Miracle-dz/UniFAR-FTUs huggingface dataset:
Fabrication of plastic microfluidic components

Plastic components have many advantages, including ease of fabrication, low cost, chemical inertness, lightweight, and disposability. We report on the fabrication of three plastics-based microfluidic components: a motherboard, a dialysis unit, and a metal sensor. Microchannels, headers, and interconnects were produced in thin sheets (≥50 microns) of polyimide, PMMA, polyethylene, and polycarbonate using a direct-write excimer laser micromachining system. Machined sheets were laminated by thermal and adhesive bonding to form leak-tight microfluidic components. The microfluidic motherboard borrowed the `functionality on a chip' concept from the electronics industry and was the heart of a complex microfluidic analytical device. The motherboard platform was designed to be tightly integrated and self-contained (i.e., liquid flows are all confined within machined microchannels), reducing the need for tubing with fluid distribution and connectivity. This concept greatly facilitated system integration and miniaturization. As fabricated, the motherboard consisted of three fluid reservoirs connected to micropumps by microchannels. The fluids could either be pumped independently or mixed in microchannels prior to being directed to exterior analytical components via outlet ports. The microdialysis device was intended to separate electrolytic solutes from low volume samples prior to mass spectrometric analysis. The device consisted of a dialysis membrane laminated between opposed serpentine microchannels containing the sample fluid and a buffer solution. The laminated metal sensor consisted of fluid reservoirs, micro-flow channels, micropumps, mixing channels, reaction channels, and detector circuitry.
THE ABUNDANCES OF THE ELEMENTS IN THE SOLAR PHOTOSPHERE-VI

Summary Low noise photoeleetrie seans of the Rb I resonanee lines (A,\ 7800 and 7947) have been obtained with the Oxford spectrometer. An analysis results in an improved abundanee determination. log N(Rb) = 2,63 on the standard scale where log N(H) = 12'00.
Molecular Evaluation of Ex3 VNTR Polymorphism of the DRD4 Gene in Patients With Autism Spectrum Disorder

Objective Autism Spectrum Disorders (ASDs) are a group of neurodevelopmental disorders that affect social and communication skills. These diseases are characterized by severe communication and social skills disabilities and limited and repetitive activities. The prevalence of these disorders appears to be steadily increasing. It is proposed that the genes involved in the dopamine pathway may play an essential role in the development of autism. In this study, we investigated the possible association between Ex3 VNTR polymorphism of the DRD4 gene and autism spectrum disorders in the Iranian population. Materials & Methods In this case-control study, 97 children with autism and 103 healthy individuals from a northwestern area of Iran as the case and control groups, respectively. After genomic xtraction from peripheral blood samples by the proteinase K method, the polymerase chain reaction (PCR) technique was used to determine the polymorphism genotypes. The data were then coded and analyzed using SPSS version 22 software. Results The study results showed that the allele frequencies differed in the two groups, some of them being statistically significant. The most common allele in both the ASD and the control group was the 700 bp allele, and its frequency was significantly different in the two groups and was more common in the ASD group (p-value=0.0018). The other allele with a statistically different frequency was the 800 bp allele which was less frequent in the ASD group (p-value=0.0017). Conclusion These results suggest a potential association between Ex3 VNTR polymorphism of the DRD4 gene and autism spectrum disorder in the Iranian population. This necessitates further studies for the evaluation of the DRD4 gene.
Permanent magnet array for the magnetic refrigerator

Recent research into the development of magnetic refrigeration (MR) operating at room temperature has shown that it can provide a reliable, energy-efficient cooling system. To enhance the cooling power of the magnetic refrigerator, it is required to use a magnetic refrigerant material with large magnetocaloric effect (MCE) at the appropriate temperature. Most advanced magnetic refrigerant materials show largest MCE at high applied magnetic fields generated by a superconducting magnet. For application of MCE to air conditioners or household refrigerators, it is essential to develop a permanent magnet array to form a compact, strong, and energy-efficient magnetic field generator. Generating a magnetic field well above the remanence of a permanent magnet material is hard to achieve through conventional designs. A permanent magnet array based on a hollow cylindrical flux source is found to provide an appropriate geometry and magnetic field strength for MR applications.
Selecting Article Segment Titles Based on Keyphrase Features and Semantic Relatedness

