Various Fields In Recent Years Research Articles

Natural Nano-Minerals (NNMs): Conception, Classification and Their Biomedical Composites.

Natural nano-minerals (NNMs) are minerals that are derived from nature with a size of less than 100 nm in at least one dimension in size. NNMs have a number of excellent properties due to their unique nanostructure and have been applied in various fields in recent years. They are rising stars in various disciplines, such as materials, biomedicine, and chemistry, taking advantage of their huge surface area, multiple active sites, excellent adsorption capacity, large quantity, low cost, and nontoxicity, etc. To provide a more comprehensive overview of NNMs and the biomedical applications of NNMs-based nanocomposites, this review classifies NNMs into three types by dimension, lists the structure and properties of typical NNMs, and illustrates their biomedical applications. Furthermore, a novel concept of natural nanomineral medical materials (NNMMs) is proposed, focusing on the medical value of NNMs. In addition, this review attempts to address the current challenges and delineate future directions for the advancement of NNMs. With the deepening of biomedical applications, it is believed that NNMMMs will inevitably play an important role in the field of human health and contribute to its promotion.

Application and challenges of informer model in financial time series prediction: A review

Time series prediction has shown excellent performance in various fields in recent years, such as stock prices, weather changes, traffic flow, and other fields. Its application development is becoming increasingly mature, and long series time prediction area of research has gained significant prominence. The excellent performance of deep learning in many models has unleashed the potential and possibility of time series prediction to a certain extent. Based on the above reasons, applying deep learning to the field of time series prediction has become a meaningful research. Therefore, the purpose of this article is to analyze the Informer algorithm model in the area of financial time series prediction and provide a comprehensive literature review on the implementation of Informer models in financial time series. Attempting to investigate and analyze the problems and challenges that Informer models may encounter in the area of financial time series prediction, with the hope of providing innovative inspiration and motivating new forms of knowledge for future workers.

The Comparison of RS Codes, LDPC Codes, And Turbo Codes

Error correction codes have been widely used in various fields in recent years, including the communication field, storage field, digital media field, computer network field, and so on. In this paper, the development history of error correction codes is briefly summarized; and the principles and performance of three kinds of error correction control codes are systematically summarized starting from RS codes, LDPC codes, and Turbo codes. Three types of error correction codes are compared and analyzed in the field of wireless optical communication, and the error correction characteristics of different codes in three different weather conditions are compared and summarized for the applicable environments. The error correction codes are also compared in the field of aviation flight telemetry, and which codes are suitable for uplink and downlink telemetry in flight telemetry are summarized. At the same time, the paper provides an outlook on the future development trend of error correction codes.

Analysis of the Principal, Fabrication and Application of AI Chips

As a matter of fact, artificial intelligence (AI) technology has developed by leaps and bounds with the rapid development computation ability and improvements of machine learning scenarios (e.g., deep learning neural network). On this basis, its applications have appeared in various fields in recent years. In order to support complex artificial intelligence computing, AI chips have gradually appeared in people's vision, as well as have played an essential role in the future development of life. With this in mind, based on the development of AI chips in recent years, this paper discusses the definition and classification, principles and design, preparation, and application of AI chips. According to the analysis, the current limitations in terms of the state-of-art facilities will be demonstrated. At the same time, this study predicts AI chips’ future development prospects, so that readers can have a more comprehensive understanding of AI chips and the field of artificial intelligence.

