Belda College

This article discusses a constrained Travelling Salesman Problem (TSP), in which the traveler determines the shortest route to take in order to place a limit on the total journey time and costs. In actual life, the length of a tour and its total cost might be scheduled. The goal of the proposed TSP is the expense of travel. It is a cost optimization based TSP. The overall cost of travel cannot be more than the proposed TSP’s total travel allowance and time ceiling. The expenses and duration of travel are regarded as type-2 fuzzy (T2F) variables. Using a defuzzification technique, we came across the crisp equivalency of fuzzy objective or fuzzy cost. An approach motivated by ant colony optimization (ACO) has been employed to solve the hypothesized TSP. To solve the suggested TSP, two features–"probabilistic selection" and "neighborhood path search"-have been added to the fundamental ACO. Furthermore, we have adopted a 2-optimal strategy for the ACO technique to obtain a better path quickly. A few common benchmark problem examples or datasets have been explored in order to illustrate the utility of the depicted approach. In addition, this paper computes a few benchmark cases that have been redefined in a random T2F circumstance.

Let R be a noncommutative prime ring of char (R) =6 2, Q(r) be its right Martindale quotient ring and C be its extended centroid. Suppose that f (r(1), ... , r(n)) is a noncentral multilinear polynomial over C and F : R -> R is an X-generalized skew derivations of R. We describe all possible forms of the maps F when R satisfies the condition a[F(f (r)), f (r)] - [F(f (r)), f (r)]a is an element of C for all r = (r(1), ... , r(n))is an element of R-n. As an application of this result, we also describe the possible forms of the maps F and G satisfying the conditions (i) [F(f (r)), f (r)] is an element of C for all r = (r(1), ... , r(n)) is an element of R-n; (ii) [[F(u), u], [G(v), v]] = 0 for all u, v is an element of f (R), where F and G are both X-generalized skew derivations of R.

Hyperspectral images (HSIs), which comprise numerous redundant spectral bands, are the most prevalent remote sensing sources for interpreting objects based on spectral band data. In the classification process, choosing a subset of spectral bands for data dimensionality reduction is known as band selection (BS). Deep learning (DL) based model with an attention mechanism can be used for BS, considering the nonlinear and global interaction among spectral bands. However, the existing DL based BS approaches using attention mechanisms are either unable to record both the spectral and spatial long-range information or depend only on queries, but not on keys and values. Moreover, the used reconstruction network (RecNet) in most of the studied BS techniques cannot recognize the features of images in compound scales since the network uses single-size kernels in convolution operations. To reduce these limitations of existing DL based BS approaches with attention mechanism, a novel DL based Hyperspectral BS (PSAA-MSRecNet) model consisting of position-sensitive axial attention (PSAA) with a multi-scale RecNet (MSRecNet) has been proposed. It takes advantage of a PSAA module that adds not only queries but also keys and values-dependent positional bias terms. The MSRecNet is employed after the PSAA module as a RecNet to search the image features in divergent scales. The proposed approach is able to effectively capture quality feature representations and, as a consequence, pick the most informative bands with higher accuracy in classification tasks than prior studied BS approaches, the majority of cases accross three standard HSI datasets.

Streptozotocin, that has a selective pharmacological toxicity toward pancreatic beta cells, in addition to high lipid diet (HLD) has been widely used to induce T2DM. However, no evidence has shown that superior dose of streptozotocin (STZ) to establish T2DM. This study was initiated to develop an animal model (Wister Albino rats) of T2DM with suitable dose of STZ. Total fifty male rats (210 ± 20 g) were arbitrarily divided into 10 groups (n = 5). Two groups were control group fed normal diet (ND) and high lipid diet (HLD). The remaining rats were induced with STZ at 20, 40, 60 and 80 mg/kg body weight, with each dose tested under ND and HLD conditions. Body weight, blood glucose, HbA1c, serum insulin, C-peptide, pancreatic glucokinase, serum triglycerides (TG), total cholesterol (TC), antioxidant enzymes (SOD, CAT, GSH, MDA) and pancreatic histology. 80 mg/kg STZ group rats expired within 7 days. After 28 days of experiment, Blood glucose was markedly raised up. Insulin and C-peptide levels were lower in STZ/HLD fed groups (P < 0.001) rats. Pancreatic glucokinase activity significantly decreased (P < 0.05). SOD, CAT, GSH and TG, TC increased significantly in STZ/HLD treated groups (P < 0.01). We observed that, β- cells were present in STZ/HLD fed rats pancreas and insulin secretion is higher than ND fed rats. We concluded that, HLD with 40mg STZ/Kg b.w. rats provide a novel animal model for T2DM without any contradictions and is suitable for the testing of antidiabetic compounds.

Partitional clustering techniques such as K-Means (KM) are simple and efficient for image segmentation. However, KM is very sensitive to the initial choice of random cluster centres and frequently it is trapped in local optima. In addition, KM is not automatic, i.e., the number of clusters is a constant user-defined value. Therefore, this study developed recent well-known metaheuristic algorithms (MAs)-based automatic partitional crisp image clustering models for better segmentation results. The density balance (DB) algorithm and superpixel strategy have been employed to find the cluster number before the beginning of the actual clustering technique. Twenty-five recent MAs-based image clustering models are developed and evaluated over BSD500 and digital pathology images. The numerical, visual, and statistical results indicate that the designed MAs-based crisp clustering models are producing very promising results. Results also demonstrate that Partial Reinforcement Optimizer (PRO) achieves the best rank and Fungal Growth Optimizer (FGO) achieves the worst rank.