bluuebunny/crossref_metadata_2025 huggingface dataset:
Deep Convolution Generative Adversarial Network-Based Electroencephalogram Data Augmentation for Post-Stroke Rehabilitation with Motor Imagery
Authors: Xu Fangzhou , Dong Gege , Li Jincheng , Yang Qingbo , Wang Lei , Zhao Yanna , Yan Yihao , Zhao Jinzhao , Pang Shaopeng , Guo Dongju , Zhang Yang , Leng Jiancai
Published: June 2022

The motor imagery brain–computer interface (MI-BCI) system is currently one of the most advanced rehabilitation technologies, and it can be used to restore the motor function of stroke patients. The deep learning algorithms in the MI-BCI system require lots of training samples, but the electroencephalogram (EEG) data of stroke patients is quite scarce. Therefore, the expansion of EEG data has become an important part of stroke clinical rehabilitation research. In this paper, a deep convolution generative adversarial network (DCGAN) model is proposed to generate artificial EEG data and further expand the scale of the stroke dataset. First, multichannel one-dimensional EEG data is converted into a two-dimensional EEG spectrogram using EEG2Image based on the modified S-transform. Then, DCGAN is used to artificially generate EEG data based on MI. Finally, the validity of the generated artificial EEG data is proved. This paper preliminarily indicates that generating artificial stroke data is a promising strategy, which contributes to the further development of stroke clinical rehabilitation.
Droplet Routing Based on Double Deep Q-Network Algorithm for Digital Microfluidic Biochips
Authors: Rajesh Kolluri , Pyne Sumanta
Published: June 2022

Digital microfluidic biochips (DMFBs) are emerging as an alternative to the cumbersome traditional laboratories for biochemical analysis. DMFBs come under micro-electro-mechanical systems and are a class of lab-on-a-chip devices. DMFBs provide automation, miniaturization and software programmability. The droplet routing algorithm determines concurrent routes for a set of droplets from their source cells to individual target cells on a DMFB. In this paper, a double deep Q-network (DDQN)-based droplet routing algorithm has been proposed. DDQN is a temporal difference-based deep reinforcement algorithm that combines Double Q-learning with a deep neural network algorithm. In the proposed work, routes for droplets are determined by DDQN, and later collisions are resolved using stalling and/or detouring. The latest arrival time of droplets arriving last at its target and cell utilization is taken as objectives for routing algorithm performance evaluation. The proposed method is evaluated on two standard benchmark suites. Simulation results show that the proposed DDQN-based droplet routing algorithm produces competitive results compared to state-of-the-art algorithms.
CONTINUOUS ADAPTIVE CONTROL OF THE KNEE JOINT MOVEMENT
Authors: TCHENDERLI-BRAHAM S. A. , BENREZKI R. R. , BENAHMED S. , KERMIA O.
Published: June 2022

In this paper, we develop a control law for Functional Electrical Stimulation (FES) application fields. This work aims to command the knee joint movement of patients in the seated position suffering from neurological disorders that lead to a partial or total paralysis of the limb. For this purpose, we propose the synthesis of an adaptive continuous control that permits to the knee joint the tracking of a time-varying desired angle. The knee movement is represented by the dynamical model of the lower limb. The designed control ensures an asymptotic stability while considering constraints such as unknown model parameters, unknown perturbation effects and unknown spasticity and fatigue parameters. The applicability and the effectiveness of the designed control law are shown through the simulation results.
Use of mobile app to enhance functional outcomes and adherence of home-based rehabilitation program for elderly with hip fracture: A randomized controlled trial
Authors: Cheng Kui Ching , Lau Kin Ming Ken , Cheng Andy S. K. , Lau Tin Sing Keith , Lau Fuk On Titanic , Lau Mun Cheung Herman , Law Sheung Wai
Published: June 2022

Background: Mobile app has been used to improve exercise adherence and outcomes in populations with different health conditions. However, the effectiveness of mobile app in delivering home-based rehabilitation program to elderly patients with hip fracture is unclear.

Objective: The aim of this study was to test the effectiveness of mobile app in delivering home-based rehabilitation program for improving functional outcomes and reducing caregiver stress with enhancing adherence among the elderly patients with hip fracture.

Methods: A randomized controlled trial with an intervention period of two months was performed. Eligible participants were randomized into either experimental group with home-based rehabilitation program using a mobile app or control group with home-based rehabilitation program using an exercise pamphlet. Primary outcomes were Modified Functional Ambulatory Category (MFAC), Elderly Mobility Scale (EMS) and Lower Extremity Functional Scale (LEFS). Secondary outcomes were exercise adherence and Modified Caregiver Strain Index (M-CSI). The outcomes were collected at pre-discharge training session, one month and two months after hospital discharge.

Results: A total of 50 participants were enrolled, with 19 participants in the experimental group and 20 participants in the control group. Eleven participants had withdrawn from the study. The experimental group showed higher exercise adherence than the control group in first month ([Formula: see text]). There were no between-group differences in MFAC, EMS, LEFS and M-CSI at the first month and second month.

