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Syringe filling of high-concentration mAb formulation during manufacturing of large-scale drug product batches may present challenges such as product deposition onto the area of the syringe barrel where the stopper is inserted, product splashing or dripping, droplets left after the fill cycle, filling needle clogging, product build-up inside the needle during line stoppages, variation in fill weight/volume, and potential impact on product quality attributes. In this article, a summary of these issues and approaches to overcome them are summarized. Potential failure modes of the syringe filling process and appropriate in-process controls are provided. In addition to developing the filling process or resolving manufacturing issues, the pharmaceutical company developing the product and associated drug product manufacturing process may want to implement long-term strategic approaches to support the portfolio progression. Potential long-term approaches such as use of a viscosity reducing formulation development approach, improving peristaltic filling technology performance, building small-scale filling capability and establishing a streamlined filling process management cycle are also summarized. The aspects summarized in this article may be used to develop a robust filling process and control strategy for high-concentration mAb products and implement long-term strategic approaches to support the portfolio progression.

In this article, we investigate a novel reinforcement-learning (RL)-based scheme to address the optimal leader-follower consensus issue for constrained-input continuous-time multiagent systems. First, as for input-constrained problems, the fundamental smooth assumption is not satisfied for the value function. To deal with this problem, we employ the method of vanishing viscosity solutions to relax this smooth assumption to a continuity assumption for the value function, which broadens the scope of RL applications. Second, the control cost functions take a more general form which guarantees the continuity of the optimal control policy instead of the specific integrand form used in previous input-constrained RL-based schemes. Based on these results, we introduce a novel identifier-critic-actor structure to extend the conventional critic-actor RL framework into a distributed model-free one, where the learning of the identifier, critic, and actor is online and simultaneous. We provide the simulation examples to validate the effectiveness of the proposed scheme.

PurposeThe purpose of this article is to understand the current research status and future development trends in the field of numerical simulation on rock mass grouting.Design/methodology/approachThis article first searched the literature database (EI, Web of Science, CNKI, etc.) for keywords related to the numerical simulation of rock mass grouting to obtain the initial literature database. Then, from the initial database, several documents with strong relevance to the numerical simulation theme of rock mass grouting and high citation rate were selected; some documents from the references were selected as supplements, forming the sample database of this review study (a total of 90 articles). Finally, through sorting out the relationship among the literature, this literature review was carried out.FindingsThe numerical simulation of rock mass grouting is mainly based on the porous media model and the fractured media model. It has experienced the development process from Newtonian fluid to non-Newtonian fluid, from time-invariant viscosity to time-varying viscosity, and from generalized theoretical model to engineering application model. Based on this, this article summarizes four scientific problems that need to be solved in the future in this research field: the law of grout distribution at the cross fissures, the grout diffusion mechanism under multi-field coupling, more accurate grouting theoretical model and simulation technology with strong engineering applicability.Originality/valueThis research systematically analyzes the current research status and shortcomings of numerical simulation on rock mass grouting, summarizes four key issues in the future development of this research field and provides new ideas for the future research on numerical simulation on rock mass grouting.

The design and optimization of many high-temperature industrial processes have great demand for viscosity models of molten slags. Due to the unsatisfactory performance of conventional models, we developed a structure-informed artificial neural network (SIANN) model for the first time to predict the viscosity of molten slags. The model database containing 1892 measurement values was constructed from carefully identified literature and covered the temperature, compositional, and structural spaces. The feed-forward four-layer perceptron artificial neural network was designed to capture the complex dependence of viscosity upon influence factors (composition, temperature, and structure). The result indicates that after quantitative atom-level information is integrated into the model, its ability to accurately predict viscosity gets significantly improved. The interpretability of the obtained SIANN mode is highlighted with selected structural features that have a strong determinant on viscosity. Furthermore, the comparisons of prediction performance indicate the obtained model outperforms other existing models, achieving the minimum predicted deviation in various component systems.

Assessing change in mechanical properties of a material has constantly been a topic drawing great attention and bringing great applications in recent years. This article proposes a new parameter using signals of real vibration, which is called viscosity resistance coefficient (IC). It is determined by adding the material’s viscosity to the linear equation of Hooke’s law. The set of IC values derived from converting equation is presented on the plane of regression using deep learning and the balancing composite motion optimization (BCMO). The article collects a set of IC values in different states of real vibration signals via a training process using deep learning. By BCMO method, these values regress to a plane with determined areas. This research is applied to two main structures of different materials and operating time namely prestressed concrete and composite concrete bridge spans by surveying four big bridges in Ho Chi Minh city, Vietnam. The result reveals that the IC values assess not only material changes over time but also work for various types of materials. This method will open new opportunities for researches and studies in future.