Preface

The 10th International Conference on Environmental and Engineering Geophysics (ICEEG) is hosted by China University of Geosciences, Beijing, China, from June 07-12, 2023. Held biennially since 2004, the ICEEG Conference is one of the most important events for geophysical research and applications in environmental and engineering. Drawing together scientists, engineers, industrial delegates and end-users, the conference is a place to see all of the advanced developments, ranging from fundamental achievement in geophysical methods to the most interesting case studies. Traditionally, the proceedings “Engineering Environmental Geophysics and Intelligent Detection Technology” of the 10th International Conference on Environment and Engineering geophysics is published for the readers from the near-surface geophysics communityShallow surface geophysics refers to the exploration theory and application research on the surface of the earth using physical principles and methods. The shallow surface supports the needs of human survival and development. On the one hand, human beings obtain water and minerals from it, and build the necessary underground space to contain water, electricity, gas, communication, transportation and other pipe network systems in modern cities. On the other hand, various kinds of waste generated by human activities is discharged and buried in it, forming a unique geological layer of the Anthropocene. Whether from the technical level (physics) or non-technical level (politics, society, law), shallow surface geophysics is becoming more and more important. Artificial intelligence technology has been widely used in various fields in recent years, and the construction of smart cities cannot be separated from the support of shallow surface geophysical exploration and monitoring methods and technologies. Due to the integration of urbanization and the development of geophysical technology in China, geophysicists have more opportunities for scientific research. In order to promote exchanges, improve the level of environmental and engineering geophysical exploration, and provide a first-class platform for extensive exchanges among workers engaged in scientific research, technology development and production applications in this field, the 10th International Conference on Environment and Engineering Geophysics will be held in China University of Geosciences (Beijing), a famous university of geosciences. We warmly welcome experts and scholars to contribute and participate in the conferenceIn the proceedings of 10th ICEEG, 160 peer-reviewed research expended abstracts are published according to the following topics: geophysical exploration of urban underground space, high frequency surface wave theory and application, ground penetrating radar technology and application, advances in shallow surface geophysics, engineering geophysics, hydrology and environmental geophysics, disaster geophysical survey, archaeological geophysics, unmanned aerial vehicle detection, smart city geophysics, intelligent detection technology, geophysical monitoring technique, geophysical instruments and software, shallow active source and passive source seismic detection, and young scientists forum.We are deeply grateful to the members of the scientific committee and institutional scientific partners of ICEEG: Chinese Geophysical Society, China University of Geosciences (Beijing), Shallow Surface Geophysical Committee of Chinese Geophysical Society, Professional Committee of Environmental Geophysics, Chinese Geophysical Society, Department of Earth Sciences, National Natural Science Foundation of China, China University of Geosciences (Wuhan), Central South University, Chengdu University of Technology, Chang’ An University, Institute of Geology and Geophysics, Chinese Academy of Sciences, China University of Mining and Technology (Beijing), Zhejiang University and Jilin UniversityWe would like to thank all reviewers for their great efforts to achieve the high quality of published articles.We would also like to extend our gratitude to all participants of 10th ICEEG for their interests and great contributions. Our special thanks go to the invited speakers for their significant contributions to the conference and the proceedings.We would also like to thank all the sponsors. It is your years’ support that makes the ICEEG conference a success.We would also extend our thanks to all committee members and student volunteers for their great efforts in organizing this wonderful event.We hope the proceedings show the challenges and the latest developments in the application of Engineering Environmental Geophysics and Intelligent Detection Technology.

Lateral flow assay: a promising rapid point-of-care testing tool for infections and non-communicable diseases.

The point-of-care testing (POCT) approach has established itself as having remarkable importance in diagnosing various infectious and non-communicable diseases (NCDs). The POCT approach has succeeded in meeting the current demand for having diagnostic strategies that can provide fast, sensitive, and highly accurate test results without involving complicated procedures. This has been accomplished by introducing rapid bioanalytical tools or biosensors such as lateral flow assays (LFAs). The production cost of these tools is very low, allowing developing countries with limited resources to utilize them or produce them on their own. Thus, their use has grown in various fields in recent years. More importantly, LFAs have created the possibility for a new era of incorporating nanotechnology in disease diagnosis and have already attained significant commercial success worldwide, making POCT an essential approach not just for now but also for the future. In this review, we have provided an overview of POCT and its evolution into the most promising rapid diagnostic approach. We also elaborate on LFAs with a special focus on nucleic acid LFAs.

Combining proteomic markers to construct a logistic regression model for polycystic ovary syndrome.

Proteomics technology has been used in various fields in recent years for the Q6 exploration of novel markers and the study of disease pathogenesis, and has become one of the most important tools for researchers to explore unknown areas. However, there are fewer studies related to the construction of clinical models using proteomics markers. In our previous study we used DIA proteomics to screen for proteins that were significant in 31 PCOS patients compared to women of normal reproductive age. In this study, we used logistic regression among these protein markers to screen out variables with diagnostic value and constructed logistic regression models. We constructed a logistic model using these protein markers, where HIST1H4A (OR=1.037) was an independent risk factor for polycystic ovary syndrome and TREML1 (OR=0.976) were protective factors for the disease. The logistic regression model equation is: Logit (PCOS) =0.036*[HIST1H4A]-0.024*[TREML1]-16.368. The ROC curve analyzing the diagnostic value of the model has an AUC value of 0.977 and a Youden index of0.903, which gives a cutoff value of 0.518 at this point. The model has a sensitivity of 93.5% and a specificity of 96.8%. Calibration curves show fair consistency of the model. Our study is the first to use proteomic results with clinical biochemical data to construct a logistic regression model, and the model is consistent. However, our study still needs a more complete sample to confirm our findings.

