Early diagnosis of Alzheimer’s disease (AD) is a proceeding hot issue along with a sharp upward trend in the incidence rate. Recently, early diagnosis of AD employing Electroencephalogram (EEG) as a ...specific hallmark has been an increasingly significant hot topic area. In consideration of the limited size of available EEG spectral images, how to extract more abstract features for better generalization still remains tremendously troubling. In this paper, we demonstrate that it can be settled well with multi-task learning strategy based on discriminative convolutional high-order Boltzmann Machine with hybrid feature maps. First, differently from our original model — Contractive Slab and Spike Convolutional Deep Boltzmann Machine (CssCDBM), we directly conduct EEG spectral image classification via inducing label layer, resulting in a discriminative version of CssCDBM, referred to as DCssCDBM. This demonstrates DCssCDBM can be extended well into the classification model instead of feature extractor alone previously. Then, the most important approach innovation is that we train our DCssCDBM with multi-task learning framework via EEG spectral images based Identification and verification tasks for overfitting reduction for the first time, which could increase the inter-subject variations and reduce the intra-subject variations respectively, both of which are essential to early diagnosis of AD. The proposed method shows the better ability of high-level representations extraction and demonstrates the advanced results over several state-of-the-art methods.
University physical education is an important public basic course in colleges and universities. The traditional teaching is usually within the class time specified in the training program; the ...teacher teaches the students the basic physical education fundamentals so that the students can master the basic skills of sports, thus improving the students’ sports level and physical quality. An improved genetic algorithm is proposed to reduce the problem of slow convergence and partial convergence of the fundamental genetic algorithm for intelligent grouping systems. To ensure the group’s stability and variety, the algorithm can rapidly extend the search space by repeatedly rejecting similar individuals. Therefore, this study proposes a new method of intelligent grouping based on the improved genetic algorithm. The new method can overcome the problem of premature convergence of the algorithm more efficiently and easily than the traditional algorithm. A large number of experiments have proved that the proposed algorithm meets all the requirements of physical education very well. The algorithm can automatically generate test papers with moderate difficulty and reasonable structure.
...the lung autonomic nervous system may be provoked by pulmonary exposure, which could then result in autonomic nervous system imbalance 15; the levels of stress hormones may also be altered 16. ...Ninety-five percent of the total Chinese population was covered by social health insurance schemes by the end of 2017 21. ...hospital admission records can provide reliable and timely information on the health status of a geographically defined population in China. Since January 1, 2013, class 3 hospitals in China have been mandated to automatically submit inpatient discharge records to the HQMS on a daily basis, in a nationally standardized format. All data used were anonymized and de-identified prior to analysis, under the supervision of Bureau of Medical Administration, National Health Commission of the People’s Republic of China. Because the data were analyzed at the aggregate level with no individual identifiers involved, institutional review board approval and participant written consent were not required for this study.
Effect of electropulsing-assisted ultrasonic surface rolling process (EP-USRP) on surface mechanical properties and microstructure of Ti-6Al-4V alloy was investigated. Compared with the original ...ultrasonic surface rolling process (USRP), the introduction of electropulsing with optimal parameters can effectively facilitate surface cracks healing, improve surface microhardness and wear resistance. In addition, the residual compressive stress is further enhanced under EP-USRP. Rapid improvement of the surface mechanical properties should be attributed to the ultra-refined grains and enhanced plastic deformation caused by the coupling effect of USRP and electropulsing. High strain rate given by USRP, the accelerated dislocation mobility and atom diffusion induced by electropulsing are likely the primary intrinsic reasons for the observed phenomena.
The mitogen-activated protein kinase (MAPK) cascade is a highly conserved signaling transduction module that transduces extracellular stimuli into intracellular responses in plants. Early studies of ...plant MAPKs focused on their functions in model plants. Based on the results of whole-genome sequencing, many MAPKs have been identified in horticultural plants, such as tomato and apple. Recent studies revealed that the MAPK cascade also plays crucial roles in the biotic and abiotic stress responses of horticultural plants. In this review, we summarize the composition and classification of MAPK cascades in horticultural plants and recent research on this cascade in responses to abiotic stresses (such as drought, extreme temperature and high salinity) and biotic stresses (such as pathogen infection). In addition, we discuss the most advanced research themes related to plant MAPK cascades, thus facilitating research on MAPK cascade functions in horticultural plants.
The present work integrates 3D digital optical microscopy (OM), nano-indentation, X-ray diffraction (XRD), scanning electron microscopy (SEM) with electron backscatter diffraction (EBSD) and ...transmission electron microscopy (TEM) to systematically investigate the effect of electropulsing on the surface mechanical properties and microstructure of AISI 304 stainless steel during the ultrasonic surface rolling process (USRP). Compared with the original USRP, the introduction of electropulsing with optimal parameters can effectively facilitate surface crack healing and improve surface hardness and wear resistance dramatically, and the residual compressive stress is further enhanced. Meanwhile, more martensite phase and fewer deformation twins can be found in the strengthened layer. Rapid improvement of the surface mechanical properties should be attributed to the ultra-refined grains, accelerated martensitic phase transformation and suppressed deformation twining induced by the coupling effect of USRP and electropulsing. The high strain rate given by USRP, increased stacking fault energy and accelerated dislocation mobility caused by electropulsing are likely the primary intrinsic reasons for the observed phenomena.
Display omitted
•A waterproof, highly sensitive, and multi-mode sensor was successfully prepared.•The sensor was constructed by 2D MXene nanosheets and 0D silicon nanoparticles.•The multifunctional ...sensor exhibited excellent sensing and broad sensing ranges.•The dual layers endowed the sensor with waterproof and antifouling performance.
