Rigorous derivation of Michaelis-Menten kinetics in the presence of diffusion

Reactions with enzymes are critical in biochemistry, where the enzymes act as catalysis in the process. One of the most used mechanisms for modeling enzyme-catalyzed reactions is the Michaelis-Menten (MM) kinetic. In the ODE level, i.e. concentrations are only on time-dependent, this kinetic can be rigorously derived from mass action law using quasi-steady-state approximation. This issue in the PDE setting, for instance when molecular diffusion is taken into account, is considerably more challenging and only formal derivations have been established. In this paper, we prove this derivation rigorously and obtain MM kinetic in the presence of spatial diffusion. In particular, we show that, in general, the reduced problem is a cross-diffusion-reaction system. Our proof is based on improved duality method, heat regularisation and a suitable modified energy function. To the best of our knowledge, this work provides the first rigorous derivation of MM kinetic from mass action kinetic in the PDE setting.Comment: Comments are welcome

Comparing the effectiveness of online and onsite learning in English proficiency classes: Learners’ perspectives

Online education has significantly gained popularity due to new technology and more importantly, the growing impact of the digitalization of the economy. Despite its prominent advantages such as accessibility, affordability and flexibility, the effectiveness of online education is still a constant debate and needs extensive investigations in different research contexts. This study aimed to evaluate the effectiveness of online learning in comparison to traditional learning in the context of English language teaching. This descriptive study was undertaken with learners of English as a foreign language (EFL) in English proficiency preparation classes, employing an online questionnaire together with final scores of proficiency tests. The results revealed that the participants had relatively positive perceptions towards online learning in all four aspects: course content, teachers, learning environment and course supports. The significant finding was that when comparing the final results of the VSTEP exams, the online learners generally were able to perform better than the learners in traditional classrooms, though the difference was not largely remarkable. Online education in the new normal will continue to excel and the effectiveness of this learning mode certainly needs further investigation from different perspectives

Establishing an Assessment Criteria System for Architectural Heritage of Colonial Educational Buildings in Hanoi

A number of educational buildings were built up by the French in Hanoi during their dominance. Most of these buildings still exist in the downtown area and have become an integral part of the valuable heritage, and their preservation is increasingly imperative. To preserve and promote more efficiently the values of the colonial building heritage assets, there is a need of a set of criteria for an assessment and classification. This paper presents the development of an assessment criteria system for the architectural heritage of colonial educational buildings in Hanoi. According to the proposals, colonial educational buildings can be classified into three groups of Special Value, Notable Value, Average Value. A set of criteria including factors related to both the internal and external values of these buildings have been proposed and validated with expert judgements. Each criterion then is incorporated with a set of scores showing the value it can bring to a colonial educational building to be assessed. The set of criteria and their scores can be used by the city authority to establish regulations to preserve and promote heritage values of the colonial educational buildings in Hanoi

Artificial intelligence in healthcare-the road to precision medicine

Precision medicine aims to integrate an individual’s unique features from clinical phenotypes and biological information obtained from imaging to laboratory tests and health records, to arrive at a tailored diagnostic or therapeutic solution. The premise that precision medicine will reduce disease-related health and financial burden is theoretically sound, but its realisation in clinical practice is still nascent. In contrast to conventional medicine, developing precision medicine solutions is highly data-intensive and to accelerate this effort there are initiatives to collect vast amounts of clinical and biomedical data. Over the last decade, artificial intelligence (AI), which includes machine learning (ML), has demonstrated unparalleled success in pattern recognition from big data in a range of domains from shopping recommendation to image classification. It is not surprising that ML is being considered as the critical technology that can transform big data from biobanks and electronic health records (EHRs) into clinically applicable precision medicine tools at the bedside. Distillation of high-dimensional data across clinical, biological, patient-generated and environmental domains using ML and translating garnered insights into clinical practice requires not only extant algorithms but also additional development of newer methods and tools. In this review, we provide a broad overview of the prospects and potential for AI in precision medicine and discuss some of the challenges and evolving solutions that are revolutionising healthcare

Design of an LMI-based Polytopic LQR Cruise Controller for an Autonomous Vehicle towards Riding Comfort

In this paper, we present an LMI-based approach for comfort-oriented cruise control of an autonomous vehicle. First, vehicle longitudinal dynamics and a corresponding parameter-dependent state-space representation are explained and discussed. An LMI-based polytopic LQR controller is then designed for the vehicle speed to track the reference value in the presence of noise and disturbances, where the scheduling parameters are functions of the vehicle mass and the speed itself. An appropriate disturbance force compensation term is also included in the designed controller to provide a smoother response. Then we detail how the reference speed is calculated online, using polynomial functions of the given desired comfort level (quantified by the vertical acceleration absorbed by the human body) and of the road type characterized by road roughness. Finally, time-domain simulations illustrate the method’s effectiveness

