bluuebunny/crossref_metadata_2025 huggingface dataset:
Computational Approaches for Understanding Sequence Variation Effects on the 3D Genome Architecture
Authors: Avdeyev Pavel , Zhou Jian
Published: May 2022

Decoding how genomic sequence and its variations affect 3D genome architecture is indispensable for understanding the genetic architecture of various traits and diseases. The 3D genome organization can be significantly altered by genome variations and in turn impact the function of the genomic sequence. Techniques for measuring the 3D genome architecture across spatial scales have opened up new possibilities for understanding how the 3D genome depends upon the genomic sequence and how it can be altered by sequence variations. Computational methods have become instrumental in analyzing and modeling the sequence effects on 3D genome architecture, and recent development in deep learning sequence models have opened up new opportunities for studying the interplay between sequence variations and the 3D genome. In this review, we focus on computational approaches for both the detection and modeling of sequence variation effects on the 3D genome, and we discuss the opportunities presented by these approaches.
Bioinformatics of Corals: Investigating Heterogeneous Omics Data from Coral Holobionts for Insight into Reef Health and Resilience
Authors: Cowen Lenore J. , Putnam Hollie M.
Published: May 2022

Coral reefs are home to over two million species and provide habitat for roughly 25% of all marine animals, but they are being severely threatened by pollution and climate change. A large amount of genomic, transcriptomic, and other omics data is becoming increasingly available from different species of reef-building corals, the unicellular dinoflagellates, and the coral microbiome (bacteria, archaea, viruses, fungi, etc.). Such new data present an opportunity for bioinformatics researchers and computational biologists to contribute to a timely, compelling, and urgent investigation of critical factors that influence reef health and resilience.
The heat shock connection: skeletal muscle hypertrophy and atrophy
Authors: Fennel Zachary J. , Amorim Fabiano T. , Deyhle Michael R. , Hafen Paul S. , Mermier Christine M.
Published: May 2022

Skeletal muscle is an integral tissue system that plays a crucial role in the physical function of all vertebrates and is a key target for maintaining or improving health and performance across the lifespan. Based largely on cellular and animal models, there is some evidence that various forms of heat stress with or without resistance exercise may enhance skeletal muscle growth or reduce its loss. It is not clear whether these stimuli are similarly effective in humans or meaningful compared with exercise alone across various heating methodologies. Furthermore, the magnitude by which heat stress may influence whole body thermoregulatory responses and the connection to skeletal muscle adaptation remains ambiguous. Finally, the underlying mechanisms, which may include interaction between relevant heat shock proteins and intracellular hypertrophy and atrophy related factors, remain unclear. In this narrative review, we examine the relevant literature regarding heat stress alone or in combination with resistance exercise emphasizing skeletal muscle hypertrophy and atrophy across cellular and animal models, as well as human investigations. In addition, we present working mechanistic theories for heat shock protein-mediated signaling effects regarding hypertrophy and atrophy-related signaling processes. Importantly, continued research is necessary to determine the practical effects and mechanisms of heat stress with and without resistance exercise on skeletal muscle function via growth and maintenance.
Using Convolutional Neural Networks for the Assessment Research of Mental Health
Authors: Liu Yanbing
Published: May 2022

Existing mental health assessment methods mainly rely on experts’ experience, which has subjective bias, so convolutional neural networks are applied to mental health assessment to achieve the fusion of face, voice, and gait. Among them, the OpenPose algorithm is used to extract facial and posture features; openSMILE is used to extract voice features; and attention mechanism is introduced to reasonably allocate the weight values of different modal features. As can be seen, the effective identification and evaluation of 10 indicators such as mental health somatization, depression, and anxiety are realized. Simulation results show that the proposed method can accurately assess mental health. Here, the overall recognition accuracy can reach 77.20%, and the F1 value can reach 0.77. Compared with the recognition methods based on face single-mode fusion, face + voice dual-mode fusion, and face + voice + gait multimodal fusion, the recognition accuracy and F1 value of proposed method are improved to varying degrees, and the recognition effect is better, which has certain practical application value.
Cyberattacks Defense in Digital Music Streaming Platforms by Mobile Distributed Machine Learning
Authors: Fan Guoxu
Published: May 2022

Given the massive popularity of digital music industry repositories and their corresponding targeting by cybercriminals, this paper presents an intelligent model for cyberattacks defense in digital music streaming platforms by mobile distributed machine learning. The basic idea of machine learning is to use large data sets to create a model that responds well to inputs it has never processed before. With the increase in data volume and complexity of models, it becomes increasingly challenging to complete machine learning processes in a single machine. Distributed ML was developed to solve this problem, and a standard procedure is completed through the collaboration of multiple servers. With the evolution of mobile devices and the increase in their number, it is possible to create an integrated and compact mobile distributed machine learning (MDML) system that could reduce the workload of servers. A distributed logit polynomial function model is proposed, which is used to model options in distributed binary regression accounting units, which are of low complexity and high stability in noisy environments.
Long-Term Follow-Up of Refractory Large Macular Hole with Autologous Neurosensory Retinal Free Flap Transplantation
Authors: Lee Po-Yen , Chang Yo-Chen , Liu Pei-Kang , Kao Tzu-En , Wu Horng-Jiun , Chen Kuo-Jen , Wu Kwou-Yeung , Cheng Kai-Chun , Wu Wen-Chuan
Published: May 2022

