bluuebunny/crossref_metadata_2025 huggingface dataset:
Visualization Display System of Gannan Hakka Paper-Cut Works Based on Computer Graphics Algorithm
Authors: Li Xingping
Published: March 2022

Today, computer graphics and graphic image processing techniques have been widely used in daily life and industrial production. Due to the development of computers, computer graphics has brought more convenience to our daily life. In order to give full play to the value of computers, this paper takes the Hakka paper-cut art with local characteristics as the starting point, first of all its development history, artistic characteristics, compositional forms, expression techniques, cultural connotations, Hakka paper-cut patterns, and the symbolic meaning of folk customs, and then we design a visualization system for the paper-cut works of Gannan Hakka based on computer graphics. In addition, the system provides a solution for the integration of Gannan Hakka paper-cut art and Jiangxi native product packaging design and provides a reference for the theory and practice of modern native product packaging design.
Image Sentiment Analysis via Active Sample Refinement and Cluster Correlation Mining
Authors: Zhang Hongbin , Shi Haowei , Hou Jingyi , Xiong Qipeng , Ji Donghong
Published: March 2022

Training an effective image sentiment analysis model using high-quality samples and the implicit cross-modal semantics among heterogeneous features is still challenging. To address this problem, we propose an active sample refinement (ASR) strategy to obtain sufficient high-quality images with definite sentiment semantics. We mine the cluster correlation among the heterogeneous SENet features. Discriminative cross-modal semantics is generated to train an effective but robust image classifier. Ensemble learning is employed to further boost performance. Our method outperforms other competitive baselines, demonstrating its effectiveness and robustness. Meanwhile, the ASR strategy is a useful supplement to the current data augmentation method.
Evaluation of Perceived Competency of Dental Graduates of Saudi Arabia
Authors: Bokhari Syed Akhtar Hussain , Rashed Hazem Tarek , Al-Farhan Mohammed Farhan
Published: March 2022

Background. Dental professional’s competency comprises a comprehensive management of the oral health care of their patients, and an evaluation of competence level is an important step towards assessing the quality of the graduating dentists. This study assessed the level of self-rated competencies of dental graduates from Saudi dental colleges. Methods. A cross-sectional study was conducted online through a structured questionnaire that consisted of three domains of “Knowledge, Skills, and Values.” The study population was dental graduates who have studied the same curriculum and were working in Saudi dental settings. Dental graduates included interns, general practitioners, and specialists/consultants, both male and female, grouped into age groups (20-24, 25-29, and ≥30 years), Saudi/non-Saudi, with qualifications (Bachelor, Master, and Fellowship/Board), working in any region inside Saudi Arabia. Results. Complete responses received from 197 graduates were analyzed. ≥75% of the respondents felt that they were well prepared for 40% and half of the respondents felt that they were not well prepared in 50% competencies in the domain of “Knowledge.” ≥75% of the respondents felt that they were well prepared for 36% and 50% were not well prepared in 36% competencies in the domain of “Skills.” ≥75% of the respondents felt that they were well prepared for 67% competencies in the domain of “Values.” Statistically, mean values were significant (

p
≤
0.001

) for female genders and nonsignificant for qualification and rank categories. The overall mean scores for self-perceived competencies for the domains of “Knowledge,” “Skills,” and “Values” were

8.9
±
3.1

,

12.8
±
3.3

, and

5.5
±
2.1

, respectively. Participants showed the highest level of “Knowledge” in medical problem diagnosis and least level in basic life support. The highest level of “Skills” was noted in the differential diagnosis and lowest in the dental emergency management. Most participants had shown a value for service to humanity. Conclusions. Dental graduates of this study sample perceived themselves well prepared for many aspects of dental practice and have highlighted areas that need improvement in their training at the undergraduate level.
MyoTac: Real-Time Recognition of Tactical Sign Language Based on Lightweight Deep Neural Network
Authors: Li Huiyong , Zhang Yifan , Cao Qian
Published: March 2022

Real-time tactical sign language recognition enables communication in a silent environment and outside the visual range, and human-computer interaction (HCI) can also be realized. Although the existing methods have high accuracy, they cannot be conveniently implemented in a portable system due to the complexity of their models. In this paper, we present MyoTac, a user-independent real-time tactical sign language classification system that makes the network lightweight through knowledge distillation, so as to balance between high accuracy and execution efficiency. We design tactical convolutional neural networks (TCNN) and bidirectional long short-term memory (B-LSTM) to capture the spatial and temporal features of the signals, respectively, and extract the soft target with knowledge distillation to compress the scale of the neural network by nearly four times without affecting the accuracy. We evaluate MyoTac on 30 tactical sign language (TSL) words based on data from 38 volunteers, including 25 volunteers collecting offline data and 13 volunteers conducting online tests. When dealing with new users, MyoTac achieves an average accuracy of 92.67% and the average recognition time is 2.81 ms. The obtained results show that our approach outperforms other algorithms proposed in the literature, reducing the real-time recognition time by 84.4% with higher accuracy.
Thermal Simulation Analysis of Internal Control Circuit Board of Steering Gear Box Based on COMSOL Three-Dimensional Simulation Software
Authors: Huo Wen
Published: March 2022