Modified Dijkstra's Routing Algorithm for Security with Different Trust Degrees

A great number of efficient methods to improve the performance of the networks have been proposed in physical-layer security for wireless communications. So far, the security and privacy in wireless communications is optimized based on a fixed assumption about the trustworthiness or trust degrees (TD) of certain wireless nodes. The nodes are often classified into different types such as eavesdroppers, untrusted relays, and trusted cooperative nodes. Wireless nodes in different networks do not completely trust each other when cooperating or relaying information for each other. Optimizing the network based on trust degrees plays an important role in improving the security and privacy for the modern wireless network. We proposed a novel algorithm to find the route with the smallest total transmission time from the source to the destination and still guarantee that the accumulated TD is larger than a trust degree threshold. Simulation results are presented to analyze the affects of the transmit SNR, node density, and TD threshold on different network performance elements

Real-time Damper Force Estimation for Automotive Suspension: A Generalized H2/LPV Approach

The real-time knowledge of the damper force is of paramount importance in controlling and diagnosing automotive suspension systems. This study presents a generalized H2/LPV observer for damper force estimation of a semi-active electro-rheological (ER) suspension system. First, an extended quarter-car model augmented with the nonlinear and dynamical model of the semi-active suspension system is written into the quasi-LPV formulation. Then, the damper force estimation method is developed through a generalized H2/LPV observer whose objective is to handle the impact of unknown road disturbances and sensor noise on the estimation errors of the state variables thanks to the H2 norm. The measured sprung and unsprung mass accelerations of the quarter-car system are used as inputs for the observer. The proposed approach is simulated with validated model of the 1/5-scaled real vehicle testbed of GIPSA-lab. Simulation results show the performance of the estimation method against unknown disturbances, emphasizing the effectiveness of the damper force estimation in real time

Hardware Architectures of Visible Light Communication Transmitter and Receiver for Beacon-based Indoor Positioning Systems

High-speed applications of Visible Light Communications have been presented recently in which response times of photodiode-based VLC receivers are critical points. Typical VLC receiver routines, such as soft-decoding of run-length limited (RLL) codes and FEC codes was purely processed on embedded firmware, and potentially cause bottleneck at the receiver. To speed up the performance of receivers, ASIC-based VLC receiver could be the solution. Unfortunately, recent works on soft-decoding of RLL and FEC have shown that they are bulky and time-consuming computations. This causes hardware implementation of VLC receivers becomes heavy and unrealistic. In this paper, we introduce a compact Polar-code-based VLC receivers. in which flicker mitigation of the system can be guaranteed even without RLL codes. In particular, we utilized the centralized bit-probability distribution of a pre-scrambler and a Polar encoder to create a non-RLL flicker mitigation solution. At the receiver, a 3-bit soft-decision filter was implemented to analyze signals received from the VLC channel to extract log-likelihood ratio (LLR) values and feed them to the Polar decoder. Therefore, the proposed receiver could exploit the soft-decoding of the Polar decoder to improve the error-correction performance of the system. Due to the non-RLL characteristic, the receiver has a preeminent code-rate and a reduced complexity compared with RLL-based receivers. We present the proposed VLC receiver along with a novel very-large-scale integration (VLSI) architecture, and a synthesis of our design using FPGA/ASIC synthesis tools

Foreign Ownership and Stock Return Volatility in Vietnam: the Destabilizing Role of Firm Size

This study aims to examine the relevance of foreign ownership to stock return volatility in the Vietnam stock market over ten years (2008 - 2017). After applying the fixed effects regressions and the extended instrumental variable regressions with fixed effects, we find that foreign ownership decreases the volatility of stock returns. However, the stabilizing impact of foreign ownership on stock return volatility becomes weaker in large firms since the coeffcient of the interaction term between firm size and foreign ownership turns out to be significantly positive. The estimated results remain robust when we use the future one-year volatility, other than the current one, as an alternative measure of the dependent variable

Comparison of dispersion characteristics of hollow-core photonic crystal fibers filled with aromatic compounds

In this paper, hollow-core photonic crystal fibers (PCFs) infiltrated with benzene and nitrobenzene are designed and investigated. Their dispersion characteristics are numerically simulated. The results show that using the aromatic-compounds-filled hollow core of PCFs makes dispersion curves flat. In addition, the dispersion curves approach the zero-dispersion line closer than previously published dispersion curves of PCFs with toluene, thus significantly improving the supercontinuum generation to create the ultra-flat spectrum expansion