Miracle-dz/UniFAR-FTUs huggingface dataset:
Supervised Contrastive Learning for Vehicle Classification Based on the IR-UWB Radar

Impulse radio ultrawideband (IR-UWB) radar has high range resolution, strong anti-jamming ability, and low power consumption and has been widely used in target detection and recognition. Currently, existing studies always extract artificial features of echo signals, such as time–frequency images, Doppler features, or time-domain features, and then distinguish these features through well-designed deep networks. However, these manual features are difficult to achieve task-invariant and disentangled representations. The target echo received by UWB radar also has amplitude, time-shift, and target-aspect sensitivity problems. To address the above problems, we propose a novel supervised contrastive learning (SupCon) framework to recognize different vehicles. Under label constraints, deep invariant representations are obtained through contrastive learning of echo signals, improving classification accuracy. First, a 1-D deep residual network (ResNet) is designed as the backbone, and the self-attention (SA) layer is added to extract long-range features of echo signals. Second, well-designed data augmentation methods can improve the performance of contrastive learning. Due to the integration of multiple data transformations, the model can learn invariant features by maximizing the mutual information between different signal transformations. Finally, we modify the SupCon loss function. It alleviates the conflict problem of simultaneously shrinking and expanding the distance between the positive samples in the feature space and improves the recognition performance of the model. Ablation experiments on the measured dataset show that the designed components of the method are effective. Comparative experiments on ultrawideband radar public datasets [Air Force Research Laboratory’s (AFRL) high-resolution range profile (HRRP), moving and stationary target acquisition and recognition (MSTAR)] also demonstrate the excellent classification performance of the proposed algorithm.
PD-1 Dynamically Regulates Inflammation and Development of Brain-Resident Memory CD8 T Cells During Persistent Viral Encephalitis

Programmed cell death-1 (PD-1) receptor signaling dampens the functionality of T cells faced with repetitive antigenic stimulation from chronic infections or tumors. Using intracerebral (i.c.) inoculation with mouse polyomavirus (MuPyV), we have shown that CD8 T cells establish a PD-1hi, tissue-resident memory population in the brains (bTRM) of mice with a low-level persistent infection. In MuPyV encephalitis, PD-L1 was expressed on infiltrating myeloid cells, microglia and astrocytes, but not on oligodendrocytes. Engagement of PD-1 on anti-MuPyV CD8 T cells limited their effector activity. NanoString gene expression analysis showed that neuroinflammation was higher in PD-L1−/− than wild type mice at day 8 post-infection, the peak of the MuPyV-specific CD8 response. During the persistent phase of infection, however, the absence of PD-1 signaling was found to be associated with a lower inflammatory response than in wild type mice. Genetic disruption and intracerebroventricular blockade of PD-1 signaling resulted in an increase in number of MuPyV-specific CD8 bTRM and the fraction of these cells expressing CD103, the αE integrin commonly used to define tissue-resident T cells. However, PD-L1−/− mice persistently infected with MuPyV showed impaired virus control upon i.c. re-infection with MuPyV. Collectively, these data reveal a temporal duality in PD-1-mediated regulation of MuPyV-associated neuroinflammation. PD-1 signaling limited the severity of neuroinflammation during acute infection but sustained a level of inflammation during persistent infection for maintaining control of virus re-infection.
A real-time algorithm for the detection of compensatory movements during reaching

Introduction: Interactive game systems can motivate stroke survivors to engage with their rehabilitation exercises. However, it is crucial that systems are in place to detect if exercises are performed correctly as stroke survivors often perform compensatory movements which can be detrimental to recovery. Very few game systems integrate motion tracking algorithms to monitor performance and detect such movements. This paper describes the development of algorithms which monitor for compensatory movements during upper limb reaching movements in real-time and provides quantitative metrics for health professionals to monitor performance and progress over time. Methods: A real-time algorithm was developed to analyse reaching motions in real-time through a low-cost depth camera. The algorithm segments cyclical reaching motions into component parts, including compensatory movement, and provides a graphical representation of task performance. Healthy participants (n = 10) performed reaching motions facing the camera. The real-time accuracy of the algorithm was assessed by comparing offline analysis to real-time collection of data. Results: The algorithm’s ability to segment cyclical reaching motions and detect the component parts in real-time was assessed. Results show that movement types can be detected in real time with accuracy, showing a maximum error of 1.71%. Conclusions: Using the methods outlined, the real-time detection and quantification of compensatory movements is feasible for integration within home-based, repetitive task practice game systems for people with stroke.
Research on the Financing Efficiency of listed Companies in China's Photovoltaic Industry