The steering gear device includes two parts, a steering gear control circuit and a transmission component. The transmission component includes a ball screw and a motor. During the operation of the steering gear, due to the presence of the steering gear ball screw motor and friction, a certain amount of heat will be generated, which will affect the steering gear control circuit in a confined space. At the same time, the steering gear is inevitable in the actual working process, and will experience a high temperature environment, which will increase the temperature of the internal structure of the steering gear, and due to the difference in thermal expansion coefficients between various materials, stress and strain will occur in the structure, which may cause mismatch or even cracks in the system structure, and the steering gear system cannot work normally. It is necessary to analyze the thermal characteristics of the overall steering gear under multiple factors. Based on this, this paper uses COMSOL three-dimensional simulation software to conduct thermal simulation analysis on the shell of the steering gear containing the control circuit board. The temperature distribution and stress-strain response law of the control circuit board in the box, and the influence of different materials and thickness of the box heat insulation layer on the thermal characteristics of the control circuit are discussed, and then a reasonable thickness and material of the heat insulation layer are obtained for the design of the rudder chassis for reference.
Experimental Study on the Strength Characteristics of Expansive Soils Improved by the MICP Method
Authors: Tian Xuwen , Xiao Hongbin , Li Zixiang , Li Zhenyu , Su Huanyu , Ouyang Qianwen
Published: March 2022

Microbially induced calcite precipitation (MICP) has been a promising method to improve geotechnical engineering properties; however, there are few literatures about the application of the MICP method to improve the strength characteristics of expansive soils with low permeability. In this paper, a series of CD triaxial tests were carried out to investigate the effect of the MICP method on the strength characteristics of the expansive soils. The results show that the shear strength of the specimens increased with the increase in the cementation solution and eventually reached a stable value. The MICP method can significantly improve the shear strength index of the expansive soils. The cohesion of the expansive soils was increased from 29.52 kPa to 39.41 kPa, and the internal friction angle was increased from 20.13° to 29.58°. The stress-strain curves of expansive soil samples improved by the MICP method show a hyperbolic relationship, which is characterized by strain hardening. The hyperbolic model was chosen to describe the stress-strain relationship of the expansive soils improved by the MICP method, and the predicted results were in good agreement with the measured results. Moreover, we performed a scanning electron microscope (SEM) experiment and revealed the mechanism of the MICP method to improve the strength characteristics of expansive soils. The conclusions above can provide a theoretical basis to further study the strength characteristics of improved expansive soils by the MICP method.
Population Structure, Regeneration Status, and Threats to Dobera glabra (Forssk.) Poir. in Chifra District, Afar Regional State, Ethiopia
Authors: Gebrehiwot Kflay , Zeynu Ali
Published: March 2022

Dobera glabra is a much branched multipurpose evergreen shrub or tree, which is distributed in Acacia savanna, Acacia open woodland with grassy clearings, and Acacia nubica scrub. This study investigates the population structure, regeneration status, cultural importance, and major threats to D. glabra and provides input for policy and decision-makers to develop conservation strategies that improve the population of the species. A sample plot of 30×20 meters and 10×10 meters were systematically laid along transect lines for the mature D. glabra population and seedling and saplings of the species, respectively. The historical distribution and principal threats to D. glabra were collected through semistructured interviews and focused group discussion with the purposively selected informants. Moreover, direct field observation of threats such as cutting and debarking was recorded. The density of D. glabra was found to be 18.33 stem ha−1. The mean DBH was 43.55 cm (SE±1.58), showing a bell-shaped pattern implying the mid-DBH class is the dominant class. The mean height and crown diameter was 5.5 m (SE±0.19) and 8.60 m (SE±0.31), respectively. Furthermore, the regeneration status of the D. glabra was found to be extremely poor. The major threats to the species were identified as drought, lightning, grazing, cutting, and windfall. The population structure of the species revealed a bell-shaped pattern. The extremely poor natural regeneration status of the species shows the need to implement a reactive conservation approach.
A New Heuristic Computation Offloading Method Based on Cache-Assisted Model
Authors: Wu Junhua , Fan Cang , Li Guangshun , Xu Zhuqing , Jin Zhenyu , Zheng Yuanwang
Published: March 2022

Mobile edge computing (MEC) solves the high latency problem of cloud computing by offloading tasks to edge servers. Due to limited resources, it is necessary to improve the efficiency of computation offloading. However, there is a lot of redundant data transmission between MEC servers and users in the existing methods. Additional data transmission increases the task processing delay. To reduce the total delay, a new cache-assisted computation offloading strategy is proposed. In response to a large number of similar requests from users, a new cache management mechanism is designed. This mechanism can select reusable calculation results more accurately in the cache space through an approximate matching method and improve the cache hit ratio. Then, aiming at the problem of offloading efficiency, the delay optimization problem is transformed into an optimal path problem, a cost function is defined to determine the optimal offloading position, and an improved path planning method is used to plan the optimal offloading path. The simulation results indicate that the proposed scheme can improve the cache hit ratio and reduce the total processing delay of tasks compared with other standard schemes.
A Novel Memetic Algorithm Based on Multiparent Evolution and Adaptive Local Search for Large-Scale Global Optimization
Authors: Zhang Wenfen , Lan Yulin
Published: March 2022