Simulation study of the light collection efficiency and the dependency on position resolution in plastic scintillator-based muography detectors

Cosmic-ray muon imaging (muography) has been applied in various fields in recent years, in which plastic scintillators are one of the frequently selected detectors internationally. Therefore, a triangular scintillator strip based muography detection system has been proposed in the development of the μ Scattering and Transmission imaging faCility (μSTC). Before the mass production of detector units, this work studied the impacts of multiple factors on the light collection efficiency (LCE) and the position resolution (σ) of plastic scintillators. These factors include configurations of wavelength shifting (WLS) fibers, fiber grooves on scintillators, coupling optical glues and silicon photomultiplier (SiPM) readout mode. According to the simulated results, an empirical formula was proposed to quantitatively describe the relation between the LCE and σ, which has seldom been studied before. In this formula, σ reduces as a power-law function of the LCE. The SiPM readout mode (single-end or double-end output) and fiber groove treatments show no significant influence on the  σ-LCE relation. LCE variations due to different factors lead to a difference in σ of less than 0.2 mm in the whole range of LCE. Accordingly, these factors are nearly equivalent in the improvement of detector position resolution. In comparison, the muon hit position reconstruction method nearly halves the σ after using angular and gap corrections. Thus, a better reconstruction method shows greater importance than the efforts made to increase LCEs. The simulation study in this work will provide good references for the construction of plastic scintillators of the μSTC platform in the near future.

TEST OF LSTM NETWORKS IN LONG-TERM BEACH MORPHOLOGICAL CHANGES

In the prediction of beach profile changes, for example, the long-term calculation of daily changes can only provide sufficiently reliable reproduction results for a few years. One of the reasons is that actual morphological change is caused by the superposition of complex processes that are unknown. The prediction of timeseries data by data-driven models in contrast to physical models has been applied to various fields in recent years with the spread of deep learning and is expected to be used as a tentative solution for problems that cannot be adequately predicted by physical modeling. Recurrent neural networks are being applied to predict shoreline change over several years (Montaño et al., 2020), but application to spatial morphological changes, such as beach profile changes, is rarely investigated due to the limitations of the large amount of observation data required for learning. Here, we used the LSTM (Long Short-Term Memory) network, one of the recurrent neural networks, and long-term beach profile data observed at the Hasaki coast, Japan for learning and prediction of beach profile changes.

Combining Bibliometric and Social Network Analysis to Understand the Scholarly Publications on Artificial Intelligence

This article aims to conduct a comprehensive study employing bibliometric and social network analysis to explore scholarly publications in artificial intelligence (AI). A co-authorship network analysis of countries/regions and institutions, a thematic analysis based on the co-occurrence of keywords, and a Spearman rank correlation test of social network analysis are conducted using VOSviewer and SPSS, respectively. According to the research power analysis, the United States and China are the most significant contributors to the relevant publications and hold dominant positions in the co-authorship network. Universities play a crucial role in promoting and carrying out relevant research. AI has been increasingly applied to address new problems and challenges in various fields in recent years. The Spearman rank correlation analysis indicates that research performance in AI is significantly and positively correlated with social network indicators. This article reveals a systematic picture of the research landscape of AI, which can provide a potential guide for future research.

Preparation and Application Analysis of MXene

As an emerging two-dimensional transition metal carbide and/or nitride in recent years, MXene has become a research hotspot in many fields such as energy storage, catalysis, and adsorption with its unique two-dimensional layered structure, large specific surface area, excellent electrical conductivity, mechanical stability and magnetic properties. In this paper, the preparation method of two-dimensional material MXenes is briefly described, and its application progress in various fields in recent years is reviewed and summarized. Finally, the existing problems at this stage and the future research direction and development prospects are analyzed.

