bluuebunny/crossref_metadata_2025 huggingface dataset:
Monolithically Integrating Non-Volatile Main Memory over the Last-Level Cache
Authors: Walden Candace , Singh Devesh , Jagasivamani Meenatchi , Li Shang , Kang Luyi , Asnaashari Mehdi , Dubois Sylvain , Jacob Bruce , Yeung Donald
Published: July 2021

Many emerging non-volatile memories are compatible with CMOS logic, potentially enabling their integration into a CPU’s die. This article investigates such monolithically integrated CPU–main memory chips. We exploit non-volatile memories employing 3D crosspoint subarrays, such as resistive RAM (ReRAM), and integrate them over the CPU’s last-level cache (LLC). The regular structure of cache arrays enables co-design of the LLC and ReRAM main memory for area efficiency. We also develop a streamlined LLC/main memory interface that employs a single shared internal interconnect for both the cache and main memory arrays, and uses a unified controller to service both LLC and main memory requests.

We apply our monolithic design ideas to a many-core CPU by integrating 3D ReRAM over each core’s LLC slice. We find that co-design of the LLC and ReRAM saves 27% of the total LLC–main memory area at the expense of slight increases in delay and energy. The streamlined LLC/main memory interface saves an additional 12% in area. Our simulation results show monolithic integration of CPU and main memory improves performance by 5.3× and 1.7× over HBM2 DRAM for several graph and streaming kernels, respectively. It also reduces the memory system’s energy by 6.0× and 1.7×, respectively. Moreover, we show that the area savings of co-design permits the CPU to have 23% more cores and main memory, and that streamlining the LLC/main memory interface incurs a small 4% performance penalty.
Size, Shape, and Material Effects in Ferroelectric Octahedral Nanoparticles
Authors: Zhu Daopei , Yan Haocheng , Tian Siyuan , Wang Zhangli
Published: July 2021

Composite materials composed of multiferroelectric nanoparticles in dielectric matrixes have attracted enormous attention for their potential applications in developing future functional devices. However, the functionalities of ferroelectric nanoparticles depend on shapes, sizes, and materials. In this paper, a time-dependent Landau-Ginzburg method has been used and combined with a method as the coupled-physics finite-element-method-based simulations are used to illustrate the polarization behavior in isolated BaTiO3 or PbTiO3 octahedral nanoparticles embedded in a dielectric medium, like SrTiO3 (ST, high dielectric permittivity) and amorphous silica (a-SiO2, low dielectric permittivity). The equilibrium polarization topology of the octahedral nanoparticle is strongly affected by the choice of inclusion and the size of matrix materials. Also, there are three equilibrium polarization patterns, i.e., monodomain, vortex-like, and multidomain, because of the various sizes and material parameters combination. There is a critical particle size below which ferroelectricity vanishes in our calculations. This size of the PbTiO3 octahedral nanoparticle is 2.5 and 3.6 nm for high- and low-permittivity matrix materials, respectively. However, this size of the BaTiO3 octahedral nanoparticle is 3.6 nm regardless of the matrix materials.
A Vibration Feature Extraction Method Based on Time-Domain Dimensional Parameters and Mahalanobis Distance
Authors: Chen Junjun , Xu Bing , Zhang Xin
Published: July 2021

To accurately describe the characteristics of a signal, the feature parameters in time domain and frequency domain are usually extracted for characterization. However, the total number of feature parameters in time domain and frequency domain exceeds twenty, and all of the feature parameters are used for feature extraction, which will result in a large amount of data processing. For the purpose of using fewer feature parameters to accurately reflect the characteristics of the vibration signal, a simple but effective vibration feature extraction method combining time-domain dimensional parameters (TDDP) and Mahalanobis distance (MD) is proposed, i.e., TDDP-MD. In this method, ten time-domain dimensional parameters are selected to extract fault features, and the distance evaluation technique based on Mahalanobis distance criterion function is also introduced to calculate the feature vector, which can be used to classify different failure types. Finally, the proposed method is applied to fault diagnosis of rolling element bearings, and experimental analysis results show that the proposed method can recognize different failure types accurately and effectively with only ten time-domain dimensional parameters and a small quantity of training samples.
Genus Viburnum: Therapeutic Potentialities and Agro‐Food‐Pharma Applications
Authors: Sharifi-Rad Javad , Quispe Cristina , Vergara Cristian Valdés , Kitic Dusanka , Kostic Milica , Armstrong Lorene , Shinwari Zabta Khan , Khalil Ali Talha , Brdar-Jokanović Milka , Ljevnaić-Mašić Branka , Varoni Elena M. , Iriti Marcello , Leyva-Gómez Gerardo , Herrera-Bravo Jesús , Salazar Luis A. , Cho William C.
Published: July 2021

