bluuebunny/crossref_metadata_2025 huggingface dataset:
Weak donor-like effect to enhance the thermoelectric performance of Bi2Te2.79Se0.21 near room temperature
Authors: Liu Xusheng , Xing Tong , Deng Tingting , Yang Hai , Wang Lei , Li Peng , Fan Jinhui , Li Xiaoya , Chen Lidong
Published: March 2022

Bismuth telluride-based materials are the most celebrated thermoelectric (TE) materials near room temperature. However, for the n-type bismuth telluride-based materials, the traditional powder metallurgy (PM) method, such as grinding and ball milling, can induce strong donor-like effect. The strong donor-like effect boosts the carrier concentration [Formula: see text] and the carrier thermal conductivity [Formula: see text], leading to the deterioration of both electrical and thermal performance. Herein, the basal deformation (BD) has been performed in the n-type Bi2Te3-based materials to introduce weak donor-like effect. The BD crystalline bulks were obtained through sintering the stacked crystals, which had been heavily deformed on the (00[Formula: see text] plane. The BD process optimizes the carrier concentration [Formula: see text] and improves the power factor (PF). Meanwhile, the lattice thermal conductivity [Formula: see text] is suppressed due to the enhanced grain boundary scattering. Consequently, a peak ZT of 1.0 at 380 K has been achieved in Bi2Te[Formula: see text]Se[Formula: see text], which is 24% and 30% higher than that of the original zone-melting (ZM) ingot and the polycrystalline sample by PM. This study sheds light on the further TE performance enhancement of bismuth telluride-based materials via introducing the weak donor-like effect.
Influence of the nanogrinding process on the performance of lithium iron phosphate
Authors: Ma Yuan , Li Rui , Zhang Qi , Jia Yingying , Pan Xuan , Du Qingqing , Ma Xiaohang , Liu Lingyun , Wu Yaodong , Meng Ying , Zi Zhenfa
Published: March 2022

Wet grinding is one of the most common forms of technology in powder production and preparation. Sand mill is the latest technology of wet grinding, which can effectively reduce the grinding particle size and improve the powder reactivity. LiFePO4 is one of the widely used cathode materials for lithium batteries at present, and the grinding process will affect the product performance. In this paper, parallel type and series type grinding processes were simulated by the single-cylinder system and the double cylinder system, which were studied and compared through the preparation of LiFePO4. It was discovered that the series type grinding process can not only increase the grinding efficiency but also improve the consistency of particle size distribution. LiFePO4 prepared by the series type grinding process has better rate performance and a higher discharge voltage platform. Attributing to the uniform particle size of LiFePO4, the lithium-ion diffusion coefficient was raised, which was proved by the cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). To sum up, based on the results of this paper, the properties of lithium iron phosphate will be affected by the particle size distribution, which is related to the grinding process. Therefore, in order to obtain the best performance of LiFePO4, it is necessary to choose the appropriate production process. This research work provides references and the basis for the research of powder grinding production technology.
ViLO — A Core Ontology of Vietnamese Legal Documents
Authors: Nguyen Minh Duc , Nguyen-Thi Giang H. , Nguyen-Dinh Cuong H.
Published: March 2022

Legal ontologies play a key role in various legal applications and have been broadly used by many stakeholders. Innovative systems and ontologies in the law hold potential to conduct legal research. With the needs for legal information management in smart applications, especially for Vietnamese law, it is vitally important to construct core legal ontologies for knowledge representation. This study proposes a core ontology for Vietnamese legal documents which covers general legal domain called as ViLO. The ViLO ontology mainly consists of related institutions of Vietnamese political system, types and structures of legal documents. The method of the NeOn-based collaborations among domain experts and ontology engineers was conducted to build up the ViLO ontology. Through FOCA-based validation results, the proposed method was shown to be effective and efficient. The resulting ontology was demonstrated to be reliable and enriched. The ViLO ontology is supposed to be a basis for further constructions of domain ontologies and artificial intelligence applications in Vietnamese law.
Revealing the importance of groundwater for potable private supplies in Wales
Authors: Farr G. , Ander E. L. , James K. , Kibble A. , Jones D. A. , Jones C.
Published: March 2022

At least 77 000 people across Wales rely on private water supplies for their drinking water, with 94% of these supplies dependent on groundwater. Potable private water supplies were mapped to Lower Super Output Area level, creating the first map of its kind for Wales. Some rural areas report nearly 43% of properties using private water supplies as their principal source of water. Simplifying the complex geology of Wales into ‘hydrostratigraphic units’ shows that 97% of private water supplies are sourced from secondary aquifers that have low productivity and storage. Ordovician and Silurian bedrock aquifers and associated Quaternary deposits support nearly 75% of all private water supplies. The total volume of groundwater abstracted by licensed and unlicensed potable private water supplies across Wales is estimated at 24.6 Ml day
−1
. In times of drought many of Wales’ low-storage aquifers can experience insufficiency events. During 2018, reports of 132 dry supplies were collated; however, we suspect that many cases were not reported. In a changing climate with more extreme weather events, and as working from home becomes more common, private water supply users in low-storage and low-permeability aquifers may find themselves at increasing risk of insufficiency events.

