Descent Point Research Articles

Synergetic evolution of the microscopic crystal orientation and macroscopic superconducting properties of REBCO tape under sever deformation

This study investigates crystal orientation evolution at microscopic level, and changes in the superconducting properties at macroscopic level of rare-earth-barium-copper-oxide (REBCO) superconductor tape under severe deformation. At the microscopic level, in-situ electron backscatter diffraction is used for real-time observation of the crystal orientation evolution during the entire tensile process. At the macroscopic level, the critical current (Ic) value and Ic angle dependence performance are systematically measured under different applied tensile stresses. By comparing the microscopic and macroscopic results, the synergetic evolution mechanism between them is successfully established. Stress at the initial descent point of the Ic value excellently fits the one where the (001)[100] orientation sharply decreases, thereby revealing the real Ic degradation mechanism of REBCO tapes. The application of a moderate applied tensile stress can increase the in-field Ic value and pinning force because new generated defects and strains act as pinning centers. A higher tensile stress can destroy the biaxial texture of the REBCO layer and become a significant barrier to the supercurrent flow. This research elucidates the REBCO degradation mechanism and may assist manufacturers in improving the electromechanical properties of commercial REBCO tape.

The design and analysis of optimal descent profiles using real flight data

This paper aims to evaluate the vertical profile efficiency of arrival traffic at Trabzon Airport in terms of fuel consumption, carbon dioxide (CO2), hydrocarbon (HC), carbon monoxide (CO), and nitrogen oxide derivatives (NOx). For this purpose, 3-deg descent profiles were created using the parameters obtained from real flight data under the near idle thrust and clear configuration restrictions and these were then compared to the reference real flight profiles. The cruise section was also considered while comparing the designed continuous descent profiles to baseline scenarios. According to the results of our analyses, the altitudes at which landing traffic deviates from the optimum profile is especially around 6000ft and after the top of descent point. It was not possible to note a high level of vertical inefficiency in the vertical profile of arrival traffic, however, holding maneuvers, waypoint restrictions play an important role in overall descent time, fuel consumption, and emissions.

DPSK modulation and demodulation system based on a novel Mach-Zehnder interference structure

As the conventional fiber Mach-Zehnder (M−Z) interference structure being sensitive to temperature variation and mismatching of arm length in Differential Phase Shift Keying (DPSK) system, we proposed a modulation and demodulation system of DPSK based on novel M−Z interference structure. This novel M−Z interference structure has some advantages of higher temperature stability, better robustness and lower restriction of laser linewidth. A detailed discussion of the working principle is presented and demonstrated, the influences of the delay induced by arm mismatching and temperature variation on the Q factor of DPSK system are analyzed. Experimental results as well as simulations show that the delay length mismatching corresponding to descent point of 3dB increases from [0.925,1.124] to [0.536,1.31], which reduces the requirement for the length accuracy of the interference arm, and the demodulation effect of the novel structure is better under the same deviation. The Q factor of the system keeps fluctuation within a narrow range when the temperature variation in the range of 20°C–120°C, and the demodulation effect of the system is almost unaffected by the variation of ambient temperature.

Regression learning assisted efficient energy harvesting method for smart city environment

Energy Harvesting in smart cities is a demanding requirement to improve the lifetime of the end-user services. It is achieved by balancing the data transmission, energy consumption, and conservation optimally. In this article, the learning assisted efficient energy harvesting method is proposed for improving the energy efficiency of the Internet of Things (IoT) devices deployed in the smart city environment. The regression learning model used in this proposed method classifies the time and energy-dependent schedules for data queuing and transmission in a view to maximising throughput. Cost-based data transmission and energy harvesting approaches are the concurrent procedures used to identify the linear descent points through the regression method. This identification helps mitigate unnecessary energy dissemination and early energy drain of the IoT devices for their allocated/transmitted data. The performance of the proposed method is evaluated using simulations, and it is verified using the metrics energy consumption, remaining energy ratio, energy harvested, cost factor, and throughput. The proposed method helps to minimise the energy consumption rate and maximise the throughput in an effective manner.