In many fields, including management, computer, and communication, Large-Scale Global Optimization (LSGO) plays a critical role. It has been applied to various applications and domains. At the same time, it is one of the most challenging optimization problems. This paper proposes a novel memetic algorithm (called MPCE & SSALS) based on multiparent evolution and adaptive local search to address the LSGO problems. In MPCE & SSALS, a multiparent crossover operation is used for global exploration, while a step-size adaptive local search is utilized for local exploitation. A new offspring is generated by recombining four parents. In the early stage of the algorithm execution, global search and local search are performed alternately, and the population size gradually decreases to 1. In the later stage, only local searches are performed for the last individual. Experiments were conducted on 15 benchmark functions of the CEC′2013 benchmark suite for LSGO. The results were compared with four state-of-the-art algorithms, demonstrating that the proposed MPCE & SSALS algorithm is more effective.
A Study of Regional Economic Disparities and Coordinated Development Based on Improving Grey Model Combinations
Authors: Yang Bowen
Published: March 2022

The positive promotion of regional economic growth is largely consistent with expectations, while there is also a promotion effect on the economic growth of neighboring regions. In this paper, the individual cognitive term of the particle swarm algorithm is introduced into the bath algorithm, and economic forecasting analysis is carried out based on the improved combination of grey model improvements. The main reason for the negative correlation between government investment and economic growth is that, although OFDI promotes economic growth, industrial upgrading, and optimizes industrial structure, it may also reduce regional export capacity, reduce employment opportunities, and increase agency costs. The estimated coefficients for human capital, labor input, capital stock, and urbanization rate are all positive, indicating that each variable is conducive to promoting regional economic growth.
Use of 3-D Models for Surgical Planning of a Malunion in a Dog
Authors: Muroi Norihiro , Fujii Kiyohisa , Shimada Masakazu , Kanno Nobuo , Harada Yasuji , Hara Yasushi
Published: March 2022

Background. An 8-year-old, 18.9 kg, male, intact Kai Ken with a femoral shaft fracture experienced recurrent implant breakage after two fracture reductions using an internal fixator. Objectives. This case report is aimed at using a three-dimensional (3-D) printer to diagnose residual femoral rotational deviation. Implant failures and malunion occurred after two attempts at synthesis. Thus, a 3-D model was designed for preoperative planning of a third surgery. Methods. To evaluate the alignment in the postoperative state after the second surgery, we removed a broken plate from the affected limb. Subsequently, a computed tomography image produced a bone replica using 3-D printing. The distal fragment was fixed and rotated externally by 42°. In addition to correcting the rotational deformity of the femur, we used an intramedullary pin and two locking plates to stabilize the proximal and distal femoral fracture segments. Results. The bone union was confirmed four months after surgery, and no postoperative complications were observed 11 months after surgery. Conclusion. 3-D printing is a valuable tool that increases the accuracy of presurgical planning.
Application of Big Data Unbalanced Classification Algorithm in Credit Risk Analysis of Insurance Companies
Authors: Wu Xian , Liu Huan
Published: March 2022

The 2008 global financial crisis triggered by subprime mortgage crisis in the United States and the ongoing European debt crisis have urged governments and academics to pay high attention to financial industry risk supervision. The financial industry has actively implemented comprehensive risk management. As an important component of the financial industry, the insurance industry implements comprehensive risk management to control the risks of insurance companies. Propose an integrated learning model based on imbalanced dataset resampling and apply it to UCI dataset (University of California Irvine). First, resampling technology is used to preprocess the unbalanced dataset to obtain a relatively balanced training set. Then, use the classic backpropagation neural network, classic k-nearest neighbor, and classic Naive Bayes three algorithms as the base classifier and use the Bagging strategy to get the ensemble learning model. In order to verify its effectiveness, F‐measure and G‐mean methods are used to measure the performance of the classifier. The subject mainly focuses on the classification of relevance vector machine (RVM) in two types of large‐scale datasets, imbalanced and balanced, and proposes solutions for these two types of data. This explains the effectiveness of the disequilibrium classification algorithm used in the risk analysis of insurance companies.
Longitudinal Alterations of Retinal and Choroidal Structure in Patients Recovered from COVID-19
Authors: Abrishami Mojtaba , Hassanpour Kiana , Zamani Ghodsieh , Hosseini Seyedeh Maryam , Shoeibi Nasser , Emamverdian Zahra , Zamani Amir , Amini Nasibeh , Abrishami Majid
Published: March 2022