Conclusion: Use of the mobile app improved exercise adherence, yet it did not improve physical performance, self-efficacy and reduce caregiver stress when compared to a standard home rehabilitation program for elderly patients with hip fracture. Further studies to investigate the benefits of mobile apps are required. (ClinicalTrials.gov ID: NCT04053348.)
Ultra-Low-Power Modulo Adder with Thermometer Coding for Uncertain RNS Applications
Authors: Vijaya Vardhan K. , Musala Sarada
Published: June 2022

The residue numeral system (RNS) is a numerical representation of integers based on modulo values. The RNS makes computing systems more efficient in terms of speed, power, energy, area, and other considerations. In recent years, the RNS has focused on a variety of topics, including digital signal and image processing, cryptography, embedded systems (ESs), communications, and the Internet of Things (IoT), among others. Forward and reverse converters, as well as arithmetic units in the channels, all use modular addition. In low-power ESs, thermometer coding (TC) has lately been utilized to reduce uncertainty. The thermometer code (TC) is more energy-efficient and consumes less power for modulo addition as compared to binary adders. Based on TC, the proposed research develops a power-efficient modular adder. The proposed design is simulated using Cadence’s NC launch encounter tool. In comparison with conventional modular adders, the proposed modular adder consumes 40% and 35% less power for one-hot coding modulo adder and thermometer code residue (TCR) modulo adder, respectively.
Influence of the aspect ratio of nitrogen-doped carbon nanotubes on their piezoelectric properties
Authors: Il’ina Marina V. , Soboleva Olga I. , Rudyk Nikolay N. , Polyvianova Maria R. , Khubezhov Soslan A. , Il’in Oleg I.
Published: June 2022

Recent studies have shown that nitrogen doping of carbon nanotubes (CNTs) can lead to the formation of piezoelectric properties in them, not characteristic of pure CNTs. In this work, nitrogen-doped CNTs were grown by plasma-enhanced chemical vapor deposition and the effect of the aspect ratio of the nanotube length to its diameter on its piezoelectric coefficient [Formula: see text] was shown. It was observed that as the aspect ratio of the nanotube increased from 7 to 21, the value of [Formula: see text] increased linearly from 7.3 to 10.7 pm/V. This dependence is presumably due to an increase in curvature-induced polarization because of an increase in the curvature and the number of bamboo-like “bridges” in the nanotube cavity formed as a result of the incorporation of pyrrole-like nitrogen into the nanotube structure. The obtained results can be used in the development of promising elements of nanopiezotronics (nanogenerators, memory elements, and strain sensors).
Position Controller for a Flapping-Wing Drone Using UWB
Authors: Gonzalez Guillermo , de Croon Guido C. H. E. , Olejnik Diana , Karásek Matěj
Published: June 2022

This paper proposes an integral approach for accurate ultra-wideband indoor position control of flapping-wing micro-air vehicles. Three aspects are considered to achieve a reliable and accurate position controller. The first aspect is a velocity/attitude flapping-wing model for drag compensation. The model is compared with real flight data and shown to be applicable for more than one type of flapping-wing drone. The second improvement regards a voltage-dependent thrust control. Lastly, a characterisation of ground effects in flapping-wing flight is obtained from hovering experiments. The proposed controller improves position control by a factor [Formula: see text], reaching a mean absolute error of 10[Formula: see text]cm for the position in [Formula: see text] and [Formula: see text], and 4.9[Formula: see text]cm for the position in [Formula: see text].
Examining the Current Status and Emerging Trends in Continuous Authentication Technologies through Citation Network Analysis
Authors: Jeong Jongkil Jay , Zolotavkin Yevhen , Doss Robin
Published: June 2022

Continuous Authentication (CA) technologies enable users to be authenticated beyond just the point of entry. In this article, we conduct a comprehensive review of over 2,300 articles to (a) identify the main components of CA research to date, and (b) explore the current gaps and future research directions. Through a Citation Network Analysis (CNA), we identified that there are currently three primary focus research areas on CA - Keystroke Dynamics; Mouse Movements; and Mobile Device Touch, as well as identify an emerging trend in more recent studies on multi-modal CA authentication which utilises the numerous sensors that are embedded in modern mobile devices. This study also highlights the current gaps in the literature such as the need for a consensus over how to evaluate the application and utility of CA, and the need to examine the feasibility of CA technologies that currently exist based on more use case studies.
FARSI: An Early-stage Design Space Exploration Framework to Tame the Domain-specific System-on-chip Complexity
Authors: Boroujerdian Behzad , Jing Ying , Tripathy Devashree , Kumar Amit , Subramanian Lavanya , Yen Luke , Lee Vincent , Venkatesan Vivek , Jindal Amit , Shearer Robert , Reddi Vijay Janapa
Published: June 2022

