Age of Information Based User Scheduling and Data Assignment in Multi-User Mobile Edge Computing Networks:An Online Algorithm

This paper investigates the age of information(AoI)-based multi-user mobile edge computing(MEC)network with partial offloading mode.The weighted sum AoI(WSA)is first analyzed and derived,and then a WSA minimization problem is formulated by jointly optimizing the user scheduling and data assignment.Due to the non-analytic expression of the WSA w.r.t.the optimization variables and the unknowability of future network information,the problem cannot be solved with known solution methods.Therefore,an online Joint Partial Offloading and User Scheduling Optimization(JPOUSO)algorithm is proposed by transforming the original problem into a single-slot data assignment subproblem and a single-slot user scheduling sub-problem and solving the two sub-problems separately.We analyze the computational complexity of the presented JPO-USO algorithm,which is of O(N),with N being the number of users.Simulation results show that the proposed JPO-USO algorithm is able to achieve better AoI performance compared with various baseline methods.It is shown that both the user’s data assignment and the user’s AoI should be jointly taken into account to decrease the system WSA when scheduling users.

Design and Application of“STEM+”6E Teaching Mode in Information Technology Teaching

The concept of“STEM+”integrates art,humanistic literacy,and social values in the traditional STEM education concept,advocates cross-disciplinary integration,and aims to cultivate compound talents equipped to tackle future challenges.In 2022,the Ministry of Education issued the“Compulsory Education Information Technology Curriculum(2022 Edition),”emphasizing the core literacy of information science and technology and the integration of interdisciplinary disciplines,and encouraging the teaching mode suitable for discipline characteristics.The 6E teaching mode is a student-centered teaching strategy characterized by active exploration and cross-disciplinary integration.This article innovatively designed the“STEM+”6E teaching mode,which is applied to junior high school information technology teaching,which can better achieve core literacy teaching goals.

Exploration of the Teaching Reform of Computer Composition Principles with Ideological and Political Education at Anqing Normal University

The teaching mode of the Computer Composition Principles includes theoretical and practical teaching.At present,there is a problem of inconsistency in the teaching content of the two methods in our school.To this end,this paper conducts online and offline hybrid teaching based on the learning platform and the virtual simulation experiment platform,and cleverly applies ideological and political elements to theoretical and practical teaching to practice ideological and political education.Finally,the combination of theoretical and practical teaching modes can further help students firmly grasp the core knowledge points and improve the teaching quality and level of this course.

Upper and Lower Bounds of the α-Universal Triple I Method for Unified Interval Implications

Theα-universal triple I(α-UTI)method is a recognized scheme in the field of fuzzy reasoning,whichwas proposed by our research group previously.The robustness of fuzzy reasoning determines the quality of reasoning algorithms to a large extent,which is quantified by calculating the disparity between the output of fuzzy reasoning with interference and the output without interference.Therefore,in this study,the interval robustness(embodied as the interval stability)of theα-UTI method is explored in the interval-valued fuzzy environment.To begin with,the stability of theα-UTI method is explored for the case of an individual rule,and the upper and lower bounds of its results are estimated,using four kinds of unified interval implications(including the R-interval implication,the S-interval implication,the QL-interval implication and the interval t-norm implication).Through analysis,it is found that theα-UTI method exhibits good interval stability for an individual rule.Moreover,the stability of theα-UTI method is revealed in the case of multiple rules,and the upper and lower bounds of its outcomes are estimated.The results show that theα-UTI method is stable for multiple rules when four kinds of unified interval implications are used,respectively.Lastly,theα-UTI reasoning chain method is presented,which contains a chain structure with multiple layers.The corresponding solutions and their interval perturbations are investigated.It is found that theα-UTI reasoning chain method is stable in the case of chain reasoning.Two application examples in affective computing are given to verify the stability of theα-UTImethod.In summary,through theoretical proof and example verification,it is found that theα-UTImethod has good interval robustness with four kinds of unified interval implications aiming at the situations of an individual rule,multi-rule and reasoning chain.

Seismic anisotropy and upper mantle dynamics in Alaska:A review of shear wave splitting analyses