Objective. To evaluate the midterm longitudinal changes in chorioretinal structures in patients with coronavirus disease 2019 (COVID-19). Methods. Thirty-four eyes of 17 COVID-19 patients were enrolled. The patients underwent retinal and choroidal imaging upon the recovery (baseline) after 1 and 3 months. Retinal measurements in fovea, parafovea, and perifovea were recorded. To calculate choroidal vascularity index (CVI), luminal and total choroidal areas were measured using Sonada's method. Choroidal thickness was measured at the subfovea 500 microns temporal and nasal to the fovea. Results. Mean CVI was 0.64 ± 0.04 at baseline that significantly increased to 0.67 ± 0.05 (P = 0.012) after 1 month and again significantly decreased to 0.63 ± 0.05 after 3 months (P < 0.001). While the stromal component showed a significant decrease between the baseline and first-month values (1.16 ± 0.29 to 1.01 ± 0.27, P = 0.03), the luminal area mostly changed between months 1 and 3 (2.03 ± 0.28 to 1.91 ± 0.23, P = 0.045). The average of subfoveal choroidal thickness and retinal thickness remained unchanged. Conclusion. CVI is increased in patients with COVID-19 1 month after recovery from COVID-19 and returns to baseline values after 3 months. Regarding the reversible nature of changes, there might be a prominent role in inflammation.
Short-Axis PET Image Quality Improvement by Attention CycleGAN Using Total-Body PET
Authors: Shang Chong , Zhao Guohua , Li Yamei , Yuan Jianmin , Wang Meiyun , Wu Yaping , Lin Yusong
Published: March 2022

The quality of positron emission tomography (PET) imaging is positively correlated with scanner sensitivity, which is closely related to the axial field of view (FOV). Conventional short-axis PET scanners (200–350 mm FOV) reduce the imaging quality during fast scanning (2–3 minutes) due to the limitation of FOV, which reduce the reliability of diagnosis. To overcome hardware limitations and improve the image quality of short-axis PET scanners, we propose a supervised deep learning model, CycleAGAN, which is based on a cycle-consistent adversarial network (CycleGAN). We introduced the attention mechanism into the generator and focus on channel and spatial representative features and supervised learning using pairs of data to maintain the spatial consistency of the generated images with the ground truth. The imaging information of 386 patients from Henan Provincial People’s Hospital was prospectively included as the dataset in this study. The training data come from the total-body PET scanner uEXPLORER. The proposed CycleAGAN is compared with traditional gray-level-based methods and learning-based methods. The results confirm that CycleAGAN achieved the best results on SSIM and NRMSE and achieved the closest distribution to ground truth in expert rating. The proposed method is not only able to improve the image quality of PET scanners with 320 mm FOV but also achieved good results on shorter FOV scanners. Patients and radiologists can benefit from the computer-aided diagnosis (CAD) system integrated with CycleAGAN.
Arterial Tortuosity and Its Correlation with White Matter Hyperintensities in Acute Ischemic Stroke
Authors: Shang Ke , Chen Xiao , Cheng Chang , Luo Xiang , Xu Shabei , Wang Wei , Liu Chenchen
Published: March 2022

Introduction. The association between arterial tortuosity and acute ischemic stroke (AIS) has been reported, but showing inconsistent results. We hypothesized that tortuosity of extra- and intracranial large arteries might be higher in AIS patients. Furthermore, we explored the correlation between artery tortuosity and white matter hyperintensity (WMH) severity in AIS patients. Methods. 166 AIS patients identified as large artery atherosclerosis, and 83 control subjects were enrolled. All subjects received three-dimensional computed tomography angiography (CTA). Arterial tortuosity was evaluated using the tortuosity index. WMHs were evaluated using magnetic resonance imaging in all AIS patients. Results. AIS patients showed significantly increased arterial tortuosity index relative to controls, including left carotid artery (CA) (

p
=
0.001

), right CA (

p
<
0.001

), left common carotid artery (CCA) (

p
<
0.001

), right CCA (

p
<
0.001

), left internal carotid artery (

p
=
0.001

), right internal carotid artery (

p
=
0.01

), left extracranial internal carotid artery (EICA) (

p
<
0.001

), right EICA (

p
=
0.01

), and vertebral artery dominance (VAD) (

p
=
0.001

). The tortuosity of all above arteries was associated with the presence of AIS. AIS patients with moderate or severe WMHs had a higher tortuosity index in left CA (

p
=
0.005

), left CCA (

p
=
0.003

), left EICA (

p
=
0.07

), and VAD (

p
=
0.001

). In addition, the tortuosity of left EICA and VAD was associated with WMH severity in AIS patients. Conclusions. Increased extra- and intracranial large arteries tortuosity is associated with AIS. The tortuosity of left carotid artery system and vertebral artery may be the independent risk factors for WMH severity in AIS patients. Clinical Trial Registration. This trial is registered with NCT03122002 (http://www.clinicaltrials.gov).
Speaker Gender Recognition Based on Deep Neural Networks and ResNet50
Authors: Alnuaim Abeer Ali , Zakariah Mohammed , Shashidhar Chitra , Hatamleh Wesam Atef , Tarazi Hussam , Shukla Prashant Kumar , Ratna Rajnish
Published: March 2022

