Mathematical and Computational Modelling for Biosensors: A Modular Approach

Biosensors are analytic devices which detect biochemical and physiological changes and represent an emerging technology for low-cost, rapid and simple-to-operate biomedical diagnostic tools. Biosensor design and functionality are based on well understood physical and chemical processes which can be easily translated into mathematical models involving ordinary and partial di erential equations. Using mathematical and computational modelling techniques to characterize the biosensor response as a function of its input parameters in a wide range of physical contexts can guide the experimental work, thus reducing development time and costs.This thesis is based on a close collaboration with Biochemistry researchers at the National Centre for Sensor Research (NCSR) and Biomedical Diagnostics Institute (BDI) at Dublin City University and the mathematical models we develop are relevant to ongoing experimental work in these centres relating to design optimization of biocatalytic and bioanity devices. Our approach is to use numerical solutions as a rst step towards determining the accuracy of these models, since the simulations successfully reproduce the experimental outcomes; future work can then concentrate on a more detailed theoretical analysis

Convolutional Long-Short Term Memory Network with Multi-Head Attention Mechanism for Traffic Flow Prediction

Accurate predictive modeling of traffic flow is critically important as it allows transportation users to make wise decisions to circumvent traffic congestion regions. The advanced development of sensing technology makes big data more affordable and accessible, meaning that data-driven methods have been increasingly adopted for traffic flow prediction. Although numerous data-driven methods have been introduced for traffic flow predictions, existing data-driven methods cannot consider the correlation of the extracted high-dimensional features and cannot use the most relevant part of the traffic flow data to make predictions. To address these issues, this work proposes a decoder convolutional LSTM network, where the convolutional operation is used to consider the correlation of the high-dimensional features, and the LSTM network is used to consider the temporal correlation of traffic flow data. Moreover, the multi-head attention mechanism is introduced to use the most relevant portion of the traffic data to make predictions so that the prediction performance can be improved. A traffic flow dataset collected from the Caltrans Performance Measurement System (PeMS) database is used to demonstrate the effectiveness of the proposed method

Pruning Large Language Models via Accuracy Predictor

Large language models(LLMs) containing tens of billions of parameters (or even more) have demonstrated impressive capabilities in various NLP tasks. However, substantial model size poses challenges to training, inference, and deployment so that it is necessary to compress the model. At present, most model compression for LLMs requires manual design of pruning features, which has problems such as complex optimization pipeline and difficulty in retaining the capabilities of certain parts of the model.Therefore, we propose a novel pruning approach: firstly, a training set of a certain number of architecture-accuracy pairs is established, and then a non-neural model is trained as an accuracy predictor. Using the accuracy predictor to further optimize the search space and search, the optimal model can be automatically selected. Experiments show that our proposed approach is effective and efficient. Compared with the baseline, the perplexity(PPL) on Wikitext2 and PTB dropped by 9.48% and 5,76% respectively, and the average accuracy of MMLU increased by 6.28%.Comment: 6 pages, 4 figs, submitted to IEEE ICASSP 202