Simultaneous multilayer coating techniques are widely known, but their industrial application remains limited to narrow market sectors. One barrier to adoption may be the mismatch between industries that are familiar with such processes but have no need, and industries that have need but are not familiar. Moreover, there are application-specific technical challenges to developing multilayer-coating processes. In this article, we describe our resolution of two specific issues in simultaneous multilayer coating of all-aqueous highly loaded slurries for new and emerging energy applications. The first issue is particle jamming (associated with shear-thickening) of the highly loaded slurries in the die internals, which we alleviated by adding small amounts of viscosity modifiers without reducing the solid loading. The second issue is a Marangoni-driven surface instability that resembles top layer de-wetting, which we solved by carefully selecting surfactants to tune the dynamic surface tensions of each slurry. Both issues were resolved early in a step-wise development, saving significant development cost which in our case was driven by expensive materials.

The increase of high demand of technological innovation in building construction have increasingly the need materials that have special structural characteristics. Notably, the use of self-compacting concrete (SCC) has been described by many researchers and engineers as a major revolution in concrete technology due to its fluidity and viscosity characteristics, besides providing energy savings during its construction. Tire waste rubber (TWR) is one of the most important residues that cause serious environmental issues. Recycling TWR in the form of aggregates as supplementary construction material is advantageous in many building fronts. To obtain an optimal SCC manufacturing parameters, this research was based on the application of design of experiments. Several mixtures, or designs, were prepared with different TRW proportions (10%, 30% and 50%). In addition, the effect of the rubber grain size was also considered in the model in two different levels. The specimens were submitted to quasi-static and dynamic tests in order to evaluate their structural performance. The considered responses were the natural frequency, damping factor, compression strength and elastic modulus for different curing periods (7 and 28 days). Statistical results showed the significance of the rubber, grain and curing time for the responses, especially for the strength and elastic modulus. The results of this paper could be a guideline for engineers using SCC in building field.

In this paper, we propose and study new inertial viscosity Tseng's extragradient algorithms with self-adaptive step size to solve the variational inequality problem (VIP) and the fixed point proble...

Machine learning (ML) is recognized as an efficient prediction tool. However, very few attempts have been made to apply it to model pressure losses in the water-assisted pipeline transportation of ...

Several numerical meshless methods have been proposed to try to solve some of the limitations of traditional mesh-based methods. Among those, the finite pointset method, which has been applied in several physical problems, shows a great potential. This work presents an extension of the available numerical approaches based on the finite pointset method to allow dealing with generalized Newtonian fluids, which possess a high viscosity. The developed finite pointset method solver is verified through the comparison of its predictions with analytical solutions, for simple flows, and with results provided by the well-established and validated open-source computational library OpenFOAM, for more complex cases studies. The excellent results obtained on the verification case studies prove the proper implementation of the new finite pointset method solver.

Abstract This piece of study attempts to accurately anticipate the apparent viscosity of the viscoelastic surfactant (VES) based self-diverting acids as a function of VES concentration, temperature, shear rate, and pH value. The focus not only is on generating computer-aided models but also on developing a straightforward and reliable explicit mathematical expression. Towards this end, Gene Expression Programming (GEP) is used to connect the aforementioned features to and the target. The GEP network is trained using a wide dataset adopted from open literature and leads to an empirical correlation for fulfilling the aim of this study. The performance of the proposed model is shown to be fair enough. The accuracy analysis indicates satisfactory Root Mean Square Error and R-squared values of 7.07 and 0.95, respectively. Additionally, the proposed GEP model is compared with literature published correlations and established itself as the superior approach for predicting the viscosity of VES-based acids. Accordingly, the GEP model can be potentially served as an efficient alternative to experimental measurements. Its obvious advantages are saving time, lowering the expenses, avoiding sophisticated experimental procedures, and accelerating the diverter design in stimulation operations. Article Highlights The Gene Expression Programming evolutionary algorithm is proposed for modeling the viscosity of Viscoelastic Surfactant-based self-diverting acids. The viscoelastic surfactant viscosity correlation presents high accuracy which is demonstrated through multiple analyses. The Gene Expression Programming algorithm is a reliable tool expediting the diverter design phase of each stimulation operation.

The particle swarm optimization (PSO) algorithm has certain disadvantages; for instance, the convergence viscosity of the algorithm is reduced in the post evolution phase, the optimization search efficiency is also reduced, the algorithm is easy to be inserted with a local extremum during the calculation of a complex problem of a high-dimensional multiple extremum, and the convergence thereof is low. To compensate for the PSO disadvantage, we propose a particle swarm optimization of the comprehensive improvement strategy, which is a simple particle swarm optimization with a dynamic adaptive hybridization of the extremum disturbance and the ecds-PSO algorithm. This new comprehensively improved particle swarm algorithm discards the particle velocity and reduces the PSO from the second-order to a first-order difference equation. The evolutionary process is controlled by the particle position variables only. The hybridization operation of increasing the extremum disturbance and introducing a genetic algorithm can accelerate the particles to overstep the local extremum. The mathematical derivation and the plurality of a comparative experiment provide the following information: the improved particle swarm optimization is a simple and effective optimization algorithm that can enhance the algorithm accuracy, the convergence viscosity and the ability of avoiding the local extremum, and effectively reduce the calculation complexity.