Several speaker recognition algorithms failed to get the best results because of the wildly varying datasets and feature sets for classification. Gender information helps reduce this effort since categorizing the classes based on gender may help lessen the impact of gender variability on the retrieved features. This study attempted to construct a perfect classification model for language-independent gender identification utilizing the Common Voice dataset (Mozilla). Most previous studies are doing manual extracting characteristics and feeding them into a machine learning model for categorization. Deep neural networks (DNN) were the most effective strategy in our research. Nonetheless, the main goal was to take advantage of the wealth of information included in voice data without requiring significant manual intervention. We trained the deep learning network to choose essential information from speech spectrograms for the classification layer, performing gender detection. The pretrained ResNet 50 fine-tuned gender data successfully achieved an accuracy of 98.57% better than the traditional ML approaches and the previous works reported with the same dataset. Furthermore, the model performs well on additional datasets, demonstrating the approach’s generalization capacity.
Pott’s Puffy Tumor Presenting as Pyogenic Meningitis in an Infant
Authors: Faridi M. M. A. , Pandey Somya , Shamsi Sumaiya
Published: March 2022

Introduction. Pott’s puffy tumor is characterized by the osteomyelitis of the frontal bone with underlying subperiosteal abscess, mostly occurring secondary to recurrent sinusitis or head trauma. Though it is a rare clinical entity in this antibiotic era, its occurrence mostly in the adolescent age group has now shown increased reporting lately in all age groups. Case Description. We describe here a case of a 4½-month-old female baby who presented to our hospital’s Emergency Room with clinical features of pyogenic meningitis following aspiration of a midline frontal swelling. The infant presented with high-grade fever, 3-4 episodes of projectile vomiting, increased irritability, and refusal to breastfeeding than usual. This was accompanied by a history of a gradually increasing midline fluctuant erythematous swelling on her forehead extending to the left eye. Aspiration of the swelling was done a day before by a local general practitioner, following which she developed the above-mentioned features of pyogenic meningitis and was brought to the hospital the next day. Examination revealed a conscious, febrile, irritable child with bulging anterior fontanel and 101.4°F axillary temperature. Vital signs were within normal limits. CSF analysis was suggestive of pyogenic meningitis, and appropriate antibiotics were given. MRI showed frontal bone osteomyelitis with erosion of the bony plate and focal cerebritis. The condition turned out to be Pott’s puffy tumor with pyogenic meningitis after detailed investigations. The infant was treated with appropriate antibiotics and other supportive therapeutic measures and discharged with the advice for further management in collaboration with otorhinolaryngologist.
Application of Machine Learning in the Reliability Evaluation of Pipelines for the External Anticorrosion Coating
Authors: Zhao Zhifeng , Chen Mingyuan , Fan Heng , Zhang Nailu
Published: March 2022

The purpose of this research is to enhance the analysis of the reliability status for external anticorrosive coatings. With the limitation and insufficiency of the static evaluation method, we study and construct an evaluation method of dynamic reliability for the anticorrosive layer, integrating the trend analysis of the Markov chain and the set pair theory. This method is implemented by the machine learning software of PyCharm community edition, based on Python language. The algorithm utilizes the connection degree in the set pair theory to determine the risk levels of the anticorrosive coating systems. According to the characteristics of the dynamic change of the anticorrosive layer with time, we built the mathematical evaluation model by combining it with the nonaftereffect property of the Markov chain. Therefore, we can make a dynamic and useful analysis for the reliability grade of the anticorrosive coating and assess the effectiveness grade of the changed reliability for the anticorrosive coating after some time. This method can effectively evaluate the reliability level of the anticorrosion coating through the example of big data of detection points. Under national standards, we provide the theoretical basis for pipeline maintenance within detection cycle requirements.
Optimal Path of Internet of Things Service in Supply Chain Management Based on Machine Learning Algorithms
Authors: Li Jing , Zhang Ruifeng , Jin Yanfeng , Zhang Haiyan
Published: March 2022

With the development of global economic integration, the scale of supply chain network is becoming larger and larger. The research on the benefits of supply chain network under the Internet of Things technology is a new interdisciplinary research issue. Starting from the complexity and evolution of supply chain network, based on machine learning algorithms, this study analyses the service modes of the Internet of Things for supply chain enterprises, such as RFID technology, EDI technology, and enterprise resource planning ERP. Finally, by analyzing the degree of application of the Internet of Things in different industries, the manufacturing industry is selected for simulation. It is found that the introduction of the Internet of Things is conducive to speeding up the evolution of enterprise supply chain. Thus, the horizontal integration of the optimized path supply chain can effectively improve the overall profits of enterprises and provide theoretical support for the Internet of Things service supply chain.
The Exploration of New Courtyard Architecture Based on the Guidance of Architectural Culture and Technology
Authors: Luo Xi , Huang Jianyun
Published: March 2022

