Drug-Induced Phase Separation in Polyelectrolyte Microgels

Polyelectrolyte microgels may undergo volume phase transition upon loading and the release of amphiphilic molecules, a process important in drug delivery. The new phase is “born” in the outermost gel layers, whereby it grows inward as a shell with a sharp boundary to the “mother” phase (core). The swelling and collapse transitions have previously been studied with microgels in large solution volumes, where they go to completion. Our hypothesis is that the boundary between core and shell is stabilized by thermodynamic factors, and thus that collapsed and swollen phases should be able to also coexist at equilibrium. We investigated the interaction between sodium polyacrylate (PA) microgel networks (diameter: 400–850 µm) and the amphiphilic drug amitriptyline hydrochloride (AMT) in the presence of NaCl/phosphate buffer of ionic strength (I) 10 and 155 mM. We used a specially constructed microscopy cell and micromanipulators to study the size and internal morphology of single microgels equilibrated in small liquid volumes of AMT solution. To probe the distribution of AMT micelles we used the fluorescent probe rhodamine B. The amount of AMT in the microgel was determined by a spectrophotometric technique. In separate experiments we studied the binding of AMT and the distribution between different microgels in a suspension. We found that collapsed, AMT-rich, and swollen AMT-lean phases coexisted in equilibrium or as long-lived metastable states at intermediate drug loading levels. In single microgels at I = 10 mM, the collapsed phase formed after loading deviated from the core-shell configuration by forming either discrete domains near the gel boundary or a calotte shaped domain. At I = 155 mM, single microgels, initially fully collapsed, displayed a swollen shell and a collapsed core after partial release of the AMT load. Suspensions displayed a bimodal distribution of swollen and collapsed microgels. The results support the hypothesis that the boundary between collapsed and swollen phases in the same microgel is stabilized by thermodynamic factors.

Global Existence and Temporal Decay of Large Solutions for the Poisson--Nernst--Planck Equations in Low Regularity Spaces

We are concerned with the global existence and decay rates of large solutions for the Poisson–Nernst–Planck equations. Based on careful observation of algebraic structure of the equations and using the weighted Chemin–Lerner type norm, we obtain the global existence and optimal decay rates of large solutions without requiring the summation of initial densities of a negatively and positively charged species is small enough. Moreover, the large solution is obtained for initial data belonging to the low regularity Besov spaces with different regularity and integral indices for the different charged species, which indicates more specific coupling relations between the negatively and positively charged species.

Geodiversity of surface karst features of geographical zones

The diversity of small, medium, and large solution features of zonal karsts and high mountain karsts is described here. It was taken into consideration how diversity changes according to the distance from the Equator in case of small, medium and large features of various zonal karsts and how it varies based on the distance from altitude in case of the karren features (small features) of high mountain karsts. It can be established that the diversity of karst features decreases according to the distance from the Equator (independent of the size of the features), while in high mountains the diversity of karren features first increases with altitude and then it decreases. The decrease of the diversity of medium and large features moving away from the Equator can be explained by the decrease of dissolution in­tensity. The diversity change of karren features shows a rela­tion with the diversity of the inclination of the bearing slope. Since on tropical karsts and in the medium elevated areas of high mountains (1600-2100 m) where bare slopes with large expansion and various slope inclination occur, the diversity of karren is great. On tropical karsts, slopes with diverse inclina­tion were created by karstification and in high mountains by glacial erosion.

Kinetic Study on CO-Selective Methanation over Nickel-Based Catalysts for Deep Removal of CO from Hydrogen-Rich Reformate

The CO-selective methanation process is considered as a promising CO removal process for compact fuel processors producing hydrogen, since the process selectively converts the trace of CO in the hydrogen-rich gas into methane without additional reactants. Two different types of efficient nickel-based catalysts, showing high activity and selectivity to the CO methanation reaction, were developed in our previous works; therefore, the kinetic models of the reactions over these nickel-based catalysts have been investigated adopting the mechanistic kinetic models based on the Langmuir chemisorption theory. In the methanation process, the product species can react with the reactant and also affect the adsorption/desorption of the molecules at the active sites. Thus, the kinetic parameter study should be carried out by global optimization handling all the rate equations for the plausible reactions at once. To estimate the kinetic parameters, an effective optimization algorithm combining both heuristic and deterministic methods is used due to the large solution space and the nonlinearity of the objective function. As a result, 14 kinetic parameters for each catalyst have been determined and the parameter sets for the catalysts have been compared to understand the catalytic characteristics.