Purpose. To evaluate the long-term anatomic and functional outcomes of autologous neurosensory retinal free flap transplantation (ART) for patients with refractory large macular hole (MH). Design. Retrospective interventional case series. Methods. We reviewed 9 patients who underwent ART for their refractory large MH. In this extended follow-up study, postoperative assessment including spectral-domain optical coherence tomography and best-corrected visual acuity (BCVA) were recorded at 12, 15, 18, 21, and 24 months after surgery. Results. The macular hole of all patients appeared successfully closed during the whole follow-up period. The mean logMAR BCVA improved from 1.61 ± 0.44 (preoperative) to 0.72 ± 0.30 (12 months after surgery) (

p
<
0.001

). Thereafter, the mean BCVA remained stable at each follow-up. At the mean 16.0 ± 0.8 months postoperatively, inner retinal cystic changes were observed in 4 eyes (44.4%), but these did not significantly affect vision. Conclusion. ART is a good alternative technique for closing large refractory macular holes. Although inner retinal cystic changes were observed in 4 eyes (44.4%), this phenomenon did not significantly affect visual acuity. It provides long-term good anatomical and functional results, especially in cases where insufficient ILM or lens capsule are left.
Teaching Mode of Augmented Reality College English Listening and Speaking Supported by Wearable Technology
Authors: Ma Xiaochun
Published: May 2022

With the development of modern technology, wearable technology has become more and more popular in the actual college English listening and speaking teaching mode. Wearable devices, as the name suggests, are smart electronic products that can be worn on the human body, such as smart glasses, smart watches, smart rings and smart clothing. Smart watches and ordinary electronic products are very different from wearable devices. Wearable devices give us a more intelligent and technological experience. Wearable technology has brought new changes and challenges to the realistic college English listening and speaking teaching mode. Wearable technology has become a hot topic and frontier in the field of science and technology. It has been widely used in education, medical, and industrial fields. It provides unprecedented convenience for people to learn, work, and live. Augmented reality technology is a kind of technology that skillfully integrates virtual information with the real world. After the simulation of computer-generated text, images, 3D models, music, video, and other virtual information, it is applied to the real world, and the two kinds of information complement each other, thus realizing the “enhancement” of the real world. This paper gives a brief overview of the definition of wearable technology, the classification, and main features of wearable technology and combines it with college English listening and speaking teaching mode to improve the classroom learning effect. Through the questionnaire survey, we know that wearable technology is a teacher’s teaching, and students’ learning provides great convenience. Wearable technology greatly enhances the learning and listening fun of students.
Application of Spectrum Analysis Technology in Music Audio Analysis
Authors: Li Yi
Published: May 2022

In order to improve the music analysis technology, this paper studies the music analysis technology combined with the spectrum analysis technology and builds an intelligent audio analysis model. In this paper, the nonlinear theoretical method is adopted, and the motion equation of the audio frequency is obtained through variable processing, so as to obtain the mean square fluctuation of the two sound wave models and then obtain the entanglement and compression. Moreover, this paper introduces the combined mode method to describe the interaction of the two laser fields and atomic matter and verifies that both the differential modes are decoupled from the interaction and only the sum mode participates in the interaction. The experiment verifies that the music audio analysis system based on spectrum analysis technology proposed in this paper can play an important role in music analysis.
Deformation Modeling and Simulation of a Novel Bionic Software Robotics Gripping Terminal Driven by Negative Pressure Based on Classical Differential Algorithm
Authors: Chen Yinuo , Yao Ligang , Wang Zhenya
Published: May 2022

