bluuebunny/crossref_metadata_2025 huggingface dataset:
Mapping tundra ecosystem plant functional type cover, height and aboveground biomass in Alaska and northwest Canada using unmanned aerial vehicles
Authors: Orndahl Kathleen M. , Ehlers Libby P. W. , Herriges Jim D. , Pernick Rachel E. , Hebblewhite Mark , Goetz Scott J.
Published: April 2022

Arctic vegetation communities are rapidly changing with climate warming, which impacts wildlife, carbon cycling and climate feedbacks. Accurately monitoring vegetation change is thus crucial, but scale mismatches between field and satellite-based monitoring cause challenges. Remote sensing from unmanned aerial vehicles (UAVs) has emerged as a bridge between field data and satellite-based mapping. We assess the viability of using high resolution UAV imagery and UAV-derived Structure from Motion (SfM) to predict cover, height and aboveground biomass (henceforth biomass) of Arctic plant functional types (PFTs) across a range of vegetation community types. We classified imagery by PFT, estimated cover and height, and modeled biomass from UAV-derived volume estimates. Predicted values were compared to field estimates to assess results. Cover was estimated with root-mean-square error (RMSE) 6.29-14.2% and height was estimated with RMSE 3.29-10.5 cm, depending on the PFT. Total aboveground biomass was predicted with RMSE 220.5 g m-2, and per-PFT RMSE ranged from 17.14-164.3 g m-2. Deciduous and evergreen shrub biomass was predicted most accurately, followed by lichen, graminoid, and forb biomass. Our results demonstrate the effectiveness of using UAVs to map PFT biomass, which provides a link towards improved mapping of PFTs across large areas using earth observation satellite imagery.
Reorienting of pramipexole as a promising therapy for acute pancreatitis in a rat model by suppressing TLR4\NF-κB p65\NLRP3 inflammasome signaling
Authors: Fawzy Hadeel A. , Mohammed Asmaa A. , Fawzy Hala M. , Fikry Ebtehal M.
Published: April 2022

Acute pancreatitis (AP), a disorder of global importance, has a growing incidence and prevalence, particularly in the Western world. Its complications include pseudocysts and chronic pancreatitis. Pramipexole (PMX), a D2/3 receptor selecting agonist used in Parkinsonism, was reported to have anti-inflammatory effects. This study explored the potential curative role of PMX in an l-arginine-induced acute pancreatitis rat model in addition to a possible mechanistic pathway. Rats were divided randomly into three groups: control, l-arginine, and l-arginine + PMX. Seven days after AP induction, rats were decapitated and estimated for serum amylase, lipase, glucose, pancreatic inflammatory mediators toll-like receptor-4, nuclear factor κ B p65, serum tumor necrosis factor-α, NOD-, LRR and pyrin domain- containing protein 3 (NLRP3) inflammasome, caspase-1, interleukin 1β, oxidative biomarkers malondialdehyde, myeloperoxidase, nitrite/nitrate, reduced glutathione, and the apoptotic marker caspase-3, with pancreatic histopathological changes. l-arginine-mediated AP was proved by elevated serum lipase and amylase and pancreatic inflammatory, oxidative, and apoptotic markers with infiltration of inflammatory cells using hematoxylin and eosin stain. PMX improved all these adverse signs of AP greatly. PMX might be considered an innovative therapy for AP due to its remarkable antioxidant, antiapoptotic, and anti-inflammatory effects, which are attributed to the suppression of the NLRP3 inflammasome and its downstream inflammatory cytokines.
Based on proteomics to explore the mechanism of mecobalamin promoting the repair of injured peripheral nerves
Authors: Xiong Zong-liang , Wang Yao , Ma Xiang-Lin , Zhou Chong , Jiang Xiao-wen , Yu Wen-hui
Published: April 2022

Mecobalamin is commonly used in the adjuvant intervention of various peripheral nerve injuries. Actin cytoskeleton plays a role in the regeneration of myelin and axon. Therefore, the purpose of this study was to explore the possibility of mecobalamin regulating actin cytoskeleton in repairing nerve injury. In this study, a crush injury on the right sciatic nerve of two groups of rats (12 in each group) was established. The control group was only given normal saline (i.g.), and the intervention group was given mecobalamin 1 mg/kg (i.g.). The rats were sacrificed on 28th day and the injured nerves were collected for proteomics. The result shows that regulation of actin cytoskeleton pathway changed significantly. The expression of protein Vav1 was verified by Western blot and immunofluorescence. In the intervention group, the nerve fiber structure was complete, the axons were dense and symmetrical, and the myelin sheath was compact and uniform in thickness. The positive rate of myelin basic protein and βⅢ-tubulin was higher than that in the control group. The findings of the study show that mecobalamin regulates the actin cytoskeleton in the repair of nerve damage and upregulates Vav1 in the regulation of actin cytoskeleton pathway.
A Literature-Based Thematic Network to Provide a Comprehensive Understanding of Agile Teamwork (106)
Authors: Freire Arthur , Neto Manuel , Perkusich Mirko , Costa Alexandre , Gorgónio Kyller , Almeida Hyggo , Perkusich Angelo
Published: April 2022