Shear wave splitting(SWS)is regarded as the most effective geophysical method to delineate mantle flow fields by detecting seismic azimuthal anisotropy in the earth's upper mantle,especially in tectonically active regions such as subduction zones.The Aleutian-Alaska subduction zone has a convergence rate of approximately 50 mm/yr,with a trench length reaching nearly 2800 km.Such a long subduction zone has led to intensive continental deformation and numerous strong earthquakes in southern and central Alaska,while northern Alaska is relatively inactive.The sharp contrast makes Alaska a favorable locale to investigate the impact of subduction on mantle dynamics.Moreover,the uniqueness of this subduction zone,including the unusual subducting type,varying slab geometry,and atypical magmatic activity and composition,has intrigued the curiosity of many geoscientists.To identify different sources of seismic anisotropy beneath the Alaska region and probe the influence of a geometrically varying subducting slab on mantle dynamics,extensive SWS analyses have been conducted in the past decades.However,the insufficient station and azimuthal coverage,especially in early studies,not only led to some conflicting results but also strongly limited the in-depth investigation of layered anisotropy and the estimation of anisotropy depth.With the completion of the Transportable Array project in Alaska,recent studies have revealed more detailed mantle structures and characteristics based on the dense station coverage and newly collected massive seismic data.In this study,we review significant regional-and continental-scale SWS studies in the Alaska region and conclude the mantle flow fields therein,to understand how a geometrically varying subducting slab alters the regional mantle dynamics.The summarized mantle flow mechanisms are believed to be conducive to the understanding of seismic anisotropy patterns in other subduction zones with a complicated tectonic setting.

Link Budget and Enhanced Communication Distance for Ambient Internet of Things

Backscatter communications will play an important role in connecting everything for beyond 5G(B5G)and 6G systems.One open challenge for backscatter communications is that the signals suffer a round-trip path loss so that the communication distance is short.In this paper,we first calculate the communication distance upper bounds for both uplink and downlink by measuring the tag sensitivity and reflection coefficient.It is found that the activation voltage of the envelope detection diode of the downlink tag is the main factor limiting the back-scatter communication distance.Based on this analysis,we then propose to implement a low-noise amplifier(LNA)module before the envelope detection at the tag to enhance the incident signal strength.Our experimental results on the hardware platform show that our method can increase the downlink communication range by nearly 20 m.

Discussion on the Construction and Application of Virtual Reality-Based Curriculum Resources for Kindergarten Education

The construction of early childhood curriculum resources serves as a vital vehicle and precondition for the implementation of collective teaching activities in kindergartens.However,traditional curriculum resources based on graphic language or the linguistic descriptions of early childhood educators often fail to provide children with an immersive experience during collective teaching activities,leading to a lack of initiative and interest in learning.This paper discusses the methods for building a kindergarten curriculum resource library and,based on this foundation,employs virtual reality technology for three-dimensional modeling of the library to enrich the curriculum resources in kindergartens.Furthermore,this paper proposes to train early childhood educators in virtual reality technology to enhance their abilities to operate and utilize virtual reality equipment,which can emphasize the children’s central role in the learning process,better achieve educational goals,and improve teaching outcomes.

Fiber-Optic Sensors and Their Practical Research in the Internet of Things

With the rapid development of the Internet of Things(IoT)technology,fiber-optic sensors,as a kind of high-precision and high-sensitivity measurement tool,are increasingly widely used in the field of IoT.This paper outlines the advantages of fiber-optic sensors over traditional sensors,such as high precision,strong resistance to electromagnetic interference,and long transmission distance.On this basis,the paper discusses the application scenarios of fiber-optic sensors in the Internet of Things,including environmental monitoring,intelligent industry,medical and health care,intelligent transportation,and other fields.It is hoped that this study can provide theoretical support and practical guidance for the further development of fiber-optic sensors in the field of the Internet of Things,as well as promote the innovation and application of IoT.

Construction of Ideological and Political Education in English Teacher Education Courses in Local Normal Universities:Objectives,Connotations,and Implementation Paths

The integration of ideological and political education into university curricula represents a new trend in holistic,comprehensive,and all-around education.The concept of“ideological and political education in English teacher education courses”embodies an interdisciplinary approach to curriculum reform.Grounded in the principle of fostering moral integrity,it aims to create high-quality courses by seamlessly integrating the transmission of knowledge and skills with the guidance of ideological values.This study explores the status quo,significance,and implementation pathways for integrating ideological and political education into English teacher education courses,providing valuable insights for future curricular development.

Research on the Integration Path of Postgraduate Professional Courses into the Construction of Curriculum Ideology and Politics

The curriculum ideology and politics play an important role in cultivating students’correct worldview,outlook on life,and values.In recent years,universities and colleges have begun to pay attention to the cultivation of postgraduates’moral education and the construction of postgraduates’curriculum ideology and politics.This paper takes the course“Theory of Software Engineering”of Anqing Normal University as an example,discusses the paths of integrating postgraduate professional courses into the construction of curriculum ideology and politics,and puts forward the paths of feeding back from scientific research,connecting with practice and summarizing and condensing,which are aimed at improving postgraduates’comprehensive quality and innovation ability,and have positive significance for cultivating high-level applied scientific and technological talents who are both moral and talented.

