bluuebunny/crossref_metadata_2025 huggingface dataset:
3D Visualization Analysis of Motion Trajectory of Knee Joint in Sports Training Based on Digital Twin
Authors: Chen Jian
Published: June 2022

With the development of China’s social economy, the pace of people’s life is also accelerating. Therefore, in the face of the increasing pressure of life and work, people have begun to pay more and more attention to improving their physical fitness and they also pay more and more attention to sports in their spare time. The knee joint is a very important body part that will be involved in sports. Therefore, in sports, it is very important for healthy sports to pay attention to the state of the knee joint at all times and avoid injury to the knee joint during sports. The three-dimensional visual analysis of the knee joint movement trajectory in sports training refers to the trajectory capture and three-dimensional visual analysis of the knee joint movement in sports training based on the computer-based description and understanding of model technology. The three-dimensional visual analysis of the motion trajectory of the knee joint in sports training is conducive to the attention and analysis of the motion state of the knee joint in sports training, so as to help find problems and avoid knee joint damage during sports training as much as possible. This paper aims to study the 3D visualization analysis of knee joint motion trajectory in sports training based on digital twin and establish a 3D visualization system of knee joint motion trajectory based on digital twin technology, and based on this system, the three-dimensional visualization experiment of knee joint movement trajectory in sports training is carried out. The experiment concluded that the signal-to-noise ratio of the three-dimensional visualization image of the knee joint movement trajectory in sports training based on the three-dimensional visualization system established by the digital twin is 68% and the clarity is 89%. It shows that the system has a good 3D visualization effect.
Clostridioides difficile Infection in Liver Cirrhosis: A Concise Review
Authors: Liu Yuanbin , Chen Mingkai
Published: June 2022

Clostridium difficile is a Gram-positive bacillus with fecal-oral transmission and is currently one of the most common nosocomial infections worldwide, which was renamed Clostridioides difficile in 2016. Clostridioides difficile infection (CDI) is a prevalent infection in cirrhosis and negatively affects prognosis. This study aimed to provide a concise review with clinical practice implications. The prevalence of CDI in cirrhotic patients increases, while the associated mortality decreases. Multiple groups of risk factors increase the likelihood of CDI in patients with cirrhosis, such as antibiotic use, the severity of cirrhosis, some comorbidities, and demographic aspects. Treatment in the general population is currently described in the latest guidelines. In patients with cirrhosis, rifaximin and lactulose have been shown to reduce CDI risk due to their modulatory effects on the intestinal flora, although conflicting results exist. Fecal microbiota transplantation (FMT) as a treatment for the second or subsequent CDI recurrences has demonstrated a good safety and efficacy in cirrhosis and CDI. Future validation in more prospective studies is needed. Screening of asymptomatic patients appears to be discouraged for the prevention currently, with strict hand hygiene and cleaning of the ward and medical equipment surfaces being the cornerstone of minimizing transmission.
An Energy-Efficient Partial Data Offloading-Based Priority Rate Controller Technique in Edge-Based IoT Network to Improve QoS
Authors: Bali Malvinder Singh , Gupta Kamali , Rani Shalli , Ratna Rajnish
Published: June 2022

In last few years, the Internet of Things (IoT) platform, which comprises a significant number of devices equipped with sensors and utilized for monitoring and actuation operations, has seen significant growth. Massive volumes of data are collected by IoT devices, which are subsequently sent to the cloud for analysis and forecasting. IoT data that is directly sent to the cloud can congest IT infrastructure that is used for other purposes. As a result, data offloading has become a popular issue in academic and industrial sectors, specifically for traffic-intensive applications that can benefit from offloading to local edge and cloud infrastructure. A lot of in-depth studies have been undertaken in areas where sensor device mobility is high in order to ensure efficient data offloading. Nevertheless, very few studies have taken into account the issue of data offloading with sensor devices that have very limited mobility, like industrial IoT sensors, which really needs more attention as it is one of the most data-trafficking areas. To counter this problem, a data offloading-based heuristic technique using edge computing is proposed in IIoT-based applications. This will reduce data transferring quantity, resulting in lower data migration capacity, bandwidth usage, and load, lowering operational costs. It is being put through extensive testing to see how it compares to existing algorithms. The experimental results show that the proposed algorithm is superior in terms of energy consumption, dependability, and resilience under various situations.
Description of Quantum Mechanics as a Branch of Mathematical Physics That Deals with the Emission and Absorption of Energy by Matter
Authors: Cheng Renxiang
Published: June 2022

Quantum mechanics is the study of the emanation and retention of energy by issue, as well as the movement of material particles. Quantum mechanics is especially relevant to elementary particles and their relationship since it holds that energy and matter exist in tiny, discrete sums. The neutron is the main known molecule that permits exploratory admittance to each of the four basic powers as well as a wide scope of speculative collaborations. In this review, we analyzed the correlation for this study to analyze the relationship between the quantum mechanics, neutrons, and dark energy interactions and zeroed in on the idea of quantum mechanics and its arrangement with emission and absorption of energy.
Embedded Demand, Policy Supply, and the Urban Spatial Effect of the Transformation of the New Generation of Migrant Workers into Citizens
Authors: Yu Lin , Yin Jianbing , Qiao Wei
Published: June 2022

