bluuebunny/crossref_metadata_2025 huggingface dataset:
Association between Stress at Work and Temporomandibular Disorders: A Systematic Review
Authors: Aranha Ricardo Luiz de Barreto , Martins Renata de Castro , de Aguilar Diego Rodrigues , Moreno-Drada Johana Alejandra , Sohn Woosung , Martins Carolina de Castro , de Abreu Mauro Henrique Nogueira Guimarães
Published: May 2021

Temporomandibular disorders (TMD) have been traditionally associated with psychosocial factors; however, occupational stress as a factor related to TMD has not been adequately assessed in the literature. The aim was to investigate the association between stress at work and TMD on adult paid workers. An electronic search included PubMed, Scopus, Web of Science, Embase, and LILACS databases. Manual searches in the included articles’ reference and gray literature were performed. There were no restrictions regarding language or publication period. The inclusion criteria comprised observational studies with paid workers of any category, of both sexes, above 18 years old, assessing occupational stress/stress or distress and TMD as diagnosis or isolated signs and symptoms. Methodological quality was evaluated using Joanna Briggs tools. We narratively assessed the evidence using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach. We collected 12 studies. 50% reported a positive association between stress and TMD diagnostic across various job categories. On the other hand, TMJ sounds (a TMD sign) and work stress were associated only in a musicians’ population. However, the shortage of eligible articles and the methodological limitations provided a very low certainty of the evidence; only 4 of the studies used validated tools for both stress and TMD (2 reporting positive association). The association between stress and TMD is inconclusive by the available data. In the future, we expect more robust epidemiologic studies addressing these relevant aspects.
Multidepot Recycling Vehicle Routing Problem with Resource Sharing and Time Window Assignment
Authors: Wang Yong , Wang Xiuwen , Guan Xiangyang , Tang Jinjun
Published: May 2021

This study aims to provide tactical and operational decisions in multidepot recycling logistics networks with consideration of resource sharing (RS) and time window assignment (TWA) strategies. The RS strategy contributes to efficient resource allocation and utilization among recycling centers (RCs). The TWA strategy involves assigning time windows to customers to enhance the operational efficiency of logistics networks. A biobjective mathematical model is established to minimize the total operating cost and number of vehicles for solving the multidepot recycling vehicle routing problem with RS and TWA (MRVRPRSTWA). A hybrid heuristic algorithm including 3D k-means clustering algorithm and nondominated sorting genetic algorithm- (NSGA-) II (NSGA-II) is designed. The 3D k-means clustering algorithm groups customers into clusters on the basis of their spatial and temporal distances to reduce the computational complexity in optimizing the multidepot logistics networks. In comparison with NSGA algorithm, the NSGA-II algorithm incorporates an elitist strategy, which can improve the computational speed and robustness. In this study, the performance of the NSGA-II algorithm is compared with the other two algorithms. Results show that the proposed algorithm is superior in solving MRVRPRSTWA. The proposed model and algorithm are applied to an empirical case study in Chongqing City, China, to test their applicability in real logistics operations. Four different scenarios regarding whether the RS and TWA strategies are included or not are developed to test the efficacy of the proposed methods. The results indicate that the RS and TWA strategies can optimize the recycling services and resource allocation and utilization and enhance the operational efficiency, thus promoting the sustainable development of the logistics industry.
Blue Light from Cell Phones Can Cause Chronic Retinal Light Injury: The Evidence from a Clinical Observational Study and a SD Rat Model
Authors: Li Huili , Zhang Ming , Wang Dahong , Dong Guojun , Chen Zhiwei , Li Suilin , Sun Xiaohong , Zeng Min , Liao Haiyang , Chen Huifang , Xiao Shengyan , Li Xiaodan
Published: May 2021