A general pneumatic soft gripper is proposed in this paper. Combined with the torque balance theory, the mathematical theoretical model of bending deformation of soft gripper is established based on Yeoh constitutive model and classical differential geometry. Assuming that the pressure in each inner cavity is evenly distributed, the input gas is in an ideal state, which is approximately treated as an isothermal condition, and all orifices experience blocked flow. In addition, compared with the mechanical work of gas, the energy related to gas flow and heat transfer is negligible. The nonlinear mechanical properties of silicone rubber are studied. It is regarded as isotropic and incompressible material, which is characterized by strain energy per unit volume. The material constant coefficients C10 and C20 are determined through the uniaxial tensile test, and the software gripper is simulated on the ABAQUS platform. The bending deformation models of grippers with three different force-bearing cavity structures are analyzed and compared, and the software clamping structure with the bending deformation most in line with the application conditions is selected. The limit input air pressure of the gripper and the situation of enveloping the clamping target object are analyzed. Through the bending deformation experiment, the maximum deformation angle is 72.4°. The relative error between the simulation analysis data and the prediction results of the mathematical model is no more than 3.5%, which verifies the effectiveness of the simulation and the correctness of the mathematical theoretical model of bending deformation. The soft manipulator proposed in this paper has good adaptability to grasping objects of different shapes and sizes. The minimum diameter of the target object that can be clamped is 0.1 mm. It can clamp the object weighing up to 1 kg. It has compact size, light weight, high ductility, and flexibility.
A Robust Pupil Localization via a Novel Parameter Optimization Strategy
Authors: Zhou Wenjun , Lu Xiaoyi , Wang Yang
Published: May 2022

With the development of iris biometrics, the pupil recognition method is widely used in the fields of identity recognition and so on. Most traditional iris recognition algorithms use an adaptive threshold to the eye image binarization processing; although this can at the maximum preserve the original characteristics of the image itself, the retained features in this method included a lot of noise points, including Gaussian noise and so on, in a series of image preprocessing. The subsequent iris recognition and pupil positioning are still prone to the loss of positioning accuracy, which cannot separate the pupil completely from the background area of the image, increasing the difficulty of connecting the pupil to domain localization, thus affecting the pupil positioning accuracy. Therefore, in order to meet the requirement of high precision pupil recognition accuracy in low-quality eye images, this paper takes the iris dataset provided by the China Research Institute of Automation as an example to improve the traditional pupil location method. A pupil localization method based on parameter optimization is proposed, which includes the setting of a three-step threshold, called as TST.
Basic Theory and Practice Teaching Method Based on the Cerebellar Model Articulation Controller Learning Algorithm
Authors: Huang Meng , Liu Shuai , Zhang Yahao , Cui Kewei , Wen Yana
Published: May 2022

With the continuous in-depth development of science and technology in my country, the field of artificial intelligence has also made considerable progress. For example, the comprehensiveness and operability of technology have been enhanced. Artificial intelligence, as a comprehensive and intersecting research field in the fields of computer science, information science, and mathematics, has also made a certain contribution to the progress of international students in colleges and universities. This article is aimed at studying the problems based on basic theories and time teaching methods, mainly through a comprehensive data analysis of artificial intelligence technology and fusion differential evolution algorithm, and then improving the learning ability of students and comprehensive quality. This paper proposes a swarm intelligence algorithm,

K

-means clustering algorithm, and vector machine algorithm and summarizes the process and application fields of the swarm intelligence algorithm. In addition, the CMAC learning algorithm is proposed, which shows that robots under the background of industrial intelligence and machine learning can organize themselves to form groups to perform complex tasks. The experimental results of this paper show that the communication range is between 0 and 1, which can enable more robots to communicate in order to improve the individual’s social learning opportunities, thereby enhancing the individual’s learning ability to improve the performance of learning behavior and improving the efficiency of practical teaching and research, making the efficiency reach 0.9.
Design of Energy-Efficient Protocol Stack for Nanocommunication Using Greedy Algorithms
Authors: Iqbal Iqra , Nazir Mohsin , Sabah Aneeqa
Published: May 2022

With the passage of time, nanotechnology has become a mature discipline. It attracts attention of researchers toward nanocommunication and trying to give comprehensive set of tools to the engineering community. Nanonetwork contains nanosized communication devices that can be used in the field of military, bio medical, environment, ICT, and industry. Its communication abilities make this field powerful enough to make the devices interact with each other in the micro/macro world. The interconnection between nanonodes is not possible with the help of traditional networking techniques. In the upcoming years, nanotechnology is expecting to bring the integration of different nanodevices ranging from one to a few hundred of nanometers. This revolutionary transition is bringing Internet of Things (IoT) to Internet of Nanothings (IoNT). Due to its limited communication and computational capabilities, the energy availability in nanodevices is very scarce, which lead to various research challenges in the field of nanocommunication. The energy constraint narrows down the overall scope of nanocommunication. This research work will provide a step ahead and propose a design of energy-efficient routing protocol. The suggested solution will increase the effectiveness of nanocommunication techniques to achieve the maximum throughput by optimizing energy harvesting procedure. The comparison of devised solution and the state-of-the-art nanocommunication techniques will be established and evaluated through simulation. The results will be displayed and discussed to pave the way for future research in the domain of Internet of Nanothings (IoNT).
Convolutional Neural Network Based Network Distance English Teaching Effect Evaluation Method
Authors: Wang Jing , Fu Jia
Published: May 2022