Domain-specific SoCs (DSSoCs) are an attractive solution for domains with extremely stringent power, performance, and area constraints. However, DSSoCs suffer from two fundamental complexities. On the one hand, their many specialized hardware blocks result in complex systems and thus high development effort. On the other hand, their many system knobs expand the complexity of design space, making the search for the optimal design difficult. Thus to reach prevalence, taming such complexities is necessary. To address these challenges, in this work, we identify the necessary features of an early-stage design space exploration framework that targets the complex design space of DSSoCs and provide an instance of one such framework that we refer to as FARSI. FARSI provides an agile system-level simulator with speed up and accuracy of 8,400× and 98.5% compared to Synopsys Platform Architect. FARSI also provides an efficient exploration heuristic and achieves up to 62× and 35× improvement in convergence time compared to the classic simulated annealing (SA) and modern Multi-Objective Optimistic Search. This is done by augmenting SA with architectural reasoning such as locality exploitation and bottleneck relaxation. Furthermore, we embed various co-design capabilities and show that, on average, they have a 32% impact on the convergence rate. Finally, we demonstrate that using development-cost-aware policies can lower the system complexity, both in terms of the component count and variation by as much as 60% and 82% (e.g., for Network-on-a-Chip subsystem), respectively.
Measurement-Based Optimization of Cell Selection in NB-IoT Networks
Authors: Chang Xiangmao , Zhan Jun , Xing Guoliang , Huang Jun , Chen Bing , Zhou Lu
Published: June 2022

Narrowband-Internet of Things (NB-IoT) is an emerging cellular communication technology designed for low-power wide-area applications. Cell selection determines the channel of user device and hence is an important issue in cellular networks. In this article, we make the first attempt to examine and optimize the cell selection in NB-IoT networks by field measurement. We conduct measurements at 30 different locations which involve five typical application scenarios of NB-IoT. Two kinds of NB-IoT modules and two network operators are also involved in the measurements. We find four potential issues on the cell selection of the User Equipment (UE) through the measurements. We propose an adaptive cell selection approach to optimize the cell selection of UE. The simulation test based on real-world measurement data shows that the cell selected by the adaptive approach can improve the coverage level and reduce the power consumption for UE.
Extending Python for Quantum-classical Computing via Quantum Just-in-time Compilation
Authors: Nguyen Thien , McCaskey Alexander J.
Published: June 2022

Python is a popular programming language known for its flexibility, usability, readability, and focus on developer productivity. The quantum software community has adopted Python on a number of large-scale efforts due to these characteristics, as well as the remote nature of near-term quantum processors. The use of Python has enabled quick prototyping for quantum code that directly benefits pertinent research and development efforts in quantum scientific computing. However, this rapid prototyping ability comes at the cost of future performant integration for tightly coupled CPU-QPU architectures with fast-feedback. Here, we present a language extension to Python that enables heterogeneous quantum-classical computing via a robust C++ infrastructure for quantum just-in-time (QJIT) compilation. Our work builds off the QCOR C++ language extension and compiler infrastructure to enable a single-source, quantum hardware-agnostic approach to quantum-classical computing that retains the performance required for tightly coupled CPU-QPU compute models. We detail this Python extension, its programming model and underlying software architecture, and provide a robust set of examples to demonstrate the utility of our approach.
Application Value of Machine Learning Method in Measuring Gray Matter Volume of AIDS Patients
Authors: Fu Danhui , Mo Kai , Deng Wenjuan , Zhao Yang , Ding QianLin , Hong Sen , Zhang Wei , Su Danke
Published: June 2022

Background. To investigate the role of gray matter (GM) volume in the identification of HIV-positive patients with HIV-associated neurocognitive impairment (HAND) using a machine learning approach from normal healthy controls. Methods. Twenty-seven HIV-infected patients and 14 healthy controls were enrolled in our study. Each set of BRAVO images was postprocessed using DPARSF3.1 to coregister all brains on the MNI template, and volume extraction of 90 brain regions was performed using custom-designed code. The machine learning method was performed using PRoNTo2.1.1 toolbox. The differences in brain volume between the HAND and non-HAND groups were analyzed. Results. GM volume effectively distinguished HIV-positive patients from healthy subjects with an AUC equals to 0.73. The sensitivity, specificity, and accuracy of the established classification were 85.19%, 42.86%, and 70.73%, respectively. GM volume value of the top ten brain regions was related to digit symbols, trail making test, digit span, vocabulary fluency, stroop C time, stroop CW time, CD4, and neuropsychological group. Conclusions. A machine learning approach facilitates early diagnosis of HAND in HIV patients by MRI-based GM volume measurement.
Music Timbre Extracted from Audio Signal Features
Authors: Mo Ying
Published: June 2022

