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In 1923, the Philosophical Transactions published G. I. Taylor’s seminal paper on the stability of what we now call Taylor–Couette flow. In the century since the paper was published, Taylor’s ground-breaking linear stability analysis of fluid flow between two rotating cylinders has had an enormous impact on the field of fluid mechanics. The paper’s influence has extended to general rotating flows, geophysical flows and astrophysical flows, not to mention its significance in firmly establishing several foundational concepts in fluid mechanics that are now broadly accepted. This two-part issue includes review articles and research articles spanning a broad range of contemporary research areas, all rooted in Taylor’s landmark paper. This article is part of the theme issue ‘Taylor–Couette and related flows on the centennial of Taylor’s seminal Philosophical Transactions paper (part 1)’.

To enhance the quality and efficiency of computer-enabled generation of papers for Test for English Majors Band 8 (TEM-8), a paper generation model supported by sparrow search algorithm-genetic algorithm was studied. First, a simplified test paper generation mathematical model was set up after analyzing and studying types and characteristics of TEM-8 tasks. In the model, quantity, type, difficulty, discrimination degree, scores, exposure, and answering time of test questions were taken into consideration. To enhance the optimizing effect of the genetic algorithm for searching test questions, the traditional genetic algorithm was improved by introducing the sparrow search algorithm into the model to achieve a better crossover rate, variance rate, optimization precision, and speed of the genetic algorithm. A new sparrow search-genetic algorithm (SSA-GA) was designed, and the optimizing effect of SSA-GA was verified to be ideal through optimizing six standard test functions. Then, SSA-GA was applied to conduct experimentation with test paper generation, and comparison with traditional genetic algorithms was also made. The values of best and average fitness of SSA-GA were better than those of the traditional genetic algorithm (GA) in the paper generation. Exposure rate and success rate in TEM-8 paper generation of SSA-GA were higher than those of traditional GA in TEM-8 paper generation. Results showed that the studied SSA-GA could implement test paper generation with higher speed and better quality.

This paper presents a comprehensive study and analysis of the intelligent grouping of papers in adult higher education using a random matrix approach. Using the results of random matrix theory on the eigenvalues of the sample covariance matrix, the energy of each subspace is estimated, and the estimated energy is then used to construct a subspace weighting matrix. The statistical properties of the sample covariance matrix eigenvectors are analyzed using the first-order perturbation approximation, and then, asymptotic results from random matrix theory on the projection of the sample covariance matrix signal subspace to the real signal parametrization are used to obtain the weighting matrix based on the random matrix eigenvectors. Dynamic adjustment according to the fitness of individuals in the population is performed to ensure population diversity, while the combination of the small habitat technique can avoid the algorithm from falling into early convergence. The algorithm introduces chaos theory to optimize the population initialization process and uses the dynamic traversal randomness of chaos to select individuals in the population so that the initial population is close to the desired target solution. The design of the fitness function in the genetic algorithm generally maps the objective function of the problem to the fitness function. A good fitness function can directly reflect the quality of the individuals in the group. Based on the in-depth study of the basic attributes of the test questions and the principles of test paper evaluation, the mathematical model and objective function of intelligent paper grouping are determined by the difficulty, knowledge points, and cognitive level of the test questions as the main constraints, and NCAGA is applied to the intelligent paper grouping method, which better completes the intelligent paper grouping session for the computer system architecture course. In the process of designing the intelligent grouping algorithm, for the situations of premature convergence and convergence to locally optimal solutions that easily occur in the traditional genetic algorithm, this paper adopts the approach of adaptive adjustment of crossover probability and variation probability to improve the algorithm and achieves satisfactory results. Based on extensive business research, this paper completes the requirement analysis of the online practice system based on the intelligent grouping of papers and presents the functional design and database design of the key functional modules in the system in detail. Finally, this paper conducts functional tests on the system and analyses the test results.

We introduce our efforts within the project ‘The science of deep specification’ to work out the key formal underpinnings of industrial-scale formal specifications of software and hardware components, anticipating a world where large verified systems are routinely built out of smaller verified components that are also used by many other projects. We identify an important class of specification that has already been used in a few experiments that connect strong component-correctness theorems across the work of different teams. To help popularize the unique advantages of that style, we dub itdeep specification, and we say that it encompasses specifications that arerich,two-sided,formalandlive(terms that we define in the article). Our core team is developing a proof-of-concept system (based on the Coq proof assistant) whose specification and verification work is divided across largely decoupled subteams at our four institutions, encompassing hardware microarchitecture, compilers, operating systems and applications, along with cross-cutting principles and tools for effective specification. We also aim to catalyse interest in the approach, not just by basic researchers but also by users in industry.This article is part of the themed issue ‘Verified trustworthy software systems’.

We formulate a simple susceptible‐infectious‐recovery (SIR) model to describe the spread of the coronavirus under strict social restrictions. The transmission rate in this model is exponentially decreasing with time. We find a formula for basic reproduction function and estimate the maximum number of daily infected individuals. We fit the model to induced death data in Italy, United States, Germany, France, India, Spain, and China over the period from the first reported death to August 7, 2020. We notice that the model has excellent fit to the disease death data in these countries. We estimate the model's parameters in each of these countries with 95% confidence intervals. We order the strength of social restrictions in these countries using the exponential rate. We estimate the time needed to reduce the basic reproduction function to one unit and use it to order the quality of social restrictions in these countries. The social restriction in China was the strictest and the most effective and in India was the weakest and the least effective. Policy‐makers may apply the Chinese successful social restriction experiment and avoid the Indian unsuccessful one. [ FROM AUTHOR] Copyright of Mathematical Methods in the Applied Sciences is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

In the current article, we aim to study in detail a novel coronavirus (2019-nCoV or COVID-19) mathematical model for different aspects under Caputo fractional derivative. First, from analysis point of view, existence is necessary to be investigated for any applied problem. Therefore, we used fixed point theorem's due to Banach's and Schaefer's to establish some sufficient results regarding existence and uniqueness of the solution to the proposed model. On the other hand, stability is important in respect of approximate solution, so we have developed condition sufficient for the stability of Ulam-Hyers and their different types for the considered system. In addition, the model has also been considered for semianalytical solution via Laplace Adomian decomposition method (LADM). On Matlab, by taking some real data about Pakistan, we graph the obtained results. In the last of the manuscript, a detail discussion and brief conclusion are provided.