Agile Software Development (ASD) has become the mainstream software development method of choice. Its core fundamentals are based on Teamwork factors and the higher value of individuals and their interactions over processes and tools. However, there is no common understanding regarding the factors that should be considered for defining an ASD Teamwork construct. Driven by this problem, we present a thematic network that synthesizes the information presented in the literature, and eases knowledge sharing by defining a terminology. The thematic network is the result of the following process: (i) studies definition to be used as data source through a literature review; (ii) data extraction from these studies; (iii) data translation into codes; (iv) codes translation into themes; (v) creation of higher-order themes model; and (vi) assessment of synthesis trustworthiness. The resulting thematic network comprises four higher-order themes: Cohesion, Orientation, Shared Leadership, and Autonomy. We also evaluate the applicability of the identified themes in ASD Teamwork constructs in the literature. We concluded that the constructed thematic network can be generalized to ASD, and used as basis by researchers who intend to explore ASD Teamwork. Further, practitioners can use our results to understand agile teams’ dynamics better and improve their efficiency.
Twitter Sentiment Analysis Using Social-Spider Lex Feature-Based Syntactic-Senti Rule Recurrent Neural Network Classification
Authors: Anuratha K. , Parvathy M.
Published: April 2022

Social platforms have become one of the major sources of unstructured text. Investigating the unstructured text and interpreting the meaning is a complex job. Sentiment Analysis is an emerging approach as the social platforms have lot of opinionated data. 1 It uses language processing, classification of texts and linguistics to retrieve the opinions from the text. Twitter is a micro blogging site which is popular amongst the social users as it is a vast open data-platform and it witnesses lot of sentiments. Twitter Sentiment Analysis is a process of automatic mining of user tweets for opinions, emotions, attitude to derive useful insights into community opinions and classify the opinions as well. Due to the enormous increase in the number of collaborative tweets, it has become complex to identify the terms that carries sentiments. Also, the unstructured tweets may have non-relevant terms and reduce the classification accuracy. To address these issues, we propose a Social-Spider Lex Feature Ensemble Model-Based Syntactic-Senti Rule prediction Recurrent Neural Network Classifier (S2LFEM-S2RRNN) to obtain better classification accuracy. Twitter is used as source of data and we have extracted the tweets using Twitter API. Initially, data pre-processing is done to remove unwanted data, symbols and content terms are extracted to improvise the dataset. Then, the significant lexical content terms are extracted employing the proposed Social Spider Lex Feature Ensemble Model (S2LFEM) based on Syntactic-Senti Rule Prediction. The semantics 4 of the terms are analysed on the verbs, subjectivity of the tweet patterns to count the overall weightage of tweets. Based on tweet weightage Recurrent Neural Network is trained to classify the tweets int to positive, negative and neutral. The experiment results show that the proposed classifier outperforms the existing models for sentiment classification in terms of accuracy with a performance score 94.1%.
Visiting Indian Hospitals Before, During and After COVID
Authors: Pavithra E. , Janakiramaiah B. , Prasad L. V. Narasimha , Deepa D. , Jayapandian N. , Sathishkumar V. E.
Published: April 2022

The prevailing COVID-19 situation has brought in temporary and permanent changes in the attitude and lifestyle of people. Starting from Hand sanitizers and face masks, it extends to online classrooms and work from home culture. In case of visiting hospitals and medications, people with pre-existing medical conditions and minor health issues tend to delay or avoid visiting hospitals due to fear of infection, which is dangerous. Further, people or patients tend to access several alternatives and precautions. The alternatives include home remedies, ayurvedic medication, yoga and meditation. On the other hand, hospitals are trying to adapt online consulting and telemedicine. Besides, Cancellation or delay of nonemergency surgeries became inevitable in the lockdown phase. This survey conducted among the people of Erode district, Tamilnadu to study the perception of people concerning visiting hospitals for health issues. The results show that fear of infection, financial and transportation difficulties are the major factors which affected people from visiting hospital. Also, changing trends like Telemedicine and home remedies are likely to be permanently opted by people. In Brief, the outcomes reveal the changing attitude of people towards medication and hospital visiting habits.
Minimizing Subtle Errors in Computing Information of TCAM by Partial “N” Search Key Implementation
Authors: Sakthi K. , Nirmal Kumar P.
Published: April 2022

