bluuebunny/crossref_metadata_2025 huggingface dataset:
Degradation of Toxic Dye Using Phytomediated Copper Nanoparticles and Its Free-Radical Scavenging Potential and Antimicrobial Activity against Environmental Pathogens
Authors: Rajeshkumar S. , Vanaja M. , Kalirajan Arunachalam
Published: December 2021

The present investigation deals with the green synthesis of copper nanoparticles in an ecofriendly manner using leaf extract of Andrographis paniculata. Green-synthesized copper nanoparticles were studied for their antibacterial, antioxidant, and catalytic activity. The leaves were powdered and extracted with water and added to copper sulphate solution. The reduction of copper ions to nanoparticles was preliminarily identified by the color change of the reaction mixture. The synthesized nanoparticle was characterized by using a UV-Vis Spectrophotometer at a different wavelength with different time intervals. Functional groups available on the surface of the nanoparticle were identified by Fourier transform infrared spectroscopy (FTIR). Surface roughness was characterized by atomic force microscopy (AFM). X-ray diffraction (XRD) analysis showed six distinct intense peaks indicating the crystalline nature of synthesized copper nanoparticles (CuNPs). A scanning electron microscope (SEM) demonstrated polydispersed nanoparticles formed in the reaction process. The antibacterial activity of the nanoparticles was evaluated by an agar well diffusion assay against pathogenic bacteria. The antioxidant activity showed the excellent reduction of DPPH free radicals by nanoparticles. These results confirmed that copper nanoparticles serve as an alternative therapeutic agent over conventional drugs. Moreover, copper nanoparticles were also used to study the effect on the dye degradation process of methyl red and eosin dyes. Copper nanoparticles effectively remove the dyes with high efficiency up to 92% and 95% of methyl red and eosin dye, respectively.
Multicentre Study Using Machine Learning Methods in Clinical Diagnosis of Knee Osteoarthritis
Authors: Zeng Ke , Hua Yingqi , Xu Jing , Zhang Tao , Wang Zhuoying , Jiang Yafei , Han Jing , Yang Mengkai , Shen Jiakang , Cai Zhengdong
Published: December 2021

Knee osteoarthritis (OA) is one of the most common musculoskeletal disorders. OA diagnosis is currently conducted by assessing symptoms and evaluating plain radiographs, but this process suffers from the subjectivity of doctors. In this study, we retrospectively compared five commonly used machine learning methods, especially the CNN network, to predict the real-world X-ray imaging data of knee joints from two different hospitals using Kellgren-Lawrence (K-L) grade of knee OA to help doctors choose proper auxiliary tools. Furthermore, we present attention maps of CNN to highlight the radiological features affecting the network decision. Such information makes the decision process transparent for practitioners, which builds better trust towards such automatic methods and, moreover, reduces the workload of clinicians, especially for remote areas without enough medical staff.
Comparative Study of Lightweight Cementitious Composite Reinforced with Different Fibre Types and the Effect of Silane‐Based Admixture
Authors: Leong Geok Wen , Chua Hui Loo , Mo Kim Hung , Ibrahim Zainah , Loh Zhi Pin
Published: December 2021

This study aims to develop a type of fine‐grained lightweight concrete, also known as lightweight cementitious composite (LCC), containing perlite microsphere (PM) and fibres with enhanced impermeability. The effect of polypropylene (PP), polyvinyl alcohol (PVA), and basalt fibres on the fresh and hardened properties of LCC was investigated. Besides, silane‐based water repellent admixture was incorporated to reduce the water absorption and enhance the hydrophobicity of LCC. The dry densities of LCC developed were in the range of 912–985 kg/m3. PP fibres have lesser influence on the strengths of LCC. However, PVA fibres enhanced the strength of LCC by up to 35.2% and 28% in the compressive strength and flexural strength, respectively, while the basalt fibres increased both strengths up to 30.1% and 43.5%, respectively. By considering the overall performance, LCC with 0.5% PVA fibres has achieved a good balance in workability and strength. Additionally, silane‐based water repellent admixture had an excellent effect in reducing the water absorption and improving the hydrophobicity of LCC. By incorporating 1% of silane‐based water repellent admixture, the LCC with 0.5% PVA fibres obtained water‐resistant properties with the softening coefficient of 0.85 and water contact angle of 128.2°. In conclusion, a combination of PVA‐LCC with 1% waterproofing admixture showed the best performance in terms of mechanical strength as well as hydrophobic properties and had the potential to be used in the fabrication of concrete façade.
Research on Fault Diagnosis Model of Generative Adss Based on Improved Semisupervised Diagnosis Algorithm
Authors: Qian Yi
Published: December 2021

With the advent of the era of big data and the rapid development of deep learning and other technologies, people can use complex neural network models to mine and extract key information in massive data with the support of powerful computing power. However, it also increases the complexity of heterogeneous network and greatly increases the difficulty of network maintenance and management. In order to solve the problem of network fault diagnosis, this paper first proposes an improved semisupervised inverse network fault diagnosis algorithm; the proposed algorithm effectively guarantees the convergence of generated against network model, makes full use of a large amount of trouble-free tag data, and obtains a good accuracy of fault diagnosis. Then, the diagnosis model is further optimized and the fault classification task is completed by the convolutional neural network, the discriminant function of the network is simplified, and the generation pair network is only responsible for generating fault samples. The simulation results also show that the fault diagnosis algorithm based on network generation and convolutional neural network achieves good fault diagnosis accuracy and saves the overhead of manually labeling a large number of data samples.
A Study of Millisecond Blasting on High Bench at Barun Iron Ore Operation
Authors: Zhang Pengfei , Bai Runcai , Sun Xue , Li Haoran , Fei Honglu , Bao Shijie
Published: December 2021