Aiming at the low accuracy of English teaching quality evaluation, a user perception evaluation modeling method based on convolutional neural network was proposed. Make full use of product usage data to establish quantitative mapping between teaching quality assessment and teaching quality to support product design improvement. On this basis, a convolutional neural network structure suitable for user perception evaluation model is established. Then, the optimal hyperparameters of the model were determined by k-fold cross validation analysis, and the overfitting problem of the model was improved. Finally, an example of user perception modeling for smart phones is taken to verify the effectiveness of the method. It is used for user perception assessment and prediction, which can help English teaching methods timely and accurately assess performance and provide decision support for English teaching improvement. SKYPE is currently the world's most popular Internet remote video, audio, text, and real-time interactive platform; the real-time audio and video communication is very clear; the remote education will expand to the family, especially to promote the remote universality and universal English education that can play a big role. In this paper, the basic function and performance of the platform are presented, and distance education in English presents a practical approach to the application aspects.
Diagnosis of Heart Failure Complicated with Sleep Apnea Syndrome by Thoracic Computerized Tomography under Artificial Intelligence Algorithm
Authors: Tian Weihong , Li Jinghua , Ma Lan
Published: May 2022

The aim of this study was to explore the application effect of thoracic computerized tomography (CT) under single threshold segmentation algorithm in the diagnosis of heart failure (HF) complicated with sleep apnea syndrome. 30 patients diagnosed with HF complicated with sleep apnea syndrome were chosen for the research. Another 30 patients without sleep apnea syndrome were selected as the control group, whose age, height, and weight were similar to those of the experimental group. Then, a model for thoracic CT image segmentation was proposed under the single threshold segmentation algorithm, and the faster region convolutional neural network (Faster RCNN) was applied to label the thoracic respiratory lesions. All the patients underwent thoracic CT examination, and the obtained images were processed using the algorithm model above. After that, the morphology of the patient’s respiratory tract after treatment was observed. The results suggested that the improved single threshold segmentation algorithm was effective for the image segmentation of patient lesions, and the Faster RCNN could effectively finish the labeling of the lesion area in the CT image. The classification accuracy of the Faster RCNN was about 0.966, and the loss value was about 0.092. With CT scanning under the algorithm, it was found that the airway collapse of the posterior palatal area, retrolingual area, and laryngopharyngeal area of the sleep apnea syndrome patients was significantly greater than that of the control group (

P
<
0.05

). But there was no significant difference of the collapse of the nasopharyngeal area between the two groups (

P
>
0.05

). The single threshold segmentation algorithm had a better segmentation accuracy for thoracic CT images in patients with HF and sleep apnea syndrome, so it had a highly promising application prospect in the diagnosis of the disease.
Anisi Stellati Fructus, a Significant Traditional Chinese Medicine (TCM) Herb and Its Bioactivity against Gastric Cancer
Authors: Khan Maryam , Shamim Saba
Published: May 2022

Anisi stellati fructus (ASF) is the fruit of Illicium verum Hook F. (Chinese star anise), which is native to many countries, and is a significant Chinese medicinal herb. Gastric cancer (GC) is one of the major fatal types of cancers with multiple stages and a poor prognosis. The present review aims to discuss the bioactive properties of ASF and its phytocompounds against GC, with a particular insight into the molecular mechanisms and signaling pathways involved in its anti-GC mechanism. Furthermore, it highlights the potential mechanism of action of major phytocompounds of ASF against GC. Clinical studies (in vitro and in vivo) regarding the action of ASF and its major bioactive compounds such as quercetin, luteolin, kaempferol, d-limonene, and honokiol against GC were reviewed. For this review, search of literature was performed in Science, PubMed, Google Scholar, Web of Science, and Scopus related to ASF and its phytocompounds, from which only relevant studies were chosen. Major bioactive compounds of ASF and their extracts have proven to be effective against GC due to the mechanistic action of these compounds involving signaling pathways that target cancer cell apoptosis, proliferation, and tumor metastasis in GC cells. Existing reports of these compounds and their combinatory effects with other modern anticancer agents have also been reviewed. From its traditional use to its role as an anticancer agent, ASF and its bioactive phytocompounds have been observed to be effective in modern research, specifically against GC. However, further studies are required for the identification of molecular targets and pharmacokinetic potential and for the formulation of anti-GC drugs.
Azadirachta indica‐Derived Silver Nanoparticle Synthesis and Its Antimicrobial Applications
Authors: Pawar Aditya Amrut , Sahoo Jyotirmayee , Verma Aakash , Alswieleh Abdullah M. , Lodh Abhijit , Raut Rajesh , Lakkakula Jaya , Jeon Byong-Hun , Islam Md. Rabiul
Published: May 2022