Background. To investigate the chronic photodamage induced by the low‐intensity blue light of phones, we carried out a clinical pilot study and established an animal model by irradiating SD rats with a homemade illuminator. Methods. Clinical investigation: A total of 25 clinical medical workers in our hospital were selected and divided into a control group and an observation group according to the daily video terminal use time. Multifocal electrophysiological system (Mf‐ERG) was used for retinal functional examination. Animal experiment: A total of sixty SD rats were randomly divided into a control group (n = 6) and an experimental group (n = 54). The experimental rats were divided into nine groups, which were exposed to the blue light illuminator of the simulated cell phone array for different time. The visual electrophysiology of the rats was tested, and changes in structure were observed by H&E staining and transmission electron microscopy. Results. In clinical investigation, macular centers near the concave area retinal photoreceptor cells have reduced amplitude. In animal experiments, the amplitude of photoreceptor cells decreased, the peak time was delayed, and the amplitudes were lower in the experimental groups. H&E staining and transmission electron microscope showed retinal tissue structure and functional damage in experimental groups. Conclusions. Long‐term exposure to low‐illuminance blue light can cause retinal tissue structure and functional damage, and the chronic damage due to low‐illuminance light warrants attention. The clinical registration number is 2018‐KY‐KS‐LHL.
Intervertebral Disc Degeneration Induced by Needle Puncture and Ovariectomy: A Rat Coccygeal Model
Authors: Tian Tao , Wang Haidong , Li Zhaohui , Yang Sidong , Ding Wenyuan
Published: May 2021

Objectives. To establish a novel animal model of intervertebral disc degeneration (IVDD) in rats and to investigate the effect of 17β‐estradiol (E2) intervention in this model. Methods. This study was divided into two parts: animal model (four groups: Sham, ovariectomy (OVX), Puncture, and OVX+Puncture; three‐time points: 4, 8, and 12 weeks; three female rats/group/time point) and drug intervention (Sham, OVX+Puncture+corn oil, and OVX+Puncture+E2; three female rats/group). The rats were analyzed by micromagnetic resonance imaging (MRI), hematoxylin and eosin (HE) staining, and safranin‐O staining. Results. MRI and histological scores significantly differed among the four groups at the three‐time points (all P < 0.05). IVDD progressed with time in the OVX, Puncture, and OVX+Puncture groups (all P < 0.05). The changes were the most obvious in the OVX+Puncture group. In the E2 intervention part, the Veh group had the worst MRI signals and histological scores (P < 0.05). The MRI scores in the E2 group were less obvious compared to the Sham group (P > 0.05). Also, the histological scores were significantly different between the Sham and E2 groups (P < 0.05). Conclusions. The combination of ovariectomy and needle puncture can synergically induce IVDD in rat coccygeal discs. Estrogen treatment can effectively ameliorate IVDD progression in the newly established IVDD models.
A New Semantic and Statistical Distance‐Based Anomaly Detection in Crowd Video Surveillance
Authors: Rezaei Fariba , Yazdi Mehran
Published: May 2021

Recently, attention toward autonomous surveillance has been intensified and anomaly detection in crowded scenes is one of those significant surveillance tasks. Traditional approaches include the extraction of handcrafted features that need the subsequent task of model learning. They are mostly used to extract low‐level spatiotemporal features of videos, neglecting the effect of semantic information. Recently, deep learning (DL) methods have been emerged in various domains, especially CNN for visual problems, with the ability to extract high‐level information at higher layers of their architectures. On the other side, topic modeling‐based approaches like NMF can extract more semantic representations. Here, we investigate a new hybrid visual embedding method based on deep features and a topic model for anomaly detection. Features per frame are computed hierarchically through a pretrained deep model, and in parallel, topic distributions are learned through multilayer nonnegative matrix factorization entangling information from extracted deep features. Training is accomplished through normal samples. Thereafter, K‐means is applied to find typical normal clusters. At test time, after achieving feature representation through deep model and topic distribution for test frames, a statistical earth mover distance (EMD) metric is evaluated to measure the difference between normal cluster centroids and test topic distributions. High difference versus a threshold is detected as an anomaly. Experimental results on the benchmark Ped1 and Ped2 UCSD datasets demonstrate the effectiveness of our proposed method in anomaly detection.
RAPOT: An Adaptive Multifactor Risk Assessment Framework on Public Opinion for Trial Management
Authors: Jiang Weina , Yong Qi , Liu Ning , Luo Yuze
Published: May 2021

Since public opinion from social media has a growing impact and supervision on trial, risk assessment on public opinion is increasingly important in refined trial management. However, the tremendous amount of public opinion and the insufficient historical logs of trial procedures bring challenges to risk assessment on public opinion. To address this, we propose an adaptive multifactor risk assessment framework on public opinion with fuzzy numbers. Initially, we establish a multilayer indicator model for assessing the risk of public opinion (POR) with multilayer analysis and decision methods. Then, we explore the association rules hidden in the process logs to update the indicator model periodically. Moreover, we design a public opinion analysis module for indicator evaluation, including analysis in public opinion sentiment, hot search, and social media coverage to deal with big data on social media. Especially, the public opinion sentiment is classified by topic‐based BiLSTM (T‐BiLSTM), which is more accurate. Finally, the fuzzy number similarity is employed to determine POR’s level in the nine‐level risk system. Experimental results validate the efficiency of our framework when assessing the POR.
Roof Failure Mechanism and Control Technology of Large Section Open‐Off Cut in Soft Rock Strata with Thin Thickness
Authors: Li Chen , Li Jun , Lian Xiaoyong , Li Yongen , Xue Qi , Feng Jicheng
Published: May 2021