The primary memory of the computer gets corrupted due to microparticles that possess a great deal of energy. This causes transient kind of errors and it is called subtle errors. High corruption of data in computer systems has been occurred because of these errors. Major sources of subtle errors include ionic particles, huge radiations say example alpha, beta and gamma rays, protons, neutrons etc., When TCAM configuration changes from 120 nm to 20 nm technology, there was a drastic increase in subtle errors more than multiples of eight. Devices operated in low power regions are more prone to subtle errors. Memory storage devices like secondary memory element called cache, registers are more feasible to subtle errors. 1 Behavior of subtle errors never damage circuit configuration but changes the information content leading to erroneous data. The proposed architecture is called N-SEARCH KEY TCAM, where in this system, it has been predicted that occurrence of subtle errors are very small. This innovative method of detecting and correcting both single and burst type of subtle errors has many modules like TCAM, Multiplexer, control circuit and Error correction code frame. Moreover, the proposed architecture N-SEARCH KEY ternary memory uses a special LUT for matching process. A key named Search key was generated which contains don’t care bits for matching subtle errors. Unlike regular TCAM, this design uses sequential LUT which enhances matching time of order [Formula: see text](wnlog2[Formula: see text]. Initially, proposed scheme have been used to find single bit subtle errors by changing forbearance factor ([Formula: see text] and chances of occurrence of error among TCAM information. The design was enhanced to detect multiple subtle errors and rectified the same. If match not occurs with predetermined information, then IP filter forbids the unauthorized users from using global network resources, thus providing better information security. By the proposed method, number of improper segregation ratio decreases with increase in Forbearance ratio parameter and time required to detect and correct the same is also considerably reduced. By controlling the forbearance factor [Formula: see text], the best duration schemes can be chosen. Through the given architecture, average time consumption for error checking of subtle errors would range from 0.6 to 0.7 [Formula: see text]s.
Semigroups with Soft AC-Ideals
Authors: Özlü Şeri̇f , Sezgi̇n Aslihan
Published: April 2022

In this paper, we define the concepts of soft anti-covered semigroup, soft anti-covered left (right) ideal, soft anti-covered interior ideal, soft anti-covered (generalized) bi-ideal, soft anti-covered quasi-ideal of a semigroup. We investigate their properties by various examples. Moreover, we focus on the interrelations of them. We construct a new approach to anti-covered ideals of a semigroup via soft set theory.
Progress on fault-tolerant locating-dominating sets
Authors: Jean Devin C. , Seo Suk J.
Published: April 2022

A locating-dominating set is a subset of vertices representing “detectors” in a graph G which can locate an “intruder” given that each detector covers its closed neighborhood and can distinguish its own location from its neighbors. We explore a fault-tolerant variant of locating-dominating sets, called error-detecting locating-dominating (DET:LD) sets, which can tolerate one false negative. The concept of DET:LD sets was originally introduced in 2002, along with several properties in various classes of graphs; we extend this work by fully characterizing DET:LD sets for general graphs and establishing existence criteria. We prove that the problem of determining the minimum density of a DET:LD set in arbitrary graphs is NP-complete. Additionally, we determine that the minimum density in cubic graphs is at least [Formula: see text], and we show an infinite family of cubic graphs achieving this density.
Increased suicidal erythrocyte death in patients with hepatitis B-related acute-on-chronic liver failure
Authors: Mei Cheng , Peng Fang , Yin Wenyu , Xu Wei , Yao Run , Li Bijuan , Zhou Rongrong , Fan Xuegong , Li Ning
Published: April 2022

Acute chronic liver failure (ACLF) is a critical syndrome characterized by multiple organ failures and high short-term mortality. A common complication of HB-ACLF is anemia, however, the mechanism of anemia in HB-ACLF remains to be elucidated. We confirm that the accelerated eryptosis is involved in the pathophysiology of anemia associated with HB-ACLF, which progressively aggravates the clinical outcome. Our study illustrates the mechanism regarding the anemia pathogenesis of HB-ACLF, which may be utilized further toward therapeutic ends.
Effects of air pollution exposure on inflammatory and endurance performance in recreationally trained cyclists adapted to traffic-related air pollution
Authors: Silveira André C. , Hasegawa Júlio S. , Cruz Ramon , Matsuda Monique , Marquezini Mônica V. , Lima-Silva Adriano Eduardo , Giles Luisa V. , Saldiva Paulo , Koehle Michael S. , Bertuzzi Romulo
Published: April 2022

The aim of the present study was to analyze the effects of traffic-related air pollution (TRAP) on markers of inflammatory, neuroplasticity, and endurance performance-related parameters in recreationally trained cyclists who were adapted to TRAP during a 50-km cycling time trial (50-km cycling TT). Ten male cyclists performed a 50-km cycling TT inside an environmental chamber located in downtown Sao Paulo (Brazil), under TRAP or filtered air conditions. Blood samples were obtained before and after the 50-km cycling TT to measure markers of inflammatory [interleukin-6 (IL-6), C-reactive protein (CRP), interleukin-10 (IL-10), intercellular adhesion molecule-1 (ICAM-1)] and neuroplasticity [brain-derived neurotrophic factor (BDNF)]. Rating of perceived exertion (RPE), heart rate (HR), and power output (PO) were measured throughout the 50-km cycling TT. There were no significant differences between experimental conditions for responses of IL-6, CRP, and IL-10 ( P > 0.05). When compared with exercise-induced changes in filtered air condition, TRAP provoked greater exercise-induced increase in BDNF levels (TRAP = 3.3 ± 2.4-fold change; Filtered = 1.3 ± 0.5-fold change; P = 0.04) and lower exercise-induced increase in ICAM-1 (Filtered = 1.1 ± 0.1-fold change; TRAP = 1.0 ± 0.1-fold change; P = 0.01). The endurance performance-related parameters (RPE, HR, PO, and time to complete the 50-km cycling TT) were not different between TRAP and filtered air conditions ( P > 0.05). These findings suggest that the potential negative impacts of exposure to pollution on inflammatory, neuroplasticity, and performance-related parameters do not occur in recreationally trained cyclists who are adapted to TRAP.
A Survey of Knowledge and Barriers of Healthcare Professionals toward Opioid Analgesics in Cancer Pain Management
Authors: Ayoub Nehad M. , Jibreel Malak , Nuseir Khawla , Al-Taani Ghaith M.
Published: April 2022

