Miracle-dz/UniFAR-FTUs huggingface dataset:
The application and enlightenment of augmented reality technology in social disorders of autistic patients

Autism spectrum disorder (ASD) is a specific type of generalized developmental disorder characterized by social communication disorders and narrow, repetitive stereotypical behaviors. Augmented reality (AR) has gained considerable attention and research in the developmental intervention of autism with its combination of virtual and real characteristics. This paper systematically understands the characteristics of social disorders in autistic patients and the current effective intervention methods, and obtains the development enlightenment for effective intervention for autistic patients. Through the in-depth exploration of the definition and characteristics of augmented reality technology, the feature matching between augmented reality technology and autistic social communication disorder is analyzed in detail, and then the feasibility of application is obtained. Finally, based on the research examples of foreign augmented reality technology in the social disorders of autistic patients, the development enlightenment of future augmented reality technology in autistic patients with social disorders is summarized.
Improved Criminisi algorithm constrained by local feature

When computing the order of target patch priority, Criminisi algorithm has defect, to solve this problem, an improved inpainting algorithm is proposed. To establish a new function about priority, the proposed algorithm takes into account local feature of image—curvature and gradient, and uses the information of curvature and gradient as a data item. Thus, the order of filling inpainting is more reliably. The experimental results show that the proposed algorithm overcomes the problem of excessive diffusion from high texture area to low area, and obtains better visual appearance.
Assessment of Individual Radiosensitivity in Breast Cancer Patients Using a Combination of Biomolecular Markers

About 5% of patients undergoing radiotherapy (RT) develop RT-related side effects. To assess individual radiosensitivity, we collected peripheral blood from breast cancer patients before, during and after the RT, and γH2AX/53BP1 foci, apoptosis, chromosomal aberrations (CAs) and micronuclei (MN) were analyzed and correlated with the healthy tissue side effects assessed by the RTOG/EORTC criteria. The results showed a significantly higher level of γH2AX/53BP1 foci before the RT in radiosensitive (RS) patients in comparison to normal responding patients (NOR). Analysis of apoptosis did not reveal any correlation with side effects. CA and MN assays displayed an increase in genomic instability during and after RT and a higher frequency of MN in the lymphocytes of RS patients. We also studied time kinetics of γH2AX/53BP1 foci and apoptosis after in vitro irradiation of lymphocytes. Higher levels of primary 53BP1 and co-localizing γH2AX/53BP1 foci were detected in cells from RS patients as compared to NOR patients, while no difference in the residual foci or apoptotic response was found. The data suggested impaired DNA damage response in cells from RS patients. We suggest γH2AX/53BP1 foci and MN as potential biomarkers of individual radiosensitivity, but they need to be evaluated with a larger cohort of patients for clinics.
Parkinson's Disease Detection Using Acoustic features from Speech recordings

With more than 10 million cases globally, Parkinson's disease is the second most common neurological disorder after Alzheimer's. Parkinson's disease is generally distinguished by a decline in motor and cognitive function. There isn't a single test that can be used to make a diagnosis. Instead, a thorough clinical analysis of the patient's medical history is what medical professionals must conduct. According to National Institute of Neurological Disorders research, the accuracy of an early diagnosis-defined as five years or less of symptoms-is only 53%. Although this isn't much better than winging it, prompt diagnosis is essential for successful treatment. The obtained most heavily weighted feature vector is used for classification. The proposed method makes use of the methods for linear discriminant analysis (LDA), K Nearest Neighbor (KNN), decision tree (DT), and neural network (NN). Three major contributions are made by the suggested method. Finding features from the Parkinson's acoustic dataset is the initial step. The second step is choosing the most important characteristics from the numerous produced feature vectors. The third is the addition of UPDRS-defined data, which includes motor symptoms for Parkinson's disease detection. As a consequence, KNN, LDA, DT, NN, and GB algorithms produced effective results for both acoustic characteristics and UPDRS specified data. The compared results unmistakably demonstrate that, among the chosen state-of-the-art procedures, the UPDRS established parameters attained the best success rate. Given the proposed approach and the outcomes, the approach is effective for identifying Parkinson's disease.
A Realistic Mixture of Persistent Organic Pollutants Affects Zebrafish Development, Behavior, and Specifically Eye Formation by Inhibiting the Condensin I Complex

