Time-optimal control of disturbance-rejection tracking systems for discrete-time time-delayed systems by state feedback

AbstractIn this paper, we solve the disturbance-rejection and tracking problem for linear multivariable discrete-time systems with time-delayed controlled inputs. A set of necessary and sufficient conditions under which the proposed problem is controllable is defined. Also, the nilpotency properties of such systems is established and used as the basis of a comprehensive design procedure. This general procedure is illustrated by designing a time-optimal disturbance rejection tracking system for a stirred-tank with time-delayed control inputs

The Application of Innovative High-Throughput Techniques to Serum Biomarker Discovery

Time-of-flight mass spectrometry continues to evolve as a promising technique for serum protein expression profiling and biomarker discovery. As seen in our initial SELDI-TOF MS and MALDI-TOF MS profiling study of serum for the assessment of breast cancer risk, many profiling strategies typically employ single chemical affinity beads or surfaces to decrease sample complexity of dynamic fluids like serum. However, most proteins, captured on a particular surface or bead, are not resolved in the lower mass range where mass spectrometers are most effective. To this end we have designed an expression profiling workflow that utilizes immobilized trypsin paramagnetic beads in order to reduce large mass proteins into peptides that are in the ideal mass range for serum expression profiling as well as for direct LIFT-MS/MS sequence determinations. We demonstrate that this bead-based trypsinization is efficient in digesting large serum proteins in short incubation times and is highly reproducible and amenable to an automated platform. Additionally, we show that this workflow may be combined in tandem with many different types of bead fractionation surfaces. Furthermore, by utilizing two different pooled human serum sample cohorts as proof-of-concept experiments, we are able to demonstrate the reproducibility of this method in the profiling of clinical samples and the ease of differential peptide identity determination. Overall, this method is an attractive strategy for high-throughput serum profiling with the goal of detecting and identifying differential peptides

Anisotropic Zeeman Splitting in YbNi4P2

The electronic structure of heavy-fermion materials is highly renormalised at low temperatures with localised moments contributing to the electronic excitation spectrum via the Kondo effect. Thus, heavy-fermion materials are very susceptible to Lifshitz transitions due to the small effective Fermi energy arising on parts of the renormalised Fermi surface. Here, we study Lifshitz transitions that have been discovered in YbNi4P2 in high magnetic fields. We measure the angular dependence of the critical fields necessary to induce a number of Lifshitz transitions and find it to follow a simple Zeeman-shift model with anisotropic g-factor. This highlights the coherent nature of the heavy quasiparticles forming a renormalised Fermi surface. We extract information on the orientation of the Fermi surface parts giving rise to the Lifshitz transitions and we determine the anisotropy of the effective g-factor to be $\eta \approx 3.8$ in good agreement with the crystal field scheme of YbNi4P2.Comment: 10 pages, 5 figures, prepared for resubmission to SciPos

A Numerical Approach for Solving Optimal Control Problems Using the Boubaker Polynomials Expansion Scheme

In this paper, we present a computational method for solving optimal control problems and the controlled Duffing oscillator. This method is based on state parametrization. In fact, the state variable is approximated by Boubaker polynomials with unknown coefficients. The equation of motion, performance index and boundary conditions are converted into some algebraic equations. Thus, an optimal control problem converts to a optimization problem, which can then be solved easily. By this method, the numerical value of the performance index is obtained. Also, the control and state variables can be approximated as functions of time. Convergence of the algorithms is proved. Numerical results are given for several test examples to demonstrate the applicability and efficiency of the method

Modelling of Growth Profile of Three Probiotic Single Strain Starter Cultures (L.acidophilus (La-5), Bifidobacterium (BB-12), S.thermophilus (STB-01)) through Turbidity Measurement Technique

Probiotics are one or more mixture of viable microorganisms which have beneficial effects on animals and human beings through propagation gastrointestinal microflora. Some instances for health benefits of these products are: alleviating gastrointestinal disorders, diarrehea, food allergies, infection of Helicobacter pylori, lactose intolerance, candidiasis, serum cholesterol, and improving immune system balance, mineral uptake and protecting the consumer from different cancers such as colon, bladder and gastrointestinal cancers.To achieve these neutraceutical purposes, a large population of probiotics( 107- 108 cfu/g) should remain alive during storage of these products up to expiring date.In this research production of probiotic ABT yogurt is taken into consideration. Single strains of two probiotic starter cultures, Bifidobacterium( BB-12) and L. acidophilus(La-5), and one single strain of S. thermophilus (STB-01) for reducing the fermentation time are used. In probiotic products the method of counting probiotic bacteria have a significant effect. Traditional microbiological methods require wide range of time and a lots of facilities. Modelling of growth profile of bacteria with the data obtained from turbidity measurement would be a helpful method for fast counting of microbial communities. Keywords: analyze ; Broth media ; Colony Count Unit; Direct-Vat-Set(DVS); Durbin-Watson statistic

Solving an Optimal Control Problem of Cancer Treatment by Artificial Neural Networks

Cancer is an uncontrollable growth of abnormal cells in any tissue of the body. Many researchers have focused on machine learning and artificial intelligence (AI) based on approaches for cancer treatment. Dissimilar to traditional methods, these approaches are efficient and are able to find the optimal solutions of cancer chemotherapy problems. In this paper, a system of ordinary differential equations (ODEs) with the state variables of immune cells, tumor cells, healthy cells and drug concentration is proposed to anticipate the tumor growth and to show their interactions in the body. Then, an artificial neural network (ANN) is applied to solve the ODEs system through minimizing the error function and modifying the parameters consisting of weights and biases. The mean square errors (MSEs) between the analytical and ANN results corresponding to four state variables are 1.54e-06, 6.43e-07, 6.61e-06, and 3.99e-07, respectively. These results show the good performance and efficiency of the proposed method. Moreover, the optimal dose of chemotherapy drug and the amount of drug needed to continue the treatment process are achieved