Miracle-dz/UniFAR-FTUs huggingface dataset:
General Expression of the Gyroviscous Force

Assuming only small gyromotion periods and Larmor radii compared to any other time and length scales, and retaining the lowest significant order in δ=ρi∕L⪡1, the general expression of the ion gyroviscous stress tensor is presented. This expression covers both the “fast dynamics” (or “magnetohydrodynamic”) ordering, where the time derivative and ion gyroviscous stress are first order in δ relative to the ion gyrofrequency and scalar pressure, respectively, and the “slow dynamics” (or “drift”) ordering, where the time derivative and ion gyroviscous stress are, respectively, second order in δ. This general stress tensor applies to arbitrary collisionality and does not require the distribution function to be close to a Maxwellian. Its exact divergence (gyroviscous force) is written in a closed vector form, allowing for arbitrary magnetic geometry, parallel gradients, and flow velocities. Considering, in particular, the contribution from the velocity gradient (rate of strain) term, the final form of the moment.
Sentiment Classification with Gated CNN for Customer Reviews

Recurrent neural networks(RNNs) have been applied to sentiment classification but RNNs is usually heavier than convolutional neural networks (CNNs), turning more interest in the application of CNNs to language tasks. In this paper we propose a method to apply gated CNN (gCNN) with Maxpooling to sentiment classification of customer reviews. In our proposal, the application of gCNN is to sentiment classification, instead of constructing a language model. Our experiment is conducted with Amazon Product Review dataset and Japanese review dataset of TripAdvisor. The whole of each review is used as an input, instead of each sentence. The result is that a simple application of gCNN to sentiment classification achieved sufficient accuracies with the two datasets. Thus, an implication is that gCNN is proven to work fine for sentiment classification much faster than RNNs with fine results in the different language datasets.
Distributed decision fusion using empirical estimation

The problem of optimal data fusion in multiple detector systems is studied in the case where training examples are available, but no a priori information is available about the probability distributions of errors committed by the individual detectors. Earlier solutions to this problem require some knowledge of the error distributions of the detectors, for example, either in a parametric form or in a closed analytical form. Here we show that, given a sufficiently large training sample, an optimal fusion rule can be implemented with an arbitrary level of confidence. We first consider the classical cases of Bayesian rule and Neyman-Pearson test for a system of independent detectors. Then we show a general result that any test function with a suitable Lipschitz property can be implemented with arbitrary precision, based on a training sample whose size is a function of the Lipschitz constant, number of parameters, and empirical measures. The general case subsumes the cases of nonindependent and correlated detectors.
Optically controlled quantum dot gated transistors with high on/off ratio

We report the design and fabrication of InAs quantum dot gated transistors, which are normally-on, where the channel current can be switched off by laser illumination. Laser light at 650 nm with a power of 850 pW switches the channel current from 5 μA to 2 pA, resulting in an on/off ratio of more than 60 dB. The switch-off mechanism and carrier dynamics are analyzed with simulated band structure.
Semantic similarity measure for Thai language

Assessing the semantic similarity of texts is a fundamental concept which has many applications in natural language processing and related fields. This work presents both word and sentence semantic similarity measures specifically for Thai language. The word similarity measure is based on word embedding vectors, WordNet database and an edit-distance measure. The sentence similarity measure relies on the word similarity measure as a baseline. The proposed measures are compared with existing methods on benchmark datasets.
Rotating antiferromagnetism in high-temperature superconductors

In order to address the issue of the puzzling properties of high-temperature superconductors in the underdoped regime, we investigated the effect of on-site Coulomb repulsion on superconductivity by proposing a two-dimensional disordered quantum state that is characterized by rotating antiferromagnetism. The underdoped and overdoped phases are found to be separated by a quantum critical point where the rotating order parameter vanishes. This point is practically equal to the optimal doping density. Our calculations of the doping dependence of the ratio ${R=2E}_{d}{/k}_{B}{T}_{C}$ shows a sharp change at the quantum critical point and accounts for the experimental variations of R in YBCO systems for example. Several other quantities are evaluated and compared to experimental data with fairly good agreement.
Hypotheses testing based on modified nonparametric estimation of an affinity measure between two distributions