Persistent organic pollutants (POPs) are posing major environmental and health threats due to their stability, ubiquity, and bioaccumulation. Most of the numerous studies of these compounds deal with single chemicals, although real exposures always consist of mixtures. Thus, using different tests, we screened the effects on zebrafish larvae caused by exposure to an environmentally relevant POP mixture. Our mixture consisted of 29 chemicals as found in the blood of a Scandinavian human population. Larvae exposed to this POP mix at realistic concentrations, or sub-mixtures thereof, presented growth retardation, edemas, retarded swim bladder inflation, hyperactive swimming behavior, and other striking malformations such as microphthalmia. The most deleterious compounds in the mixture belong to the per- and polyfluorinated acids class, although chlorinated and brominated compounds modulated the effects. Analyzing the changes in transcriptome caused by POP exposure, we observed an increase of insulin signaling and identified genes involved in brain and eye development, leading us to propose that the impaired function of the condensin I complex caused the observed eye defect. Our findings contribute to the understanding of POP mixtures, their consequences, and potential threats to human and animal populations, indicating that more mechanistic, monitoring, and long-term studies are imperative.
Demodulation Method of Tilted Fiber Bragg Grating Refractometer Based on Gramian Angle Field Algorithm and 2D Residual Convolutional Neural Networks

Tilted fiber Bragg grating (TFBG) has been widely for refractive index sensing due to their high sensitivity, but there is a lack of demodulation method with high accuracy and good real-time performance. In this paper, a novel demodulation method based on deep learning is proposed and verified experimentally. As demonstration, a bare TFBG working as refractometer was immersed in sodium chloride solution with different concentrations, whose RI ranged from 1.3350 to 1.3790 RIU. The dimension of the acquired transmission spectra was ascended by the Gramian Angle Field (GAF) algorithm and then demodulated by a 2D residual convolutional neural networks (CNN). As a result, the accuracy represented by the coefficient of determination reaches 99.75% and the mean square error (MSE) is only 4.049 × 10-7. The proposed method makes it possible to achieve a fast, robust, and accurate demodulation TFBG refractive index sensing scheme.
Whole Genome Resequencing Reveals Genetic Diversity and Selection Signatures of Ethiopian Indigenous Cattle Adapted to Local Environments

Cattle are among the most important domesticated bovid species in the world, of which Ethiopia possesses large populations adapted to different agro-ecologies and production systems. Though several molecular population genetic studies have been done on Ethiopian indigenous cattle, genomic diversity and selection signatures pertinent to adaptation to the different local environments have yet to be comprehensively characterized. Here, the whole genome sequences of 151 samples from 14 Ethiopian indigenous cattle populations were analyzed to assess genomic diversity and differentiation as well as signatures of positive selection (using Hp, iHS, FST, and XP-CLR) in comparison to Sudanese zebu, Asian zebu, Ankole, and African and European taurine cattle. High genomic differentiation was observed between Ethiopian and non-Ethiopian cattle populations, while low genomic differentiation and inbreeding were present between and within Ethiopian cattle populations. Sixteen genome regions overlapping with 40 candidate genes were commonly identified by at least three genome scan methods. High frequencies of missense SNPs in ITPR2, CHADL, GNAS, STING1, and KIT genes with high haplotype differentiations were observed in Ethiopian cattle compared to non-Ethiopian cattle. The candidate genes were significantly associated with several biological functions and molecular pathways responsible for nutrient metabolism, skeletal development, immune response, reproduction, water balance, coat color pigmentation, and circulatory homeostasis. Our results provide new insights into the adaptation of the Ethiopian indigenous cattle to the country’s diverse environments.
Construction and Accurate Retrieval Method of Knowledge Graph of Automobile Engine Fault

