Search

This paper proposes a blended teaching design model on WeChat Platform-based SPOC and carries out an empirical study of its applicability and effectiveness in lower-secondary school science teaching. Participants were from two eighth grade classes with different academic performance, and after conducting the same blended teaching, it was found that pre-class knowledge transmission on the WeChat platform considerably enhanced students' classroom participation, which was also highly related to their improved post-class test scores. The improvement in the performance of the weak groups is more obvious. In addition, backend data from the WeChat platform revealed that the number of resource clicks was significantly higher than the total number of students, indicating that the sharing function of the WeChat platform facilitated the sharing of educational resources. Seven in-service science teachers were also interviewed to qualitatively evaluate and analyze the teaching model from the perspective of in-service teachers. The comprehensive results indicate that the teaching model constructed in this study achieves good results, facilitates the development of students' independent learning ability, and allows for the integration of this blended teaching model into lower-secondary curriculum.

Delineating the gene-regulatory programs underlying complex cell types is fundamental for understanding brain function in health and disease. Here, we comprehensively examined human brain cell epigenomes by probing DNA methylation and chromatin conformation at single-cell resolution in 517 thousand cells (399 thousand neurons and 118 thousand non-neurons) from 46 regions of three adult male brains. We identified 188 cell types and characterized their molecular signatures. Integrative analyses revealed concordant changes in DNA methylation, chromatin accessibility, chromatin organization, and gene expression across cell types, cortical areas, and basal ganglia structures. We further developed single-cell methylation barcodes that reliably predict brain cell types using the methylation status of select genomic sites. This multimodal epigenomic brain cell atlas provides new insights into the complexity of cell-type-specific gene regulation in adult human brains.

Simple Summary: Electroencephalograms provide valuable insights into brain activity with diverse medical utility that encompasses diagnosis, monitoring, drug discovery, and treatment evaluation. We propose an artificial intelligence model that is uniquely optimized to analyze electroencephalogram signals by directly processing raw data. This model captures intricate spatial and temporal patterns in electroencephalograms through dedicated components like spatial channel attention and sparse transformer encoding. Through being evaluated extensively, our model demonstrates high accuracy in detecting brain diseases and classifying psychotropic drugs. By automatically learning the representations from raw electroencephalogram data, it adapts well across diseases, subjects, and tasks. The model's end-to-end learning aptitude and task versatility constitutes a robust and widely applicable automated electroencephalogram analytics solution. We believe it has the potential to significantly advance electroencephalogram-based diagnosis and personalized medicine. Electroencephalogram (EEG) analysis plays an indispensable role across contemporary medical applications, which encompasses diagnosis, monitoring, drug discovery, and therapeutic assessment. This work puts forth an end-to-end deep learning framework that is uniquely tailored for versatile EEG analysis tasks by directly operating on raw waveform inputs. It aims to address the challenges of manual feature engineering and the neglect of spatial interrelationships in existing methodologies. Specifically, a spatial channel attention module is introduced to emphasize the critical inter-channel dependencies in EEG signals through channel statistics aggregation and multi-layer perceptron operations. Furthermore, a sparse transformer encoder is used to leverage selective sparse attention in order to efficiently process long EEG sequences while reducing computational complexity. Distilling convolutional layers further concatenates the temporal features and retains only the salient patterns. As it was rigorously evaluated on key EEG datasets, our model consistently accomplished a superior performance over the current approaches in detection and classification assignments. By accounting for both spatial and temporal relationships in an end-to-end paradigm, this work facilitates a versatile, automated EEG understanding across diseases, subjects, and objectives through a singular yet customizable architecture. Extensive empirical validation and further architectural refinement may promote broader clinical adoption prospects. [ABSTRACT FROM AUTHOR]

Biochar is now recognized as an excellent fuel with excellent performance in the combustion process and environmental friendness. As the main raw material of biochar, agricultural, and forestry biomass waste has a high waste rate. Therefore, researchers have conducted long-term research on the technology of generating biochar from agricultural and forestry biomass. Based on the current research and analysis of the existing molding fuel technology, the influence of different research parameters on the preparation of different biomass fuel is explored. The influences of molding temperature, molding pressure, raw material moisture content and the ratio of adhesive to toner on the performance of biomass fuel are focused on analysis. Finally, a conclusion is drawn based on the experimental data.

