Does Preschool Education Experience Help Disadvantaged Students Become Academically Resilient? Empirical Evidence from CEPS Data

Supporting socio-economically disadvantaged students to improve their academic resilience is vital in promoting social mobility and halting intergenerational transmission of poverty. Based on the baseline data 2013-2014 from the China Education Panel Survey (CEPS) database, this paper examined the impact of preschool education experience on academic resilience of disadvantaged students using coarsened exact matching (CEM) and hierarchical logistic regression. Research findings show that disadvantaged children have less access to preschool education than their advantaged peers, with a kindergarten attendance rate lower than 70%; that preschool education can significantly improve children’s cognitive ability and effectively predict academic resilience of disadvantaged students; and that preschool education is a better predictor of resilience of rural disadvantaged students than that of their urban counterparts. The government should reinforce subsidies for early childhood education that can serve as compensation for disadvantaged kids; and should further universalize preschool education to secure the opportunity of high-quality pre-primary school education for disadvantaged children

A New Fusion Fault Diagnosis Method for Fiber Optic Gyroscopes

The fiber optic gyroscope (FOG) is a high precision inertial navigation device, and it is necessary to ensure its reliability for effective use. However, the extracted fault features are easily distorted due to the interference of vibrations when the FOG is in operation. In order to minimize the influence of vibrations to the greatest extent, a fusion diagnosis method was proposed in this paper. It extracted features from fault data with Fast Fourier Transform (FFT) and wavelet packet decomposition (WPD), and built a strong diagnostic classifier with a sparse auto encoder (SAE) and a neural network (NN). Then, a fusion neural network model was established based on the diagnostic output probabilities of the two primary classifiers, which improved the diagnostic accuracy and the anti-vibration capability. Then, five fault types of the FOG under random vibration conditions were established. Fault data sets were collected and generated for experimental comparison with other methods. The results showed that the proposed fusion fault diagnosis method could perform effective and robust fault diagnosis for the FOG under vibration conditions with a high diagnostic accuracy

Forbidden Pairs of Disconnected Graphs for Traceability of Block-Chains

Each traceable graph must be a block-chain; however, a block-chain is not necessarily traceable in general. Whether a given graph is a block-chain or not can be easily verified by a polynomial algorithm. It occurs to us that forbidden subgraph conditions for a block-chain are traceable. In this article, we characterize all pairs of disconnected forbidden subgraphs for the traceability of block-chains, so as to completely solve pairs of forbidden subgraphs for the traceability of block-chains (including disconnected and connected)

A New Fusion Fault Diagnosis Method for Fiber Optic Gyroscopes

The fiber optic gyroscope (FOG) is a high precision inertial navigation device, and it is necessary to ensure its reliability for effective use. However, the extracted fault features are easily distorted due to the interference of vibrations when the FOG is in operation. In order to minimize the influence of vibrations to the greatest extent, a fusion diagnosis method was proposed in this paper. It extracted features from fault data with Fast Fourier Transform (FFT) and wavelet packet decomposition (WPD), and built a strong diagnostic classifier with a sparse auto encoder (SAE) and a neural network (NN). Then, a fusion neural network model was established based on the diagnostic output probabilities of the two primary classifiers, which improved the diagnostic accuracy and the anti-vibration capability. Then, five fault types of the FOG under random vibration conditions were established. Fault data sets were collected and generated for experimental comparison with other methods. The results showed that the proposed fusion fault diagnosis method could perform effective and robust fault diagnosis for the FOG under vibration conditions with a high diagnostic accuracy

Forbidden Pairs of Disconnected Graphs for Traceability of Block-Chains

Each traceable graph must be a block-chain; however, a block-chain is not necessarily traceable in general. Whether a given graph is a block-chain or not can be easily verified by a polynomial algorithm. It occurs to us that forbidden subgraph conditions for a block-chain are traceable. In this article, we characterize all pairs of disconnected forbidden subgraphs for the traceability of block-chains, so as to completely solve pairs of forbidden subgraphs for the traceability of block-chains (including disconnected and connected)

An Integrated Compensation Method for the Force Disturbance of a Six-Axis Force Sensor in Complex Manufacturing Scenarios

As one of the key components for active compliance control and human–robot collaboration, a six-axis force sensor is often used for a robot to obtain contact forces. However, a significant problem is the distortion between the contact forces and the data conveyed by the six-axis force sensor because of its zero drift, system error, and gravity of robot end-effector. To eliminate the above disturbances, an integrated compensation method is proposed, which uses a deep learning network and the least squares method to realize the zero-point prediction and tool load identification, respectively. After that, the proposed method can automatically complete compensation for the six-axis force sensor in complex manufacturing scenarios. Additionally, the experimental results demonstrate that the proposed method can provide effective and robust compensation for force disturbance and achieve high measurement accuracy

A Wind-Solar Hybrid Energy Harvesting Approach Based on Wind-Induced Vibration Structure Applied in Smart Agriculture