Altered squat movement pattern in patients with chronic low back pain

Low back pain (LBP) is a problem which causes human suffering and entails costs for treatment. Chronic LBP is considered a civilization diseases in developed countries and one of the majorpublic healthproblem worldwide. Squatting is a part of such daily activities as sitting down, standing up or lifting objects. In this study, hip, knee, and ankle joint motions during squatting are analyzed in the sagittal plane. The aim of the study was to find a relationship between the function of the lower limb during squatting and LBP incidence. Eleven healthy subjects and eight subjects suffering from low back pain were enrolled in tes study. Participants performed squat while maintaining heel contact with the ground throughout the movement. The participants attempted to make a squat as deep as possible. Kinematic data were captured using a Vicon optical motion capture system. The low back pain group had a greater range of motion of the hip and knee with reference to the ankle joint motion, compared to the control group. There was no statistically significant difference in ankle dorsiflexion between groups. Control group reached maximal ankle dorsiflexion at higher point of descent than LBP group. The findings support the hypothesis that the strategy of movement is different in subjects with and without back pain. They may suggest that subjects with low back pain in maximal squat use the hip joint and knee joint to a greater extent than subjects without back pain, with comparable ankle dorsiflexion. Clinicians should take into consideration the influence of ankle motion and timing on exercise biomechanics in persons suffering from low back pain.

Evaluation of two vaginal, uterus sparing operations for pelvic organ prolapse: modified Manchester operation (MM) and sacrospinous hysteropexy (SSH), a study protocol for a multicentre randomized non-inferiority trial (the SAM study)

BackgroundPelvic organ prolapse (POP) affects up to 40% of parous women which adversely affects the quality of life. During a life time, 20% of all women will undergo an operation. In general the guidelines advise a vaginal operation in case of uterine descent: hysterectomy with uterosacral ligament plication (VH), sacrospinous hysteropexy (SSH) or a modified Manchester operation (MM). In the last decade, renewed interest in uterus sparing techniques has been observed. Previous studies have shown non-inferiority between SSH and VH. Whether or not SSH and MM are comparable concerning anatomical and functional outcome is still unknown. The practical application of both operations is at least in The Netherlands a known cause of practice pattern variation (PPV). To reveal any difference between both techniques the SAM-study was designed.MethodsThe SAM-study is a randomized controlled multicentre non-inferiority study which compares SSH and MM. Women with symptomatic POP in any stage, uterine descent and POP-Quantification (POP-Q) point D at ≤ minus 1 cm are eligible. The primary outcome is the composite outcome at two years of absence of prolapse beyond the hymen in any compartment, the absence of bulge symptoms and absence of reoperation for pelvic organ prolapse. Secondary outcomes are hospital parameters, surgery related morbidity/complications, pain perception, further treatments for prolapse or urinary incontinence, POP-Q anatomy in all compartments, quality-of-life, sexual function, and cost-effectiveness. Follow-up takes place at 6 weeks, 12 and 24 months. Additionally at 12 weeks, 6 and 9 months cost-effectiveness will be assessed. Validated questionnaires will be used and gynaecological examination will be performed. Analysis will be performed following the intention-to-treat and per protocol principle. With a non-inferiority margin of 9% and an expected loss to follow-up of 10%, 424 women will be needed to prove non-inferiority with a confidence interval of 95%.DiscussionThis study will evaluate the effectiveness and costs of SSH versus MM in women with primary POP. The evidence will show whether the existing PPV is detrimental and a de-implementation process regarding one of the operations is needed.Trial registrationDutch Trial Register (NTR 6978, http://www.trialregister.nl). Date of registration: 29 January 2018. Prospectively registered.

Research and Demonstration of the Refraction Problems

The introduction of function extremum promotes the development of calculus, which is the prerequisite and important condition for the development and development of many mathematical ideas. Except for extreme problems in social life or science and technology, the issue of cost in economic issues, the shortest distance in mathematics problem can be solved by the function extreme value thought. However, because the extreme value problem is not well described, learners cannot easily observe and learn. This paper takes the uniform speed and uniform acceleration and descent in the descent model as an example. In the uniform acceleration, the dichotomy is introduced to solve the function extremum problem. Through MATLAB simulation, the position of the steepest descent point is found intuitively and relevant conclusions are obtained. It is hoped that the research process of the problem can be helpful to the study of refractive problems, and it is hoped that the visually intuitive simulation results can enable learners to understand the descent model more objectively.