Application Progress of Cellulose Aerogel Modified by GMA

Cellulosic aerogels have attracted much attention because of their low density, high porosity and low thermal conductivity in many modified composite materials. Glycidyl methacrylate (GMA) is a new type of polymeric monomer with low cost, low toxicity, good biocompatibility, and easy chemical modification that has emerged in recent years, and it was widely used in the modification of rubber, fiber and other polymer materials. Cellulose composite aerogel, which is constructed by using GMA as a bridge to connect other materials, has rich functionality and shows great application potential in biomedicine, catalysis, adsorption and other fields. In this paper, the classification and properties of aerogels and cellulose are briefly described at first, then the synthesis methods of cellulose aerogels are introduced, and the applications of composite aerogels synthesized from cellulose modified by GMA in various fields in recent years are reviewed. This paper reviews the application of GMA-modified cellulose composite aerogels about the heavy metal and dye adsorption, biological tissues, wound dressings, drug carriers, biosensor and catalytic carriers. Finally, the future development prospect and current limitations of GMA modified cellulose aerogel are briefly introduced.

Study on the Technical Model Design of a Tourist Attraction from the Metaverse Perspective

With the development of digital technology and the epidemic's impact in recent years, the public's demand for "cloud tourism" using various virtual technologies has gradually increased. The concept of building a tourist attraction in a metaverse is studied based on the metaverse perspective and the current development of metaverse technology. The rationality and feasibility of building a metaverse tourist attraction are analyzed. The application of high technology in various fields in recent years in constructing metaverse tourism scenery is studied and analyzed. A feasible technical model of metaverse tourism scenery is proposed based on this research.

Private Blockchain in the Field of Health Services

Blockchain is a technology that is quite popular and has been adopted in various fields in recent years. This technology has caught the attention of researchers in the health sector because of its innovation which is considered capable of providing the necessary guarantees for the safe processing, sharing, and management of sensitive patient data. There are many problems with falsifying reports and withholding important information from patients, which is considered medical fraud. Hyperledger, a type of private Blockchain, is very suitable for healthcare applications. A private blockchain is a restricted type of blockchain network created by an entity. This type of network is limited to those with access permissions. In addition, private blockchains usually use a centralized verification system and are controlled by the network's creators. Hyperledger Fabric is one example of a permissioned blockchain that can play a role in implementing patient-centric, interoperable healthcare systems

Research on Management Mode of Gas Industry under the New Situation

With the continuous development of China's economy, the structure of energy allocation has also undergone many changes. As a basic enterprise serving urban residents, gas enterprises need to better carry out all aspects of operation and management. The price formation mechanism excludes the positive role of the market mechanism, which is embodied in the following aspects: the price formation mechanism excludes the positive role of the market mechanism; Price constitutes the function of rejecting the market; Pricing method can't provide production efficiency stimulation. As an integral part of public utilities, gas industry has the characteristics of public welfare, natural monopoly, competitive source, obvious geographical boundary, continuity of gas production and imbalance of consumption. Under the franchise system, the government will gradually withdraw its investment from the gas industry; At the same time, a new regulatory system for public utilities has been established to clarify the rights and obligations of franchising enterprises. Therefore, it has been widely used in various fields in recent years. However, at present, many urban gas enterprises in China have encountered many problems in their operation, which seriously affect their economic benefits. Therefore, analyzing the problems existing in the marketing management of urban gas enterprises is of great significance for promoting the healthy development of the natural gas industry.

Reinforcement learning - based adaptation and scheduling methods for multi-source DASH

Dynamic adaptive streaming over HTTP (DASH) has been widely used in video streaming recently. In DASH, the client downloads video chunks in order from a server. The rate adaptation function at the video client enhances the user?s quality-of-experience (QoE) by choosing a suitable quality level for each video chunk to download based on the network condition. Today networks such as content delivery networks, edge caching networks, contentcentric networks, etc. usually replicate video contents on multiple cache nodes. We study video streaming from multiple sources in this work. In multi-source streaming, video chunks may arrive out of order due to different conditions of the network paths. Hence, to guarantee a high QoE, the video client needs not only rate adaptation, but also chunk scheduling. Reinforcement learning (RL) has emerged as the state-of-the-art control method in various fields in recent years. This paper proposes two algorithms for streaming from multiple sources: RL-based adaptation with greedy scheduling (RLAGS) and RL-based adaptation and scheduling (RLAS). We also build a simulation environment for training and evaluation. The efficiency of the proposed algorithms is proved via extensive simulations with real-trace data.