Thematic collection:
This article is part of the Climate change and resilience in Engineering Geology and Hydrogeology collection available at:
https://www.lyellcollection.org/cc/climate-change-and-resilience-in-engineering-geology-and-hydrogeology
A Quasi-digital QPSK Modulator Design for Biomedical Devices
Authors: Li Dawei , Zhou Yang , Chen Shaopin , Xu Xiaowei
Published: March 2022

For the biomedical transceiver, the data transmission is often asymmetric. At the downlink, the transceiver only needs to receive a simple command to control the operation of the external device, and the receiving data rate is low, about hundreds of Kb/s. However, data collected by external devices such as temperature sensors, pressure sensors, or cameras are often very large, which results in a transmitting data rate of several Mb/s. Therefore, a high energy-efficient modulator is needed. Compared with conventional digital modulator, analog modulator circuits have demonstrated superior energy efficiency at high data rates. This article presents a quasi-digital quadrature phase-shift keying (QPSK) modulator design realized by pure analog circuits which follows a logic design flow. The simulation results show that the system can generate a stable carrier of 64 MHz that meets intra-body communications (IBCs) requirements with a data transmission rate of 10 Mb/s. When the signal-to-noise ratios (SNRs) of the Gaussian channel is 14 dB, it can still maintain a bit error rate (BER) below 10
4
.
Scalable Representation Learning for Dynamic Heterogeneous Information Networks via Metagraphs
Authors: Fang Yang , Zhao Xiang , Huang Peixin , Xiao Weidong , de Rijke Maarten
Published: March 2022

Content representation is a fundamental task in information retrieval. Representation learning is aimed at capturing features of an information object in a low-dimensional space. Most research on representation learning for heterogeneous information networks (HINs) focuses on static HINs. In practice, however, networks are dynamic and subject to constant change. In this article, we propose a novel and scalable representation learning model,
M-DHIN
, to explore the evolution of a dynamic HIN. We regard a dynamic HIN as a series of snapshots with different time stamps. We first use a static embedding method to learn the initial embeddings of a dynamic HIN at the first time stamp. We describe the features of the initial HIN via metagraphs, which retains more structural and semantic information than traditional path-oriented static models. We also adopt a complex embedding scheme to better distinguish between symmetric and asymmetric metagraphs. Unlike traditional models that process an entire network at each time stamp, we build a so-called
change dataset
that only includes nodes involved in a triadic closure or opening process, as well as newly added or deleted nodes. Then, we utilize the above metagraph-based mechanism to train on the change dataset. As a result of this setup,
M-DHIN
is scalable to large dynamic HINs since it only needs to model the entire HIN once while only the changed parts need to be processed over time. Existing dynamic embedding models only express the existing snapshots and cannot predict the future network structure. To equip
M-DHIN
with this ability, we introduce an LSTM-based deep autoencoder model that processes the evolution of the graph via an LSTM encoder and outputs the predicted graph. Finally, we evaluate the proposed model,
M-DHIN
, on real-life datasets and demonstrate that it significantly and consistently outperforms state-of-the-art models.
HintStor: A Framework to Study I/O Hints in Heterogeneous Storage
Authors: Ge Xiongzi , Cao Zhichao , Du David H. C. , Ganesan Pradeep , Hahn Dennis
Published: March 2022

To bridge the giant semantic gap between applications and modern storage systems, passing a piece of tiny and useful information, called I/O access hints, from upper layers to the storage layer may greatly improve application performance and ease data management in storage systems. This is especially true for heterogeneous storage systems that consist of multiple types of storage devices. Since ingesting external access hints will likely involve laborious modifications of legacy I/O stacks, it is very hard to evaluate the effect and take advantages of access hints. In this article, we design a generic and flexible framework, called HintStor, to quickly play with a set of I/O access hints and evaluate their impacts on heterogeneous storage systems. HintStor provides a new application/user-level interface, a file system plugin, and performs data management with a generic block storage data manager. We demonstrate the flexibility of HintStor by evaluating four types of access hints: file system data classification, stream ID, cloud prefetch, and I/O task scheduling on a Linux platform. The results show that HintStor can execute and evaluate various I/O access hints under different scenarios with minor modifications to the kernel and applications.
MBN: Towards Multi-Behavior Sequence Modeling for Next Basket Recommendation
Authors: Shen Yanyan , Ou Baoyuan , Li Ranzhen
Published: March 2022