Identification of time-varying neural dynamics from spike train data using multiwavelet basis functions

BackgroundTracking the changes of neural dynamics based on neuronal spiking activities is a critical step to understand the neurobiological basis of learning from behaving animals. These dynamical neurobiological processes associated with learning are also time-varying, which makes the modeling problem challenging. New methodWe developed a novel multiwavelet-based time-varying generalized Laguerre–Volterra (TVGLV) modeling framework to study the time-varying neural dynamical systems using natural spike train data. By projecting the time-varying parameters in the TVGLV model onto a finite sequence of multiwavelet basis functions, the time-varying identification problem is converted into a time invariant linear-in-the-parameters one. An effective forward orthogonal regression (FOR) algorithm aided by mutual information (MI) criterion is then applied for the selection of significant model regressors or terms and the refinement of model structure. A generalized linear model fit approach is finally employed for parameter estimation from spike train data. ResultsThe proposed multiwavelet-based TVGLV approach is used to identify both synthetic input-output spike trains and spontaneous retinal spike train recordings. The proposed method gives excellent the performance of tracking either sharply or slowly changing parameters with high sensitivity and accuracy regardless of the a priori knowledge of spike trains, which these results indicate that the proposed method is shown to deal well with spike train data. Comparison with existing methodsThe proposed multiwavelet-based TVGLV approach was compared with several state-of-art parametric estimation methods like the steepest descent point process filter (SDPPF) or Chebyshev polynomial expansion method. The conventional SDPPF algorithm, or SDPPF with B-splines wavelet expansion method was shown to have the poor performance of tracking the time-varying system changes with the synthetic spike train data due to the slow convergence of the adaptive filter methods. Although the Chebyshev polynomial basis function method gave the good parametric estimation results, it requires prior parameter estimation. It was shown that the proposed multiwavelet-based TVGLV method can track the time-varying parameter changes rapidly and accurately. ConclusionsThe multiwavelet-based TVGLV modeling framework developed in this paper can not only provide a computational modeling scheme for investigating such nonstationary properties, track more general forms of changes in time-varying neural dynamics, and but also may potentially be applied to investigate the spatial-temporal information underlying biomedical spiking signals.

Complete characterization of the first descent point distribution for the <i>k</i>-error linear complexity of 2<sup><i>n</i></sup>-periodic binary sequences

In this paper, a new constructive approach of determining the first descent point distribution for the \begin{document}$k$\end{document} -error linear complexity of \begin{document}$2^n$\end{document} -periodic binary sequences is developed using the sieve method and Games-Chan algorithm. First, the linear complexity for the sum of two sequences with the same linear complexity and minimum Hamming weight is completely characterized and this paves the way for the investigation of the \begin{document}$k$\end{document} -error linear complexity. Second we derive a full representation of the first descent point spectrum for the \begin{document}$k$\end{document} -error linear complexity. Finally, we obtain the complete counting functions on the number of \begin{document}$2^n$\end{document} -periodic binary sequences with given \begin{document}$2^m$\end{document} -error linear complexity and linear complexity \begin{document}$2^n-(2^{i_1}+2^{i_2}+···+2^{i_m})$\end{document} , where \begin{document}$0≤ i_1 In summary, we depict a full picture on the first descent point of the \begin{document}$k$\end{document} -error linear complexity for \begin{document}$2^n$\end{document} -periodic binary sequences and this will help us construct some sequences with requirements on linear complexity and \begin{document}$k$\end{document} -error complexity.

Structure analysis on the <i>k</i>-error linear complexity for 2<sup><i>n</i></sup>-periodic binary sequences

In this paper, in order to characterize the critical error linear complexity spectrum (CELCS) for $2^n$-periodic binary sequences, we first propose a decomposition based on the cube theory. Based on the proposed $k$-error cube decomposition, and the famous inclusion-exclusion principle, we obtain the complete characterization of the $i$th descent point (critical point) of the k-error linear complexity for $i=2,3$. In fact, the proposed constructive approach has the potential to be used for constructing $2^n$-periodic binary sequences with the given linear complexity and $k$-error linear complexity (or CELCS), which is a challenging problem to be deserved for further investigation in future.