Purpose Speech-language pathologists (SLPs) are trained to evaluate and treat dysphagia. One treatment method is to modify diet consistency or viscosity to compensate for swallowing dysfunction and promote a safer intake; this typically involves softening solids and thickening liquids. Thickening liquids is not safer for all patients, and modification of dysphagia diets without adequate training may reduce the quality of dysphagia patient care. Over 90% of SLPs working in health care report exposure to nurses who regularly downgrade dysphagia diets without an SLP consult. This study explores dysphagia diet modification practices of nursing staff with and without dysphagia training. Method Practicing nurses and student nurses ( N = 298) in the United States were surveyed regarding their dysphagia diet modification training and practice patterns. Additionally, a pre-/posttest design was used to determine the efficacy of a short general tutorial on willingness to modify diets without an SLP consult. Results Downgrading diets without an SLP consult is a common practice. Fewer than one third of nurses (31.41%) would avoid it, whereas 73.65% would avoid upgrading without SLP consult. Formal dysphagia training made little difference to this practice. The short general tutorial also had no beneficial effect, in fact slightly reducing the willingness to consult SLPs. Conclusions Dysphagia diet modification practice by nurses is pervasive in U.S. health care. This is a previously unexplored but common issue SLPs face in work settings. This study identifies a need to clarify guidelines and increase interprofessional education between both professions to improve patient care.

The quality of cooking oil sold in the market needs to be checked to ensure its health. cooking oil quality detector is designed to make it easier for the public to know the quality of the cooking oil. The research method is to make tools and conduct testing. The test is carried out by measuring the viscosity and density using the tool made. When the viscosity of 985 fuzzification was "good", and the density was 542.93 Kg/mL of "good" fuzzification, the fuzzification was processed by a fuzzy inference system, then defuzzification occurred in the form of oil quality results. fried "good". When the viscosity of 932 fuzzification is "sufficient", and the density is 618.69 Kg/mL of "moderate" fuzzification, a fuzzy inference system occurs, a defuzzification process is "moderate", when the viscosity of 926 fuzzification is "bad", and a density of 631.31 Kg/mL fuzzification "bad", fuzzy inference system occurs, defuzzification process occurs with "bad" results. To ensure that the results are accurate, the sample is taken to the BPOM which measures free fatty acids. From the BPOM test results converted to viscosity and density. In order to obtain an accurate conversion value between viscosity and density, it is recommended that a large number of samples be tested..Keywords: viscosity, density, fuzzy logic

Purpose To better understand how to retain hospitality customers in the fierce competition among mobile applications, this study aims to propose and empirically validates an integrative framework, which elaborates how conscious and subconscious factors, together with affective factors, may induce app loyalty and how brand viscosity moderates such effects. Design/methodology/approach The authors conducted an online survey to collect data and received a total of 268 valid responses. This study splits the data into two groups (brand viscosity vs non-viscosity). Then, the authors performed a multi-group structural equation modeling with Chi-square difference tests to compare the model between the two groups. Findings The findings support the integrative model and reveal that the influence of app satisfaction on loyalty is stronger for app users who do not stick to one brand across the website and mobile app channels. Moreover, for those with brand viscosity, habit and switching cost are two significant determinants that exert positive effects in inducing app loyalty. Research limitations/implications Brand viscosity across different channels matters for the effects of habit and switching costs in shaping app loyalty. E-commerce managers should elaborate on brand management among various booking channels and establish effective digital marketing strategies to facilitate the formation of usage habits and switching costs and to enhance brand viscosity across channels. Originality/value This research advances the knowledge of app loyalty in hospitality by providing a comprehensive explanatory framework from affective, conscious and subconscious lenses. This research is among the first to unveil the impact of brand viscosity on the links between loyalty and its determinants.