Traditional fossil fuels are being replaced by pyrolytic carbonization fuel from agricultural and forestry biomass to address the energy shortage crisis and the environmental pollution caused by the massive burning of fossil fuels in recent years. This paper introduces the research progress in the preparation of agriculture and forestry biomass pyrolysis carbonization molding fuel. The advantages and disadvantages of different biomass conversion technology are presented. The effects of different technological parameters on the preparation of pyrolytic carbon from agricultural and forestry biomass waste were reviewed. Agriculture and forestry biomass combustion characteristics and their regularity are analyzed.

Currently, the primary method for leaching rare earth ores is through in situ leaching. This approach involves contact between clay minerals and liquids, which can lead to the potential swelling of clay minerals with water, triggering natural disasters such as landslides. The main purpose of this study is to select the suitable anti-swelling solution for Hunan Jianghua ionic rare earth ore. According to the ore composition analysis, 88 wt% of Hunan Jianghua ionic rare earth ore is composed of halloysite clay mineral. Therefore, halloysite clay mineral is used to investigate its anti-swelling behavior in order to provide a reference for future research on the selection of raw ore swelling inhibitors. In this study, the traditional leaching agent, MgSO4 solution, was used as the solvent along with two additional compounds, CH3COOK and KCl, which were prepared in different concentrations to form a new composite swelling inhibitor solution to observe their effect on the swelling rate of halloysite clay mineral. At the same time, the seepage velocity of halloysite clay mineral with different anti-swelling solutions is studied. The results indicate that the optimal concentration in the CH3COOK + MgSO4 solution system is 0.05 mol/dm3. At this concentration, the swelling rate is 5.129%, the inhibition rate is 20.08%, and the seepage velocity rate is 12.51 × 10−3 cm/min, respectively. In KCl + MgSO4 solution, the swelling rate is 4.868%, the inhibition rate is 24.15% and the seepage velocity rate is 13.23 × 10−3 cm/min at the concentration of 0.02 mol/dm3, which is the optimum concentration. In addition, FTIR and TG studies have further demonstrated the mechanism by which these two composite bulking inhibitors inhibit the swelling of halloysite clay mineral. [ABSTRACT FROM AUTHOR]

This article employs a three-stage slack-based data envelopment analysis (SBM-DEA) model to evaluate the construction safety performance (CSP) of 30 provinces and cities in China, focusing on enhancing the sustainable development of construction safety in the industry, in line with the concept of sustainable development. The research findings indicate that the supervision environment of each province and city exerts a more substantial influence on the sustainable development of construction safety compared with the level of socio-economic development. Significant changes have been observed in the regional distribution of construction safety management levels within the construction industry by eliminating the impact of economic development, the supervision environment, and random errors. The original pattern of "East > West > Central > Northeast" has shifted to "East > Central > Northeast > West." Moreover, it has been discovered that high-efficiency values of safety performance in certain provinces and cities are partially attributed to external environmental (EE) pressure. In contrast, low-efficiency values cannot be solely attributed to their lack of willingness to implement safety management. Finally, the article proposes strategies, including government policy-led approaches, technology prioritization, and management prioritization, to enhance the sustainable development of construction safety in the construction industry based on the internal safety performance of each province. [ABSTRACT FROM AUTHOR]

The heterogeneity in extracellular vesicles (EVs) introduces an extra level of complexity in small EV‐based liquid biopsy for cancer diagnosis. Surface plasmon resonance microscopy (SPRM) offers sensitive and multifunctional analysis for single tiny EVs. However, the EV SPRM image analysis has been extremely laborious and time‐consuming due to the low contrast and bad signal‐to‐noise ratio. Herein, we present a digital EV analyzer (DEA), a software package, for the automatic analysis of EV data in the SPRM platform. This package enabled fully automated single EV identification, digital counting, sizing EV, and quantification of dwell time. Integrated with a classification algorithm and intelligent searching algorithm for subpopulation analysis, DEA was able to accurately group EVs with five different origins. We hope this software tool would largely promote the application of SPRM technology into cancer diagnosis with EVs. [ABSTRACT FROM AUTHOR]