Dynamic Evolutionary Game-based Modeling,Analysis and Performance Enhancement of Blockchain Channels

The recent development of channel technology has promised to reduce the transaction verification time in blockchain operations.When transactions are transmitted through the channels created by nodes,the nodes need to cooperate with each other.If one party refuses to do so,the channel is unstable.A stable channel is thus required.Because nodes may show uncooperative behavior,they may have a negative impact on the stability of such channels.In order to address this issue,this work proposes a dynamic evolutionary game model based on node behavior.This model considers various defense strategies'cost and attack success ratio under them.Nodes can dynamically adjust their strategies according to the behavior of attackers to achieve their effective defense.The equilibrium stability of the proposed model can be achieved.The proposed model can be applied to general channel networks.It is compared with two state-of-the-art blockchain channels:Lightning network and Spirit channels.The experimental results show that the proposed model can be used to improve a channel's stability and keep it in a good cooperative stable state.Thus its use enables a blockchain to enjoy higher transaction success ratio and lower transaction transmission delay than the use of its two peers.

Automatic detection of small bowel lesions with different bleeding risks based on deep learning models

BACKGROUND Deep learning provides an efficient automatic image recognition method for small bowel(SB)capsule endoscopy(CE)that can assist physicians in diagnosis.However,the existing deep learning models present some unresolved challenges.AIM To propose a novel and effective classification and detection model to automatically identify various SB lesions and their bleeding risks,and label the lesions accurately so as to enhance the diagnostic efficiency of physicians and the ability to identify high-risk bleeding groups.METHODS The proposed model represents a two-stage method that combined image classification with object detection.First,we utilized the improved ResNet-50 classification model to classify endoscopic images into SB lesion images,normal SB mucosa images,and invalid images.Then,the improved YOLO-V5 detection model was utilized to detect the type of lesion and its risk of bleeding,and the location of the lesion was marked.We constructed training and testing sets and compared model-assisted reading with physician reading.RESULTS The accuracy of the model constructed in this study reached 98.96%,which was higher than the accuracy of other systems using only a single module.The sensitivity,specificity,and accuracy of the model-assisted reading detection of all images were 99.17%,99.92%,and 99.86%,which were significantly higher than those of the endoscopists’diagnoses.The image processing time of the model was 48 ms/image,and the image processing time of the physicians was 0.40±0.24 s/image(P<0.001).CONCLUSION The deep learning model of image classification combined with object detection exhibits a satisfactory diagnostic effect on a variety of SB lesions and their bleeding risks in CE images,which enhances the diagnostic efficiency of physicians and improves the ability of physicians to identify high-risk bleeding groups.

Contribution of groundwater to the formation of sand dunes in the Badain Jaran Desert, China

The tallest sand dune worldwide is located in the Badain Jaran Desert(BJD),China,and has been standing for thousands of years.Previous studies have conducted limited physical exploration and excavation on the formation of sand dunes and have proposed three viewpoints,that is,bedrock control,wind dominance,and groundwater maintenance with no unified conclusion.Therefore,this study analyzed the underlying bedding structure of sand dunes in the BJD.Although the bedrock of sand dunes is uplifted and wind controls the shape of dunes,the main cause of dune formation is groundwater that maintains the deposition of calcareous sandstone and accumulation of aeolian sand.According to water transport model and vapor transports in the unsaturated zone of sand dunes,capillary water transport height is limited with film water constituting the main form of water in dunes.Chemical properties and temperature of groundwater showed that aquifers in different basins receive relatively independent recharge from deep sources in the crater.Result of dune formation mechanism is of considerable importance in understanding groundwater circulation and provides a new perspective on water management in arid desert areas.

Virtual sample generation for model-based prognostics and health management of on-board high-speed train control system

In view of class imbalance in data-driven modeling for Prognostics and Health Management(PHM),existing classification methods may fail in generating effective fault prediction models for the on-board high-speed train control equipment.A virtual sample generation solution based on Generative Adversarial Network(GAN)is proposed to overcome this shortcoming.Aiming at augmenting the sample classes with the imbalanced data problem,the GAN-based virtual sample generation strategy is embedded into the establishment of fault prediction models.Under the PHM framework of the on-board train control system,the virtual sample generation principle and the detailed procedures are presented.With the enhanced class-balancing mechanism and the designed sample augmentation logic,the PHM scheme of the on-board train control equipment has powerful data condition adaptability and can effectively predict the fault probability and life cycle status.Practical data from a specific type of on-board train control system is employed for the validation of the presented solution.The comparative results indicate that GAN-based sample augmentation is capable of achieving a desirable sample balancing level and enhancing the performance of correspondingly derived fault prediction models for the Condition-based Maintenance(CBM)operations.