International audience; In highly dense crowds of humans, collisions between people occur often. It is common to simulate such a crowd as one fluid-like entity (macroscopic), and not as a set of individuals (microscopic, agent-based). Agent-based simulations are preferred for lower densities because they preserve the properties of individual people. However, their collision handling is too simplistic for extreme-density crowds. Therefore, neither paradigm is ideal for all possible densities. In this paper, we combine agent-based crowd simulation with Smoothed Particle Hydrodynamics (SPH), a particle-based method that is popular for fluid simulation. We integrate SPH into the crowd simulation loop by treating each agent as a fluid particle. The forces of SPH (for pressure and viscosity) then augment the usual navigation behavior and contact forces per agent. We extend the standard SPH model with a dynamic rest density per particle, which intuitively controls the crowd density that an agent is willing to accept. We also present a simple way to let agents blend between individual navigation and fluid-like interactions depending on the SPH density. Experiments show that SPH improves agent-based simulation in several ways: better stability at high densities, more intuitive control over the crowd density, and easier replication of wave-propagation effects. Also, density-based blending between collision avoidance and SPH improves the simulation of mixed-density scenarios. Our implementation can simulate tens of thousands of agents in real-time. As such, this work successfully prepares the agent-based paradigm for crowd simulation at all densities.

High fidelity display has a big impact on the user’s sense of immersion in virtual surgery. To achieve a high degree of visual realism, we propose a new subsurface scattering method, which is based on screen-space and texture-space and an improved two-layer surface reflection model, for living soft tissue rendering. The method can be described as a two-step strategy. First, the subsurface scattering in the screen space is computed. The biggest difference from traditional methods is that the back light information stored in the irradiance map of the texture space and the diffusion profile in the dipole model are used to calculate the subsurface scattering distribution formed by the light from the back of the object, which can render more rich details. Second, considering that the living soft tissues are usually covered with a thin layer of mucus, a mucus texture is mapped to the soft tissues as the second layer texture and an improved two-layer surface reflection model is proposed to render the mucus. Moreover, to show the viscosity and smoothness of the mucus, the calculation of highlight component is used in our model, which further enhances the reality of living soft tissue. Experimental results show that our method outperforms the two traditional methods of subsurface scattering based on texture space and screen space, and the rendering time of the proposed two-layer surface reflection model is 2/3 of that of the traditional model. We conducted a survey on the fidelity of soft tissues rendered using different algorithms, and the professional surgeons (60%-85%) thought that the rendered soft tissue with our method is more consistent with the characteristics of the in vivo soft tissues under a real surgical scene.

In this paper, based on the framework of the single-period model, we establish an optimal control model for the inheriting period which allows inter-phase banking and borrowing of allowances under the cap-and-trade system. By considering the abatement control policy and the initial auction amount of allowances, we optimize the problem in two steps. The two models can then be expressed using the Hamilton–Jacobi–Bellman (HJB) equations. In the framework of viscosity solution, we prove that the value functions in two models are the unique viscosity solutions of the corresponding HJB equations. Finally, we analyze the properties of optimal policy by referring to numerical results and a comparison of the two models is presented. Our results show that under the same circumstances, allowing inter-phase banking and borrowing of allowances indeed reduces the company’s abatement costs and has an incentive-based effect on the company’s emission reductions.

The material point method (MPM) recently demonstrated its efficacy at simulating many materials and the coupling between them on a massive scale. However, in scenarios containing debris, MPM manifests more dissipation and numerical viscosity than traditional Lagrangian methods. We have two observations from carefully revisiting existing integration methods used in MPM. First, nearby particles would end up with smoothed velocities without recovering momentum for each particle during the particle-grid-particle transfers. Second, most existing integrators assume continuity in the entire domain and advect particles by directly interpolating the positions from deformed nodal positions, which would trap the particles and make them harder to separate. We propose an integration scheme that corrects particle positions at each time step. We demonstrate our method's effectiveness with several large-scale simulations involving brittle materials. Our approach effectively reduces diffusion and unphysical viscosity compared to traditional integrators.

The purpose of this paper is to invent an accelerated algorithm for the convex minimization problem which can be applied to the image restoration problem. Theoretically, we first introduce an algorithm based on viscosity approximation method with the inertial technique for finding a common fixed point of a countable family of nonexpansive operators. Under some suitable assumptions, a strong convergence theorem of the proposed algorithm is established. Subsequently, we utilize our proposed algorithm to solving a convex minimization problem of the sum of two convex functions. As an application, we apply and analyze our algorithm to image restoration problems. Moreover, we compare convergence behavior and efficiency of our algorithm with other well-known methods such as the forward-backward splitting algorithm and the fast iterative shrinkage-thresholding algorithm. By using image quality metrics, numerical experiments show that our algorithm has a higher efficiency than the mentioned algorithms.

We study an optimal control problem consisting in minimizing the L ∞ norm of a Borel measurable cost function, in finite time, and over all trajectories associated with a controlled dynamics which is reflected in a compact prox-regular set. The first part of the paper provides the viscosity characterization of the value function for uniformly continuous costs. The second part is concerned with linear programming formulations of the problem and the ensued by-products as e.g. dynamic programming principle for merely measurable costs.