Let F and G denote two distribution functions defined on the same probability space which are absolutely continuous with respect to the Lebesgue measure with probability density functions f and g, respectively. Ahmad and Van Belle (1974) proposed a measure of the closeness between F and G as follows: . Ahmad (1980) proposed to estimate λ by , where F n(x) and G n(x)) are empirical distribution functions of F(x) and G(x) respectively and are the well-known kernel estimates of f(x) and g(x) respectively. This paper generalizes the estimator to a family of modified estimators of λ indexed by a constant γ , say, where 0≤γ≤1, which includes special case (when γ = 0). We derive the limiting distribution of normalized for 0 < y = 1by using the theory of U-statistics and show that the limiting distribution of for γ = 0, i.e., of , when normalized, isdegenerate. Consequently, cannot be used to construct an asymptotically valid goodness-of-fit test. The normalized estimator for any 0
Terahertz antenna based on graphene

We have investigated several configurations of antennas based on graphene. We show that patterned metallic dipole antennas or arrays of dipole antennas deposited on graphene highly benefit from the reversible high-resistivity-to-low-resistivity transition in graphene, tuned by a gate voltage. The radiation pattern and the efficiency of such antennas are changed via the gate voltage applied on graphene.
Hydrodynamics affected by submerged vegetation with different flexibility under unidirectional flow

Submerged vegetation changes the hydrodynamic characteristics of rivers, lakes, wetlands, and coastal zones. However, only a few studies have focused on the effect of flexible submerged vegetation on hydrodynamic characteristics under unidirectional flow. Therefore, laboratory experiments were conducted to study the effects of submerged vegetation with different flexibility on the flow structure and turbulence characteristics under unidirectional flow. The results showed that the reconfiguration and coordination of wave motion of flexible submerged vegetation redistribute flow velocity, Reynolds stress, and turbulent kinetic energy inside and outside of the vegetation canopy. With a gradual decrease in the deflection height of vegetation, the differences in dimensionless velocity, dimensionless mixed layer thickness, bulk drag coefficient, averaged turbulent kinetic energy, and the averaged contribution rate of its shear production term for the vegetation canopy also decrease; the trend of the penetration depth of Reynolds stress is opposite. Based on the turbulent kinetic energy budget equation, a turbulent kinetic energy model (TKE model) was established, which can be used to predict the turbulent kinetic energy and its shear production term within the vegetation canopy. Here, the scaling factor was determined by the vegetation canopy Cauchy number. The TKE model can be applied under unidirectional flow conditions for submerged vegetation with different flexibilities with high accuracy. It is a simple method to predict vegetation-induced turbulence and the characteristics of sediment and material transport under the influence of submerged vegetation with different flexibility.
Continuous cryotherapy vs. traditional cryotherapy after total knee arthroplasty: A systematic review and meta-analysis of randomized controlled trials

Background Cryotherapy is widely applied to relieve pain and improve functional outcomes after total knee arthroplasty (TKA). New cryotherapy devices have recently been developed to guarantee a fixed temperature for a prolonged time. Therefore, we conducted a systematic review and meta-analysis to compare continuous cryotherapy and traditional cryotherapy (ice bag or gel pack) for patients after TKA. Methods This study was conducted according to a predefined protocol registered on PROSPERO. Two independent reviewers performed an electronic database search of PubMed, Embase, Cochrane, Web of Science, Google Scholar, and ClinicalTrials.gov. Dichotomous outcomes were reported as risk difference (RD) with 95% confidence intervals (CIs), and continuous outcomes were reported as mean difference (MD), or standardized mean difference (SMD) with 95% CIs. Results Seven trials enrolling a total of 519 patients were included. There were no differences in pain intensity (MD: −0.54, 95% CI: −1.55 to 0.47; P = 0.30), analgesics consumption (MD: −0.37, 95% CI: −1.28 to 0.55; P = 0.43), postoperative range of motion (MD: 0.47, 95% CI: −4.09 to 5.03; P = 0.84), swelling of the knee joint, blood loss, change in hemoglobin, or transfusion rate. Meanwhile, there were no differences in length of hospital stay (MD: −0.77, 95% CI: −1.62 to 0.08; P = 0.07) and adverse events (RD: 0, 95% CI: −0.02 to 0.03; P = 0.74). In addition, continuous cryotherapy leads to extra costs and resources than traditional cryotherapy. Conclusions Continuous cryotherapy does not appear to offer significant benefits for TKA when compared with traditional cryotherapy. Based on currently available evidence, traditional cryotherapy is still recommended as continuous cryotherapy is not cost-effective. Further well-designed studies with larger sample sizes are warranted to further confirm these preliminary results. PROSPERO Registration: Identifier [CRD42022308217].
Whole-genome resequencing reveals genetic diversity, differentiation, and selection signatures of yak breeds/populations in Qinghai, China

