately and to choose the most exquisite treatment for the corresponding degeneration of every patient. Furthermore, we discuss the sensitivity analysis with parameter [Formula: see text] in GIVmPNCIAO to investigate the patient’s improvement record.

Fecundity and food habits of the slender stonebasher (Hippopotamyrus ansorgii Boulenger, 1905) (Mormyridae) in Ogbese river

Fecundity study is important in order to evaluate the reproductive potentials of fish species. Thus, fecundity and food habits of Hippopotamyrus ansorgii (H. ansorgii) from Ogbese river were investigated. Total of 32 specimens of H. ansorgii were used. The major fishing method employed for collecting the specimens were cast and gill netting. Fecundity and food habits the fish were studied. The total lengths ranged from 14.6 cm to 22.1cm while the body weights ranged from 30.0 to 91.0 g. The food items found in the examined stomachs were rotifer, algae, insects, crustacean, detritus and plant parts but the major food items based on the ranking index were insects and crustaceans. The sex ratio of H. ansorgii was 1.13: 1 for males: females. The gonadosomatic index showed that H. ansorgii in Ogbese river uses an average of 11.69% of its body weight in egg production. Fecundity ranged from 600 to 7200 eggs with an average of 3231 eggs per female. In conclusion, H. ansorgii in Ogbese river was an omnivore and highly fecund fish.

Assessment of the potential of multifarious demand response programs in reducing transformer loss of life

Transformer may enter the ageing cycle sooner if it is loaded more than the rated value for longer periods of time in its life cycle. This paper exploits demand response as a way to improve transformer life by reducing the hottest spot temperature (HST) which is caused due to better load profile. The aim of the paper is to investigate the impact of various types of price-based and incentive-based demand response programs (DRPs) on the transformer life and other attributes like energy consumption, peak to average ratio, etc. Entropy method is used to determine the weights of multi-attributes in a multi-attribute decision-making (MADM) model formed by the various attributes and the multifarious demand response programs. Using these weights, the various DRPs are ranked using Program Ranking Index to assist the utility in deciding which DRP is to be employed. IEEE transformer model is used to calculate the transformer ageing for two cases with and without demand response programs. The simulation results validate the effectiveness of demand response in mitigating transformer loss of life. Furthermore, the economic and technical benefits of employing demand response are quantified.

Schedule Optimization in a Smart Microgrid Considering Demand Response Constraints

Smart microgrids (SMGs) may face energy rationing due to unavailability of energy resources. Demand response (DR) in SMGs is useful not only in emergencies, since load cuts might be planned with a reduction in consumption but also in normal operation. SMG energy resources include storage systems, dispatchable units, and resources with uncertainty, such as residential demand, renewable generation, electric vehicle traffic, and electricity markets. An aggregator can optimize the scheduling of these resources, however, load demand can completely curtail until being neglected to increase the profits. The DR function (DRF) is developed as a constraint of minimum size to supply the demand and contributes solving of the 0-1 knapsack problem (KP), which involves a combinatorial optimization. The 0-1 KP stores limited energy capacity and is successful in disconnecting loads. Both constraints, the 0-1 KP and DRF, are compared in the ranking index, load reduction percentage, and execution time. Both functions turn out to be very similar according to the performance of these indicators, unlike the ranking index, in which the DRF has better performance. The DRF reduces to 25% the minimum demand to avoid non-optimal situations, such as non-supplying the demand and has potential benefits, such as the elimination of finite combinations and easy implementation.