Nowadays people can find almost all kinds of information they want from the Internet. However, in most cases, users are not willing to find their target among segment among long paragraphs, by spending much time browsing texts. Existing work on topic labeling works effectively and performs well on document categorization, but inadequate for granularity of detailed contents. Thus we propose a method for selecting titles for segments in long documents. We analyze the characteristics of high quality titles for article segments, from the aspect of semantic relatedness between the target segment and related articles as well as other segments. Then we revise three features proposed before. We improve the phraseness feature, for giving appropriate scores for long titles. Meanwhile, we combine the features SimPF and Embedding-vector to enhance the efficiency and rationality. We use Wikipedia articles for experimental evaluations, in which a large number of article segments are titled manually, and a great number of articles lack detailed segment titles. We evaluate scoring functions by where hidden original segment titles are ranked, through precision@K. Through rigorous evaluations, we show an optimum combination of the features.
Qing Fei Hua Xian Decoction ameliorates bleomycin-induced pulmonary fibrosis by suppressing oxidative stress through balancing ACE-AngII-AT1R/ACE2-Ang-(1-7)-Mas axis

Objective(s): We aimed to investigate the preventative effect of Qing Fei Hua Xian Decoction (QFHXD) against pulmonary fibrosis (PF) and its potential mechanisms. Materials and Methods: Bleomycin (BLM)-induced rats were respectively treated with 413.3, 826.6, and 1239.9 mg/kg of QFHXD and prednisone for 28 days. The lung tissues of rats were collected on day 28 for histological and western blotting analysis. Results: QFHXD significantly reduced alveolus inflammation, collagen accumulation, and fibrosis deposition in BLM-induced PF rats (P<0.05). Collagen I and III, vimentin, and α-smooth muscle actin(α-SMA) expression levels were likewise decreased in PF rats treated with QFHXD (P<0.05). Additionally, QFHXD increased the expression of nuclear factor erythroid 2-related factor 2 (Nrf2) while decreasing NADPH oxidase 4 (NOX4) expression (P<0.05). Furthermore, QFHXD suppressed the PF progression by down-regulating Angiotensin-Converting Enzyme (ACE) -Angiotensin II (AngII) -Angiotensin II Type 1 Receptor (AT1R) axis (P<0.01) and up-regulating Angiotensin-Converting Enzyme 2 (ACE2) -Angiotensin-(1-7) (Ang-(1-7)) -Mas axis (P<0.05). Conclusion: QFHXD suppressed inflammatory infiltration and PF brought on by BLM in lung tissues through reducing oxidative stress by maintaining the equilibrium of ACE-AngII-AT1R and ACE2-Ang-(1-7) -Mas axes. This study may provide a novel clinical therapy option for PF.
The effects of mulberry (Morus alba Linn.) leaf supplementation on growth performance, blood parameter, and antioxidant status of broiler chickens under high stocking density

Background and Aim: A stocking density system in boilers is well known for increasing productivity. However, this system increases stress and affects the growth performance of broilers. Mulberry is a valuable plant with therapeutic applications in traditional medicine; moreover, it reduces free radicals and improves growth performance in broilers. This study was conducted to investigate the effects of mulberry on the blood biochemistry parameters and the antioxidant status of broilers exposed to various raising systems. Materials and Methods: Two hundred and seventy-six 3-week-old male broilers were randomly assigned to nine categories composed of three growing systems: Semi-intensive, low stocking density, and high stocking density. Each group was fed with a control diet mixed with and without 10% mulberry leaf extract; the positive control group was provided with vitamin C. During the study, phytochemical screening of mulberry leaf extract, growth performances, hematological parameters, and antioxidant profiles were measured over the 4 weeks of the treatment. Results: In the high stocking density group, lipid peroxidation gradually increased while antioxidant activities decreased; however, the level of lipid peroxidation was reduced, whereas catalase and superoxide dismutase activities were significantly increased. The growth performance and blood biochemistry were improved after being fed with 10% mulberry leaf extract. Conclusion: This finding indicates that mulberry leaf extract reduced oxidative stress, activated antioxidant enzyme activities, and enhanced broilers’ growth performance when raised under stress conditions.
Lipotoxicity-related sarcopenia: a review

