Research on the Teaching Design of Advanced Mathematics Courses Based on Outcome-Based Education Concept

This paper first analyzes the characteristics and current situation of the Advanced Mathematics course;secondly,it proposes a teaching model that integrates the outcome-based education(OBE)philosophy and blended teaching method,reorganizing the teaching objectives,teaching content,and assessment evaluation process of the Advanced Mathematics course;lastly,through practice,it is proved that this approach can effectively improve students’mastery of course content,enhance students’ability to apply mathematical knowledge,and strengthen teaching effectiveness.

Recent progress on fabrication and flat-band physics in 2D transition metal dichalcogenides moiré superlattices

Moiré superlattices are formed when overlaying two materials with a slight mismatch in twist angle or lattice constant. They provide a novel platform for the study of strong electronic correlations and non-trivial band topology, where emergent phenomena such as correlated insulating states, unconventional superconductivity, and quantum anomalous Hall effect are discovered. In this review, we focus on the semiconducting transition metal dichalcogenides(TMDs) based moiré systems that host intriguing flat-band physics. We first review the exfoliation methods of two-dimensional materials and the fabrication technique of their moiré structures. Secondly, we overview the progress of the optically excited moiré excitons, which render the main discovery in the early experiments on TMD moiré systems. We then introduce the formation mechanism of flat bands and their potential in the quantum simulation of the Hubbard model with tunable doping, degeneracies, and correlation strength. Finally, we briefly discuss the challenges and future perspectives of this field.

Crowned Dens Syndrome: Recent Progress on Diagnosis and Treatment

Defined as an association of acute cervical pain and calcifications in the peri-odontoid space, crowned dens syndrome (CDS) is a major imaging manifestation of “coronary”. CDS is a rare but under-recognized cause of severe neck pain in older adults. As such, it is often misdiagnosed. So, we review the literature with particular attention to the clinical and radiological aspects of this syndrome.

MOF-Like 3D Graphene-Based Catalytic Membrane Fabricated by One-Step Laser Scribing for Robust Water Purification and Green Energy Production

Increasing both clean water and green energy demands for survival and development are the grand challenges of our age.Here,we successfully fabricate a novel multifunctional 3D graphene-based catalytic membrane(3D-GCM)with active metal nanoparticles(AMNs)loading for simultaneously obtaining the water purification and clean energy generation,via a“green”one-step laser scribing technology.The as-prepared 3D-GCM shows high porosity and uniform distribution with AMNs,which exhibits high permeated fluxes(over 100 L m^(−2) h^(−1))and versatile super-adsorption capacities for the removal of tricky organic pollutants from wastewater under ultra-low pressure-driving(0.1 bar).After adsorption saturating,the AMNs in 3D-GCM actuates the advanced oxidization process to self-clean the fouled membrane via the catalysis,and restores the adsorption capacity well for the next time membrane separation.Most importantly,the 3D-GCM with the welding of laser scribing overcomes the lateral shear force damaging during the long-term separation.Moreover,the 3D-GCM could emit plentiful of hot electrons from AMNs under light irradiation,realizing the membrane catalytic hydrolysis reactions for hydrogen energy generation.This“green”precision manufacturing with laser scribing technology provides a feasible technology to fabricate high-efficient and robust 3D-GCM microreactor in the tricky wastewater purification and sustainable clean energy production as well.

Room-temperature infrared photodetectors with hybrid structure based on two-dimensional materials

Two-dimensional(2D) materials, such as graphene, transition metal dichalcogenides(TMDs), black phosphorus(BP),and related derivatives, have attracted great attention due to their advantages of flexibility, strong light–matter interaction,broadband absorption, and high carrier mobility, and have become a powerful contender for next-generation infrared photodetectors. However, since the thickness of 2D materials is on the order of nanometers, the absorption of 2D materials is very weak, which limits the detection performance of 2D materials-based infrared photodetectors. In order to solve this problem, scientific researchers have tried to use optimized device structures to combine with 2D materials for improving the performance of infrared photodetectors. In this review, we review the progress of room-temperature infrared photodetectors with hybrid structure based on 2D materials in recent years, focusing mainly on 2D–nD(n = 0, 1, 2) heterostructures, the integration between 2D materials and on-chip or plasmonic structure. Finally, we summarize the current challenges and point out the future development direction.