The Qinghai Province of China is located in the northeast region of the Qinghai–Tibetan Plateau (QTP) and carries abundant yak genetic resources. Previous investigations of archaeological records, mitochondrial DNA, and Y chromosomal markers have suggested that Qinghai was the major center of yak domestication. In the present study, we examined the genomic diversity, differentiation, and selection signatures of 113 Qinghai yak, including 42 newly sequenced Qinghai yak and 71 publicly available individuals, from nine yak breeds/populations (wild, Datong, Huanhu, Xueduo, Yushu, Qilian, Geermu, Tongde, and Huzhu white) using high-depth whole-genome resequencing data. We observed that most of Qinghai yak breeds/populations have abundant genomic diversity based on four genomic parameters (nucleotide diversity, inbreeding coefficients, linkage disequilibrium decay, and runs of homozygosity). Population genetic structure analysis showed that Qinghai yak have two lineages with two ancestral origins and that nine yak breeds/populations are clustered into three distinct groups of wild yak, Geermu yak, and seven other domestic yak breeds/populations. F ST values showed moderate genetic differentiation between wild yak, Geermu yak, and the other Qinghai yak breeds/populations. Positive selection signals were detected in candidate genes associated with disease resistance (CDK2AP2, PLEC, and CYB5B), heat stress (NFAT5, HSF1, and SLC25A48), pigmentation (MCAM, RNF26, and BOP1), vision (C1QTNF5, MFRP, and TAX1BP3), milk quality (OPLAH and GRINA), neurodevelopment (SUSD4, INSYN1, and PPP1CA), and meat quality (ZRANB1), using the integrated PI, composite likelihood ratio (CLR), and F ST methods. These findings offer new insights into the genetic mechanisms underlying target traits in yak and provide important information for understanding the genomic characteristics of yak breeds/populations in Qinghai.
An electrical vehicle-assisted demand response management system: A reinforcement learning method

With the continuous progress of urbanization, determining the charging and discharging strategy for randomly parked electric vehicles to help the peak load shifting without affecting users’ travel is a key problem. This paper design a reinforcement learning-based method for the electric vehicle-assisted demand response management system. Specifically, we formalize the charging and discharging sequential decision problem of the parking lot into the Markov process, in which the state space is composed of the state of parking spaces, electric vehicles, and the total load. The charging and discharging decision of each parking space acts as the action space. The reward comprises the penalty term that guarantees the user’s travel and the sliding average value of the load representing peak load shifting. After that, we use a Deep Q-Network (DQN)-based reinforcement learning architecture to solve this problem. Finally, we conduct a comprehensive evaluation with real-world power usage data. The results show that our proposed method will reduce the peak load by 10% without affecting the travel plan of all electric vehicles. Compared with random charging and discharging scenarios, we have better performance in terms of state-of-charge (SoC) achievement rate and peak load shifting effect.
Early-stage fusion of EEG and fNIRS improves classification of motor imagery