Advancement of biologically driven experimental procedure for the generation of metal nanoparticles is a major aspect in the domain of nanotechnology. Herein, we synthesized one pot silver nanoparticles (AgNPs) using Azadirachta indica (Neem plant) via green synthesis method. The alkaloids and flavonoids in the plant extract served as capping and reducing agent to develop silver nanoparticles. The characterization of synthesized AgNPs was performed using analytical techniques such as UV‐visible spectroscopy, FTIR, and SEM. The synthesized AgNPs were further impregnated onto a cotton cloth and the antimicrobial studies were performed. The AgNPs embedded in cotton fabric were found to show zone of inhibition against the fungi Candida albicans, gram‐negative bacteria Escherichia coli, and gram‐positive bacteria Staphylococcus aureus. This experimental study suggests that the AgNPs impregnated on cotton cloth could have great potential for application in smart nanobandages for wound dressing, medical textiles, sports clothing, etc.
Development and Characterization of Multigrain Pan Bread Prepared Using Quinoa, Lupin, and Fenugreek Seeds with Yellow Maize as a Gluten-Free Diet
Authors: Alshehry Garsa , Algarni Eman , Aljumayi Huda , Algheshairy Reham M. , Alharbi Hend F.
Published: May 2022

Celiac disease causes serious health problems for humans. Therefore, the consumption of gluten-free diets (GFDs) is the only therapy to prevent patients from developing the disease. The objective of the current study was to investigate the proximate analysis, mineral compositions, and antioxidant activities of the quinoa, germinated sweet lupin, fenugreek, and yellow maize, and they were used to develop gluten-free multigrain pan breads. A total of four different grain blend formulations were used to develop the pan bread. The textural properties, color, and sensory evaluation of the developed multigrain pan bread were also determined. The results of the present study showed a significantly higher fat content was found in germinated lupin (13.56%) and quinoa (12.76%), followed by germinated fenugreek and yellow maize (9.68% and 4.67%, respectively). The results indicated that the development of multigrain pan bread with fortification of quinoa, germinated lupin, germinated fenugreek, and yellow maize imparted significant improvement in the nutritional content. Therefore, it could be recommended that the addition of up to 15% of germinated lupin and fenugreek, 60% quinoa, and 10% yellow maize does not negatively affect the sensory characteristics and quality attributes of pan bread.
Multicategory Image Recognition Based on Image Semantic Features and Transformer
Authors: Chen Hewei , Xiang Chen , Qiu Dong , Huang Xuxiang
Published: May 2022

In the field of computer vision research, generative adversarial networks (GAN) are used for general object recognition. In recent years, however, GAN have learned only from image data without using label information. In recent years, however, unsupervised learning, which learns GAN only from image data without using label information, where GAN are learned from image data alone without using label information, has been introduced. In this paper, we describe research on unsupervised learning of GAN since the introduction of transformer, reviewing trends in computer vision/artificial intelligence-related research since the introduction of transformer from a visual neuroscience perspective.
Deep Learning of 3D High-Precision Model Digital Engraving of Next-Generation Games Based on Artificial Intelligence
Authors: Zhao Yue
Published: May 2022

In order to improve the intelligence of the next-generation game model, a new game artificial intelligence came into being. Through the in-depth understanding and research of the next generation of game model production technology, the current industry general production process is summarized, and a set of effective production methods is expounded. In the context of the rapid development of digital technology, the game industry increasingly recognizes the importance of digital carving technology for the design and production of the next generation of games. The design and production of the next-generation game model can make up for the lack of refinement of the traditional next-generation game character model and the unreal mapping effect. This paper focuses on two ways: behavior tree and machine learning, designing game AI with more anthropomorphic perception and flexible behavior. Finally, the basketball player competition AI designed with a behavior tree and machine learning demonstrates its intelligent behavior.
Sparse Covariance Matrix Reconstruction-Based Nulling Broadening for UAV 2D Antenna Arrays
Authors: Han Chuang , Lei Haoyang , Gong Yanyun , Wang Ling
Published: May 2022

Since the antennas on UAVs may have slight vibrations or the interference source is in the state of rapid movement in practice, the interference suppression performance and robustness of the traditional methods may suffer a decline. In this paper, we propose a flexible asymmetric null widening technique, which allows flexible adjustment of the null width to accommodate the variation of the interference source. This method has a good effect of spreading zero trap on the two-dimensional array and can effectively reduce the waste of degrees of freedom. Firstly, the flexible asymmetric null widening method is extended to two-dimensional arrays to accommodate 2D array antennas of UAVs. Secondly, when the SMI algorithm is applied in adaptive beamforming, the desired signal appears in sampling snapshots or using data samples, resulting in a model mismatch. To solve the model mismatch problem of UAV antenna arrays, this paper applies a sparsity-based interference plus noise covariance matrix reconstruction technique. Finally, for the application scenario that the UAV may receive signals from multiple directions, we apply the linear constrained minimum variance criterion (LCMV) to achieve the main beam gain formation in multiple directions. The simulation results show that we can generate a wide null and adjust the null width asymmetrically. The results also show that the model mismatch problem is avoided, and the performance of the adaptive beamforming is almost optimal. For the UAV antenna, we also implemented multiple beams to receive multiple signals .
Power Allocation Intelligent Optimization for Mobile NOMA Communication System
Authors: Fu Xiaobin , Tang Zhen , Xiao Pingping
Published: May 2022

