bluuebunny/crossref_metadata_2025 huggingface dataset:
Early Detection of Medical Image Analysis by Using Machine Learning Method
Authors: Khasawneh Ahmad M. , Bukhari Amal , Al-Khasawneh Mahmoud Ahmad
Published: February 2022

We develop effective medical image classification techniques, with an emphasis on histopathology and magnetic resonance imaging (MRI). The trainer utilized the curriculum as a starting point for a set of data and a restricted number of samples, and we used it as a starting point for a set of data. As calibrating a machine learning model is difficult, we used alternative methods as unsupervised feature extracts or weight-conditioning factors for identifying pathological histology pictures. As a result, the pretrained models will be trained on 3-channel RGB pictures, while the MRI sample has more slices. To alter the working model using the MRI data, the convolutional neural network (CNN) must be fine-tuned. Pretrained models are placed and then used as feature snippets. However, there is a scarcity of well-done medical photos, making training machine learning models a difficult endeavor to begin with. In any case, data augmentation aids in the generation of sufficient training samples; however, it is unclear if data augmentation aids in the prediction of unknown data samples. As a result, we fine-tuned machine learning models without using any additional data. Furthermore, rather than utilizing a standard machine learning classifier for the MRI data, we created a unique CNN that uses both 3D shear descriptors and deep features as input. This custom network identifies the MRI sample after processing our representation of the characteristics from beginning to end. On the hidden MRI dataset, our bespoke CNN outperforms traditional machine learning. Our CNN model is less prone to overfitting as a result of this. Furthermore, we have given cutting-edge outcomes employing machine learning.
Dynamic Multicommodity Contraflow Problem with Asymmetric Transit Times
Authors: Gupta Shiva Prakash , Pyakurel Urmila , Dhamala Tanka Nath
Published: February 2022

A maximum dynamic multicommodity flow problem concerns with the transportation of several different commodities through the specific source-sink path of an underlying capacity network with the objective of maximizing the sum of commodity flows within a given time horizon. Motivated by the uneven road condition of transportation network topology, we introduce the dynamic multicommodity contraflow problem with asymmetric transit times on arcs that increase the outbound lane capacities by reverting the orientation of lanes towards the demand nodes. Moreover, a pseudo-polynomial time algorithm by using a time-expanded graph and an FPTAS by using aΔ-condensed time-expanded network are presented.
Results Concerning the Analysis of Multi‐Index Whittaker Function
Authors: Khan Nabiullah , Husain Saddam , Usman Talha , Araci Serkan
Published: February 2022

A variety of functions, their extensions, and variants have been extensively investigated, mainly due to their potential applications in diverse research areas. In this paper, we aim to introduce a new extension of Whittaker function in terms of multi‐index confluent hypergeometric function of first kind. We discuss multifarious properties of newly defined multi‐index Whittaker function such as integral representation, integral transform (i.e., Mellin transform and Hankel transform), and derivative formula. The results presented here, being very general, are pointed out to reduce to yield some known or new formulas and identities for relatively functions.
Treatment with Light‐Emitting Diodes of Wavelength 863 nm Delays DMBA/TPA‐Induced Skin Tumor Formation and Decreases Proinflammatory Cytokine Levels in ICR Mice
Authors: Park Hyeong Ju , Cho Yeon Hee , Goo Hyeyoon , Choi Sun-hyang , Kim Eun Young , Hong Namgue , Mo SangJoon , Park Ji On , Lee Byeong-Il , Lee Min Young , Ahn Jin-Chul
Published: February 2022

The popularity of light/energy devices for cosmetic purposes (e.g., skin care) is increasing. However, the effects and underlying mechanisms remain poorly understood. Commencing in the 1960s, various studies have evaluated the beneficial effects of a light source on cells and tissues. The techniques evaluated include low‐level light (laser) therapy and photobiomodulation (PBM). Most studies on PBM used red light sources, but, recently, many studies have employed near‐infrared light sources including those of wavelength 800 nm. Here, we used a light‐emitting diode (LED) array with a wavelength of 863 nm to treat DMBA/TPA‐induced mouse skin tumors; treatment with the array delayed tumor development and reduced the levels of systemic inflammatory cytokines. These results suggest that light therapy could be beneficial. However, the effects were small. Further studies on different skin tumors using an optimized LED setup are required. Combination therapies (conventional methods and an LED array) may be useful.
Research on DCE-MRI Images Based on Deep Transfer Learning in Breast Cancer Adjuvant Curative Effect Prediction
Authors: Ye Guolin , He Suqun , Pan Ruilin , Zhu Lewei , Zhou Dan , Lu RuiLiang
Published: February 2022