The genus Viburnum (Adoxaceae, Dipsacales) is of scientific interest due to the chemical components and diverse biological activities found across species of the genus, which includes more than 230 species of evergreen, semievergreen, or deciduous shrubs and small trees. Although frequently used as an ornament, the Viburnum species show biological properties with health‐promoting effects. Fruits, flowers, and barks of certain species are used for pharmaceutical purposes or as cooking ingredients, hence containing biochemical compounds with health‐promoting activity such are carotenoids, polyphenols, and flavonoids. However, its taxonomical determination is difficult, due to its wide distribution and frequent hybridizations; therefore, an objective classification would allow us to understand its biological activity based on its phytochemical components. More than sixty phytochemical compounds have been reported, where vibsanin‐type diterpenes and their derivatives are the most prevalent. Leaves and twigs of V. dilatatum contain the largest number of phytochemicals among the genus. Through preclinical evidence, this study provides insight regarding antioxidant, antibacterial, anti‐inflammatory, cytotoxic, and anticancer activities of genus Viburnum.
Big Data and Deep Learning Model for FMS Score Prediction of Aerobics Athletes
Authors: Xiong Wenying , Huang Dongqin , Xu Wei
Published: July 2021

In recent years, competitive aerobics has developed rapidly in my country, and the corresponding sports injury risks have gradually increased. A number of studies have shown that due to the characteristics of aerobics itself, difficult movement requirements, fast-paced music accompaniment and coherent coordinated movements, athletes will suffer sports injuries if they are not paying attention. Therefore, discovering the causes of athletes’ injuries in time and preventing them in time is crucial for improving athletes’ skill level and prolonging sports life. Through the functional movement screening (FMS) test, understanding young aerobics athletes’ insufficiency in trunk stability, joint flexibility, muscle extension, and core strength can further help athletes reduce the risk of sports injuries. Therefore, this article proposes a novel sports injury risk model based on big data technology and deep learning, which can effectively predict the risk of sports injury and can play a positive role in improving the quality of athletes’ movements and prolonging their sports life.
Modeling the Internal Control Constraint of University Research Funding System
Authors: Wang Jiacan , Hu Liangzhang
Published: July 2021

With the in-depth development of the trend of scientific and technological innovation and the implementation of the “Double First-Class” strategy, universities shouldering the scientific research task are constantly promoting the development of scientific research projects with the support of national policies and the scientific research funds are also significantly increased. However, due to the defects of the existing internal control system of scientific research funds in universities, scientific research corruption cases occur frequently. Therefore, it is urgent to build a strong internal control system of scientific research funds to meet the development requirements of the “Double First-class” strategy. For this reason, this paper uses the method of case study, combines the five elements of the COSO internal control system and the internal control norms of Chinese administrative institutions, selects three typical cases of university research corruption, and finally constructs the internal control constraint model of university research funds. This model puts forward suggestions to improve the internal control system of the scientific research funds in universities from four aspects of environmental constraint, psychological constraint, control constraint, and regulatory constraint. Through the construction of the model, it is expected to provide some reference for the construction of the internal control system of scientific research funds in universities.
Complexity of the Analysis of Financial Cloud Based on Fuzzy Theory in the Wisdom of Sustainable Urban Development
Authors: Chen Yi , Yao Dong , Duan Yinying
Published: July 2021

With the rapid growth of our country’s economy, fuzzy theory has become more and more widely used in the financial field. This research mainly explores the application of financial cloud based on fuzzy theory in the sustainable development of smart cities. This research will apply the related knowledge of fuzzy theory, combine the traditional information system risk assessment thought and cloud computing system risk assessment, and propose a series of risk assessment models for the cloud computing system. First, design a smart city model to analyze the potential security issues of the financial cloud computing system. The security level model of cloud computing establishes an index system for evaluation objects based on the level of the security model and uses expert evaluation methods to build models for all levels of the risk profile through the analysis‐level process and establish a fuzzy relationship model for each evaluation object value. We objectively evaluate the smart city model based on the designed financial cloud platform. Then, specific statistical analysis is performed on the fuzzy relationship model corresponding to the weight value of the evaluation object, the calculation result is finally obtained, and the risk assessment report of the financial cloud computing system is provided. For every 2% increase in trade dependence, the informatization level of smart cities will drop by about 0.03% on average. The findings have long strengthened the overall coordinated development of the financial system, optimized the financial structure, improved development efficiency, promoted close integration of science and technology and finance, and played the role of government leader it was to fulfill. It shows that we need to maximize it and improve the informatization when building smart cities. The level of development is very important for accelerating urban construction.
Study on Promoting Intelligent Manufacturing Path Choice of Manufacturing Enterprises Based on Coevolution Strategy
Authors: Liu Tie , Yang Xu , Guo Yu
Published: July 2021

The basic guarantee of promoting intelligent manufacturing ability of manufacturing enterprises is to enhance the comprehensive strength of the country and realize industrialization. Based on the coevolution strategy, considering the time and space structure, this paper determines that the path of manufacturing enterprises to promote intelligent manufacturing is adaptive, visionary, planned, and emerging. According to the factors influencing the promotion of intelligent manufacturing by manufacturing enterprises, the paper proposes the following steps: adaptive strategy path, vision strategic path, planned strategic path, and emerging strategy path. The paper constructs an evaluation system for manufacturing enterprises to promote the selection of intelligent manufacturing path with 25 evaluation indexes and establishes the three-dimensional model of promoting intelligent manufacturing path selection by using the combination of Choquet fuzzy integral method and SPACE method. Through the empirical analysis of ARR, it is found that the planned strategic path is the most suitable path to promote intelligent manufacturing, and the scientificity and objectivity of the three-dimensional path selection model are shown. It is of great theoretical innovation and practical significance to develop a new perspective for manufacturing enterprises to promote the research and selection of intelligent manufacturing path.
The Correlation of Texture and the Formation of the Adiabatic Shear Band in 7XXX Aluminum Alloy during Dynamic Loading
Authors: Wu Xiaodong
Published: July 2021