Non-orthogonal multiple access (NOMA) technology can greatly improve user access and spectral efficiency. This paper considers the power allocation optimization problem of a two-user mobile NOMA communication system. Firstly, a mobile NOMA communication system model is established. Then, we analyze the outage probability (OP) of mobile NOMA communication system and the relationship between OP performance and user power allocation coefficient. Finally, the optimization objective function is established, and a power allocation optimization algorithm employing monarch butterfly optimization (MBO) is proposed. Compared with firefly algorithm and artificial fish swarm algorithm, the efficiency of MBO algorithm is increased by 20.7%, which can better improve the OP performance.
Application of Mung Bean Protein Separation and Purification Combined with Artificial Intelligence MLR Classifier Technology in the Study of Protein Physical and Chemical Properties
Authors: Wang Xianqing , Bai Jimin , Luan Yueting
Published: May 2022

Within the scope of this project, a spectroscopy-dependent machine learning (ML) method will be utilized to estimate the optimal harvest time for mung bean, which will be used to examine the changes in physical and chemical attributes of the bean as it develops. It was decided to harvest mung bean from the R5 (initial seed), R6 (full seed), and R7 (beginning maturity) stages. The spectral reflectance of the pods was measured, and their physical and chemical characteristics were characterized. The experiment was carried out using a spectrophotometer with a wavelength range of 360–740 nm. On the basis of the qualities that have been identified so far in the study, early, ready, and late specimens have all been included. The results showed that the pod/bean weight and pod thickness reached their maximum at R6. After that, everything remained the same as before. Around R6, there was an increase in sugar, carbs, amino acids, and glycine, among other things. The ML approach (random forest classification) achieved an accuracy of 0.95 for the classification of pods dependent on their spectral reflectance. Specimens can be classed as “early” or “late” depending on whether or not they are “ready” or “not ready” when they are collected or processed. As a result, this procedure is the most effective choice available. It can figure out when the best time is to harvest mung bean.
Improved Feature Pyramid Convolutional Neural Network for Effective Recognition of Music Scores
Authors: Li Lei
Published: May 2022

Music written by composers and performed by multidimensional instruments is an art form that reflects real-life emotions. Historically, people disseminated music primarily through sheet music recording and oral transmission. Among them, recording music in sheet music form was a great musical invention. It became the carrier of music communication and inheritance, as well as a record of humanity's magnificent music culture. The advent of digital technology solves the problem of difficult musical score storage and distribution. However, there are many drawbacks to using data in image format, and extracting music score information in editable form from image data is currently a challenge. An improved convolutional neural network for musical score recognition is proposed in this paper. Because the traditional convolutional neural network SEGNET misclassifies some pixels, this paper employs the feature pyramid structure. Use additional branch paths to fuse shallow image details, shallow texture features that are beneficial to small objects, and high-level features of global information, enrich the multi-scale semantic information of the model, and alleviate the problem of the lack of multiscale semantic information in the model. Poor recognition performance is caused by semantic information. By comparing the recognition effects of other models, the experimental results show that the proposed musical score recognition model has a higher recognition accuracy and a stronger generalization performance. The improved generalization performance allows the musical score recognition method to be applied to more types of musical score recognition scenarios, and such a recognition model has more practical value.
Wireless Control Industrial Robot Processing Irradiation System Based on Artificial Intelligence Technology
Authors: Zhao Lijun , Li Qingsheng , Ding Guanhua
Published: May 2022

Radiation processing technology has been more and more widely used in light industry, chemical industry, food preservation, medical care, and other industries. Therefore, this paper designs a kind of wireless control system based on artificial intelligence technology γ radiation method, to strengthen the application of radiation processing equipment, and γ radiation monitoring can strengthen the radiation monitoring of its surrounding environment. The experimental results show that the C/Si atomic ratio of oxidized SiC fiber in air treated by this scheme is about 1.75, while the C/Si atomic ratio of SiC(Be) fiber treated by electron irradiation is close to 1 : 1.
Transcription Factor E2F1 Exacerbates Papillary Thyroid Carcinoma Cell Growth and Invasion via Upregulation of LINC00152
Authors: Yang Junjie , Ying Yong , Zeng Xiangtai , Liu Jiafeng , Xie Yang , Deng Zefu , Hu Zhiqiang , Li Zanbin
Published: May 2022

