Spin dynamics during chirped pulses: applications to homonuclear decoupling and broadband excitation

Swept-frequency pulses have found applications in a wide range of areas including spectroscopic techniques where efficient control of spins is required. For many of these applications, a good understanding of the evolution of spin systems during these pulses plays a vital role, not only in describing the mechanism of techniques, but also in enabling new methodologies. In magnetic resonance spectroscopy, broadband inversion, refocusing, and excitation using these pulses are among the most used applications in NMR, ESR, MRI, and $in$ $vivo$ MRS. In the present survey, a general expression for chirped pulses will be introduced, and some numerical approaches to calculate the spin dynamics during chirped pulses via solutions of the well-known Liouville-von Neumann equation and the lesser-explored Wei-Norman Lie algebra along with comprehensive examples are presented. In both cases, spin state trajectories are calculated using the solution of differential equations. Additionally, applications of the proposed methods to study the spin dynamics during the PSYCHE pulse element for broadband homonuclear decoupling and the CHORUS sequence for broadband excitation will be presented

Cyclist Detection, Tracking, and Trajectory Analysis in Urban Traffic Video Data

The major objective of this thesis work is examining computer vision and machine learning detection methods, tracking algorithms and trajectory analysis for cyclists in traffic video data and developing an efficient system for cyclist counting. Due to the growing number of cyclist accidents on urban roads, methods for collecting information on cyclists are of significant importance to the Department of Transportation. The collected information provides insights into solving critical problems related to transportation planning, implementing safety countermeasures, and managing traffic flow efficiently. Intelligent Transportation System (ITS) employs automated tools to collect traffic information from traffic video data. In comparison to other road users, such as cars and pedestrians, the automated cyclist data collection is relatively a new research area. In this work, a vision-based method for gathering cyclist count data at intersections and road segments is developed. First, we develop methodology for an efficient detection and tracking of cyclists. The combination of classification features along with motion based properties are evaluated to detect cyclists in the test video data. A Convolutional Neural Network (CNN) based detector called You Only Look Once (YOLO) is implemented to increase the detection accuracy. In the next step, the detection results are fed into a tracker which is implemented based on the Kernelized Correlation Filters (KCF) which in cooperation with the bipartite graph matching algorithm allows to track multiple cyclists, concurrently. Then, a trajectory rebuilding method and a trajectory comparison model are applied to refine the accuracy of tracking and counting. The trajectory comparison is performed based on semantic similarity approach. The proposed counting method is the first cyclist counting method that has the ability to count cyclists under different movement patterns. The trajectory data obtained can be further utilized for cyclist behavioral modeling and safety analysis

Optimal Control of Spins by Analytical Lie Algebraic Derivatives

Computation of derivatives (gradient and Hessian) of a fidelity function is one of the most crucial steps in many optimization algorithms. Having access to accurate methods to calculate these derivatives is even more desired where the optimization process requires propagation of these calculations over many steps, which is in particular important in optimal control of spin systems. Here we propose a novel numerical approach, ESCALADE (Efficient Spin Control using Analytical Lie Algebraic Derivatives) that offers the exact first and second derivatives of the fidelity function by taking advantage of the properties of the Lie group of $2\times 2$ Hermitian matrices, $\mathrm{SU}(2)$, and its Lie algebra, the Lie algebra of skew-Hermitian matrices, $\mathfrak{su}(2)$. A full mathematical treatment of the proposed method along with some numerical examples are presented

Lactation performance and serum biochemistry of dairy cows fed supplemental chromium in the transition period

This study was conducted to evaluate the effect of supplemental chromium on performance and blood serum biochemistry of dairy cows. Thus, 20 multiparous Holstein cows (parity 3) were equally divided into two groups, group one (control), which received no chromium supplementation and group two (treatment) which received 5 g/day chromium methionine from week 5 prior to parturition until 12 weeks thereafter. Milk production and milk composition were evaluated on 4, 8 and 12 weeks after parturition. Serum biochemistry concentrations (serum glucose, cholesterol, triglyceride, total protein, and cortisol and insulin concentration) and blood hematology (red blood cell, hematocrit, hemoglobin concentration and percentage neutrophils, lymphocytes, monocytes, basophiles, eosinophils and ratio of neutrophils to lymphocytes) were measured on 2 and 5 weeks prior to parturition and 1 and 4 weeks thereafter. Results indicate that milk production was significantly affected by chromium-methionine supplementation during the entire period (P<0.01) but no significant effect on milk composition was found. Supplemental chromium had no significant effect on serum glucose, cholesterol, triglycerides and insulin concentration and blood hematology parameters (P<0.05). However, chromium supplementation tended to increase significantly, serum total protein concentration and decrease cortisol concentration (P<0.05). The results of this experiment showed that chromium methionine supplementation in multiparous dairy cows diet may improve their milk yield in transition period.Key words: Dairy cow, transition period, chromium-methionine, milk yield, serum biochemistry

