Miracle-dz/UniFAR-FTUs huggingface dataset:
A Case of Mucormycosis Obstructing the Trachea

Mucormycosis is the common name given to several different diseases caused by fungi of the order Mucorales. The mucoraceae are ubiquitous fungi and are common inhabitants of decaying matter. In contrast to the widespread distribution of these fungi, disease in humans is limited, in most cases, to people with severe immunocompromised, diabetes mellitus, or trauma. 1be fungus gains entry to the body through the respiratory tract. The spores are presumably deposited in the nasal turbinates and may be inhaled into the pulmonary alveoli. The manifestations of mucormycosis are rhinocerebral, pulmonary, cutaneous, gastrointestinal, central nervous system, and miscellaneous. Sporadic reports can be found of mucormycosis involving other areas : heart, bones, kidney, bladder, mediastinum, and trachea. However, isolated tracheal mucormycosis is very rare. Therefore, we report a 57-year old, noninsulin dependent diabetic woman who presented with acute, severe degree of upper airway obstruction due to isolated mucormycosis of the trachea.
Asymptotic theory and inference of predictive mean matching imputation using a superpopulation model framework

Predictive mean matching imputation is popular for handling item nonresponse in survey sampling. In this article, we study the asymptotic properties of the predictive mean matching estimator for finite‐population inference using a superpopulation model framework. We also clarify conditions for its robustness. For variance estimation, the conventional bootstrap inference is invalid for matching estimators with a fixed number of matches due to the nonsmoothness nature of the matching estimator. We propose a new replication variance estimator, which is asymptotically valid. The key strategy is to construct replicates directly based on the linear terms of the martingale representation for the matching estimator, instead of individual records of variables. Simulation studies confirm that the proposed method provides valid inference.
Ultrasharp LSPR Temperature Sensor Based on Grapefruit Fiber Filled With a Silver Nanoshell and Liquid

A grapefruit fiber (GF) based temperature sensor with ultrasharp localized surface plasmon resonance (LSPR) is designed and characterized by finite element method. The silver nanoshell (SNS), formed by a dielectric core and a silver shell together with chloroform own large thermo-optic coefficient is sealed into one large air hole of the GF as the core sensing region. Benefiting from the plasmon hybridization in SNS and birefringence, the designed sensor can support two separate ultrasharp resonance peaks with full width half maximum (FWHM) only 4.6 nm in orthogonal polarizations, which is the narrowest to our best knowledge and the highest figure of merit (FOM) of 0.43/°C can be obtained. Results show that by following the shifts of two polarized peaks, dual channel temperature sensing can be realized simultaneously at 5 °C–55 °C with the maximum sensitivity of −17.8 nm/°C and −17.2 nm/°C, about 3–17 times higher than other same type works. Moreover, the influences of structure parameters of SNS like deposition position, number, diameter, and length on the sensing performances are discussed in detail.
Learning better physics: a machine learning approach to lattice gauge theory

In this work we explore how lattice gauge theory stands to benefit from new developments in machine learning, and look at two specific examples that illustrate this point. We begin with a brief overview of selected topics in machine learning for those who may be unfamiliar, and provide a simple example that helps to show how these ideas are carried out in practice. After providing the relevant background information, we then introduce an example of renormalization group (RG) transformations, inspired by the tensor RG, that can be used for arbitrary image sets, and look at applying this idea to equilibrium configurations of the two-dimensional Ising model. The second main idea presented in this thesis involves using machine learning to improve the efficiency of Markov Chain Monte Carlo (MCMC) methods. Explicitly, we describe a new technique for performing Hamiltonian Monte Carlo (HMC) simulations using an alternative leapfrog integrator that is parameterized by weights in a neural network. This work is based on the L2HMC (‘Learning to Hamiltonian Monte Carlo’) algorithm introduced in [1].
ALGORITMA KLASIFIKASI C4.5 BERBASIS PARTICLE SWARM OPTIMIZATION UNTUK PREDIKSI PENERIMA BANTUAN PANGAN NON TUNAI