COVID-19 pandemic has affected all aspects of people's lives and disrupted the economy. Forecasting the number of cases infected with this virus can help authorities make accurate decisions on the interventions that must be implemented to control the pandemic. Investigation of the studies on COVID-19 forecasting indicates that various techniques such as statistical, mathematical, and machine and deep learning have been utilized. Although deep learning models have shown promising results in this context, their performance can be improved using auxiliary features. Therefore, in this study, we propose two hybrid deep learning methods that utilize the statistical features as auxiliary inputs and associate them with their main input. Specifically, we design a hybrid method of the multihead attention mechanism and the statistical features (ATT_FE) and a combined method of convolutional neural network and the statistical features (CNN_FE) and apply them to COVID-19 data of 10 countries with the highest number of confirmed cases. The results of experiments indicate that the hybrid models outperform their conventional counterparts in terms of performance measures. The experiments also demonstrate the superiority of the hybrid ATT_FE method over the long short-term memory model.

The article deals with the analysis of the fractional COVID‐19 epidemic model (FCEM) with a convex incidence rate. Keeping in view the fading memory and crossover behavior found in many biological phenomena, we study the coronavirus disease by using the noninteger Caputo derivative (CD). Under the Caputo operator (CO), existence and uniqueness for the solutions of the FCEM have been analyzed using fixed point theorems. We study all the basic properties and results including local and global stability. We show the global stability of disease‐free equilibrium using the method of Castillo‐Chavez, while for disease endemic, we use the method of geometrical approach. Sensitivity analysis is carried out to highlight the most sensitive parameters corresponding to basic reproduction number. Simulations are performed via first‐order convergent numerical technique to determine how changes in parameters affect the dynamical behavior of the system.

Pulp washing process has the features of multivariate, time delay, nonlinearity. Considering the difficulties of modeling and optimal control in pulp washing process, a data-driven operational-pattern optimization method is proposed to model and optimize the pulp washing process in this paper. The most important quality indexes of pulp washing performance are residual soda in the washed pulp and Baume degree of extracted black liquor. Considering the difficulties of modeling, online measurement of these indexes, two-step neural networks, and multivariate logistic regression are used to establish the prediction models of residual soda and Baume degree. The mathematical model of the washing process can be identified, and the indexes can meet the production requirements. In the target of better product quality, low cost, and low energy consumption, a multiobjective problems is solved by ant colony optimization algorithm based on the optimized operational-pattern database. It shows that the theoretical analyses are correct and the practical applications are feasible, optimization control system has been designed for the pulp washing process, and the practical results show that pulp production increased by 20% and water consumption decreased by nearly 30%. This method is effective in the pulp washing process.

The objective of this work was to evaluate the ability of CCC as an adsorbent material for the acidity removal of RFO, aiming at the application of the oil in biodiesel production. For that, a RCCD was used for FFA removal by applying the CCC and CAC for comparative purposes. In the RCCD removal assays the effect of the Temperature, Agitation and Mass factors were assessed over acidity removal of the oil. Under the best conditions from RCCD, an evaluation of adsorption kinetics was performed, wherein it was observed the equilibrium was reached within 4 h, for the CCC. Also, the influence of the adsorbent dosage was performed. It was verified that 4 g was sufficient to allow the system to reach the maximum FFA removal. Overall, the CCC presented results approximately twice as high than those obtained by the CAC, mainly due to the pore size distribution which led to a “molecular sieving effect” for the CCC adsorbent. It allowed the major diffusion of the FFA molecules inside its narrow-distributed pores, whereas the CAC with a wider pore distribution (up to 260 Å) resulted in the larger molecules competition for the active sites inside the porous structure. The adsorbents’ characterization also evidenced that CCC adsorbent presented a higher content of oxygenated groups in its surface which acted as potential active sites for the FFA molecules resulting in an enhanced adsorbent-adsorbate affinity. Lastly, the wastes generated in the adsorption experiments, were evaluated as to their calorific power resulting in a value of 31,933 J g-1, suggesting that it could be further used for energetic purposes, such as a solid fuel for boilers and furnaces to generate thermal energy. Based on these results, the CCC stands out as a promising material for RFO acidity removal.

Considering the increased generation of solid waste and the difficulty of proper final disposal, it is of utmost importance to study the reuse of solid waste, seeking a beneficial alternative for the population and the environment. This study aimed to produce wood particle boards incorporated with different percentage of waste from the manufacture of labels and tags, commonly known as paper shavings, containing biaxially oriented polypropylene (BOPP), aiming its reuse. Physical, chemical and mechanical tests were performed. The difference in density between the materials used to manufacture the boards influenced the production process as well as the amount of waste added. Values of moisture content and thickness swelling remained within the range set by the regulations. The results for water absorption analysis are in agreement with those in the literature on the incorporation of different types of waste in the boards. According to our findings, it was observed the importance of a homogeneous mixture of the materials, and pH control. The incorporation of waste containing BOPP into particle boards may be a promising disposal alternative for this waste, given the development of a by-product that encourages sustainable development.

Zinc uptake is essential for crop development; thus, knowledge about soil zinc availability is fundamental for fertilization in periods of higher crop demand. A nonlinear first-order kinetic model has been employed to evaluate zinc availability. Studies usually employ few observations; however, inference in nonlinear models is only valid for sufficiently large samples. An alternative is the Bayesian method, where inferences are made in terms of probability, which is effective even with small samples. The aim of this study was to use Bayesian methodology to evaluate the fitness of a nonlinear first-order kinetic model to describe zinc extraction from soil with sewage sludge using seven different extraction solutions. The analysed data were obtained from an experiment using a completely randomized design and three replicates. Fifteen zinc extractions were evaluated for each extraction solution. Posterior distributions of a study that evaluated the nonlinear first-order kinetic model were used as prior distributions in the present study. Using the full conditionals, samples of posterior marginal distributions were generated using the Gibbs sampler and Metropolis-Hastings algorithms and implemented in R. The Bayesian method allowed the use of posterior distributions of another study that evaluated the model used as prior distributions for parameters in the present study. The posterior full conditional distributions for the parameters were normal distributions and gamma distributions, respectively. The Bayesian method was efficient for the study of the first-order kinetic model to describe zinc extraction from soil with sewage sludge using seven extraction solutions.