The open‐off cut is used for equipment installation of working face before underground mining, and its sectional size is larger than that of the mining roadway. Therefore, the stability of open‐off cut surrounding rock determines whether the panel can be put into operation. To solve the roof instability of open‐off cut in the Wanli No.1 coal mine, the roof failure mechanism of open‐off cut under weak composite rock strata with thin thickness was studied by field monitoring, theoretical analysis, and numerical simulation. First, the characteristics of surrounding rock and the basic law of strata behaviors were obtained by detailed field monitoring. Afterward, FLAC3D numerical simulation and mechanical analysis were used to obtain the main mechanical control parameters of surrounding rock instability, and the existence of a soft interlayer above the roof is the main cause of roof instability. Based on this, the supporting parameters of the open‐off cut were optimized and adjusted. The optimized parameters were applied to the adjacent 31207 open‐off cut. The engineering practice showed that the optimized supporting parameters have an ideal control effect on roof stability.
Improved Functional Causal Likelihood-Based Causal Discovery Method for Diabetes Risk Factors
Authors: Gao Xiue , Xie Wenxue , Wang Zumin , Chen Bo , Zhou Shengbin
Published: May 2021

Diabetes mellitus is a disease that has reached epidemic proportions globally in recent years. Consequently, the prevention and treatment of diabetes have become key social challenges. Most of the research on diabetes risk factors has focused on correlation analysis with little investigation into the causality of these risk factors. However, understanding the causality is also essential to preventing the disease. In this study, a causal discovery method for diabetes risk factors was developed based on an improved functional causal likelihood (IFCL) model. Firstly, the issue of excessive redundant and false edges in functional causal likelihood structures was resolved through the construction of an IFCL model using an adjustment threshold value. On this basis, an IFCL-based causal discovery algorithm was designed, and a simulation experiment was performed with the developed algorithm. The experimental results revealed that the causal structure generated using a dataset with a sample size of 2000 provided more information than that produced using a dataset with a sample size of 768. In addition, the causal structures obtained with the developed algorithm had fewer redundant and false edges. The following six causal relationships were identified: insulin→plasma glucose concentration, plasma glucose concentration→body mass index (BMI), triceps skin fold thickness→BMI and age, diastolic blood pressure→BMI, and number of times pregnant→age. Furthermore, the reasonableness of these causal relationships was investigated. The algorithm developed in this study enables the discovery of causal relationships among various diabetes risk factors and can serve as a reference for future causality studies on diabetes risk factors.
Effect of NH3 Alkalization and MgO Promotion on the Performance of Ni/SBA-15 Catalyst in Combined Steam and Carbon Dioxide Reforming of Methane
Authors: Phuong Phan H. , Loc Luu C. , Tri Nguyen , Anh Nguyen P. , Anh Ha C.
Published: May 2021

In this work, 31.4 wt.% Ni/SBA-15 (Ni/SBA-15) nonpromoted and alkalized with ammonia solution and by MgO promoter catalysts were prepared and used for combined steam and CO2 reforming of CH4 (bireforming). Effect of concentration of ammonia solution (NH3(aq)) (10–25 vol.%) and Mg content (3–12 wt.%) on the properties of the Ni/SBA-15 catalysts was investigated by low-angle and powder X-ray diffraction (XRD), N2-BET isothermal adsorption, SEM, TEM, EDS mapping, H2-TPR, and CO2-TPD methods. The performance of the catalysts in bireforming was assessed in the temperature range of 550–800°C. The enhancement of dispersion of NiO particles, reducibility, and basicity of alkalized Ni/SBA-15 catalysts were responsible for improving the catalytic performance of this catalyst. The results revealed that the Ni/SBA-15 treated with 15-25% NH3(aq) solution and promoted with 3-9% Mg exhibited high activity for CH4 conversion. Meanwhile, Ni6Mg/SBA-15 showed the highest CO2 conversion. Among tested catalysts, Ni/SBA-15-20NH3 and Ni9Mg/SBA-15 samples had an almost equal activity with a CH4 conversion of nearly 97% and a CO2 conversion of about 84% at 700°C thanks to its moderate affinity with both CO2 and CH4. However, the H2/CO ratio of the product mixture remained at 2.02 on the Ni/SBA-15-20NH3 catalyst and almost 1 on the Ni9Mg/SBA-15 sample. These results might be related to the fact that the alkalization of the Ni/SBA-15 catalyst by NH3(aq) solution had an advantage over using MgO because side reactions were unlikely to occur.
Pavement Crack Detection Method Based on Deep Learning Models
Authors: Hu Guo X. , Hu Bao L. , Yang Zhong , Huang Li , Li Ping
Published: May 2021