Breast cancer is a serious threat to women's physical and mental health. In recent years, its incidence has been on the rise and it has become the top female malignant tumor in China. At present, adjuvant chemotherapy for breast cancer has become the standard mode of breast cancer treatment, but the response results usually need to be completed after the implementation of adjuvant chemotherapy, and the optimization of the treatment plan and the implementation of breast-conserving therapy need to be based on accurate estimation of the pathological response. Therefore, to predict the efficacy of adjuvant chemotherapy for breast cancer patients is to find a predictive method that is conducive to individualized choice of chemotherapy regimens. This article introduces the research of DCE-MRI images based on deep transfer learning in breast cancer adjuvant curative effect prediction. Deep transfer learning algorithms are used to process images, and then, the features of breast cancer after adjuvant chemotherapy are collected through image feature collection. Predictions are made, and the research results show that the accuracy of the prediction reaches 70%.
Generalized Mandelbrot Sets of a Family of Polynomials

P

n

z

=

z

n

+
z
+
c
;

n
≥
2
Authors: Farris Salma M.
Published: February 2022

In this paper, we study the general Mandelbrot set of the family of polynomials

P

n

z

=

z

n

+
z
+
c
;

n
≥
2

, denoted by GM(

P

n

). We construct the general Mandelbrot set for these polynomials by the escaping method. We determine the boundaries, areas, fractals, and symmetry of the previous polynomials. On the other hand, we study some topological properties of

GM

P

n

. We prove that

GM

P

n

is bounded and closed; hence, it is compact. Also, we characterize the general Mandelbrot set as a union of basins of attraction. Finally, we make a comparison between the properties of famous Mandelbrot set

M

z

2

+
c

and our general Mandelbrot sets.
Factors Associated with Poor Treatment Outcome among Hospitalized COVID‐19 Patients in South Central, Ethiopia
Authors: Kaso Abdene Weya , Hareru Habtamu Endashaw , Kaso Taha , Agero Gebi
Published: February 2022

Background. Deaths due to COVID‐19 are common among the elderly, especially among individuals with underlying illnesses. The pandemic of the COVID‐19 impaired the mental, psychological, and physical well‐being of people admitted to hospitals. Furthermore, in underdeveloped countries, scarcity of medical equipment was a challenge to manage cases in public health facilities. Thus, understanding the epidemiology and clinical outcomes of COVID‐19 patients who are receiving treatment is critical for developing effective treatments and assessing service quality. Therefore, this study is aimed at assessing the treatment outcomes and associated factors among patients affected by the COVID‐19 virus. Method. We used an institutional‐based retrospective cross‐sectional analysis of 398 patients discharged in South Central, Ethiopia, between June 1, 2020, and July 5, 2021. Data were extracted using the data abstraction format. Data were entered, coded, and analyzed using the STATA 16 software. Bivariate and multivariate logistic regression analysis was used to assess the factors associated with poor treatment outcomes. A 95% confidence interval with adjusted odds ratio (AOR) and p value less than 0.05 were considered statistically significant. Result. In our study, the proportion of poor treatment outcomes was 61 (15.3%). Chronic pulmonary disease (AOR = 5.62; 95% CI: 2.49–12.70), asthma (AOR = 2.8; 95% CI: 1.17–6.67), chronic kidney disease (AOR = 4.81; 95% CI: 1.27–18.22),diabetic mellitus (AOR = 2.27; 95% CI: 1.02–5.09), HIV positive (AOR = 10.44; 95% CI: 3.0–36.35), worsening conditions (AOR = 3.73, 95% CI: 1.17–11.95), and age 55 and above years (AOR = 4.35, 95% CI: 1.30–14.60) were statistically associated with poor treatment outcomes.Conclusion. We found a significant number of patients had favourable treatment. Moreover, aging, having complicated situations at admission, and chronic illnesses such as COPD, CKD, asthma, diabetic mellitus, and HIV/AIDS participants were significantly associated with poor treatment outcomes. Therefore, critical follow–up and management of patients with underlying diseases and worsening health conditions during admission is required.
Performance Comparison of Most Recently Proposed Evolutionary, Swarm Intelligence, and Physics-Based Metaheuristic Algorithms for Retinal Vessel Segmentation
Authors: Çetinkaya Mehmet Bahadır , Duran Hakan
Published: February 2022