A Clique-Based Approach to the Identification of Common Gene Association Sub-Networks

We developed a computational framework to identify common gene association sub-network. This framework combines graphical lasso model, graph product and a replicator equation based clique solver. We applied this method to find common stress responsive sub-networks from two related Deinococcus-Thermus bacterial species.

The Guangdong Mode of Integration of Science-Education and Industry-Education for Postgraduates in Engineering

Based on the theory of collaborative innovation and the triple helix theory combined with the needs for innovative development in the modern manufacturing industry in Guangdong Province,the construction of open bases as education carriers by the governments was explored in terms of implement joint training for postgraduates in engineering through the integration of science-education and industry-education,to improve the practical innovation ability of postgraduates in engineering.The integration training mode has significantly improved the effectiveness of collaborative education and innovation,and it has become an important talent and intellectual support for Guangdong’s modern manufacturing industry.It has further advanced the theory and practice of joint training conducted by governments,industries,universities,and scientific research institutes.

Research on the Diversified Mentoring Models for Open Base Engineering Graduates-A Case Study of Foshan

The key to guaranteeing the quality of engineering graduate cultivation lies in establishing a dual mentorship model,involving both,universities and enterprises.By analyzing the existing problems in dual mentorship for engineering graduates,the open base for joint graduate cultivation in Foshan is taken as an example.Under the“2+2”diversified mentoring model constructed for engineering practice cultivation,an open base guarantees the implementation of the diversified mentoring model,while the diversified mentoring model improves the effectiveness of collaborative education between universities and enterprises.Strengthening the qualification examination of collaborative education and reinforcing the academic ability training of enterprise mentors are proposed as strategies for the problems faced in the implementation.

A Research on the Strategy of Integrating PBL with Curriculum Ideological and Political Elements-Taking College Mathematics as an Example

Taking college mathematics as an example,a questionnaire was designed for college mathematics teachers and students to fully comprehend the teaching situation.This paper mainly discusses the problems existing in the teaching of college mathematics and proposes the strategy of integrating PBL with curriculum ideological and political elements in college mathematics,so as to change the teaching process and improve the teaching quality.

Reform and Practice of an Engineering Postgraduate Training Pattern in an Open-Ended Base:Taking Foshan Base as an Example

The flawed engineering practice is considered the main factor that is affecting to the development quality of engineering postgraduates.Taking Foshan Base as an example,this paper has analyzed the operational pattern,practice teaching model,and internal governance system of the open-ended base as a new system for engineering practice and proposed several suggestions for the reformation of engineering postgraduates based on the construction effect.

Total process of fault diagnosis for wind turbine gearbox,from the perspective of combination with feature extraction and machine learning:A review

With the increasing of the installed capacity of wind power,the condition monitoring and maintains technique is becoming more important.Wind Turbines(WT)gearbox is one of the key wind power components as it plays the role of power transmission and speed regulation.Towards this,a number of scholars have pay attention to the fault diagnosis of WT gearbox.The efficiency of Machine Learning(ML)algorithms is highly correlated with signal type,data quality,and extracted features employed.The implementation of ML techniques has proven to be advantageous in simplifying the comprehension prerequisites for fault diagnosis technology concerning fault mechanisms.More and more current studies predominantly concentrate on the utilization and fine-tuning of ML algorithms,while providing limited insights into the features of the acquired data.Therefore,it is necessary to review the research in recent years from the perspective of the combination of feature extraction and ML algorithms,and provide a detailed direction for future WT gearbox fault diagnosis technology research.In this paper,data processing algorithms and typical fault diagnosis methods based on ML methods for WT gearbox are reviewed.For the using of ML method in WT gearbox fault diagnosis,the data prepared for training is very important.The paper firstly reviewed the data analysing method which will support the ML method.The data analysing methods include data acquisition,data preprocessing and feature extraction method.Feature extraction plays a pivotal role in the realm of gearbox fault diagnosis,as it serves as the essence of effective detection.This review will primarily focus on exploring methods that enable the utilization of efficient features in combination with ML techniques to achieve accurate gearbox fault diagnosis.Then typical ML method for WT gearbox fault diagnosis are carefully reviewed.Moreover,some prospects for future research directions are discussed in the end.