Next basket recommendation aims at predicting the next set of items that a user would likely purchase together, which plays an important role in e-commerce platforms. Unlike conventional item recommendation, the next basket recommendation focuses on capturing item correlations among baskets and learning the user’s temporal interest from the past purchasing basket sequence. In practice, most users interact with items in various kinds of behaviors. The multi-behavior data sheds light on user’s potential purchasing intention and resolves noisy signals from accidentally purchased items. In this article, we conduct an empirical study on real datasets to exploit the characteristics of multi-behavior data and confirm its positive effects on next basket recommendation. We develop a novel Multi-Behavior Network (MBN) model that captures item correlations and acquires meta-knowledge from multi-behavior basket sequences effectively. MBN employs the meta multi-behavior sequence encoder to model temporal dependencies of each individual behavior and extract meta-knowledge across different behaviors. Furthermore, we design the recurring-item-aware predictor in MBN to realize the high degree of the repeated occurrences of items, leading to better recommendation performance. We conduct extensive experiments to evaluate the performance of our proposed MBN model using real-world multi-behavior data. The results demonstrate the superior recommendation performance of MBN compared with various state-of-the-art methods.
Online Learning Bipartite Matching with Non-stationary Distributions
Authors: Chen Weirong , Zheng Jiaqi , Yu Haoyu , Chen Guihai , Chen Yixin , Li Dongsheng
Published: March 2022

Online bipartite matching has attracted wide interest since it can successfully model the popular online car-hailing problem and sharing economy. Existing works consider this problem under either
adversary
setting or
i.i.d.
setting. The former is too pessimistic to improve the performance in the general case; the latter is too optimistic to deal with the varying distribution of vertices. In this article, we initiate the study of the non-stationary online bipartite matching problem, which allows the distribution of vertices to vary with time and is more practical. We divide the non-stationary online bipartite matching problem into two subproblems, the matching problem and the selecting problem, and solve them individually. Combining Batch algorithms and deep Q-learning networks, we first construct a candidate algorithm set to solve the matching problem. For the selecting problem, we use a classical online learning algorithm, Exp3, as a selector algorithm and derive a theoretical bound. We further propose CDUCB as a selector algorithm by integrating distribution change detection into UCB. Rigorous theoretical analysis demonstrates that the performance of our proposed algorithms is no worse than that of any candidate algorithms in terms of competitive ratio. Finally, extensive experiments show that our proposed algorithms have much higher performance for the non-stationary online bipartite matching problem comparing to the state-of-the-art.
B

h

BF: A Bloom Filter Using B

h

Sequences for Multi-set Membership Query
Authors: Pei Shuyu , Xie Kun , Wang Xin , Xie Gaogang , Li Kenli , Li Wei , Li Yanbiao , Wen Jigang
Published: March 2022

Multi-set membership query is a fundamental issue for network functions such as packet processing and state machines monitoring. Given the rigid query speed and memory requirements, it would be promising if a multi-set query algorithm can be designed based on Bloom filter (BF), a space-efficient probabilistic data structure. However, existing efforts on multi-set query based on BF suffer from at least one of the following drawbacks: low query speed, low query accuracy, limitation in only supporting insertion and query operations, or limitation in the set size. To address the issues, we design a novel B

h

sequence-based Bloom filter (B

h

BF) for multi-set query, which supports four operations: insertion, query, deletion, and update. In B

h

BF, the set ID is encoded as a code in a B

h

sequence. Exploiting good properties of B

h

sequences, we can correctly decode the BF cells to obtain the set IDs even when the number of hash collisions is high, which brings high query accuracy. In B

h

BF, we propose two strategies to further speed up the query speed and increase the query accuracy. On the theoretical side, we analyze the false positive and classification failure rate of our B

h

BF. Our results from extensive experiments over two real datasets demonstrate that B

h

BF significantly advances state-of-the-art multi-set query algorithms.
PSL: An Algorithm for Partial Bayesian Network Structure Learning
Authors: Ling Zhaolong , Yu Kui , Liu Lin , Li Jiuyong , Zhang Yiwen , Wu Xindong
Published: March 2022

Learning partial Bayesian network (BN) structure is an interesting and challenging problem. In this challenge, it is computationally expensive to use global BN structure learning algorithms, while only one part of a BN structure is interesting, local BN structure learning algorithms are not a favourable solution either due to the issue of false edge orientation. To address the problem, this article first presents a detailed analysis of the false edge orientation issue with local BN structure learning algorithms and then proposes PSL, an efficient and accurate
P
artial BN
S
tructure
L
earning (PSL) algorithm. Specifically, PSL divides V-structures in a Markov blanket (MB) into two types: Type-C V-structures and Type-NC V-structures, then it starts from the given node of interest and recursively finds both types of V-structures in the MB of the current node until all edges in the partial BN structure are oriented. To further improve the efficiency of PSL, the PSL-FS algorithm is designed by incorporating
F
eature
S
election (FS) into PSL. Extensive experiments with six benchmark BNs validate the efficiency and accuracy of the proposed algorithms.
Impact of altered vena cava flow rates on right atrium flow characteristics
Authors: Parker Louis P. , Svensson Marcial Anders , Brismar Torkel B. , Broman Lars Mikael , Prahl Wittberg Lisa
Published: March 2022