Biomedical image analysis based on metaheuristic algorithms is one of the most important research areas encountered in recent years. Due to the low contrast differences between the diseased areas and the image background in high-contrast biomedical images, effective methods are required to diagnose diseases with high accuracy. To overcome the difficulties encountered in this field, metaheuristic approaches may offer effective solutions due to their advantages such as the ability of converging to the global optimum, higher convergence rate, and having few control parameters. In this work, Jellyfish Search (JS), Marine Predators (MPA), Tunicate Swarm (TSA), Mayfly Optimization (MA), Chimp Optimization (ChOA), Slime Mould Optimization (SMA), Archimedes Optimization (AOA), and Equilibrium Optimizer (EO) algorithms, which are the most recently proposed metaheuristic algorithms in the literature, have been improved as clustering based in order to achieve vessel segmentation with high precision. Also, a detailed performance comparison of these algorithms has been realized for the rate of convergences, error values reached, CPU time, standard deviation, sensitivity, specificity, accuracy, F-score, and Wilcoxon rank sum-test. In order to present the compatibility of the results obtained with the literature, the performances of these novel algorithms have also been compared to that of Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Differential Evolution (DE) algorithms. The simulation results represent that each algorithm produces similar convergence and error performance. Also, it can be emphasized from the statistical analyses that the stability and robustness of each metaheuristic approach are quite adequate in separating the vessel pixels and the background pixels of a retinal image. In general, this paper proves that although having fewer number of control parameters, the JS, MPA, TSA, MA, ChOA, SMA, AOA, and EO algorithms produce similar but a bit better results in terms of image segmentation when compared to PSO, GWO, and DE algorithms.
A Study on the Application of Distributed System Technology-Guided Machine Learning in Malware Detection
Authors: Jin Shi , Guo Zhaofeng , Liu Dongli , Yang Yanhua
Published: February 2022

In recent years, with the development of information technology, the Internet has become an essential tool for human daily life. However, as the popularity and scale of the Internet continue to expand, malware has also emerged as an increasingly widespread trend, and its development has brought many negative impacts to the society. As the number of types of malware is getting enormous, the attacks are constantly updated, and at the same time, the spread is very fast, causing more and more damage to the network, the requirements and standards for malware detection are constantly rising. How to effectively detect malware is a research trend; in order to tackle the new needs and problems arising from the development of malware, this paper proposes to guide machine learning algorithms to implement malware detection in a distributed environment: firstly, each detection node in the distributed network performs anomaly detection on the captured software information and data, then performs feature analysis to discover unknown malware and obtain its samples, updates the new malware features to all feature detection nodes in the whole distributed network, and trains the random forest-based machine learning algorithm for malware classification and detection, thus completing the global response processing capability for malware. By building a distributed system framework, the global capture capability of malware detection is enhanced to robustly respond to the increasing and rapid spread of malware, and machine learning algorithms are integrated into it to achieve effective detection of malware. Extended experiments on the Ember 2017 and Ember 2018 databases show that our proposed approach achieves advanced performance and effectively addresses the problem of malware detection.
Safety Risk Evaluation of Large and Complex Bridges during Construction Based on the Delphi‐Improved FAHP‐Factor Analysis Method
Authors: Ji Teng , Liu Ji-Wei , Li Qing-Fu
Published: February 2022

With the increase in construction scale and difficulty of large and complex bridges in China, it has become increasingly difficult to assess the safety risks of bridges during the construction period. Therefore, how to reasonably assess the safety risk of large, complex bridges during construction has become particularly important. Existing assessment methods are subjective in assigning weights, and it is difficult to select representative important factors to focus on for the prevention and control of numerous risk sources; they do not comprehensively consider the correlation of various risk sources during the construction period. To address the above shortcomings, a safety risk assessment of large and complex bridges during the construction period based on the Delphi‐improved fuzzy analytic hierarchy process (FAHP) factor analysis method is proposed in this paper. First, the Delphi method was used to conduct a general survey of safety risk factors during the bridge construction period, and then the work breakdown structure‐risk substructure (WBS‐RBS) was used to establish the evaluation index system. Second, the improved FAHP was combined with it to calculate the weight of each risk factor. Finally, the factor analysis method was used to determine the correlation degree of each risk factor, and representative factors were selected to express the risk degree of the object to be evaluated to screen out major risk factors in the construction process. Finally, the feasibility and practicality of the method are verified by combining an actual engineering case with AHP (analytic hierarchy process) to perform a comparative study, which provides a reference basis for subsequent bridge construction risk prevention.
Hybrid Model of Machine Learning Refractory Data Prediction Based on IoT Smart Cities
Authors: Li Xuewei , Huang Kai , Xu Lei
Published: February 2022