The mechanical behavior of the extruded 7003-T6 aluminum profiles used as automotive buffer beams is investigated. The correlation of the texture and the formation of the adiabatic shear band is analyzed. Copper texture, R texture, and S texture are the main reasons for the anisotropy of mechanical behavior of the profile, resulting in that the stress of the profile along the extrusion direction is higher than that perpendicular to the extrusion direction. Through finite element modeling (FEM), it can be found that the adiabatic shear band is developed in the sample if the dynamic loading direction is parallel to the extrusion direction, but it does not appear if the loading direction is perpendicular to the extrusion direction. When the dynamic loading direction is parallel to the extrusion direction, higher stress results in a lower energy barrier for shear localization. Therefore, the formation of the adiabatic shear band is susceptible along but is not sensitive perpendicular to the extrusion direction. This study provides technical support for the service of 7003 aluminum alloy in automobiles, which has important academic and engineering application value.
Effect of Microwave Pretreatment on Grindability of Lead-Zinc Ore
Authors: Yu Qing , Ding Dexin , Chen Wenguang , Hu Nan , Wu Lingling , Zhang Qiucai , Liu Yulong , Zhang Zhijun , Li Feng , Xue Xilong , Li Zhaopeng , He Guicheng
Published: July 2021

The influence of microwave pretreatment on grindability of lead-zinc ore was studied through comparison analysis on the changes of particle size distribution, percentage of below 0.074 mm, energy consumption, and other indexes of grinding products before and after microwave pretreatment in the ball milling process. The results showed that the grindability of lead-zinc ore was improved obviously by microwave pretreatment. The particle size distribution curve of the grinding products was obviously higher than that of the samples without microwave irradiation. The yield of size fraction below 0.074 mm was also improved in a certain degree. Pulsed microwave irradiation was more effective than continuous microwave irradiation when other microwave parameters were consistent. The comprehensive energy consumption of lead-zinc ore pretreated by different microwave parameters was lower than that without microwave irradiation under the same grinding fineness. The total energy consumption was down by 30.1% when irradiated for 15 s at 7 kW power, and it was lower than that without microwave irradiated. The results showed that pulsed microwave pretreatment was more effective in reducing the comprehensive energy consumption of grinding process for lead-zinc ore. And water quenching after microwave irradiation can improve the grindability and reduce the energy consumption of grinding for lead-zinc ore.
An Implicit Memory-Based Method for Supervised Pattern Recognition
Authors: Ma Yu , Wang Shafei , Yang Junan , Bao Yanfei , Yang Jian
Published: July 2021

How the human brain does recognition is still an open question. No physical or biological experiment can fully reveal this process. Psychological evidence is more about describing phenomena and laws than explaining the physiological processes behind them. The need for interpretability is well recognized. This paper proposes a new method for supervised pattern recognition based on the working pattern of implicit memory. The artificial neural network (ANN) is trained to simulate implicit memory. When an input vector is not in the training set, the ANN can treat the input as a “do not care” term. The ANN may output any value when the input is a “do not care” term since the training process needs to use as few neurons as possible. The trained ANN can be expressed as a function to design a pattern recognition algorithm. Using the Mixed National Institute of Standards and Technology database, the experiments show the efficiency of the pattern recognition method.
Pulsed Electromagnetic Field Affects the Development of Postmenopausal Osteoporotic Women with Vertebral Fractures
Authors: Liu Wei , Jin Xiao , Guan Zhiqiang , Zhou Qiyun
Published: July 2021

Background. Postoperative pain, dysfunction, and significant bone loss may occur after vertebral fractures, which will lead to the occurrence of refractures and shorten the survival time, so postoperative rehabilitation is very important. Pulsed electromagnetic field therapy is noninvasive, pain‐relieving, and beneficial to reduce bone loss and is an important treatment for patients to recover after surgery. Therefore, this study analyzed the effect of postmenopausal women’s vertebral fracture rehabilitation after pulsed electromagnetic field treatment. Method. This study uses a randomized controlled study, respectively, in the pulsed electromagnetic field treatment group (40 cases) and the control group (42 cases), respectively. We studied the results of health‐related quality of life scores (HRQOL), back pain, body function, hip bone density, bone microstructure of tibia, and radius after 1 month and 3 months after surgery. Results. Compared with the control group, the pulsed electromagnetic field treatment group (PEMF) can improve significantly the psychological score, 6‐minute walk test, and Chair Sit‐and‐Reach one month after the operation. And at 3 months after surgery, the pulsed electromagnetic field treatment group can improve significantly in health‐related quality of life scores (HRQOL), back pain, and body function. Regarding the effect of changes in bone mass, compared with the control group, pulsed electromagnetic field treatment had no significant effect on changes in hip bone density. As a result of changes in bone microstructure, pulsed electromagnetic field treatment can significantly improve the bone microstructure of the radius and tibia three months after vertebral fractures. Conclusion. Pulsed electromagnetic field therapy has positive significance for improving pain, body functional changes, and bone loss after vertebral fracture surgery.
Analysis of Environmental Costs’ Effect in Green Mining Strategy Using a System Dynamics Approach: A Case Study
Authors: Jafarpour Amir , Khatami Siamak
Published: July 2021