Acerola is a tropical fruit characterized by a high content of vitamin C. The food sorption isotherms reflect the way that the water binds to the food. This study aimed to fit mathematical models to the adsorption isotherms of the acerola pulp powder obtained by spray-drying, and. It was used acerola pulp containing 17.1% (m m -1 ) maltodextrin, and a spray-dryer with drying air temperature of 154°C. The mathematical models of GAB, BET, Henderson, and Oswin were applied to determine the isotherms, using temperatures of 25, 35 and 45°C. The hygroscopicity, degree of caking and solubility analysis were performed to characterize the powders. The powder showed hygroscopicity of 5.18, degree of caking of 3.67 and solubility of 94.88%. The evaluated mathematical models showed mean relative errors ranging from 10.18 to 29.13% and a coefficient of determination (R 2 ) ranging from 0.939 to 0.977. At 25°C, the Oswin model was the best fitted, whereas at 35 and 45°C the best fitting was obtained by the Henderson model. Type III (J-shape) isotherms were obtained, typical of high-sugar foods. The powder showed a low hygroscopicity and degree of caking in addition to a high solubility.

Membranes processes, in particular, nanofiltration and reverse osmosis, represent an important alternative for treatment of contaminated waters. In this context, the present study aims to evaluate the efficiency of the removal of the carbamates: carbaryl, carbofuran and methomyl at different concentrations from water fortified with these compounds. A pilot nanofiltration unit, operating in the tangential flow, provided with a membrane in the spiral configuration, Model NF90-4040 (Dow Filmtec Membranes) was used. Two types of fortified water with carbamates were performed: distilled water and pond water. Nanofiltration tests with distilled water were performed in a closed system, in order to maintain a constant concentration of carbamates in the feed water. In this case, the efficiency of carbamates removal was greater or equal than 89, 100 and 74% for carbaryl, carbofuran, and methomyl, respectively. When using pond water, the removal of carbamates was higher or equal: 98, 100 and 90% for carbaryl, carbofuran, and methomyl, respectively. The results indicate that by nanofiltration, it is possible to obtain drinking water from the contaminated water with carbamates, according to international guidelines.

Cellulose nanopapers have gained significant attention in recent years as large-scale reinforcement for high-loading cellulose nanocomposites, substrates for printed electronics and filter nanopapers for water treatment. The mechanical properties of nanopapers are of fundamental importance for all these applications. Cellulose nanopapers can simply be prepared by filtering a suspension of nanocellulose, followed by heat consolidation. It was already demonstrated that the mechanical properties of cellulose nanopapers can be tailored by the fineness of the fibrils used or by modifying nanocellulose fibrils for instance by polymer adsorption, but nanocellulose blends remain underexplored. In this work, we show that the mechanical and physical properties of cellulose nanopapers can be tuned by creating nanopapers from blends of various grades of nanocellulose, i.e. (mechanically refined) bacterial cellulose or cellulose nanofibrils extracted from never-dried bleached softwood pulp by chemical and mechanical pre-treatments. We found that nanopapers made from blends of two or three nanocellulose grades show synergistic effects resulting in improved stiffness, strength, ductility, toughness and physical properties. This article is part of a discussion meeting issue ‘New horizons for cellulose nanotechnology’.

The Flash Profile is a descriptive analysis method derived from Free-Choice Profile, in which each taster chooses and uses his/her own words to evaluate the product while comparing several attributes. Four passion fruit juices were analyzed, two juices were produced with concentrated juice, one with pulp and one with reconstituted juice; all juices had different levels of sugar, some had gum and dyes. This study aimed to evaluate the physicochemical properties (color, titratable acidity and solid content) as well as sensory analysis like Flash profile and affective test. In physicochemical characterization and in Flash Profile, the juice A (pulp) had higher solid content and consistence, the juice B (concentrated juice) was the least acidic and presented the lowest value of soluble solids and presented strong aroma and flavor of passion-fruit, the juice C (reconstituted juice) was pale yellow and showed artificial flavor and the juice D (concentrated juice) was the most acidic, consistent with the natural flavor. In the acceptance test, all the juices scored 5-6, indicating that panelists tasters neither liked nor disliked. Flash Profile proved to be an easy and rapid technique showing a good correlation between panelists and the attributes and confirmed the results of physicochemical characterization.

Curaua (Ananas erictifolius) fiber was investigated as sorbent of diesel and biodiesel oils, and compared with peat, which is used commercially. The sorption tests were carried out for the unclassified fiber and within the granulometry ranges ≤ 180; 180-425; 425-850 and 850-3350 μm, in the times 5, 10, 20, 40, 60 and 1440 min. The sorbents were also submitted to a physycal-chemical and energetic characterization. The unclassified curaua fiber presented a medium sorption capacity of the diesel oil of 1.12 g oil g fiber -1 , similar to the one found for peat, which was 1.25 g oil g peat -1 , whereas the biodiesel sorption was 1.16 g oil g fiber -1 , higher than the peat sorption, 0.85 g oil g peat -1 . The peat showed the highest mean sorption value of both oils in the 180-425 μm granulometry, decreasing with its increase. With the granolumetry increase, the curaua fiber experienced an increase in the biodiesel sorption, but remained constant regarding the diesel sorption. The calorific power of the sorbents after sorption was higher, due to the sorbed oil

The manufacture of sugar with sulfur dioxide during the clarification process faces market difficulties and rejection by consumers who are more and more concerned on food safety. The alternative may be sugar clarification with carbon dioxide which consists in adding carbon dioxide and calcium hydroxide to sugar juice at constant pH. Under these conditions, a calcium carbonate complex is formed which adsorbs and precipitates impurities. Current assay analyzes the clarification of sugarcane juice in a laboratory carbonatation process. Assays were performed randomly with a two-level factorial design with three replications at the center point to evaluate the effect of pH and carbon dioxide flow in clarification. Process efficiency was assessed according to rates of starch removal, ICUMSA color and calcium left in the juice. Rates of starch removal at 89.19 and 85.75% and of ICUMSA color at 92.93 and 91.66% were obtained, respectively, in assays with carbon dioxide flow at 200 NL h-1 and pH at 8.0 and 9.0. Results show that total added calcium was almost removed as calcium carbonate.