With the advent of the digital age in recent years, the application of artificial intelligence in urban Internet of Things (IoT) systems has become increasingly important. The concept of smart cities has gradually formed, and smart firefighting under the smart city system has also become important. The method of machine learning is now applied in various fields, but seldom to the data prediction of smart firefighting. Various types of applications including data applications of machine learning algorithms in smart firefighting have yet to be explored. In this article, we propose using machine learning algorithms to predict building fire-resistance data, aiming to provide more theoretical and technical support for IoT smart cities. This article adopts the fire-resistance data of building beam components in a real fire environment, using three integrated machine learning algorithms, Extreme random Tree (ET), AdaBoost, and Gradient Boosting Machine (GBM), and the grey wolf optimization algorithm to optimize. We improve the grey wolf algorithm and combine the grey wolf algorithm with the machine learning model. The algorithm constitutes three machine learning hybrid models: GWO-ET, GWO-AdaBoost, and GWO-GBM. Compared with traditional grid tuning, particle swarm optimization (PSO), and genetic algorithm (GA) optimization, the robustness and accuracy of the three optimization algorithms and the machine learning hybrid algorithm on the data set are compared and analyzed. Performance is measured through various performance comparisons and experimental result comparisons. For various building beam component data sets under real fires, the optimization and comparison show that the mean square error (MSE) of the proposed algorithm is extremely small. The results indicate that the GWO machine learning hybrid model is superior to other models and has a smaller prediction error.
A Novel Traffic Surveillance System Using an Uncalibrated Camera
Authors: Gupta Sandeep , Kartha Ranju S , Ramirez-Asis Edwin , Flores-Albornoz Judith , Vílchez-Vásquez Rosa , Singh Sitesh Kumar
Published: February 2022

The objective of this paper is to present an effective and reliable method for the traffic surveillance using the concepts of digital image processing. The paper proposes a system that can detect, track, and estimate velocity of vehicles using an uncalibrated camera and also detect and recognize their registration number plate. This approach provides a cost-effective alternative for traffic flow monitoring and surveillance. This robust system finds its applications in urban traffic management systems, military installation, and research facility security systems. This approach is a computationally efficient approach for detecting and tracking moving cars on the road utilizing uncalibrated cameras mounted on the road. It is also helpful for military installation because all the security issues have been detected by using this approach.
Clinical Care of Hyperthyroidism Using Wearable Medical Devices in a Medical IoT Scenario
Authors: Wei Lili , Hou Sujuan , Liu Qiuxia
Published: February 2022

This paper presents an in-depth study and analysis of clinical care of patients with hyperthyroidism using wearable medical devices in the context of medical IoT scenarios. According to the use scenario of the gateway and the connectivity of the equipment, the hardware architecture, hardware interfaces, functionality, and performance of the gateway were briefly designed, so as to monitor patients with hyperthyroidism more comprehensively and save labor costs. The gateway can provide access to different devices and adaptation functions to different hardware interfaces and provide hardware support for the subsequent deployment of the proposed new medical communication protocols and related information systems. A medical data convergence information system based on multidevice management and multiprotocol parsing was designed and implemented. The system enables the management and configuration of different medical devices and access to data through the targeted parsing of the underlying medical device communication protocols. The system also provides the automatic adaptation of multiple types of underlying medical device communication protocols and automatic parsing of multiple versions and can provide multiple devices to process fused data streams or device information and data from a single device. The use of event-driven asynchronous communication eliminates the tight dependency on service invocation in the synchronous communication approach. The use of a metadata-based data model structure enables model extensions to accommodate the impact of iterative business requirements on the database structure. Real-time patient physiological data transmission for intraoperative monitoring based on the MQTT protocol and video transmission for intraoperative patient monitoring based on the RTMP protocol were implemented. The development of the intelligent medical monitoring service system was completed, and the system was tested, optimized, and deployed. The functionality and performance of the system were tested, the performance issue of slow query speed was optimized, and the deployment of the project using Docker containers was automated.
A Comparative Analysis of Business Machine Learning in Making Effective Financial Decisions Using Structural Equation Model (SEM)
Authors: Sujith A. V. L. N. , Qureshi Naila Iqbal , Dornadula Venkata Harshavardhan Reddy , Rath Abinash , Prakash Kolla Bhanu , Singh Sitesh Kumar
Published: February 2022

Globally, organisations are focused on deriving more value from the data which has been collected from various sources. The purpose of this research is to examine the key components of machine learning in making efficient financial decisions. The business leaders are now faced with huge volume of data, which needs to be stored, analysed, and retrieved so as to make effective decisions for achieving competitive advantage. Machine learning is considered to be the subset of artificial intelligence which is mainly focused on optimizing the business process with lesser or no human interventions. The ML techniques enable analysing the pattern and recognizing from large data set and provide the necessary information to the management for effective decision making in different areas covering finance, marketing, supply chain, human resources, etc. Machine learning enables extracting the quality patterns and forecasting the data from the data base and fosters growth; the machine learning enables transition from the physical data to electronically stored data, enables enhancing the memory, and supports with financial decision making and other aspects. This study is focused on addressing the application of machine learning in making the effective financial decision making among the companies; the application of ML has emerged as a critical technology which is being applied in the current competitive market, and it has offered more opportunities to the business leaders in leveraging the large volume of data. The study is intended to collect the data from employees, managers, and business leaders in various industries to understand the influence of machine learning in financial decision making .
Effective of Smart Mathematical Model by Machine Learning Classifier on Big Data in Healthcare Fast Response
Authors: Al-Khasawneh Mahmoud Ahmad , Bukhari Amal , Khasawneh Ahmad M.
Published: February 2022