Nowadays, one of the most significant problems in mining activities is the significance of analyzing environmental issues along with mining, concentration, and mineral processing operations to achieve the goals of sustainable development. Nevertheless, mine owners refuse to include environmental costs (EC) and consider them unprofitable. Due to the ever-increasing importance of environmental and social topics in recent years, there is a vital need for assessing the EC and its impact on total mining costs and implementing green strategies by the mining managers and engineers. The current study tries to model the mining cost structure by considering the causal relationships between different factors affecting open-pit mining costs to highlight the EC’s role. Furthermore, this research evaluates the effectiveness of implementing each possible mining green strategy in a large-scale copper mine using the System Dynamics (SD) approach. In this regard, seven scenarios and a combination of different environmental strategies, including mine reclamation, an environmental strategy for a condensation and processing plant, and environmental mining operations, have been considered for the SD-based economic analysis. The simultaneous use of the green mining strategies for the concentration and processing plant (Scenario 4) shows a high impact on cost reduction in the mining operation.
Conditional Symmetry of a Memristive System with Amplitude and Frequency Modulation Control
Authors: Zang Hongyan , Huang Lili , Lei Tengfei , Wang Yanling
Published: July 2021

In this study, we studied the effects of offset boosting on the memristive chaotic system. A system with symmetry and conditional symmetry was constructed, by adding the absolute value function to an offset boosting system. It is proved that the symmetric system or a conditionally symmetric system can be constructed with similar or the same dynamic characteristics by using certain correction and offset boosting in an asymmetric system. In addition to multiple stability, the memristive system can also realize the amplitude and frequency control by introducing a parameter. The simulation circuit verifies the amplitude modulation characteristics of the system.
[Retracted] Deep Unsupervised Hashing for Large‐Scale Cross‐Modal Retrieval Using Knowledge Distillation Model
Authors: Li Mingyong , Li Qiqi , Tang Lirong , Peng Shuang , Ma Yan , Yang Degang
Published: July 2021

Cross‐modal hashing encodes heterogeneous multimedia data into compact binary code to achieve fast and flexible retrieval across different modalities. Due to its low storage cost and high retrieval efficiency, it has received widespread attention. Supervised deep hashing significantly improves search performance and usually yields more accurate results, but requires a lot of manual annotation of the data. In contrast, unsupervised deep hashing is difficult to achieve satisfactory performance due to the lack of reliable supervisory information. To solve this problem, inspired by knowledge distillation, we propose a novel unsupervised knowledge distillation cross‐modal hashing method based on semantic alignment (SAKDH), which can reconstruct the similarity matrix using the hidden correlation information of the pretrained unsupervised teacher model, and the reconstructed similarity matrix can be used to guide the supervised student model. Specifically, firstly, the teacher model adopted an unsupervised semantic alignment hashing method, which can construct a modal fusion similarity matrix. Secondly, under the supervision of teacher model distillation information, the student model can generate more discriminative hash codes. Experimental results on two extensive benchmark datasets (MIRFLICKR‐25K and NUS‐WIDE) show that compared to several representative unsupervised cross‐modal hashing methods, the mean average precision (MAP) of our proposed method has achieved a significant improvement. It fully reflects its effectiveness in large‐scale cross‐modal data retrieval.
Automatic Breast Tumor Diagnosis in MRI Based on a Hybrid CNN and Feature‐Based Method Using Improved Deer Hunting Optimization Algorithm
Authors: Ha Weitao , Vahedi Zahra
Published: July 2021

Breast cancer is an unusual mass of the breast texture. It begins with an abnormal change in cell structure. This disease may increase uncontrollably and affects neighboring textures. Early diagnosis of this cancer (abnormal cell changes) can help definitively treat it. Also, prevention of this cancer can help to decrease the high cost of medical caring for breast cancer patients. In recent years, the computer‐aided technique is an important active field for automatic cancer detection. In this study, an automatic breast tumor diagnosis system is introduced. An improved Deer Hunting Optimization Algorithm (DHOA) is used as the optimization algorithm. The presented method utilized a hybrid feature‐based technique and a new optimized convolutional neural network (CNN). Simulations are applied to the DCE‐MRI dataset based on some performance indexes. The novel contribution of this paper is to apply the preprocessing stage to simplifying the classification. Besides, we used a new metaheuristic algorithm. Also, the feature extraction by Haralick texture and local binary pattern (LBP) is recommended. Due to the obtained results, the accuracy of this method is 98.89%, which represents the high potential and efficiency of this method.
The Role of Computer Remote Monitoring Technology for Nursing Care in Elderly Breast Cancer Complications
Authors: Wang Ling , Yan Huang , Yan Jing , Qian Liyuan
Published: July 2021