Severe weather and long‐term driving of vehicles lead to various cracks on asphalt pavement. If these cracks cannot be found and repaired in time, it will have a negative impact on the safe driving of vehicles. Traditional artificial detection has some problems, such as low efficiency and missing detection. The detection model based on machine learning needs artificial design of pavement crack characteristics. According to the pavement distress identification manual proposed by the Federal Highway Administration (FHWA), these categories have three different types of cracks, such as fatigue, longitudinal crack, and transverse cracks. In the face of many types of pavement cracks, it is difficult to design a general feature extraction model to extract pavement crack features, which leads to the poor effect of the automatic detection model based on machine learning. Object detection based on the deep learning model has achieved good results in many fields. As a result, those models have become possible for pavement crack detection. This paper discusses the latest YOLOv5 series detection model for pavement crack detection and is to find out an effective training and detection method. Firstly, the 3001 asphalt crack pavement images with the original size of 2976 × 3978 pixels are collected using a digital camera and are randomly divided into three types according to the severity levels of low, medium, and high. Then, for the dataset of crack pavement, YOLOv5 series models are used for training and testing. The experimental results show that the detection accuracy of the YOLOv5l model is the highest, reaching 88.1%, and the detection time of the YOLOv5s model is the shortest, only 11.1 ms for each image.
Sleep Quality and Its Correlates among Adolescents of Western Nepal: A Population-Based Study
Authors: Gautam Prayas , Dahal Maginsh , Baral Kushalata , Acharya Rohit , Khanal Sudip , Kasaju Aastha , Sangroula Raj Kumar , Gautam Koshish Raj , Pathak Kabita , Neupane Anu
Published: May 2021

Sleep quality has a long-term impact on health leading to depression among adolescent students. We conducted a cross-sectional study to assess the prevalence of sleep quality and its associated factors among adolescents of western, Nepal. 514 adolescents from different schools were selected by the probability proportionate to size (PPS) method. The Pittsburgh Sleep Quality Index (PSQI) was used to assess the sleep quality among adolescents. The collected data were entered in EpiData 3.2 version, then extracted to excel 2019 and was analyzed with the help of RStudio (version 1.2.5033). Frequency distribution and percentage were identified as descriptive analysis whereas chi-square test was done. Variables that were found statistically significant (

P
<
0.05

) were further analyzed using the logistic regression model. The prevalence of sleep quality in this study was 39.1%. In a bivariate analysis, ethnicity, religion, place of residence, drinking status of father, reason for selecting the currently studying faculty, satisfaction with academic performance, use of tobacco, relationship with friends or classmates, more use of internet per day, and use of internet before falling asleep were found to be statistically significant with sleep quality. Those students who left their home without informing their parents were more than three times at the risk of sleep quality than those students who never ran away from their home without informing their parents (

AOR
=
3.435

, CI: 1.237-9.540). The overall prevalence of sleep quality among school going adolescent students was 39.1 percent which was comparatively high.
A Deep Learning Model with Conv-LSTM Networks for Subway Passenger Congestion Delay Prediction
Authors: Chen Wei , Li Zongping , Liu Can , Ai Yi
Published: May 2021

When urban rail transit is faced with a large number of commuter passengers during peak periods, passengers are often waiting for the next train because the subway is running at full load, which causes delays to the overall travel time of passengers. The calculation and prediction of the congestion delay in subway stations can guide the operation department and passengers to make better planning and selection. In this paper, we use a new method based on deep learning technology to evaluate the congestion delay of subway stations. Firstly, we use automatic fare collection (AFC) system data to evaluate the congestion delays of stations. Then, we use a convolutional long short-term memory (Conv-LSTM) network to extract spatial and temporal characteristics to solve the short-term prediction problem of the subway congestion delay in the network structure. The spatiotemporal variables include inbound passenger flow, outbound passenger flow, number of passengers delayed, and average delay time. As a spatiotemporal sequence, the input and prediction targets are both spatiotemporal three-dimensional tensors in the end-to-end training model. The effectiveness of the method is verified by a case study of the Chongqing Rail Transit. Experimental results show that Conv-LSTM is better than the benchmark models in capturing spatial and temporal correlation.
Current Status of Emerging PV Technologies: A Comparative Study of Dye-Sensitized, Organic, and Perovskite Solar Cells
Authors: Giannouli Myrsini
Published: May 2021