In the past few years, big data related to healthcare has become more important, due to the abundance of data, the increasing cost of healthcare, and the privacy of healthcare. Create, analyze, and process large and complex data that cannot be processed by traditional methods. The proposed method is based on classifying data into several classes using the data weight derived from the features extracted from the big data. Three important criteria were used to evaluate the study as well as to benchmark the current study with previous studies using a standard dataset.
Statistical Analysis of the People Fully Vaccinated against COVID-19 in Two Different Regions
Authors: Ahmadini Abdullah Ali H. , Elgarhy Mohammed , Shawki A. W. , Baaqeel Hanan , Bazighifan Omar
Published: February 2022

Motivation. Currently, the COVID-19 pandemic represents a critical issue all over the world. On May 11, 2020, at 05 : 41 GMT, approximately 0.28 million individuals had perished because of the COVID-19 pandemic, and the figure is continuously growing rapidly. Unfortunately, millions of people have died due to this pandemic. As a result, this issue forced governments and other corresponding organizations to take significant action, such as the lockdown and vaccinations. Furthermore, scientists have developed several vaccinations, and the World Health Organization (WHO) has urged governments and people to get vaccinated to eradicate this pandemic. Consequently, the findings of any scientific research into this phenomenon are highly interesting. Problem Statement. To enhance individual protection, it is now critical to analyze and compare the percentage of people fully vaccinated against COVID-19. It is constantly of interest in the field of big data science and other related disciplines to provide the best analysis and modeling of COVID-19 data. Methodology. Through this paper, we aimed to compare individuals who have been completely vaccinated against COVID-19 in two locations: North American countries and Arabian Peninsula countries. Simple techniques for comparing individuals who have been completely vaccinated against COVID-19 have been applied, which may be used to generate the foundation for conclusions. Most significantly, a modern statistical model was created to present the best assessment of individuals completely vaccinated against COVID-19 data in nations in North America and the Arabian Peninsula. Some of the suggested statistical model features were proposed. Furthermore, the estimate of the model parameters was driven using the maximum likelihood estimation method. Results. The flexibility provided by the proposed statistical model is useful for describing the percentage of the individuals completely vaccinated against COVID-19, which provides a close fit with the COVID-19 data. Implications. The proposed statistical model can be used for statistics and generate new statistical distributions that can be used to compare and predict the process of people’s willingness to vaccinate and take the vaccine to try to eliminate COVID-19.
Deep Learning-Based Emergency Care Process Reengineering of Interventional Data for Patients with Emergency Time-Series Events of Myocardial Infarction
Authors: Gao Na , Xu Yue , Tu Lili , Zhu Siyue , Zhang Shuhong
Published: February 2022

This paper proposes a representation learning framework HE-LSTM model for heterogeneous temporal events, which can automatically adapt to the multiscale sampling frequency of multisource heterogeneous data. The proposed model also demonstrates its superiority over other typical approaches on real data sets. A controlled study is performed according to computerized randomization, with 38 patients in each of the two groups. The study group has a higher resuscitation success rate and patient satisfaction than the conventional group (P < 0.05), and the time from the first consultation to the completion of the first ECG, the time from the completion of the ECG to the activation of the path lab, and the time from the emergency admission to the balloon dilation were significantly shorter in the study group than in the conventional group (P < 0.05). The emergency care process reengineering intervention helps patients with acute myocardial infarction to be treated quickly and effectively, thus improving their resuscitation success rate and satisfaction rate, and is worthy to be caused in the clinic and widely applied.
[Retracted] Evaluation Model of Enterprise Lean Management Effect Based on Data Mining
Authors: Chang Rui
Published: February 2022

This article discusses the task allocation decision of single management ability and multiple management ability in view of the operation stage of the specific project of enterprise lean management service supply chain. First of all, the basic process of the management service integrator’s task allocation to the functional management service provider is given. Based on factors such as business satisfaction and other factors, we construct a management task allocation model based on service income price discounts in the case of fuzzy demand. On this basis, we further dig out the external and internal related factors of supply risk, use enterprise research and data mining methods to verify theoretical hypotheses and identify related factors of supply risk, analyze the impact of the characteristics of cooperation with different types of suppliers in data mining to determine the key factors of supply risk for different types of supply and demand cooperation, and determine the focus of supply risk control for different types of suppliers for data mining. In the process of simulation experiment, the method of maximum satisfaction of fuzzy multiobjective programming is adopted, and the influence of factors such as the preference of decision‐makers and price discounts on the result of task allocation is analyzed in combination with specific examples. Finally, on the basis of fully considering the characteristics of management services, we build a comprehensive evaluation index system for management service providers in terms of service capabilities, service quality, informatization capabilities, and cooperation risks and comprehensively consider the uncertain factors in the decision‐making process. The experimental results show that the proposed interval data mining solution method based on the degree of possibility can realize the ranking of management service providers by combining the subjective wishes of decision‐makers with the objective evaluation of data mining.
[Retracted] Acupuncture in the Treatment of Parkinson’s Disease with Sleep Disorders and Dose Response
Authors: Li Lihong , Jin Xiaoqing , Cong Wenjie , Du Tingting , Zhang Wei
Published: February 2022