A body of literature supports the postulation that a persistent lipid metabolic imbalance causes lipotoxicity, “an abnormal fat storage in the peripheral organs”. Hence, lipotoxicity could somewhat explain the process of sarcopenia, an aging-related, gradual, and involuntary decline in skeletal muscle strength and mass associated with several health complications. This review focuses on the recent mechanisms underlying lipotoxicity-related sarcopenia. A vicious cycle occurs between sarcopenia and ectopic fat storage via a complex interplay of mitochondrial dysfunction, pro-inflammatory cytokine production, oxidative stress, collagen deposition, extracellular matrix remodeling, and life habits. The repercussions of lipotoxicity exacerbation of sarcopenia can include increased disability, morbidity, and mortality. This suggests that appropriate lipotoxicity management should be considered the primary target for the prevention and/or treatment of chronic musculoskeletal and other aging-related disorders. Further advanced research is needed to understand the molecular details of lipotoxicity and its consequences for sarcopenia and sarcopenia-related comorbidities.
On the origin of the 1 keV diffuse X-ray background

High galactic latitude data (b<-15/sup 0/) from the Wisconsin soft X-ray sky survey are used to constrain simple geometric models for the source of the diffuse X-ray background at energies near 1 keV. Two extended enhanced features are apparent in the intensity maps. One is in Eridanus and one is in the direction of the galactic center. Away from these features, the sky is relatively uniform. The observed degree of isotropy is consistent with the model that the 0.5--1.2 keV background consists of an isotropic extragalactic component plus a thick disk galactic component. If an 11E/sup -1.4/ photons cm/sup -2/ s/sup -1/ sr/sup -1/ keV/sup -1/ spectrum is assumed for the extragalactic component, then a temperature of 2--3 x 10/sup 6/ K and an emission measure perpendicular to the galactic plane of 0.004 cm/sup -6/ pc are derived for the galactic component. A local and isotropically distributed source model is also consistent with the data, but is difficult to accommodate on physical grounds. Several other source distributions, such as a simple isotropic extragalactic component or a spherical galactic halo, are in conflict with the data.
Numerical Determination of Fundamental Modes

A convenient and practical method of numerical determination of the fundamental eigenfunction and eigenvalue in a class of linear eigenvalue problems has been developed and applied in two and three dimensions. The method is based on use of a network and of difference equations, but departs from previous methods in that it is not iterative. Rather, a polynomial operator is applied to a trial function just once, to accomplish a determinable degree of reduction in all eigenfunctions other than the fundamental that are contained in the trial function. In the case of the diffusion equation, the polynomial operator is a Tschebyscheff polynomial of a simple averaging operator. It is shown that this operator, when of degree m, is ``better'' than any other polynomial operator of this degree and much ``better'' than m iterations of a simple averaging operator—``better'' in the sense of accomplishing to a greater degree the elimination of all unwanted eigenfunctions. Techniques for the use of computing equipment for.
Piezoelectric constant of InP

The piezoelectric modulus d14 of InP was not determined experimentally up to now. By doping InP with Fe now high compensated bulk material is available by liquid‐encapsulated Czochralski crystals with a conductivity lower than 10−8 (Ω cm)−1. Excitation of vibration modes leads to a set of closely spaced resonance lines. These data were used to calculate a set of parameters for an electrical equivalent circuit with which the impedance behavior can be described successfully. From this the piezoelectric modulus of InP was derived to be −1.8 pC/N at room temperature. The sign of the constant was determined by qualitative static measurements of the piezovoltage by compressing the crystal.
Towards AI-Empowered Crowdsourcing