Purpose. Pain is among the most frequent and troubling symptoms in cancer patients. Despite the availability of updated treatment guidelines and effective pharmacological therapies, undertreatment of cancer pain remains a global problem. Opioids are the mainstay analgesics to treat moderate-to-severe cancer pain. The goal of this study was to assess the knowledge and barriers towards opioid analgesics for cancer pain management among healthcare professionals in Oncology Units in Jordan. Methods. A structured questionnaire was administered to healthcare professionals (consultant doctors, resident doctors, pharmacists, and nurses) at three Oncology Units in a cross-sectional study design. Results. A total of 201 healthcare professionals completed the questionnaire. The average age was 34.8 ± 8.1 years (range 23–58) and 49.3% of respondents were nurses. The mean score for the knowledge of opioids was 12.5 ± 3.2 out of 24 points (range 2–20). An acceptable level of knowledge was observed in 50.7% of participants, while 49.3% had poor knowledge. Knowledge items mostly answered incorrectly were related to opioid administration, pharmacology, dosing, adverse events, rotation, and toxicity. Knowledge scores were significantly higher for consultant doctors compared to pharmacists and nurses (

p
=
0.016

and

p
<
0.001

, respectively). Healthcare professionals who handled opioid analgesics had significantly higher mean knowledge scores than those who did not (

p
=
0.012

). Linear regression analysis revealed that being a consultant physician has an independent, statistically significant association with higher knowledge scores. Among perceived barriers to using opioids, fear of addiction by patients was the most frequently reported barrier by respondents (79.6%). Other highly recognized barriers were fear of adverse effects by patients (67.2%) and lack of training programs on opioid dosing and monitoring (63.7%). Conclusions. This study revealed major gaps in the knowledge of opioids and pain management among healthcare professionals. There is an urgent need for developing innovative interventions to improve the knowledge of opioid analgesics and the understanding of pain management guidelines among healthcare professionals in Jordan.
Natural Language Description Generation Method of Intelligent Image Internet of Things Based on Attention Mechanism
Authors: Ouyang Jianquan , Yu Haoyang
Published: April 2022

With the rapid development of Internet of Things technology, the image data on the Internet are growing at an amazing speed. How to describe the semantic content of massive image data is facing great challenges. Attentional mechanisms originate from the study of human vision. In cognitive science, due to bottlenecks in information processing, humans selectively attend to a portion of all information while ignoring the rest of the visible information. This study mainly discusses the natural language description generation method of Internet of Things intelligent image based on attention mechanism. In this study, a CMOS sensor based on Internet of Things technology is used for image data acquisition and display. FPGA samples cis16bit parallel port data, writes FIFO, stores image data, and then transmits it to host computer for display through network interface. In order to minimize the value of cross-entropy loss function, maximum-likelihood estimation is used to maximize the joint probability of word sequences in the language model when sentence descriptions are generated using the encoder-decoder framework. At each moment, in addition to image features, additional text features are input. Image feature vector and text feature vector are weighted and summed by attention mechanism at each time. In decoding, the attention mechanism gives each image region feature weight, and the long-term and short-term memory network decodes in turn, but the long-term and short-term memory network has limited decoding ability. We use bidirectional long-term and short-term memory network instead of long-term memory network, and dynamically focus on context information through forward LSTM and reverse LSTM. The specificity of the proposed network is 5% higher than that of the 3D convolution residual link network. The results show that the performance of image description model is improved by inputting image context and text context into long-term memory network decoder.
Emotional Interaction and Behavioral Decision-Making Mechanism in Network Science Education Based on Deep Learning
Authors: Li Pengjiao , Meng Qian
Published: April 2022