Acupuncture can effectively improve the sleep state, and most PD patients have sleep disorders. In this study, we used acupuncture to intervene in the sleep state of PDSD, so as to observe the changes and dose effect of Acutreatment on PDSD. 57 patients with PDSD, during medical treatment, aged 40–70 years were recruited to enroll in this trial. Each participant completed one condition, namely, Acutreatment (n = 30) and sham Acutreatment (placebo, stick flat needle on skin, n = 27). The Acutreatment was applied for 30 min once a day for a 30‐day observation. UPDRSIII scores for motor symptom assessment and sleeping quality were assessed by PDSS‐2, ESS as well as ActiGraph. Scale evaluation was made on the first day of admission and the thirtieth day. There were significant differences on all outcome indicators, except UPDRSIII, on day 30 compared with day 1 (P < 0.01). Compared with sham Acutreatment therapy, Acutreatment therapy has better performance in sleep latency, total sleep time, and sleep efficiency (P < 0.01). ActiGraph indicated that sleep efficiency of sham or Acutreatment in day 6 was significantly lower than that in day 5 (P < 0.05 and P < 0.01) and Acutreatment in day 7 was significantly lower than that in day 6 (P < 0.01). The sleep efficiency of Acutreatment in days 5, 6, and 7 was significantly higher than that in sham Acutreatment (P < 0.01). Moreover, Acutreatment in days 26, 27, and 28 was significantly higher than that in sham Acutreatment (P < 0.01). There was a close correlation between the difference of UPDRSIII and PDSS‐2 (r = 0.5090, P < 0.05), sleep latency (r = 0.7201, P < 0.01), TST (r = −0.6136, P < 0.01), and sleep efficiency (r = −0.6707, P < 0.01). The sleep condition of PDSD patients can be improved by acupuncture, which can effectively relieve sleep quality, can also be shown by ActiGraph, and shows a dose‐response relationship. Future research should explore Acutreatment with a larger sample size and compare the Acutreatment protocol goal formation of the system scheme.
A Two-Level Nonconforming Rotated Quadrilateral Finite Element Method for the Stationary Navier–Stokes Equations
Authors: Tian Weijun , Mei Liquan , He Yinnian
Published: February 2022

In this paper, we propose a two-level nonconforming rotated finite element (TNRFE) method for solving the Navier–Stokes equations. A new nonconforming rotated finite element (NRFE) method was proposed by Douglas added by conforming bubbles to velocity and discontinuous piecewise constant to the pressure on quadrilateral elements possessing favorable stability properties. The TNRFE method involves solving a small Navier–Stokes problem on a coarse mesh with mesh size

H

and a large linearized Navier–Stokes problem on a fine mesh with mesh size

h

by the NRFE method. If we choose

h
=
O

H

2

, the TNRFE method gives the convergence rate of the same order as that of the NRFE method. Compared with the NRFE method, the TNRFE method can save a large amount of CPU time. In this paper, the stability of the approximate solutions and the error estimates are proved. Finally, the numerical experiments are given, and results indicate that the method is practicable and effective.
A New Generalized Fractional-Order Derivative and Bifurcation Analysis of Cholera and Human Immunodeficiency Co-Infection Dynamic Transmission
Authors: Regassa Cheneke Kumama , Purnachandra Rao Koya , Kenassa Edesssa Gereme
Published: February 2022

In this study, the co-infection of HIV and cholera model has been developed and analyzed. The new fractional-order derivative is applied and the behavior of the solution is interpreted. The order of new generalized fractional-order derivative implication is presented. A new method is incorporated to determine the forward bifurcation at a threshold

 

R

0

=
1

. The developed method is used to determine the stability of steady-state points. The full model and the submodels’ disease-free equilibrium are locally asymptotically stable if the corresponding reproduction number is less than one and unstable if the production number is greater than one. The only HIV model exhibits forward bifurcation at the threshold point,

 

R

0

=
1

. The numerical simulations solutions obtained using a new generalized fractional-order derivative shows that the total human population size approaches the disease-free equilibrium if the order of the fractional derivative is higher. Also, the simulated results show that the memory effects toward the invading disease are less whenever the order of the fractional derivative is near 0 but higher whenever the order of the fractional derivative is near 1. Furthermore, V. cholerae concentration in the environment increases whenever the intrinsic growth rate increases. The numerical solutions are carried out using MATLAB software.
Short-Term Traffic Flow Prediction and Signal Timing Optimization Based on Deep Learning
Authors: Qin Hao , Zhang Weishi
Published: February 2022

