Investigation into the optoelectrowetting droplet transport mechanism

To explore the optoelectronic wetting droplet transport mechanism, a transient numerical model of optoelectrowetting (OEW) under the coupling of flow and electric fields is established. The study investigates the impact of externally applied voltage, dielectric constant of the dielectric layer, and interfacial tension between the two phases on the dynamic behavior of droplets during transport. The proposed model employs an improved Young's equation to calculate the instantaneous voltage and contact angle of the droplet on the dielectric layer. Results indicate that, under the influence of OEW, significant variations in the interface contact angle of droplets occur in bright and dark regions, inducing droplet movement. Moreover, the dynamic behavior of droplet transport is closely associated with various parameters, including externally applied voltage, dielectric layer material, and interfacial tension between the two phases, all of which impact the contact angle and, consequently, the transport process. By summarizing the influence patterns of the three key parameters studied, the optimization of droplet transport performance is achieved. The study employs two-dimensional simulation models to emulate the droplet motion under the influence of the electric field, investigating the OEW droplet transport mechanism. The continuous movement of droplets involves three stages: initial wetting, continuous transport, and reaching a steady position. The findings contribute theoretical support for the efficient design of digital microfluidic devices for OEW droplet movement and the selection of key parameters for droplet manipulation.

Simulation of Membrane Fabrication via Solvent Evaporation and Nonsolvent-Induced Phase Separation

Block copolymer membranes offer a bottom-up approach to form isoporous membranes that are useful for ultrafiltration of functional macromolecules, colloids, and water purification. The fabrication of isoporous block copolymer membranes from a mixed film of an asymmetric block copolymer and two solvents involves two stages: First, the volatile solvent evaporates, creating a polymer skin, in which the block copolymer self-assembles into a top layer, comprised of perpendicularly oriented cylinders, via evaporation-induced self-assembly (EISA). This top layer imparts selectivity onto the membrane. Subsequently, the film is brought into contact with a nonsolvent, and the exchange between the remaining nonvolatile solvent and nonsolvent through the self-assembled top layer results in nonsolvent-induced phase separation (NIPS). Thereby, a macroporous support for the functional top layer that imparts mechanical stability onto the system without significantly affecting permeability is fabricated. We use a single, particle-based simulation technique to investigate the sequence of both processes, EISA and NIPS. The simulations identify a process window, which allows for the successful in silico fabrication of integral-asymmetric, isoporous diblock copolymer membranes, and provide direct insights into the spatiotemporal structure formation and arrest. The role of the different thermodynamic (e.g., solvent selectivity for the block copolymer components) and kinetic (e.g., plasticizing effect of the solvent) characteristics is discussed.

Zr-Doped h-BN Monolayer: A High-Sensitivity Atmospheric Pollutant-Monitoring Sensor

In the post-epidemic era, industrial production has gradually recovered, and the attendant air pollution problem has attracted much attention. In this study, the Zr-doped h-BN monolayer (Zr-BN) is proposed as a new gas sensor for air pollution. Based on density functional theory (DFT), we calculated and compared the adsorption energies (Eads), geometric parameters, the shortest distance between gas and substrate (dsub/gas), density of states (DOS), electron localization function (ELF), charge density difference (CDD), band structure, band gap energy change rate (ΔEg), and sensitivity (S) of Zr-BN adsorption systems (SO2F2, SOF2, SO2, NO, and CO2 adsorption systems). The results show that Zr-BN had strong adsorption and high sensitivity to the above-mentioned polluted gases, and the sensitivity was in the order of SOF2 > SO2F2 > CO2 > SO2 > NO. Therefore, this study provides a theoretical basis for the preparation of Zr-BN gas sensors and provides new ideas and methods for the development of other gas sensors.

Development of Induction Heating System Ensuring Increased Heating Efficiency of the Charge Material in a Forging

This study performs a complex analysis and review of the currently applied methods of inductively heating the charge material in hot die forging processes, as well as elaborates and verifies a more effective heating method. On this basis, a device for inductive heating using variable frequency inductors was designed and constructed, which made it possible to reduce the scale and decarburization with respect to the heater used so far. In the first place, the temperature distributions in the heater in the function of time were modeled with the use of the CEDRAT FLUX software. The aim of the research was to analyze the temperature gradient and value diversification on the surface and in the material core, as well as to determine the process stability. The following stage was designing and constructing a heater with an automatic system of loading and positioning of the charge on the exit, as well as with a possibility of working in a fully automated system adjusted to the work center. The last stage of investigations was the verification of the elaborated effective heating method on the basis of a short production series and a continuous work for the period of 8 h, both in the quantitative and qualitative aspect (reduced oxidation and decarburization as well as a gradient between the core and the surface). The obtained results confirm the effectiveness of the proposed solution referring to heating the charge material, especially in the aspect of stability and repeatability of the process, as well as a significant reduction in oxidation and decarburization of the material surface.