The imminent depletion of conventional energy sources has motivated the advancement of renewable energy technologies. Third-generation photovoltaic technologies, such as dye-sensitized solar cells (DSSCs), organic solar cells (OSCs), and perovskite solar cells (PSCs), are being developed as alternatives to silicon solar cells. In recent years, there has been considerable interest in the market development of these emerging photovoltaic technologies, especially for sustainable solar energy applications. However, these technologies have not yet reached the maturity required for large-scale commercialization. Further research is required in order to improve the efficiency and stability of these devices, while keeping their production costs to a minimum. In this study, a comparative assessment of DSSCs, OSCs, and PSCs is conducted and the current state of the art of these promising technologies is investigated. Advanced techniques and research trends are examined from the perspective of novel materials, device modelling, and innovative device structures. The comparative advantages and limitations of each of these photovoltaic technologies are assessed in terms of device efficiency, durability, ease of fabrication, and performance-price ratio. Emphasis is placed on assessing the potential of these solar cell technologies for sustainable solar energy applications. Finally, the future outlook of these technologies is featured, and avenues for progress beyond the state of the art are explored.
Systems Pharmacology and In Silico Docking Analysis Uncover Association of CA2, PPARG, RXRA, and VDR with the Mechanisms Underlying the Shi Zhen Tea Formula Effect on Eczema
Authors: Wang Zhen-Zhen , Jia Yuan , Srivastava Kamal D. , Huang Weihua , Tiwari Raj , Nowak-Wegrzyn Anna , Geliebter Jan , Miao Mingsan , Li Xiu-Min
Published: May 2021

Eczema is a complex chronic inflammatory skin disease impacted by environmental factors, infections, immune disorders, and deficiencies in skin barrier function. Shi Zhen Tea (SZT), derived from traditional Chinese medicine Xiao-Feng-San, has shown to be an effective integrative therapy for treating skin lesions, itching, and sleeping loss, and it facilitates reduction of topical steroid and antihistamine use in pediatric and adult patients with severe eczema. Yet, its active compounds and therapeutic mechanisms have not been elucidated. In this study, we sought to investigate the active compounds and molecular mechanisms of SZT in treating eczema using systems pharmacology and in silico docking analysis. SZT is composed of 4 medicinal herbs, Baizhu (Atractylodis macrocephalae rhizome), Jingjie (Schizonepetae herba), Kushen (Sophorae flavescentis radix), and Niubangzi (Arctii fructus). We first identified 51 active compounds from SZT and their 81 potential molecular targets by high-throughput computational analysis, from which we identified 4 major pathways including Th17 cell differentiation, metabolic pathways, pathways in cancer, and the PI3K-Akt signaling pathway. Through network analysis of the compound-target pathway, we identified hub molecular targets within these pathways including carbonic anhydrase II (CA2), peroxisome proliferator activated receptor γ (PPAR γ), retinoid X receptor α (RXRA), and vitamin D receptor (VDR). We further identified top 5 compounds including cynarine, stigmasterin, kushenol, β-sitosterol, and (24S)-24-propylcholesta-5-ene-3β-ol as putative key active compounds on the basis of their molecular docking scores with identified hub target proteins. Our study provides an insight into the therapeutic mechanism underlying multiscale benefits of SZT for eczema and paves the way for developing new and potentially more effective eczema therapies.
Mechanistic Investigation on the Regulation of FABP1 by the IL‐6/miR‐603 Signaling in the Pathogenesis of Hepatocellular Carcinoma
Authors: Lin Ye-xin , Wu Xiong-bo , Zheng Chu-wei , Zhang Qing-lin , Zhang Guo-qiang , Chen Ke , Zhan Qiang , An Fang-mei
Published: May 2021