With the globalization of network education and the design and construction of all aspects of engineering, network science education is playing an increasingly important role in higher education and even the lifelong education system of college students. The purpose of this article is to study emotional interaction in deep learning network education and analyze the status quo of its behavioral decision-making mechanism. It uses research literature method, algorithmic statistical method, and questionnaire survey method to investigate specific groups of people; analyzes the status quo of emotional interaction and behavioral decision-making mechanism; improves statistical algorithms; and explores an old style emotional cognitive decision-making model. In this paper, a questionnaire survey of a university shows that the proportion of students whose online learning time is 1.5–2 hours is about 10.3% and the proportion of 1–1.5 hours is about 6.8%. The study time of students’ online courses is mainly concentrated. The study time between 0.5 and 1 hour accounts for about 83.2%; about 2.3% of learners rarely use the Internet, less than 0.5 hour; and 1% of students hardly use online courses and may rely more on traditional classroom teaching. Further research showed the behavior of their emotional interaction: interactive teaching network in six modules reached the upper level, the peak value of the curve was 0.737, the bottom value was 0.115, and the transitivity was above 0.115. From deep statistical learning algorithms to completing network science education, designing or modifying more comprehensive and faster bpq-l learning algorithms based on traditional learning algorithms can allow us to find target sentiments.
Application of Artificial Intelligence in the Intervention of Sports on Adolescent Health Risk Behavior
Authors: Ha Jin
Published: April 2022

Through school education to intervene in the behavior of adolescent health risk behaviors students and guide students to have a correct concept of quality and health, this paper uses artificial intelligence technology to mine students’ body language to analyze students’ behavior in quality education dance classes, so as to achieve effective intervention for AHRB students. Before the experimental study, AHRB students and normal students were mixed into groups in a ratio of not less than 1 : 18. The results of the study showed that the implementation of intervention strategies could reduce the recurrence of various risky behaviors in adolescents. When non-AHRB students and AHRB students intervene together, there will be no adverse effects on non-AHRB students.
Evolutionary Analysis of Innovation Development in a Metropolitan Area from a Symbiosis Perspective: Empirical Research on the Shanghai Metropolitan Area
Authors: Xue Quanxiang , Liu Can , Zhao Min , Wu Jie
Published: April 2022

Promoting the innovative development of cities in the metropolitan area holds great significance for China’s implementation of the “innovation‐driven development” strategy. According to symbiosis theory, this paper constructed a city‐based Lotka‐Volterra symbiosis model and innovation‐level index system. We used the Shanghai metropolitan area as a sample for empirical analysis to explore the evolution of the comprehensive development level of urban innovation under different symbiotic relationships. The research results showed that under a reciprocal relationship, the innovative development of core city and node cities was higher than the actual level; under a mutual inhibition symbiosis or parasitic symbiotic relationship, the innovative development of core city and node cities was lower than the actual level; and under an independent symbiotic relationship, the innovative development of the core city was higher than the actual level, while that of the node cities was lower than the actual level.
Discussion on the Construction of Interactive Chinese Teaching Mode of Mobile App Application under the Internet Background
Authors: He Xuelian , Liu Zhenhuan
Published: April 2022

Under the new situation of Chinese internationalization promotion and educational informatization, online learning of Chinese as a foreign language is the key to learning Chinese as a foreign language in the future. Today, people learning Chinese as a second language must learn Chinese quickly online. However, it is not the best thing to open a website for learning Chinese as a foreign language at present. Learning Chinese online may not meet the needs of students, and it is not uncommon for students to give up in the process of Chinese learning. Therefore, the article discusses the teaching methods of Chinese as a foreign language and the use of new media and analyzes the shortcomings of Chinese teaching. This paper demonstrates the possibility and scientificity of using mobile app to conduct online interactive teaching of Chinese as a foreign language from both theoretical and practical aspects, aiming to promote the development of online teaching of Chinese as a foreign language, and then promote the promotion of foreign language Chinese as a foreign language. The results of the survey show that 75%, 96.1%, and 96.2% of the students who have studied Chinese for three months, six months, and one year, respectively, have downloaded the app Chinese language-assisted teaching software to learn Chinese.
High Similarity Image Recognition and Classification Algorithm Based on Convolutional Neural Network
Authors: Liu Zhizhe , Sun Luo , Zhang Qian
Published: April 2022

Nowadays, the information processing capabilities and resource storage capabilities of computers have been greatly improved, which also provides support for the neural network technology. Convolutional neural networks have good characterization capabilities in computer vision tasks, such as image recognition technology. Aiming at the problem of high similarity image recognition and classification in a specific field, this paper proposes a high similarity image recognition and classification algorithm fused with convolutional neural networks. First, we extract the image texture features, train different types, and different resolution image sets and determine the optimal texture different parameter values. Second, we decompose the image into subimages according to the texture difference, extract the energy features of each subimage, and perform classification. Then, the input image feature vector is converted into a one-dimensional vector through the alternating 5-layer convolution and 3-layer pooling of convolutional neural networks. On this basis, different sizes of convolution kernels are used to extract different convolutions of the image features, and then use convolution to achieve the feature fusion of different dimensional convolutions. Finally, through the increase in the number of training and the increase in the amount of data, the network parameters are continuously optimized to improve the classification accuracy in the training set and in the test set. The actual accuracy of the weights is verified, and the convolutional neural network model with the highest classification accuracy is obtained. In the experiment, two image data sets of gems and apples are selected as the experimental data to classify and identify gems and determine the origin of apples. The experimental results show that the average identification accuracy of the algorithm is more than 90%.
Evaluation of Low-Carbon Scientific and Technological Innovation-Economy-Environment of High Energy-Consuming Industries
Authors: Zhao Zhao , Liu Zheng , Peng Tianqi , Li Lingling , Zhao Yuanjun
Published: April 2022