Synthesis and up-to-date applications of 2D microporous g-C3N4 nanomaterials for sustainable development.

In recent years, with the development of industrial technology and the increase of people's environmental awareness, the research on sustainable materials and their applications has become a hot topic. Among two-dimensional (2D) materials that have been selected for sustainable research, graphitic phase carbon nitride (g-C3N4) has become a hot research topic because of its many outstanding advantages such as simple preparation, good electrochemical properties, excellent photochemical properties, and better thermal stability. Nevertheless, the inherent limitations of g-C3N4 due to its relatively poor specific surface area, rapid charge recombination, limited light absorption range, and inferior dispersion in aqueous and organic media have limited its practical application. In the review, we summarize and analyze the unique structure of the 2D microporous nanomaterial g-C3N4, its synthesis method, chemical modification method, and the latest application examples in various fields in recent years, highlighting its advantages and shortcomings, with a view to providing ideas for overcoming the difficulties in its application. Furthermore, the pressing challenges faced by g-C3N4 are briefly discussed, as well as an outlook on the application prospects of g-C3N4 materials. It is expected that the review in this paper will provide more theoretical strategies for the future practical application of g-C3N4-based materials, as well as contributing to nanomaterials in sustainable applications.

A Lightweight Method for Defense Graph Neural Networks Adversarial Attacks.

Graph neural network has been widely used in various fields in recent years. However, the appearance of an adversarial attack makes the reliability of the existing neural networks challenging in application. Premeditated attackers, can make very small perturbations to the data to fool the neural network to produce wrong results. These incorrect results can lead to disastrous consequences. So, how to defend against adversarial attacks has become an urgent research topic. Many researchers have tried to improve the model robustness directly or by using adversarial training to reduce the negative impact of an adversarial attack. However, the majority of the defense strategies currently in use are inextricably linked to the model-training process, which incurs significant running and memory space costs. We offer a lightweight and easy-to-implement approach that is based on graph transformation. Extensive experiments demonstrate that our approach has a similar defense effect (with accuracy rate returns of nearly 80%) as existing methods and only uses 10% of their run time when defending against adversarial attacks on GCN (graph convolutional neural networks).

Molecular represention in AI-assisted synthesis chemistry: from sequence to graph

<p indent="0mm">With the rapid development of deep neural networks and their derived algorithms, artificial intelligence (AI) has flourished in various fields in recent years. In the field of computer-aided synthetic route design, artificial intelligence algorithms such as generative adversarial networks and variational autoencoders have performed very well. How to deal with the geometric representation of molecules is a key step in transforming chemical problems into algorithmic problems. Starting from different dimensions, this paper systematically summarizes the application of various molecular sequence models, graph theory and other representation methods in AI-assisted synthesis chemistry, and discusses the shortcomings and challenges of current molecular representation.

Iterative Dual CNNs for Image Deblurring

Image deblurring attracts research attention in the field of image processing and computer vision. Traditional deblurring methods based on statistical prior largely depend on the selected prior type, which limits their restoring ability. Moreover, the constructed deblurring model is difficult to solve, and the operation is comparatively complicated. Meanwhile, deep learning has become a hotspot in various fields in recent years. End-to-end convolutional neural networks (CNNs) can learn the pixel mapping relationships between degraded images and clear images. In addition, they can also obtain the result of effectively eliminating spatial variable blurring. However, conventional CNNs have some disadvantages in generalization ability and details of the restored image. Therefore, this paper presents an iterative dual CNN called IDC for image deblurring, where the task of image deblurring is divided into two sub-networks: deblurring and detail restoration. The deblurring sub-network adopts a U-Net structure to learn the semantical and structural features of the image, and the detail restoration sub-network utilizes a shallow and wide structure without downsampling, where only the image texture features are extracted. Finally, to obtain the deblurred image, this paper presents a multiscale iterative strategy that effectively improves the robustness and precision of the model. The experimental results showed that the proposed method has an excellent effect of deblurring on a real blurred image dataset and is suitable for various real application scenes.