The paper interprets the “shape” and “meaning” of the new courtyard‐style building from the two dimensions of architectural culture and architectural technology, as well as three levels of interpretation, construction, and cooperation. At the same time, it takes the building of “Waterside Cube” in Solar Decathlon China Competition as an example, which puts forward the concept of “Re‐Traditional” and analyzes the generation logic and possibility of development of the new courtyard‐style building which is combined with architectural culture and architectural aesthetics. Meanwhile, it puts forward the application of building technology and coping methods for rapid construction problems and finally summarizes the new ideas for the development of new courtyard‐style buildings.
RUNX3/H3K27me3 Co-Expression Defines a Better Prognosis in Surgically Resected Stage I and Postoperative Chemotherapy-Naive Non-Small-Cell Lung Cancer
Authors: Chen Xiaohui , Deng Yujie , Huang Chuanzhong , Shi Yi , Lu Jianping , Weng Guibin , Zhu Weifeng , Zhu Kunshou , Chen Junqiang , Li Jiancheng
Published: March 2022

The purpose of this study is to investigate the significance of RUNX3/H3K27me3 co-expression in surgically resected non-small-cell lung cancer (NSCLC) patients. Using tissue microarray (TMA), immunohistochemistry, fluorescent double immunostaining, and western blotting, 208 NSCLC and 5 benign pulmonary patients were studied of their expression of runt-related transcription factor 3 (RUNX3), trimethylated histone H3 at lysine 27 (H3K27me3), enhancer of zeste homolog 2 (EZH2), and Ki-67. Apoptotic index in cancerous tissue was evaluated via TdT-mediated dUTP-biotin nick end labeling (TUNEL). The correlation between clinicopathologic parameters and overall survival was determined by Cox regression and Kaplan–Meier survival estimates and log-rank test. GEPIA and KM plotter were used for validation of some survival analyses. As a result, together with other regular prognostic factors, RUNX3/H3K27me3 co-expression was found to be closely correlated with better prognosis in either pTNM-I or POCT-naive NSCLC patients, which might partially result from a higher cancerous apoptotic index. In conclusion, RUNX3/H3K27me3 co-expression defined some specific NSCLC population with better prognosis and longer OS and could probably be used as a biomarker in the prediction of better postoperative outcomes.
Production Task Allocation Decision Based on Cloud Robot Cell-Line
Authors: Du Yi , Wang Yongqiang , Wang Jialin , Zhang Tianpeng
Published: March 2022

Cloud manufacturing is a new service-oriented efficient and low-consumption agile manufacturing mode integrating information, manufacturing, Internet of Things, and other technologies. One of the key decisions of production enterprises is production task allocation based on cloud robot cell-line, which determines the efficiency and flexibility of the production system and affects various production links, such as job-shop logistics, production planning, and production scheduling. This paper explores the production task allocation, from the angle of the optimal combination of cloud manufacturing resources. First, a mathematical model was established, based on the transport cost of different sub-tasks and the tardiness cost of product delivery, and solved by quantum firefly algorithm (QFA). Next, QFA was proved superior to traditional firefly algorithm (FA), improved FA, and the FA optimized by cat swarm optimization (CSO-FA), in terms of time complexity and spatial complexity. The research enriches the theory and methodology of allocating operation-level cloud manufacturing resources based on cloud robot cell-line and provides decision support to manufacturers, which want to implement operation-level allocation of cloud manufacturing resources based on cloud robot cell-line.
Bushen Jianpi Formula Combined with Entecavir for the Treatment of HBeAg-Negative Chronic Hepatitis B: A Multicenter, Randomized, Double-Blind, Placebo-Controlled Trial
Authors: Zhang Jing-Hao , Zhang Xin , Zhou Zhen-Hua , Zhu Xiao-Jun , Zheng Chao , Li Man , Jin Shu-Gen , Mao De-Wen , Xue Jing-Dong , Shi Wei-Bing , Chi Xiao-Ling , Wang Xian-Bo , Li Xiao-Dong , Li Yong , Wang Hui , Li Qin , Zhou Da-Qiao , Wang Cheng-Bao , Shi Chang-He , Li Cheng-Zhong , Wu Jian-Hua , Kong Xiao-Ni , Sun Xue-Hua , Gao Yue-Qiu
Published: March 2022

Background. Bushen Jianpi formula (BSJPF, also known as Lingmao formula) is a traditional Chinese medicine for chronic hepatitis B (CHB). The previous study has suggested that the treatment combination of BSJPF and entecavir (ETV) can achieve a significant loss of hepatitis B e antigen (HBeAg) and a significant decrease in serum level of hepatitis B virus (HBV) DNA in HBeAg-positive CHB patients with mildly elevated alanine aminotransferase. Objective. This study aimed to evaluate the efficacy and safety of BSJPF combined with ETV for treating HBeAg-negative CHB patients. Methods. A total of 640 patients were assigned randomly to the treatment group (receiving BSJPF combined with ETV for 96 weeks) or the control group (receiving a placebo combined with ETV for 96 weeks) in a 1 : 1 ratio. The primary endpoints are the rate of loss of hepatitis B surface antigen (HBsAg). The secondary outcomes included the rate of decrease in the HBsAg concentration to ≥1 lg·IU/mL, the HBV DNA suppression, the decline of the level of covalently closed circular DNA (cccDNA) in the liver, histological improvements, and the rate of ALT normalization. Results. The rate of HBsAg loss in the treatment group was significantly higher than that of the control group (5.5% versus 1.8%,