Drawing on the urban space theory, based on the interaction between policy supply and right demand, this paper constructs a theoretical model of the spatial fit and inhibition effect of the urban transformation of the new generation of migrant workers from the perspective of right demand and policy supply, to explore the urban space fit and restraining effect caused by the policy supply on the new generation of migrant workers. For that reason, under the guidance of the theoretical model, this paper makes an investigation and empirical analysis on the transformation of some new-generation migrant workers in the Yangtze River Delta, and the conclusion is as follows: the basic rights demand of the new generation of migrant workers embedded in urban space has a positive effect on the realization of their citizens’ transformation will, but the supply policy of citizens’ transformation has a restraining effect.
Experimental Study on the Effect of Large Temperature Difference on Compressive Strength and Pore Structure of Semirigid Base
Authors: Dai Ming , Pan Jia , Shen Yanjun , Deng Jianbo , Muhadeer Yeermulati
Published: June 2022

Understanding the evolution of mechanical properties and pore structure of semirigid base under large temperature difference is of great significance for evaluating the durability and safety of semirigid base structure and studying the damage cracking mechanism and prevention technology of semirigid base induced by large temperature difference climate. This paper studies the variation law of peak stress and dynamic modulus, the evolution characteristics of pore structure, and the pore size distribution of semirigid base after different cycles at different temperature intervals. Based on the analysis of peak stress and dynamic modulus test results, the degradation effect of freeze‐thaw environment (−20°C∼20°C) on semirigid base is far greater than that of high‐temperature environment (20°C∼60°C) and low‐temperature environment (−5°C∼−30°C). There are significant decreases in peak stress and dynamic modulus of semirigid base in the late cycle (12 to 15 cycles). Under low‐temperature and freeze‐thaw environments, the axial load resistance of semirigid base is significantly correlated with the deformation resistance, and the correlation between the two is not significant under high‐temperature environment. The variability of the thermal expansion and contraction characteristics of the internal microscopic phases of the semirigid base and the force characteristics of the pore interface phases are the root causes of the damage and cracking of the pavement base in a large temperature difference climate.
A Sports Video Behavior Recognition Using Local Spatiotemporal Patterns
Authors: Liu Yuanqiang , Jing Huaiguo
Published: June 2022

These days, many researchers are interested in sports video behavior recognition (SVBR), which has emerged as a core research domain for visual understanding and analysis of athlete performance. Despite significant results in simple scenarios, SVBR remains a difficult task due to numerous challenges such as independence, obstruction, and interclass visual appearance in real situations. Recognizing the sports video behavior is one of the crucial challenges in computer vision for sports video behavior data, with implementation in monitoring, mental illness diagnostics, video information extraction, and multitarget tracking. This paper provides a local pattern activity discrimination model for the detection and localization of active individuals in a video to address issues of multitarget tracking in video behavior recognition caused by mutual occlusion of targets and the complexity of ambient background. This work solves the problem of low recognition accuracy due to incomplete extraction of trajectories or overly complex backgrounds using a trajectory-based approach. Our model is applied to the video segments of 38 matches associated with goal events in Euro 2012, and an average accuracy of 91.3% is obtained. The experimental results verify the high accuracy and applicability of our method for the recognition of target object behavior in the videos.
Recurrent Events’ Impacts on foF2 Diurnal Variations at Dakar Station during Solar Cycles 21-22
Authors: Sandwidi Sibri Alphonse , Ouattara Frédéric
Published: June 2022

This paper deals with recurrent events’ (REs’) impacts on foF2’s diurnal variations at Dakar station (lat: 14.8° N, long: 342.6° E, Senegal) during solar cycles 21 and 22. Data were analyzed by taking in account solar phases and seasons influence. REs generate positive storms in all seasons and on all solar phases and also weak negative storms in all seasons and at minimum and maximum solar phases. The results suggest on the one hand that vertical drift

E
×
B

due to strong electrojet currents cannot be considered an important mechanism of positive storms and on the other hand that prereversal enhancement (PRE) phenomenon (with a delay due to Dakar station position) and equatorward thermospheric neutral winds circulation (TNWC) contribute mainly to their generation. The intense positive storms, which appear mostly at night, are observed in decreasing phase and in spring which correspond, respectively, to the solar phase and the season of maximum REs occurrence. Then, positive storms’ strength presents seasonal and solar cycle dependence and it is related to REs days occurrence. REs’ negative storms strength are linked directly with REs days’ occurrence on solar phase scale, while they are not on seasonal scale.
Analysis of Risk Factors in Global Software Development: A Cross-Continental Study Using Modified Firefly Algorithm
Authors: Iftikhar Asim , Ali Syed Mubashir , Alam Muhammad , Musa Shahrulniza , Su’ud Mazliham Mohd
Published: June 2022