Program Bantuan Pangan Non Tunai (BPNT) merupakan peralihan dari program RASTRA atau RASKIN. BPNT adalah bantuan sosial pangan dalam bentuk non tunai dari pemerintah yang diberikan kepada Keluarga Penerima Manfaat (KPM) setiap bulannya melalui mekanisme akun elektronik yang digunakan hanya untuk membeli bahan pangan di pedagang bahan pangan atau e-warong. Bahan pangan dalam program BPNT ini adalah beras dan atau telur. Salah satu kesulitan yang terkadang dihadapi oleh pemerintah dalam penyaluran BPNT adalah proses pembagian bantuan sosial yang tidak merata dan tidak tepat sasaran. Munculnya masalah tersebut diakibatkan kurangnya pertimbangan atau kemantapan analisis penerima manfaat BPNT dalam menentukan kelayakan pada saat mengajukan pendataan. Oleh karena itu perlu dilakukan analisis lanjutan sehingga dapat mengetahui kelayakan dari suatu permasalahan prediksi penerima manfaat BPNT. Melalui hasil analisis pendataannya, dapat diketahui apakah warga layak atau tidak untuk menerima BPNT. Dari permasalahan yang ada digunakan metode klasifikasi untuk memprediksi kelayakan penerima manfaat BPNT yaitu dengan menggunakan dua model, model algoritma klasifikasi C4.5 dan model algoritma klasifikasi C4.5 berbasis Particle Swarm Optimization (PSO) tanpa pruning . Karena proses pengujian yang dilakukan tanpa pruning menghasilkan akurasi yang lebih tinggi dibandingkan dengan yang menggunakan pruning. Setelah dilakukan pengujian dengan dua model tersebut didapatkan hasil yaitu algoritma klasifikasi C4.5 menghasilkan nilai akurasi sebesar 98,56% dan nilai AUC sebesar 0,979 dengan tingkat diagnosa Excellent Classification , namun setelah dilakukan optimasi dengan algoritma klasifikasi C4.5 berbasis Particle Swarm Optimization nilai akurasi sebesar 98,92% dan nilai AUC sebesar 0,988 dengan tingkat diagnosa Excellent Classification . Sehingga kedua metode tersebut memiliki perbedaan tingkat akurasi sebesar 0,36%.
Seismic Signal Denoising Using $f-x$ Variational Mode Decomposition

Ground roll is a common coherent noise in land seismic acquisition, which is usually characterized by high amplitudes, low frequencies, and low propagation velocities. Removing ground roll is a key procedure in seismic data processing. In this letter, we develop a novel ground roll suppression approach termed f-x variational mode decomposition (VMD), which has the characteristic of a frequency-dependent, high-wavenumber filter. This new algorithm first applies VMD on constant frequency slices in the frequency-offset domain within the frequency range of ground roll in order to extract the ground roll, and then the denoised data can be obtained by subtracting them adaptively from the original data. A synthetic example is given to demonstrate that the proposed method is able to suppress ground roll well and preserve seismic reflections effectively compared with other conventional techniques such as frequency filtering and f-k filtering. Application of the method on field data further demonstrates the potential of the f-x VMD algorithm in industrial applications.
Adaptive Fuzzy Tracking Control for a Class of Strict-Feedback Nonlinear Systems With Time-Varying Input Delay and Full State Constraints

This article investigates the adaptive fuzzy tracking control problem for a class of strict-feedback nonlinear systems with time-varying input delay and full state constraints. By using state vector transformation and barrier Lyapunov function techniques, the effect of time-varying input delay and full state constraints are compensated. The unknown nonlinear functions are approximated by utilizing fuzzy logic systems, and then a novel adaptive fuzzy backstepping control strategy is proposed to guarantee that the closed-loop nonlinear system is semiglobally ultimately uniformly bounded. Finally, two simulation examples of an inverted pendulum system and three-degrees-of-freedom helicopter nonlinear system are studied to verify the effectiveness of the proposed control strategy.
Small time local controllability of driftless nonholonomic systems in a task-space