To improve the productivity and efficient of modern large-scale open-cut mines, a number of technologies are developed and trialed, including new blasting equipment, larger blasting holes, high benches, air spacing, and short-delay blasting within holes. However, the relative blasting parameters need field calibration and further investigation of theories on these techniques are required. This paper studied the open-cut bench blasting at Barun Eboxi Mine of Baotou Iron and Steel Group via theoretical analysis on shock wave, numerical simulation, and field test. According to the technical conditions of the site, three sets of vertical boreholes at 310 mm diameter were drilled on 24 m high batter; and three sets of air-spaced charges were set up. The digital electronic detonator was used to initiate at millisecond intervals. The study found that under the condition of 24 m high bench, the use of intermediate air interval is beneficial to the rock fragmentation. The delay time within the hole is 3-8 ms. The bottom of the lower explosives and the top of the upper explosives were devised for initiation to optimize the initiation location. The peak effective stress points are 63.6%, 52.2%, and 8.9% higher, respectively. The field test of high-bench intrahole millisecond blasting in Barun Eboxi mine shows that the intrahole millisecond blasting parameters proposed in this study are feasible.
Molecular Confirmation of Vancomycin-Resistant Staphylococcus aureus with vanA Gene from a Hospital in Kathmandu
Authors: Maharjan Meera , Sah Anil Kumar , Pyakurel Susil , Thapa Sabita , Maharjan Susan , Adhikari Nabaraj , Rijal Komal Raj , Ghimire Prakash , Thapa Shrestha Upendra
Published: December 2021

Staphylococcus aureus, a commensal on the skin and in the nasal cavity of humans, is one of the most serious cases of nosocomial infections. Moreover, methicillin-resistant S. aureus (MRSA) is a leading cause of morbidity and mortality worldwide. For the treatment of MRSA infections, vancomycin is considered as a drug of choice. However, the emergence of vancomycin resistance among MRSA isolates has been perceived as a formidable threat in therapeutic management. To estimate the rate of vancomycin-resistant S. aureus (VRSA) and to detect the vancomycin-resistant genes, namely, vanA and vanB, among the isolates, a hospital-based cross-sectional study was conducted from July to December 2018 in Annapurna Neurological Institute and Allied Science, Kathmandu, Nepal. S. aureus was isolated and identified from different clinical samples and processed for antibiotic susceptibility testing by the modified Kirby–Bauer disc diffusion method. The screening of MRSA was performed as per Clinical and Laboratory Standard Institute (CLSI) guidelines. VRSA was confirmed by the minimum inhibitory concentration (MIC) method by employing E-test strips. All the phenotypically confirmed VRSA were further processed to detect the vanA and vanB gene by using the conventional polymerase chain reaction (PCR) method. A total of 74 (20.3%) S. aureus were isolated, and the highest percentage of S. aureus was from the wound samples (36.5%). Of 74 S. aureus isolates, the highest number (89.2%) was resistant to penicillin, and on the other hand, linezolid was found to be an effective drug. Likewise, 45 (60.81%) were found to be MRSA, five (11.11%) were VRSA, and 93.2% of S. aureus isolates showed an MAR index greater than 0.2. Two VRSA isolates (40%) were positive for the vanA gene. The higher prevalence of MRSA and significant rate of VRSA in this study recommend routine surveillance for the MRSA and VRSA in hospital settings before empirical therapy.
Evaluation of pH, Calcium Ion Release, and Dimensional Stability of an Experimental Silver Nanoparticle-Incorporated Calcium Silicate-Based Cement
Authors: Sheethal Dsouza Teena , Shetty Aditya , Dsouza Neevan
Published: December 2021

An experimental calcium silicate-based root-end filling material incorporated with silver nanoparticles intended for use in periapical surgeries was developed with the purpose to overcome the drawbacks of existing materials and to satisfy the ideal requirements of root-end filling materials. This study was designed to evaluate the physicochemical properties, pH, calcium ion release, and dimensional stability of the experimental cement, and compare the results with commercially available ProRoot MTA (Dentsply). An independent sample test was used to analyze the data. Mean initial pH (immediately after mixing) of the experimental cement was 10.42 ± 0.04 which was higher than that of MTA. However, there was a significant increase in pH of MTA at 1 day, 2 days, and 7 days. Presence of calcium chloride favored the release of calcium ions which was significantly increased in the experimental group at 24 hours. At the end of 30 days, MTA showed a significant expansion when compared to the experimental cement

p
<
0.001

. In conclusion, the experimental nanoparticle-incorporated calcium silicate-based cement showed clinically acceptable physicochemical properties.
Optimization of an Intelligent Sorting and Recycling System for Solid Waste Based on Image Recognition Technology
Authors: Chen Haitao
Published: December 2021

