Miracle-dz/UniFAR-FTUs huggingface dataset:
Trigonometrische Reihen und Potenzreihen mit mehrfach monotoner Koeffizientenfolge

d.h. mit trigonometrischen Reiken und Potenzreilen. Hier durchlauft m entweder die Gesamtheit der nichtnegativen ganzen Zahlen 0, 1, 2, 3, . oder aber diese von einer bestimmten positiven ganzen Zahl n + 1 angefangen, d.h. m=n+1, n+2, n+3, . Im letzteren Falle nenne ich diese Reihen Restreihen, genauer trigonometriscie Restreiken oder Potenzrestreihen. Eingehend werden auch die Falle untersucht, in denen m entweder nur alle nichtnegativen geraden Zahlen, oder alle positiven ungeraden Zahlen durchlauft, bzw. diese nur von einer bestimmten angefangen. Schliesslich wird, wenn auch nur kurz, von Polynomen entsprechender Form die Rede sein, wo also die Koeffizienten cm von einem gewissen Index an gleich Null sind. 2. Die Grundvoraussetzung wird in der Regel die sein, dass die Koeffizientenfolge, welche die zugelassenen Indizes aufweist, und die ich immer mit
Two Ferulic Acid Derivatives Inhibit Neuroinflammatory Response in Human HMC3 Microglial Cells via NF-κB Signaling Pathway

Various physiological and pathological changes are related to the occurrence and development of neurodegenerative diseases. Neuroinflammation is a major trigger and exacerbation of neurodegenerative diseases. One of the main symptoms of neuritis is the activation of microglia. Thus, to alleviate the occurrence of neuroinflammatory diseases, an important method is to inhibit the abnormal activation of microglia. This research evaluated the inhibitory effect of trans-ferulic acid (TJZ-1) and methyl ferulate (TJZ-2), isolated from Zanthoxylum armatum, on neuroinflammation, by establishing the human HMC3 microglial cell neuroinflammation model induced by lipopolysaccharide (LPS). The results showed both compounds significantly inhibited the production and expression of nitric oxide (NO), tumor necrosis factor-α (TNF-α), and interleukin-1β (IL-1β) contents, and increased the level of anti-inflammatory factor β-endorphin (β-EP). Furthermore, TJZ-1 and TJZ-2 can inhibit LPS-induced activation of nuclear factor kappa B (NF-κB). It was found that of two ferulic acid derivatives, both had anti-neuroinflammatory effects by inhibiting the NF-κB signaling pathway and regulating the release of inflammatory mediators, such as NO, TNF-α, IL-1β, and β-EP. This is the first report that demonstrates that TJZ-1 and TJZ-2 had inhibitory effects on LPS-induced neuroinflammation in human HMC3 microglial cells, which indicates that two ferulic acid derivates from Z. armatum could be used as potential anti-neuroinflammatory agents.
Analysis of spectrally resolved white light interferometry by Hilbert transform method

Scanning White Light Interferometry (SWLI) is a tool for measuring discontinuous surface profile. A short coherence length white light source is used in the interferometer so that the fringes are localized in the vicinity of zero Optical Path Difference (OPD). The object surface is scanned along the height axis to get the height variation over the object field. Another technique using white light is Spectrally Resolved White Light Interferometry (SRWLI) in which the white light interferogram is spectrally decomposed by a spectrometer. The interferogram displayed at the exit plane of the spectrometer has a continuous variation of wavelength along the chromaticity axis. This interferogram encodes the phase as a function of wave number. For a given OPD, the phase is different for different spectral component of the source. The OPD can be determined as the slope of the phase versus wave number linear fit. To determine the phase of the different spectral components, temporal phase shifting technique which typically uses five frames has been proposed. Since the OPD is related to the height of the test object at a point, a line profile of the object can be determined. In this paper we discuss the Hilbert Transform method for determination of phase in SRWLI. This procedure requires only one spectrally resolved white light interferogram.
Optimal adaptive clutter filtering in color flow imaging