Crowdsourcing, in which human intelligence and productivity is dynamically mobilized to tackle tasks too complex for automation alone to handle, has grown to be an important research topic and inspired new businesses (e.g., Uber, Airbnb). Over the years, crowdsourcing has morphed from providing a platform where workers and tasks can be matched up manually into one which leverages data-driven algorithmic management approaches powered by artificial intelligence (AI) to achieve increasingly sophisticated optimization objectives. In this paper, we provide a survey presenting a unique systematic overview on how AI can empower crowdsourcing to improve its efficiency - which we refer to as AI-Empowered Crowdsourcing(AIEC). We propose a taxonomy which divides AIEC into three major areas: 1) task delegation, 2) motivating workers, and 3) quality control, focusing on the major objectives which need to be accomplished. We discuss the limitations and insights, and curate the challenges of doing research in each of these areas to highlight promising future research directions.
BiMLP: Compact Binary Architectures for Vision Multi-Layer Perceptrons

This paper studies the problem of designing compact binary architectures for vision multi-layer perceptrons (MLPs). We provide extensive analysis on the difficulty of binarizing vision MLPs and find that previous binarization methods perform poorly due to limited capacity of binary MLPs. In contrast with the traditional CNNs that utilizing convolutional operations with large kernel size, fully-connected (FC) layers in MLPs can be treated as convolutional layers with kernel size $1\times1$. Thus, the representation ability of the FC layers will be limited when being binarized, and places restrictions on the capability of spatial mixing and channel mixing on the intermediate features. To this end, we propose to improve the performance of binary MLP (BiMLP) model by enriching the representation ability of binary FC layers. We design a novel binary block that contains multiple branches to merge a series of outputs from the same stage, and also a universal shortcut connection that encourages the information flow from the previous stage. The downsampling layers are also carefully designed to reduce the computational complexity while maintaining the classification performance. Experimental results on benchmark dataset ImageNet-1k demonstrate the effectiveness of the proposed BiMLP models, which achieve state-of-the-art accuracy compared to prior binary CNNs. The MindSpore code is available at \url{https://gitee.com/mindspore/models/tree/master/research/cv/BiMLP}.
Block diagonalization of block circulant quaternion matrices and the fast calculation for T-product of quaternion tensors

With the great success of the T-product based real tensor methods in the color image and gray video processing, the establishment of T-product based quaternion tensor methods in the color video processing has encountered a challenge, which is the block diagonalization of block circulant quaternion matrices. In this paper, we show that the discrete Fourier matrix F p cannot diagonalize p × p circulant quaternion matrices, nor can the unitary quaternion matrices F p j and F p (1+ j ) / √ 2 with j being an imaginary unit of quaternion algebra, due to 1 c = c 1 but j c = ¯ c j for any complex number c . Further, we establish suﬃcient and necessary conditions for a unitary quaternion matrix being a diagonalization matrix of circulant quaternion matrices, which shows that achieving the diagonalization of circulant quaternion matrices in the quaternion domain is too hard. Noting that l q = ¯ q l holds for any quaternion q with l being an imaginary unit of octonion algebra, we turn to the octonion domain for achieving the diagonalization of circulant quaternion matrices. We obtain several special multiplication cases between special octonions and quaternions. Based on that, the unitary octonion matrix F p p with p = l , il or ( l + il ) / √ 2 can diagonalize a circulant quaternion matrix of size p × p , at the cost of O ( p log p ) via the fast Fourier transform (FFT); and unitary matrices F p p ⊗ I m and F p p ⊗ I n can block diagonalize a block circulant quaternion matrix of size mp × np , at the cost of O ( mnp log p ) via the FFT. As a result, we propose a fast algorithm to calculate the T-product between m × n × p and n × s × p third-order quaternion tensors via FFTs, at the cost of O ( mnsp ), which is almost 1 /p of the computational magnitude of computing T-product by its deﬁnition. Numerical calculations verify the correctness of the complexity analysis.
Can 5th} Generation Local Training Methods Support Client Sampling? Yes!