In this paper a small time local controllability, naturally deﬁned in a conﬁguration space, is transferred into a task-space. It was given its analytical characterization and practical impli-cations. A special attention was put on singular conﬁgurations. Theoretical considerations were illustrated with two calculation examples. An extensive comparison of the proposed construction with the controllability deﬁned in an endogenous conﬁguration space approach was presented pointing out to their advantages and disadvantages.
Physical properties of hexagonal BaPtAs with noncentrosymmetric SrPtSb-type and centrosymmetric YPtAs-type crystal structures: Effects of spin-orbit coupling

Recently, superconductivity in BaPtAs with the noncentrosymmetric SrPtSb and the centrosymmetric YPtAs-structure has been discovered. Its noncentrosymmetric and centrosymmetric structures make BaPtAs a charming compound for searching the effect of spin-orbit coupling (SOC) onto superconductors with broken and preserved spatial inversion symmetry. We have investigated the effect of SOC on the physical and electron-phonon interaction properties of these phases of BaPtAs by using the generalized gradient approximation of the density functional theory and the plane-wave pseudopotential method. The inclusion of SOC has moderate effect on the elastic and electronic properties, and on phonon frequencies for both phases of BaPtAs. However, SOC makes an opposite impact on their Eliashberg spectral function. For the noncentrosymmetric phase, this coupling decreases the strength of dominant peaks of Eliashberg spectral function, reducing the values of average electron-phonon coupling and superconducting transition temperature. The scenario is exactly opposite for the centrosymmetric phase. For both phases, the hybridized Pt-As vibrations play a strongly significant role in determining their electron-phonon interaction properties since their electronic states have the dominant contribution near the Fermi level. The superconducting temperature is estimated to be 2.87 K for the SrPtSb phase and 2.38 K for the YPtAs phase. These values compare well with the reported measured values of 2.8 K and 2.1--3.0 K, respectively.
Spurious Peak Elimination for Sliding Spotlight SAR Imaging With Steering Angle Quantization

This letter proposes a method for eliminating the paired-echo-like spurious peaks in sliding spotlight synthetic aperture radar (SAR) images caused by the electronic step-by-step azimuth beam steering. The basic idea of the method originates from the extended ideal filtering for suppressing the SAR azimuth ambiguities. The concept of deconvolving filtering is further extended and a new form of deconvolving filter is presented. Using the new filtering, not only the first and odd spurious peaks of point-like targets but also the even spurious peaks can be removed all at once from the detected SAR images. Simulated data from a sliding-spotlight SAR system with quantized azimuth steering are used to verify the improvement in image quality by using the new method.
Accounting for time‐dependent treatment use when developing a prognostic model from observational data: A review of methods

Failure to account for time‐dependent treatment use when developing a prognostic model can result in biased future predictions. We reviewed currently available methods to account for treatment use when developing a prognostic model. First, we defined the estimands targeted by each method and examined their mechanisms of action with directed acyclic graphs (DAGs). Next, methods were implemented in data from 1,906 patients; 325 received selective β‐blockers (SBBs) during follow‐up. We demonstrated seven Cox regression modeling strategies: (a) ignoring SBB treatment; (b) excluding SBB users or (c) censoring them when treated; (d) inverse probability of treatment weighting after censoring (IPCW), including SBB treatment as (e) a binary or (f) a time‐dependent covariate; and (g) marginal structural modeling (MSM). Using DAGs, we demonstrated IPCW and MSM have the best properties and target a similar estimand. In the case study, compared to (a), approaches (b) and (e) provided predictions that were 1% and 2% higher on average. Performance (c‐statistic, Brier score, calibration slope) varied minimally between approaches. Our review of methods confirmed that ignoring treatment is theoretically inferior, but differences between the prediction models obtained using different methods can be modest in practice. Future simulation studies and applications are needed to assess the value of applying IPCW or MSM to adjust for treatments in different treatment and disease settings.
The Littlewood–Paley Theory: A Common Thread of Many Works in Nonlinear Analysis