Right atrium turbulent kinetic energy increases as the proportion of flow entering from the superior vena cava is increased. Although the commonly reported large right atrium vortex was confirmed across all flow scenarios, a new smaller vortex is observed in the right auricle. The caval veins exhibit highly helical flow and this appears to be the result of distal venous morphology.
A model for self-organization of sensorimotor function: the spinal monosynaptic loop
Authors: Enander Jonas M. D. , Jones Adam M. , Kirkland Matthieu , Hurless Jordan , Jörntell Henrik , Loeb Gerald E.
Published: March 2022

A novel model of self-organization of early spinal circuitry based on a biologically realistic plant, sensors, and neuronal plasticity in conjunction with empirical observations of fetal development. Without explicit need for guiding genetic rules, connection matrices emerge that support functional self-organization of the mature pattern of Ia to motoneuron connectivity in the spinal circuitry.
Properties of the Rotation and Mergence of Twisted Gaussian Schell Model Array Beams Propagating in Turbulent Biological Tissues
Authors: Xianyang Yang , Wenyu Fu
Published: March 2022

In this paper, an analytical expression for describing propagation properties of twisted Gaussian Schell model array (TGSMA) beams through turbulent biological tissues is derived based on the extended Huygens Fresnel integral. With the help of the formulae, properties of the rotation and mergence for the TGSMA beams in turbulent biological tissues are researched in detail. It is found that the TGSMA beams go through the distinct mergence period in the far field besides phenomena of abruption and rotation in the near field, and turbulent biological tissues play a dominated role in mergence of the TGSMA beams. These novel results may be helpful in optical trapping.
Impact of Industry 4.0 on Green Decoration Materials in Public Architectural Engineering for Application of Energy Conservation and Environmental Protection
Authors: Cui Wei , Liu Hui , Xu Bo , Zhong Chen
Published: March 2022

Industry 4.0 is derived from the concept of German industry, and its essence is to use technology for efficient production. However, the evolution in wireless technologies has opened the door to a new class of plant automation architecture that offers adopters a significant strategic advantage. Under the historical background of advocating low-carbon production, energy conservation and environmental protection, and sustainable development in various fields, the awareness of ecological energy conservation and environmental protection has gradually become a mainstream ideology. This paper principally studies the application of energy conservation and environmental protection green decoration materials in public architectural engineering projects to show the advantages of energy conservation and environmental protection green decoration materials in architectural decoration construction and analyzes the future development direction of public architectural decoration in China. Therefore, it is of great significance to improve the public environment and develop new ways of housing renovation with sustainable development goals to create a sustainable Industry 4.0 combining environmental protection and sustainability.
Enhancing Personalized Recommendation by Transductive Support Vector Machine and Active Learning
Authors: Wang Xibin , Li Yunji , Chen Jing , Yang Jianfeng
Published: March 2022

As an important component of information service networks, personalized recommendation technology provides users with better options and enables them to obtain information anytime and anywhere. Collaborative filtering (CF) is a successful and widely used form of this technology. However, the traditional CF recommendation algorithm is ineffective in environments with frequent entry of new users and high levels of data sparsity. For new users in the system, few or no scores, labels, or other such information is available, leading to the user cold start problem. Simultaneously, data sparsity leads to the selection of unreasonable neighbors, which reduces the recommendation accuracy. In addition, the traditional CF recommendation algorithm ignores the inherent connections between users’ preferences and their basic information (such as demographics). Users with similar demographic information are likely to have similar preferences, which can serve as a good basis for finding neighbors. To address the aforementioned problems, we propose a recommendation model that combines active learning (AL) and a semi-supervised transductive support vector machine (TSVM). To enable neighbors to be found quickly and accurately, similar users are clustered together on the basis of their basic information. Then, the TSVM-based classifier is trained on each cluster. To improve the quality of sample labeling and thus the classifier performance, an active learning method based on the distance strategy and a multiclassifier voting mechanism is implemented. Finally, the TSVM-based recommendation model is trained on the labeled samples. The extensive experiments conducted using a real data set from MovieLens demonstrate that the proposed model effectively alleviates the aforementioned cold start and data sparsity problems.
The Improving Effect of Intelligent Speech Recognition System on English Learning
Authors: Luo Qi
Published: March 2022