In today’s competitive world, software organizations are moving towards global software development (GSD). This became even more significant in times such as COVID-19 pandemic, where team members residing in different geographical locations and from different cultures had to work from home to carry on their tasks and responsibilities as travelling was restricted. These teams are distributed in nature and work on the same set of goals and objectives. Some of the key challenges which software practitioners face in GSD environment are cultural differences, communication issues, use of different software models, temporal and spatial distance, and risk factors. Risks can be considered as a biggest challenge of other challenges, but not many researchers have addressed risks related to time, cost, and resources. In this research paper, a comprehensive analysis of software project risk factors in GSD environment has been performed. Based on the literature review, 54 risk factors were identified in the context of software development. These were further classified by practitioners into three dimensions, i.e., time, cost, and resource. A Pareto analysis has been performed to discover the most important risk factors, which could have bad impact on software projects. Furthermore, a modified firefly algorithm has been designed and implemented to evaluate and prioritize the pertinent risk factors obtained after the Pareto analysis. All important risks have been prioritized according to the fitness values of individual risks. The top three risks are “failure to provide resources,” “cultural differences of participants,” and “inadequately trained development team members.”
Electrochemical Mechanical Properties of Beam-Column Joints of Building Steel Structures under Impact Loads
Authors: Qian Liu
Published: June 2022

To optimize the seismic performance in high-rise buildings, the performance changes of beam-column joints of high-rise steel structures of buildings when they are impacted are analyzed to obtain the optimal choice of joint combination. The most commonly used steel beam and steel column joints are selected as the research objects. Firstly, the finite element model and the shell element model of the force are established and then the model of the finite element combination of six kinds of steel beam and steel column joints is established. Secondly, the applied data of common impact force in actual buildings are collected, and the simulated impact force in this experiment is established according to the sampled data. Then, the changes in each performance of the joints under the action of the impact external force are deeply explored through simulation. Finally, the accuracy of the model is confirmed by calculation. The experimental results comprehensively show that the transmission mechanisms of the impact force are different for the six joints. Through calculation and comparison, the bending stiffness, strength, deformation, the displacement degree of the joints, and the bearing degree of the impact external force are comprehensively analyzed. It is concluded that the bolt-type and external diaphragm-type steel tube column concrete and steel-beam concrete composite joints have better seismic resistance than the other five joints. The bolt-type does not need to be welded on site, and the on-site construction workers only need to assemble, which can reduce the technical requirements for construction workers, improving the practicability and applicability. Therefore, it shows reliable reference significance for the joint selection of building steel structures.
Identification of Gene-Tyrosine Kinase 2 (TYK2) in Head and Neck Squamous Cell Carcinoma Patients—An Integrated Bioinformatics Approach
Authors: Gong Xiaoyan , Ren Fukai
Published: June 2022

Background. The human tyrosine kinase 2 (TYK2) has been found to be associated with at least 20 autoimmune diseases; however, its tumor-regulating role in head and neck squamous cell carcinoma (HNSC) has not been researched by using an integrative bioinformatics approach, yet. Objective. To investigate the regulating mechanisms of the TYK2 gene in HNSC in terms of its expression pattern, prognostic values, involved biological functions, and implication of tumor immunity. Methods. The TYK2 gene expression pattern and regulatory involvement in HNSC were investigated using publically accessible data from TCGA database. R software tools and public web servers were utilized to conduct statistical analysis on cancer and noncancerous samples. Results. TYK2 was found to be significantly upregulated in HNSC samples compared with healthy control samples. The expression of TYK2 gene was shown to be associated with the prognosis of HNSC by showing its upregulation represented better survival outcome. The regulating role of TYK2 in HNSC was found mainly in several pathways including DNA replication, base excision repair, apoptosis, p53 signaling pathway, and NF-kappa B signaling pathway. The gene set enrichment analysis (GSEA) results showed that TYK2-significantly correlated genes were mainly enriched in several biological functional terms including cell cycle, DNA replication, PLK1 pathway, ATR pathway, and Rho GTPase pathway. In addition, TYK2 was found to be involved in tumor immunity, showing positive correlation with the majority of tumor infiltrating immune cells, immune checkpoint genes, and significant representative components of tumor microenvironment, according to the ESTIMATE-Stromal-Immune score. Conclusions. Given the dysregulation, prognostic values, regulating tumor progression-related pathways, and the tumor immune-modulatory role of TYK2 in HNSC, the TYK2 gene should be regarded as a potential therapeutic target in treating head and neck cancer.
Modeling of Solitary Wave Interaction with Curved Face Seawalls Using Numerical Method
Authors: Ghassemizadeh Seyyed-Mahmood , Ketabdari Mohammad Javad
Published: June 2022