The rapid development of traffic industry has caused traffic congestion and environmental pollution and other problems. Traffic flow prediction and signal timing optimization under different road environments can improve the current traffic environment. In this paper, based on historical traffic data and BP neural network, according to the characteristics of different deep learning modes, SVM neural network is integrated to achieve accurate prediction of traffic flow, fully analyze the characteristics of traffic flow in different traffic environments, and accurately estimate the current traffic capacity. Meanwhile, on the basis of traffic flow prediction data and fuzzy control method, the signal timing optimization of three-phase intersections is realized to ensure the maximum capacity and the lowest average delay time of intersections. Simulation experiments show that the accuracy of the traffic flow prediction method designed in this paper is about 19.5% higher than that of the traditional mean prediction method, and fusion neural network model is more accurate than single neural network model. The optimized signal timing method can accurately control the traffic process with different phases and has obvious improvement in traffic capacity and average delay time compared with the traditional method.
Construction and Analysis of College Students' Entrepreneurship Guidance Model from the Perspective of Ideological and Political Education under Big Data
Authors: Hu Rong , Hu Jingwen
Published: February 2022

The idea of infusing entrepreneurship into education has incited much enthusiasm in the last few decades. A myriad of effects have been stated to result from this, including economic growth, job creation, and increased societal resilience, as well as personal growth, increased school involvement, and improved equality. However, in addition to the purported favorable consequences, putting this theory into practice has presented major challenges. The study aims to improve the success rate of college students' entrepreneurship and strengthen their entrepreneurship education. First, two aspects of the current state of political and ideological education are examined: ideological education and entrepreneurship education, and ideological education and big data technology. Next, ideological education, entrepreneurship education, and big data are integrated to implement an impact index evaluation model of college students' entrepreneurship education from the perspective of the Internet under big data. Finally, based on the implemented model, seven influencing indicators of entrepreneurship education curriculum, practical education, educational equipment, educational awareness, basic ability, social ability, and data usability are obtained. Through detailed analysis experiments, it is found that three factors have the greatest impact on college students' entrepreneurship education, including basic ability, data usability, and psychological pressure resistance in the face of difficulties. The results show that the cultivation of basic entrepreneurial ability and the development of psychological quality should be enhanced in college students' entrepreneurship education, providing a definite reference base for college students' entrepreneurship education.
Application of Intelligent Medical Equipment Management System Based on Internet of Things Technology
Authors: Ren Wei , Wu Xiaochen
Published: February 2022

The management level of medical equipment is an important index to evaluate the modernization degree of hospitals and it is also the basic element of medical and scientific research work, which plays a key role in the development of medical clinical science. Under the background of the rapid development of modern Internet of Things (IoT) technology, hospitals should be fully aware of the role of Internet of Things technology in hospital medical equipment management. With the increasing demand for refined management of medical equipment in hospitals, the application potential of the Internet of Things technology in medical equipment management has gradually become prominent. The management of medical equipment in a hospital is an important part of the management of the hospital, which is related to the medical level, service quality, and economic benefits of the hospital. In this paper, we have analyzed current problems in the management of medical equipment in hospitals, discussed the application of the Internet of Things technology in the management of medical equipment, and promoted the management of medical equipment to be more intelligent and automated. Experimental results have verified that the proposed scheme could be one of the trustworthy automated systems, preferably technology assisted, for the proper management of the medical equipment particularly in hospitals.
And the Oscar Goes to Peripheral Blood Film for the Detection of Lead Poisoning in a Complicated Toxic Patient: A Case Report with a Review of Laboratory Clues
Authors: Zamani Amir , Kazerooni Ehsan Sarraf , Kasaee S. Saeed , Anbardar Mohammad Hossein , Mohammadzadeh Sahand , Shekarkhar Golsa , Soleimani Neda
Published: February 2022

Background. Peripheral blood smear examination is an invaluable laboratory test, which provides the complete hematologic and/or nonhematologic picture of a case. In addition to verifying the results of automated cell counters, it has the potential to identify some pathologic and morphologic changes that remain hidden using the cell counters alone. Case Presentation. A 40-year-old man with a three-year history of alcohol intake and marijuana abuse presented with severe lower extremities of the bone and abdominal pain. Physical examination showed high blood pressure, high pulse rate, and abdominal tenderness. He underwent extensive laboratory and imaging tests, and cholecystectomy and bone marrow studies were associated with no definite diagnosis. Right after all these invasive, expensive, and time-consuming investigations during a month, finding coarse basophilic stippling in the red blood cells in the peripheral blood smear by an expert led to the final diagnosis. Elevated blood lead level and the presence of ring sideroblasts in the bone marrow study confirmed the diagnosis of lead poisoning, and the patient responded well to chelator therapy in a short period. Conclusion. This case clearly showed the value of peripheral blood smear review and its impact on patient care. In order not to lose the cases, laboratories are recommended to design their own policy for peripheral blood smear review. The peripheral blood smear is the fastest, simplest, and most available screening test, which can prevent many misdiagnoses and malpractices. It provides rich morphological information, among which basophilic stippling is highly suggestive of lead poisoning.
Microstructure, Mechanical, and Nanotribological Properties of Ni, Ni-TiN, and Ni90Cu10-TiN Films Processed by Reactive Magnetron Cosputtering
Authors: Kumar Mukesh , Sharma Ashutosh
Published: February 2022