In this paper, the technique of image recognition algorithm is used to conduct an in-depth study and analysis of the intelligent classification and recycling system of solid waste and to optimize the design of its system. The network structure and detection principle of the YOLO target detection algorithm based on convolutional neural nets are analysed, images of construction solid waste are collected as a dataset, and the image dataset is expanded using data enhancement techniques, and the target objects in the dataset are labelled and used to train their own YOLO detection models. To facilitate testing the images and to design a YOLO algorithm-based construction solid waste target detection system. Using the detection system for construction solid waste recognition, the YOLO model can accurately detect the location, class, and confidential information of the target object in the image. Image recognition is a technique to recognize images by capturing real-life images through devices and performing feature extraction, and this technique has been widely used since its inception. The deep learning-based classification algorithm for recyclable solid waste studied in this paper can classify solid waste efficiently and accurately, solving the problem that people do not know how to classify solid waste in daily life. The convolutional layer, pooling layer, and fully connected layer in a convolutional neural network are responsible for feature extraction, reducing the number of parameters, integrating features into high-level features, and finally classifying them by SoftMax classifier in turn. However, the actual situation is intricate and often the result is not obtained as envisioned, and the use of migration learning can be a good way to improve the overfitting phenomenon. In this paper, the combination of lazy optimizer and lookahead can improve the generalization ability and fitting speed as well as greatly improve the accuracy and stability. The experimental results are tested, and it is found that the solid waste classification accuracy can be as high as 95% when the VGG19 model is selected and the optimizer is combined.
A Novel Description on Vague Graph with Application in Transportation Systems
Authors: Kou Zheng , Kosari Saeed , Akhoundi Maryam
Published: December 2021

Fuzzy graph (FG) models embrace the ubiquity of existing in natural and man-made structures, specifically dynamic processes in physical, biological, and social systems. It is exceedingly difficult for an expert to model those problems based on a FG because of the inconsistent and indeterminate information inherent in real-life problems being often uncertain. Vague graph (VG) can deal with the uncertainty associated with the inconsistent and determinate information of any real-world problem, where FGs many fail to reveal satisfactory results. Regularity definitions have been of high significance in the network heterogeneity study, which have implications in networks found across biology, ecology, and economy; so, adjacency sequence (AS) and fundamental sequences (FS) of regular vague graphs (RVGs) are defined with examples. One essential and adequate prerequisite has been ascribed to a VG with maximum four vertices is that it should be regular based on the adjacency sequences concept. Likewise, it is described that if

ζ

and its principal crisp graph (CG) are regular, then all the nodes do not have to have the similar AS. In the following, we obtain a characterization of vague detour (VD) g-eccentric node, and the concepts of vague detour g-boundary nodes and vague detour g-interior nodes in a VG are examined. Finally, an application of vague detour g-distance in transportation systems is given.
Towards the Segmentation and Classification of White Blood Cell Cancer Using Hybrid Mask-Recurrent Neural Network and Transfer Learning
Authors: Das Sumit Kumar , Islam Kazi Soumik , Neha Tanzila Ahsan , Khan Mohammad Monirujjaman , Bourouis Sami
Published: December 2021

Inside the bone marrow, plasma cells are created, and they are a type of white blood cells. They are made from B lymphocytes. Antigens are produced by plasma cells to combat bacteria and viruses and prevent inflammation and illness. Multiple myeloma is a plasma cell cancer that starts in the bone marrow and causes the formation of abnormal plasma cells. Multiple myeloma is firmly identified by examining bone marrow samples under a microscope for myeloma cells. To diagnose myeloma cells, pathologists have to be very selective. Furthermore, because the ultimate decision is based on human sight and opinion, there is a possibility of error in the result. The nobility of this research is that it provides a computer-assisted technique for recognizing and detecting myeloma cells in bone marrow smears. For recognizing purposes, we have used Mask-Recurrent Convolutional Neural Network, and for detection purposes, Efficient Net B3 has been used. There are already many studies on white blood cell cancer, but very few with both segmentation and classification. We have designed two models. One is for recognizing myeloma cells, and the other is for differentiating them from nonmyeloma cells. Also, a new data set has been made from the multiple myeloma data sets, which has been used in our classification model. This research focuses on hybrid segmentation models and increases the accuracy level of the classification model. Both of our models are trained pretty well, where the Mask-RCNN model gives a mean average precision (mAP) of 93% and the Efficient Net B3 model gives 94.68% accuracy. The result of this research indicates that the Mask-RCNN model can recognize multiple myeloma and Efficient Net B3 can distinguish between myeloma and nonmyeloma cells and beats most of the state of the art in myeloma recognition and detection.
A Hybrid Mathematical and Decision-Making Model to Determine the Amount of Economic Order considering the Discount
Authors: Hazrati Hanif , Barzegarinegad Abbas , Siaby-Serajehlo Hamid
Published: December 2021