Introduction Many research papers have reported successful implementation of hybrid brain-computer interfaces by complementarily combining EEG and fNIRS, to improve classification performance. However, modality or feature fusion of EEG and fNIRS was usually designed for specific user cases, which were generally customized and hard to be generalized. How to effectively utilize information from the two modalities was still unclear. Methods In this paper, we conducted a study to investigate the stage of bi-modal fusion based on EEG and fNIRS. A Y-shaped neural network was proposed and evaluated on an open dataset, which fuses the bimodal information in different stages. Results The results suggests that the early-stage fusion of EEG and fNIRS have significantly higher performance compared to middle-stage and late-stage fusion network configuration (N = 57, P < 0.05). With the proposed framework, the average accuracy of 29 participants reaches 76.21% in the left-or-right hand motor imagery task in leave-one-out cross-validation, using bi-modal data as network inputs respectively, which is in the same level as the state-of-the-art hybrid BCI methods based on EEG and fNIRS data.
Arc fault detection on load-side based on sensitive features tracking

The detection of series arc faults using fault current is difficult to overcome the influence of load types, making it difficult to establish a unified fault detection criterion. In contrast, since the arc voltage waveform of fault point is less affected by the load types and is basically a square wave shape, which provide conditions for constructing a unified fault criterion. In terms of the fault information, the fault distortion point of voltage on load-side caused by the arc voltage transition edge provides the position information of the arc voltage transition edge, and its polarity, amplitude and rate of change make it possible to distinguish from the transition edge caused by normal harmonic voltage drop, which provide the theoretical basis for fault detection using the voltage on load-side. Based on the basic analysis of arc voltage waveform features, this paper proposes an arc fault detection method based on load-side voltage sensitive feature tracking for the purpose of identifying the existence of arc voltage transition edges. The method proposed in this paper highlights the transition edge by eliminating the fundamental wave component of the voltage on load-side, the phase areas where the fault distortion points may exist are used as the sensitive area for fault detection, and the identification and tracking of the transition edge is achieved based on the same direction of voltage change, finally, the presence of arc fault voltage is characterized through the polarity, amplitude and rate of transition edge by fusion. The detection method proposed in this paper has a clear physical meaning and has the advantage of being less affected by the load types. Compared with other similar methods, the method proposed in this paper has higher detection sensitivity and stronger ability to distinguish from voltage drop distortion. The experimental results show that the average detection accuracy of the proposed method for faults detection under various loads exceeds 96%, which verifies the effectiveness of the method.
Deciphering the mechanism of Tinospora cordifolia extract on Th17 cells through in-depth transcriptomic profiling and in silico analysis

Naive CD4+ T cells differentiate into effector (Th1, Th2, Th17) cells and immunosuppressive (Treg) cells upon antigenic stimulation in the presence of a specific cytokine milieu. The T cell in vitro culture system provides a very efficient model to study compounds’ therapeutic activity and mechanism of action. Tinospora cordifolia (Willd.) Hook.f. & Thomson (Family. Menispermaceae) is one of the widely used drugs in Ayurveda (ancient Indian system of medicine) for various ailments such as inflammatory conditions, autoimmune disorders, and cancer as well as for promoting general health. In vitro and in vivo studies on immune cells comprising dendritic cells, macrophages, and B cells suggest its immune-modulating abilities. However, to date, the effect of T. cordifolia on individual purified and polarized T cell subsets has not been studied. Studying drug effects on T cell subsets is needed to understand their immunomodulatory mechanism and to develop treatments for diseases linked with T cell abnormalities. In this study, we examined the immunomodulatory activity of T. cordifolia on primary CD4+ T cells, i.e., Th1, Th17, and iTreg cells. An aqueous extract of T. cordifolia was non-cytotoxic at concentrations below 1500 µg/ml and moderately inhibited the proliferation of naive CD4+ T cells stimulated with anti-CD3ε and anti-CD28 for 96 h. T. cordifolia treatment of naive CD4+ T cells differentiated under Th17-polarizing conditions exhibited reduced frequency of IL-17 producing cells with inhibition of differentiation and proliferation. For the first time, in-depth genome-wide expression profiling of T. cordifolia treated naive CD4+ T cells, polarized to Th17 cells, suggests the broad-spectrum activity of T. cordifolia. It shows inhibition of the cytokine-receptor signaling pathway, majorly via the JAK-STAT signaling pathway, subsequently causing inhibition of Th17 cell differentiation, proliferation, and effector function. Additionally, the molecular docking studies of the 69 metabolites of T. cordifolia further substantiate the inhibitory activity of T. cordifolia via the cytokine-receptor signaling pathway. Furthermore, in vitro polarized Th1 and iTreg cells treated with T. cordifolia extract also showed reduced IFN-γ production and FoxP3 expression, respectively. This study provides insight into the plausible mechanism/s of anti-inflammatory activity of T. cordifolia involving T cells, mainly effective in Th17-associated autoimmune and inflammatory diseases.
LDMD: A database of microbes in human lung disease