Geriatric patients undergoing mastectomy have a weakened organism and slow recovery of gastrointestinal function after surgery, which may lead to various complications, affect the absorption of intestinal nutrients, and prolong the healing rate of wounds. Therefore, it is necessary to find an effective nursing program to promote the recovery of gastrointestinal function and prevent postoperative complications in elderly patients undergoing mastectomy. With the continuous development and advancement of computer and communication technologies, telecare is gaining more and more attention and has become an important part of medical information technology construction. Falls endanger the elderly and other special populations, especially after a sudden but unassisted fall, which may be life-threatening. Timely fall detection and rescue can win valuable time for treatment and rescue, which is very important to protect users’ health and improve medical monitoring. In order to provide better medical care to the elderly population and reduce the harm caused by falls, this paper will focus on the fall problem of the elderly in telecare. In order to facilitate the detection of falls of the elderly, we design an Android sensor-based data acquisition scheme, using the built-in acceleration sensor in the Android system to collect the human acceleration information, and through the JMS middleware technology, the collected data are transmitted to MATLAB for analysis and processing in real time. This paper preprocesses and synthesizes the collected human body data and visualizes the acceleration changes of various typical daily activities of the human body and breast cancer, then extracts the relevant data features according to the synthesized SVM curve, constructs a pattern recognition algorithm using the extracted features, and verifies the effectiveness of the pattern recognition algorithm through experiments.
Cardiac Manifestations in COVID-19 Patients: A Focus on the Pediatric Population
Authors: Abi Nassif Tania , Fakhri Ghina , Younis Nour K. , Zareef Rana , Al Amin Farah , Bitar Fadi , Arabi Mariam
Published: July 2021

Background. SARS-CoV-2 is a new strain of the coronavirus family that emerged by the end of 2019 and led to the unpreceded COVID-19 pandemic. The virus affects multiple organs simultaneously and leads to a high rate of morbidity and mortality in all age groups. The cardiovascular system is one of the major affected organ systems. Various mechanisms including direct myocardial injury contribute to the cardiac manifestations of COVID-19 patients. Methods. We performed a comprehensive and updated search on the cardiac manifestations of COVID-19. Our search included laboratory and imaging evaluations. In addition, we added a unique section on the effect of SARS-CoV-2 on the cardiovascular system in the pediatric population. Results. COVID-19 might have an effect on the cardiovascular system at various levels leading to myocardial ischemia, arrhythmia, heart failure, myocarditis, and multisystem inflammatory syndrome in children. The incidence of cardiovascular complications varies among patients. This paper also provides a comprehensive summary of all the reported pediatric cases with cardiac manifestations. Conclusion. Multidisciplinary teams are crucial for adequate management of patients with COVID-19 regardless of age. Timely diagnosis is critical in reducing mortality.
Research on Damping Characteristics of Interconnected Hydropneumatic Suspension considering the Effects of Hose and Check Valve
Authors: Zhang Hongmin , Fang Xin
Published: July 2021

The interconnected hydropneumatic suspension (ICHPS) has not only the nonlinear stiffness and damping of the independent hydropneumatic suspension (IDHPS) but also antiroll and antipitch functions. The existing analysis of hydropneumatic suspension damping mainly focuses on the orifice and check valve in the suspension cylinder. In this study, the calculation formula of the damping force of ICHPS is established, and the numerical simulation results show that the damping characteristics of the hydraulic hose cannot be ignored. The influence of check valve and hose on the damping characteristics is analyzed. Through the equivalent energy method, the equivalent compression damping ratio and the equivalent recovery damping ratio of the ICHPS are established. It is pointed out that when designing the damping characteristics of the ICHPS, it is necessary to select the orifices, check valves, and hose damping reasonably to make the damping characteristics get the best match.
Exosomal miR‐218‐5p/miR‐363‐3p from Endothelial Progenitor Cells Ameliorate Myocardial Infarction by Targeting the p53/JMY Signaling Pathway
Authors: Ke Xiao , Yang Rongfeng , Wu Fang , Wang Xing , Liang Jiawen , Hu Xun , Hu Chengheng
Published: July 2021

Accumulating evidence has shown that endothelial progenitor cell‐derived exosomes (EPC‐Exos) can ameliorate myocardial fibrosis. The purpose of the present study was to investigate the effects of EPC‐Exos‐derived microRNAs (miRNAs) on myocardial infarction (MI). A miRNA‐Seq dataset of miRNAs differentially expressed between EPCs and exosomes was collected. Quantitative real‐time polymerase chain reaction (qRT‐PCR) was used to validate the miRNA expression indicated by miRNA‐Seq. Immunofluorescence, cell proliferation, and angiogenesis assays were employed to investigate the effects of miRNAs on cardiac fibroblasts (CFs) in vitro. Interactions between miRNAs and their respective targets were examined via immunoblotting, qRT‐PCR, and luciferase reporter assays. An MI rat model was constructed, and various staining and immunohistochemical assays were performed to explore the mechanisms underlying the miRNA‐mediated effects on MI. miR‐363‐3p and miR‐218‐5p were enriched in EPC‐Exos, and miR‐218‐5p and miR‐363‐3p mimic or inhibitor enhanced or suppressed CF proliferation and angiogenesis, respectively. miR‐218‐5p and miR‐363‐3p regulated p53 and junction‐mediating and regulatory protein (JMY) by binding to the promoter region of p53 and the 3 ′ untranslated region of JMY. Additionally, treatment of CFs with Exo‐miR‐218‐5p or Exo‐miR‐363‐3p upregulated p53 and downregulated JMY expression, promoted mesenchymal‐endothelial transition, and inhibited myocardial fibrosis. Administration of exosomes containing miR‐218‐5p mimic or miR‐363‐3p mimic ameliorated left coronary artery ligation‐induced MI and restored myocardial tissue integrity in the MI model rats. In summary, these results show that the protective ability of EPC‐Exos against MI was mediated by the shuttled miR‐218‐5p or miR‐363‐3p via targeting of the p53/JMY signaling pathway.
A Tripartite Knowledge Translation Program: Innovative Patient-Centered Approach to Clinical Research Participation for Individuals with Multiple Sclerosis
Authors: Saini Aman , Cochran Colleen , Zucker-Levin Audrey , Donkers Sarah J. , Kumar Pawan , Knox Katherine B. , MacPherson Jessica , Salapa Hannah , Levin Michael C.
Published: July 2021