Regression filters provide an efficient method for clutter rejection. The clutter is removed by subtracting the projection of the N-dimensional signal vector into a K-dimensional subspace containing the low frequency clutter signal. With a predefined set of basis vectors, e.g. the Legendre polynomials, the filter cut-off frequency can be adjusted by varying K. In this study the correlation matrix of the clutter signal is estimated by spatial averaging. The eigenvectors corresponding to the K largest eigenvalues are an optimal basis for the clutter space, and the corresponding filter have maximum clutter attenuation. Since stationarity is not assumed, the filter will also adapt to nonlinear tissue movement. Nonlinear movement often occurs when the operator moves the probe in search for blood vessels, and causes artifacts when a standard clutter filter is used. Digital RF data from the carotid artery was recorded with transmit frequency 5.7 MHz and PRF=5 kHz. Doppler angio images were generated by displaying the power of the filtered signal as gray scale images. In a frame with extensive probe movement, the clutter to blood ratio was measured by comparing the mean power in a region with strong tissue echo and regions inside blood vessels. The signal-to-clutter ratio was significantly improved by the adaptive filter. With N=9 and K=3 the improvement was 12.9 dB.
Improving the Visualization of the Adrenal Veins Using Virtual Monoenergetic Images from Dual-Energy Computed Tomography before Adrenal Venous Sampling

(1) Background: This study explored the optimal energy level in advanced virtual monoenergetic images (VMI+) from dual-energy computed tomography angiography (DE-CTA) for adrenal veins visualization before adrenal venous sampling (AVS). (2) Methods: Thirty-nine patients were included in this prospective single-center study. The CT value, noise, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were measured in both adrenal veins and abdominal solid organs and were then compared between VMI+ within the range of 40–80 kiloelectron volt (keV). The visualization rate of the adrenal veins and the overall image quality of solid organs were subjectively compared among different keV VMI+. The AVS success rate was recorded for 20 patients. (3) Results: For the adrenal veins, 40 keV VMI+ had the peak CT value, noise and CNR (p < 0.05). Subjectively, the visualization rate was the highest at 40 keV (100% for the right adrenal vein, and 97.4% for the left adrenal vein) (p < 0.05). For solid organs, the CT value, noise and CNR at 50 keV were lower than those at 40 keV (p < 0.05), but the SNR was similar between 40 keV and 50 keV. The overall subjective image quality of solid organs at 50 keV was the best (p < 0.05). The AVS success rate was 95%. (4) Conclusions: For VMI+, 40 keV was the preferential energy level to obtain a high visualization rate of the adrenal veins and a high success rate of AVS, while 50 keV was the favorable energy level for the depiction of abdominal organs.
Double-Reinforced Fish Gelatin Composite Scaffolds for Osteochondral Substitutes

Genipin crosslinked composite blends of fish gelatin/kappa-carrageenan (fG/κC) with different concentrations of graphene oxide (GO) for osteochondral substitutes were prepared by a simple solution-blending method. The resulting structures were examined by micro-computer tomography, swelling studies, enzymatic degradations, compressions tests, MTT, LDH, and LIVE/DEAD assays. The derived findings revealed that genipin crosslinked fG/κC blends reinforced with GO have a homogenous morphology with ideal pore dimensions of 200–500 µm for bones alternative. GO additivation with a concentration above 1.25% increased the blends’ fluid absorption. The full degradation of the blends occurs in 10 days and the gel fraction stability increases with GO concentration. The blend compression modules decrease at first until fG/κC GO3, which has the least elastic behavior, then by raising the GO concentration the blends start to regain elasticity. The MC3T3-E1 cell viability reveals less viable cells with the increase of GO concentration. The LDH together with the LIVE/DEAD assays reports a high concentration of live and healthy cells in all types of composite blends and very few dead cells at the higher GO content.
Fabrication of large periodic arrays of AlGaAs microdisks by laser-interference lithography and selective etching