When solving multidimensional problems of gas dynamics, finite-volume schemes using complete (i.e., based on a three-wave configuration) solvers of the Riemann problem suffer from shock-wave instability. It can appear as oscillations that cannot be damped by slope limiters, or it can lead to a qualitatively incorrect solution (carbuncle effect). To combat instability, one can switch to incomplete solvers based on a two-wave configuration near the shock wave, or introduce artificial viscosity. The article compares these two approaches on unstructured grids in relation to the EBR-WENO scheme for approximating convective terms and the classical Galerkin method for approximating diffusion terms. It is shown that the method of introducing artificial viscosity usually makes it possible to more accurately reproduce the flow pattern behind the shock front. However, on a three-dimensional unstructured grid, it causes dips ahead of the front, the depth of which depends on the quality of the grid, which can lead to an emergency stop of the calculation. Switching to an incomplete solver in this case gives satisfactory results with a much lower sensitivity to the quality of the mesh.

Foams are relevant in many food products and food production processes. During the bottling of beverages, foam can severely impair the process as overflowing foam causes underfilled bottles and poses a high contamination risk. Consequently, the filling speed is limited by the foaming properties of the beverage. Several filling experiments with different juices were executed to improve the understanding of the influence of flow and material properties on the foaming behavior. The foam evolution in time was evaluated using a convolutional neural network (CNN) to detect visible foam regions in video recordings of the experiments. The use of image data has the advantage that its acquisition is noninvasive and no supplementary sensor hardware with direct product contact has to be installed into the process. The CNN was able to detect image regions that contain foam with errors of only a few millimeters. The detection worked for different juice colors, regardless of the lighting and noise in the image. Based on the filling speed, viscosity, surface tension, and density, the foaming behavior during the filling process was modeled with a recurrent neural network (RNN). The RNN was able to predict the foam height evolution with average errors below five millimeters. Both models are easily transferable to new use cases by retraining the networks.

Desktop 3D filament printers have spread among different categories of users, whether amateurs or professionals. And the most common filament is PLA Polylactic Acid for its ease of use and its environmentally friendly print, in the 3D printing process for extrusion filament / thermoplastics, the hot end Extruder raises the temperature of it to obtain the appropriate viscosity for its flow through the Nozzel, the nozzle is then moved from place to place within the print space by the 3D printer movement system to gradually extrude an entire object layer by layer as the filament moves inside The printer is through the hot end and the nozzle, it is under the control of the printer through the commands of the G-CODE file, so its behavior is controlled by its temperature and flow velocity controlling, but once it leaves the nozzle, and the deposition process begins, it becomes out of control, leaving its properties easily affected by environmental factors. Because of its soft state, it helps ease the control of the forces of gravity and pulls its strings down, causing deformation Its layers. It is therefore of utmost importance to ensure that it does not move or deform after leaving the nozzle. This is done by returning it to its solid state as soon as possible, and here it appears the importance of cooling. Keywords: Statement of the problem: The quality of 3D PLA prints produced by fused filament fabrication printers was affected by the cooling process during printing. Importance: The importance of the research lies in showing the effect of cooling on the quality of 3D PLA printed parts and showing methods of controlling it to improve the quality of 3D printed models. Objectives: The research aims toidentify problems in PLA printed models related to cooling and improve its quality by adjusting part cooling. Research Methodology: The research depends on the descriptive approach and the experimental method in applying solutions and observing the results. Main results:The most important results of the research are that cooling is one of the important factors affecting the quality of models printed with poly-lactic acid filament. Cooling is closely related to the problems of wrinkling of protruding parts, deformation of bridges and accurate ends of models, as well as problems of dimensional accuracy. And the design of the air distribution duct attached to the part cooling fan affects the print quality.

Muscle contractions (or equivalent mechanical elements) are responsible for joint movement in systems with musculoskeletal structure. Because muscles can only transmit force in the tensile direction in such systems, the internal force exists between the muscles. By utilizing the potential field generated by the internal force, the musculoskeletal potential method makes it possible to control the position without complex real-time calculations or sensory feedback by entering step-inputs of the balanced internal force at the target posture. However, the conditions of convergence to the target posture strongly depend on muscular arrangement. Previous studies have elucidated the mathematical conditions of the muscular arrangement; however, they provide sufficient conditions that must be satisfied by the muscular arrangement to converge to the target posture, which do not necessarily lead to optimal muscular arrangement conditions. This study proposes a method to determine the optimal muscular arrangement of a two-joint six-muscle system, wherein muscle viscosity is considered, that uses a genetic algorithm and an evaluation function considering the motion response time. The effect of the obtained muscular arrangement is verified in a simulation.

Let the VI indicate a variational inclusion, the CFPP denote a common fixed point problem of countably many nonexpansive mappings, and the SVI represent a system of variational inequalities. We int...