The paper deals with a theoretical study of electrokinetic flow of a rheological Herschel-Bulkley fluid through a cylindrical tube of variable cross-section. The concern of this study is to analyze combined pressure-driven and electroosmotic flow of Herschel-Bulkley fluid. The wall potential is considered to vary slowly and periodically along the axis of the tube. With reference to flow in the micro-vessels, the problem has been solved using the lubrication theory. The Helmholtz-Smoluchowski (HS) slip boundary condition has been employed in this study. Volumetric flow rate $Q$ is found to be significantly affected by the yield stress parameter $\nu$ only if an applied pressure force is active. The linear superposition of flow components separately due to the hydrodynamic and electric force occurs only for a strictly uniform tube. This linear relationship fails if non-uniformity appears in either tube radius or in distribution of the electrokinetic slip boundary condition. Moreover, converging/diverging nature of the mean tube radius plays a crucial role on the fluid transport. For the benefit of readers, along with the original contribution, some applications of external electrical stimulation (ES) in the human body and HS slip velocity, studied in the past by previous researchers have been discussed in the paper., Comment: 44 pages and 28 figures

The minimum spanning tree (MST) is widely used in planning the most economical network. The algorithm for finding the MST of a connected graph is essential. In this paper, a new algorithm for finding MST is proposed based on a deduced theorem of MST. The algorithm first sorts the edges in the graph according to their weight, from large to small. Next, on the premise of ensuring the connectivity of the graph, it deletes the edges in this order until the number of edges of the graph is equal to the number of vertexes minus 1. Finally, the remaining graph is an MST. This algorithm is equivalent to an inverse Kruskal algorithm. For sparse graphs, the proposed algorithm has higher efficiency than the Kruskal algorithm. Experiments and analysis verify the effectiveness of the algorithm proposed in this paper. The algorithm provides a better choice for finding the MST of the sparse graph.

The goal of this paper is to establish a feasible three-species equilibrium model to analyze the symbiotic relationship of an automobile manufacturing community. In order to extend the Lotka–Volterra model to empirical analysis, this paper proposes an enterprise community symbiosis model based on a three-dimensional Lotka–Volterra model, as the classical two-dimensional Lotka–Volterra model has limited application scenarios. This paper takes the innovation assets of three communities of the Chinese automobile manufacturing industry as samples. The industrial community related to automobile manufacturing consists of the automobile manufacturing population, automobile parts population, and enterprise service industry population. The symbiosis system is empirically analyzed from two aspects: the balanced development of the three populations and the competitive evolution of the three populations. The stability of the model is tested by the data from information technology and the intelligent manufacturing community. In the process of dynamic simulation, the symbiotic relationship between automobile manufacturing-related populations shows a significant “skew symbiotic relationship.” This paper reconstructs the “whole population symbiosis” optimization model as skew distribution is difficult to apply to support collaborative development. The symbiosis optimization under the equilibrium state of the three populations shows that the growth of the three automobile manufacturing industry populations has the possibility of equilibrium and reciprocity. The empirical analysis fully demonstrates the feasibility of this research paradigm. The evolution analysis of the symbiotic system shows that cooperative behavior is better than competitive strategy. The research paradigm proposed in this paper can better analyze the symbiosis mechanism of the enterprise community.

The key to the sustainable development of family farms and ranches is to solve the problem of financing difficulty, and the construction of the credit rating system of family farms and ranches is the basis of solving the financing difficulty. Therefore, the construction of the credit rating system of family farms and ranches is not only a theoretical problem but also has strong practical significance. On the basis of dichotomous method and improved DEA technology, this paper sets up a credit rating model based on dynamic cycle dichotomous DEA method with the goal of maximizing the efficiency value of credit scoring of family farm and ranch and the constraint of increasing default loss rate. The first feature of this paper is that it constructs a direct mapping from the credit index system to the credit score through the three-stage DEA model, which avoids the disadvantages of the classical linear weighting formula that relies heavily on the measurement results of different weights, and obtains more accurate and stable credit score results of family farms and pastures. Second, the three-stage DEA efficiency value is used as the credit score value of the family farm and pasture, and whether it is located in the optimal preset interval is taken as the basis for efficiency dichotomy. The DEA credit score of the family farm and pasture is calculated by dynamic cycle, and the samples belonging to each level of the family farm and pasture are calculated successively according to the order of the credit grade from high to low. Finally, the result of reasonable credit rating of family farm and ranch with high default loss rate and low credit score was obtained. The pyramid test results of matching credit rating and default loss rate show that this research method is effective and has a good pyramid shape.

Management accounting plays a greater role in providing business decisions for enterprises. As the core technology of big data processing, data mining technology can process a large amount of complex, structured, and unstructured information very efficiently. In this paper, the nonlinear system is taken as the research object, and the obtained system data is directly used. In the framework of divide and conquer strategy, multi-model method is combined with data clustering and a local modeling algorithm to study the nonlinearity of management accounting data mining. Through online adaptive estimation of parameters, aiming at the zero dynamic problem of the non-minimum phase system, through the decoupling matrix analysis of the nonlinear system, the state of the original strategy is dynamically extended to make the estimated parameters asymptotically approach the true value. The estimated parameters are applied to the nonlinear hypothesis strategy to achieve the goal of linearization control. It is assumed that the generation strategy is applied to the mining of different related types of rules without manually setting parameters, which greatly improves the efficiency of rule discovery. Through case analysis, this paper demonstrates the feasibility of applying nonlinear hypothesis strategy of data mining technology to management accounting and puts forward the relevant application process framework. The validity and feasibility of nonlinear hypothesis strategy data mining technology in management accounting are verified.