This paper considers the solitary wave interaction with seawalls of different curved geometries and sloped faces using numerical modeling. This interaction was simulated using the Finite Volume Method‐Volume of Fraction (FVM‐VOF) approach. To model the turbulent free‐surface flow, coupled VOF and k‐ε‐RNG methods were used. To validate the model, the numerical results for a conventional sloping seawall were compared with the available experimental data. Then the interaction of solitary waves and seawalls with different sinusoidal, logarithmic, and parabolic functions front faces and linear slope was modeled. The results showed that for these seawalls in general by increasing the solitary wavelength, the wave reflection coefficient (Cr) increases. However, the wave run‐up on seawalls demonstrates an oscillatory decrease. Furthermore, for parabolic walls in comparison to conventional linear sloping seawalls, the wave run‐up and wave reflection increased by 4.1% and 4.7%. For sinusoidal walls, the wave run‐up and wave reflection increased by 5% and 1.8%. For logarithmic walls, the wave run‐up and wave reflection increased by 6.3% and decreased by 1.1%, respectively. This means that wave run‐up on logarithmic walls is more than that of the sinusoidal, parabolic, and sloped walls. The simulation results revealed that normalized maximum run‐up increases with an increase in normalized incident wave height for all types of curved walls.
Coordination between Tourism and Ecological Environment and High-Quality development: Based on the Study of the Yellow River Basin
Authors: Meng Fei
Published: June 2022

The coordinated development of tourism and EE (ecological environment) is of practical significance to the HQD (high-quality development) of the YRB (Yellow River Basin), and the excellent EE provides a strong guarantee for the sustainable and healthy development of the tourism industry. Objective. To explore the coupling relationship between tourism and EE coordination and HQD in the YRB. Methods. This paper uses the improved soil moisture weight method to quantitatively measure the coupling CT (coordination between tourism) and EE and HQD in the YRB from 2014 to 2020. The results show that from 2014 to 2020, on the whole, the evaluation values of tourism economy and EE in the YRB show an increasing trend, but the growth trend of tourism economy is obviously stronger than that of EE. From the perspective of eco-environmental quality index, the overall increase of eco-environmental quality index of the YRB from 2014 to 2020 is small, and the eco-environmental quality of the YRB has improved to some extent in the recent years, but the effect is not significant. And they all passed the significance test at 1% level. During 2014–2020, the overall Moran index of the coordinated development level of tourism and EE and HQD in the YRB showed a wave-like fluctuation, and the fluctuation range increased gradually, and the overall trend was decreasing. Government control, industrial structure (proportion of tertiary industry), population size, and people’s living standard have significant positive effects on the coupling and coordination of local tourism economy and EE, while industrial R&D investors have significant negative effects on the coupling and coordination of local tourism economy and EE, while natural disasters have no significant effects on the coupling and coordination of local tourism economy and EE. Conclusion. The HQD of tourism in the YRB should establish the idea of giving priority to EE, construct the mechanism of regional coordinated development, and implement the driving strategy of scientific and technological innovation and the development model of culture + ecology + tourism.
IGST Combined with Conventional Drug Therapy and TCM Therapy for Treatment of Bilateral Vestibular Hypofunction
Authors: Zhao Mingming , Han Jing , Li Liang
Published: June 2022

Objective. To investigate the effects of intensive gaze stability training (IGST) combined with conventional drug therapy and traditional Chinese medicine (TCM) therapy on hearing recovery and quality of life (QOL) in patients with bilateral vestibular dysfunction. Materials and Methods. A total of 120 patients with bilateral vestibular dysfunction admitted to our hospital from January 2019 to January 2020 were recruited and assigned to group A (n = 60) and group B (n = 60) based on the order of admission. Group B received conventional medication and TCM therapy, and group A received IGST plus conventional medication and TCM therapy. The serum biochemical indexes, blood rheology indexes, symptoms disappearance time, hearing recovery time, pure-tone hearing threshold, vestibular symptom index (VSI), and QOL of the two groups were compared. Results. The serum biochemical indexes and blood rheology indexes in group A were lower than those in group B after treatment (

P
<
0.001

). Patients in group A had a shorter time lapse before symptoms disappearance and hearing recovery than those in group B (

P
<
0.001

). Group A had lower results of the pure-tone hearing threshold (

P
<
0.001

) and of VSI (

P
<
0.05

) than group B. Patients in group A had higher QOL scores than those in group B after treatment (

P
<
0.001

). Conclusion. IGST plus conventional drug therapy effectively improves the serum biochemical indexes and blood rheological indexes of patients with bilateral vestibular hypofunction, accelerates their hearing recovery process, and alleviates their clinical symptoms, thereby improving their QOL, which shows good potential in clinical application.
Research on Communication Optimization of Power Carrier Sensor Control Network Based on Ant Colony Algorithm
Authors: Ma Xiang , Workneh Tewabe Chekole
Published: June 2022