The coordination of scientific and technological innovation with economy and environment is conducive to the sustainable development of high energy-consuming industries. Under the background of realizing the “carbon peak and neutrality” goal in China, this paper constructs the evaluation index system of scientific and technological innovation, economy, and environment of high energy-consuming industries. Based on the coupling coordination theory, this paper analyzes the coordinated development of scientific and technological innovation, economy, and environment of high energy-consuming industries from 2011 to 2019 and analyzes the factors restricting the coordinated development of the three systems. The results show that with the emphasis on scientific and technological innovation and ecological environment, the coordination degree of the complex system of scientific and technological innovation, economy, and environment of high energy-consuming industries is gradually increasing. R & D investment, the proportion of total industrial output value in GDP, and coal consumption per 10000 yuan of industrial output value are the main influencing factors of the coordination of the three systems.
Impact of Music in Males and Females for Relief from Neurodegenerative Disorder Stress
Authors: Salankar Nilima , Mishra Anjali , Koundal Deepika , Hoang Vinh Truong , Tran-Trung Kiet , Zaguia Atef , Belay Assaye
Published: April 2022

Neurological imbalance sometimes resulted in stress, which is experienced by the number of people at some moment in their life. A considerable measurement scheme can quantify the stress level in an individual, in which music has always been considered as the best therapy for stress relief in healthy human being as well in severe medical conditions. In this work, the impact of four types of music interventions with the lyrics of Hindi music and varying spectral centroid has been studied for an analysis of stress relief in males and females. The self‐reported data for stress using state‐trait anxiety (STA) and electroencephalography (EEG) signals for 14 channels in response to music interventions have been considered. Features such as Hjorth (activity, mobility, and complexity), variance, standard deviation, skew, kurtosis, and mean have been extracted from five bands (delta, theta, alpha, beta, and gamma) of each channel of the recorded EEG signals from 9 males and 9 females of the age category between 18 and 25 years. The support vector machine classifier has been used to classify three subsets: (i) male and female, (ii) baseline and female, and (iii) baseline and male. The noteworthy accuracy of 100% was found at the delta band for the first subset, beta and gamma bands for the second subset, and beta, gamma, and delta bands for the third subset. STA score has shown more deviation in the male category than in female, which gives a clear insight into the impact of music intervention with varying spectral centroid that has a higher impact to relieve stress in the male category than the female category.
Triple Solutions with Stability Analysis of MHD Mixed Convection Flow of Micropolar Nanofluid with Radiation Effect
Authors: Lanjwani Hazoor Bux , Chandio Muhammad Saleem , Anwar M. Imran , Al-Johani Amnah S. , Khan Ilyas , Alam Md. Nur
Published: April 2022

This paper deals with two‐dimensional steady boundary layer flow, heat, and mass transfer characteristics of micropolar nanofluid past on exponentially stretching/shrinking surface. The effect of different physical parameters like magnetic field, buoyancy, thermal radiation, and connective heat transfer are examined. Furthermore, similarity solutions are obtained by similarity transformation on the governing system of partial differential equations. The shooting method with help of the Maple software is used to achieve the numerical solutions of the equations. For the different ranges of the applied parameters, triple solutions are obtained for both cases of the surface. In view of the triple solutions, stability analysis is performed by bvp4c in the MATLAB software, where only first solution is found feasible which is discussed. The main findings of the first solution indicate the skin friction, drag force, heat, and mass transfer rates are increasing for the λ > 0 and decreasing for λ < 0 as the K is enhanced. The velocity profiles decrease with increase in magnetic, slip velocity, and suction parameters. The temperature profiles increase with increase in magnetic, thermophoresis, thermal radiation, and Brownian motion parameters, whereas concentration profiles reduce with increase in Schmitt number and Brownian motion.
A Bibliometrics Analysis on Teacher’s Feedback Research Based on CiteSpace through Data Visualization
Authors: Xie Wanji
Published: April 2022

Teacher feedback has played a pivotal role in students’ foreign language learning. This article aims to gain a more comprehensive insight into the current situation and development trend of teacher feedback in core leading Chinese Academic Journal within the last decade using both CiteSpace and narrative analysis. Results show that (1) research on teacher feedback has undergone a dynamic increase in the past 10 years, with 2013 the summit; (2) research subjects have become more diversified rather than focusing only on undergraduates learning English; written and oral feedback are still two major research classifications; (3) empirical studies still dominates the field while theory exploration still employs nonempirical research. Future research should emphasize more on sustained development of authors and explore this field using more cross-discipline theories and taking multiple theoretical perspectives.
Research on Teaching Reform of College Student Training Mode Based on Engineering Project Economic Evaluation of Driving Behavior with Internet of Vehicles Data
Authors: He Shuilong , Zou Zhihong , Li Hui , Deng Jucai , Xu Enyong , Tang Rongjiang , Zhou Yanyan
Published: April 2022