In this article we present the Littlewood-Paley theory and illustrate the effectiveness of this microlocal analysis tool in the study of partial differential equations, in a context which is the least technical possible. As we shall see below, the Littlewood-Paley theory provides a robust approach not only to the separate study of the various regimes of solutions to nonlinear partial differential equations, but also to the fine study of functional inequalities, and to make them accurate. 1. The Littlewood-Paley theory : a tool that has become indispensable The Littlewood-Paley theory is a localization procedure in the frequency space that, since about three decades ago, has established itself as a very powerful tool in harmonic analysis. The first goal of this text is to present it in a way as simple as possible 1. Its basic idea is contained in two fundamental inequalities known as Bernstein's inequalities, that describe some properties of functions whose Fourier transform have compact support. The first inequality says that, for a tempered distribution 2 in R d whose Fourier transform is supported in a annulus of size λ, to differentiate first and then take the L p norm is the same as to apply a homothety of ratio λ on the L p norm. In the L 2 setting this remarkable property is an easy consequence of the action of the Fourier transform on derivatives and of the Fourier-Plancherel formula. The proof in the case of general L p spaces uses Young's inequalities and the fact that the Fourier transform of a convolution is the product of the Fourier transforms. In the other hand, the second inequality tells us that, for such a distribution, the change from the L p norm to the L q norm, with q ≥ p ≥ 1, costs λ d 1 p − 1 q , which must be understood as a Sobolev embedding. It is proved like the first inequality, using Young's inequalities and the relation between the Fourier transform and the convolution product. Fourier Analysis is at the heart of the Littlewood-Paley theory, which has inspired a large number of my works. It was in conducting experiments on the propagation of heat that Joseph Fourier at the end of the 18th century opened the door to that theory, which was hugely expanded on the 20th century and intervenes in the majority of branches of Physics. In this theory having the name of its creator, one performs the frequency analyis of a function f of L 1 (R d) by the formula : f (ξ) = R d e −ix·ξ f (x) dx .
Joint-designed achromatic diffractive optics for full-spectrum computational imaging

Diffractive optical elements (DOEs) are promising lens candidates in computational imaging because they can drastically reduce the size and weight of image systems. The inherent strong dispersion hinders the direct use of DOEs in full spectrum imaging, causing an unacceptable loss of color fidelity. State-of-the-art methods of designing diffractive achromats either rely on hand-crafted point spread functions (PSFs) as the intermediate metric, or frame a differential end-to-end design pipeline that interprets a 2D lens with limited pixels and only a few wavelengths. In this work, we investigate the joint optimization of achromatic DOE and image processing using a full differentiable optimization model that maps the actual source image to the reconstructed one. This model includes wavelength-dependent propagation block, sensor sampling block, and imaging processing block. We jointly optimize the physical height of DOEs and the parameters of image processing block to minimize the errors over a hyperspectral image dataset. We simplify the rotational symmetric DOE to 1D profle to reduce the computational complexity of 2D propagation. The joint optimization is implemented using auto differentiation of Tensor ow to compute parameter gradients. Simulation results show that the proposed joint design outperforms conventional methods in preserving higher image fidelity.
On the stability of Micken's type NSFD schemes for generalized Burgers Fisher equation

ABSTRACT We propose exact finite difference scheme (EFD) for Generalized Burgers Fisher (GBF) Equation using solitary wave solution. Moreover a non-standard finite difference (NSFD) scheme is also proposed. The proposed EFD and NSFD scheme works for all This scheme preserves the positivity and boundedness properties which is also discussed here. The method is shown to be stable, consistent and first-order accurate in both space and time. Approximate solutions of the GBF equation are obtained using NSFD scheme and in order to show the accuracy, the computed solutions are compared with the exact solution. Comparisons are shown with the other available methods to indicate that our method gives better result. These methods give accurate results even for relatively bigger values of step size.
Differences and similarities between reinforcement learning and the classical optimal control framework