As a hot topic in military research, small combat robots are always in a dynamic process when traveling, causing the body tube to vibrate which affects its firing accuracy. Different from the traditional two-degree-of-freedom combat robot, this paper establishes a rigid-flexible coupling dynamics model for the new three-degree-of-freedom combat robot to study the response characteristics of its body tube during travel. To accurately reflect the driving conditions of the mobile robot, a road surface unevenness model was established using the sine wave superposition method. The firing accuracy of a small combat robot can be affected by the speed and road conditions, as shown in numerous studies. The effects of arm structure, arm mounting position, and firing angle on the vibration response of the body tube are analyzed in this paper. The results show that a reference for improving the design of small combat robots can be found in a correlation between the structure, mounting position, and firing angle of the robotic arm, and the body tube’s vibration response.

Considering the widely existing processing time uncertainty in the real-world production process, this paper constructs a fuzzy mathematical model for the silicon single crystal production batch scheduling problem to minimize the maximum completion time. In this paper, a two-stage hybrid optimization algorithm (TSHOA) is proposed for solving the scheduling model. Firstly, the improved differential evolution algorithm (IDE) is used to solve the order quantity allocation problem of silicon single crystal with different sizes to obtain the quantity of silicon single crystal rods with different sizes produced by different types of single crystal furnaces. Secondly, the variable neighborhood search (VNS) algorithm is adopted to optimize the order quantity sequencingof batch production processes. Finally, simulations and comparisons demonstrate the feasibility of the model and the effectiveness of TSHOA.

This paper has been designed to introduce the method for solving the Bi-level Multi-objective (BL-MO) Fully Quadratic Fractional Optimization Model through Fuzzy Goal Programming (FGP) approach by utilising non-linear programming. In Fully Quadratic Fractional Optimization Model, the objective functions are in fractional form, having quadratic functions in both numerator and denominator subject to quadratic constraints set. The motive behind this paper is to provide a solution to solve the BL-MO optimization model in which number of decision-makers (DM) exists at two levels in the hierarchy. First, the fractional functions with fuzzy demand, which are in the form of fuzzy numbers, are converted into crisp models by applying the concept of α-cuts. After that, membership functions are developed which are corresponding to each decision-maker’s objective and converted into simpler form to avoid complications due to calculations. Finally, the model is simplified by applying FGP approach, and a compromised solution to the initial model is obtained. An algorithm, flowchart and example are also given at the end to explain the study of the proposed model.

Previous comparisons of experimental data with nonlinear numerical simulations of density stratified Taylor–Couette (TC) flows revealed nonlinear interactions of strato-rotational instability (SRI) modes that lead to periodic changes in the SRI spirals and their axial propagation. These pattern changes are associated with low-frequency velocity modulations that are related to the dynamics of two competing spiral wave modes propagating in opposite directions. In the present paper, a parameter study of the SRI is performed using direct numerical simulations to evaluate the influence of the Reynolds numbers, the stratification, and of the container geometry on these SRI low-frequency modulations and spiral pattern changes. The results of this parameter study show that the modulations can be considered as a secondary instability that are not observed for all SRI unstable regimes. The findings are of interest when the TC model is related to star formation processes in accretion discs. This article is part of the theme issue ‘Taylor–Couette and related flows on the centennial of Taylor’s seminal Philosophical Transactions paper (part 2)’.

The dynamic transitions of the Brusselator model has been recently analyzed in Y. Choi et’al (2021) and T. Ma, S. Wang (2011). Our aim in this paper is to address the relation between the pattern formation and dynamic transition results left open in those papers. We consider the problem in the setting of a 2D rectangular box where an instability of the homogeneous steady state occurs due to the perturbations in the direction of several modes becoming critical simultaneously. Our main results are two folds: (1) a rigorous characterization of the types and structure of the dynamic transitions of the model from basic homogeneous states and (2) the relation between the dynamic transitions and the pattern formations. We observe that the Brusselator model exhibits different transition types and patterns depending on the nonlinear interactions of the pattern of the critical modes.

Since Taylor’s seminal paper, the existence of large-scale quasi-axisymmetric structures has been a matter of interest when studying Taylor–Couette flow. In this article, we probe their formation in the highly turbulent regime by conducting a series of numerical simulations at a fixed Reynolds number Re s = 3.6 × 10 4 while varying the Coriolis parameter to analyse the flow characteristics as the structures arise and dissipate. We show how the Coriolis force induces a one-way coupling between the radial and azimuthal velocity fields inside the boundary layer, but in the bulk, there is a two-way coupling that causes competing effects. We discuss how this complicates the analogy of narrow-gap Taylor–Couette to other convective flows. We then compare these statistics with a similar shear flow without no-slip boundary layers, showing how this double coupling causes very different effects. We finish by reflecting on the possible origins of turbulent Taylor rolls. This article is part of the theme issue ‘Taylor–Couette and related flows on the centennial of Taylor’s seminal Philosophical Transactions paper (part 1)’.

Emulsions are common in many natural and industrial settings. Recently, much attention has been put on understanding the dynamics of turbulent emulsions. This paper reviews some recent studies of emulsions in turbulent Taylor-Couette flow, mainly focusing on the statistics of the dispersed phase and the global momentum transport of the system. We first study the size distribution and the breakup mechanism of the dispersed droplets for turbulent emulsions with a low volume-fraction (dilute) of the dispersed phase. For systems with a high volume-fraction of the dispersed phase (dense), we address the detailed response of the global transport (effective viscosity) of the turbulent emulsion and its connection to the droplet statistics. Finally, we will discuss catastrophic phase inversions, which can happen when the volume fraction of the dispersed phase exceeds a critical value during dynamic emulsification. We end the manuscript with a summary and an outlook including some open questions for future research. This article is part of the theme issue `Taylor-Couette and Related Flows on the Centennial of Taylor's Seminal Philosophical Transactions Paper'., 15 pages, 4 figures