P
=
0.031

). There were 11.1% of patients in the treatment group who recorded a reduction in HBsAg ≥1 lg·IU/mL, which is better than 5.9% of patients in the control group (

P
=
0.043

). There was no significant difference between the two groups with regard to the rate of HBV DNA clearance, the reduction in intrahepatic cccDNA, and the rate of ALT normalization (

P
>
0.05

). The rate of liver fibrosis improvement in the treatment group was better than that of the control group (35.5% versus 11.8%,

P
=
0.031

), but there was no difference in necroinflammatory improvement (

P
>
0.05

). The adverse events (AEs) were similar between the two groups, except for the abnormal kidney function, with 2.2% in the control group and 0.0% in the treatment group (

P
=
0.028

). Conclusion. The combination of BSJPF and ETV can increase the rate of HBsAg loss and the rate of histological fibrosis improvement without serious adverse events in CHB patients. Trial Registration. This trial is registered with ChiCTR-IOR-16009880 on November 16, 2016—retrospectively registered, http://www.chictr.org.cn/showproj.aspx?proj=16836.
Application of Clustering and Recommendation Algorithm in Sports Competition Pressure Source
Authors: Zhang Lipeng , Guo Lingling
Published: March 2022

With the vigorous development of China’s sports industry, the rules and number of events are increasing, and the competition pressure on the playground is also increasing. The increase of competition pressure will bring many negative effects to athletes. In order to relieve the pressure of athletes in sports competition and eliminate the negative significance of pressure to athletes, this paper mainly introduces the clustering algorithm of sports source and competition. The clustering algorithm uses the similarity of attributes between data objects to calculate the clustering structure of fractional clustering. In this paper, the original data of sports competition pressure are obtained through the questionnaire survey, using clustering and recommendation algorithms to calculate and analyze the original data, the data utilization rate is as high as 98%, and the analysis efficiency is as high as 97%. Dividing athletes into three categories, the magnitude and source of stress are analyzed, respectively, and application methods are recommended according to their respective stress distributions, so as to assist psychologists in the diagnosis, and the corresponding height is 80%; this enables athletes to receive good counseling advice and remain mentally healthy.
Global Existence and Blow-Up for a Weakly Dissipative Modified Two-Component Camassa-Holm System
Authors: Han Xuanxuan , Wang Tingting , Lu Yibin
Published: March 2022

In this paper, we consider a weakly dissipative modified two-component Camassa-Holm system. We demonstrate a simple sufficient condition on initial data to guarantee blow-up of solutions in finite time, and the solutions exist globally in time.
Assessing the Nutritional-Value-Based Therapeutic Potentials and Non-Destructive Approaches for Mulberry Fruit Assessment: An Overview
Authors: Manzoor Muhammad Faisal , Hussain Abid , Tazeddinova Diana , Abylgazinova Aizhan , Xu Bin
Published: March 2022

Among different fruits, mulberry is the most highlighted natural gift in its superior nutritional and bioactive composition, indispensable for continuing a healthy life. It also acts as a hepatoprotective immunostimulator and improves vision, anti-microbial, anti-cancer agent, anti-stress activity, atherosclerosis, neuroprotective functions, and anti-obesity action. The mulberry fruits also help reduce neurological disorders and mental illness. The main reason for that is the therapeutic potentials present in the nutritional components of the mulberry fruit. The available methods for assessing mulberry fruits are mainly chromatographic based, which are destructive and possess many limitations. However, recently some non-invasive techniques, including chlorophyll fluorescence, image processing, and hyperspectral imaging, were employed to detect various mulberry fruit attributes. The present review attempts to collect and explore available information regarding the nutritional and medicinal importance of mulberry fruit. Besides, non-destructive methods established for the fruit are also elaborated. This work helps encourage many more research works to dug out more hidden information about the essential nutrition of mulberry that can be helpful to resolve many mental-illness-related issues.
Dual-Path Residual “Shrinkage” Network for Side-Scan Sonar Image Classification
Authors: Ruan Fengxue , Dang Lanxue , Ge Qiang , Zhang Qian , Qiao Baojun , Zuo Xianyu
Published: March 2022