In this contribution , we discuss Reinforcement Learning as an alternative way to solve optimal control problems. Especially for biomechanical models, well‐established classical techniques can become complex and time‐consuming, because biomechanical models have often much more actuators than degrees of freedom. Furthermore, the solution of such a technique is normally only applicable to this specific setting. This means, that a slightly change of the initial value of the model or the desired end position does make the computed solution useless. We give a short overview to Reinforcement Learning and apply it to an optimal control problem containing the above mentioned challenges. We use an algorithm, which updates the weights and biases of a neural network, which takes the role of a controller, using simulated trajectories of the model generated by the current neural network.
Effect of melatonin on mild Alzheimer's disease in elderly male patients

Objective To investigate the effect of melatonin on mild Alzheimer's disease.Methods Thirty-six male patients with mild AD were randomly assigned to melatonin group or placebo control group.Each subject respectively took melatonin or placebo for 24 weeks.Mini mental state examination(MMSE) and activity of daily living(ADL) chart were used to evaluate patients' cognitive function and activity of daily living before and after treatment.Results There were five subjects departed from the trial,three from melatonin group and two from control group,then the actual number of subject was thirty-one.After treatment for 24 weeks,score of MMSE in control group degraded,at the same time,the score of ADL upgraded.However,the above scores of MMSE and ADL in melatonin group had an opposite change.The diferences in the scores of MMSE or ADL in the two groups was not significant before treatment and 12 weeks after treatment(P0.05),but was significant 24 weeks after treatment(P0.05). Conclusions Taking melatonin for a long time could efficiently improve the scores of MMSE and ADL in elderly male patients with mild AD,and early usage of melatonin in the early stage of AD can efficiently delay the progression of the disease.
Control of carrier injection and transport in quantum dot light emitting diodes (QLEDs) via modulating Schottky injection barrier and carrier mobility

Carrier injection and transport in the quantum dot (QD) layer was modulated by tuning the Schottky injection barrier and mobility of charge transport layers. The analyses indicate that stages of the J-V curve for hole injection must match with those of electron injection to achieve the charge balance in the QD layer. In addition, it was demonstrated that not only the parameters investigated but also other parameters such as charge trap density and energy levels must be considered to enhance the efficiency of the QLEDs. With the improved charge balance in the QD layer, the current efficiency of the quantum dot light-emitting diode display was improved from 26 to 40 cd/A.Carrier injection and transport in the quantum dot (QD) layer was modulated by tuning the Schottky injection barrier and mobility of charge transport layers. The analyses indicate that stages of the J-V curve for hole injection must match with those of electron injection to achieve the charge balance in the QD layer. In addition, it was demonstrated that not only the parameters investigated but also other parameters such as charge trap density and energy levels must be considered to enhance the efficiency of the QLEDs. With the improved charge balance in the QD layer, the current efficiency of the quantum dot light-emitting diode display was improved from 26 to 40 cd/A.
Analysis and Design of Continuous-Time Multivariable Periodic Controllers: An $\mathcal {H}_{\infty }$/LTR-Based Approach

This article proposes a new high-frequency continuous-time two degrees of freedom (DOF) periodic control scheme to compensate linear time-invariant (LTI), square/nonsquare multivariable plants. The proposed scheme is more effective when the plant is nonminimum phase, or has more inputs than outputs. It is proved that, in averaged sense, the design of periodic controller can be recast as designing a 2-DOF LTI controller for an equivalent LTI plant. The conditions for which this equivalent plant becomes minimum phase are also obtained. When this plant is minimum phase and has no more inputs than outputs, the synthesis of the above-mentioned LTI controller is carried out using an linear matrix inequality-based <inline-formula><tex-math notation="LaTeX">$\mathcal {H}_{\infty }$</tex-math></inline-formula>/loop transfer recovery (LTR) approach to achieve linear quadratic regulator (LQR) loop behavior asymptotically. The design method being based on LTR, the reference-input-to-output transfer function of the averaged compensated system becomes same as that using LQR. The periodic controller is shown to exhibit negligible ripples in the plant output, but <inline-formula><tex-math notation="LaTeX">$O(1)$</tex-math></inline-formula> ripples in control input. Suitable examples are considered to show the superiority of the proposed control.