To improve the effect of English learning in the context of smart education, this study combines speech coding to improve the intelligent speech recognition algorithm, builds an intelligent English learning system, combines the characteristics of human ears, and studies a coding strategy of a psychoacoustic masking model based on the characteristics of human ears. Moreover, this study analyzes in detail the basic principles and implementation process of the psychoacoustic model coding strategy based on the characteristics of the human ear and completes the channel selection by calculating the masking threshold. In addition, this study verifies the effectiveness of the algorithm in this study through simulation experiments. Finally, this study builds a smart speech recognition system based on this model and uses simulation experiments to verify the effect of smart speech recognition on English learning. To improve the voice recognition effect of smart speech, band-pass filtering and envelope detection adopt the gammatone filter bank and Meddis inner hair cell model in the mathematical model of the cochlear system; at the same time, the masking effect model of psychoacoustics is introduced in the channel selection stage to prevent noise. Sex has been improved, and the recognition effect of smart voice has been improved. The analysis shows that the intelligent speech recognition system proposed in this study can effectively improve the effect of English learning. In particular, it has a great effect on improving the effect of oral learning.
Privacy-Preserving Minority Oversampling Protocols with Fully Homomorphic Encryption
Authors: Sun Maohua , Yang Ruidi , Liu Mengying
Published: March 2022

In recent years, blockchain and machine-learning techniques have received increasing attention both in theoretical and practical aspects. However, the applications of these techniques have many challenges, one of which is the privacy-preserving issue. In this paper, we focus on, specifically, the privacy-preserving issue of imbalanced datasets, a commonly found problem in real-world applications. Built based on the fully homomorphic encryption technique, this paper presents two new secure protocols, Privacy-Preserving Synthetic Minority Oversampling Protocol (PPSMOS) and Borderline Privacy-Preserving Synthetic Minority Oversampling Protocol (Borderline-PPSMOS). Our analysis reveals that PPSMOS is generally more efficient in performance than Borderline-PPSMOS. However, Borderline-PPSMOS achieves a better TP rate and F-Value than PPSMOS.
Study on Machine Learning Applications in Ideological and Political Education under the Background of Big Data
Authors: Li Yanjie , Mao He
Published: March 2022

With the development of big data and data mining technology, machine learning has been applied in many fields. However, there are a large number of difficulties for students who majored in ideological and political education. It is very necessary for those students to integrate machine learning technology into ideological and political education courses. In this paper, we introduced how to integrate machine learning into ideological and political education courses in class. Firstly, we explained what teachers should do before/in/after class for teaching machine learning courses and what students should prepare. Secondly, we took the introduction section of machine learning courses as an example to connect each content with ideological and political education and illustrate them in the way of ideological and political education. Thirdly, we took the decision tree algorithm that belongs to machine learning as an example to explore the ideological and political education philosophy in the decision tree algorithm. Finally, we make a questionnaire from the perspective of learning attitude, learning influence, and learning effect to investigate the outcomes of students with our teaching way. Our results presented valuable meaningful information for students who majored in not only computer science but also ideological and political education, thus promoting the progress of interdisciplinary and making machine learning courses understood more easily in the class of ideological and political education.
[Retracted] Computational Intelligence and Things Technology‐Based Collection and Design of Inheritance Characteristics of Tea Product Packaging Art Form
Authors: Wu Shilin , Mojtahe Kaza
Published: March 2022

In view of the core process data such as temperature and humidity of the tea product packaging process and the automatic control operation parameters of the production line, this paper designs a smart tea product packaging 5G Internet of Things gateway system, which realizes the 5G Internet of Things onsite monitoring and remote monitoring of the tea product packaging production line equipment. The design and implementation of the 5G Internet of Things gateway system are divided into three aspects, namely, 5G Internet of Things gateway configuration design, data communication protocol conversion. First, the 5G Internet of Things gateway configuration is to design the relevant control information of the tea processing production line equipment and complete the configuration storage of the PLC controller; secondly, the data communication protocol conversion is to pass the Modbus/TCP OPC standard to transfer the collected 5G Internet of Things. The sensor data undergoes unified standardized conversion, and then the standardized converted data is sent to the database and server using the MQTT protocol; the data processing of the 5G Internet of Things gateway system is the design of the internal data of the 5G Internet of Things gateway. Finally, the test was carried out and the results were obtained, which proved that the designed 5G Internet of Things gateway system is useable, reliable, has good stability and scalability, and provides an important solution for the construction of smart tea product packaging in the industry. The tea product packaging system based on 5G Internet of Things technology compiles electronic production management files for tea garden production, tea picking, tea processing, tea packaging and other links in accordance with the requirements of the national agricultural product quality and safety related systems and industry standards, and has become the traceability of tea quality The recorded important production management information provides important information support for the traceability of the whole process of tea.
A Comprehensive Investigation of the Performances of Different Machine Learning Classifiers with SMOTE-ENN Oversampling Technique and Hyperparameter Optimization for Imbalanced Heart Failure Dataset
Authors: Muntasir Nishat Mirza , Faisal Fahim , Jahan Ratul Ishrak , Al-Monsur Abdullah , Ar-Rafi Abrar Mohammad , Nasrullah Sarker Mohammad , Reza Md Taslim , Khan Md Rezaul Hoque
Published: March 2022