Among the basic elements of music, timbre is one of the most important elements of sound, and it is also the main basis for distinguishing one pronunciation from another. People usually have the ability to “listen and argue” because everyone’s pronunciation is different. However, the existing audio extraction technology has low efficiency and low accuracy. Therefore, this paper aims to discuss the algorithm that can make music timbre feature extraction more accurate and efficient. For audio signal feature extraction, this paper proposed an audio feature based on harmonic components to describe the harmonic structure information in the audio signal spectrum. The algorithm in this paper extracts timbre features from the sound data of Western musical instruments and national musical instruments and analyzes the recognition accuracy. The experimental results showed that the classification accuracy of the four feature extractors is above 92%, among which B has the worst effect, with an accuracy of 92.42%, and D has the best classification effect, with an accuracy of 99.15%, which shows that the feature extraction algorithm designed in this paper combined with the traditional feature extraction algorithm can achieve better results.
[Retracted] Prediction Performance of Deep Learning for Colon Cancer Survival Prediction on SEER Data
Authors: Gupta Surbhi , Kalaivani S. , Rajasundaram Archana , Ameta Gaurav Kumar , Oleiwi Ahmed Kareem , Dugbakie Betty Nokobi
Published: June 2022

Colon and rectal cancers are the most common kinds of cancer globally. Colon cancer is more prevalent in men than in women. Early detection increases the likelihood of survival, and treatment significantly increases the likelihood of eradicating the disease. The Surveillance, Epidemiology, and End Results (SEER) programme is an excellent source of domestic cancer statistics. SEER includes nearly 30% of the United States population, covering various races and geographic locations. The data are made public via the SEER website when a SEER limited‐use data agreement form is submitted and approved. We investigate data from the SEER programme, specifically colon cancer statistics, in this study. Our objective is to create reliable colon cancer survival and conditional survival prediction algorithms. In this study, we have presented an overview of cancer diagnosis methods and the treatments used to cure cancer. This paper presents an analysis of prediction performance of multiple deep learning approaches. The performance of multiple deep learning models is thoroughly examined to discover which algorithm surpasses the others, followed by an investigation of the network’s prediction accuracy. The simulation outcomes indicate that automated prediction models can predict colon cancer patient survival. Deep autoencoders displayed the best performance outcomes attaining 97% accuracy and 95% area under curve‐receiver operating characteristic (AUC‐ROC).
The Embedding of Sports Social Organizations in Rural Governance Based on the Collaborative Governance Model of Multiple Subjects
Authors: Yu Yanglu , Gao Kuiting
Published: June 2022

Rural governance is an important part of national governance, and the participation of social organizations is also very important. This research explores the multiple relationships between rural governance subjects and governance methods through the basic requirements of “co-construction and sharing” of multi-governance and the status quo of creation activities, integrates an innovative rural community governance evaluation and supervision system, and builds rural community governance evaluation and supervision system mechanism. Mutually exclusive constraints on the diversity of governance subjects will provide strategic guidance for sports social organizations to participate in diversified management.
Similar Inflammatory Adaptation in Women following 10 Weeks of Two Equalized Resistance Training with Different Muscle Action Duration
Authors: Pinto Kelerson Mauro de Castro , Diniz Rodrigo César Ribeiro , Tourino Frank Douglas , de Lacerda Lucas Túlio , Bearzoti Eduardo , Costa Karine Beatriz , de Souza Débora Maria Soares , Lima Fernando Vitor , Rocha-Vieira Etel , Chagas Mauro Heleno , Talvani Andre
Published: June 2022

This study is aimed at evaluating the profile of inflammatory markers and components of redox regulation in untrained women after 10 weeks of resistance training using equalized protocols but different muscle action duration (MAD). Twenty‐two women underwent progressive resistance training exercising the knee extensor muscles for 10 weeks—3x/week, with 3‐5 sets of 6 repetitions at 50% of the 1 repetition maximum strength test (1RM), with a rest of 180 s between the series, following the training protocol (i) 5 s of concentric muscle action for 1 s of eccentric muscle action (5C‐1E) and (ii) 1 s of concentric muscle action for 5 s of eccentric muscle action (1C‐5E). Quadriceps muscle hypertrophy maximum strength (1RM) and redox regulation/muscle damage/inflammatory markers (CAT, SOD, TBARS, FRAP, CH, LDH, CXCL8, and CCL2) were evaluated. Plasma markers were evaluated before and 30 minutes after the first and last training sessions. A similar gain in hypertrophy and maximum strength was observed in both groups. However, in the 5C‐1E, a significant major effect was observed for SOD (F1.19 = 10.480, p = 0.004) and a significant major time effect, with a reduction in the last training session, was observed for CXCL8 (F1.37 = 27.440, p < 0.001). In conclusion, similar protocols of resistance training, with different MAD, produced similar inflammatory and adaptive responses to strength training. As the training load is progressive, the maintenance of this inflammatory and redox regulation profile suggests an adaptive response to the proposed strength training.
Gliosarcoma: The Distinct Genomic Alterations Identified by Comprehensive Analysis of Copy Number Variations
Authors: Cheng Chuan-dong , Chen Cheng , Wang Li , Dong Yong-fei , Yang Yang , Chen Yi-nan , Niu Wan-xiang , Wang Wen-chao , Liu Qing-song , Niu Chao-shi
Published: June 2022