Suppliers are one of the most important parts of the supply chain, whose performance indirectly has a significant impact on customer satisfaction. Because customer demands are different from organizations, organizations have to consider different criteria for selecting their suppliers. In recent years, many studies in this field have been conducted using various criteria and methods. The main purpose defined in this research is to develop a model for simultaneous item ordering systems in real business conditions. In this research, a model is developed by considering the two objectives of minimizing overall costs and maximizing the amount of products ordered from different suppliers based on their weight value. Weights are calculated based on different criteria using the fuzzy analytic hierarchy process method for each supplier in different periods. Then, due to the multiobjective nature of the model, the proposed model has been solved by using the epsilon constraint in GAMS and nondominated sorting genetic algorithm II in MATLAB software. Considering the simultaneous order of inventory of multiproduct with several suppliers in several periods of time in discrete space with discount is one of the contributions of this research. To validate the proposed model, the results of the exact solution are compared with the meta-heuristic solution. Comparison results and assessment metrics indicate that the results of the proposed solution approach with an error of less than 1% had good performance. The results show that the system cost increases, by increasing the amount of discount, because of the increase in the amount of demand. Therefore, with a 30% increase in the discount, the system costs will increase to 36,496 units. Also, with a 20% reduction, the cost reduction will be reduced to 14,170 units.
Does the Capital Market Opening Improve the Price Discovery Efficiency of Stock Market? An Empirical Research Based on Shanghai-Hong Kong Stock Connect
Authors: Yu Xueyan , Yu Yanbing
Published: December 2021

Whether capital market opening improves the price discovery efficiency of stock market is an important issue. Shanghai-Hong Kong Stock Connect (hereafter, SHKSC) is a milestone event in the opening up of China’s capital market. Based on SHKSC, using the method of PSM + DID, we study the impact of capital market opening on the price discovery efficiency from two dimensions-stock price information content and price reaction speed to information. Our research shows that capital market opening did not increase stock price information content, but speed up the reaction of price to information. Therefore, capital market opening improves capital market’s price discovery efficiency in terms of response speed of stock price to information. Further analysis shows that capital market opening affects stock price reaction speed through improving market information environment and reducing insider trading, but it has not yet had a substantial impact on listed companies’ earnings quality, which is the main participant in the capital market, and therefore has failed to influence the stock price information content. In order to maximize the effectiveness of capital market opening, it is necessary to introduce more effective policies to improve the information disclosure quality of listed firms and reduce the level of insider trading.
Different Approaches to Atopic Dermatitis by Allergists, Dermatologists, and Pediatricians
Authors: Zuccolo de Bortoli Suzieni Padoin , Chong Neto Herberto José , Rosário Filho Nelson Augusto
Published: December 2021

Objectives. Atopic dermatitis (AD) is the most prevalent chronic inflammatory skin disease, with a vast drug arsenal and guidelines available for its management and diagnosis and different medical specialties engaged in providing care. This study aimed to outline the therapeutic and diagnostic approaches to the AD of allergists, dermatologists, and pediatricians and verify whether they are compliant with the guidelines. Methods. A cross-sectional study using an electronic questionnaire administered through the SurveyMonkey® platform was disclosed by participating medical societies to their medical associates. Results. Of the 1,473 participating physicians, the use of moisturizers as part of AD treatment was observed among pediatricians (91.9%), dermatologists (97.5%), and allergists (100%;

p
=
0.07

). The preference for the use of new emollients was lower among pediatricians (57%) than dermatologists (75.9%) and allergists (71.4%;

p
<
0.001

). The prevalence of wet-wrap therapy was lower among dermatologists (16.3%) than allergists (51%;

p
<
0.001

). The recommendation of proactive treatment with topical corticosteroids was more frequently reported by allergists (65.3%) than pediatricians (43.3%) and dermatologists (40.8%;

p
<
0.001

), and the same trend was observed in relation to proactive treatment using calcineurin inhibitors. The use of oral anti-histamines to control pruritus was mainly considered by pediatricians (69.2%) and dermatologists (59.2%

p
<
0.001

). Clinical experience with systemic immunomodulating agents was greater among allergists (77.5%) and dermatologists (60.8%;

p
<
0.001

), with cyclosporine being the most cited systemic immunomodulating agent. Environmental control of aeroallergens was recommended by pediatricians (89.8%), dermatologists (86.9%), and allergists (100%;

p
=
0.01

). Conclusion. There were differences in the therapeutic and diagnostic approaches to AD used by allergists, dermatologists, and pediatricians and those recommended by the guidelines, especially regarding the use of wet-wrap therapy, proactive treatment with topical corticosteroids or calcineurin inhibitors, prescription of anti-histamines, recommendation of phototherapy, and control of aeroallergens.
Strategies for the Architectural Space Design of Special Education Resource Center in China
Authors: Ge Xin , Tian Xin
Published: December 2021

In view of the current problems in China’s special education resource centers, such as lack of functional cognition and unreasonable architectural function and spatial organization, which result in the inability to coordinate regional education resources, the formulation and implementation of special education ISP program are undertaken, and the development of integrated education is promoted. This paper draws on the concept of integrated education in developed areas and the design wisdom of resource centers. The research is carried out from the perspective of meeting the behavior patterns and special needs of different users, and the perfect and reasonable functional composition and the key points of special space design are put forward, and the feasible design strategies are expressed through establishing the model and plane layout.
Application Value and Relevance Analysis of the Risk Evaluation System for Arteriovenous Fistula Puncture in Thrombosis after Puncture
Authors: Li Mei , Sun Chao , Du Xuan
Published: December 2021