An Improved Algorithm for Super-Resolution Video

Aiming at the characteristics of the UAV camera, camera data nowadays, a new improved method is proposed based on putting the low-resolution video reconstruction into high-resolution video. First, the low-resolution video frame is done spectrum analysis by Fourier transform. Second, find the maximum gradient descent point to determine the cut off frequency. Finally making use of high-resolution images with high frequency detail, then motion compensated. Through POCS algorithm, then iterated, obtaining super-resolution reconstruction video and realizing the above by MATLAB simulation.

Foucault and Soviet biopolitics

The article addresses the puzzling silence of the Foucaldian studies of biopolitics about Soviet socialism by revisiting Foucault’s own account of socialism in his 1970s work, particularly his 1975–6 course ‘Society Must Be Defended’. Foucault repeatedly denied the existence of an autonomous governmentality in socialism, demonstrating its dependence on the techniques of government developed in 19th-century western Europe. For Foucault Soviet socialism was fundamentally identical to its ideological antagonist in its biopolitical rationality, which he defined in terms of racism. This article challenges Foucault’s reading, demonstrating that his notion of racism is ill-suited to describe the governmental rationalities of Soviet socialism during both the formation and the consolidation of the Stalinist regime. While the Soviet project was paradigmatically biopolitical in its ambition to transform the forms of life of the population in line with the communist ideology, its biopolitics was fundamentally different from the security-oriented logic of racism, focusing instead on the exposure of the population to the violent transformation of their forms of life. Revisiting Foucault’s genealogy of racism, we argue that the point of descent of this biopolitics lies in the 19th-century split of the ‘counter-historical’ discourse of the struggle of the races into the discourses of state racism and class struggle. While Foucault’s genealogy focuses on the development of the former into liberal and totalitarian biopolitics as we know them, it leaves class struggle out of the history of biopolitics and is therefore unable to account for the biopolitical specificity of the Soviet project.

Three‐dimensional transperineal ultrasound for imaging mesh implants following sacrocolpopexy

To characterize, using three-dimensional (3D) transperineal ultrasound, the appearance, position and dimensions of mesh implants following minimally invasive abdominal sacrocolpopexy. In women who underwent sacrocolpopexy, mesh was evaluated at rest and on maximal Valsalva, on all 3D orthogonal planes and rendered views. Mesh dimensions were obtained by 3D processing in the midsagittal and coronal planes (anterior, posterior and sacral arm) and were analyzed offline, the operator blinded to clinical data. Overall, 62 women, mean age 58.4 (range, 42-79) years were evaluated at a median of 9 (range, 1-26) months following surgery. The anterior arm of the mesh was caudal to the lowermost point of descent of the anterior compartment in 56 (90.3%) women, was equally positioned in five (8.1%) and was cranial in one. The posterior arm was caudal in 44 (71%) women, was equally positioned in 16 (25.8%) and was cranial in two (3.2%). The Y connection and the sacral arm of the mesh could not be adequately seen because of physical limitations of ultrasound (lower resolution at greater depth), large recurrent rectoceles, echogenic stools or folding of mesh remnants. Folding of the mesh was seen in 46 (74.2%) women, folding of the anterior arm in five (8.1%) and folding of the posterior arm in 23 (37.1%). Folding occurred caudally in 26 (41.9%) women, proximally in 11 (17.7%) and in both areas in nine (14.5%). There were no erosions. Mesh visualization following minimally invasive abdominal sacrocolpopexy procedures using transperineal 3D/four-dimensional (4D) ultrasound is feasible. Studies are needed to evaluate the correlation between ultrasound measures and prolapse recurrence or mesh erosion.

A new hybrid descent method with application to the optimal design of finite precision FIR filters

In this paper, the problem of the optimal design of discrete coefficient FIR filters is considered. A novel hybrid descent method, consisting of a simulated annealing algorithm and a gradient-based method, is proposed. The simulated annealing algorithm operates on the space of orthogonal matrices and is used to locate descent points for previously converged local minima. The gradient-based method is derived from converting the discrete problem to a continuous problem via the Stiefel manifold, where convergence can be guaranteed. To demonstrate the effectiveness of the proposed hybrid descent method, several numerical examples show that better discrete filter designs can be sought via this hybrid descent method.