By laser-interference lithography, reactive-ion etching, and selective wet-chemical etching using a citric acid-based solution, we have fabricated large periodic arrays of AlGaAs microdisks with periods of 4 μm and disk diameters between 1.5 and 2 μm. The arrays are characterized by temperature-dependent photoluminescence spectroscopy. Taking into account the below-threshold absorption of the quantum wells inside the disks, we get disk quality factors close to the theoretical maximum value. We demonstrate that our technique allows one also to produce one-dimensionally or two-dimensionally coupled arrays of microdisks.
3D mesh optimization based on contour recovery and least-squares vertex movement allocation

In order to solve the problems of edge and plane passivation and inaccurate distribution of pixel power to vertex in 3D mesh optimization using image matching best surface estimation method, a 3D mesh method based on contour restoration and least square vertex movement is proposed to optimize the initial mesh. Firstly, large scale simplification and subdivision method are combined, the global contour of the level of detail in mesh is restored, and then the vertex gradient accumulation of the least square method is used to allocate the moving amount of the energy of the object in the triangle to the three vertices more accurately. Finally, the smoothing term based on curvature is used to replace the homogenized Laplacian smoothing. The mesh shape is retained while the matching noise is filtered to achieve more refined mesh optimization. After the experimental verification, the feasibility of this method is proved.
Spherically symmetric models for x-ray damage and the movement of electrons produced in non-spherically symmetric targets such as bio-molecules

This paper develops spherically symmetric models as well as a more accurate model for the calculation of the damage and the movement of free and quasi-free electrons which are produced from the irradiation of x-ray free electron lasers (XFELs). The behaviour of free and quasi-free electrons is studied for targets with various shapes such as spheres and ellipsoids by treating the space distribution of free and quasi-free electrons. Furthermore, the limits of the application of spherically symmetric models to various shapes are discussed. The spherically symmetric model developed here is also applied to a bio-molecule. The results obtained are useful for the analysis of the three-dimensional structures of large bio-molecules in the experiments of XFELs.
Area-time complexity for VLSI

The complexity of the Discrete Fourier Transform (DFT) is studied with respect to a new model of computation appropriate to VLSI technology. This model focuses on two key parameters, the amount of silicon area and time required to implement a DFT on a single chip. Lower bounds on area (A) and time (T) are related to the number of points (N) in the DFT: AT2≥ N2/16. This inequality holds for any chip design based on any algorithm, and is nearly tight when T = θ(N1/2) or T = θ(log N). A more general lower bound is also derived: ATx = Ω(N1+x/2), for 0≤×≤2.
An On-Chip Fully Connected Neural Network Training Hardware Accelerator Based on Brain Float Point and Sparsity Awareness

In recent years, deep neural networks (DNNs) have brought revolutionary progress in various fields with the advent of technology. It is widely used in image pre-processing, image enhancement technology, face recognition, voice recognition, and other applications, gradually replacing traditional algorithms. It shows that the rise of neural networks has led to the reform of artificial intelligence. Since neural network algorithms are computationally intensive, they require GPUs or accelerated hardware for real-time computation. However, the high cost and high power consumption of GPUs result in low energy efficiency. It recently led to much research on accelerated digital circuit hardware design for deep neural networks. In this paper, we propose an efficient and flexible neural network training processor for fully connected layers. Our proposed training processor features low power consumption, high throughput, and high energy efficiency. It uses the sparsity of neuron activations to reduce the number of memory accesses and memory space to achieve an efficient training accelerator. The proposed processor uses a novel reconfigurable computing architecture to maintain high performance when operating Forward Propagation and Backward Propagation. The processor is implemented in Xilinx Zynq UltraSacle+MPSoC ZCU104 FPGA, with an operating frequency of 200MHz and power consumption of 6.444W, and can achieve 102.43 GOPS.
Presynaptic spike-driven plasticity based on eligibility trace for on-chip learning system