Heart failure is a chronic cardiac condition characterized by reduced supply of blood to the body due to impaired contractile properties of the muscles of the heart. Like any other cardiac disorder, heart failure is a serious ailment limiting the activities and curtailing the lifespan of the patient, most often resulting in death sooner or later. Detection of survival of patients with heart failure is the path to effective intervention and good prognosis in terms of both treatment and quality of life of the patient. Machine learning techniques can be critical in this regard since they can be used to predict the survival of patients with heart failure in advance, allowing patients to receive appropriate treatment. Hence, six supervised machine learning algorithms have been studied and applied to analyze a dataset of 299 individuals from the UCI Machine Learning Repository and predict their survivability from heart failure. Three distinct approaches have been followed using Decision Tree Classifier, Logistic Regression, Gaussian Naïve Bayes, Random Forest Classifier, K-Nearest Neighbors, and Support Vector Machine algorithms. Data scaling has been performed as a preprocessing step utilizing the standard and min–max scaling method. However, grid search cross-validation and random search cross-validation techniques have been employed to optimize the hyperparameters. Additionally, the synthetic minority oversampling technique and edited nearest neighbor (SMOTE-ENN) data resampling technique are utilized, and the performances of all the approaches have been compared extensively. The experimental results clearly indicate that Random Forest Classifier (RFC) surpasses all other approaches with a test accuracy of 90% when used in combination with SMOTE-ENN and standard scaling technique. Therefore, this comprehensive investigation portrays a vivid visualization of the applicability and compatibility of different machine learning algorithms in such an imbalanced dataset and presents the role of the SMOTE-ENN algorithm and hyperparameter optimization for enhancing the performances of the machine learning algorithms.
The Visual Movement Analysis of Physical Education Teaching considering the Generalized Hough Transform Model
Authors: Liu Jianmin , Li Yuan
Published: March 2022

In order to promote the effect of college physical education reform, this paper combines the generalized Hough transform model to analyze the visual movement of physical education teaching. The idea proposed in this paper uses the position information of the edge image itself and the direction information of the curve segment to directly eliminate the impossible targets, which fundamentally alleviates the problem of invalid sampling and accumulation. Moreover, this paper greatly constrains the parameter space based on the results of each segment of the curve, which greatly reduces the search burden of high-dimensional parameters, and combines the improved algorithm to construct a sports teaching video action analysis system. The experimental research shows that the visual movement analysis system of physical education teaching considering the generalized Hough transform model proposed in this paper can effectively analyze the sports teaching actions and improve the efficiency of physical education.
Ingestion of High-Oleic Peanut Improves Endurance Performance in Healthy Individuals
Authors: Kato Morimasa , Omiya Mayuko , Horiuchi Makino , Kurata Daisuke
Published: March 2022

This study aimed at evaluating whether high-oleic peanuts (with skin), which are rich in oleic acid, could serve as an energy substrate for prolonged exercise and improve endurance performance. We evaluated changes in blood biomarker (triglycerides, free fatty acid (FFA), biological antioxidant potential (BAP), malondialdehyde-modified low-density lipoprotein (MDA-LDL), and serum total protein) levels at 2-h intervals for 6 h after the ingestion of 10 g and 30 g of peanuts. The results were used to determine the timing of peanut ingestion before the endurance performance test. As a result, there was a significant change in the 30-g peanut-ingested condition, and lipid levels increased 2 h after the ingestion of 30 g of peanuts. Accordingly, the endurance performance test was conducted 2 h after ingesting 30 g of peanuts. The endurance performance test involved 70 min of pedaling exercise. We measured pre- and postexercise levels of 8-hydroxy-2′-deoxyguanosine (8-OHdG), which is a biomarker of oxidative stress. There was a significantly improved workload in the endurance performance test in the high-oleic peanut-ingested condition than in the control condition. Furthermore, the rate of increase in 8-OHdG was significantly lower in the high-oleic peanut-ingested condition than in the control condition. This suggests that the increase in FFA levels resulting from the ingestion of high-oleic peanuts and the inherent antioxidant effects of peanuts improved the workload during endurance exercise.
hsa_circ_0077837 Alleviated the Malignancy of Non-Small Cell Lung Cancer by Regulating the miR-1178-3p/APITD1 Axis
Authors: Xu Shanlin , Jiang Yanyan , Duan Yaqian
Published: March 2022