Gliosarcoma (GSM), a histologic variant of glioblastoma (GBM), carries a poor prognosis with less than one year of median survival. Though GSM is similar with GBM in most clinical and pathological symptoms, GBM has unique molecular and histological features. However, as the rarity of GSM samples, the genetic information of this tumor is still lacking. Here, we take a comprehensive analysis of DNA copy number variations (CNV) in GBM and GSM. Whole genome sequencing was performed on 21 cases of GBM and 15 cases of GSM. CNVKIT is used for CNV calling. Our data showed that chromosomes 7, 8, 9, and 10 were the regions where CNV frequently happened in both GBM and GSM. There was a distinct CNV signal in chromosome 2 especially in GSM. The pathway enrichment of genes with CNV was suggested that the GBM and GSM shared the similar mechanism of tumor development. However, the CNV of some screened genes displayed a disparate form between GBM and GSM, such as AMP, BEND2, HDAC6, FOXP3, ZBTB33, TFE3, and VEGFD. It meant that GSM was a distinct subgroup possessing typical biomarkers. The pathways and copy number alterations detected in this study may represent key drivers in gliosarcoma oncogenesis and may provide a starting point toward targeted oncologic analysis with therapeutic potential.
Feasibility of the Development of New PVC/Fine Sand Imitation Stone Composite Materials to Promote Rural Urbanization
Authors: Wei Yi
Published: June 2022

At present, with the acceleration of rural urbanization, people’s market demand for new building materials is also increasing, and the research and development of new PVC/fine sand imitation stone composite materials are also in continuous progress and development. The purpose of this article is to analyze the feasibility of developing new PVC/fine sand imitation stone composite materials to promote rural urbanization and propose the application of more new PVC/fine sand imitation stone composite materials to our urbanization construction. In, we first conducted research on the new PVC/fine sand imitation stone composite material and rural urbanization, and then, we conducted research on the various properties of the new PVC/fine sand imitation stone composite material and the development of rural urbanization. The research results show that the new PVC/fine sand imitation stone composite material has at least 400% improvement in compressive performance compared with the traditional PVC material, and it is also better than the traditional PVC material in terms of the material expansion and contraction performance, which is more conducive to the application our rural urbanization is under construction.
[Retracted] Multiple Influences of Intelligent Technology on Network Behavior of College Students in the Metaverse Age
Authors: Ge Jiajia
Published: June 2022

The advent of the metaverse age has gradually transformed digital survival from a fantasy in science fiction to a reality. Especially in recent years, the college students, as the objects of ideological and political education in universities, have been deeply embedded in their learning, social interaction, entertainment, and consumption behaviors, presenting new characteristics of the times. From the aspects of the background of intelligent technology on College Students’ network behavior, the types of College Students’ network behavior, the multiple effects of intelligent technology, the nature of College Students’ network behavior, etc., provide some basis for ideological and political education.
Sports Feedback System Based on Video Digital Processing
Authors: Wang Lei
Published: June 2022

The current sports feedback system has high application value and is widely used in physical education courses, but it has problems such as large feedback error, long response time, and incomplete data, leading to poor effect of sports feedback system. In order to improve the effectiveness of sports feedback system, a sports feedback system based on video digital processing is designed. By constructing the overall architecture of video digital sports feedback system, the positioning module, interface module, power supply circuit module, sampling module, input module, signal extraction module, and video digital processing module are designed. Using information communication mechanism, TCP/IP network protocol, serial communication, and shared memory to improve video digital processing, experimental results show that the designed system has good error convergence of 0.1, short response time of 32 s, and high data integrity of 94%, which has strong practicability.
Synthesis and Properties of a New Type of Terpyridine Cholesterol Derivative Gelator with Applications to Medical Treatments
Authors: Qiu Lina , Liu Yang , Miao Yunhe , Zhang Weiwei , Gong Aijun , Liu Zhipeng
Published: June 2022

Low molecular mass gelators (LMMGs), as a new type of intelligent soft material, possess good response properties to light, electricity, heat, and ultrasound and have many potential applications in fields such as intelligent sensing, biological materials, and drug release. Additionally, steroid derivatives have been a focus in the study of LMMGs for their desirable properties as well, such as their rigid framework, multichiral center, and strong van der Waals accumulation. Furthermore, the coordination ability of terpyridine has been an emphasis in the study of supramolecular chemistry and coordination chemistry as well. Attempts have been made with terpyridine groups that have special responses, such as terpyridine with steroid derivatives, to build more specialized and functional gelators. In this study, we used 2-acetylpyridine, 2-formaldehyde, and cholesterol to synthesize 6-(2,2:6