Superhydrophobic wearable textile electronics are of great promising devices due to their broad application in healthcare monitoring and artificial intelligence, etc. MXenes, the emerging two‐dimensional (2D) carbides and carbonitrides, are ideal candidates for fabricating wearable electronic devices. However, in an effort to maintain its highly conductive and long-term stability of cotton fabrics under harsh condition, it is an imperative demand to prepare superhydrophobic MXenes-textile sensor. Herein, a waterproof, highly sensitive, and wearable multi-mode sensor was successfully fabricated by the construction of 2D MXene nanosheets and zero-dimension (0D) silicon nanoparticles (SiNPs) onto a cotton fibers substrate (MX@SiNPs cotton). The as-cultivated MX@SiNPs cotton exhibited an integration of high-performance sensing toward pression, bending, and torsion, superior sensitivity (S1 = 12.23 kPa−1), a stable response under press-relaxing cycles, and broad sensing ranges (pressure: 8.8 Pa – 70 kPa, bending: 0 – 180°, torsion: 0 – 628 rad m−1). By virtue of its hierarchical structure and low surface energy of the SiNPs layer, the MXenes-textile sensor can maintain intrinsic conductivity under wet and corrosive conditions. The waterproof and highly sensitive MXene-reinforced cotton fabrics lay a basis for the broad-range application and large-scale preparation of the sensor and prolong its service life.
The Qilian Mountain ecosystems play an irreplaceable role in maintaining ecological security in western China. Vegetation, as an important part of the ecosystem, has undergone considerable changes in ...recent decades in this area, but few studies have focused on the process of vegetation change. A long normalized difference vegetation index (NDVI) time series dataset based on remote sensing is an effective tool to investigate large-scale vegetation change dynamics. The MODerate resolution Imaging Spectroradiometer (MODIS) NDVI dataset has provided very detailed regional to global information on the state of vegetation since 2000. The aim of this study was to explore the spatial-temporal characteristics of abrupt vegetation changes and detect their potential drivers in the Qilian Mountain area using MODIS NDVI data with 1 km resolution from 2000 to 2017. The Breaks for Additive Season and Trend (BFAST) algorithm was adopted to detect vegetation breakpoint change times and magnitudes from satellite observations. Our results indicated that approximately 80.1% of vegetation areas experienced at least one abrupt change from 2000 to 2017, and most of these areas were distributed in the southern and northern parts of the study area, especially the area surrounding Qinghai Lake. The abrupt browning changes were much more widespread than the abrupt greening changes for most years of the study period. Environmental factors and anthropogenic activities mainly drove the abrupt vegetation changes. Long-term overgrazing is likely the main cause of the abrupt browning changes. In addition, our results indicate that national ecological protection policies have achieved positive effects in the study area.
Abstract In order to investigate the effects of strain rate and water saturation on the energy dissipation and crack growth of tuff, uniaxial compression tests were carried out on dry and water ...saturated tuff with different strain rates using an electro-hydraulic servo press and a 50 mm diameter split Hopkinson pressure rod (SHPB) device. High-speed camera and Image J image analysis software were used to obtain the crack growth process of the specimen under impact load, and fractal dimension was introduced to quantitatively study the crack growth degree. The results show that more than 90% of the energy is stored in the specimen as elastic energy when it reaches the peak stress under static load. The average total energy of water-saturated specimens is 67.55% of that of dry specimens. The average energy dissipation density of water-saturated specimens under 0.3 MPa, 0.4 MPa and 0.5 MPa air pressure is 0.79, 0.91 and 0.92 times of that of dry specimens, respectively. Water-saturated specimens will deteriorate and thus reduce their energy storage and energy absorption effects. The reflected energy, transmitted energy, absorbed energy and incident energy are linear, logarithmic and linear functions, respectively, and the energy absorptivity and specific energy absorptivity of water-saturated specimens are lower than those of dry specimens. Due to the existence of “stefan” effect, the increase of energy dissipation density of water-saturated specimen at high strain rate is greater than that of dry specimen. The mean fractal dimension of water-saturated specimens under 0.3 MPa, 0.4 MPa and 0.5 MPa is 1.09, 1.05 and 1.16 times that of dry specimens. At the same strain rate, the number and width of cracks in water-saturated specimens are larger than that in dry specimens. Water-saturated behavior reduces the energy absorption capacity of tuff, increases the fractal dimension of crack growth, and significantly reduces the resistance of water-saturated rock to external loads.
This paper proposes a novel soft sensor modeling approach, MIC-TCA-INGO-LSSVM, to address the decline in performance of soft sensor models during the fermentation process of
, caused by changes in ...working conditions. Initially, the transfer component analysis (TCA) method is utilized to minimize the differences in data distribution across various working conditions. Subsequently, a least squares support vector machine (LSSVM) model is constructed using the dataset adapted by TCA, and strategies for improving the northern goshawk optimization (INGO) algorithm are proposed to optimize the parameters of the LSSVM model. Finally, to further enhance the model's generalization ability and prediction accuracy, considering the transfer of knowledge from multiple-source working conditions, a sub-model weighted ensemble scheme is proposed based on the maximum information coefficient (MIC) algorithm. The proposed soft sensor model is employed to predict cell and product concentrations during the fermentation process of
. Simulation results indicate that the
of the INGO-LSSVM model in predicting cell and product concentrations is reduced by 47.3% and 42.1%, respectively, compared to the NGO-LSSVM model. Additionally, TCA significantly enhances the model's adaptability when working conditions change. Moreover, the soft sensor model based on TCA and the MIC-weighted ensemble method achieves a reduction of 41.6% and 31.3% in the
for predicting cell and product concentrations, respectively, compared to the single-source condition transfer model TCA-INGO-LSSVM. These results demonstrate the high reliability and predictive performance of the proposed soft sensor method under varying working conditions.
PDF