Background. Papillary thyroid carcinoma (PTC) is the most common thyroid neoplasm, whereas transcription factor E2F1 has been previously implicated in PTC progression. The current study sought to elucidate the underlying mechanism of E2F1 in PTC cell biological activities via regulation of long intergenic noncoding RNA 152 (LINC00152). Methods. Firstly, the expression patterns of LINC00152 and E2F1 in PTC were determined. Besides, TPC-1 and IHH-4 cells were adopted to carry out a series of experiments. Cell proliferation was detected by means of a cell counting kit-8 assay and colony formation assay, while cell migration and invasion abilities were assessed using a Transwell assay. Next, the interaction between E2F1 and LINC00152 was certified. Lastly, xenograft transplantation was carried out to validate the effects of E2F1 depletion on PTC. Results. Both LINC00152 and E2F1 were highly expressed in PTC cells. Knockdown of LINC00152 led to reduced cell activity, while LINC00152 overexpression brought about the opposing trends. Likewise, E2F1 knockdown quenched cell proliferation, migration, and invasion. However, the combination of E2F1 knockdown and LINC00152 overexpression resulted in augmented cell growth. In addition, E2F1 induced LINC00152 overexpression, which accelerated cell proliferation, migration, and invasion by activating the PI3K/AKT axis, whereas the administration of LY294002, the inhibitor of PI3K, led to reversal of the same. Finally, xenograft transplantation validated that E2F1 inhibition could suppress LY294002, thereby discouraging tumor growth. Conclusion. Our findings highlighted that E2F1 augmented PTC cell proliferation and invasion by upregulating LINC00152 and the PI3K/AKT axis. Our discovery provides therapeutic implications for PTC alleviation.
Treatment of COVID-19 Patients Using Some New Topological Indices
Authors: Amin Shahid , Rehman M. A. , Naseem Amir , Khan Ilyas , Andualem Mulugeta
Published: May 2022

COVID-19 is causing havoc to human health and the world economy right now. It is a single standard positive-sense RNA virus which is transferred by inhalation of a viral droplet. Its genome forms four structural proteins such as nucleocapsid protein, membrane protein, spike protein, and envelop protein. The capsid of coronavirus is a protein shell within which a positive strand of RNA is present which enables the virus to control the machinery of human cells. It has several variants, e.g., SARS, MERS, and now a new variant identified in 2019, which is a novel coronavirus that causes novel coronavirus disease (COVID-19). COVID-19 is a novel coronavirus disease that originally arose in Wuhan, China, and quickly spread around the world. Clinically, we identified the virus presence by a PCR-based test. Preventive measures and vaccination are the only treatment against coronavirus. Some of these include Remdesivir (GS-5734), Chloroquine, Hydroxychloroquine, and Theaflavin. A topological index (TI) is a mathematical function that assigns a numerical value to a (molecular) graph and predicts many physical, chemical, biological, thermodynamical, and structural features of that network. In this work, we will calculate a new topological index, namely, the first and second Gourava and Hyper-Gourava indices for the molecular graph of Remdesivir (GS-5734), Chloroquine, Hydroxychloroquine, and Theaflavin. We also plotted our computed results to examine how they were affected by the parameters involved. These findings could contribute in the development of new COVID-19 therapy options.
Application of Neural Network in the Stability of Biped Robot and Embedded Control of Walking Mode
Authors: Zhang Jianrui , Yuan Zhaohui , Geng Huan , Dong Sheng , Zhang Fuli , Li Jingchao
Published: May 2022

The biped robot adopts the human movement mode. Compared with other movement modes, the gait has good flexibility and adaptability. It is very important in the research of robotics, so it has become a hot spot of robotics research. This article aims to study the application of neural networks in the stability of biped robots and the embedded control of walking mode. A method of establishing precise mathematical modeling and stability analysis is proposed. Based on this model, the motion characteristics of the biped robot’s walking mode and the local stability of joints are studied, and the motion mode of passive walking under the control of the neural network is deeply analyzed, using a neural network to control the stability of biped robot motion and adopting the research method of the embedded control system in walking mode. Essentially, the output value of the physical network is used to judge whether the robot is in a stable position so as to perform appropriate actions and control the robot’s stability of walking. The experimental results show that the biped robot can detect movement and overcome obstacles through related networks and embedded control systems. Through the control of the embedded system, the errors of each joint of the biped robot on flat ground, stairs, and obstacles are greatly reduced. The most obvious reduction of the deviation is that the ankle joint decreases from 2.5 to 0.07 when rotating, and the knee joint angle deviation is reduced from 3.8 to 2, which greatly improves the stability of the biped robot’s walking mode.
Intelligent Analysis and Application of Preschool Education Language Teaching Quality Based on Deep Neural Network
Authors: Li Hongping , Niu Chuanming
Published: May 2022