Objective. To analyze the application value and relevance of risk evaluation system for arteriovenous fistula (AVF) puncture in thrombosis after puncture. Methods. The clinical data of 180 patients treated with hemodialysis in the hemodialysis center of our hospital from November 2017 to November 2019 were retrospectively analyzed. After puncture, all patients received the digital subtraction angiography (DSA) examination, and based on whether they had AVF thrombosis, they were divided into the nonthrombosis group (n = 102) and thrombosis group (n = 78), and then, according to the parity of their admission numbers, the patients in the thrombosis group were subdivided into the study group (n = 39) and the reference group (n = 39), so as to analyze the risk factors of thrombosis after AVF puncture and the application value of the risk evaluation system for AVF puncture in preventing and treating thrombosis. Results. Compared with the reference group after intervention, the study group had significantly higher mean internal fistula blood flow volume (

P

 < 0.001) and significantly lower total incidence rate of vascular complications in fistulas (

P

 < 0.05); according to the multifactor binary logistic regression analysis, it was found that diabetes, systolic blood pressure reduction, hemoglobin, low-density lipoprotein cholesterol (LDL-C), ultrafiltration rate, and elevation of blood phosphorus and platelet levels were the risk factors of thrombosis after AVF puncture in hemodialysis patients. Conclusion. When risk factors of thrombosis are found in patients treated with hemodialysis, timely detection and intervention shall be applied in the early stage. Adopting the AVF puncture risk evaluation system has an extremely high application value in the clinic and is of important meaning in prolonging the service life of fistulas.
Cavitation Analysis in Centrifugal Pumps Based on Vibration Bispectrum and Transfer Learning
Authors: Hajnayeb Ali
Published: December 2021

Detection of cavitation in centrifugal pumps is critical in their condition monitoring. In order to detect cavitation more accurately and confidently, more advanced signal processing techniques are needed. For the classification of a pump conditions based on the outputs of these techniques, advanced machine learning techniques are needed. In this research, an automatic system for cavitation detection is proposed based on machine learning. Bispectral analysis is used for analyzing the vibration signals. The resulting bispectrum images are given to convolutional neural networks (CNNs) as inputs. The CNNs are a pretrained AlexNet and a pretrained GoogleNet, which are used in this application through transfer learning. On the contrary, a laboratory test setup is used for generating controlled cavitation in a centrifugal pump. The suggested algorithm is implemented on the vibration dataset acquired from the laboratory pump test setup. The results show that the cavitation state of the pump can be detected accurately using this system without any need to image processing or feature extraction.
Polyvinyl Alcohol Fiber Length Optimization for High Ductility Cementitious Composites with Different Compressive Strength Grades
Authors: Cong Ding , Liping Guo , Jinming Ren , Yongming Wang , Xinyu Li , Bing Yu , Yuan Gao , Wanpeng Liu
Published: December 2021

The fiber length has a significant impact on the fiber bridging capacity and the mechanical properties of high ductility cementitious composites (HDCCs), which is related to fiber/matrix interfacial bonding. However, this fundamental knowledge of HDCCs design has rarely been investigated systematically. To this end, this study deeply investigates the effect of the fiber length on the bridging stress and the complementary energy with various fiber/matrix interfacial bonds in theory. Then, the mechanical performances of HDCCs with various fiber lengths and compressive strengths were evaluated experimentally. In micromechanical design, longer fibers can achieve stronger bridging stress and more sufficient complementary energy regardless of the fiber/matrix interfacial bonding properties. However, it should be noted that the increase in bridging capacity was quite slow for the overlong fibers and excessive interfacial bonding. The experiments indicated that overlong fibers (18 mm and 24 mm) easily twined on the mixer blade and were hard to disperse evenly. The HDCCs with shorter fibers displayed better workability. The compressive strength was less affected by the fiber length, and most striking differences were less than 5.0%, while the flexural properties and the tensile properties first increased and then decreased when the fiber length ranged from 6 mm to 24 mm. Consequently, the fibers with lengths of 9 mm and the fibers with lengths of 12 mm were better candidates for the HDCCs with compressive strengths of 30 MPa to 80 MPa, and fibers with lengths of 9 mm caused the HDCCs to exhibit higher ductility properties in general.
Detection of Tuberculosis Disease Using Image Processing Technique
Authors: Alsaffar Mohammad , Alshammari Gharbi , Alshammari Abdullah , Aljaloud Saud , Almurayziq Tariq S. , Hamad Abdulsattar Abdullah , Kumar Vishal , Belay Assaye
Published: December 2021

Machine learning is a branch of computing that studies the design of algorithms with the ability to “learn.” A subfield would be deep learning, which is a series of techniques that make use of deep artificial neural networks, that is, with more than one hidden layer, to computationally imitate the structure and functioning of the human organ and related diseases. The analysis of health interest images with deep learning is not limited to clinical diagnostic use. It can also, for example, facilitate surveillance of disease-carrying objects. There are other examples of recent efforts to use deep learning as a tool for diagnostic use. Chest X-rays are one approach to identify tuberculosis; by analysing the X-ray, you can spot any abnormalities. A method for detecting the presence of tuberculosis in medical X-ray imaging is provided in this paper. Three different classification methods were used to evaluate the method: support vector machines, logistic regression, and nearest neighbors. Cross-validation and the formation of training and test sets were the two classification scenarios used. The acquired results allow us to assess the method’s practicality.
Robust Controller Designing for an Air-Breathing Hypersonic Vehicle with an HOSVD-Based LPV Model
Authors: He Xing , Jiang Wei , Jiang Caisheng
Published: December 2021