Radiographic Characteristics of Soft Tissue Calcification on Digital Panoramic Images

Objective: To assess the prevalence of soft tissue calcifications and their panoramic radiographic characteristics. Material and Methods: This descriptive retrospective study evaluated 2027 panoramic radiographs. The type and location of calcifications and the age and gender of patients were evaluated by two radiologists. Data were analyzed via SPSS and the Chi-square, Fisher’s exact and Kappa tests were used to compare the categorical demographic variables among the groups. The confidence interval was set to 95% and p<0.05 was considered statistically significant. Results: The prevalence of calcified stylohyoid ligament was 11.24%. This value was 3.99% for tonsillolith, 1.33% for calcified carotid plaque, 0.69% for antrolith, 0.39% for calcified lymph node, 0.29% for phleboliths, and 0.19% for sialoliths. The prevalence of these conditions had no significant association with gender or age (p=0.102). The prevalence of bilateral calcified stylohyoid ligament, tonsillolith, and a calcified carotid plaque was significantly higher (p<0.001). The most prevalent type of calcified stylohyoid ligament, according to O'Carroll’s classification, belonged to types 1, 4, 3 and 2 (p<0.001). The most commonly observed radiographic pattern was multiple, well-defined tonsilloliths (75.3%, p<0.001). Conclusion: The prevalence of soft tissue calcifications on panoramic radiographs was relatively low in this Iranian population. The most calcifications were respectively calcified stylohyoid ligament, tonsillolith, calcified carotid plaque, antrolith, calcified lymph node, phleboliths and sialoliths. Calcified stylohyoid ligament, tonsillolith and calcified carotid plaque were more bilaterally. Thereby panoramic imaging can help in primary assessment, epidemiologic and screening evaluation of these calcifications

The Social Adjustment and Depression during Outbreak of COVID-19 among Iranian People

Background: The sudden outbreak of Corona has created public depression and social changes, which affected countries and communities in terms of psychosocial issues. Social adjustment and physical activity play a very significant role to improve mental health. The purpose of this study was to compare social adjustment and depression during the outbreak of COVID-19 in Iranian employees regarding physical activity participation before the outbreak. Methods: The research method was a causal-comparative type. The statistical population of the study included all men and women over the age of 25 workings in governmental agencies, among which 403 people participated in the online survey by random method. The data about age, occupational, family, and educational condition were collected by demographic questionnaire. Using the short-form depression questionnaire (BDI-13), the depression during an outbreak of COVID-19 was evaluated. A 25-item social adjustment questionnaire (self-made questionnaire) was used for collecting the data of social adjustment specific to new social norms caused by COVID-19. Results: Data analysis using ANOVA showed that active individuals had higher social adjustment than inactive individuals (F = 10.398). Social adjustment was also statistically significant related to depression (r = -0.165). Conclusions: Overall, applying physical activity with the observance of the guidelines approved by the Ministry of Health and other reference institutions will be a great help for the promotion of the social adjustments and mental health. Keywords: COVID-19, Social adjustment, Physical activity, Depressio

Adaptive optimal control of entangled qubits

Developing fast, robust, and accurate methods for optimal control of quantum systems comprising interacting particles is one of the most active areas of current science. Although a valuable repository of algorithms is available for numerical applications in quantum control, the high computational cost is somewhat overlooked. Here, we present a fast and accurate optimal control algorithm for systems of interacting qubits, QOALA (quantum optimal control by adaptive low-cost algorithm), which is predicted to offer [Formula: see text] (M(2)) speedup for an M-qubit system, compared to the state-of-the-art exact methods, without compromising overall accuracy of the optimal solution. The method is general and compatible with diverse Hamiltonian structures. The proposed approach uses inexpensive low-accuracy approximations of propagators far from the optimum, adaptively switching to higher accuracy, higher-cost propagators when approaching the optimum. In addition, the utilization of analytical Lie algebraic derivatives that do not require computationally expensive matrix exponential brings even better performance

Adaptive optimal control of entangled qubits

Developing fast, robust, and accurate methods for optimal control of quantum systems comprising interacting particles is one of the most active areas of current science. Although a valuable repository of algorithms is available for numerical applications in quantum control, the high computational cost is somewhat overlooked. Here, we present a fast and accurate optimal control algorithm for systems of interacting qubits, QOALA (quantum optimal control by adaptive low-cost algorithm), which is predicted to offer O(M 2) speedup for an M-qubit system, compared to the state-of-the-art exact methods, without compromising overall accuracy of the optimal solution. The method is general and compatible with diverse Hamiltonian structures. The proposed approach uses inexpensive low-accuracy approximations of propagators far from the optimum, adaptively switching to higher accuracy, higher-cost propagators when approaching the optimum. In addition, the utilization of analytical Lie algebraic derivatives that do not require computationally expensive matrix exponential brings even better performance.</p