Introduction Recurrent spiking neural network (RSNN) performs excellently in spatio-temporal learning with backpropagation through time (BPTT) algorithm. But the requirement of computation and memory in BPTT makes it hard to realize an on-chip learning system based on RSNN. In this paper, we aim to realize a high-efficient RSNN learning system on field programmable gate array (FPGA). Methods A presynaptic spike-driven plasticity architecture based on eligibility trace is implemented to reduce the resource consumption. The RSNN with leaky integrate-and-fire (LIF) and adaptive LIF (ALIF) models is implemented on FPGA based on presynaptic spike-driven architecture. In this architecture, the eligibility trace gated by a learning signal is used to optimize synaptic weights without unfolding the network through time. When a presynaptic spike occurs, the eligibility trace is calculated based on its latest timestamp and drives synapses to update their weights. Only the latest timestamps of presynaptic spikes are required to be stored in buffers to calculate eligibility traces. Results We show the implementation of this architecture on FPGA and test it with two experiments. With the presynaptic spike-driven architecture, the resource consumptions, including look-up tables (LUTs) and registers, and dynamic power consumption of synaptic modules in the on-chip learning system are greatly reduced. The experiment results and compilation results show that the buffer size of the on-chip learning system is reduced and the RSNNs implemented on FPGA exhibit high efficiency in resources and energy while accurately solving tasks. Discussion This study provides a solution to the problem of data congestion in the buffer of large-scale learning systems.
Maternal Pre-Pregnancy Nutritional Status and Infant Birth Weight in Relation to 0–2 Year-Growth Trajectory and Adiposity in Term Chinese Newborns with Appropriate Birth Weight-for-Gestational Age

Being born with appropriate weight-for-gestational age (AGA, ~80% of newborns) is often considered as low risk for future obesity. This study examined differential growth trajectories in the first two years by considering pre- and peri-natal factors among term-born AGA infants. We prospectively investigated 647 AGA infants and their mothers enrolled during 2012–2013 in Shanghai, China, and obtained repeated anthropometric measures at ages 42 days, 3, 6, 9, and 18 months from postnatal care records, and onsite measurements at age 1 and 2 years (skinfold thickness, mid-upper arm circumference (MUAC)). Birthweight was classified into sex-and gestational age-specific tertiles. Among mothers, 16.3% were overweight/obese (OWO), and 46.2% had excessive gestational weight gain (GWG). The combination of maternal prepregnancy OWO and high birthweight tertile identified a subset of AGA infants with 4.1 mm higher skinfold thickness (95% CI 2.2–5.9), 1.3 cm higher MUAC (0.8–1.7), and 0.89 units higher weight-for-length z-score (0.54, 1.24) at 2 years of age with adjustment for covariates. Excessive GWG was associated with higher child adiposity measures at 2 years of age. AGA infants manifested differential growth trajectories by the combination of maternal OWO and higher birthweight, suggesting that additional attention is needed for those “at increased risk” of OWO in early intervention.
Adaptive Spatial Transformation Networks for Periocular Recognition

Periocular recognition has emerged as a particularly valuable biometric identification method in challenging scenarios, such as partially occluded faces due to COVID-19 protective masks masks, in which face recognition might not be applicable. This work presents a periocular recognition framework based on deep learning, which automatically localises and analyses the most important areas in the periocular region. The main idea is to derive several parallel local branches from a neural network architecture, which in a semi-supervised manner learn the most discriminative areas in the feature map and solve the identification problem solely upon the corresponding cues. Here, each local branch learns a transformation matrix that allows for basic geometrical transformations (cropping and scaling), which is used to select a region of interest in the feature map, further analysed by a set of shared convolutional layers. Finally, the information extracted by the local branches and the main global branch are fused together for recognition. The experiments carried out on the challenging UBIRIS-v2 benchmark show that by integrating the proposed framework with various ResNet architectures, we consistently obtain an improvement in mAP of more than 4% over the “vanilla” architecture. In addition, extensive ablation studies were performed to better understand the behavior of the network and how the spatial transformation and the local branches influence the overall performance of the model. The proposed method can be easily adapted to other computer vision problems, which is also regarded as one of its strengths.
Passivity Fractional-Order Sliding-Mode Control of Grid-Connected Converter With LCL Filter