Background. Knowledge translation (KT) models that represent an individual’s perspective are a sign of effective KT. Some common challenges in KT include participant engagement, organization of the team, and time demands of the participants. We implemented a unique tripartite KT program to (1) share current research, (2) inform persons living with multiple sclerosis (pwMS) about the clinical research process, and (3) invite pwMS to immediately participate in clinical research. The primary aim was to determine participants’ perspectives on the value and acceptability of an experiential research program offered at a patient and family educational conference. Methods. A team of researchers identified factors that would impact the logistics of hosting an experiential research program at a conference and designed a unique tripartite KT program. The local multiple sclerosis (MS) society was engaged to select an appropriate location and invite stakeholders to the conference. A survey to determine participants’ perspectives on the value and acceptability of the experiential research program was developed and analyzed. Results. 65 pwMS attended the conference, and 44 (67.7%) participated in the on-site experiential research program. 72.7% of the participants completed the survey, of which 93.8% stated that they strongly agree or agree with the following statements: “Did you feel like participating in research today was a valuable experience to you?” and “Did you feel like you were contributing to MS research?” 100% of the participants agreed or strongly agreed when asked “would you like to see more research activities taking place at these kinds of events?” Conclusions. This paper describes the logistics and challenges of conducting an experiential KT program, which proved to be rewarding for pwMS. The majority of pwMS attending the conference agreed to participate in the on-site experiential research program and an overwhelming majority of participants felt the experience was valuable.
Research on Road Adhesion Condition Identification Based on an Improved ALexNet Model
Authors: Wang QiMing , Xu JinMing , Sun Tao , Lv ZhiChao , Zong GaoQiang
Published: July 2021

Automotive intelligence has become a revolutionary trend in automotive technology. Complex road driving conditions directly affect driving safety and comfort. Therefore, by improving the recognition accuracy of road type or road adhesion coefficient, the ability of vehicles to perceive the surrounding environment will be enhanced. This will further contribute to vehicle intelligence. In this paper, considering that the process of manually extracting image features is complicated and that the extraction method is random for everyone, road surface condition identification method based on an improved ALexNet model, namely, the road surface recognition model (RSRM), is proposed. First, the ALexNet network model is pretrained on the ImageNet dataset offline. Second, the weights of the shallow network structure after training, including the convolutional layer, are saved and migrated to the proposed model. In addition, the fully connected layer fixed to the shallow network is replaced by 2 to 3, which improves the training accuracy and shortens the training time. Finally, the traditional machine learning and improved ALexNet model are compared, focusing on adaptability, prediction output, and error performance, among others. The results show that the accuracy of the proposed model is better than that of the traditional machine learning method by 10% and the ALexNet model by 3%, and it is 0.3 h faster than ALexNet in training speed. It is verified that RSRM effectively improves the network training speed and accuracy of road image recognition.
Prevalence and Antimicrobial Susceptibility Profile of Staphylococcus aureus in Milk and Traditionally Processed Dairy Products in Addis Ababa, Ethiopia
Authors: Lemma Fikirte , Alemayehu Haile , Stringer Andrew , Eguale Tadesse
Published: July 2021