The underwater environment is complicated and changeable and contains many noises, making it difficult to detect a particular object in the underwater environment. At present, the main seabed detection technology explores the seabed environment with sonar equipment. However, the characteristics of underwater sonar imaging (e.g., low contrast, blurred edges, poor texture, and unsatisfactory quality) have serious negative influences on such image classification. Therefore, in this study, we propose a dual-path deep residual “shrinkage” network (DP-DRSN) module, which is a simple and effective neural network attention module that can classify side-scan sonar images. Specifically, the module can extract background and feature texture information of the input feature mapping through different scales (e.g., global average pooling and global max pooling), whereas scale information passes through a two-layer 1 × 1 convolution to increase nonlinearity. This helps realize cross-channel information interaction and information integration simultaneously before outputting threshold parameters in a sigmoid layer. The parameters are then multiplied by the average value of the input feature mapping to obtain a threshold, which is used to denoise the image features using the soft threshold function. The proposed DP-DRSN study provided higher classification accuracy and efficiency than other models. In this way, the feasibility and effectiveness of DP-DRSN in image classification of side-scan sonar are proven.
An Early Warning Risk and Control Model for Manpower Capital Investment Using Data Warehousing and Computational Intelligence
Authors: Liu Yang , Wang Zongyu
Published: March 2022

Human capital plays an important role in the development of enterprises. Investing in human capital is the main focus of enterprises to improve personnel quality and enhance their core competitiveness. With the development of market economy, the function of human resource market allocation has been improved and the mobility of enterprise human resources has been enhanced leading to the increase in investment risk of enterprise human capital. Enterprise human capital investment risk has a negative impact on enterprises, reduces the income of enterprises’ human capital investment, and affects their growth. Hence, enterprises need to avoid the risk of human capital investment or minimize the negative impact of risk. Using the data warehouse and computational intelligence, this paper constructs the early warning and control model for human capital investment risk and analyzes the existing approaches during the recruitment process and training, investment, and production, among enterprises. Finally, this paper proposes the corresponding control method according to the model inspiration.
Efficient E-Mail Spam Detection Strategy Using Genetic Decision Tree Processing with NLP Features
Authors: Ismail Safaa S. I. , Mansour Romany F. , Abd El-Aziz Rasha M. , Taloba Ahmed I.
Published: March 2022

In this modern era, each and everything is computerized, and everyone has their own smart gadgets to communicate with others around the globe without any range limitations. Most of the communication pathways belong to smart applications, call options in smartphones, and other multiple ways, but e-mail communication is considered the main professional communication pathway, which allows business people as well as commercial and noncommercial organizations to communicate with one another or globally share some important official documents and reports. This global pathway attracts many attackers and intruders to do a scam with such innovations; in particular, the intruders generate false messages with some attractive contents and post them as e-mails to global users. This kind of unnecessary and not needed advertisement or threatening mails is considered as spam mails, which usually contain advertisements, promotions of a concern or institution, and so on. These mails are also considered or called junk mails, which will be reflected as the same category. In general, e-mails are the usual way of message delivery for business oriented as well as any official needs, but in some cases there is a necessity of transferring some voice instructions or messages to the destination via the same e-mail pathway. These kinds of voice-oriented e-mail accessing are called voice mails. The voice mail is generally composed to deliver the speech aspect instructions or information to the receiver to do some particular tasks or convey some important messages to the receiver. A voice-mail-enabled system allows users to communicate with one another based on speech input which the sender can communicate to the receiver via voice conversations, which is used to deliver voice information to the recipient. These kinds of mails are usually generated using personal computers or laptops and exchanged via general e-mail pathway, or separate paid and nonpaid mail gateways are available to deal with certain mail transactions. The above-mentioned e-mail spam is considered in many past researches and attains some solutions, but in case of voice-based e-mail aspect, there will be no options to manage such kind of security parameters. In this paper, a hybrid data processing mechanism is handled with respect to both text-enabled and voice-enabled e-mails, which is called Genetic Decision Tree Processing with Natural Language Processing (GDTPNLP). This proposed approach provides a way of identifying the e-mail spam in both textual e-mails and speech-enabled e-mails. The proposed approach of GDTPNLP provides higher spam detection rate in terms of text extraction speed, performance, cost efficiency, and accuracy. These all will be explained in detail with graphical output views in the Results and Discussion.
The Development and Management Work Innovation of University Students by Artificial Intelligence and Deep Learning in the Wireless Network Era
Authors: Li Qiao , Qi Tiaolan , Liu Lin
Published: March 2022

This study is aimed at promoting the growth and development of college students from two aspects: independent student learning and school management. Firstly, deep neural networks (DNNs) are used to build a personalized learning resource recommendation model for college students based on an online education platform. The model is tested and passed the experiment. A new comprehensive quality evaluation index system for college students is established in school management based on the traditional complete quality evaluation method. The thorough fuzzy evaluation (FCE) method and the backpropagation neural network (BPNN) model are combined to construct a comprehensive quality evaluation model for college students. The students’ comprehensive quality evaluation scores obtained using this model are compared with the actual scores through experiments. The results show that the number of learners is 50 in the constructed personalized learning resource recommendation model for college students. When the number of learning resources is 180,

F
1
‐
score
=
3

is more significant than other programs. The FCE method and the comprehensive quality evaluation model constructed by BPNN scored six students. The maximum error value between the evaluation result and the actual value is only 0.039. This shows that the effect of the constructed personalized learning resource recommendation model and college students’ comprehensive quality evaluation model is excellent and practical. This provides a feasible method for improving students’ independent learning ability and school management efficiency.