Language is the cornerstone of children’s learning knowledge and exploring the world. Language teaching is an indispensable part of early childhood education, which can help children improve their communication ability and help children communicate with classmates and teachers in school. The teaching form of preschool language education is too single, which does not conform to children’s own learning ability. Therefore, in teaching, teachers should actively optimize the language environment to make children willing to express themselves in language: introducing game activities to make children want to express themselves in language. With the help of situational display, children like to express themselves in language and carry out performance activities to give children the opportunity to express themselves in language. With the help of home force, children dare to express themselves in language. Children are in a dynamic stage of rapid growth. In order to enable children to learn language effectively, teachers should make clear the development center of children and their mastery of language. To guide language teaching in accordance with students’ aptitude for a long time, at the same time, we should update our rigid views on children at any time, boldly innovate the content and thinking mode of children’s language teaching activities, and mobilize children’s initiative and enthusiasm to participate in language learning, so that they can achieve the language learning goals of being able to speak, loving to speak and bravely speaking. This paper selects the learning situation of preschool children as the object, establishes an evaluation model by using the hybrid GA-BP neural network method, and makes an empirical analysis on the development of preschool education in Liaoning Province. The GA-BP neural network model is trained by MATLAB 7.0, and the simulation results show that the model can evaluate the development of preschool education scientifically, and it is scientific and practical. According to the results of this paper, the corresponding evaluation model of preschool education language teaching quality is put forward, which makes a good prediction preparation for children to learn language better in advance.
5G and Artificial Intelligence Interactive Technology Applied in Preschool Education Courses
Authors: Liao Lijuan , Gu Fanjun
Published: May 2022

To explore the application of artificial intelligence interaction technology in preschool education practice courses, preschool education, 5G wireless network, artificial intelligence interaction technology, and other relevant theories are integrated, questionnaire survey is adopted as the research method, and six-year-old kindergarten students in a city of China are taken as the research objects. Adaptive optimization suggestions are put forward from the preschool children practice curriculum learning situation, cooperation and exchange situation, learning effect, and learning attitude research. The results show that in terms of learning of practical courses, students’ cognition of learning has changed after the implementation of wisdom teaching, but the average value of some

P

values < 0.05 is low in the real classroom adaptation stage. In the exchange and cooperation of practical courses, it is found that wisdom teaching has the greatest influence on helping other students solve problems, with a specific

P

of 0.009. In terms of learning effect and learning attitude, the effect is significant after the introduction of artificial intelligence technology, with an average of 3.56 before the introduction and 4.01 after the introduction. It means that to a certain extent, preschool children can realize the role and significance of wisdom classroom teaching, which plays a certain role in teaching practice. Therefore, research on the application of artificial intelligence interaction technology in preschool education practice courses under 5G wireless network environment provides a new theoretical basis and optimization direction for preschool education in the future.
A Path-Based Feature Selection Algorithm for Enterprise Credit Risk Evaluation
Authors: Du Marui , Ma Yue , Zhang Zuoquan
Published: May 2022

In recent years, there has been increasing interest in exploring diversified features to measure small and medium-sized enterprises (SMEs) credit risk. Path-based features, revealing logical connections between SMEs, are widely adopted as informative feature kinds for causal inference in credit risk evaluation. Since there may exist thousands of feature paths to the target enterprise, to evaluate its credit risk, how to select the most informative path-based features becomes a challenging problem. To solve the problem, in this paper, we propose a novel method of feature selection, considering both similarity and importance on features’ structured semantics as the factors of informativeness. With this, the proposed method can effectively rank both conventional and path-based features together. Furthermore, to improve the efficiency of the method, a heuristic algorithm is proposed to fast search for the candidate features. Through extensive experiments, we show our method performs competitively with other state-of-the-art selection methods.
Application of Regression Analysis to Identify the Soil and Other Factors Affecting the Wheat Yield
Authors: Hayat Azhar , Amin Muhammad , Afzal Saima , Muse Abdisalam Hassan , Egeh Omer Mohamed , Hayat Hafiz Saqib
Published: May 2022

In farming and related fields, numerous connections exist that should be distinguished quantitatively. Several factors affect the various crop yields in different dimensions. These factors may have relation with farmer’s practices or with quality of soil. In this study, our main focus is to explore the effect of soil and other factors on the wheat yield. Regression modeling plays an important role in the identification of such factors that greatly affect the crops yield. For reliable and valid results, one has to check the data for outliers and other critical results. In this study, we have fitted the regression models with and without satisfying some regression assumptions to determine the factors affecting yield of wheat. For analysis purposes, the required data were collected from the district Multan. It was observed that when the regression assumptions were satisfied, then coefficient of determination (R2) was improved from 45% to 48%, R2 (adjusted) was improved from 40% to 46%, and the standard error of the estimates was reduced from 2.772 to 2.649. These results indicate that the soil characteristics, such as saturation, electrical conductivity, organic matter, phosphorus, potassium, calcium carbonate, and micronutrients (zinc, copper, iron, manganese, and boron), are the significant factors for wheat yield. While among all other factors, urea, chemical coating of seed, use of compost, and previously sown crops are the significant factors for wheat yield.