Background Lungs were initially thought to be sterile. However, with the development of sequencing technologies, various commensal microorganisms, especially bacteria, have been observed in the lungs of healthy humans. Several studies have also linked lung microbes to infectious lung diseases. However, few databases have focused on the metagenomics of lungs to provide microbial compositions and corresponding metadata information. Such a database would be handy for researching and treating lung diseases. Methods To provide researchers with a preliminary understanding of lung microbes and their research methods, the LDMD collated nearly 10,000 studies in the literature covering over 30 diseases, gathered basic information such as the sources of lung microbe samples, sequencing methods, and processing software, as well as analyzed the metagenomic sequencing characteristics of lung microbes. Besides, the LDMD also contained data collected in our laboratory. Results In this study, we established the Lung Disease Microorganisms Database (LDMD), a comprehensive database of microbes involved in lung disease. The LDMD offered sequence analysis capabilities, allowing users to upload their sequencing results, align them with the data collated in the database, and visually analyze the results. Conclusion In conclusion, the LDMD possesses various functionalities that provide a convenient and comprehensive resource to study the lung metagenome and treat lung diseases.
Transcriptome-wide analysis reveals the molecular mechanisms of cannabinoid type II receptor agonists in cardiac injury induced by chronic psychological stress

Background: Growing evidence has supported that chronic psychological stress would cause heart damage, However the mechanisms involved are not clear and effective interventions are insufficient. Cannabinoid type 2 receptor (CB2R) can be a potential treatment for cardiac injury. This study is aimed to investigate the protective mechanism of CB2R agonist against chronic psychological stress-induced cardiac injury. Methods: A mouse chronic psychological stress model was constructed based on a chronic unpredictable stress pattern. Mice were performed a three-week psychological stress procedure, and cardiac tissues of them were collected for whole-transcriptome sequencing. Overlap analysis was performed on differentially expressed mRNAs (DE-mRNAs) and ER stress-related genes (ERSRGs), and bioinformatic methods were used to predict the ceRNA networks and conduct pathway analysis. The expressions of the DE-ERSRGs were validated by RT-qPCR. Results: In the comparison of DE mRNA in Case group, Control group and Treatment group, three groups of ceRNA networks and ceRNA (circ) networks were constructed. The DE-mRNAs were mainly enriched in chromatid-relevant terms and Hematopoietic cell lineage pathway. Additionally, 13 DE-ERSRGs were obtained by the overlap analysis, which were utilized to establish a ceRNA network with 15 nodes and 14 edges and a ceRNA (circ) network with 23 nodes and 28 edges. Furthermore, four DE-ERSRGs (Cdkn1a, Atf3, Fkbp5, Gabarapl1) in the networks were key, which were mainly enriched in response to extracellular stimulus, response to nutrient levels, cellular response to external stimulus, and FoxO signaling pathway. Finally, the RT-qPCR results showed almost consistent expression patterns of 13 DE-ERSRGs between the transcriptome and tissue samples. Conclusion: The findings of this study provide novel insights into the molecular mechanisms of chronic psychological stress-induced cardiac diseases and reveal novel targets for the cardioprotective effects of CB2R agonists.