Effects of Baicalein and Chrysin on the Structure and Functional Properties of β-Lactoglobulin

Two flavonoids with similar structures, baicalein (Bai) and chrysin (Chr), were selected to investigate the interactions with β-lactoglobulin (BLG) and the influences on the structure and functional properties of BLG by multispectral methods combined with molecular docking and dynamic (MD) simulation techniques. The results of fluorescence quenching suggested that both Bai and Chr interacted with BLG to form complexes with the binding constant of the magnitude of 105 L·mol−1. The binding affinity between BLG and Bai was stronger than that of Chr due to more hydrogen bond formation in Bai–BLG binding. The existence of Bai or Chr induced a looser conformation of BLG, but Chr had a greater effect on the secondary structure of BLG. The surface hydrophobicity and free sulfhydryl group content of BLG lessened due to the presence of the two flavonoids. Molecular docking was performed at the binding site of Bai or Chr located in the surface of BLG, and hydrophobic interaction and hydrogen bond actuated the formation of the Bai/Chr–BLG complex. Molecular dynamics simulation verified that the combination of Chr and BLG decreased the stability of BLG, while Bai had little effect on it. Moreover, the foaming properties of BLG got better in the presence of the two flavonoids compounds and Bai improved its emulsification stability of the protein, but Chr had the opposite effect. This work provides a new idea for the development of novel dietary supplements using functional proteins as flavonoid delivery vectors.

Designing an Optimal Model of Blood Logistics Management with the Possibility of Return in the Three-Level Blood Transfusion Network

Inventory managers in the blood supply chain always seek timely and proper response to their customers, which is essential because of the perishability and uncertainty of blood demand and the direct relationship of its presence or non-presence with human life. On the other hand, timely and regular delivery of blood to consumers is vital, as the weakness in delivery and transportation policies results in increased shortages, returns, blood loss and significant decrease in the quality of blood required by patients. Given the significance of this for the blood transfusion network, the paper tried to design a comprehensive and integrated optimal model of blood transfusion network logistics management by blood group to reduce the cost of losses, returns and blood shortages. This model is divided into two parts: Inventory management and routing. A combination of simulation techniques and neural network with several recurrent layers was used to evaluate the optimal inventory management and a multi-objective planning model was designed to determine the delivery and distribution of blood to consumers. The model designed was implemented in Khorasan Razavi Blood Transfusion Network with a main base, six central bases and 54 hospitals. Solving the model led to estimating the f consumer demand, the optimal value of target inventory and re-ordering point of central bases and hospitals, and blood distribution from the supplier to its consumers that decreased the units of blood returned to bases, increased inventory availability, and reduced costs significantly.

Self-Driving Robotic Cars

Even the behavior of a single driver can have a dramatic impact on hundreds of cars, making it more difficult to manage traffic. While the attempts to analyze and correct the traffic patterns that lead to congestion began as early in the 1930s, it wasn't until recently that scientists developed simulation techniques and advanced algorithms to create more realistic visualizations of traffic flow. In experiments conducted by Alexandre Bayen and the Liao-Cho, which included several dozen cars in a small-scale closed circuit, a single autonomous vehicle could eliminate traffic jams by moderating the speed of every car on the road. In larger simulations, the research showed that once their number rises to 5-10% of all cars in the traffic, they can manage localized traffic even in complex environments, such as merging multiple lanes of traffic into two or navigating extremely busy sections.

Political Decision Making in the COVID-19 Pandemic: The Case of Germany from the Perspective of Risk Management

The COVID-19 pandemic is permanently changing modern social and economic coexistence. Most governments have declared infection control to be their top priority while citizens face great restrictions on their civil rights. A pandemic is an exemplary scenario in which political actors must decide about future, and thus uncertain, events. This paper tries to present a tool well established in the field of entrepreneurial and management decision making which could also be a first benchmark for political decisions. Our approach builds on the standard epidemiological SEIR model in combination with simulation techniques used in risk management. By our case study we want to demonstrate the opportunities that risk management techniques, especially risk analyses using Monte Carlo simulation, can provide to policy makers in general, and in a public health crisis in particular. Hence, our case study can be used as a framework for political decision making under incomplete information and uncertainty. Overall, we want to point out that a health policy that aims to provide comprehensive protection against infection should also be based on economic criteria. This is without prejudice to the integration of ethical considerations in the final political decision.