The prediction of drilling mud rheological properties is a crucial topic with significant importance in analyzing frictional pressure loss and modeling the hole cleaning. Based on Marsh viscosity, mud density, and solid percent, this paper implements a committee machine intelligent system (CMIS) to predict apparent viscosity (AV) and plastic viscosity (PV) of oil-based mud. The established CMIS combines radial basis function neural network (RBFNN) and multilayer perceptron (MLP) via a quadratic model. Levenberg–Marquardt algorithm was applied to optimize the MLP, while differential evolution, genetic algorithm, artificial bee colony, and particle swarm optimization were used to optimize the RBFNN. A databank of 440 and 486 data points for AV and PV, respectively, gathered from various Algerian fields was considered to build the proposed models. Statistical and graphical assessment criteria were employed for investigating the performance of the proposed CMIS. The obtained results reveal that the developed CMIS models exhibit high performance in predicting AV and PV, with an overall average absolute relative deviation (AARD %) of 2.5485 and 4.1009 for AV and PV, respectively, and a coefficient of determination (R2) of 0.9806 and 0.9753 for AV and PV, respectively. A comparison of the CMIS-AV with Pitt's and Almahdawi's models demonstrates its higher prediction capability than these previously published correlations.

Topology optimization (TopOpt) is a mathematical-driven design procedure to realize optimal material architectures. This procedure is often used to automate the design of devices involving flow through porous media, such as micro-fluidic devices. TopOpt offers material layouts that control the flow of fluids through porous materials, providing desired functionalities. Many prior studies in this application area have used Darcy equations for primal analysis and the minimum power theorem (MPT) to drive the optimization problem. But both these choices (Darcy equations and MPT) are restrictive and not valid for general working conditions of modern devices. Being simple and linear, Darcy equations are often used to model flow of fluids through porous media. However, two inherent assumptions of the Darcy model are: the viscosity of a fluid is a constant, and inertial effects are negligible. There is irrefutable experimental evidence that viscosity of a fluid, especially organic liquids, depends on the pressure. Given the typical small pore-sizes, inertial effects are dominant in micro-fluidic devices. Next, MPT is not a general principle and is not valid for (nonlinear) models that relax the assumptions of the Darcy model. This paper aims to overcome the mentioned deficiencies by presenting a general strategy for using TopOpt. First, we will consider nonlinear models that take into account the pressure-dependent viscosity and inertial effects, and study the effect of these nonlinearities on the optimal material layouts under TopOpt. Second, we will explore the rate of mechanical dissipation, valid even for nonlinear models, as an alternative for the objective function. Third, we will present analytical solutions of optimal designs for canonical problems; these solutions not only possess research and pedagogical values, but also facilitate verification of computer implementations. We have considered a pressure-driven problem with axisymmetry and got optimal material layouts using topology optimization by maximizing the total rate of dissipation. The left figure shows unphysical finger-like design patterns when the primal analysis does not enforce explicitly the underlying radial symmetry. The right figure shows that one can avoid such numerical pathologies if the primal analysis invokes axisymmetry conditions.

In this paper, we consider a common solution of three problems in real Hilbert spaces including the split generalized equilibrium problem, the variational inequality problem and the fixed point problem for nonexpansive multivalued mappings. For finding the solution, we present a modified viscosity approximation method and prove a strong convergence theorem under mild conditions. Moreover, we also provide a numerical example to illustrate the convergence behavior of the proposed iterative method.

In this article, a notion of viscosity solutions is introduced for second-order Hamilton–Jacobi–Bellman (HJB) equations with delays associated with optimal control problems for stochastic different...

To assess the reliability of a tear film (TF) viscosity video grading system. Thirty-four dynamic TF viscosity videos were obtained by a clinically available TF analyzer and objectively sorted according to the movement speed of three arbitrary reflective light particles. A 4-grade system was constructed on a specially designed window for simultaneous comparison with the three standard videos. Two masked graders were invited to grade these videos under a randomized procedure. Observer reliabilities were determined by Spearman’s correlation analysis and Bland-Altman agreement analysis. For this four-grade system, the intra-observer correlation was very strong in the two graders (ρ = 0.96 and 0.82; both P < 0.0001). However, the inter-observer correlation showed moderate strength in normal playback speed (ρ = 0.53, P = 0.002 and ρ = 0.52, P = 0.003 for 1st and 2nd gradings, respectively). In slower playback videos, the inter-observer correlation of the two graders was higher (ρ = 0.70 and P < 0.0001) when reduced to 0.8-times playback speed. Moreover, the 0.8-times mode was also significantly better than normal playback mode (P = 0.0204) in terms of inter-observer agreement. The dynamic 4-grade system has an excellent intra-observer reliability and a good inter-observer reliability under 0.8-times playback speed. The grading system established in this study provides a promising solution for rapidly determining the level of TF viscosity.

In this paper, we introduce two different kinds of iterative algorithms, which are based on the inertial Tseng's method and the viscosity method. They are intended to solve the variational inequali...

