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

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

Microbial biodegradable potato starch based low density polyethylene

Plastic materials remain in the nature for decades. Slow degradation of plastics in the environment caused a public trend to biodegradable polymers. The aim of this research was to produce the microbial biodegradable low density polyethylene with potato starch. Degradation of potato starch based low density polyethylene (LDPE) was investigated in soil rich in microorganisms for 8 months. Weight differences of polymeric samples before and after degradation in soil indicated soil biodegradation. Fourier transform spectroscopy (FTIR) approved the result. Scanning electron microscope (SEM) and weight change after 84 days’ exposure to Pseudomonas aeruginosa confirmed degradation by microorganisms. In addition, potato starch based LDPE was exposed to 8 different kinds of fungi and the degradation was studied visually. Result confirmed the microbialbiodegradability of potato starch based LDPE blend in natural and laboratory condition

Cascade of magnetic field induced Lifshitz transitions in the ferromagnetic Kondo lattice material YbNi4P2

A ferromagnetic quantum critical point is thought not to exist in two and three-dimensional metallic systems yet is realized in the Kondo lattice compound YbNi4(P,As)2, possibly due to its one-dimensionality. It is crucial to investigate the dimensionality of the Fermi surface of YbNi4P2 experimentally but common probes such as ARPES and quantum oscillation measurements are lacking. Here, we studied the magnetic field dependence of transport and thermodynamic properties of YbNi4P2. The Kondo effect is continuously suppressed and additionally we identify nine Lifshitz transitions between 0.4 and 18 T. We analyze the transport coefficients in detail and identify the type of Lifshitz transitions as neck or void type to gain information on the Fermi surface of YbNi4P2. The large number of Lifshitz transitions observed within this small energy window is unprecedented and results from the particular flat renormalized band structure with strong 4f-electron character shaped by the Kondo lattice effect.Comment: 6 pages, 4 figure

Expression analysis of carbohydrate antigens in ductal carcinoma in situ of the breast by lectin histochemistry

<p>Abstract</p> <p>Background</p> <p>The number of breast cancer patients diagnosed with ductal carcinoma <it>in situ </it>(DCIS) continues to grow. Laboratory and clinical data indicate that DCIS can progress to invasive disease. Carbohydrate-mediated cell-cell adhesion and tumor-stroma interaction play crucial roles in tumorigenesis and tumor aggressive behavior. Breast carcinogenesis may reflect quantitative as well as qualitative changes in oligosaccharide expression, which may provide a useful tool for early detection of breast cancer. Because tumor-associated carbohydrate antigens (TACA) are implicated in tumor invasion and metastasis, the purpose of this study was to assess the expression of selected TACA by lectin histochemistry on DCIS specimens from the archival breast cancer tissue array bank of the University of Arkansas for Medical Sciences.</p> <p>Methods</p> <p>For detection of TACA expression, specimens were stained with <it>Griffonia simplicifolia </it>lectin-I (GS-I) and <it>Vicia vilosa </it>agglutinin (VVA). We studied associations of lectin reactivity with established prognostic factors, such as tumor size, tumor nuclear grade, and expression of Her-2/neu, p53 mutant and estrogen and progesterone receptors.</p> <p>Results</p> <p>We observed that both lectins showed significant associations with nuclear grade of DCIS. DCIS specimens with nuclear grades II and III showed significantly more intense reactivity than DCIS cases with nuclear grade I to GS-1 (Mean-score chi-square = 17.60, DF = 2; <it>P </it>= 0.0002) and VVA (Mean-score chi-square = 15.72, DF = 2; <it>P </it>= 0.0004).</p> <p>Conclusion</p> <p>The results suggest that the expression of VVA- and GS-I-reactive carbohydrate antigens may contribute to forming higher grade DCIS and increase the recurrence risk.</p