In order to improve the efficiency and accuracy of automobile engine fault maintenance, an accurate retrieval method of automobile engine fault driven by knowledge graph was proposed. Firstly, the definition and framework of knowledge graph are discussed. The entity extraction of engine fault features was carried out by multi-source neural network, and the disambiguation of fault entities was carried out by integrating entity link technologies; Secondly, fault knowledge reasoning is carried out to eliminate the wrong knowledge in the knowledge base and infer new knowledge to form a complete knowledge graph. On this basis, the retrieval subgraph of engine fault semantics is designed. Combined with the influence of physical distance and proximity, the retrieval result evaluation model is established, and the subgraph matching was carried out based on the similarity calculation of graph structure and semantic information. Finally, four knowledge graphs including entity equipment graph, ontology graph, maintenance rule graph and history graph were constructed by selecting some automobile engine fault cases from 2017 to 2020. Finally, the process architecture of engine fault search and analysis is constructed and the effectiveness of the proposed method was verified by precision rate and recall rata, which provides a new idea for accurate and efficient engine maintenance.
A New One-Tube Reaction Assay for the Universal Determination of Sweet Cherry (Prunus avium L.) Self-(In)Compatible MGST- and S-Alleles Using Capillary Fragment Analysis

The sweet cherry plant (Prunus avium L.) is primarily self-incompatible, with so-called S-alleles responsible for the inability of flowers to be pollinated not only by their own pollen grains but also by pollen from other cherries having the same S-alleles. This characteristic has wide-ranging impacts on commercial growing, harvesting, and breeding. However, mutations in S-alleles as well as changes in the expression of M locus-encoded glutathione-S-transferase (MGST) can lead to complete or partial self-compatibility, simplifying orchard management and reducing possible crop losses. Knowledge of S-alleles is important for growers and breeders, but current determination methods are challenging, requiring several PCR runs. Here we present a system for the identification of multiple S-alleles and MGST promoter variants in one-tube PCR, with subsequent fragment analysis on a capillary genetic analyzer. The assay was shown to unequivocally determine three MGST alleles, 14 self-incompatible S-alleles, and all three known self-compatible S-alleles (S3′, S4′, S5′) in 55 combinations tested, and thus it is especially suitable for routine S-allele diagnostics and molecular marker-assisted breeding for self-compatible sweet cherries. In addition, we identified a previously unknown S-allele in the ’Techlovicka´ genotype (S54) and a new variant of the MGST promoter with an 8-bp deletion in the ´Kronio´ cultivar.
Optic fiber magnetic field sensor based on the miniature pull-taper twin-core photonic crystal fiber

In this paper, a optical fiber magnetic field sensor with the tapered twin-core photonic crystal fiber coupling structure has been presented and experimentally demonstrated. The magnetic fluid(MF) was injected into the air hole between two parallel fiber cores of the photonic crystal fiber, and then the two fiber cores of the twin-core photonic crystal fiber infiltrated with MF were tapered horizontally. A total reflection film was plated on the end face of the fusion cone of the twin-core photonic crystal fiber. The guided light was transmitted from the input port to the pull-cone area of the fiber, reflected by the reflection end face after mode coupling, and finally reflected from the output port to the output fiber. According to the above light transmission mode of reflective fiber coupler, The magnetic field measurement principle was studied, which the coupling region of twin-core photonic crystal fiber had been injected MF. The change of the effective refractive index will lead to the central wavelength shift of the fiber magnetic field coupler, and the magnetic field is measured by inquiring the wavelength shift of the transmission spectrum. The magnetic field response characteristics of the sensor were studied and analyzed through experiments. The experimental results show that the sensitivity of this method can reach 116pm/Oe. The fiber magnetic field sensor has the advantages of miniaturization, high sensitivity, and no electromagnetic interference. The magnetic field sensor can be widely used in the field of aviation, military, transportation, medicine and other fields of magnetic field accurate measurement.
Realization Criteria of Terminal Radiation Disposition on Circular Aperture Antenna