Dysphagia patients need to carefully control their intake liquid's viscosity to reduce choking and aspiration risks. However, accurate liquid viscosity measurement requires expensive rheometers still unavailable in daily life. Though the existing approximate testing methods are low-cost, they are not convenient for everyday use as they require either tedious procedures or dedicated apparatus. This paper presents ViscoCam, the first liquid viscosity classification system for dysphagia patients or carers, which only requires a smartphone. It is easy to operate, widely deployable, and robust for daily use. ViscoCam classifies visually indistinguishable liquid of various viscosity levels by exploiting the fact that the sloshing motion of viscous liquid decays faster than thin liquid. To perform a measurement, the user shakes a cup of liquid and their smartphone to induce the liquid sloshing motion. Then, ViscoCam senses the cup's motion using the smartphone's built-in accelerometer or microphone and infers liquid viscosity from the fluid surface motion captured by flashlight camera. To combat changes in camera position, lighting conditions, and liquid sloshing motion, a 3D convolutional neural network is trained to extract reliable motion features for classification. We evaluate ViscoCam's performance in classifying three levels in the IDDSI standard, which is the most up-to-date and internationally adopted one for dysphagia patients. Results show that ViscoCam achieves an overall accuracy of 96.52% in controlled cases. It is robust to unseen liquid heights or container sizes, and >81% accuracy is maintained under extreme testing cases.

Systems of nonlinear parabolic equations with small parameter multiplying the highest derivative and stochastic models associated with them are considered. It is shown that the vanishing viscosity method, which makes it possible to choose physical solutions to the Cauchy problem for systems of nonlinear conservation laws, has a natural justification in terms of stochastic models. A similar result for balance laws is also obtained.

In this article, we suggest and analyze the splitting type viscosity methods for inclusion and fixed point problem of a nonexpansive mapping in the setting of Hadamard manifolds. We derive the convergence of sequences generated by the proposed iterative methods under some suitable assumptions. Several special cases of the proposed iterative methods are also discussed. Finally, some applications to solve the variational inequality, optimization and fixed point problems are given on Hadamard manifolds.

With the help of the generalized viscosity implicit method and hybrid steepest-descent method, we introduce an iterative scheme for approximating the solution of a variational inequality over the set of fixed points of an asymptotically nonexpansive mapping in the intermediate sense. Some strong convergence results for our proposed iterative scheme are established in the framework of Banach spaces. Applicability of our proposed method is shown in variational inclusion problem and convex minimization problem. We discuss some examples to demonstrate the numerical implementation and efficiency of our main results in comparison of other related results. Our results improve, extend and unify previously known results given in literature.

The rapid growth in technological advances and quantity of scientific data over the past decade has led to several challenges including data storage and analysis. Accurate models of complex datasets were previously difficult to develop and interpret. However, improvements in machine learning algorithms have since enabled unparalleled classification and prediction capabilities. The application of machine learning can be seen throughout diverse industries due to their ease of use and interpretability. In this review, we describe popular machine learning algorithms and highlight their application in pharmaceutical protein development. Machine learning models have now been applied to better understand the nonlinear concentration dependent viscosity of protein solutions, predict protein oxidation and deamidation rates, classify sub-visible particles and compare the physical stability of proteins. We also applied several machine learning algorithms using previously published data and describe models with improved predictions and classification. The authors hope that this review can be used as a resource to others and encourage continued application of machine learning algorithms to problems in pharmaceutical protein development.

In this paper, we propose a new viscosity extragradient algorithm for solving variational inequality problems of pseudo-monotone and non-Lipschitz continuous operator in real Hilbert spaces. We prove a strong convergence theorem under some appropriate conditions imposed on the parameters. Finally, we give some numerical experiments to illustrate the advantages of our proposed algorithms. The main results obtained in this paper extend and improve some related works in the literature.

The aim of this article is to introduce a new algorithm by integrating a viscosity-type method with the subgradient extragradient algorithm to solve the equilibrium problems involving pseudomonotone and Lipschitz-type continuous bifunction in a real Hilbert space. A strong convergence theorem is proved by the use of certain mild conditions on the bifunction as well as some restrictions on the iterative control parameters. Applications of the main results are also presented to address variational inequalities and fixed-point problems. The computational behaviour of the proposed algorithm on various test problems is described in comparison to other existing algorithms.