Background. Abnormal lipid metabolism is closely associated with the invasiveness and metastasis of cancer. Fatty acid‐binding proteins (FABPs) play essential roles in lipid metabolism, and miRNAs can affect lipid metabolism by targeting FABPs. However, the exact mechanism is unknown. Methods. FABP1 expression in HCC tissues was analyzed by immunochemistry with tissue microarrays. The lipid content was detected by Oil Red O staining, and the interaction between FABP1 and free fatty acid (FFA) was studied by a labeling and tracking method. miRNA arrays were used to detect the expression of miRNAs in IL‐6‐stimulated HCC cells. miR‐603 expression was verified by qPCR. The proteins were checked by Western blot analysis. Gain and loss function evaluation was assessed by lentivirus and miRNA mimic transfection in Huh‐7 cells, while reactive oxygen species (ROS) were detected by fluorescence. Results. FABP1 expression was significantly decreased in approximately 90% (81/90) of HCC patients. FABP1 expression in adjacent tissues was closely associated with overall survival. Meanwhile, lipid was abundant in the adjacent tissues, yet significantly reduced in HCC tissues. FABP1 and FFA can promote each other for being uptaken by Huh‐7 cells. FABP1 overexpression induced apoptosis and inhibited the proliferation, migration, invasion, and metastasis of Huh‐7 cells. IL‐6 treatment affected the expression of miRNAs, and miR‐603 was overexpressed in HCC tissues. Also, miR‐603 overexpression promoted the proliferation, migration, invasion, and metastasis of Huh‐7 cells. Bioinformatic analysis predicted that miR‐603 targets the 3 ′‐UTR region of FABP1. However, miR‐603 overexpression inhibited the expression of the FABP1 but increased the CPT1A, PPAR‐α, and SREBP1 expressions. FABP1 overexpression reduced ROS in HCC cells, while miR‐603 can reverse these effects. Conclusion. Our results indicate that in the pathogenesis of HCC, IL‐6 induces miR‐603 expression, which subsequently inhibits FABP1 expression, promotes the lipid metabolism‐ and synthesis‐related proteins, and finally increases the cellular oxidative stress level and leads to the metastasis of HCC.
Design and Implementation of Human-Computer Interaction System in Parallel Digital Library System Based on Neural Network
Authors: Cao Jun
Published: May 2021

Information and communication technologies are well thought-out as probable assets for the development of socioeconomics in developing countries. Studies have shown that enhanced infrastructure of telecommunication has facilitated means for underserved population development by various ways. Among the existing applications of ICT, the digital library systems provide with better solutions and respond to a variety of unmet needs of research institutions, scientific communities, and developments. With the development of digital library technology, the parallel database system has become the main tool for efficient information processing in the digital library system. On this basis, based on the parallel environment of the computer cluster, by coordinating the communication in the parallel environment, the coordinator, the collection machine, and the query processor can complete the operation of distribution, load, and maintenance, which has high efficiency and saves much precious time, supports the digital library to meet user requirements effectively, and meets the digital library’s performance requirements for data, and also, the key problem in the parallel algorithm has been solved. The experimental results show that this parallel technique has very good performance and efficiency.
Simulating the Sustainability of Xiong’an New Area Undertaking the Industrial Transfer from Beijing
Authors: Niu Fangqu
Published: May 2021

The national new area Xiong’an has been established to take over the noncapital functions of Beijing in China. In light of local resources and environmental constraints, it is important to clarify the mode of industrial transfer for Xiong’an new area (XNA) to achieve the developmental goals. This study simulates and analyzes the speed of industrial transfer and the capacities of XNA in light of resource and environmental constraints. The results show that, to just realize modernization in 2035, the transfer rate of the secondary industry and tertiary industry is annually 3.7 billion yuan and 6.4 billion yuan, respectively. But at this speed, the atmospheric environment will be overloaded by 2028. The minimum and maximum transfer rate to realize modernization in 2035 without overloading resources and environment are also specified as well as the total size of economy and population. The results indicate that land for the construction of XNA is rich, but local water is not sufficient to support industrial growth. The atmospheric environment of XNA is also an important limiting factor. It is thus necessary to reduce air pollution and increase the population and industrial scale of the area by increasing the transfer rate of the tertiary industry.
On Fault‐Tolerant Resolving Sets of Some Families of Ladder Networks
Authors: Wang Hua , Azeem Muhammad , Nadeem Muhammad Faisal , Ur-Rehman Ata , Aslam Adnan
Published: May 2021