In order to optimize the communication research of dynamic carrier sensing control network based on ant colony algorithm, this paper proposes an improved ant colony routing algorithm. Firstly, this paper establishes the experimental model, sets up the relevant experimental parameters, and builds the MATLAB experimental platform. The simulation results show that compared with the basic ant colony algorithm, the improved algorithm performs better in the final jumps. The average residual energy of the sensor node is more. The total energy consumption is less, and the consumption in each round is relatively stable. The residual energy fluctuation range of the sensor node is small, and the energy consumption of each node is relatively balanced. Therefore, the improved ant colony algorithm is important for improving the survival cycle of the WSN. The algorithm comprehensively considers the factors such as convergence speed, node communication distance, node residual energy, and optimal solution and can finally find the path with low node energy consumption and high node residual energy.
Prediction of Differentially Expressed Genes and a Diagnostic Signature of Preeclampsia via Integrated Bioinformatics Analysis
Authors: Huang Shan , Cai Shuangming , Li Huibin , Zhang Wenni , Xiao Huanshun , Yu Danfeng , Zhong Xuan , Tao Pei , Luo Yiping
Published: June 2022

Background. Preeclampsia (PE), which has a high incidence rate worldwide, is a potentially dangerous syndrome to pregnant women and newborns. However, the exact mechanism of its pathogenesis is still unclear. In this study, we used bioinformatics analysis to identify hub genes, establish a logistic model, and study immune cell infiltration to clarify the physiopathogenesis of PE. Methods. We downloaded the GSE75010 and GSE10588 datasets from the GEO database and performed weighted gene coexpression network analysis (WGCNA) as well as Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The online search tool for the retrieval of interacting genes and Cytoscape software were used to identify hub genes, which were then used to establish a logistic model. We also analyzed immune cell infiltration. Finally, we verified the expression of the genes included in the predictive model via RT-PCR. Results. A total of 100 and 212 differently expressed genes were identified in the GSE75010 and GSE10588 datasets, respectively, and after overlapping with WGCNA results, 17 genes were identified. KEGG and GO analyses further indicated the involvement of these genes in bioprocesses, such as gonadotropin secretion, immune cell infiltration, and the SMAD and MAPK pathways. Additionally, protein-protein interaction network analysis identified 10 hub genes, six (FLT1, FLNB, FSTL3, INHA, TREM1, and SLCO4A1) of which were used to establish a logistic model for PE. RT-PCR analysis also confirmed that, except FSTL3, these genes were upregulated in PE. Our results also indicated that macrophages played the most important role in immune cell infiltration in PE. Conclusion. This study identified 10 hub genes in PE and used 6 of them to establish a logistic model and also analyzed immune cell infiltration. These findings may enhance the understanding of PE and enable the identification of potential therapeutic targets for PE.
Three-Dimensional Ultrasound Imaging under Optimized Nuclear Regression Reconstruction Algorithm in the Diagnosis Vaginal Delivery and Cesarean Section on the Anal Sphincter Complex of Primiparas
Authors: Wang Han , He Xiaolan , He Yi
Published: June 2022

This study was aimed at analyzing the injury of anal sphincter (AS) for primipara caused by the vaginal delivery and cesarean section under the guidance of three-dimensional (3D) ultrasound images. A total of 160 patients who underwent postpartum reexamination were enrolled as the research subjects, including 80 cases of natural delivery (group A) and 80 cases of cesarean section pregnant women (group B), all of whom underwent three-dimensional ultrasound imaging scans. At the same time, an optimized kernel regression reconstruction (KRR) algorithm was proposed for the enhancement of ultrasound images. It was found that the running time after acceleration by the graphics processing unit (GPU) was obviously superior to that of a single-threaded CPU and a multithreaded CPU, showing statistical differences (

P
<
0.05

). The thickness of the proximal and distal external AS in group A was much thinner in contrast to that in group B, showing statistical difference (

P
<
0.05

). Therefore, the 3D ultrasound image based on the optimized KRR algorithm can accurately assess the morphology of AS injury in primipara, and the adverse effect of natural delivery on the AS complex in primipara was greater than that of cesarean section.
Breast Tumor Detection Using Robust and Efficient Machine Learning and Convolutional Neural Network Approaches
Authors: Khan Mohammad Monirujjaman , Tazin Tahia , Zunaid Hussain Mohammad , Mostakim Monira , Rehman Taeefur , Singh Samender , Gupta Vaishali , Alomeir Othman
Published: June 2022

Breast cancer develops when cells in the breast expand and divide uncontrollably, resulting in a lump of tissue known as a tumor. This lump of tissue is called a tumor. After skin cancer, breast cancer is the second most common cancer among women. It is more common in women over the age of 50. Men may also acquire breast cancer, albeit it is uncommon. Each year, approximately 2,600 men in the United States are diagnosed with breast cancer, accounting for less than 1% of all cases. Transgender women are more likely than cisgender men to acquire breast cancer. Additionally, transgender males are less likely than cisgender women to acquire breast cancer. Breast cancer is more common in women over the age of 50, although it can affect anyone at any age. Early detection of a breast tumor may significantly lower the risk of developing breast cancer. A public dataset of breast tumor features was used instead to build models for identifying breast tumors through machine learning and deep learning. Prediction models were built using logistic regression (LR), decision tree (DT), random forest (RF), voting classifier (VC), support vector machine (SVM), and a proprietary convolutional neural network (CNN). These models were used to find critical prognostic indicators linked to breast cancer. The proposed network performs far better, with an average accuracy of 99%. This study has six types of models: LR, RF, SVM, VC, DT, and a custom CNN model. They all had 96% to 99% accuracy in this study. CNN, LR, RF, SVM, VC, and DT achieved 99%, 96%, 98%, 97%, 97%, and 96% F1 score, respectively. There were many machine learning algorithms used in this study that were very accurate, which means that these techniques could be used as alternative prognostic tools in breast tumor detection studies in Asia.
Changes in the Profile of Antibiotic Prescriptions by Dentists in Brazil during the Pandemic
Authors: Fiol Fernando de Sá Del , de Andrade-Jr Isaltino Pereira , da Silva Marcus Tolentino , Barberato-Filho Silvio , Lopes Luciane Cruz , Bergamaschi Cristiane de Cassia
Published: June 2022