Decentralized architecture known as fog computing is situated between the cloud and data-producing devices. It acts as a conduit between cloud services and IoT devices. In order to reduce latency, fog computing can handle a significant amount of computation for time-sensitive IoT applications. The Fog layer is simultaneously vulnerable to numerous assaults. To defend the fog nodes from attacks, fog computing paradigms may be suited for deep learning-based intrusion detection systems (IDS). In this paper, a combined Ensemble Intrusion Detection Model using Deep learning with Efficient Feature Selection using Random forests is proposed for Fog Computing Environments by using two deep learning models of traditional CNN and IDS-AlexNet model called Ensemble CNN-IDS with Random Forest and showed this model gives high accuracy of attack detection. The respective model implementations demonstrated on the UNSW-NB15 dataset that consists of 9 classes of attacks namely Fuzzers, Analysis, Backdoors, DoS, Exploits, Generic, Reconnaissance, Shellcodes and Worms. The proposed combined Ensemble Intrusion Detection Model using Deep learning with Efficient Feature Selection for intrusions detection is shown to be accurate and efficient by using different classifiers. Our proposed model provides high the accuracy in attack detection of about 97.5% that it outperformed various other traditional and recent models. Decentralized architecture known as fog computing is situated between the cloud and data-producing devices. It acts as a conduit between cloud services and IoT devices. In order to reduce latency, fog computing can handle a significant amount of computation for time-sensitive IoT applications. The Fog layer is simultaneously vulnerable to numerous assaults. To defend the fog nodes from attacks, fog computing paradigms may be suited for deep learning-based intrusion detection systems (IDS). In this paper, a combined Ensemble Intrusion Detection Model using Deep learning with Efficient Feature Selection using Random forests is proposed for Fog Computing Environments by using two deep learning models of traditional CNN and IDS-AlexNet model called Ensemble CNN-IDS with Random Forest and showed this model gives high accuracy of attack detection. The respective model implementations demonstrated on the UNSW-NB15 dataset that consists of 9 classes of attacks namely Fuzzers, Analysis, Backdoors, DoS, Exploits, Generic, Reconnaissance, Shellcodes and Worms. The proposed combined Ensemble Intrusion Detection Model using Deep learning with Efficient Feature Selection for intrusions detection is shown to be accurate and efficient by using different classifiers. Our proposed model provides high the accuracy in attack detection of about 97.5% that it outperformed various other traditional and recent models.

This paper studies the monic semi-classical Laguerre polynomials based on previous work by Boelen and Van Assche \cite{Boelen}, Filipuk et al. \cite{Filipuk} and Clarkson and Jordaan \cite{Clarkson}. Filipuk, Van Assche and Zhang proved that the diagonal recurrence coefficient $\alpha_n(t)$ satisfies the fourth Painlev\'{e} equation. In this paper we show that the off-diagonal recurrence coefficient $\beta_n(t)$ fulfills the first member of Chazy II system. We also prove that the sub-leading coefficient of the monic semi-classical Laguerre polynomials satisfies both the continuous and discrete Jimbo-Miwa-Okamoto $\sigma$-form of Painlev\'{e} IV. By using Dyson's Coulomb fluid approach together with the discrete system for $\alpha_n(t)$ and $\beta_n(t)$, we obtain the large $n$ asymptotic expansions of the recurrence coefficients and the sub-leading coefficient. The large $n$ asymptotics of the associate Hankel determinant (including the constant term) is derived from its integral representation in terms of the sub-leading coefficient., Comment: 20 pages

Lot sizing models involve operational and tactical decisions. These decisions may entail multi-level production processes such as assembly operations with multiple plants and limited capacities. Lot sizing problems are widely recognized as NP-hard problems therefore difficult to solve, especially the ones with multiple plants and capacity constraints. The level of complexity rises to an even higher level when there is an interplant transfer between the plants. This paper presents a Genetic Algorithm (GA) based solution methodology applied to large scale multi-plant capacitated lot sizing problem with interplant transfer (MPCLSP-IT). Although the GA has been a very effective and widely accepted meta-heuristic approach used to solve large scale complex problems, it has not been employed for MPCLSP-IT problem. This paper solves the MPCLSP-IT problem in large scale instances by using a genetic algorithm, and in doing so successfully obtains a better solution in terms of computation time when compared to the results obtained by the other methods such as Lagrangian relaxation, greedy randomized adaptive search procedure (GRASP) heuristics, and GRASP-path relinking techniques used in extant literature.

In this paper a thorough analysis is carried out of the type of order reduction that Lawson methods exhibit when used to integrate nonlinear initial boundary value problems. In particular, we focus on nonlinear reaction-diffusion problems, and therefore, this study is important in a large number of practical applications modeled by this type of nonlinear equations. A theoretical study of the local and global error of the total discretization of the problem is carried out, taking into account both, the error coming from the space discretization and that due to the integration in time. These results are also corroborated by the numerical experiments performed in this paper., Ministerio de Ciencia e Innovación and Regional Development European Funds, Grant/Award Number: PGC2018-101443-B-I00; Junta de Castilla y León and Feder, Grant/Award Number: VA169P20

In a companion paper, we develop the theoretical background of a stochastic approach to causality with the objective of formulating necessary conditions that are operationally useful in identifying or falsifying causality claims. Starting from the idea of stochastic causal systems, the approach extends it to the more general concept of hen-or-egg causality, which includes as special cases the classic causal, and the potentially causal and anti-causal systems. The framework developed is applicable to large-scale open systems, which are neither controllable nor repeatable. In this paper, we illustrate and showcase the proposed framework in a number of case studies. Some of them are controlled synthetic examples and are conducted as a proof of applicability of the theoretical concept, to test the methodology witha prioriknown system properties. Others are real-world studies on interesting scientific problems in geophysics, and in particular hydrology and climatology.

This paper exploits the Markov reward method to availability and importance evaluation for CNC machine tools. The presented paper regards CNC machine tools as a multi-state system that state transitions process caused by elements and subsystem’s failures and corresponding maintenance activities during the lifetime. Simultaneously, considering the time-varying characteristics of the failure rates of subsystems, the non-homogeneous Markov reward model is introduced for evaluation of availability and subsystem importance identification for aging multi-state CNC machine tools under minimal repair. Corresponding procedures for the failure rates, state transition matrix, and reward matrix definition are suggested for the availability and importance measures. A numerical example is presented in order to illustrate the approach.