In computer networks, vertices represent hosts or servers, and edges represent as the connecting medium between them. In localization, some special vertices (resolving sets) are selected to locate the position of all vertices in a computer network. If an arbitrary vertex stopped working and selected vertices still remain the resolving set, then the chosen set is called as the fault‐tolerant resolving set. The least number of vertices in such resolving sets is called the fault‐tolerant metric dimension of the network. Because of the variety of applications of the metric dimension in different areas of sciences, many generalizations were proposed, and fault tolerant is one of them. In this paper, we computed the fault‐tolerant metric dimension of triangular snake, ladder, Mobius ladder, and hexagonal ladder networks. It is important to observe that, in all these classes of networks, the fault‐tolerant metric dimension and metric dimension differ by 1.
Methanolic Crude Extract of Hagenia abyssinica Possesses Significant Antidiarrheal Effect: Evidence for In Vivo Antidiarrheal Activity
Authors: Kifle Zemene Demelash , Atnafie Seyfe Asrade , Yimer Tadesse Tesfaye , Belachew Teshome Fentik , Kidanu Birhanu Berihun
Published: May 2021

Background. Hagenia abyssinica is one of the most commonly used medicinal plants for the treatment of diarrhea in Ethiopia. Therefore, this study aimed to evaluate the antidiarrheal effect of methanol crude extract of H. abyssinica leaves in mice. Methods. Acute toxicity testing was conducted using Organization for Economic Cooperation and Development guidelines. The antidiarrheal activity of the crude extract of H. abyssinica was investigated using three animal models such as small intestine transit, enteropooling, and castor oil-induced diarrhea models. The extract was administered at three different doses (100, 200, and 400 mg/kg) to the test groups, while the positive control group received 3 mg/kg of loperamide and the negative control group received 10 ml/kg of vehicle (distilled water). Results. The crude extract of H. abyssinica did not exhibit death at the limit dose (2 g/kg) throughout the observation period. In the castor oil-induced model, the crude extract at a dose of 200 and 400 mg/kg exhibited a significant (

P
<
0.05

) antimotility effect as compared to the negative control. The crude extract revealed a significant reduction in the volume and weight of intestinal contents at 200 and 400 mg/kg doses of the extract. Moreover, the highest antidiarrheal index (ADI) was obtained with the dose of 400 mg/kg of crude extract, which was comparable to the standard drug. Conclusion. The crude extract of Hagenia abyssinica possesses antidiarrheal activity and supports the traditional use of H. abyssinica for the management of diarrhea.
Oral Administration of Mifepristone Combined with Ultrasound-Guided Radiofrequency Ablation in Treating Patients with Uterine Fibroids: Efficacy, Safety, and Alternations of Inflammatory Cytokines, Adhesion Molecules, and Growth Factors
Authors: Hou Aiqin , Yan Zhen , Zhang Yuanyuan , Hou Jing
Published: May 2021

Uterine fibroids are one of the most common and yet understudied diseases in women mainly during their reproductive years. The study analyzed the efficacy and safety of oral administration of mifepristone combined with ultrasound-guided radiofrequency ablation in treating patients with uterine fibroids, as well as changes of inflammatory cytokines including procalcitonin (PCT), high-sensitivity C-reactive protein (hs-CRP), and tumor necrosis factor-alpha (TNF-α), adhesion molecules including monocyte chemotactic protein 1 (MCP-1) and soluble intercellular adhesion molecule 1 (sICAM-1), and growth factors including vascular endothelial growth factor (VEGF), epidermal growth factor (EGF), basic fibroblast growth factor (bFGF), transforming growth factor-β (TGF-β), and TGF-β receptor. A total of 130 patients who were admitted into our hospital for uterine fibroids from December 2016 to June 2018 were included, among which 65 women were given ultrasound-guided radiofrequency ablation alone (control group) and the remaining women were given oral administration of mifepristone combined with ultrasound-guided radiofrequency ablation (experimental group). Reduced volumes of uterus and fibroids were observed in both two groups after treatment (

P
<
0.05

). It was found that the experimental group exhibited higher total effective rate and smaller volumes of uterus and fibroids than the control group (

P
<
0.05

). Following treatment, the levels of MCP-1, sICAM-1, VEGF, EGF, bFGF, TGF-β, TGF-β receptor, PCT, hs-CRP, and TNF-α in the serum in the two groups were declined (

P
<
0.05

), while this decline was more significantly in the experimental group than the control group (

P
<
0.05

). Besides, there was no significant difference in the incidence rate of adverse reactions between the two groups (