In this study, nanocrystalline Ni, Ni-TiN, and Ni90Cu10-TiN coatings were processed using reactive magnetron cosputtering of Ni, Cu, and Ti targets under Ar and N2 gas environment. The phase evolution and structure of coatings were analyzed by the X-ray diffraction (XRD) technique. The morphology of the as-prepared nanocomposite films were investigated by high-resolution transmission electron microscopy (HRTEM). The elastic modulus, nanohardness, and scratch resistance of the investigated films were measured using the nanoindentation technique and compared. The results showed that Ni, Ni-TiN and Ni90Cu10-TiN coatings exhibited nanocrystalline structure. The Ni90Cu10-TiN nanocomposite films showed optimum nanohardness and tribological properties due to the additional TiN additives which enhanced the dispersion hardening of the composite matrix significantly.
Metabolic Syndrome and the Risk of Preclinical Heart Failure: Insights after 17 Years of Follow-Up from the STANISLAS Cohort
Authors: Sharma Abhinav , Razaghizad Amir , Ferreira João Pedro , Machu Jean-Loup , Bozec Erwan , Girerd Nicolas , Rossignol Patrick , Zannad Faiez
Published: February 2022

Background: We used data from people initially free of clinical cardiovascular disease to evaluate the association between metabolic syndrome (MS) and incident preclinical heart failure (pHF). Methods and Results: STANISLAS was a familial, single-center, longitudinal prospective cohort study composed of 1,006 families from Nancy, France (median follow-up, 17 years [1993–2016]). Age- and sex-adjusted logistic regression and inverse probability weighting models were used to evaluate the association between MS and pHF, which was defined by diastolic dysfunction, atrial enlargement, ventricular hypertrophy, or elevated natriuretic peptides. Among 944 people who were adults at the first and final visit, those with baseline MS were more likely to be older (63 vs. 61 vs. 59 years of age) and male (73% vs. 55% vs. 45%) compared to people who developed incident MS and people who had no baseline MS, respectively. Furthermore, compared to people without baseline MS, the risk of pHF was numerically larger among people with baseline MS (adjusted odds ratio [aOR] 2.27, 95% CI: 1.07–4.81) and people who developed incident MS (aOR 1.56, 95% CI: 1.00–2.43). Concerning the metabolic determinants of MS, the risk of pHF was most elevated in people with baseline hypertension (aOR 3.19, 95% CI: 1.80–5.63) and elevated waist circumference (aOR 2.59, 95% CI: 1.47–4.57). Conclusion: Overall, HF is an important public health concern given the high risk of mortality when patients with MS or elevated fasting glucose become established with the disease. Early aggressive lifestyle modification and medical intervention among patients free of cardiovascular disease with an obese-hypertensive phenotype may be warranted to prevent HF development.
Joaquim Guerra e o debate às margens da tradução de Confúcio: marcas do discurso polêmico
Authors: Jye Chen Tsung , Menezes Jr Antonio José Bezerra de
Published: February 2022

O presente trabalho tem por objetivo identificar as marcas do discurso polêmico encontradas nas Notas Críticas da tradução dos clássicos chineses feitas pelo jesuíta portugués Joaquim Guerra (1908-1993). No quadro teórico, na linha de Bakhtin e Ducrot, verifica-se comoo dialógico e opolifônico concorrem para o discurso polêmico. Na análise do corpus, verificam-se duas estratégias discursivas baseadas no grau ou intensidade da argumentação e no ethos do enunciador-polemista, aqui designadas refutações brandas e refutações enérgicas. Nas considerações finais, procura-se identificar as vozes e motivações presentes no debate sobre a tradução dos textos confucianos.
Escritoras israelenses dialogam com a História
Authors: Rozenchan Nancy
Published: February 2022

Contrasting to the first period of Modern Hebrew literature, when books by female authors were not privileged in the national cannon, books by women writers published in the 20th century final years became very popular. Part of this success is due tothe historical background adopted by sevéral books of that period. This paper deals with two novels of the period: Shulamit Lapid's book has a woman in the beginning of the 19thcentury colonization period as its heroine while Shifra Horn's book, dealing with the sameapproximately, has several female characters whose names and features remind biblical matriarchs.