Staphylococcus aureus is a contagious pathogen that can cause various diseases in both humans and animals. Antimicrobial‐resistant S. aureus is becoming an extremely important global health problem. A cross‐sectional study was conducted from December 2019 to May 2020 to assess the occurrence of S. aureus and its antimicrobial susceptibility profiles in milk and traditionally processed dairy products in selected subcities of Addis Ababa. A total of 255 dairy product samples (175 raw milk and 80 traditionally processed dairy products) were collected from farms and retail markets. Samples were cultured for S. aureus according to standard microbiology techniques, and the Kirby–Bauer disk diffusion method was used to assess antimicrobial susceptibility of isolates to a panel of 12 antimicrobials. Susceptibility to methicillin was determined based on the sensitivity of isolates to cefoxitin, and resistant isolates were investigated for the presence of mecA and mecC genes using PCR. Staphylococcus aureus was isolated from 43 (24.6%) of milk, 7 (17.5%) of yogurt, and 2 (5%) of cottage cheese. A significantly higher rate of contamination with S. aureus was recorded among milk samples compared to yogurt and cottage cheese (p = 0.019). Out of 52 S. aureus isolates investigated for susceptibility to 12 antimicrobials, 49 (94.2%) of the isolates were resistant to ampicillin and 42 (80.8%) to amoxicillin+clavulanic acid. Twenty (38.5%) of the isolates were methicillin‐resistant S. aureus (MRSA) based on susceptibility to cefoxitin. However, only one of these isolates (5%) was positive for mecA gene, and none of them were positive for the mecC gene. There was no significant difference (p > 0.05) in the rate of occurrence of MRSA among isolates from different sources. In conclusion, this study demonstrated a significant level of contamination of milk and dairy products with S. aureus and most isolates were multidrug resistant. The occurrence of MRSA in raw milk and dairy products signifies a serious public health threat as the practice of consuming raw dairy products in the study area is widespread. The lack of agreement between phenotypic and genotypic detection of MRSA suggests the need for further study to identify the genetic basis for the observed resistance phenotype.
Low-Grade Endometrial Stromal Sarcoma in a 22-Year-Old Maiden Female: A Rare Case Report from Syria
Authors: Nseir Lana , Mansour Georget , Makhoul Junior , Skaf Luna , Dahhan Mohammad Ziad , AlShehabi Zuheir
Published: July 2021

Endometrial stromal sarcoma (ESS) is a rare and challenging type of endometrial tumor, constituting only about 0.2% of all uterine malignancies and occurring in women 42–58 years of age. ESS is usually misdiagnosed as leiomyoma. They both have nonspecific symptoms, which makes the diagnosis of ESS difficult to achieve. As the ESS is infrequently encountered, optimal management is quite debatable. Here, we report a rare case of a 22-year-old Syrian female who presented with abnormal uterine bleeding; the preoperative diagnosis suggested leiomyoma while histopathological and immunohistochemical studies confirmed the diagnosis of LG-ESS stage IIA. Therefore, the treatment plan was shifted from a conservative myomectomy to a total hysterectomy with both adnexa. The aim of this case report is to draw attention to this uncommon tumor at young age of patients as well as to have awareness of the necessity to suspect this diagnosis especially with the presentation of rapid enlargement of uterine leiomyoma.
Solution of the Systems of Delay Integral Equations in Heterogeneous Data Communication through Haar Wavelet Collocation Approach
Authors: Wu Hualing , Amin Rohul , Khan Asmatullah , Nazir Shah , Ahmad Sultan
Published: July 2021

In this work, the Haar collocation scheme is used for the solution of the class of system of delay integral equations for heterogeneous data communication. The Haar functions are considered for the approximation of unknown function. By substituting collocation points and applying the Haar collocation technique to system of delay integral equations, we have obtained a linear system of equations. For the solution of this system, an algorithm is developed in MATLAB software. The method of Gauss elimination is utilized for the solution of this system. Finally, by using these coefficients, the solution at collocation points is obtained. The convergence of Haar technique is checked on some test problems.
Image Enhancement Algorithm Based on Depth Difference and Illumination Adjustment
Authors: Li Dan , Bao Jinan , Yuan Sizhen , Wang Hongdong , Wang Likai , Liu Weiwei
Published: July 2021

In order to improve the clarity and color fidelity of traffic images under the complex environment of haze and uneven illumination and promote road traffic safety monitoring, a traffic image enhancement model based on illumination adjustment and depth of field difference is proposed. The algorithm is based on Retinex theory, uses dark channel principle to obtain image depth of the field, and uses spectral clustering algorithm to cluster image depth. After the subimages are divided, the local haze concentration is estimated according to the depth of field and the subimages are adaptively enhanced and fused. In addition, the illumination component is obtained by multiscale guided filtering to maintain the edge characteristics of the image, and the uneven illumination problem is solved by adjusting the curve function. The experimental results show that the proposed model can effectively enhance the uneven illumination and haze weather image in the traffic scene and the visual effect of the images is good. The generated image has rich details, improves the quality of traffic images, and can meet the needs of traffic practical application.
Current and Emerging Approaches for Hepatic Fibrosis Treatment
Authors: Li Jingguo , Tuo Biguang
Published: July 2021

Liver fibrosis resulting from chronic liver injury is a key factor to develop liver cirrhosis and risk of hepatocellular carcinoma (HCC) which are major health burden worldwide. Therefore, it is necessary for antifibrotic therapies to prevent chronic liver disease progression and HCC development. There has been tremendous progress in understanding the mechanisms of liver fibrosis in the last decade, which has created new opportunities for the treatment of this condition. In this review, we aim to make an overview on information of different potential therapies (drug treatment, cell therapy, and liver transplantation) for the liver fibrosis and hope to provide the therapeutic options available for the treatment of liver fibrosis and discuss novel approaches.
Evaluation of Nutrients Intake for a Group of Jordanian Older Adults with Sarcopenia Syndrome in Amman: An Explorative and Pilot Study
Authors: Al-Majali Sarah Ziad , Ali Ghazzawi Hadeel , Amawi Adam Tawfiq
Published: July 2021