Through sorting out the theory of financing efficiency, the influencing factors and empirical methods of financing efficiency at home and abroad, taking the panel data of 29 listed companies in the photovoltaic industry in China from 2017 to 2021 as the research object, the DEA-Tobit model was used to study the financing efficiency and influencing factors of China's photovoltaic industry. The results show that in recent years, the financing efficiency of listed companies in the domestic photovoltaic industry has fluctuated greatly, and there is a certain downward trend as a whole, and there is still a broad room for improvement; There are significant positive effects on net profit, net profit margin on sales, cost of main business, cash ratio and corporate financing efficiency, while current liabilities and corporate financing efficiency show significant negative effects. Based on this, suggestions are put forward to optimize the scale of listed companies, broaden financing channels, and introduce new financing methods by relevant government departments.
AMELIORATING EFFECT OF CARICA PAPAYA L. FRUIT EXTRACT ON ENTAMOEBA HISTOLYTICA IN MICE

Entamoeba histolytica is a major public health problem in tropical and subtropical countries. Treatment failure with specific chemotherapy suggested the development of new plant origin therapy. Different parts of Carica papaya Linn. have anti-parasitic agent. The study evaluated the efficacy of C. papaya fruit methanolic and aqueous extracts against Entamoeba histolytica as compared with the used Albendazole ® in experimentally infected mice. Male CD-1 mice were divided into two groups of 50 each; immunocompetent (IC) and dexamethasone immunosuppressed (IS). Each group was subdivided into 5 subgroups, normal, infected and infected treated with crude & pure C. papaya extracts (500mg/kg) or albendazole (200mg/kg). Each mouse was infected orally with 1000 E. histolytica cysts. Six weeks after infection, treatment with pure and crude extracts (3doses/ week) for 3 weeks which started from the 3 rd week after oral administration of dexamethasone, significantly reduced the trophozoites (78.20% & 76.29%, respectively) in IC mice compared to albendazole (88.72% & 81.81%). Serum IgG, IgM, IgA antibody levels and IFN-P < 0.001) increased in treated mice compared to normal ones. Histopathological results showed improvement in the hepatic and intestinal architecture of infected mice treated with papaya extracts and albendazole with various degrees. Results of IS groups were the same as those of IC ones but, with lower levels.
Efficiency Enhancement of CMOS Power Amplifier for RF Applications

This paper presented a new structure for the CMOS power amplifiers as a more effective trend to amplify radio frequency (RF) signals compared to polar power amplifiers PA's by using envelope removal and restoration (EER) technique. Polar PA offers high-efficiency RF-modulated linear signal amplification. However, these amplifiers need high uniformity between the amplitude and phase of the modulated signals. The last translates promptly into higher energy consumption. Rather than deconstructing the quadrature signals into the combination of phase and amplitude signals, it is suggested that the CMOS quadrature power amplifier (QPA's) technique had been used to amplify RF signals immediately. The linearity, bandwidth, efficiency, and power consumption of the QPA's has been improved by separate amplitude and phase quadrature signals. The quadratic geometry architecture contains two bridges from modulated PA's that are able to treat negative or positive voltages, modulation, and RF-power bundling. The design of the new structure is compared with respect to parameters such as fundamental frequency, power gain, PAE, output power, technology or fabrication process, and number of stage transistors. Simulation results for PA's design using CMOS process show an effective quadrature model by a power-added-efficiency (PAE) of 78.413% at a maximum output power of 21.619dBm. The third intermodulation IDM3 is -49.2dBc at output power driven at frequency 2.4 GHz and input power greater than 20dBm. The amplitude and phase distortion has been obtained of 1.4 and 0.26 ᵒ/dB respectively at 50MHz of bandwidth for modulated signals. This is an open access article under the CC BY 4.0 license ( http://creativecommons.org/licenses/by/4.0/ ).
Introduction to Modern Canonical Quantum General Relativity