This paper proposes a semi-automatic methodology to assist the user in creating surveys about FMEA and Risk Analysis, based on a customized use of the tools for semantic analysis and in particular a home-developed syntactic parser called Kompat Cognitive. The core of this work has been the analysis of the specific FMEA-related jargon and its common modalities of description within scientific papers and patents in order to systematize the linguistic analysis of the reference documents within the proposed step-divided procedure. The main goals of the methodology are to assist not skilled in the art users about FMEA during the analysis of generic and specific features, by considering large moles of contributions in restricted amounts of time. The methodology has then been tested on the same pool of 286 documents, divided between 177 and 109 patents, manually analyzed in our previous survey, in order to replicate part of its classifications through the proposed new modality. In this way we evaluated the abilities of the methodology both to automatically suggesting the main features of interest and to classify the documents according to them.

Travel time estimation & reliability evaluation of any means of transportation in every type of travel mode- land, rail, sea and air has been of immense interest of the researchers; primarily due to growing economic concern in the field of logistics & passenger movement. In situations like quantitative data inaccessibility or data imprecision, fuzzy set based possibilistic approach is recognized as a practical choice in obtaining the reliability estimates. This paper proposes and advocates possibilistic approach for travel time reliability computation of any type transportation vehicle under fuzzy type of data. The proposed approach is a novel way of computing the travel time & obtaining the related reliability value. Initially, the paper proposes the general methodology for travel time reliability evaluation. Individual travel time components of a transportation vehicle are considered as fuzzy; as a result, travel time is modelled as a fuzzy variable. Travel time reliability of a transportation vehicle has been defined with the help of possibilistic measures. The proposed procedure is then demonstrated with an application to marine vessel carrying the bulk. After illustration of the proposed methodology, sensitivity analysis is carried out. The paper ends with the comments on comparative features of the three cases.

As the crime is increasing day by day around the world. To stop the crime many security gadgets and machines have been developed by various security agencies. The tripod turnstile machine also helps in stopping the crime and restricting the entry of unauthorized person on the premises. In this paper, the aim is to analyze the reliability measures of the two tripod turnstile machines which work in a parallel configuration. In the case of non-functionality of the machine, unauthorized persons may enter into the premises. Due to security and safety reason, organizations install these machines. Hence these machines must be highly reliable in the operation to avoid any unwanted event. In this paper, for the considered system, the transition state diagram has been drawn with the help of the Markov model. The Chapman-Kolmogorov differential equations are developed from the transition state diagram and solved using Laplace transformation. The failure and repair rates are assumed to be constant and follow negative exponential distribution. The system upstate and downstate probabilities are determined. The explicit expression of the system Availability, Reliability and MTTF are also obtained. The sensitivity analysis is also performed to determine which machine affects the system reliability the most.

When it comes across problems in creating Internet of Things (IOT) architecture, the major problem that arises is an automatic stipulation of resources. At the same time in today’s era, it is very important to integrate this problem with better Quality of Services (QoS) because of which the cloud computing is taking a shift. As being well acquainted that in fog computing, network’s bandwidth is limited, therefore it becomes quite important to build a joint architecture with resource allocation problem giving it a better quality of services with enhanced efficiency and low latency communication. Priority of QoS is determined by Systematic Ladder Process (SLP) and decision parameter evaluation by RECK algorithm. In this paper, there will be a design of a better framework for IOT resource allocation scheme with better efficiency and better QoS. The paper too highlights the comparison of the previous works of the resource allocation algorithms and schemes with RECK algorithm.

The goal of this paper is to explore the interrelated relationships between the quality of corporate governance, Stakeholder’s pressure, and Sustainability Reporting. We claim that sustainable development strategies are the result of the interaction of two variables: the standard of corporate governance and the pressure of stakeholders rather than the outcome of a single variable. The results indicate that the company size and corporate governance rating are positively correlated with sustainability reporting Scores disclosed by the Turkish companies. The analysis of 125 firm-year data suggest that the company provides more SRS if it has a better corporate governance rating; or larger total assets. The paper also reveals that social, environmental and economic indicators, as pillars of sustainable development, are not the result of corporate governance practices or of stakeholder pressure alone, but represent a holistic reflection of the accumulated effects of various factors related to both corporate governance practices and stakeholder theory. This paper extends the work of Taşkirmaz, and Gamze (2017) which examined the relationship between corporate governance and corporate social responsibility. Our results increase understanding of sustainable practices of the circular economy in Turkish industrial companies and indicate future trends for these applications. This research will help managers of manufacturing companies in formulating their plans to maximize the use of available resources and improve efficiency in the context of the circular economy.

A normalized version of the ubiquitous two-by-two contingency matrix is associated with a variety of marginal, conjunctive, and conditional probabilities that serve as appropriate indicators in diagnostic testing. If this matrix is enhanced by being interpreted as a probabilistic Universe of Discourse, it still suffers from two inter-related shortcomings, arising from lack of length/area proportionality and a potential misconception concerning a false assumption of independence between the two underlying events. This paper remedies these two shortcomings by modifying this matrix into a new Karnaugh-map-like diagram that resembles an eikosogram. Furthermore, the paper suggests the use of a pair of functionally complementary versions of this diagram to handle any ternary problem of conditional probability. The two diagrams split the unknowns and equations between themselves in a fashion that allows the use of a divide-and-conquer strategy to handle such a problem. The method of solution is demonstrated via four examples, in which the solution might be arithmetic or algebraic, and independently might be numerical or symbolic. In particular, we provide a symbolic arithmetic derivation of the well-known formulas that express the predictive values in terms of prevalence, sensitivity and specificity. Moreover, we prove a virtually unknown interdependence among the two predictive values, sensitivity, and specificity. In fact, we employ a method of symbolic algebraic derivation to express any one of these four indicators in terms of the other three. The contribution of this paper to the diagnostic testing aspects of mathematical epidemiology culminates in a timely application to the estimation of the true prevalence of the contemporary world-wide COVID-19 pandemic. It turns out that this estimation is hindered more by the lack of global testing world-wide rather than by the unavoidable imperfection of the available testing methods.