Ammonia converter Simulation and Optimization Based on an Innovative Correlation for (KP) Prediction

In this paper, the simulation and optimization of an industrial ammonia synthesis reactor is illustrated. The converter under study is of a vertical design, equipped with three radial-flow catalyst beds with inter-stage cooling and two quenching points. For building the model, a modified kinetic equation of ammonia synthesis reaction, based on Temkin- Pyzhev equation and an innovative correlation for (KP) prediction, was developed in suitable form for the implementation in Aspen HYSYS plug flow reactor using the spreadsheet embedded in the software with the introduction of some invented simulation techniques. A new parameter, which is a function of (T, P and α), was introduced into the reaction rate equation to account for the variation of KP with pressure. The simulation model is able to describe the converter behavior with acceptable accuracy. A case study was done, using Aspen HYSYS Optimizer, illustrated the optimum reactor temperature profile, after 12 years of operation, to achieve maximum production. The result predicts an increase of 8 tons ammonia per day accompanied with an increase of steam production of 12 tons per day.

Analysis of large-scale UHF-RFID use-cases utilizing full-wave simulation techniques

UHF-RFID is a mature and widespread technology that has the potential to increase the reliability and efficiency of processes in logistics and production environments. However, complex interference effects in indoor environments pose challenges to the implementation of reliable wireless communication systems like RFID. This work proposes a method for tag performance evaluation utilizing a coherent two-stage rating process. This enables the abstraction of physical quantities and facilitates the interpretation of tag readability. For this purpose, two well-established full-wave techniques are utilized to perform deterministic simulations of a logistical UHF-RFID use-case. The setup of large-scale simulation environments is discussed and important quantities to be considered in RFID-systems are derived. Based on the simulation results and the proposed rating method, the RFID use-case is evaluated. Results are visualized in full-3D, facilitating the identification of critical spots. Furthermore, a subsequent cross-validation of the simulation results is performed, verifying the validity of the simulation results. By performing a priori propagation analysis, issues can effectively be revealed beforehand and costly modifications after system deployment can be avoided.

Adaptation Strategies for Personalized Gait Neuroprosthetics

Personalization of gait neuroprosthetics is paramount to ensure their efficacy for users, who experience severe limitations in mobility without an assistive device. Our goal is to develop assistive devices that collaborate with and are tailored to their users, while allowing them to use as much of their existing capabilities as possible. Currently, personalization of devices is challenging, and technological advances are required to achieve this goal. Therefore, this paper presents an overview of challenges and research directions regarding an interface with the peripheral nervous system, an interface with the central nervous system, and the requirements of interface computing architectures. The interface should be modular and adaptable, such that it can provide assistance where it is needed. Novel data processing technology should be developed to allow for real-time processing while accounting for signal variations in the human. Personalized biomechanical models and simulation techniques should be developed to predict assisted walking motions and interactions between the user and the device. Furthermore, the advantages of interfacing with both the brain and the spinal cord or the periphery should be further explored. Technological advances of interface computing architecture should focus on learning on the chip to achieve further personalization. Furthermore, energy consumption should be low to allow for longer use of the neuroprosthesis. In-memory processing combined with resistive random access memory is a promising technology for both. This paper discusses the aforementioned aspects to highlight new directions for future research in gait neuroprosthetics.

Coalescent tree recording with selection for fast forward-in-time simulations

Forward simulations are increasingly important in evolutionary genetics to simulate selection with realistic demography, mating systems and ecology. To reach the performance needed for genome-wide simulations a number of new simulation techniques have been developed recently. Kelleher et al. (2018) introduced a technique consisting in recording the entire genetic history of the population and placing mutations on the coalescent tree. This method cannot model selection. I recently introduced a simulation technique that speed up fitness calculation by assuming that fitness effects among haplotypes are multiplicative (Matthey-Doret, 2021). More precisely, fitness measures are stored for subsets of the genome and, at time of reproduction, if no recombination happen within a given subset, then the fitness for this subset for the offspring haplotype is directly inferred from the parental haplotype. Here, I present a hybrid of the above two techniques. The algorithm records the genetic history of a species, directly places the mutations on the tree and infers fitness of subsets of the genome from parental haplotypes. At recombinant sites, the algorithm explores the tree to reconstruct the genetic data at the recombining segment. I benchmarked this new technique implemented in SimBit and report an important improvement of performance compared to previous techniques to simulate selection. This improvement is particularly drastic at low recombination rate. Such developments of new simulation techniques are pushing the horizon of the realism with which we can simulate species molecular evolution.