This is an introduction to the by now fifteen years old research field of canonical quantum general relativity, sometimes called "loop quantum gravity". The term "modern" in the title refers to the fact that the quantum theory is based on formulating classical general relativity as a theory of connections rather than metrics as compared to in original version due to Arnowitt, Deser and Misner. Canonical quantum general relativity is an attempt to define a mathematically rigorous, non-perturbative, background independent theory of Lorentzian quantum gravity in four spacetime dimensions in the continuum. The approach is minimal in that one simply analyzes the logical consequences of combining the principles of general relativity with the principles of quantum mechanics. The requirement to preserve background independence has lead to new, fascinating mathematical structures which one does not see in perturbative approaches, e.g. a fundamental discreteness of spacetime seems to be a prediction of the theory providing a first substantial evidence for a theory in which the gravitational field acts as a natural UV cut-off. An effort has been made to provide a self-contained exposition of a restricted amount of material at the appropriate level of rigour which at the same time is accessible to graduate students with only basic knowledge of general relativity and quantum field theory on Minkowski space.
PEDAGOGICAL-PSYCHOLOGICAL FEATURES OF THE DEVELOPMENT OF NON-STANDARD WAY OF THINKING IN STUDENTS

Today’s educators need to take a creative approach to their work, using effective means to absorb education at the intersection of purposeful individual and group interests in non-standard situations. Of course, this process is based on the demonstration of creative abilities from each edu-cator. This article describes the pedagogical and psychological features of the development of non-standard thinking in students.
Review of Road Dust Resuspension Modelling Approaches and Comparisons Analysis for a UK Case Study

This report provides a review of several measurement studies and campaigns focusing on road dust resuspension (RDS), which is becoming an important source of ambient particulate matter (PM) concentrations. There is a growing requirement from stakeholders, policy makers and scientists for gathering additional data around RDS, since there is a lack of well-established methodologies or those developed for measuring or modelling this source of emissions are not very recent. Moreover, conventional inventory methodologies are not available for this source of emissions, and it is not yet covered in the UK’s National Atmospheric Emissions Inventory (NAEI) neither in the inventory guidebooks at European level. Based on the available literature and models, this paper also seeks to provide some preliminary considerations whether an approach can be tailored for application in air quality models used for modelling ambient PM concentrations in the UK in a consistent manner and demonstrated using a state-of-art air quality modelling tool, namely RapidAir®. The impact of including resuspension emissions on roadside concentrations was modelled in a UK case study with the greatest increases in modelled road PM10 concentrations observed on or near the road surface. The paper concludes with recommendations for longer-term implementation of the proposed improvements such as investigation of the temporal effects of RDS on PM levels; of the influence of different type of tyre materials and mixture (e.g., composition) and designs (e.g., shape, dimensions and tread); and influence of different existing pavements or of new road surface types.
Medication-Wide Association Study Plus (MWAS+): A Proof of Concept Study on Drug Repurposing

The high cost and time for developing a new drug or repositioning a partially-developed drug has fueled interest in “repurposing” drugs. Drug repurposing is particularly of interest for Alzheimer’s disease (AD) or AD-related dementias (ADRD) because there are no unrestricted disease-modifying treatments for ADRD. We have designed and pilot tested a 3-Step Medication-Wide Association Study Plus (MWAS+) approach to rigorously accelerate the identification of drugs with a high potential to be repurposed for delaying and preventing AD/ADRD: Step 1 is a hypothesis-free exploration; Step 2 is mechanistic filtering; And Step 3 is hypothesis testing using observational data and prospective cohort design. Our results demonstrated the feasibility of the MWAS+ approach. The Step 1 analysis identified potential candidate drugs including atorvastatin and GLP1. The literature search in Step 2 found evidence supporting the mechanistic plausibility of the statin-ADRD association. Finally, Step 3 confirmed our hypothesis that statin may lower the risk of incident ADRD, which was statistically significant using a target trial design that emulated randomized controlled trials.
Synthesis and field-emission properties of aligned MoO3 nanowires