During the COVID-19 pandemic, people worldwide, including the scientific community, were insecure and fearful. The lack of vaccines at the beginning of the pandemic and the high mortality rate led to a search for alternative treatments for COVID-19. Among these proposals, a postulated activity of azithromycin was frequently studied in early treatment. In view of this, many countries saw an increase in the consumption of this antibiotic. Thus, the objective of this study was to evaluate, in Brazil, whether there was an increase in azithromycin prescriptions made by dentists, as they may have been prescribing this antibiotic as a probable treatment for COVID-19. This is an interrupted time series that analyzed antimicrobial prescriptions data between January 2014 and July 2021. The data were taken from the National System of Controlled Products Management, and pre- and postpandemic periods were compared. To assess changes in azithromycin consumption, Joinpoint regression and analysis of variance, followed by Dunnett’s test, were used. More than 38 million prescriptions written during the period were analyzed. Amoxicillin (72.3%), azithromycin (18.0%), cephalexin (6.1%), and metronidazole (3.58%) were the most prescribed antibiotics. At the beginning of the pandemic, there was a drop in amoxicillin prescriptions motivated by a decrease in consultations, but conversely, in less than three months, azithromycin prescriptions grew by more than 100%. The exaggerated use of this antibiotic during the pandemic will certainly have consequences in the short and medium term on indicators of bacterial resistance. The use of guidelines and respect for the therapeutic protocols of government agencies should be fundamental for collective and strategic action in the fight against health emergencies.
Machine Translation of English Language Using the Complexity-Reduced Transformer Model
Authors: Li Qin
Published: June 2022

Previous translation models like statistical machine translation (SMT), rule-based machine translation (RBMT), hybrid machine translation (HMT), and neural machine translation (NMT) have reached their performance bottleneck. The new Transformer-based machine translation model has become the favorite choice for English language translation. For instance, Google’s BERT translation model organizes the Transformer module into bidirectional encoder representations. It is aware of the users’ search intentions as well as the material that the search engine has indexed. It does not need to evaluate previous searches to comprehend what people mean, unlike RankBrain. BERT comprehends words, sentences, and complete information in the same way that we do. It achieves remarkable translation quality improvement over the other state-of-the-art benchmarks. It demonstrates the great potential of the Transformer model. The Transformer-based translation model mainly improves the performance at the cost of growing model sizes and complexity, usually requiring million-scale parameters. It is hard for the traditional computing systems to cope with the growing memory and computation requirements. However, the latest computers can easily run this model without any lag. The biggest challenge of applying the Transformer model is to deploy these models efficiently onto real-time or embedded devices. In this work, we propose a quantization scheme to reduce the parameter and computation complexity. It is of great importance to promote the usage of the Transformer model. Our experiment results show that the original Transformer model in 32 bit floating-point can be quantized to only 8 bits to 12 bits with only negligible translation quality loss. However, due to the perfect transformation of the block part, this quality loss part can easily be managed by the users. Meanwhile, our algorithm achieves

2.6
×

to

4.0
×

compression ratio, which is helpful to save the required complexity and energy during the inference phase.
Predicting Risk through Artificial Intelligence Based on Machine Learning Algorithms: A Case of Pakistani Nonfinancial Firms
Authors: Khalid Shamsa , Khan Muhammad Anees , Mazliham M.S. , Alam Muhammad Mansoor , Aman Nida , Taj Muhammad Tanvir , Zaka Rija , Jehangir Muhammad
Published: June 2022

AI (artificial intelligence) is a significant technological advancement that has everyone buzzing about its incredible potential. The current research study evaluates the influence of supervised artificial intelligence techniques, i.e., machine learning techniques on the nonfinancial firms of Pakistan and focuses on the practical application of AI techniques for the accurate prediction of corporate risks which in turn will lead to the automation of corporate risk management. So, in this study, we used financial ratios for accurate risk assessment and for the automation of corporate risk management by developing machine learning algorithms using techniques, namely, random forest, decision tree, naïve Bayes, and KNN. A secondary data collection technique will be used. For this purpose, we collected annual data of nonfinancial companies in Pakistan for the period ranging from 2006 to 2020, and the data are analyzed and tested through Python software. Our results prove that AI techniques can accurately predict risk with minimum error values, and among all the techniques used, the random forest technique outperforms as compared to the rest of the techniques.
Font Design in Visual Communication Design of Genetic Algorithm
Authors: Wang Yue , Chung Won-jun
Published: June 2022