This paper focuses on the linear parameter varying (LPV) modeling and controller design for a flexible air-breathing hypersonic vehicle (AHV). Firstly, by selecting the measurable altitude and velocity as gain-scheduled variables, the original longitudinal nonlinear model for AHV is transformed into the LPV model via average gridding division, vertex trimming, Jacobian linearization, and multiple linear regression within the entire flight envelope. Secondly, using the tensor product model transformation method, the obtained LPV model is converted into the polytopic LPV model via high-order singular value decomposition (HOSVD). Third, the validity and applicability of the HOSVD-based LPV model are further demonstrated by designing a robust controller for command tracking control during maneuvering flight over a large envelope.
EZH2 Regulates Lipopolysaccharide-Induced Periodontal Ligament Stem Cell Proliferation and Osteogenesis through TLR4/MyD88/NF-κB Pathway
Authors: Wang Pengcheng , Tian Huan , Zhang Zheng , Wang Zuomin
Published: December 2021

Background. Periodontitis induced by bacteria especially Gram-negative bacteria is the most prevalent chronic inflammatory disease worldwide. Emerging evidence supported that EZH2 plays a significant role in the inflammatory response of periodontal tissues. However, little information is available regarding the underlying mechanism of EZH2 in periodontitis. This study is aimed at determining the potential role and underlying mechanism of EZH2 in periodontitis. Methods. The protein levels of EZH2, H3K27ME, p-p65, p-IKB, TLR4, MyD88, Runx2, and OCN were examined by western blot assay. Proliferation was evaluated by CCK8 assay. The levels of TNFα, IL1β, and IL6 were detected by ELISA assay. Migration was detected by wound healing assay. The distribution of p65 was detected by immunofluorescence. The formation of mineralized nodules was analyzed using alizarin red staining. Results. LPS stimulation significantly promoted EZH2 and H3K27me3 expression in primary human periodontal ligament stem cells (PDLSCs). Targeting EZH2 prevented LPS-induced upregulation of the inflammatory cytokines and inhibition of cell proliferation and migration. Furthermore, EZH2 knockdown attenuated the TLR4/MyD88/NF-κB signaling to facilitate PDLSC osteogenesis. Conclusions. Modulation of the NF-κB pathway through the inhibition of EZH2 may offer a new perspective on the treatment of chronic apical periodontitis.
Achieve Efficient and Privacy-Preserving Compound Substring Query over Cloud
Authors: Yin Fan , Lu Rongxing , Zheng Yandong , Tang Xiaohu
Published: December 2021

The cloud computing technique, which was initially used to mitigate the explosive growth of data, has been required to take both data privacy and users’ query functionality into consideration. Searchable symmetric encryption (SSE) is a popular solution that can support efficient attribute queries over encrypted datasets in the cloud. In particular, some SSE schemes focus on the substring query, which deals with the situation that the user only remembers the substring of the queried attribute. However, all of them just consider substring queries on a single attribute, which cannot be used to achieve compound substring queries on multiple attributes. This paper aims to address this issue by proposing an efficient and privacy-preserving SSE scheme supporting compound substring queries. In specific, we first employ the position heap technique to design a novel tree-based index to support substring queries on a single attribute and employ pseudorandom function (PRF) and fully homomorphic encryption (FHE) techniques to protect its privacy. Then, based on the homomorphism of FHE, we design a filter algorithm to calculate the intersection of search results for different attributes, which can be used to support compound substring queries on multiple attributes. Detailed security analysis shows that our proposed scheme is privacy-preserving. In addition, extensive performance evaluations are also conducted, and the results demonstrate the efficiency of our proposed scheme.
Existence and Multiplicity of Solutions for a Biharmonic Equation with p(x)-Kirchhoff Type
Authors: Zhang Qi , Miao Qing
Published: December 2021

Based on the basic theory and critical point theory of variable exponential Lebesgue Sobolev space, this paper investigates the existence and multiplicity of solutions for a class of nonlocal elliptic equations with Navier boundary value conditions when (AR) condition does not hold and improves or generalizes the original conclusions.
Studies on Dry Sliding Wear and Solid Particle Erosive Wear Behaviours of Natural Fibre Composite Developed from Water Hyacinth Aquatic Plant for Automotive Application
Authors: Ajithram A. , Winowlin Jappes J. T. , Adam Khan M. , Brintha N. C. , Waleed Fekry Faris
Published: December 2021

