Numerical Simulation of Cubic-Quartic Optical Solitons with Perturbed Fokas–Lenells Equation Using Improved Adomian Decomposition Algorithm

The current manuscript displays elegant numerical results for cubic-quartic optical solitons associated with the perturbed Fokas–Lenells equations. To do so, we devise a generalized iterative method for the model using the improved Adomian decomposition method (ADM) and further seek validation from certain well-known results in the literature. As proven, the proposed scheme is efficient and possess a high level of accuracy.

A Bi-level Decomposition Algorithm for Multi-factory Scheduling With Batch Delivery Problem

This paper introduces a multi-factory scheduling with batch delivery problem. A novel mixed-integer programming model is proposed to minimize the sum of total tardiness, holding and batching costs. A bi-level decomposition algorithm (BLDA) is developed involving two sub-problems: scheduling problem in the upper level and batching problem in the lower level. Four versions of the BLDA are created by combinations of CPLEX and simulated annealing in both levels, which interactively collaborate until the algorithm converges to a solution. The BLDAs are examined on several random and real-life test instances. A statistical analysis is performed by comparing the BLDAs’ solutions with the exact minimum and lower bound values of the total cost. The results indicate that about all versions of the developed BLDA provide high quality solutions for real-world zinc industry problems as well as generated instances in a reasonably short time. Finally, some managerial insights are derived based on sensitivity analysis.

The Art of Positronics in Contemporary Nanomaterials Science: A Case Study of Sub-Nanometer Scaled Glassy Arsenoselenides

The possibilities surrounding positronics, a versatile noninvasive tool employing annihilating positrons to probe atomic-deficient sub-nanometric imperfections in a condensed matter, are analyzed in application to glassy arsenoselenides g-AsxSe100−x (0 < x < 65), subjected to dry and wet (in 0.5% PVP water solution) nanomilling. A preliminary analysis was performed within a modified two-state simple trapping model (STM), assuming slight contributions from bound positron–electron (Ps, positronium) states. Positron trapping in g-AsxSe100−x/PVP nanocomposites was modified by an enriched population of Ps-decay sites in PVP. This was proven within a three-state STM, assuming two additive inputs in an overall trapping arising from distinct positron and Ps-related states. Formalism of x3-x2-CDA (coupling decomposition algorithm), describing the conversion of Ps-decay sites into positron traps, was applied to identify volumetric nanostructurization in wet-milled g-As-Se, with respect to dry-milled ones. Under wet nanomilling, the Ps-decay sites stabilized in inter-particle triple junctions filled with PVP replaced positron traps in dry-milled substances, the latter corresponding to multi-atomic vacancies in mostly negative environments of Se atoms. With increased Se content, these traps were agglomerated due to an abundant amount of Se-Se bonds. Three-component lifetime spectra with nanostructurally- and compositionally-tuned Ps-decay inputs and average lifetimes serve as a basis to correctly understand the specific “rainbow” effects observed in the row from pelletized PVP to wet-milled, dry-milled, and unmilled samples.

Application of improved variational mode decomposition method based on two-dimensional sparrow search algorithm in natural gas pipeline leakage signal denoising

To eliminate noise interference in pipeline leakage detection, a signal denoising method based on an improved variational mode decomposition algorithm is proposed. This work adopts a standard variational mode decomposition algorithm with decomposition level K and the penalty factor α. The improvements consist of using a two-dimensional sparrow search algorithm to find K and α. To verify the superiority of the sparrow search algorithm to find K and α, it is compared with three earlier studies. These studies used the firefly algorithm, particle swarm optimization, and whale optimization algorithm to perform the optimization. The main result of this study is to demonstrate that the variational mode decomposition improved by sparrow search algorithm gives a much improved signal-to-noise ratio compared to the other methods. In all other respects, the results are comparable.

Ultrasound Image Features under Decomposition Algorithm to Analyze the Nursing Intervention on Patients with Colon Polyps Undergoing Endoscopic Resection

Based on the ultrasonic imaging and endoscopic resection of the intelligent segmentation algorithm, this study is aimed at exploring whether nursing intervention can promote the good recovery of patients with colon polyps, hoping to find a new method for clinical treatment of the colon polyps. Patients with colon polyps were divided into an experimental group (fine nursing) and a control group (general nursing). The colonoscopy polyp ultrasound image was preprocessing to select the seed points and background points. The random walk decomposition algorithm was applied to calculate the probability of each marked point, and then, the marked image was outputted. The accuracy of the intelligent segmentation algorithm was 81%. The incidence of complications in the experimental group was 4.83%, which was lower than 16.66% in the control group, and the difference was statistically obvious ( P < 0.05 ). Perioperative refined nursing intervention for colon polyp patients undergoing endoscopic electrosurgical resection can decrease postoperative adverse reactions; reduce postoperative mucosal perforation, blood in the stool, abdominal pain, and small bleeding; lower the incidence of postoperative complications; and allow patients to recover quickly, enhancing the life comfort of patient.