The text has played an essential role in the advancement of human civilization. It is used now as a valuable cultural heritage that has been experienced for a thousand years. The passing text must have its irreplaceable advantages and charm. People are extremely sensitive to visual symbols, and it is also the first step in which people can know things. Most of the books in today’s market focus on pictures and colors, which ignores the design of the text. This makes the text boring taste, causing the reader’s visual fatigue, which is not conducive to readers absorbing information through books. Therefore, this paper studies the font design in the design of the visual algorithm based on the genetic algorithm, and the font design is analyzed by the particle swarming algorithm, the decision MIMO-SCMA system of the genetic algorithm. It aims to address the dryness of today’s texts by constructing texts that are instantly recognizable and visually appealing to readers. Through innovative visual concepts, readers can enjoy the process of acquiring knowledge. In this paper, to investigate the effectiveness of the genetic algorithm in font design, the number of experimental research subjects was set to 300, and 280 valid questionnaires were collected to investigate the satisfaction of users with the newly designed fonts. Experiments showed that the visual communication design based on a genetic algorithm has increased by 6.52% for the design satisfaction and the number of fonts that use the system is also increasing.
Optimal Scheduling Algorithm of Wireless Communication Packets Based on Knapsack Theory
Authors: Zou Kun , Shen Yao
Published: June 2022

Due to the instability of the wireless channel, data packet loss occurs during wireless communication information transmission. Wireless communication can effectively improve transmission efficiency, study the optimal transmission method of wireless communication data packets, improve the information transmission efficiency and quality of wireless communication, and realize the optimal transmission of wireless communication data packets in order to ensure the reliable transmission of data. It is crucial for increasing wireless communication quality. Natural scheduling and priority scheduling are the only two types of wireless resource scheduling algorithms available so far. These scheduling algorithms are unable to solve the problem of executing the best scheduling under the same priority constraints. This problem is solved using the knapsack theory data knapsack filling procedure. The optimal scheduling of wireless communication packets is investigated in this study using the knapsack theory. The number of destination nodes grows from two to sixteen over time, two at a time. Set the system parameters to a total of 50 original data packets, a packet loss rate of 0.4 in the optimization process, and a packet loss probability of 0.26 in the feedback connection. The KFA approach employed in this study has the highest optimization rate in the three experiments, which can be seen to reduce data packet loss, according to the simulation test. Hybrid coding of data packet transmission is used in conjunction with the buffer feedback method to increase data transmission security. At the same time, it gives practical data for wireless reactive power coded data packet transfer using the time complexity measurement algorithm.
Ginkgolide C Alleviates Acute Lung Injury Caused by Paraquat Poisoning via Regulating the Nrf2 and NF-κB Signaling Pathways
Authors: Zhang Rui , Zhao Cuirong , Gong Xianwei , Yang Jie , Zhang Guifang , Zhang Wen
Published: June 2022

Paraquat (PQ), a highly toxic herbicide and primary attack for lung, results in severe acute lung injury (ALI) appeared as evident oxidative stress, inflammation, and apoptosis. Increasing evidence elucidates that nuclear factor erythroid-2-related factor 2 (Nrf2) and its associated nuclear factor-κB (NF-κB) exhibit many merits for protection of ALI by coordinating a fine-turned response to oxidative stress, inflammation, and apoptosis. Ginkgolide C (GC) has been reported to be a safe and potent therapeutic agent against ALI. However, whether GC could protect ALI induced by PQ poisoning and the possible underlining mechanisms have remained not to be fully elucidated. A rat model of ALI and a model of acute type II alveolar epithelial cell (RLE-6TN) injury constructed by exposure to PQ were applied to discuss the protective effect of GC. Furthermore, Nrf2 gene silencing RLE-6TN cells were used to discuss the exact mechanism. We confirmed that GC significantly ameliorated the histopathological damages, ultrastructural changes, lung injury score, W/D ratio, and Hyp activity of lung tissue and inhibited polymorphonuclear neutrophil (PMN) infiltration after PQ poisoning. Further results revealed that GC remarkably activated Nrf2-based cytoprotective system and inhibited NF-κB-induced inflammatory injury as well as apoptosis. Taken together, we concluded that GC preserved protection of PQ-induced ALI via the Nrf2-NF-κB dependent signal pathway, which may provide us novel insights into the treatment strategies for PQ poisoning.
Target Nuclear Factor Erythroid 2-Related Factor 2 in Pulmonary Hypertension: Molecular Insight into Application
Authors: Qin Yuhan , Qiao Yong , Wang Dong , Li Linqing , Li Mingkang , Yan Gaoliang , Tang Chengchun
Published: June 2022