Practice is one of the essential teaching links in application-oriented professional teaching in engineering colleges. Reasonable design of practical teaching mode has an important influence on the development of scientific research activities and training of applied talents. In order to better experience the learning combined college student teaching mode, the analysis and mining based on the Internet of Vehicles data is taken as the research scene. Theoretical research and engineering verification of drivers’ driving behavior economy are carried out by using the learning method of theoretical research under the guidance of teachers and engineering practice under the guidance of enterprises. An economic evaluation model and energy saving potential calculation method based on fuzzy analytic hierarchy process are established, and the model is verified and improved in engineering practice. Among them, the analysis of personalized characteristics of driver behavior indicators shows that there are obvious differences in individual preference characteristics, generally manifested as fast acceleration and deceleration, low speed driving, gear mismatch. In addition, some drivers’ bad driving behavior has an energy saving potential of up to 4.98%. The results show that the combination of school theory research and enterprise engineering practice has positive effects on the development of students’ scientific research and the cultivation of applied talents that contribute to the development of enterprises.
Automatic Image Caption Generation Based on Some Machine Learning Algorithms
Authors: Predić Bratislav , Manić Daša , Saračević Muzafer , Karabašević Darjan , Stanujkić Dragiša
Published: April 2022

This paper is dedicated to machine learning, the branches of machine learning, which include the methods for solving this issue, and the practical implementation of the solution to the automatic image description generation. Automatic image caption generation is one of the frequent goals of computer vision. Image description generation models must solve a larger number of complex problems to have this task successfully solved. The objects in the image must be detected and recognized, after which a logical and syntactically correct textual description is generated. For that reason, description generation is a complex problem. It is an extremely important challenge for machine learning algorithms because it represents an impersonation of a complicated human ability to encapsulate huge amounts of highlighted visual pieces of information in descriptive language. The results of the generated descriptions are compared depending on the used pretrained convolutional networks. The BLEU metrics are used to calculate the quality of the image description. Although the solution to the problem of image description automatic generation does provide us with good results, there is yet room for improvement since there are images that are not adequately described.
Time-Division Multiarray Beamforming for UAV Communication
Authors: Duan Zhengxiang , Yang Xin , Xu Qian , Wang Ling
Published: April 2022

Recently, unmanned aerial vehicles (UAVs) have been widely used in various industries. However, the communication links of UAVs are also threatened by eavesdropping. To enhance physical layer security (PLS) for UAV communications, a time-division multiarray beamforming (TDMB) scheme is proposed. Multiple antenna arrays steer their beamforming vectors based on their position relative to the legitimate user (LU). Thus, angle-distance-dependent directional modulation (DM) can be achieved. Time-division means multiple antenna arrays take turns transmitting different symbols from a same packet. The receiver in undesired directions suffers from intersymbol interference (ISI) because of the path differences between the receiver and different antenna arrays. This paper shows the signal-to-interference-plus-noise ratio (SINR) distribution with the proposed method in a 2-dimensional plane. Also, the improvement of the secrecy rate with the proposed method under different total antennas and artificial noise power is studied. Overall, these results indicate that the security rate has improved more with the proposed method, where the numbers of antennas and the power of AN are limited. Therefore, this method is suitable for UAV security communication.
On a Possible Mechanism of Hydrostatic Pressure on Mineshaft Linings in Western China
Authors: Kuang Lian-Fei , Zhu Qi-Yin , Wang Jian-Zhou , Wang Bo
Published: April 2022

Reasonable determination of the water pressure on the shaft linings is the key to the optimization design of the vertical shaft of mining. The traditional water pressure calculation model is not suitable for the stratum conditions in western China. A new shaft-surrounding rock seepage model is proposed based on the coordinated seepage conditions of surrounding rock and shaft linings. In the model, the seepage within the shaft depth is assumed happening in a unit thickness confined aquifer. Thereby, the water pressure on the shaft linings is derived by the steady seepage theory, and the water pressure reduction coefficient is defined by normalizing the hydrostatic pressure. The influences of shaft diameter and shaft depth, the permeability coefficient of the surrounding rock, and the seepage flow on the water pressure reduction coefficient are analyzed. After that, the mechanism of reducing water pressure by grouting the surrounding rock is studied by considering the stratum conditions in western China. Finally, a practical equation, which only relating to the depth of the shaft, is fitted on account of the averaged water pressure reduction coefficient from the theoretical model. The effectiveness of the theoretical model and the fitted equation are verified by the measured data and data in the literature.
Comprehensive Analysis of Phagocytosis-Related Regulators to Aid Prognostic Prediction and Immunotherapy in Patients with Low-Grade Glioma
Authors: Li Jianwen , Huang Qianrong , Mo Ligen
Published: April 2022