Induction Machine Modeling and Emulation for Asymmetrical Conditions

The induction machine can be used as generator or motor, to convert mechanical power to electrical power or via versa. The induction generator is an essential element of many renewable energy systems such as wind power plants, etc. The induction motor is commonly adopted in industry. The advantages of induction machines are well-known compared to other types of machines. Thus, it is important to investigate induction machines with accurate models and replace expensive test benches and equipment by more efficient and economical test procedures. To describe induction machines, different mathematical models have been devoted over the years to examine different problems. For instance, the abc frame model, dq frame model, hybrid model of abc and dq, and multiple coupled circuit (MCC) models. Based on those models, the developed model-based induction machine emulator is able to offer a flexible and easy platform for testing and analyzing characteristics of the induction machine in the laboratory environment. Therefore, accurate mathematical models are essential for further applications of induction machines. Generally, the induction machine works under balanced conditions, but the unbalanced condition is inevitable in practice. Self-excited induction generator (SEIG) is a good option for standalone wind energy conversion systems and other renewable energy sources. In such SEIG systems, the majority of unbalanced cases occur due to load disconnection. On the other hand, the generator will have to supply nonlinear loads in most scenarios. Thus, the SEIG supplying three-phase unbalanced loads and nonlinear loads is relatively common. Inside the induction machine, due to the combination of working environment, installation, and manufacturing factors, unbalance caused by internal faults can occur. Stator windings, rotor bars, and end rings are the most common internal faults. Such faults not only reduce the machine working efficiency and cause excessive heating but also cause potential hazards for continuous work and safety. As a result, it can lead to the failure of the machine. Continuing to drive the induction machine with asymmetrical conditions can cause consequent failures and even permanent damage to the machine. This thesis proposes a power electronic converter-based machine emulator replacing the actual machine to investigate the performance of the machine under different kinds of asymmetrical conditions. The machine emulator provides a laboratory environment to test and analyze the characteristics of the actual machine, especially under critical operating conditions. Therefore, the risk, time and cost associated with generating real faults can be reduced, helping to overcome safety issues with actual faulted machines. Such techniques can also be applied in fault detection, diagnosis, and fault control areas. In this thesis, the emulation of a SEIG supplying unbalanced and nonlinear loads, an induction motor with stator winding faults and rotor cage faults conditions are researched. The mathematical model of the SEIG system is established and the emulation results of balanced loads, unbalanced loads, transients during loading and nonlinear load conditions are compared with an actual SEIG system. The mathematical model of an induction machine with stator winding faults is also built. The emulator setup for a faulted induction motor is proposed and established, the experimental results of an actual machine with 5% and 10% faults have been done and then compared with simulation and emulation results. Different loading conditions are investigated. For the rotor cage fault induction motor, a novel machine parameter measurement method is introduced, which is able to measure the machine parameters without opening the machine, making it easier and more convenient to acquire rotor parameters. Usually, the faults inside the machine are hard to distinguish. In this thesis, the rotor cage fault is identified by analyzing the stator current frequency components, then the emulation setup is established. The simulation and emulation are compared with an actual faulted induction motor including loading conditions, which demonstrates the validity of the machine emulator

Spectral tracing of deuterium for imaging glucose metabolism.

Cells and tissues often display pronounced spatial and dynamical metabolic heterogeneity. Common glucose-imaging techniques report glucose uptake or catabolism activity, yet do not trace the functional utilization of glucose-derived anabolic products. Here we report a microscopy technique for the optical imaging, via the spectral tracing of deuterium (STRIDE), of diverse macromolecules derived from glucose. Based on stimulated Raman-scattering imaging, STRIDE visualizes the metabolic dynamics of newly synthesized macromolecules, such as DNA, protein, lipids and glycogen, via the enrichment and distinct spectra of carbon-deuterium bonds transferred from the deuterated glucose precursor. STRIDE can also use spectral differences derived from different glucose isotopologues to visualize temporally separated glucose populations using a pulse-chase protocol. We also show that STRIDE can be used to image glucose metabolism in many mouse tissues, including tumours, brain, intestine and liver, at a detection limit of 10 mM of carbon-deuterium bonds. STRIDE provides a high-resolution and chemically informative assessment of glucose anabolic utilization

A Novel Microwave Tunable Band-Pass Filter Integrated Power Divider Based on Liquid Crystal

This paper proposes a novel microwave continuous adjustable band-pass filter integrated power divider based on nematic liquid crystals (LCs). The proposed power divider uses liquid crystal (LC) as the dielectric material. It can realize phase shift by changing the dielectric anisotropy, when biasing the high anisotropy nematic liquid crystal. It is mainly used in microwave frequencies. It has a large number of advantages compared to conventional filter integrated power divider, such as low loss, multifunction integration, continuous adjustable, miniaturization, low processing costs, low operating voltage, high phase shift, and convenient manufacture. Therefore, it has shown great potential for application