DDPG-Based Energy-Efficient Flow Scheduling Algorithm in Software-Defined Data Centers

With the rapid development of data centers, the energy consumption brought by more and more data centers cannot be underestimated. How to intelligently manage software-defined data center networks to reduce network energy consumption and improve network performance is becoming an important research subject. In this paper, for the flows with deadline requirements, we study how to design the rate-variable flow scheduling scheme to realize energy-saving and minimize the mean completion time (MCT) of flows based on meeting the deadline requirement. The flow scheduling optimization problem can be modeled as a Markov decision process (MDP). To cope with a large solution space, we design a DDPG-EEFS algorithm to find the optimal scheduling scheme for flows. The simulation result reveals that the DDPG-EEFS algorithm only trains part of the states and gets a good energy-saving effect and network performance. When the traffic intensity is small, the transmission time performance can be improved by sacrificing a little energy efficiency.

An Algorithm for the Broad Evaluation of Potential Matches between Actuator Concepts and Heavy-Duty Mobile Applications

In recent years, a variety of novel actuator concepts for the implements of heavy-duty mobile machines (HDMMs) has been proposed by industry and academia. Mostly, novel concepts aim at improving the typically low energy efficiency of state-of-the-art hydraulic valve-controlled actuators. However, besides energy-efficiency, many aspects that are crucial for a successful concept integration are often neglected in studies. Furthermore, most of the time, a specific HDMM is focused as an application while other HDMM types can show very different properties that might make a novel concept less suitable. In order to take more aspects and HDMM types into account when evaluating actuator concepts, this paper proposes a novel evaluation algorithm, which calculates so-called mismatch values for each potential actuator-application match, based on different problem aspects that can indicate a potential mismatch between a certain actuator concept and an HDMM. The lower the mismatch value, which depends on actuator characteristics as well as HDMM attributes, the more potential is the match. At the same time, the modular nature of the algorithm allows to evaluate a large number of possible matches at once, with low effort. For the performance demonstration of the algorithm, 36 potential matches formed out of six actuator concepts and six HDMM types are exemplarily evaluated. The resulting actuator concept ratings for the six different HDMMs are in line with general reasoning and confirm that the evaluation algorithm is a powerful tool to get a first, quick overview of a large solution space of actuator-HDMM matches. However, analyzing the limitations of the algorithm also shows that it cannot replace conventional requirements engineering and simulation studies if detailed and reliable results are required.

A Framework for Implementing Metaheuristic Algorithms Using Intercellular Communication

Metaheuristics (MH) are Artificial Intelligence procedures that frequently rely on evolution. MH approximate difficult problem solutions, but are computationally costly as they explore large solution spaces. This work pursues to lay the foundations of general mappings for implementing MH using Synthetic Biology constructs in cell colonies. Two advantages of this approach are: harnessing large scale parallelism capability of cell colonies and, using existing cell processes to implement basic dynamics defined in computational versions. We propose a framework that maps MH elements to synthetic circuits in growing cell colonies to replicate MH behavior in cell colonies. Cell-cell communication mechanisms such as quorum sensing (QS), bacterial conjugation, and environmental signals map to evolution operators in MH techniques to adapt to growing colonies. As a proof-of-concept, we implemented the workflow associated to the framework: automated MH simulation generators for the gro simulator and two classes of algorithms (Simple Genetic Algorithms and Simulated Annealing) encoded as synthetic circuits. Implementation tests show that synthetic counterparts mimicking MH are automatically produced, but also that cell colony parallelism speeds up the execution in terms of generations. Furthermore, we show an example of how our framework is extended by implementing a different computational model: The Cellular Automaton.

Test optimization selection method based on NSGA-3 and improved Bayesian network model

Most of the solutions to existing test selection problems are based on single-objective optimization algorithms and multi-signal models, which maybe lead to some problems such as rough index calculation and large solution set limitations. To solve these problems, a test optimization selection method based on NSGA-3 algorithm and Bayesian network model is proposed. Firstly, the paper describes the improved Bayesian network model, expounds the method of model establishment, and introduces the model's learning ability and processing ability on uncertain information. According to the constraints and objective functions established by the design requirements, NSGA-3 is used to calculate the test optimization selection scheme based on the improved Bayesian network model. Taking a certain component of the missile airborne radar as an example, the fault detection rate and isolation rate are selected as constraints, and the false alarm rate, misdiagnosis rate, test cost, and test quantity are the optimization goals. The method of this paper is used for test optimization selection. It has been verified that this method can effectively solve the problem of multi-objective test selection, and has guiding significance for testability design.