Boosting

AbstractIn this contribution, we review boosting, one of the most effective machine learning methods for classification and regression. Most of the article takes the gradient descent point of view, even though we do include the margin point of view as well. In particular, AdaBoost in classification and various versions of L2boosting in regression are covered. Advice on how to choose base (weak) learners and loss functions and pointers to software are also given for practitioners. Copyright © 2009 John Wiley & Sons, Inc.This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Classification and Regression Trees (CART)

Construction of Point Process Adaptive Filter Algorithms for Neural Systems Using Sequential Monte Carlo Methods

The stochastic state point process filter (SSPPF) and steepest descent point process filter (SDPPF) are adaptive filter algorithms for state estimation from point process observations that have been used to track neural receptive field plasticity and to decode the representations of biological signals in ensemble neural spiking activity. The SSPPF and SDPPF are constructed using, respectively, Gaussian and steepest descent approximations to the standard Bayes and Chapman-Kolmogorov (BCK) system of filter equations. To extend these approaches for constructing point process adaptive filters, we develop sequential Monte Carlo (SMC) approximations to the BCK equations in which the SSPPF and SDPPF serve as the proposal densities. We term the two new SMC point process filters SMC-PPFs and SMC-PPFD, respectively. We illustrate the new filter algorithms by decoding the wind stimulus magnitude from simulated neural spiking activity in the cricket cercal system. The SMC-PPFs and SMC-PPFD provide more accurate state estimates at low number of particles than a conventional bootstrap SMC filter algorithm in which the state transition probability density is the proposal density. We also use the SMC-PPFs algorithm to track the temporal evolution of a spatial receptive field of a rat hippocampal neuron recorded while the animal foraged in an open environment. Our results suggest an approach for constructing point process adaptive filters using SMC methods.

A novel three-phase trajectory informed search methodology for global optimization

A new deterministic method for solving a global optimization problem is proposed. The proposed method consists of three phases. The first phase is a typical local search to compute a local minimum. The second phase employs a discrete sup-local search to locate a so-called sup-local minimum taking the lowest objective value among the neighboring local minima. The third phase is an attractor-based global search to locate a new point of next descent with a lower objective value. The simulation results through well-known global optimization problems are shown to demonstrate the efficiency of the proposed method.

A Hybrid Descent Method for Global Optimization

In this paper, a hybrid descent method, consisting of a simulated annealing algorithm and a gradient-based method, is proposed. The simulated annealing algorithm is used to locate descent points for previously converged local minima. The combined method has the descent property and the convergence is monotonic. To demonstrate the effectiveness of the proposed hybrid descent method, several multi-dimensional non-convex optimization problems are solved. Numerical examples show that global minimum can be sought via this hybrid descent method.

Mutations in the gene encoding KRIT1, a Krev-1/rap1a binding protein, cause cerebral cavernous malformations (CCM1).

Cerebral cavernous malformations (CCM) are congenital vascular anomalies of the brain that can cause significant neurological disabilities, including intractable seizures and hemorrhagic stroke. One locus for autosomal dominant CCM ( CCM1 ) maps to chromosome 7q21-q22. Recombination events in linked family members define a critical region of approximately 2 Mb and a shared disease haplotype associated with a presumed founder effect in families of Mexican-American descent points to a potentially smaller region of interest. Using a genomic sequence-based positional cloning strategy, we have identified KRIT1, encoding a protein that interacts with the Krev-1/rap1a tumor suppressor, as the CCM1 gene. Seven different KRIT1 mutations have been identified in 23 distinct CCM1 families. The identical mutation is present in 16 of 21 Mexican-American families analyzed, substantiating a founder effect in this population. Other Mexican-American and non-Hispanic Caucasian CCM1 kindreds harbor other KRIT1 mutations. Identification of a common Mexican-American mutation has potential clinical significance for presymptomatic diagnosis of CCM in this population. In addition, these data point to a key role for the Krev-1/rap1a signaling pathway in angiogenesis and cerebrovascular disease.

Dynamic tunneling technique for efficient training of multilayer perceptrons

A new efficient computational technique for training of multilayer feedforward neural networks is proposed. The proposed algorithm consists two learning phases. The first phase is a local search which implements gradient descent, and the second phase is a direct search scheme which implements dynamic tunneling in weight space avoiding the local trap thereby generates the point of next descent. The repeated application of these two phases alternately forms a new training procedure which results into a global minimum point from any arbitrary initial choice in the weight space. The simulation results are provided for five test examples to demonstrate the efficiency of the proposed method which overcomes the problem of initialization and local minimum point in multilayer perceptrons.