′

,2

″

-terpyridine-4

′

-carboxamide group) hexanoic acid (with a yield of 64.39%, P1), glycine cholesterol ester (with a yield of 70.36%, P2), and DMTCP (with a yield of 88.92%). Infrared spectroscopy, nuclear magnetic resonance spectroscopy, mass spectrometry, elemental analysis, and gelator performance tests were then conducted to measure the gelation effect of the materials and to explore their gelation mechanisms. Compared to P1 and P2, the DMTCP synthesized by P1 and P2 was able to form gel in more kinds of solvents. In addition, when it contains both terpyridine and cholesterol functional groups, the gelation properties of DMTCP were also significantly improved, and all the gels prepared in the four solvents in which DMTCP can form gels were stimulus responsive.
Analysis on Scientific and Technological Innovation of Grain Production in Henan Province Based on SD‐GM Approach
Authors: Zhang Shuhua , Li Bingjun , Yang Yingjie , Zhang Yifan
Published: June 2022

A sustainable growth of grain outputs mainly relies on improving output per unit area through scientific and technological innovation. From the perspective of scientific and technological innovation, taking the grain production process as the research object, a systems model of scientific and technological innovation in grain production is constructed based on the relevant data of Henan Province from 2010 to 2019. Firstly, the internal mechanism of grain production scientific and technological innovation is explored, and the feedback loop of grain production scientific and technological innovation is then established. Secondly, system dynamics and grey system theory are combined to construct table functions and logistic functions to establish the grain production scientific and technological innovation system model. Through testing the model, the stability and feasibility of the model are demonstrated, and the simulation and prediction of the innovation system of grain production scientific and technological in Henan Province are carried out. Thirdly, in order to explore the impact of feasible policy schemes on grain production, seven policy plans are designed to simulate grain production policy scenarios from the perspective of scientific and technological innovation. The results show that: (1) The adjustment of individual policies, especially the adjustment of the protection policy of scientific and technological innovation in grain production or the subsidy policy of agricultural materials, has a weak influence on the improvement of grain output. The progress of agricultural technology is the main support for improving the comprehensive grain production capacity. (2) The future grain growth potential of Henan Province should focus on increasing the yield per unit area, and the protection of cultivated land resources should not be ignored. (3) The combination of policies has mutually reinforcing effect, which leads to an ideal system simulation effect. Finally, from the perspective of the composition of scientific and technological innovation system in Henan Province, this study puts forward countermeasures and suggestions for the implementation of the strategy of “storing grain in technology” in Henan Province.
Immersive Learning Method of Ideological and Political Education under Big Data and Artificial Intelligence
Authors: Yu Yan
Published: June 2022

The study aims to explore the influence of big data on college students’ learning methods of ideological and political education (IPE) under artificial intelligence. First, the characteristics of big data are analyzed. Second, big data is introduced into IPE’s classroom teaching, and its influence on students’ learning methods is discussed. Finally, the questionnaire is designed to investigate students’ learning attitudes and teachers’ effectiveness. The results show that the scores of the students are poor, and the traditional teaching method should be updated to improve the teaching quality of IPE. Motivating students’ enthusiasm and updating the teaching content are the main tasks to improve students’ learning effect on IPE. College students who receive IPE based on big data have better academic performance and a positive learning attitude, and each factor is more significant than 0.05. It is concluded that big data has a considerable impact on IPE. It can promote students’ mastery of the knowledge and improve their learning efficiency.
A Deep Learning Approach to Optimal Sampling Problems
Authors: Wang Xinxin , Meng Xiangyu , Li Fangfei
Published: June 2022

Time-triggered and event-triggered sampling methods have been widely adopted in control systems. Optimal sampling problems of the two mechanisms have also received great attentions. However, for high-dimensional systems, analytical methods have some limitations. In this study, we propose a model-free method, called soft greedy policy for neural network fitting, to calculate the optimal sampling period of the time-triggered impulse control and the optimal threshold of the event-triggered impulse control. A neural network is used to approximate the objective function and then is trained. This approach is more widely applicable than the analytical method. At the same time, compared with different ways of generating data, the algorithm can carry out real-time update with greater flexibility and higher accuracy. Simulation results are provided to verify the effectiveness of the proposed algorithm.
Long Noncoding RNAs and Circular RNAs in the Metabolic Reprogramming of Lung Cancer: Functions, Mechanisms, and Clinical Potential
Authors: Zhou Yuhao , Zhan Yuan , Jiang Weiling , Liu Huiguo , Wei Shuang
Published: June 2022

As key regulators of gene function, long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs) are generally accepted to be involved in lung cancer pathogenesis and progression. Recent research has clarified the phenomenon of metabolic reprogramming in lung cancer because of its significant role in tumor proliferation, migration, invasion, metastasis, and other malignant biological behaviors. Emerging evidence has also shown a relationship between the aberrant expression of lncRNAs and circRNAs and metabolic reprogramming in lung cancer tumorigenesis. This review provides insight regarding the roles of different lncRNAs and circRNAs in lung cancer metabolic reprogramming, by how they target transporter proteins and key enzymes in glucose, lipid, and glutamine metabolic signaling pathways. The clinical potential of lncRNAs and circRNAs as early diagnostic biomarkers and components of therapeutic strategies in lung cancer is further discussed, including current challenges in their utilization from the bench to the bedside and how to adopt a proper delivery system for their therapeutic use.
Integrated Design and Development of Intelligent Scenic Area Rural Tourism Information Service Based on Hybrid Cloud
Authors: Zhang Hong , Li Mingyang
Published: June 2022