High-performance formaldehyde prediction for indoor air quality assessment using time series deep learning

Indoor air pollution resulting from volatile organic compounds(VOCs),especially formaldehyde,is a significant health concern needed to predict indoor formaldehyde concentration(Cf)in green intelligent building design.This study develops a thermal and wet coupling calculation model of porous fabric to account for the migration of formaldehyde molecules in indoor air and cotton,silk,and polyester fabric with heat flux in Harbin,Beijing,Xi’an,Shanghai,Guangzhou,and Kunming,China.The time-by-time indoor dry-bulb temperature(T),relative humidity(RH),and Cf,obtained from verified simulations,were collated and used as input data for the long short-term memory(LSTM)of the deep learning model that predicts indoor multivariate time series Cf from the secondary source effects of indoor fabrics(adsorption and release of formaldehyde).The trained LSTM model can be used to predict multivariate time series Cf at other emission times and locations.The LSTM-based model also predicted Cf with mean absolute percentage error(MAPE),symmetric mean absolute percentage error(SMAPE),mean absolute error(MAE),mean square error(MSE),and root mean square error(RMSE)that fell within 10%,10%,0.5,0.5,and 0.8,respectively.In addition,the characteristics of the input dataset,model parameters,the prediction accuracy of different indoor fabrics,and the uncertainty of the data set are analyzed.The results show that the prediction accuracy of single data set input is higher than that of temperature and humidity input,and the prediction accuracy of LSTM is better than recurrent neural network(RNN).The method’s feasibility was established,and the study provides theoretical support for guiding indoor air pollution control measures and ensuring human health and safety.

Distinguished biological adaptation architecture aggravated population differentiation of Tibeto-Burman-speaking people

Tibeto-Burman(TB)people have endeavored to adapt to the hypoxic,cold,and high-UV high-altitude environments in the Tibetan Plateau and complex disease exposures in lowland rainforests since the late Paleolithic period.However,the full landscape of genetic history and biological adaptation of geographically diverse TB-speaking people,as well as their interaction mechanism,remain unknown.Here,we generate a whole-genome meta-database of 500 individuals from 39 TB-speaking populations and present a comprehensive landscape of genetic diversity,admixture history,and differentiated adaptative features of geographically different TB-speaking people.We identify genetic differentiation related to geography and language among TB-speaking people,consistent with their differentiated admixture process with incoming or indigenous ancestral source populations.A robust genetic connection between the Tibetan-Yi corridor and the ancient Yellow River people supports their Northern China origin hypothesis.We finally report substructure-related differentiated biological adaptative signatures between highland Tibetans and Loloish speakers.Adaptative signatures associated with the physical pigmentation(EDAR and SLC24A5)and metabolism(ALDH9A1)are identified in Loloish people,which differed from the high-altitude adaptative genetic architecture in Tibetan.TB-related genomic resources provide new insights into the genetic basis of biological adaptation and better reference for the anthropologically informed sampling design in biomedical and genomic cohort research.

Application of transfer learning and ensemble learning in image-level classification for breast histopathology

Background Breast cancer has the highest prevalence among all cancers in women globally.The classification of histopathological images in the diagnosis of breast cancers is an area of clinical concern.In computer-aided diagnosis,most traditional classification models use a single network to extract features,although this approach has significant limitations.Moreover,many networks are trained and optimized on patient-level datasets,ignoring lower-level data labels.Methods This paper proposed a deep ensemble model based on image-level labels for the binary classification of breast histopathological images of benign and malignant lesions.First,the BreaKHis dataset was randomly divided into training,validation,and test sets.Then,data augmentation techniques were used to balance the numbers of benign and malignant samples.Third,based on their transfer learning performance and the complementarity between networks,VGG16,Xception,ResNet50,and DenseNet201 were selected as base classifiers.Results In a ensemble network model with accuracy as the weight,the image-level binary classification achieved an accuracy of 98.90%.To verify the capabilities of our method,it was experimentally compared with the latest transformer and multilayer perception(MLP)models on the same dataset.Our ensemble model showed a 5%-20%advantage,emphasizing its far-reaching abilities in classification tasks.Conclusions This research focuses on improving the performance of a classification model with an ensemble algorithm.Transfer learning has an essential role in classification of small datasets,improving training speed and accuracy.Our model may outperform many existing approaches with respect to accuracy and has applications in the field of auxiliary medical diagnosis.