Antibody-dependent cellular phagocytosis- (ADCP-) related regulators (PRs) have been confirmed an important role in immunotherapy. However, the characterization of specific PRs in low-grade glioma (LGG) has not been comprehensively explored. In this study, we retrieved RNA-seq and CRISPR-Cas9 data to identify specific PRs in LGG patients and constructed a PRs-signature using the LASSO-Cox algorithm. The ROC analysis and Kaplan-Meier analysis showed that PRs-signature had a good predictive effect, and the multivariate Cox regression analysis showed that PRs-risk scores were independent prognostic factors correlated with overall survival (OS). In addition, CIBERSORT, ssGSEA, and MCP counter algorithms were used to explore immune cell content in different risk groups, especially in the correlation between macrophages and specific PRs. Finally, mRNA expression was upregulated in the high-risk group compared with the low-risk group at most immune checkpoints and proinflammatory factors. In conclusion, we constructed a prediction model for prognostic management and revealed the cross-talk between specific PRs and immunotherapy in LGG patients.
Research on Multimicrogrid Transaction Model and Cross-Chain Transaction Mechanism Based on Blockchain
Authors: Hu Wei , Xia Xue , Zhang Yi
Published: April 2022

The traditional microgrid electricity transactions face a low security, poor economic benefits, and weak stability. To solve these problems, this paper proposes a multimicrogrid cross-chain transaction model based on quantum blockchain. Specifically, a bidding strategy was developed for the noncooperative dynamic game of aggregator-multimicrogrid alliance, aiming to balance and optimize the benefits of all parties and effectively enhance the consumption rate of electric energy. To improve the transaction efficiency between aggregators, a consensus mechanism was designed for multimicrogrid cross-chain communication, realizing consistent self-adaptation to cross-chain information. In addition, the quantum threshold signature technology was adopted to ensure the reliability of transaction data and create an unconditional secure communication environment. Case analysis shows that our model proposed in this paper not only ensures the security and stability of transactions but also enhances the economic benefits, providing theoretical support and decision support for the optimization of multimicronetwork cross-chain transaction model.
Enthesitis in Bowel-Associated Dermatosis-Arthritis Syndrome in an Ulcerative Colitis Patient
Authors: Alshahrani Khalid S. , Almohaya Abdulellah M. , Hussein Rayan S. , Ali Rania H.
Published: April 2022

Bowel-associated dermatosis-arthritis syndrome (BADAS) is a rare and recurrent neutrophilic dermatosis condition. Due to the rarity of this syndrome and the difficulty of the disease recognition and management, there was no clear reported incidence rate of this syndrome. 20% of patients after ileojejunal bypass surgery for morbid obesity were reported, by Jorizzo et al., to have BADAS. The underlying etiology of BADAS is not fully understood; therefore, the diagnosis of this condition is difficult and the approach for the management as well. Herein, we report a case of BADAS with unusual musculoskeletal presentation.
Discrete Dynamic Modeling of Learner Behavior Analysis in Physical Education Teaching
Authors: Shi Jia , Sun Jun , Zheng Zhonghua
Published: April 2022

With the advent of the big data era, the combination of information technology and education has become an important way for the development of the industry. The large-scale realization of teaching tasks under the background of information data requires the prediction and analysis of learners' characteristics, behavior, and development trend. Based on the above situation, this paper uses discrete dynamic modeling technology in big data environment to study the learners' behavior in physical education teaching. By quantifying the learning process data, the feature points of each learner are extracted to realize the personalized construction of dynamic learning data. Due to the rapid development of network technology, we mainly analyze the online education platform and explore the influencing factors of learners' behavior characteristics from many aspects. Finally, it carries out dynamic modeling and prediction for physical education learners from the aspect of achievement change, uses the grey model to build the achievement change system, and combines the dynamic modeling technology to reflect the development trend of achievement. The results show that the main factor affecting learners' behavior change in physical education is video learning. Most students are passive and lack initiative in the learning process. Discrete dynamic modeling technology can improve the accuracy of predicting student achievement changes and provide effective data for the research content.
Geiparvarin Inhibits OS Metastasis through Upregulation of ANGPTL4 Expression by Inhibiting miRNA-3912-3p Expression
Authors: Jiang Fuling , Wang Guang-Jie , Huang Ping , Chen Shu , Xiao He , Zhang Liang , Zou Hua
Published: April 2022

Background. Geiparvarin (GN) is a natural compound with anticancer activity. However, the effect of GN on osteosarcoma (OS) and the anticancer mechanism of GN are still unclear. Methods. Cell viability was measured by MTT assay. Invasion and migration were measured by transwell assay. The miRNAs, genes, and signaling pathways affected by GN were confirmed by whole-genome sequencing and bioinformatics analysis. The expression level of mRNA and protein was measured by qRT-PCR and western blot. Animal experiment was performed for confirming the GN anticancer effect and side effect in vivo. Results. Our results show that GN significantly inhibits OS cell growth and metastasis in vitro. In vivo experiment also showed that GN dramatically suppressed OS lung metastasis and no side effects were found. GN treatment inhibited OS metastasis through upregulating the ANGPTL4 expression. In addition, GN inhibited the expression of miR-3912-3p, which targets ANGPTL4. Conclusion. Our data clearly indicate that GN is a candidate drug for OS treatment, and GN plays its role through miR-3912-3p/ANGPTL4 in OS.