Toward Rigorous Parameterization of Underconstrained Neural Network Models Through Interactive Visualization and Steering of Connectivity Generation

Simulation models in many scientific fields can have non-unique solutions or unique solutions which can be difficult to find. Moreover, in evolving systems, unique final state solutions can be reached by multiple different trajectories. Neuroscience is no exception. Often, neural network models are subject to parameter fitting to obtain desirable output comparable to experimental data. Parameter fitting without sufficient constraints and a systematic exploration of the possible solution space can lead to conclusions valid only around local minima or around non-minima. To address this issue, we have developed an interactive tool for visualizing and steering parameters in neural network simulation models. In this work, we focus particularly on connectivity generation, since finding suitable connectivity configurations for neural network models constitutes a complex parameter search scenario. The development of the tool has been guided by several use cases-the tool allows researchers to steer the parameters of the connectivity generation during the simulation, thus quickly growing networks composed of multiple populations with a targeted mean activity. The flexibility of the software allows scientists to explore other connectivity and neuron variables apart from the ones presented as use cases. With this tool, we enable an interactive exploration of parameter spaces and a better understanding of neural network models and grapple with the crucial problem of non-unique network solutions and trajectories. In addition, we observe a reduction in turn around times for the assessment of these models, due to interactive visualization while the simulation is computed.

A practical approach to parameter estimation applied to model predicting heart rate regulation

Mathematical models have long been used for prediction of dynamics in biological systems. Recently, several efforts have been made to render these models patient specific. One way to do so is to employ techniques to estimate parameters that enable model based prediction of observed quantities. Knowledge of variation in parameters within and between groups of subjects have potential to provide insight into biological function. Often it is not possible to estimate all parameters in a given model, in particular if the model is complex and the data is sparse. However, it may be possible to estimate a subset of model parameters reducing the complexity of the problem. In this study, we compare three methods that allow identification of parameter subsets that can be estimated given a model and a set of data. These methods will be used to estimate patient specific parameters in a model predicting baroreceptor feedback regulation of heart rate during head-up tilt. The three methods include: structured analysis of the correlation matrix, analysis via singular value decomposition followed by QR factorization, and identification of the subspace closest to the one spanned by eigenvectors of the model Hessian. Results showed that all three methods facilitate identification of a parameter subset. The "best" subset was obtained using the structured correlation method, though this method was also the most computationally intensive. Subsets obtained using the other two methods were easier to compute, but analysis revealed that the final subsets contained correlated parameters. In conclusion, to avoid lengthy computations, these three methods may be combined for efficient identification of parameter subsets.

A performance evaluation framework for association mining in spatial data

The evaluation of the process of mining associations is an important and challenging problem in database systems and especially those that store critical data and are used for making critical decisions. Within the context of spatial databases we present an evaluation framework in which we use probability distributions to model spatial regions, and Bayesian networks to model the joint probability distribution and the structural relationships among spatial and non-spatial predicates. We demonstrate the applicability of the proposed framework by evaluating representatives from two well-known approaches that are used for learning associations, i.e., dependency analysis (using statistical tests of independence) and Bayesian methods. By controlling the parameters of the framework we provide extensive comparative results of the performance of the two approaches. We obtain measures of recovery of known associations as a function of the number of samples used, the strength, number and type of associations in the model, the number of spatial predicates associated with a particular non-spatial predicate, the prior probabilities of spatial predicates, the conditional probabilities of the non-spatial predicates, the image registration error, and the parameters that control the sensitivity of the methods. In addition to performance we investigate the processing efficiency of the two approaches.

An efficient dynamic point algorithm for line-based collision detection in real time virtual environments involving haptics

In real time computer graphics, "interactivity" is limited to a display rate of 30 frames per second. However, in multimodal virtual environments involving haptic interactions, a much higher update rate of about 1 kHz is necessary to ensure continuous interactions and smooth transitions. The simplest and most efficient interaction paradigm in such environments is to represent the haptic cursor as a point. However, in many situations, such as those in the development of real time medical simulations involving the interactions of long slender surgical tools with soft deformable organs, such a paradigm is nonrealistic and at least a line-based interaction is desirable. While such paradigms exist, the main impediment to their widespread use is the associated computational complexity. In this paper, we introduce, for the first time, an efficient algorithm for computing the interaction of a line-shaped haptic cursor and polygonal surface models which has a near constant complexity. The algorithm relies on space-time coherence, topological information, and the properties of lines in 3D space to maintain proximity information between a line segment and triangle meshes. For interaction with convex objects, the line is represented by its end points and a dynamic point, which is the closest point on the line to any potentially colliding triangle. To deal with multiple contacts and non-convexities, the line is decomposed into segments and a dynamic point is used for each segment. The algorithm may be used to compute collision detection and response with rigid as well as deformable objects with no performance penalty. Realistic examples are presented to demonstrate the effectiveness of our approach.