In this research, an attempt is made to investigate the abrasive and erosion wear resistance of aquatic waste plant water hyacinth converted fibre‐reinforced polymer composites. From a novel approach, the new fibre extraction machine is designed to extract the hyacinth fibre from the parent plant and reinforce it to the epoxy matrix material to produce a natural fibre composite for frictional applications. The extracted fibre is dried in the open sunlight area for 22 to 35 days to remove moisture and external dust particles. Then, different weight percentages (15, 20, 25, 30, and 35) of composite samples are produced with the help of the hot press compression moulding technique. Improved hyacinth composite tribology properties are tested by utilizing the pin on the disk machine. This setup included various processing parameters like load (10, 20, and 30 N), velocities (1, 2, and 3 m/s), speed (160, 320, and 479 rpm), and constant sliding distance condition, and the erosion setup also influences the essential parameters like impact angle (30, 45, and 60°), erodent velocity (1, 2.5, and 3.3 m/s), and discharge rate (28, 41, and 72 g/m). The factorial techniques are used to identify the important design factors. The final results represent the weight loss, volume loss, and erosion rate of hyacinth fibre composite. By utilizing the SEM (scanning electron microscope), the worn surface morphology of different weight percentages of hyacinth fibre samples are analysed. To upgrade the usage of hyacinth reinforced composites for different industrial applications, wear and erosion studies are conducted with different parameter conditions.
Yeasts with Fermentative Potential Associated with Fruits of Camu-Camu (Myrciaria dubia, Kunth) from North of Brazilian Amazon
Authors: Matos Ítalo Thiago Silveira Rocha , de Souza Vanderly Andrade , D’Angelo Giovana do Rosário , Astolfi Filho Spartaco , do Carmo Edson Júnior , Vital Marcos José Salgado
Published: December 2021

Considering the high biotechnological potential of yeasts associated to edible fruits, a screening for these microorganisms, capable of alcoholic fermentation, was performed in ripe fruits of camu-camu (Myrciaria dubia, Kunth). The fruits were collected from north of Brazilian Amazon, in the floodplain of the Cauamé River. Yeasts were isolated, and fermentation capability was evaluated using Durham tubes. Quantitative assays were performed to calculate ethanol yield (g g−1), specific growth rate (h−1), and ethanol productivity (g L−1·h−1). Taxonomic identification was performed by ribosomal gene nucleotide sequence analysis by alignment using BLASTN. A total of fifteen yeast colonies were isolated, and three of them presented promising ability to ferment glucose to ethanol. These isolates were identified as Candida orthopsilosis, Pichia kudriavzevii, and Meyerozyma caribbica. When cultured in broth containing 180 g·L−1 of glucose, M. caribbica CC003 reached 91.7 percent of the maximum theoretical ethanol concentration (84.4 g·L−1), presenting an ethanol yield and productivity of 0.4688 g·g−1 and 0.781 g·L−1·h−1, respectively. These results indicate a promising potential of this isolate for bioprocess applications. This paper is a rare report of C. orthopsilosis with endophytic habit because most of the references indicate it as a human pathogen. Besides this, M. caribbica is a promising fermenter for alcoholic beverages due to its osmotolerance and high ethanol yield. This is the first paper reporting endophytic yeasts associated with fruits of Myrciaria dubia.
Combination Intensity-Modulated Radiotherapy and Sorafenib Improves Outcomes in Hepatocellular Carcinoma with Portal Vein Tumor Thrombosis
Authors: Abulimiti Munire , Li Zhenyu , Wang Haifeng , Apiziaji Palida , Abulimiti Yisikandaer , Tan Yao
Published: December 2021

Purpose. To compare the difference in outcome of hepatocellular carcinoma (HCC) with portal vein thrombosis (PVTT) between intensity-modulated radiotherapy (IMRT) concurrent with sorafenib and IMRT alone. Methods. A total of 82 patients with PVTT from 2014 to 2019 were included. Of these, 36 received IMRT concurrent with sorafenib treatment (group A), while 46 underwent IMRT alone (group B). The dose of IMRT was 40.0–62.5 Gy/2–2.5 Gy/4–6 w, and patients received orally administered sorafenib 400 mg twice a day in combination with IMRT. Overall survival (OS), progression-free survival (PFS), and median distant metastasis-free survival (DMFS) were evaluated by using LIFETEST procedure of SAS. Results. The median survival time was 11.0 months in group A versus 9.0 months in group B. The 1- and 2-year OS in group A were 44.9% and 3.8% versus 28.6% and 2.6% in group B (

P
=
0.036

), respectively. The median PFS was 6.0 months in group A versus 3.0 months in group B. The 1- and 2-year PFS in group A were 20.7% and 6.9% versus 2.7% and 0.0% in group B (

P
=
0.012

), respectively. The 1- and 2-year DMFS in group A were 38.0% and 7.9% versus 16.7% and 0.0% in group B (

P
=
0.019

), respectively. Multivariate analysis showed that Child–Pugh classification, AFP response, and overall response were independent risk factors for OS (

P
<
0.05

). There were no significant differences in adverse events except fatigue and skin reactions between the two groups. Conclusion. Compared with IMRT alone, IMRT concurrent with sorafenib can improve the long-term efficacy of HCC patients with PVTT, without increasing adverse reactions. The patients with Child–Pugh A, overall response, and AFP response obtained better OS.
Analyzing Vibration Mechanism of Angular Contact Ball Bearing with Compound Faults on Inner and Outer Rings
Authors: Chen Lihai , Fang Ma , Qiu Ming , Dong Yanfang , Pang Xiaoxu , Li Junxing , Yang Chuanmeng
Published: December 2021