Although the “Internet+” technologies (big data and cloud computing) have been implemented in many industries, each industry involved in rural tourism economic information services has its own database, and there are still vast economic information resources that have not been exploited. Z travel agency through rural tourism enterprise third-party information services and mobile context-awareness-based Z travel has achieved good economic and social benefits by deep value mining and innovative application of the existing data of the enterprise through the third-party information service of rural tourism enterprises and mobile context-aware travel recommendation service. It clearly demonstrates that, in order to maximise the benefits of economic data, rural tourist businesses should focus not only on the application of new technologies and methodologies but also on the core of demand and data-driven and thoroughly investigate the potential value of current data. This paper mainly analyzes the problems related to how rural tourism can be upgraded under the smart tourism platform, with the aim of improving the development of China’s rural tourism industry with the help of an integrated smart tourism platform, and proposes a hybrid cloud-based integrated system of smart scenic rural tourism information services, which can meet the actual use needs of rural tourism, with good shared service effect and platform application performance, and promote the development of rural tourism and resource utilization rate.
Impact of Alexithymia on the Lipid Profile in Major Depressed Individuals
Authors: Point Camille , Wacquier Benjamin , Dosogne Marjorie , Al Faker Mohammed , Willame Hadrien , Loas Gwenolé , Hein Matthieu
Published: June 2022

Background. The cooccurrence of major depression and dyslipidaemia is associated with negative cardiovascular outcome, which seems to justify a better identification of the factors favouring the development of dyslipidaemia in major depressed individuals. In the literature, there are arguments in favour of a special relationship between dyslipidaemia and alexithymia. However, despite a high prevalence of alexithymia in major depressed individuals, no study has investigated the impact of this personality trait on the lipid profile in this particular subpopulation. Given these elements, the aim of this study was therefore to investigate the risk of dyslipidaemia associated with alexithymia in major depressed individuals to allow better cardiovascular prevention in this subpopulation. Subjects and Methods. Demographic and polysomnographic data from 242 major depressed individuals recruited from the clinical database of the sleep laboratory were analysed. Only individuals with a diagnosis of dyslipidaemia according to the diagnostic criteria of the International Diabetes Federation at admission were included in the “dyslipidaemia” group. Logistic regression analyses were used to determine the risk of dyslipidaemia associated with alexithymia in major depressed individuals. Results. The prevalence of dyslipidaemia was 43.8% in our sample of major depressed individuals. After adjusting for the main confounding factors, multivariate logistic regression analyses demonstrated that alexithymia was a risk factor for dyslipidaemia in major depressed individuals. Conclusions. In this study, we found that alexithymia is a risk factor for dyslipidaemia in major depressed individuals, which seems to justify better identification and adequate management of this personality trait in order to allow a better lipid profile in this subpopulation at high cardiovascular risk.
Application Effect of Robot-Assisted Laparoscopy in Hepatectomy for Colorectal Cancer Patients with Liver Metastases
Authors: Li Shiguang , Fu Linxi
Published: June 2022

Objective. Application effect of Leonardo’s robot-assisted laparoscopy in hepatectomy for colorectal cancer patients with liver metastases. Methods. A total of 122 patients with sCRLM treated in our hospital from May 2015 to June 2018 were selected and divided into observation group (

n
=
61

) and control group (

n
=
61

) according to random number table method. The observation group was treated with robot-assisted laparoscopic hepatectomy, while the control group was treated with conventional laparoscopic hepatectomy. The perioperative time, intraoperative blood transfusion, intraoperative blood loss, average intraoperative blood transfusion, and hepatic portal occlusion time of the two groups were observed. Serum cortisol (Cor), norepinephrine (NE), and glucose (Glu) levels were detected before and after surgery in the two groups. The oxygen consumption and carbon dioxide output of patients were measured 1 day before surgery and 1~3 days after surgery, and the resting energy expenditure (REE) value was calculated. The levels of CD3+, CD4+, and CD8+ were determined by flow cytometry. The incidence of complications was compared between the two groups. Patients were followed up for 3 years after discharge, and Kaplan-Meier method was used to analyze the survival of the two groups. Results. The operation time, intraoperative blood transfusion, intraoperative blood loss, and average intraoperative blood transfusion in the observation group were all less than those in the control group, and the differences were statistically significant (

P
<
0.05

). Three days after operation, the levels of serum Cor, NE, and Glu were increased in both groups, and the observation group was lower than the control group; the difference was statistically significant (

P
<
0.05

). The REE level of observation group was lower than that of control group after 1 day, 2 days, and 3 days after surgery, and the difference was statistically significant (