Solar energy harvesting devices are widely used in smart agriculture nowadays. However, when lighting conditions are weak, such as through the night or on cloudy days, efficiency decays a lot. Additionally, as time goes by, more and more dust and bird droppings accumulate on the panel, which decreases the performance significantly. This paper aims to overcome the disadvantages mentioned above, and a novel wind–solar hybrid energy harvesting approach is proposed with an oscillation-induced dust-cleaning function. A wind-induced vibration device is specially designed in order to generate electrical energy and/or clean the photovoltaic panel. While in good lighting conditions, the device could keep the panel in a stable state and optimize the photovoltaic power generation efficiency. Such a hybrid energy harvesting approach is called a “suppress vibration and fill vacancy” algorithm. The experimental platform of the proposed device is introduced, and both experimental and simulation results are attained, which prove that using this device, we could realize multiple purposes at the same time

Interplay between Gut Microbiota and NLRP3 Inflammasome in Intracerebral Hemorrhage

The pathophysiological process of intracerebral hemorrhage (ICH) is very complex, involving various mechanisms such as apoptosis, oxidative stress and inflammation. As one of the key factors, the inflammatory response is responsible for the pathological process of acute brain injury and is associated with the prognosis of patients. Abnormal or dysregulated inflammatory responses after ICH can aggravate cell damage in the injured brain tissue. The NOD-like receptor family pyrin domain-containing 3 (NLRP3) inflammasome is a multiprotein complex distributed in the cytosol, which can be triggered by multiple signals. The NLRP3 inflammasome is activated after ICH, thus promoting neuroinflammation and aggravating brain edema. In addition, there is evidence that the gut microbiota is crucial in the activation of the NLRP3 inflammasome. The gut microbiota plays a key role in a variety of CNS disorders. Changes in the diversity and species of the gut microbiota affect neuroinflammation through the activation of the NLRP3 inflammasome and the release of inflammatory cytokines. In turn, the gut microbiota composition can be influenced by the activation of the NLRP3 inflammasome. Thereby, the regulation of the microbe–gut–brain axis via the NLRP3 inflammasome may serve as a novel idea for protecting against secondary brain injury (SBI) in ICH patients. Here, we review the recent evidence on the functions of the NLRP3 inflammasome and the gut microbiota in ICH, as well as their interactions, during the pathological process of ICH

Agrin-Lrp4-Ror2 signaling regulates adult hippocampal neurogenesis in mice.

Adult neurogenesis in the hippocampus may represent a form of plasticity in brain functions including mood, learning and memory. However, mechanisms underlying neural stem/progenitor cells (NSPCs) proliferation are not well understood. We found that Agrin, a factor critical for neuromuscular junction formation, is elevated in the hippocampus of mice that are stimulated by enriched environment (EE). Genetic deletion of the Agrn gene in excitatory neurons decreases NSPCs proliferation and increases depressive-like behavior. Low-density lipoprotein receptor-related protein 4 (Lrp4), a receptor for Agrin, is expressed in hippocampal NSPCs and its mutation blocked basal as well as EE-induced NSPCs proliferation and maturation of newborn neurons. Finally, we show that Lrp4 interacts with and activates receptor tyrosine kinase-like orphan receptor 2 (Ror2); and Ror2 mutation impairs NSPCs proliferation. Together, these observations identify a role of Agrin-Lrp4-Ror2 signaling for adult neurogenesis, uncovering previously unexpected functions of Agrin and Lrp4 in the brain

Metabolite profiles of essential oils in citrus peels and their taxonomic implications

China is an important center of origin for the genus Citrus L. of the family Rutaceae and is rich in wild Citrus species. The taxonomy of Citrus has been a subject of controversy for more than a half century. We propose that the metabolite profiles of Chinese native Citrus species can be used for classification and understanding of the taxonomic relationships within the Citrus germplasm. In this study, triplicate gas chromatography-mass spectrometry (GC-MS) metabolite profiles of 20 Citrus species/varieties were acquired, including 10 native varieties originating in China. R-(+)-limonene, alpha-pinene, sabinene and alpha-terpinene were found to be major characteristic components of the essential oils analyzed in this study, and these compounds contributed greatly to the metabolic classification. The three basic species of the subgenus Eucitrus (Swingle's system), i.e., C. reticulata Blanco, C. medica L. and C. grandis Osb., were clearly differentiated based upon their metabolite profiles using hierarchical cluster analysis (HCA) and partial least square-discriminant analysis (PLS-DA). All the presumed hybrid genotypes, including sweet orange (C. sinensis Osb.), sour orange (C. aurantium L.), lemon (C. limon Burm.f.), rough lemon (C. jambhiri Lush.), rangpur lime (C. limonia Osb.) and grapefruit (C. paradisi Macf.), were grouped closely together with one of their suggested parent species in the HCA-dendrogram and the PLS-DA score plot. These results clearly demonstrated that the metabolite profiles of Citrus species could be utilized for the taxonomic classification of the genus and are complementary to the existing taxonomic evidence, especially for the identification and differentiation of hybrid species