H2A.Z Represses Gene Expression by Modulating Promoter Nucleosome Structure and Enhancer Histone Modifications in Arabidopsis

Deposition of the histone variant H2A.Z at gene bodies regulates transcription by modifying chromatin accessibility in plants. However, the role of H2A.Z enrichment at the promoter and enhancer regions is unclear, and how H2A.Z interacts with other mechanisms of chromatin modification to regulate gene expression remains obscure. Here, we mapped genome-wide H2A.Z, H3K4me3, H3K27me3, Pol II, and nucleosome occupancy in Arabidopsis inflorescence. We showed that H2A.Z preferentially associated with H3K4me3 at promoters, while it was found with H3K27me3 at enhancers, and that H2A.Z deposition negatively correlated with gene expression. In addition, we demonstrated that H2A.Z represses gene expression by establishing low gene accessibility at ＋1 nucleosome and maintaining high gene accessibility at -1 nucleosome. We further showed that the high measures of gene responsiveness correlate with the H2A.Z-associated closed ＋1 nucleosome structure. Moreover, we found that H2A.Z represses enhancer activity by promoting H3K27me3 and preventing H3K4me3 histone modifications. This study provides a framework for future studies of H2A.Z functions and opens up new aspects for decoding the interplay between chromatin modification and histone variants in transcrip- tional control.

Hole-dominated Fowler–Nordheim tunneling in 2D heterojunctions for infrared imaging

Heterostructures based on diverse two-dimensional(2D)materials are effective for tailoring and further promoting device performance and exhibit considerable potential in photodetection.However,the problem of high-density thermionic carriers can be hardly overcome in most reported heterostructure devices based on type I and type II band alignment,which leads to an unacceptably small Iphoto/Idark and strong temperature dependence that limit the performance of photodetectors.Here,using the MoTe_(2)/h-BN/MoTe_(2)/h-BN heterostructure,we report the hole-dominated Fowler–Nordheim quantum tunneling transport in both on and off states.The state-of-the-art device operating at room temperature shows high detectivity of>10^(8) Jones at a laser power density of<0.3 nW μm^(-2) from the visible to near infrared range.In addition,the fast on–off switching and highly sensitive photodetection properties promise superior imaging capabilities.The tunneling mechanism,in combination with other unique properties of 2D materials,is significant for novel photodetection.

Analysis and Control of Modular Multi-terminal DC Power Flow Controller with Fault Current Limiting Function

In order to overcome the problems of power flow control and fault current limiting in multi-terminal high voltage direct current(MTDC)grids,this paper proposes a modular multi-terminal DC power flow controller(MM-DCPFC)with fault current limiting function.The topology structure,operation principle,and equivalent circuit of MM-DCPFC are introduced,and such a structure has the advantages of modularity and scalability.The power balance mechanism is studied and a hierarchical power balance control strategy is proposed.The results show that MM-DCPFC can achieve internal power exchange,which avoids the use of external power supply.The fault characteristics of MM-DCPFC are analyzed,fault current limiting and self-protection methods are proposed,and the factors affecting the current limiting capability are studied.The simulation models are established in PLECS,and the simulation results verify the effectiveness of MM-DCPFC in power flow control,fault current limiting,and scalability.In addition,a prototype is developed to validate the function and control method of MM-DCPFC.