Grid-connected converter with LCL filter plays an important role in renewable power generation systems. However, existing control techniques face several challenges in practice (e.g., difficulty in parameter design, slow dynamic response, and poor robustness under slowly time-varying filter parameter, deadtime of power devices, and external disturbances). To solve these difficulties, a passivity-based fractional-order sliding-mode control (PBC-FOSMC) hybrid controller, which combines the merits of passivity-based control (PBC) and FOSMC, is proposed, where the inputs of the passive controller are the outputs of the FOSMC controller. First, a passive current controller is designed based on the Euler–Lagrange model established by damping injection according to the PBC theory, which can make the system automatically converge to meet the energy dissipation law. Second, an FOSMC controller is designed to further enhance the system robustness to counter disturbances and compensate the reference current accuracy of PBC. The introduced fractional order can successfully suppress the undesired chattering due to the switching behaviors in conventional sliding-mode control. Third, the hybrid PBC-FOSMC controller is derived and system stability is analyzed. Finally, experimental results under 10-kW prototype validate the excellent performance of the proposed method in terms of robustness, dynamic performance, strong perturbation rejection ability, etc., and the desired control targets are achieved.
Social and Health Determinants of Quality of Life of Community-Dwelling Older Adults in Malaysia

Quality of life (QOL) of older adults is a complex issue that requires an understanding of the intersection between socioeconomic and health factors. A poor quality of life (QOL) is frequently reported as sub-optimal among older adults whereby concerted and collective actions are required through an evidence-based approach. Hence, this cross-sectional study aims to determine the social and health predictors of the QOL of a community-dwelling older adult Malaysian population through a quantitative household survey using multi-stage sampling. A total of 698 respondents aged 60 years old and older were recruited and the majority of them had a good quality of life. Risk of depression, disability, living with stroke, low household income, and lack of social network were identified as the predictors of a poor QOL among the community-dwelling older Malaysians. The identified predictors for QOL provided a list of priorities for the development of policies, strategies, programmes, and interventions to enhance the QOL of the community-dwelling older Malaysians. Multisectoral approaches, especially collective efforts from both social and health sectors, are required to address the complexities of the ageing issues.
Fermented Lettuce Extract Containing Nitric Oxide Metabolites Attenuates Inflammatory Parameters in Model Mice and in Human Fibroblast-Like Synoviocytes

Lettuce (Lactuca sativa L.) contains various bioactive compounds that can reduce the severity of inflammatory diseases. This study aimed to identify therapeutic effects and underlying mechanisms of fermented lettuce extract (FLE) containing stable nitric oxide (NO) on collagen-induced arthritis (CIA) in mice and fibroblast-like synoviocytes (MH7A line) from patients with rheumatoid arthritis (RA). DBA/1 mice were immunized with bovine type II collagen and orally administered FLE for 14 days. On day 36, mouse sera and ankle joints were collected for serological and histological analysis, respectively. Consuming FLE inhibited RA development, suppressing pro-inflammatory cytokine productions, synovial inflammation, and cartilage degradation. The therapeutic effects of FLE in CIA mice were similar to those of methotrexate (MTX), which is typically used to treat RA. In vitro, FLE suppressed the transforming growth factor-β (TGF-β)/Smad signaling pathway in MH7A cells. We also demonstrated that FLE inhibited TGF-β-induced cell migration, suppressed MMP-2/9 expression, inhibited MH7A cell proliferation, and increased the expression of autophagy markers LC3B and p62 in a dose-dependent manner. Our data suggest that FLE could induce autophagosome formations in the early of stages of autophagy while inhibiting their degradation in the later stages. In conclusion, FLE is a potential therapeutic agent for RA.
On the theory of optical activity

The scattering matrix techniques developed in a previous series of papers (Atkins & Barron 1968a, b) are applied to the problem of formulating a theory of optical activity on the basis of Kirkwood’s independent group model. We restrict ourselves to the case of non-degenerate subsystems interacting in the dipole approximation, though those restrictions are convenient simplifications, not essential features. Retardation effects are explicitly evaluated and shown to be negligible over relevant intramolecular distances. The translation invariance of the theory is demonstrated. The unique feature of the theory is that unlike previous theories it is developed without reference to the Rosenfeld equation for the rotation angle. Furthermore, it provides a physically appealing interpretation of the ‘coupled oscillator’ terms in the expression.