Objective. circRNAs were a group of the most promising molecular biomarkers for clinical prognosis and diagnosis of non-small cell lung cancer (NSCLC). It was a pity that academic circle still struggled to figure out how circRNAs acted on NSCLC. This article aimed to study the function and mechanism of hsa_circ_0077837 in NSCLC progression. Methods. Cell viability was measured via CCK-8, while apoptosis was evaluated with flow cytometry. The transwell assay and scratch test were used to detect invasion and migration, respectively. The dual-luciferase reporter gene assay verified the regulatory effect of miR-1178-3p on hsa_circ_0077837 and miR-1178-3p on apoptosis-inducing, TAF9-like domain 1 (APITD1). The TUNEL assay and immunohistochemistry were used to assess cells apoptosis and proliferation in lung tumor tissues in mice. Results. Hsa_circ_0077837 and APITD1 expression were suppressed in NSCLC tissues and cells, and miR-1178-3p level was promoted. High amount of hsa_circ_0077837 intensely prevented cell proliferation, migration, and invasion, promoted cell apoptosis in vitro, and delayed tumor growth in mice. Further analysis indicated that hsa_circ_0077837 acted as a miR-1178-3p sponge to stabilize APITD1, the target of miR-1178-3p. Mechanistically, we discovered that hsa_circ_0077837 could prevent proliferation, viability, migration, and invasion of NSCLC cells through stimulating the miR-1178-3p/APITD1 pathway. Conclusion. Collectively, our findings validated that hsa_circ_0077837 served as a miR-1178-3p sponge by targeting APITD1 that alleviated NSCLC progression.
An In Situ Spectral Monitoring Scheme for Advanced Manufacturing of Novel Nanodevices
Authors: Qi Hui , Fu Xing
Published: March 2022

The advanced manufacturing of ultra-thin-film devices, especially the nano-semiconductor products, has drawn a significant research interest over the past decades. In this field, monitoring the properties and thickness of the semiconductor layers is of paramount importance, which has significant impact on the device quality. In this study, an in situ monitoring scheme for manufacturing of nanodevices has been proposed, which is able to accurately analyse the optical absorption properties of the semiconductor layers of varying thickness in nanodevices. The in situ reflectance spectral analysis of monolayer, bilayer, and bulk-phase samples confirms the practicability and reliability of the monitoring scheme. The findings reported in this study form the basis for the advanced manufacturing of nano- and sub-nanodevices in the future.
Circular RNA hsa_circ_0004543 Aggravates Cervical Cancer Development by Sponging MicroRNA hsa-miR-217 to Upregulate Hypoxia-Inducible Factor
Authors: Liu Jun , Liu Yingqiao , Zhang Yang , Zheng Jing , Wang Shuzhen , Cao Guangming
Published: March 2022

Cervical cancer (CC) is the 4th principal source of cancer death in females with 604,000 new patients and 342,000 deaths in 2020 worldwide. It has been extensively shown that circRNAs are involved in regulating CC development. Nevertheless, the function and mechanisms of hsa_circ_0004543 in regulating CC need to be clearly elucidated. Herein, hsa_circ_0004543 expressions were compared between 40 paired paracancerous and cancerous specimens from CC patients and between 6 CC cell lines and a normal human cervical epithelial cell line based on qRT-PCR. Potential complementary binding sites between hsa-miR-217 and hsa_circ_0004543 were predicted using the interactome, while binding sites for the hypoxia-inducible factor-1a (HIF-1a) were predicted by TargetScan. The function and mechanism of hsa_circ_0004543 in the development of CC were estimated by silencing hsa_circ_0004543 with/without hsa-miR-217 or HIF-1a overexpression. The association between gene expressions was evaluated with Pearson’s correlation analysis. Molecular mechanisms were explored by ribonucleic acid (RNA) pulldown, dual-luciferase activity, and rescue experimental assays. Our results revealed that the hsa_circ_0004543 expression was considerably increased in CC tissues and cells. Its silencing repressed proliferation and metastasis, while it increased apoptosis of CC cells. The investigation of the mechanism showed that hsa-miR-217 silencing or HIF-1a overexpression rescued hsa_circ_0004543, and silencing inhibited malignant phenotypes of CC cells. hsa_circ_0004543 upregulated the HIF-1α expression by sponging hsa-miR-217 in CC development. Therefore, the hsa_circ_0004543 functioned as a competing endogenous RNA (ceRNA) of hsa-miR-217 to increase CC oncogenesis and metastasis by the upregulation of the HIF-1α expression. Consequently, targeting the hsa_circ_0004543/hsa-miR-217/HIF-1α axis might be a potential treatment approach for CC.
Pyroptosis Patterns Are Involved in Immune Microenvironment Regulation of Dilated Cardiomyopathy
Authors: Wang Kexin , Lv Zhan , Fang Chenggang , Xu Chengkai , Yu Zhimin , Gao Liuming , Wang Yanggan
Published: March 2022