Hosting seasonal urban events in open public spaces has a significant impact on urban vitality. By reviewing the categorisation of urban events, this paper explains the charactersitics of ‘Seasonal Urban Event’ through time, scale, location, format, content, function, and aim. It mainly focuses on leisure and entertainment events through two cases, Summer Beach and Winter Wonderland at Old Market Square in Nottingham, UK. The research explores ‘Seasonal Urban Event’ by literature review, open data, observation, and the survey data analysis of participants’ responses. The survey result indicates positive spatial and social engagement levels ranging from accessibility, legibility, permeability, flexibility to participation, satisfaction, and inclusivity. The potential benefits of social impact have been emphasised by participants’ responses to elevating civic pride and improving the level of social inclusion. It concludes that ‘Seasonal Urban Event’, as a useful supplement method, may enhance urban vitality in the hosting city Hosting seasonal urban events in open public spaces has a significant impact on urban vitality. By reviewing the categorisation of urban events, this paper explains the charactersitics of ‘Seasonal Urban Event’ through time, scale, location, format, content, function, and aim. It mainly focuses on leisure and entertainment events through two cases, Summer Beach and Winter Wonderland at Old Market Square in Nottingham, UK. The research explores ‘Seasonal Urban Event’ by literature review, open data, observation, and the survey data analysis of participants’ responses. The survey result indicates positive spatial and social engagement levels ranging from accessibility, legibility, permeability, flexibility to participation, satisfaction, and inclusivity. The potential benefits of social impact have been emphasised by participants’ responses to elevating civic pride and improving the level of social inclusion. It concludes that ‘Seasonal Urban Event’, as a useful supplement method, may enhance urban vitality in the hosting city

With the rise of low-carbon concept and the trend of low-carbon planning, the evaluation of low-carbon planning has been paid more and more attention by the academic and planning circles. Carbon evaluation has become a hot topic after “environmental evaluation” and “green evaluation.” In order to promote the important content of energy conservation and sustainable development in the construction of low-carbon cities, this paper constructs and plans the content of low-carbon city planning and construction. On the basis of the effectiveness evaluation index system, this paper discusses the linkage between analytic hierarchy process and fuzzy comprehensive evaluation method and makes a comprehensive quantitative evaluation on the effectiveness of case planning EIA. Combining the advantages of analytic hierarchy process and fuzzy comprehensive evaluation method, the subobjectives and weights of each index are determined by the analytic hierarchy process. The multi-level fuzzy comprehensive evaluation method is used to evaluate the factors that affect the low-carbon effect of urban high-rise residential district planning. This study leads the new trend of future urban construction, and the construction of low-carbon residential areas, as an important part of promoting urban sustainable development, is the core component of future low-carbon urban planning and construction.

In order to effectively improve the quality of video image transmission, this paper proposes a method of digital multimedia video image coding. The transmission of digital multimedia video image fault-tolerant coding requires sparse decomposition of a digital multimedia video image to obtain the linear form of the image and complete the transmission of video image fault-tolerant coding. The traditional method of fault-tolerant coding is based on human visual characteristics but ignores the linear form of the digital multimedia video image, which leads to the unsatisfactory effect of coding and transmission. In this paper, a fault-tolerant coding method based on wavelet transform and vector quantization is proposed to decompose and reconstruct digital multimedia video images. The smoothness of wavelet transform can remove visual redundancy; the decomposed image is vector quantized. The mean square deviation method and the similar scalar optimal quantization method are used to select and calculate the image vector, construct the over complete database of a digital multimedia video image, and normalize it; the digital multimedia video image is thinly decomposed by asymmetric atoms, and a linear representation of the image is obtained. According to the above-given operations, we can master the distribution range and law of pixels and realize fault-tolerant coding. The experimental results show that when the number of iterations is 15, the CR index is the same, PSNR increases by 8.7%, coding is 23.7% faster and decoding is 15% faster. Conclusion. The proposed method can not only improve the speed of fault-tolerant coding but also improve the quality of video image transmission.

Higher education question and answer system is a current research hotspot in the field of natural language processing and artificial intelligence, which can return accurate answers directly and also allow users to input in natural language, avoiding the need to return large collections of valid and invalid data links for users to filter twice. In recent years, deep learning techniques have made great progress in the field of natural language processing, thus making it possible to apply it in the field of teaching and learning in junior high school. Most of the traditional quiz systems based on traditional retrieval techniques have problems such as insufficient semantic portrayal of text, inability to extract semantic features in context, and poor processing of complex utterances. In order to fill the gap and deficiencies of question and answer systems in junior high school teaching, this paper applies multimodal and adversarial network-related technologies to build a multimodal adversarial network-based question and answer system for higher education. The experimental results show that the principles of the traditional application system and the multimodal adversarial system designed in this paper are similar, but the current application system is relatively more effective.

For nonsmooth Filippov systems, the stability of the system is assumed to be proved by nonsmooth Lyapunov functions, such as piecewise smooth Lyapunov functions. This extension was based on the Filippov solution and Clarke generalized gradient. However, it is difficult to estimate the gradient of a non-smooth Lyapunov function. In some cases, the nonsmooth system can be divided into continuous and discontinuous components. If the Lebesgue measure of the discontinuous components is zero, the smooth Lyapunov function can be utilized to prove the stability of the system owing to the inner product of the gradient of the Lyapunov function of the discontinuous components being zero. In this paper, we apply the smooth Lyapunov function to prove the stability of the nonsmooth ratio-dependent predator-prey system. In contrast to the existing literature, in this paper, although the system is divided into continuous and discontinuous components, the inner product of the gradient of the Lyapunov function of the discontinuous part is not zero but negative. In the proof of stability, the negative value condition is stricter than the zero-value condition. This proof method only needs to construct a smooth Lyapunov function, which is simpler than a non-smooth Lyapunov function or other methods.

At present, China’s academic circles have made some research results on the hidden dropout of junior middle school students, but they have not formed a relatively mature theory. Based on Durkheim’s “social integration” theory, this paper studies hidden dropout from the perspective of class sociology and puts forward the influencing mechanism of “class integration” of junior middle school students’ hidden dropout behavior. By class observation, this paper obtains the hidden dropout theoretical model of the interaction between individuals and class organization.