Aim. Sarcopenia is an age-related syndrome that is characterized by a progressive loss of muscle mass, strength, and function. This study was performed in order to evaluate nutrients intake and physical activity level and to investigate the effect of sarcopenia syndrome on food intake for a group of Jordanian older adults with sarcopenia syndrome in Amman. Methodology. The study sample consisted of 25 nonsarcopenic people and 25 sarcopenic patients aged over 60 years old with a male to female ratio of 1 : 1. A special questionnaire was used to collect demographic data, health data, data about syndrome characteristics, nutritional assessment, and physical activity level. A 24-hour recall was also used to collect food intake data. Body weight, height, and skinfold thicknesses were measured. Results. The mean of the sarcopenic patients’ age was 77.5 ± 6.9 years, and the mean of the weight was significantly lower in sarcopenic patients than the nonsarcopenic people. In this study, all macronutrients and micronutrients from dietary intake information were analyzed. Vitamin intake (water- and fat-soluble), as well as minerals (major and trace), amino acids, and essential fatty acids, was assessed. The mean intake of energy and carbohydrates, fat, and dietary fiber was lower than their recommendations, while the mean intake of protein was within the range of its recommendations in the sarcopenia group. The mean of the intake of omega 3 and omega 6 was below their recommendations. Conclusion. It could be concluded that sarcopenic older patients in Jordan have similar characteristics with patients studied worldwide with regard to age of patients, female to male ratio, and main symptoms. Sarcopenic older patients in Jordan generally have lower weight and BF% than nonsarcopenic adults and have inadequate dietary intake compared to their recommendations and compared to nonsarcopenic older adults. Therefore, the diet of sarcopenic patients needs modification and follow-up. The level of physical activity and daily living activities for sarcopenic older patients is lower than that of nonsarcopenic older adults.
Anthropometric Measurements and Correlations to Glucometabolic and Cardiovascular Risk in Obese Patients Undergoing Gastric Bypass Surgery
Authors: Aldenbäck Erica , Johansson Hans-Erik
Published: July 2021

Abdominal obesity is associated with hypertension, increased fasting glucose, HbA1c, and cholesterol. Body mass index (BMI) is frequently used to measure and define obesity and as inclusion criteria for bariatric surgery. Sagittal abdominal diameter (SAD) has been suggested to predict the amount of visceral fat, metabolic traits, and cardiometabolic risk superior to BMI. The aim was to test whether SAD has stronger correlations to glucometabolic traits compared to BMI. One hundred and fifty-five (108 women, 47 men) morbidly obese patients undergoing bariatric surgery were evaluated before (baseline), 6 and 12 months after Roux-en-Y gastric bypass (RYGBP). BMI was reduced from 43.7 kg/m2 (baseline) to 31.3 kg/m2 (12 months) and SAD from 32.6 to 23.2 cm (both

p
<0
.001

). SAD correlated with CRP (

p
=
0.04

), fasting glucose (

p
=
0.008

), HbA1c (

p
=
0.016

), triglycerides (

p
=
0.017

), systolic blood pressure (

p
=
0.032

), and vitamin D (

p
=
0.027

). BMI correlated with CRP (

p
=
0.006

), triglycerides (

p
=
0.016

), vitamin D (

p
=
0.002

), and magnesium (

p
=
0.037

). Despite RYGBP surgery, vitamin D was significantly increased. Liver enzymes were significantly lowered after RYGBP and the change over time in SAD correlated with gamma-glutamyltransferase. SAD was superior to BMI to predict glucose disturbance and dyslipidemia implying increased use of SAD as it is cost effective and simple to perform in the clinic and could be of value when considering patients for bariatric surgery.
Understanding Service Providers’ Competency in Knowledge‐Intensive Crowdsourcing Platforms: An LDA Approach
Authors: Yang Biyu , Wang Xu , Ding Zhuofei
Published: July 2021

Knowledge‐intensive crowdsourcing (KIC) is becoming one of the most promising domains of crowdsourcing by leveraging human intelligence and building a large labor‐intensive service network. In this network, the service providers (SPs) constitute the backbone of the KIC platform and play an important role in connecting the platform and service requesters. The SPs are a group of distributed crowds with a complex composition and high level of uncertainty, resulting in great challenges in service quality and platform management. Understanding the SPs’ competency is an effective way for the platform to manage them. Therefore, we attempt to connect the competency analysis to the environment of KIC to investigate and identify the criteria of SPs’ competency (i.e., the competency factors and dimensions required for being competent for the SPs’ business). To this end, we leverage the Latent Dirichlet Allocation (LDA) model to explore and extract hidden competency dimensions from online interview records. We then introduce the competency theory to identify and label the competency factors and dimensions and construct the three‐level KSAT competency model, which presents a comprehensive vision of the SPs’ performance standards in the context of KIC. Given the competency criteria in the KSAT competency model, we use the Best‐Worst Method (BWM) to determine their weights, which reflect their importance when evaluating the SPs’ competency from the platforms’ perspective. The results show that skill and knowledge are the two most important competency factors, and customer relationship management and communication ability are the two most valuable competency dimensions when evaluating the SPs’ competency. Furthermore, the KSAT competency model can be applied to analyze the competency of individuals or organizations in many other industries as well.