A radiation pattern in three dimensions is observed for proper evaluation of the Terminal Radiation Disposition (TRD) of circular aperture antenna. The clarity on stroboscopic symmetry, well-defined for larger number of points from a database, allows for rigorous practical designs to be realized. Evaluation is computationally performed with Scilab using simple mathematical manipulations for using predefined Bessel functions in integration. All the results look similar unless a particular value is sought after.
Phylogenetic and Structural Analysis of Porcine Circovirus Type 2 from 2016 to 2021 in Jilin Province, China

Porcine circovirus disease (PCVD) caused by porcine circovirus type 2 (PCV2) is widely distributed in pig farms. Up until now, nine genotypes of PCV2, PCV2a to 2i, have been identified in diseased pigs worldwide. This study analyzed 302 samples collected in the Jilin Province of China from 2016 to 2021, followed by genetic analysis of the PCV2 isolates. Meanwhile, the antigen epitopes, amino acid mutations, 3D structure of the PCV2 isolates and commercially available vaccine strains were evaluated and compared. The results showed that the predominant genotypes of PCV2 were PCV2b, followed by PCV2e and PCV2d in Jilin Province during 2016–2021. Although mutations were detected in the isolates, no recombination occurred in the PCV2 isolates, indicating a stable genotype of PCV2 in Jilin Province during these years. Moreover, the B cell epitopes in the Cap and Rep proteins of eighteen PCV2 isolates and T cell epitopes in the Cap of the isolates were changed compared to three currently used vaccine strains. The mutations in the Cap and Rep proteins did not affect their spatial conformation. Therefore, bivalent or multivalent vaccines with different genotypes of PCV2 might improve the protective effect of vaccines.
Interaction between Cigarette Smoke and Human Papillomavirus 16 E6/E7 Oncoproteins to Induce SOD2 Expression and DNA Damage in Head and Neck Cancer

Even though epidemiological studies suggest that tobacco smoking and high-risk human papillomavirus (HR-HPV) infection are mutually exclusive risk factors for developing head and neck cancer (HNC), a portion of subjects who develop this heterogeneous group of cancers are both HPV-positive and smokers. Both carcinogenic factors are associated with increased oxidative stress (OS) and DNA damage. It has been suggested that superoxide dismutase 2 (SOD2) can be independently regulated by cigarette smoke and HPV, increasing adaptation to OS and tumor progression. In this study, we analyzed SOD2 levels and DNA damage in oral cells ectopically expressing HPV16 E6/E7 oncoproteins and exposed to cigarette smoke condensate (CSC). Additionally, we analyzed SOD2 transcripts in The Cancer Genome Atlas (TCGA) Head and Neck Cancer Database. We found that oral cells expressing HPV16 E6/E7 oncoproteins exposed to CSC synergistically increased SOD2 levels and DNA damage. Additionally, the SOD2 regulation by E6, occurs in an Akt1 and ATM-independent manner. This study suggests that HPV and cigarette smoke interaction in HNC promotes SOD2 alterations, leading to increased DNA damage and, in turn, contributing to development of a different clinical entity.
Design of Three Mode Groups EDFA for Modal Gain Equalization and Wavelength Gain Flatness in the C+L Band

In this paper, a novel design of three mode groups Erbium-doped fiber amplifier (FM-EDFA) with a cladding pump and large area Erbium-doped structure for modal gain equalization and wavelength gain flatness in the C+L band is proposed. The simulation results show that the differential modal gain (DMG) in our proposed FM-EDFA is less than 0.67dB at 1550nm. The DMG is less than 0.7dB and the wavelength gain difference is less than 1.1dB in the whole C+L band (1538-1605nm).
Enhancing Tool Wear Prediction Accuracy Using Walsh–Hadamard Transform, DCGAN and Dragonfly Algorithm-Based Feature Selection