This paper investigates a method to dynamically model compound faults on the inner and outer rings of an angular contact ball bearing as well as their effects on its dynamic behavior. Gupta’s dynamic modeling method is used to consider changes in the deformation and direction of the contact load when the ball passes through the damaged area and to develop a dynamic model of compound faults in the angular contact ball bearing. The step‐changing fourth‐order Runge–Kutta method is used to solve the dynamic compound fault model. The time‐domain signal of vibration responses in the case of a single fault in the inner and outer rings exhibited a certain periodicity, and the frequency of faults in the envelope spectrum was clear. By comparison, the periodicity of compound faults was not clear. The signals of compound faults were decomposed by the dual‐tree complex wavelet transform to identify their characteristic frequency. Errors occurred between the characteristic frequency of the theoretical fault and its simulated value. They increased with the rotational speed and decreased with an increase in axial load, whereas the influence of radial load on them was minor. For compound faults on the inner and outer rings of an angular contact ball bearing, this study provides a modeling method that can describe changes in the deformation and direction of the contact load when the ball passes through the damaged area of the inner and outer rings. The work here can provide an important foundation for fault identification in angular contact ball bearings.
Persistence of ctDNA in Patients with Breast Cancer During Neoadjuvant Treatment Is a Significant Predictor of Poor Tumor Response
Authors: Zhou Qing , Gampenrieder Simon P. , Frantal Sophie , Rinnerthaler Gabriel , Singer Christian F. , Egle Daniel , Pfeiler Georg , Bartsch Rupert , Wette Viktor , Pichler Angelika , Petru Edgar , Dubsky Peter C. , Bago-Horvath Zsuzsanna , Fesl Christian , Rudas Margaretha , Ståhlberg Anders , Graf Ricarda , Weber Sabrina , Dandachi Nadia , Filipits Martin , Gnant Michael , Balic Marija , Heitzer Ellen
Published: December 2021

Purpose:

Accurate response assessment during neoadjuvant systemic treatment (NST) poses a clinical challenge. Therefore, a minimally invasive assessment of tumor response based on cell-free circulating tumor DNA (ctDNA) may be beneficial to guide treatment decisions.

Experimental Design:

We profiled 93 genes in tissue from 193 patients with early breast cancer. Patient-specific assays were designed for 145 patients to track ctDNA during NST in plasma. ctDNA presence and levels were correlated with complete pathological response (pCR) and residual cancer burden (RCB) as well as clinicopathologic characteristics of the tumor to identify potential proxies for ctDNA release.

Results:

At baseline, ctDNA could be detected in 63/145 (43.4%) patients and persisted in 25/63 (39.7%) patients at mid-therapy (MT) and 15/63 (23.8%) patients at the end of treatment. ctDNA detection at MT was significantly associated with higher RCB (OR = 0.062; 95% CI, 0.01–0.48; P = 0.0077). Of 31 patients with detectable ctDNA at MT, 30 patients (96.8%) were nonresponders (RCB II, n = 8; RCB III, n = 22) and only one patient responded to the treatment (RCB I). Considering all 145 patients with baseline (BL) plasma, none of the patients with RCB 0 and only 6.7% of patients with RCB I had ctDNA detectable at MT, whereas 30.6% and 29.6% of patients with RCB II/III, respectively, had a positive ctDNA result.

Conclusions:

Overall, our results demonstrate that the detection and persistence of ctDNA at MT may have the potential to negatively predict response to neoadjuvant treatment and identify patients who will not achieve pCR or be classified with RCB II/III.
Effectiveness and Safety of Aromatherapy in Managing Behavioral and Psychological Symptoms of Dementia: A Mixed-Methods Systematic Review
Authors: Li Becky Siu Yin , Chan Carmen Wing Han , Li Minjie , Wong Irene Kit Yee , Yu Yvonne Hoi Un
Published: December 2021

Introduction: Behavioral and psychological symptoms of dementia (BPSD) is the most prominent and distressing manifestation for older persons with dementia (PWD) and caregivers. Aromatherapy has demonstrated its effectiveness in managing BPSD in various studies. However, previous studies and systematic reviews have obtained inconsistent findings, and a review of qualitative studies is yet to be conducted. Method: A mixed-methods systematic review with a convergent segregated approach was performed to evaluate the effectiveness of aromatherapy in improving the BPSD and quality of life (QoL) of PWD and in relieving the distress and burden of caregivers, as well as its safety for PWD. Both published and unpublished quantitative and qualitative studies written in English and Chinese between January 1996 and December 2020 were retrieved from 28 databases, including MEDLINE, EMBASE, and Web of Science, based on the prespecified criteria. The methodological quality was assessed by using critical appraisal tools from the Joanna Briggs Institute. Quantitative synthesis, qualitative synthesis, and integration of quantitative and qualitative evidence were performed. Results: A total of 12 randomized controlled trials, 10 quasi-experimental studies, and 2 qualitative studies were included in the review. Some inconsistent findings regarding the effectiveness of aromatherapy in reducing the severity of BPSD were observed. Some studies reported that aromatherapy significantly improved the QoL of PWD and relieved the distress and burden of caregivers, promoted a positive experience among caregivers, and had very low adverse effects on PWD (with aromatherapy inhalation reporting no adverse effects). Conclusion: Aromatherapy, especially in the inhalation approach, could be a potentially safe and effective strategy for managing BPSD. However, more structuralized and comparable studies with sufficient sample size, adherence monitoring, and sound theoretical basis could be conducted to obtain conclusive findings.