Enhancement of Information Transmission by Array Induced Stochastic Resonance in the Processes of Amplitude and Frequency Modulations

Information transmission is studied in the cases of amplitude and frequency modulations where there is an impulsive jamming in the signal. By using the array approach of nonlinear elements, we find that for both the periodic and aperiodic modulations, the information transmission can be enhanced by adding independent external noise on every element of the array. The dependence of information transmission on the size of array and the impulsive interval of the jamming are also studied.

Detection accuracy of target accelerations based on vortex electromagnetic wave in keyhole space

The influence of the longitudinal acceleration and the angular acceleration of detecting target based on vortex electromagnetic waves in keyhole space are analyzed.The spectrum spreads of different orbital angular momentum(OAM)modes in different non-line-of-sight situations are simulated.The errors of target accelerations in detection are calculated and compared based on the OAM spectra spreading by using two combinations of composite OAM modes in the keyhole space.According to the research,the effects about spectrum spreads of higher OAM modes are more obvious.The error in detection is mainly affected by OAM spectrum spreading,which can be reduced by reasonably using different combinations of OAM modes in different practical situations.The above results provide a reference idea for investigating keyhole effect when vortex electromagnetic wave is used to detect accelerations.

Multi-Level Image Segmentation Combining Chaotic Initialized Chimp Optimization Algorithm and Cauchy Mutation

To enhance the diversity and distribution uniformity of initial population,as well as to avoid local extrema in the Chimp Optimization Algorithm(CHOA),this paper improves the CHOA based on chaos initialization and Cauchy mutation.First,Sin chaos is introduced to improve the random population initialization scheme of the CHOA,which not only guarantees the diversity of the population,but also enhances the distribution uniformity of the initial population.Next,Cauchy mutation is added to optimize the global search ability of the CHOA in the process of position(threshold)updating to avoid the CHOA falling into local optima.Finally,an improved CHOA was formed through the combination of chaos initialization and Cauchy mutation(CICMCHOA),then taking fuzzy Kapur as the objective function,this paper applied CICMCHOA to natural and medical image segmentation,and compared it with four algorithms,including the improved Satin Bowerbird optimizer(ISBO),Cuckoo Search(ICS),etc.The experimental results deriving from visual and specific indicators demonstrate that CICMCHOA delivers superior segmentation effects in image segmentation.

From Big Data to Intelligent Tutoring: Exploring the Application of Big Data Technology in the Ideological and Political Education for College Counselors

This paper explores the application of big data technology in ideological and political education for college counselors.Big data-driven personalized counseling for students,social network analysis,and public opinion monitoring can optimize the efficiency of counselor work.However,there are still challenges concerning data privacy protection,technical support,and training.By scientifically evaluating and addressing these challenges,big data technology is expected to enhance the effectiveness of college counselors and improve the quality of ideological and political education.In the future,with technological advancements,big data technology will become an essential tool for higher education management.

A Self-Adapting and Efficient Dandelion Algorithm and Its Application to Feature Selection for Credit Card Fraud Detection

A dandelion algorithm(DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA,which might not be appropriate for all optimization problems. A self-adapting and efficient dandelion algorithm is proposed in this work to lower the number of DA's parameters and simplify DA's structure. Only the normal sowing operator is retained;while the other operators are discarded. An adaptive seeding radius strategy is designed for the core dandelion. The results show that the proposed algorithm achieves better performance on the standard test functions with less time consumption than its competitive peers. In addition, the proposed algorithm is applied to feature selection for credit card fraud detection(CCFD), and the results indicate that it can obtain higher classification and detection performance than the-state-of-the-art methods.

Virtual Keyboard:A Real-Time Hand Gesture Recognition-Based Character Input System Using LSTM and Mediapipe Holistic

In the digital age,non-touch communication technologies are reshaping human-device interactions and raising security concerns.A major challenge in current technology is the misinterpretation of gestures by sensors and cameras,often caused by environmental factors.This issue has spurred the need for advanced data processing methods to achieve more accurate gesture recognition and predictions.Our study presents a novel virtual keyboard allowing character input via distinct hand gestures,focusing on two key aspects:hand gesture recognition and character input mechanisms.We developed a novel model with LSTM and fully connected layers for enhanced sequential data processing and hand gesture recognition.We also integrated CNN,max-pooling,and dropout layers for improved spatial feature extraction.This model architecture processes both temporal and spatial aspects of hand gestures,using LSTM to extract complex patterns from frame sequences for a comprehensive understanding of input data.Our unique dataset,essential for training the model,includes 1,662 landmarks from dynamic hand gestures,33 postures,and 468 face landmarks,all captured in real-time using advanced pose estimation.The model demonstrated high accuracy,achieving 98.52%in hand gesture recognition and over 97%in character input across different scenarios.Its excellent performance in real-time testing underlines its practicality and effectiveness,marking a significant advancement in enhancing human-device interactions in the digital age.