Tool wear is an important concern in the manufacturing sector that leads to quality loss, lower productivity, and increased downtime. In recent years, there has been a rise in the popularity of implementing TCM systems using various signal processing methods and machine learning algorithms. In the present paper, the authors propose a TCM system that incorporates the Walsh–Hadamard transform for signal processing, DCGAN aims to circumvent the issue of the availability of limited experimental dataset, and the exploration of three machine learning models: support vector regression, gradient boosting regression, and recurrent neural network for tool wear prediction. The mean absolute error, mean square error and root mean square error are used to assess the prediction errors from three machine learning models. To identify these relevant features, three metaheuristic optimization feature selection algorithms, Dragonfly, Harris hawk, and Genetic algorithms, were explored, and prediction results were compared. The results show that the feature selected through Dragonfly algorithms exhibited the least MSE (0.03), RMSE (0.17), and MAE (0.14) with a recurrent neural network model. By identifying the tool wear patterns and predicting when maintenance is required, the proposed methodology could help manufacturing companies save money on repairs and replacements, as well as reduce overall production costs by minimizing downtime.
Spurs in Millimeter-Wave FMCW Radar System-on-Chip

In this article, we present a nonlinear system model to evaluate the impact of circuit nonlinearities on spurs in millimeter-wave frequency-modulated continuous-wave (FMCW) radar system-on-chips. The developed model includes the harmonics of the frequency multiplier, nonlinearity of the power amplifier (PA) and low-noise amplifier (LNA), the switching operation of the receiver (RX) mixer, the limited bandwidth of the PA, LNA, transmitter (TX) and RX antennas, as well as the TX-to-RX leakage. The developed nonlinear model can be used to derive the frequency and amplitude of spurs in the radar IF spectrum. The major insights about the impact of different nonlinearities and their interactions are as follows: 1) harmonics of the frequency multiplier appear as spurs in the IF spectrum; 2) PA can be driven in its nonlinear region to mitigate the harmonics generated by the frequency multiplier; 3) bandwidth of the TX and RX systems should be limited to attenuate the undesired harmonics; and 4) interaction between the TX-to-RX leakage signal and the LNA nonlinearity can lead to spurs close to the target echo in the IF spectrum.
Fault diagnosis method of fan bearing at non- extended end of motor based on resonant sparse decomposition and variational modal decomposition

A fan is usually installed at the non-extended end of the motor to cool down the motor during operation. When this fan bearing fails, there is interference of high amplitude accidental shock in the vibration signal, and the difficulty of obtaining fault characteristic frequencies is thus greatly increased. With the aim of achieving a steady acquisition of failure frequency characteristics, a combination of resonant sparse and variational modal decomposition is suggested to determine the fault condition of the fan bearing at the non- extended end of the motor. Resonance-based sparse signal decomposition method isolates the low resonance components from the vibration signal that carry fault information, based on the different quality factors of the individual components of the signal, and then broken down into a number of modal elements using the variational modal discretization method, with the highest cliff element selected to conduct a Hilbert transformation envelope spectrum and select the fault characteristic frequencies. Results of the experiments have demonstrated that the present presented algorithm mitigates the distraction of the man-made empirical adaptation of the quality faction in the Resonant Sparse Decomposition, which is difficult to remove the more non periodic high magnitude shock elements in the low frequencies components. In addition, the experiments also compare the bearing fault diagnosis method of resonant sparse decomposition combined with wavelet decomposition, and the results show that the selection of variational modal decomposition in the envelope demodulation session can highlight the fault characteristic frequencies better than the selection of traditional wavelet decomposition, which distinguishes more precisely between types of bearing failures and has certain practical value.