Aligned MoO3 nanowires have been grown on silicon substrates without using any catalyst. They were prepared in a two-step process: first by thermal evaporation and then further processed by oxidation. The MoO3 nanowires are of crystalline and have an orthorhombic structure. They also have high purity. Field-emission measurement showed that, typically, their turn-on field and threshold field were about 3.5 and 7.65 MV/m, respectively. Furthermore, the spatial distribution of emission sites was studied using transparent anode technique and the emission current of the sites was relatively uniform. These may be attributed to very good uniformity in the height and diameter of the nanowires, and to the separation between nanowires. Finally, the stability of the emission current over time was found to be within 10%. These findings indicate that MoO3 nanowires as a cold cathode have a potential future.
Addition of Organic Acids and Lactobacillus acidophilus to the Leguminous Forage Chamaecrista rotundifolia Improved the Quality and Decreased Harmful Bacteria of the Silage

Simple Summary The leguminous forage Chamaecrista rotundifolia contains a high crude protein content and can be an important feed source for livestock. Forage is preserved mainly as silage, and microbial fermentation is key to high-quality silage. The fermentation quality of silage depends on the microbial community structure and the metabolites produced during silage fermentation. The current study provides evidence that the addition of malic or citric acid and/or Lactobacillus acidophilus (L) improves the quality of silage and inhibits the growth of harmful microorganisms. Abstract This study aimed to investigate the effects of citric acid, malic acid, and Lactobacillus acidophilus (L) on fermentation parameters and the microbial community of leguminous Chamaecrista rotundifolia silage. Fresh C. rotundifolia was treated without any additive (CK), or with L (106 CFU/g fresh weight), different levels (0.1, 0.3, 0.5, and 1% fresh weight) of organic acid (malic or citric acid), and the combinations of L and the different levels of organic acids for 30, 45, and 60 days of ensiling. The effects of malic acid and citric acid were similar during the ensiling process. Treatment with either citric or malic acid and also when combined with L inhibited crude protein degradation, lowered pH and ammonia nitrogen, and increased lactic acid concentration and dry matter content (p < 0.05). The neutral detergent fiber and acid detergent fiber increased initially and then decreased with fermentation time in all treatments (p < 0.05). Increasing the level of organic acid positively affected the chemical composition of C. rotundifolia silage. In addition, the addition of 1% organic acid increased the relative abundance of Lactobacillus, while the relative abundances of Clostridium and Enterobacter decreased at 60 days (p < 0.05). Moreover, both organic acids and combined additives increased (p < 0.05) the relative abundance of Cyanobacteria at 60 days of fermentation. We concluded that adding malic acid, citric acid, and L combined with an organic acid could improve the quality of C. rotundifolia silage and increase the relative abundance of beneficial bacteria. The addition of organic acid at a level of 1% was the most effective.
On holographic non-local operators and multiple M2-branes theories

