The Practical Path of Labor Curriculum in Compulsory Education in China from the Perspective of Overlapping Influence Domain Theory

Since the new curriculum reform,labor education has gradually shed its marginalized position in the“five educations,”and the labor curriculum has become an independent course officially separated from comprehensive practical activity courses.Exploring the practical path of labor curriculum in compulsory education in China has become the primary task of labor education in China.Based on the practical situation of labor curriculum in compulsory education in China,drawing on the theory of overlapping influence domains,and from the perspective of collaborative education among family,school,and community,this paper proposes a curriculum practical path of“school-led”family-school-community collaboration and a curriculum practical path guided by“student-centered”sentiment,in order to provide references for the practice of labor curriculum in compulsory education in China.

Regulation of the intermittent release of giant unilamellar vesicles under osmotic pressure

Osmotic pressure can break the fluid balance between intracellular and extracellular solutions.In hypo-osmotic so-lution,water molecules,which transfer into the cell and burst,are driven by the concentration difference of solute across the semi-permeable membrane.The complicated dynamic processes of intermittent bursts have been previously observed.However,the underlying physical mechanism has yet to be thoroughly explored and analyzed.Here,the intermittent re-lease of inclusion in giant unilamellar vesicles was investigated quantitatively,applying the combination of experimental and theoretical methods in the hypo-osmotic medium.Experimentally,we adopted a highly sensitive electron multiplying charge-coupled device to acquire intermittent dynamic images.Notably,the component of the vesicle phospholipids af-fected the stretch velocity,and the prepared solution of vesicles adjusted the release time.Theoretically,we chose equations and numerical simulations to quantify the dynamic process in phases and explored the influences of physical parameters such as bilayer permeability and solution viscosity on the process.It was concluded that the time taken to achieve the balance of giant unilamellar vesicles was highly dependent on the molecular structure of the lipid.The pore lifetime was strongly related to the internal solution environment of giant unilamellar vesicles.The vesicles prepared in viscous solution were able to visualize long-lived pores.Furthermore,the line tension was measured quantitatively by the release velocity of inclusion,which was of the same order of magnitude as the theoretical simulation.In all,the experimental values well matched the theoretical values.Our investigation clarified the physical regulatory mechanism of intermittent pore forma-tion and inclusion release,which provides an important reference for the development of novel technologies such as gene therapy based on transmembrane transport as well as controlled drug delivery based on liposomes.

E2E-MFERC:AMulti-Face Expression Recognition Model for Group Emotion Assessment

In smart classrooms, conducting multi-face expression recognition based on existing hardware devices to assessstudents’ group emotions can provide educators with more comprehensive and intuitive classroom effect analysis,thereby continuouslypromotingthe improvementof teaching quality.However,most existingmulti-face expressionrecognition methods adopt a multi-stage approach, with an overall complex process, poor real-time performance,and insufficient generalization ability. In addition, the existing facial expression datasets are mostly single faceimages, which are of low quality and lack specificity, also restricting the development of this research. This paperaims to propose an end-to-end high-performance multi-face expression recognition algorithm model suitable forsmart classrooms, construct a high-quality multi-face expression dataset to support algorithm research, and applythe model to group emotion assessment to expand its application value. To this end, we propose an end-to-endmulti-face expression recognition algorithm model for smart classrooms (E2E-MFERC). In order to provide highqualityand highly targeted data support for model research, we constructed a multi-face expression dataset inreal classrooms (MFED), containing 2,385 images and a total of 18,712 expression labels, collected from smartclassrooms. In constructing E2E-MFERC, by introducing Re-parameterization visual geometry group (RepVGG)block and symmetric positive definite convolution (SPD-Conv) modules to enhance representational capability;combined with the cross stage partial network fusion module optimized by attention mechanism (C2f_Attention),it strengthens the ability to extract key information;adopts asymptotic feature pyramid network (AFPN) featurefusion tailored to classroomscenes and optimizes the head prediction output size;achieves high-performance endto-end multi-face expression detection. Finally, we apply the model to smart classroom group emotion assessmentand provide design references for classroom effect analysis evaluation metrics. Experiments based on MFED showthat the mAP and F1-score of E2E-MFERC on classroom evaluation data reach 83.6% and 0.77, respectively,improving the mAP of same-scale You Only Look Once version 5 (YOLOv5) and You Only Look Once version8 (YOLOv8) by 6.8% and 2.5%, respectively, and the F1-score by 0.06 and 0.04, respectively. E2E-MFERC modelhas obvious advantages in both detection speed and accuracy, which can meet the practical needs of real-timemulti-face expression analysis in classrooms, and serve the application of teaching effect assessment very well.

Towards Public Integrity Audition for Cloud-IoT Data Based on Blockchain

With the rapidly developing of Internet of Things (IoT), the volume ofdata generated by IoT systems is increasing quickly. To release the pressure ofdata management and storage, more and more enterprises and individuals preferto integrate cloud service with IoT systems, in which the IoT data can be outsourced to cloud server. Since cloud service provider (CSP) is not fully trusted,a variety of methods have been proposed to deal with the problem of data integritychecking. In traditional data integrity audition schemes, the task of data auditing isusually performed by Third Party Auditor (TPA) which is assumed to be trustful.However, in real-life TPA is not trusted as people thought. Therefore, theseschemes suffer from the underlying problem of single-point failure. Moreover,most of the traditional schemes are designed by RSA or bilinear map techniqueswhich consume heavy computation and communication cost. To overcome theseshortcomings, we propose a novel data integrity checking scheme for cloud-IoTdata based on blockchain technique and homomorphic hash. In our scheme, thetags of all data blocks are computed by a homomorphic hash function and storedin blockchain. Moreover, each step within the process of data integrity checking issigned by the performer, and the signatures are stored in blockchain through smartcontracts. As a result, each behavior for data integrity checking in our scheme canbe traced and audited which improves the security of the scheme greatly. Furthermore, batch-audition for multiple data challenges is also supported in our scheme.We formalize the system model of our scheme and give the concrete construction.Detailed performance analyses demonstrate that our proposed scheme is efficientand practical without the trust-assumption of TPA.

TGAIN:Geospatial Data Recovery Algorithm Based on GAIN-LSTM

Accurate geospatial data are essential for geographic information systems(GIS),environmental monitoring,and urban planning.The deep integration of the open Internet and geographic information technology has led to increasing challenges in the integrity and security of spatial data.In this paper,we consider abnormal spatial data as missing data and focus on abnormal spatial data recovery.Existing geospatial data recovery methods require complete datasets for training,resulting in time-consuming data recovery and lack of generalization.To address these issues,we propose a GAIN-LSTM-based geospatial data recovery method(TGAIN),which consists of two main works:(1)it uses a long-short-term recurrent neural network(LSTM)as a generator to analyze geospatial temporal data and capture its temporal correlation;(2)it constructs a complete TGAIN network using a cue-masked fusion matrix mechanism to obtain data that matches the original distribution of the input data.The experimental results on two publicly accessible datasets demonstrate that our proposed TGAIN approach surpasses four contemporary and traditional models in terms of mean absolute error(MAE),root mean square error(RMSE),mean square error(MSE),mean absolute percentage error(MAPE),coefficient of determination(R2)and average computational time across various data missing rates.Concurrently,TGAIN exhibits superior accuracy and robustness in data recovery compared to existing models,especially when dealing with a high rate of missing data.Our model is of great significance in improving the integrity of geospatial data and provides data support for practical applications such as urban traffic optimization prediction and personal mobility analysis.