To satisfy the customer needs and to fulfil the competitive market requirement it becomes essential for manufacturing enterprises to reduce the rejection. The present work is focused on reduction of various painting defects like uncover, paint rundown, peel-off, orange peel by optimizing the properties of paint materials and different pre-treatment paint processes. These paint defects arises due to different reasons such as viscosity of the paint material, pH of paint material and paint material to thinner ratio. The pre-treatment of material before painting like Phosphating, Degreasing, Derusting and Passivation of the material also affect the quality of painted products. The properties of paint materials and pre-treatment processes are optimised using Lean Six Sigma (LSS) and Robust Taguchi Design (RTD). The define phase uses the tools like pareto charts, project charter and voice of business that shows the critical stage involves paint material properties and pre-treatment in the spray-painting process. In analysing phase, main factors that cause defects are spotted as viscosity, cleaning temperature, air pressure by using fishbone diagram. The improve phase focuses on refining the main factors responsible for rejection by using Robust Taguchi design methodology. In control phase, the procured results are implemented. This case study deals with selection of paint material properties in the organization manufacturing the agro products facing the rejection (about 12% monthly) problem dominantly due to paint defects. The data collected from the company is investigated deeply for analysing and observing the rejection causes. The implementation of adopted methodology disclosed a fall in rejection rate from 12% to 5% by optimizing the paint material properties. The implemented methodologies improve the sigma level from 2.8 to 4.1. The improved paint material properties and optimized process parameters result in better quality of agro products and good market share of agriculture equipment manufacturing organization.

In this paper, strong convergence results for $ \alpha - $inverse strongly monotone operators under new algorithms in the framework of Hilbert spaces are discussed. Our algorithms are the combination of the inertial Mann forward-backward method with the CQ-shrinking projection method and viscosity algorithm. Our methods lead to an acceleration of modified inertial Mann Halpern and viscosity algorithms. Later on, numerical examples to illustrate the applications, performance, and effectiveness of our algorithms are presented.

We prove that viscosity solutions of Hamilton--Jacobi--Bellman (HJB) equations, corresponding either to deterministic optimal control problems for systems of $n$ particles or to stochastic optimal ...

In this work, we suggest a general viscosity implicit midpoint rule for nonexpansive mapping in the framework of Hilbert space. Further, under the certain conditions imposed on the sequence of parameters, strong convergence theorem is proved by the sequence generated by the proposed iterative scheme, which, in addition, is the unique solution of the variational inequality problem. Furthermore, we provide some applications to variational inequalities, Fredholm integral equations, and nonlinear evolution equations and give a numerical example to justify the main result. The results presented in this work may be treated as an improvement, extension and refinement of some corresponding ones in the literature.

We establish a convergence theorem for the vanishing discount problem for a weakly coupled system of Hamilton-Jacobi equations. The crucial step is the introduction of Mather measures and their relatives for the system, which we call respectively viscosity Mather and Green-Poisson measures. This is done by the convex duality and the duality between the space of continuous functions on a compact set and the space of Borel measures on it. This is part 1 of our study of the vanishing discount problem for systems, which focuses on the linear coupling, while part 2 will be concerned with nonlinear coupling., Comment: 26 pages. arXiv admin note: text overlap with arXiv:1906.07979

Maintaining the viscosity and density of drilling fluids within their optimal performance margins is of utmost importance in running a safe and efficient operation when drilling oil and gas wells. Safety and efficiency may be adversely affected by current measurement practices of these two key properties as they still mostly rely on onsite personnel to carry out periodic manual measurements. These laborious measurement protocols limit the quantity and quality of the produced data. Here we describe an automated system for viscosity/density measurements of drilling fluids based on a tuning fork electromechanical resonator. The resonator works as the frequency-defining element of an oscillator circuit, and a digital gain control algorithm is used to control the feedback loop and actuate the device. The self-oscillating circuit allows the measurement to be repeated several times per second, enabling higher measurement precision and real-time monitoring in changing flows. The resonator was integrated into an in-tank system, which performed continuous measurements at the suction tank of the mud circulating system during drilling operations. Measurements in three different wells are presented. The results were in very good agreement with manual measurements reported from standard mud reports and further highlighted opportunities in detecting operational problems from the real-time data streams obtained.

The article examines the goals and objectives of the methodology for forming the research competence of students, using the example of the study of the internal viscosity coefficient of a liquid. The results of the study allowed us to establish components of subject research competence and find out its main structural elements - the readiness, ability of students to perform educational research, following the stages of organizing research activities, which are based on the logic of implementation of a research project. The practical implementation of the formation of this competence is one of the topical issues of educational practice of the pedagogical University, since its results deepen, expand and specify modern ideas about the information and communication capabilities of cognitive activity of students.

We introduce a viscosity iterative algorithm for approximating a common solution of a modified split generalized equilibrium problem and a fixed point problem for a quasi-pseudocontractive mapping which also solves some variational inequality problems in real Hilbert spaces. The proposed iterative algorithm is constructed in such a way that it does not require the prior knowledge of the operator norm. Furthermore, we prove a strong convergence theorem for approximating the common solution of the aforementioned problems. Finally, we give a numerical example of our main theorem. Our result complements and extends some related works in the literature.