Continuous cropping of alfalfa (Medicago sativa L.) reduces bacterial diversity and simplifies cooccurrence networks in aeolian sandy soil

Alfalfa is a perennial herbaceous forage legume that is remarkably and negatively affected by monocropping.However,the contribution of the changes in bacterial communities to soil sickness in alfalfa have not been elucidated.Therefore,we investigated bacterial community structures in response to monocropped alfalfa along the chronosequence.Continuous cropping remarkably reduced bacterial alpha diversity and altered community structures,and soil pH,total P and available P were strongly associated with the changes of bacterial diversity and community structures.Intriguingly,10 years of monocropped alfalfa might be a demarcation point separating soil bacterial community structures into two obvious groups that containing soil samples collected in less and more than 10 years.The relative abundances of copiotrophic bacteria of Actinobacteria and Gammaproteobacteria significantly increased with the extension of continuous cropping years,while the oligotrophic bacteria of Armatimonadetes,Chloroflexi,Firmicutes and Gemmatimonadetes showed the opposite changing patterns.Among those altered phyla,Actinobacteria,Chloroflexi,Alphaproteobacteria and Acidobacteria were the most important bacteria which contributed 50.86%of the community variations. Additionally, the relative abundances of nitrogen fixation bacteria ofBradyrhizobium and Mesorhizobium obviously increased with continuous cropping years, while theabundances of Arthrobacter, Bacillus, Burkholderiaceae and Microbacterium with potential functionsof solubilizing phosphorus and potassium remarkably decreased after long-term continuouscropping. Furthermore, bacterial cooccurrence patterns were significantly influenced by continuouscropping years, with long-term monocropped alfalfa simplifying the complexity of the cooccurrencenetworks. These findings enhanced our understandings and provided references for forecasting howsoil bacterial communities responds to monocropped alfalfa.

Constructing data literacy evaluation system for social science scholars

To establish a data literacy evaluation system for social science scholars is a part of transformation to data-intensive scientific research paradigm in social science.Based on the literature review of data literacy and survey of social science data management features,this paper analyzed the elements of data literacy of social science scholars.The data literacy of social science scholars ma inly consists of data awareness level,data discovery and access ability,data management and organization ability,data processing and analysis ability,data utilization and preservation ability,and data ethics level.Each of these primary indexes has several secondary indexes.It constructed the evaluation system of social science scholars data literacy.The weights of the primary and secondary indexes in the system were calculated by applying AHP.The data literacy evaluation system for social science scholars can provide a reference for assessing and promoting social science scholars'data literacy ability in China.

Stimuli-responsive combination therapy of cisplatin and Nrf2 siRNA for improving antitumor treatment of osteosarcoma

Cisplatin is a widely applied therapeutics for the treatment of osteosarcoma.However,its clinical applications have been hindered due to low efficacy and bioavailability,and particularly frequent emergence of reactive oxygen species(ROS)-decrease induced drug resistance.The transcription factor NF-E2-related factor 2(Nrf2)is increased in cancer patients and induces poor outcome in osteosarcoma treatment,making it a novel target to improve the efficacy of chemotherapy.Herein,a hyaluronidase-responsive multi-layer liposome(HLCN)for co-delivery of cisplatin and Nrf2 siRNA(siNrf2)is developed.It is composed of Vpr52-96 modified liposome covered with hyaluronic acid(HA).HLCN selectively accumulates in osteosarcoma by targeting tumor-specific CD44,and can be degraded by endosomal hyaluronidase to generate cationic liposome,which promotes the endosomal escape of Vpr52-96,cisplatin and siNrf2.HLCN can effectively decrease Nrf2 level,promote ROS generation,activate itochondrial apoptotic pathway,and consequently enhance anticancer efficacy of cisplatin.Particularly,HLCN shows high cytotoxicity to osteosarcoma cells with an IC50 value of about 1µM,which is four-fold lower than liposomal cisplatin(IC504µM),indicating that Nrf2 silence can significantly improve cisplatin sensitivity in cancer cells.Importantly,HLCN can remarkably inhibit tumor growth in the xenograft osteosarcoma mice with minimal systemic adverse effects.Therefore,this novel stimuli-responsive combination therapy of cisplatin and siNrf2 provides a promising strategy for the treatment of osteosarcoma.