Nuclear factor erythroid 2-related factor 2 (Nrf2) is a key transcription factor involved in maintaining redox balance and activates the expression of downstream antioxidant enzymes. Nrf2 has received wide attention considering its crucial role in oxidative and electrophilic stress. Large amounts of studies have demonstrated the protective role of Nrf2 activation in various pulmonary hypertension (pH) models. Additionally, various kinds of natural phytochemicals acting as Nrf2 activators prevent the development of pH and provide a novel and promising therapeutic insight for the treatment of pH. In the current review, we give a brief introduction of Nrf2 and focus on the role and mechanism of Nrf2 in the pathophysiology of pH and then review the relevant research of Nrf2 agonists in pH in both experimental research and clinical trials.
Fundamentals, Thermophysical Properties, and Heat Transfer Characteristics of Nanorefrigerants: A Review
Authors: Feroskhan M. , Venugopal T. , Almakayeel Naif Mana , Khan T. M. Yunus , Alghamdi Saleh , Almuflih Ali Saeed , Gobinath N.
Published: June 2022

Cooling applications that utilize latent heat property of fluids involve large heat transfer and throw more space for energy conservative research studies. Heating ventilation air‐conditioning and refrigeration systems, which have now grown as an integral part in human life, circulate refrigerants and necessitate developments to become energy‐efficient. The heat removal characteristics of the fluids in such large capacity systems can be enhanced using nanoparticles seeded in to them. Being smaller in size, the nanoparticles possess larger effective surface area and believed to help in expediting the conduction rate of heat from the refrigerants in the HVAC‐R systems. Spectrums of nanoparticles are studied for their heat transfer behavior either in pure refrigerants or with refrigerant/oil mixtures. Suspension of the particles in the refrigerants for a prolonged time is found to be critical and must be validated prior to its implementation in the systems. Seeding of nanoparticles exclusively in a phase change fluid inside the refrigeration systems is reported to be of challenging. Additionally, to harvest the benefits of nanoscale particles, literature proposes the usage of surfactants which may lead to complex situation in a vapor compression refrigeration system. In the present work, all the relevant study details specifically on synthesis and characterization, thermophysical properties, and heat transfer characteristics about the nanorefrigerants are presented.
Optimized Deconvolutional Algorithm‐based CT Perfusion Imaging in Diagnosis of Acute Cerebral Infarction
Authors: Chen Xiaoxia , Bai Xiao , Shu Xin , He Xucheng , Zhao Jinjing , Guo Xiaodong , Wang Guisheng
Published: June 2022

To apply deconvolution algorithm in computer tomography (CT) perfusion imaging of acute cerebral infarction (ACI), a convolutional neural network (CNN) algorithm was optimized first. RIU‐Net was applied to segment CT image, and then equipped with SE module to enhance the feature extraction ability. Next, the BM3D algorithm, Dn CNN, and Cascaded CNN were compared for denoising effects. 80 patients with ACI were recruited and grouped for a retrospective analysis. The control group utilized the ordinary method, and the observation group utilized the algorithm proposed. The optimized model was utilized to extract the feature information of the patient’s CT images. The results showed that after the SE module pooling was added to the RIU‐Net network, the utilization rate of the key features was raised. The specificity of patients in observation group was 98.7%, the accuracy was 93.7%, and the detected number was (1.6 ± 0.2). The specificity of patients in the control group was 93.2%, the accuracy was 87.6%, and the detected number was (1.3 ± 0.4). Obviously, the observation group was superior to the control group in all respects (P < 0.05). In conclusion, the optimized model demonstrates superb capabilities in image denoising and image segmentation. It can accurately extract the information to diagnose ACI, which is suggested clinically.
An Efficient CNN for Radiogenomic Classification of Low-Grade Gliomas on MRI in a Small Dataset
Authors: Liu Jun , Deng Feng , Yuan Geng , Yang Changdi , Song Houbing , Luo Liang
Published: June 2022

Gliomas, often known as low-grade gliomas, are malignant brain tumors. Codeletion of chromosomal arms 1p/19q has been connected with a good response to treatment in low-grade gliomas (LGG) in several studies. For treatment planning, the ability to anticipate 1p19q status is crucial. This research’s purpose is to develop a noninvasive approach based on MR images using our efficient CNNs. While public networks like VGGNet, GoogleNet, and other well-known public networks can use transfer learning to identify brain cancer on MRI, the model contains a large number of components that are unrelated to brain tumors. We build a model from the bottom-up, rather than relying on transfer learning. Our network structure flexibly uses a deep convolution stack mixed with dropout and dense operation, which reduces overfitting and enhances performance. We increase the number of samples by augmenting the dataset. The Gaussian noise is introduced during the model training. To address the issue of data imbalance, we use stratified

k

-fold cross-validation during training to find the best model. Our proposed model is compared with models fine-tuned through transfer learning, such as MobileNetV2, InceptionResNetV2, and VGG16. Our model achieves better results than these models on the same small dataset. In the test set, when deciding whether or not an image should be 1p/19q codeleted, the proposed architecture achieved an F1-score of 96.50%, precision of 96.50%, recall of 96.49%, and accuracy of 96.50%. By comparing with the transfer model, we found that transfer learning does not outperform CNN on a small dataset.