The importance of cell pyroptosis in immunity regulation is becoming increasingly obvious, especially in diseases of the cardiovascular system. Nevertheless, it is unknown whether the pyroptosis signalling pathway is involved in the immune microenvironment regulation of dilated cardiomyopathy (DCM). Therefore, the purpose of the study was to investigate the influence of pyroptosis on the immune environment in dilated cardiomyopathy. We found that expression of 19 pyrolysis-related genes (PRGs) in DCM samples was altered compared to healthy samples. Subsequently, based on these 12 hub pyrolysis-related genes, we developed a classifier that can distinguish between healthy samples and DCM samples. Among the hub pyrolysis-related genes, RT–PCR analyses demonstrated that five of them exhibited significant differential expression in DCM. Interestingly, we observed that immune characteristics are correlated with pyroptosis: higher expression of GSDMD is positively correlated with infiltrating activated pDCs; GSDMD is negatively correlated with Tregs; CASP1 is positively related to parainflammation; and CASP9 is negatively related to the type II IFN response. In addition, distinct pyroptosis-mediated patterns were identified, and immune characteristics under distinct patterns were revealed: pattern B mediates an active immune response, and pattern A leads to a relatively mild immune response to DCM. We also compared the biological functions between these patterns. Compared with pattern A, pattern B had more abundant pathways, such as the NOTCH signalling pathway and pentose phosphate pathway. In summary, this study proves the important influence of pyrolysis on the immune microenvironment of dilated cardiomyopathy and provides new clues for understanding the pathogenesis of dilated cardiomyopathy.
Hot-Air Drying Characteristics of Sea Cucumber (Apostichopus japonicus) and Its Rehydration Properties
Authors: Jiang Pengfei , Jin Wengang , Liu Yan , Sun Na , Zhu Kaiyue , Bao Zhijie , Dong Xiuping
Published: March 2022

Drying is one of the most common methods for processing and preserving sea cucumber. Based on the research of pretreatment and drying process, this paper develops a dried sea cucumber product that can be quickly rehydrated in only 8 hours. By setting the pretreatment at heated water temperature of 80°C and the drying temperature from 30°C to 60°C, the fitting model is found by comparing empirical formulas and artificial neural networks. The ANN-based model was demonstrated to fit the experimental data for the adequate drying of sea cucumber. Following the increased drying temperature, the drying time was decreased and the rehydration ratio was increased. The sensory evaluation and texture properties dried at 40°C and 50°C were much better than those dried at 30°C and 60°C. Microstructure of rehydrated dried sample showed that increasing the temperature leads to the increase of fiber pore space, and the rehydration rate increases. The results of drying time and the rehydration properties of sea cucumber showed that 50°C is the best drying condition for hot-air drying of sea cucumber. The developed rapid rehydration dried sea cucumber can effectively simplify the rehydration time and steps of the dried sea cucumber and improve the quality of the sea cucumber, and it is considered to be a good technology for drying the sea cucumber and making a fast rehydration dried sea cucumber that can be further used for value-added product.
A Novel Solution for Seepage Problems Implemented in the Abaqus UEL Based on the Polygonal Scaled Boundary Finite Element Method
Authors: Yang Yang , Zhang Zongliang , Feng Yelin , Wang Kun
Published: March 2022

The scaled boundary finite element method (SBFEM) is a semianalytical computational scheme based on the characteristics of the finite element method (FEM) and boundary element method that combines their respective advantages. In this paper, the SBFEM and polygonal mesh technique are integrated into a new approach to solve steady-state and transient seepage problems. The proposed method is implemented in Abaqus employing a user-defined element (UEL). A detailed implementation of the procedure is presented in which the UEL element is defined, the internal variables RHS and AMATRX are updated, and the stiffness/mass matrix is solved using eigenvalue decomposition. Several benchmark problems are solved to verify the proposed implementation. The results show that the polygonal element of the polygonal SBFEM (PSBFEM) is more accurate than the standard FEM element of the same element size. For transient problems, the results for the PSBFEM and FEM are in excellent agreement. Hence, the proposed method is robust and accurate for solving steady-state and transient seepage problems. The developed UEL source code and the associated input files can be downloaded from GitHub.
Teaching Design of “Three-Dimensional” Blended Ideological and Political Courses from the Perspective of Deep Learning
Authors: Dong Hui
Published: March 2022

Since the increasing development of information technology, its integration and curriculum resources have become a new point of view in education and teaching reform. As a result, educational concepts, teaching models, and learning methods have been changed. Moreover, new requirements for the teaching reform of ideological and political courses in colleges and universities have also been put forward. Aiming at the current educational mechanism of ideological education courses in colleges and universities, which has a single form, is not well targeted and lacks synergy. This paper studies and establishes a brand-new three-dimensional hybrid education concept. The characteristics of this concept include (1) timeliness; that is, the basic views of Marxism are unified with the characteristics of the times; (2) pertinence, that is, the combination of ideological education and the characteristics of the students’ growth stage; (3) openness; that is, the new curriculum content and creative thinking are connected. Therefore, the three-dimensional hybrid teaching that combines networked teaching with the traditional teacher-centered teaching model has become an inevitable trend of classroom teaching reform at this stage. This paper develops a college ideological education course recommendation system based on deep learning, based on a hybrid collaborative filtering algorithm, and by introducing the effectiveness of the gradually forgetting curve based on changes in user feature, it better solves the shortcomings of traditional collaborative filtering algorithms, such as low efficiency and weak adaptability. Further, a corresponding recommendation system for ideological guidance courses in colleges and universities has been developed. The system runs stably and has strong practicability and robustness. It is of positive significance for creating an ideological and educational atmosphere with different forms and innovation for teachers and students.