Exploration of the Path of Intelligent Development of Continuing Education in the Digital Age

With the rapid development of big data and intelligent technology,opportunities and challenges coexist in continuing education work,and the integration of continuing education and intelligent education is imperative.Therefore,China’s continuing education should strengthen the construction of the education system,make long-term plans,strengthen overall management in system construction,promote the transformation of continuing education models,and accelerate the modernization process of education.Based on this,this article analyzes and studies the path of intelligent development of continuing education in the digital era,explores its inevitability,analyzes the main characteristics of intelligent continuing education,explores the problems of intelligent development of continuing education,and proposes strategies for the intelligent development of continuing education.

Investigating and predicting the role of photovoltaic, wind, and hydrogen energies in sustainable global energy evolution

The global shift toward next-generation energy systems is propelled by the urgent need to combat climate change and the dwindling supply of fossil fuels.This review explores the intricate challenges and opportunities for transitioning to sustainable renewable energy sources such as solar,wind,and hydrogen.This transition economically challenges traditional energy sectors while fostering new industries,promoting job growth,and sustainable economic development.The transition to renewable energy demands social equity,ensuring universal access to affordable energy,and considering community impact.The environmental benefits include a significant reduction in greenhouse gas emissions and a lesser ecological footprint.This study highlights the rapid growth of the global wind power market,which is projected to increase from$112.23 billion in 2022 to$278.43 billion by 2030,with a compound annual growth rate of 13.67%.In addition,the demand for hydrogen is expected to increase,significantly impacting the market with potential cost reductions and making it a critical renewable energy source owing to its affordability and zero emissions.By 2028,renewables are predicted to account for 42%of global electricity generation,with significant contributions from wind and solar photovoltaic(PV)technology,particularly in China,the European Union,the United States,and India.These developments signify a global commitment to diversifying energy sources,reducing emissions,and moving toward cleaner and more sustainable energy solutions.This review offers stakeholders the insights required to smoothly transition to sustainable energy,setting the stage for a resilient future.

Detection of Real-Time Distributed Denial-of-Service (DDoS) Attacks on Internet of Things (IoT) Networks Using Machine Learning Algorithms

The primary concern of modern technology is cyber attacks targeting the Internet of Things.As it is one of the most widely used networks today and vulnerable to attacks.Real-time threats pose with modern cyber attacks that pose a great danger to the Internet of Things(IoT)networks,as devices can be monitored or service isolated from them and affect users in one way or another.Securing Internet of Things networks is an important matter,as it requires the use of modern technologies and methods,and real and up-to-date data to design and train systems to keep pace with the modernity that attackers use to confront these attacks.One of the most common types of attacks against IoT devices is Distributed Denial-of-Service(DDoS)attacks.Our paper makes a unique contribution that differs from existing studies,in that we use recent data that contains real traffic and real attacks on IoT networks.And a hybrid method for selecting relevant features,And also how to choose highly efficient algorithms.What gives the model a high ability to detect distributed denial-of-service attacks.the model proposed is based on a two-stage process:selecting essential features and constructing a detection model using the K-neighbors algorithm with two classifier algorithms logistic regression and Stochastic Gradient Descent classifier(SGD),combining these classifiers through ensemble machine learning(stacking),and optimizing parameters through Grid Search-CV to enhance system accuracy.Experiments were conducted to evaluate the effectiveness of the proposed model using the CIC-IoT2023 and CIC-DDoS2019 datasets.Performance evaluation demonstrated the potential of our model in robust intrusion detection in IoT networks,achieving an accuracy of 99.965%and a detection time of 0.20 s for the CIC-IoT2023 dataset,and 99.968%accuracy with a detection time of 0.23 s for the CIC-DDoS 2019 dataset.Furthermore,a comparative analysis with recent related works highlighted the superiority of our methodology in intrusion detection,showing improvements in accuracy,recall,and detection time.

Effect of microwave-vacuum drying(MVD)method on quality properties of agro-products

Microwave-vacuum drying is an accepted drying method for agro-products, which is nonetheless still relatively unknown to some. This paper attempted to give an overview of the most important aspects of microwaves-vacuum drying （MVD） and their relevance to agro-products processing. Some advantages on microwave-vacuum drying properties were discussed to provide a better insight into the reasons for the use of microwaves. Also the effects of the MVD on the quality of several agro-products and reasonable processing parameters were given, which develop the guidance to the application of MVD on the agro-products dehydration. As a potential drying technology, MVD will be broadly utilized for the other agro-products processing

Electroreduction of hexavalent chromium using a porous titanium flow-through electrode and intelligent prediction based on a back propagation neural network

Flow-through electrodes have been demonstrated to be effective for electroreduction of Cr(VI),but shortcomings are tedious preparation and short lifetimes.Herein,porous titanium available in the market was studied as a flow-through electrode for Cr(VI)electroreduction.In addition,the intelligent prediction of electrolytic performance based on a back propagation neural network(BPNN)was developed.Voltametric studies revealed that Cr(VI)electroreduction was a diffusion-controlled process.Use of the flow-through mode achieved a high limiting diffusion current as a result of enhanced mass transfer and favorable kinetics.Electroreduction of Cr(VI)in the flow-through system was 1.95 times higher than in a parallel-plate electrode system.When the influent(initial pH 2.0 and 106 mg/L Cr(VI))was treated at 5.0 V and a flux of 51 L/(h·m2),a reduction efficiency of~99.9%was obtained without cyclic electrolysis process.Sulfate served as the supporting electrolyte and pH regulator,as reactive CrSO72−species were formed as a result of feeding HSO4−.Cr(III)was confirmed as the final product due to the sequential three-electron transport or disproportionation of the intermediate.The developed BPNN model achieved good prediction accuracy with respect to Cr(VI)electroreduction with a high correlation coefficient(R2=0.943).Additionally,the electroreduction efficiencies for various operating inputs were predicted based on the BPNN model,which demonstrates the evolutionary role of intelligent systems in future electrochemical technologies.