The celebrated FedAvg algorithm of McMahan et al. (2017) is based on three components: client sampling (CS), data sampling (DS) and local training (LT). While the first two are reasonably well understood, the third component, whose role is to reduce the number of communication rounds needed to train the model, resisted all attempts at a satisfactory theoretical explanation. Malinovsky et al. (2022) identified four distinct generations of LT methods based on the quality of the provided theoretical communication complexity guarantees. Despite a lot of progress in this area, none of the existing works were able to show that it is theoretically better to employ multiple local gradient-type steps (i.e., to engage in LT) than to rely on a single local gradient-type step only in the important heterogeneous data regime. In a recent breakthrough embodied in their ProxSkip method and its theoretical analysis, Mishchenko et al. (2022) showed that LT indeed leads to provable communication acceleration for arbitrarily heterogeneous data, thus jump-starting the $5^{\rm th}$ generation of LT methods. However, while these latest generation LT methods are compatible with DS, none of them support CS. We resolve this open problem in the affirmative. In order to do so, we had to base our algorithmic development on new algorithmic and theoretical foundations.
Holonomic Poisson geometry of Hilbert schemes

We undertake a detailed study of the geometry of Bottacin's Poisson structures on Hilbert schemes of points in Poisson surfaces, i.e. smooth complex surfaces equipped with an effective anticanonical divisor. We focus on three themes that, while logically independent, are linked by the interplay between (characteristic) symplectic leaves and deformation theory. Firstly, we construct the symplectic groupoids of the Hilbert schemes and develop the classification of their symplectic leaves, using the methods of derived symplectic geometry. Secondly, we establish local normal forms for the Poisson brackets, and combine them with a toric degeneration argument to verify that Hilbert schemes satisfy our recent conjecture characterizing holonomic Poisson manifolds in terms of the geometry of the modular vector field. Finally, using constructible sheaf methods, we compute the space of first-order Poisson deformations when the anti-canonical divisor is reduced and has only quasi-homogeneous singularities. (The latter is automatic if the surface is projective.) Along the way, we find a tight connection between the Poisson geometry of the Hilbert schemes and the finite-dimensional Lie algebras of affine transformations, which is mediated by syzygies. In particular, we find that the Hilbert scheme has a natural subvariety that serves as a global counterpart of the nilpotent cone, and we prove that the Lie algebras of affine transformations have holonomic dual spaces -- the first such series of Lie algebras to be discovered.
Meta-Path Based Attentional Graph Learning Model for Vulnerability Detection

In recent years, deep learning (DL)-based methods have been widely used in code vulnerability detection. The DL-based methods typically extract structural information from source code, e.g., code structure graph, and adopt neural networks such as Graph Neural Networks (GNNs) to learn the graph representations. However, these methods fail to consider the heterogeneous relations in the code structure graph, i.e., the heterogeneous relations mean that the different types of edges connect different types of nodes in the graph, which may obstruct the graph representation learning. Besides, these methods are limited in capturing long-range dependencies due to the deep levels in the code structure graph. In this paper, we propose a Meta-path based Attentional Graph learning model for code vulNErability deTection, called MAGNET. MAGNET constructs a multi-granularity meta-path graph for each code snippet, in which the heterogeneous relations are denoted as meta-paths to represent the structural information. A meta-path based hierarchical attentional graph neural network is also proposed to capture the relations between distant nodes in the graph. We evaluate MAGNET on three public datasets and the results show that MAGNET outperforms the best baseline method in terms of F1 score by 6.32%, 21.50%, and 25.40%, respectively. MAGNET also achieves the best performance among all the baseline methods in detecting Top-25 most dangerous Common Weakness Enumerations (CWEs), further demonstrating its effectiveness in vulnerability detection.