Gauge-string duality has provided a powerful framework for the study of strongly coupled gauge theories and non-perturbative string models. This thesis analyzes the holographic description of non-local gauge theory operators and some aspects of the BaggerLambert theory. The latter, as a proposal for a multiple M2-branes effective theory, is conjectured to be the holographic dual of a compactification of M-theory. We show that all half-BPS Wilson loop operators in N = 4 SYM – which are labeled by Young tableaus – have a gravitational dual description in terms of D5-branes or alternatively in terms of D3-branes in AdS5×S. We prove that the insertion of a half-BPS Wilson loop operator in the N = 4 SYM path integral is achieved by integrating out the degrees of freedom on a configuration of bulk D5-branes or alternatively on a configuration of bulk D3-branes. We construct a new class of supersymmetric surface operators in N = 4 SYM and find the corresponding dual supergravity solutions. Consistency requires constructing N =4 SYM in the D7 supergravity background and not in flat space. This enlarges the class of holographic gauge theories dual to string theory backgrounds to gauge theories in non-trivial supergravity backgrounds. We write down a maximally supersymmetric one parameter deformation of the field theory action of Bagger and Lambert and we show that this theory on R×T 2 is invariant under the superalgebra of the maximally supersymmetric Type IIB plane wave. It is argued that this theory holographically describes the Type IIB plane wave in the discrete light-cone quantization (DLCQ). Finally, we show by explicit computation that the Bagger-Lambert Lagrangian realizes the M2-brane superalgebra, including also two p-form central charges that encode the M-theory intersections involving M2-branes.
The classical multivariate regression model with singular covariance matrix

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Evaluation of the potential use of Amazonian palm species for the production of edge-glued panels

The potential of Attalea maripa and Attalea speciosa was evaluated for the production of edge-glued panels. The chemical and physical properties and the quality of the bonding of the species and mixed treatment between them, under different conditions, were evaluated. The length bonding was performed with polyvinyl acetate (PVA) adhesive and the side with PVA and emulsion polymer isocyanate (EPI) in two spreading rates. The species A. maripa has a lower content of extractives in cold and hot water, total extractives, ash and pH, and higher density compared to A. speciosa. The tangential, radial and volumetric shrinkage and shrinkage anisotropy of the species were statistically equal. On the length bonding, only in the tensile test after drying pre-treatment were the requirements of the technical standards not met. As regards side bonding, the treatments of A. maripa and mixed joints with EPI reached the standard for the pre-treatment in cold water.
Unconventional Fermi surface in two-dimensional systems of Dirac fermions

At the low energy regime, the decay rate of two-dimensional massless Dirac fermions due to interactions can be written as $\mathrm{Im}\Sigma(\omega) \propto |\omega|^{x}$ at zero temperature. We find that the fermion system has: I) no sharp Fermi surface and no well-defined quasiparticle peak for $0 1$. In the presence of long-range gauge/Coulomb interaction or certain massless boson mode, the system exhibits unusual behavior belonging to class II.
(S)-3-(3-((7-Ethynyl-9H-pyrimido[4,5-b]indol-4-yl)amino)piperidin-1-yl)propanenitrile

The title compound (S)-3-(3-((7-ethynyl-9H-pyrimido[4,5-b]indol-4-yl)amino)piperidin-1-yl)propanenitrile (2) was synthesized in five steps, starting from 4-chloro-7-iodo-9H-pyrimido[4,5‑b]indole (3), and was characterized by 1H-NMR, 13C-NMR, MS and HPLC. Moreover, its structure was confirmed by single crystal X-ray diffraction. Pyrimido[4,5‑b]indole 2 demonstrated an IC50 value of 2.24 µM in a NanoBRETTM TE intracellular glycogen synthase kinase-3β assay.
Comparison Between Features Extraction Techniques for Impairments Arabic Speech

Automatic Speech Recognition (ASR) is a tough task, with the existence of related noise and high unpredictability in a speech presenting the most severe problems. Especially with regard to the noise of speech impairments, whether due to disability or mispronunciation in children. Extraction of noise-resistant features to compensate for speech degradation due to noise impact has remained a difficult challenge in the last few years. This research investigated the impact of different wavelet generations for extracting speech features, then test the produced dataset from each technique with two types of deep learning techniques : deep long short-term memory (LSTM) and hyper deep learning model convolutional neural network with long short-term memory (CNN-LSTM). The result shows that the deep long short-term memory of MFCC has reached 93% as an accuracy while in the hyper deep learning model of CNN-LSTM the accuracy of MFCC was 91%, as the highest recorded accuracy which proves that MFCC would be the best feature extraction technique for our developed dataset.