P
<
0.05

). Three days after operation, the levels of serum CD3+ and CD4+ were decreased in both groups, and the observation group was higher than the control group; the difference was statistically significant (

P
<
0.05

). The incidence of complications in the observation group (3.28%) was lower than that in the control group (13.11%); the difference was statistically significant (

P
<
0.05

).There was no significant difference in survival rate between the two groups after 1, 2, and 3 years of follow-up (

P
>
0.05

). Conclusion. The application of robot-assisted laparoscopy in patients with sCRLM can effectively improve the perioperative situation of patients, reduce stress, energy metabolism, and immune damage, and reduce the incidence of complications.
[Retracted] Protection and Inheritance of Traditional Culture in Urbanization Construction Based on Genetic Algorithm under the Concept of Environmental Protection
Authors: Guo Lin
Published: June 2022

Chinese traditional culture is a typical “moral culture,” and traditional morality is the core and essence of culture. However, in recent years, the phenomenon of following the trend of rural construction has been particularly serious, and many villages have lost their original features. To solve the above problems, the genetic algorithm can be used to further explore the traditional culture of urbanization construction. A genetic algorithm is a natural evolutionary process that imitates natural selection and genetic operation in nature to obtain optimal solution, in which genetic operation mainly includes the processes of gene replication, crossover, and mutation. This paper studies the traditional culture of urbanization construction based on the genetic algorithm under the concept of environmental protection. Among the accuracy of urban construction land expansion, in 2018, the accuracy of ant colony algorithm, data mining algorithm, and particle swarm optimization algorithm is 58%, 51.8%, and 56.7%, respectively. The accuracy of this genetic algorithm is as high as 58.8%. It can be seen that the genetic algorithm in this paper has the highest accuracy in the expansion of urban construction land. Therefore, in the process of large‐scale urbanization based on the genetic algorithm, we should pay attention to not being separated from traditional culture, not letting farmers lose their regional culture, local culture, and grassroots culture, and protecting the cultural‐ecological environment on which these cultures depend.
Context-Based Emotion Predictor: A Decision- Making Framework for Mobile Data
Authors: Anwar Zahid , Jahangir Rashid , Nauman Muhammad Asif , Alroobaea Roobaea , Alzahrani Sabah M. , Ali Ihsan
Published: June 2022

The proliferation of big data for web-enabled technologies allows users to publish their views, suggestions, sentiments, emotions, and opinionative content about several real-world entities. These available opinionative texts have greater importance to those who are inquisitive about their desired entities, but it becomes an arduous task to capture such a massive volume of user-generated content. Emotions are an inseparable part of communication, which is articulated in multiple ways and can be used for making better decisions to reshape business strategies. Emotion detection is a subdiscipline at the crossroads of text mining and information retrieval. Context is a common phenomenon in emotions and is inherently hard to capture not only for the machine but even for a human. This study proposes a decision-making framework for efficient emotion detection of mobile reviews. An unsupervised lexicon-based algorithm has been developed to tackle the problem of emotion prediction. Dictionaries and corpora are used as backend resources in the semantic orientation of emotion words, whereas the major contribution is to cope with contextualized emotion detection. The proposed framework outperformed the existing emotion detection systems by achieving 86% accuracy over mobile reviews.
Analysis of Infertility Factors Caused by Gynecological Chronic Pelvic Inflammation Disease Based on Multivariate Regression Analysis of Logistic
Authors: Liu Linmei , Yang Gang , Ren Jigang , Zhang Limei , Wu Ting , Zheng Qiao
Published: June 2022

In order to solve the complex and recurrent problem of chronic pelvic inflammation disease (CPID) in the process of the clinical treatment, a method of understanding the influencing factors of CPID by investigating the actual situation of clinical cases and using logistics regression analysis was proposed in this study. A total of 204 outpatients were selected from a certain hospital. The ratio of the cases in the experimental group to those in the control group stands at 1 : 1. The results were obtained as follows. According to the data of CPID patients collected in the paper, the majority of patients had a high school education background or below technical secondary school education background, accounting for 66.7%. And the majority of patients were manual workers, accounting for 69.1%. All the exp (

B

) values of the frequency of sex life per

month
≥
9

times, frequent sex life during menstruation, IUD contraception, no contraception,

abortion
≥
3

times, vaginal irrigation per

week
≥
1

time, and intrauterine

surgery
≥
3

times were more than 1. These seven factors were the risk factors for chronic pelvic inflammation. Oral contraceptives were a weak protective factor of chronic pelvic inflammation. These factors including early drug withdrawal (53.1%), without understanding the condition of the disease (35.7%), no time to review the disease (24.5%), and irregular medication (21.4%) accounted for a large proportion. They were associated with the recurrence of CPID. This method is aimed at providing some foundations for establishing effective prevention and control measures for chronic pelvic inflammation and providing a recognized clinical diagnosis and efficacy evaluation criteria for the treatment of chronic pelvic inflammation.