χ

2

=
0.781

,

P
>
0.05

). These results collectively indicate the high efficacy and safety of oral administration of mifepristone combined with ultrasound-guided radiofrequency ablation in treating patients with uterine fibroids, as this combination therapy could inhibit the expressions of inflammatory cytokines, adhesion molecules, and growth factors.
Propofol Combined with Fentanyl Is Superior to Propofol Alone in Sedation Protocols for Painless Gastrointestinal Endoscopy
Authors: Chang Jie , Yang Chun
Published: May 2021

Sufficient propofol or fentanyl doses necessary to prevent the response to skin incision do not necessarily reduce hemodynamic responses during surgery. The purpose of this study was to characterize the pharmacodynamic interaction between propofol and fentanyl with respect to the sedative effects and safety during painless gastrointestinal endoscopy. From October 2018 to October 2020, 200 patients undergoing painless gastrointestinal endoscopy in our department’s outpatient or inpatient clinic were selected and randomly divided into a control group and an observation group, 100 patients per group. Prior to surgery, the patients in the two groups were required to be connected to an electrocardiogram monitor and then were instructed to lie on the left side and receive continuous oxygen infusion of 2-3 L/min with a nasal cannula. The control group was injected with propofol injection (100 mg/min). The observation group was given fentanyl intravenous injection at a dose of 0.1 μg/kg followed by propofol injection (100 mg/min). Painless gastrointestinal endoscopy was performed after the patients entered a sleep state and the eyelash reaction disappeared. Outcome analysis was performed on preoperative and intraoperative hemodynamic indicators, including heart rate, blood oxygen saturation, dose of propofol, time for endoscopy, recovery time, hospitalization after recovery, Montreal Cognitive Assessment (MoCA) score, and Mini-Mental State Examination (MMSE) score. The incidence rate of adverse reactions in the observation group was 6%, which was notably lower than that of the control group (18%). The total response rate of the observation group was 98%, which was significantly higher than that of the control group (90%) (

P
<
0.05

). The intraoperative heart rate and blood oxygen saturation of the observation group were higher than those of the control group (

P
<
0.0
ss

). The patients receiving sedation with propofol plus fentanyl had fewer doses of propofol and shorter recovery time than those receiving propofol alone (

P
<
0.05

). It was also revealed that the patients receiving sedation with propofol plus fentanyl exhibited more MoCA and MMSE scores than those receiving propofol alone 30 min after sedation during painless gastrointestinal endoscopy. Taken together, sedation with propofol combined with fentanyl was more effective and safer than that with propofol alone in painless gastrointestinal endoscopy, which can ensure a wake-up state, stable breathing cycle, and better gastrointestinal painless procedure.
Pricing of Embedded Options in Bank Deposits and Loans Based on Jump‐Diffusion Interest Rate Model
Authors: Tang Enlin , Xu Song
Published: May 2021

The marketization of interest rate is an inevitable requirement for China’s financial reform and joining the WTO to connect with the international financial market. It is also an important link to improve the marketization degree of China’s financial system. The marketization of interest rate in China is gradually advancing according to its preset mode. In the process of interest rate marketization, an unavoidable problem is that while the interest rate marketization gives the commercial banks the autonomy of capital pricing, the fluctuation of interest rate is more and more frequent. However, due to the fluctuation of interest rate, the loan as the main assets of commercial banks will be prepayed by borrowers, and the time deposit as the main liabilities of commercial banks will be withdrawn by depositors in advance; that is, embedded options are implied in asset liability items, which makes it difficult for commercial banks to accurately calculate the actual interest margin of deposits and loans and manage the interest rate risk. Therefore, it is of great significance to identify and price such embedded option value. On the basis of identifying and decomposing the embedded options in deposit and loan of commercial banks, according to the change characteristics of deposit and loan interest rate of Chinese commercial banks, this paper chooses jump‐diffusion interest rate model to describe the change of benchmark interest rate of deposit and loan in China and demonstrates the advantages of this model compared with other models. Based on Monte Carlo simulation technology, the embedded options of five‐year fixed deposit and ten‐year prepayable loan in China are priced. On this basis, it points out that the real interest margin of commercial bank’s deposit and loan should be the nominal interest margin minus the value of deposit and loan’s embedded options. In the process of interest rate risk management, we should pay attention to the existence of embedded options and carry out effective management.
Semantic Matching Efficiency of Supply and Demand Text on Cross‐Border E‐Commerce Online Technology Trading Platforms
Authors: Chen Xuhua
Published: May 2021

With the innovation of global trade business models, more and more foreign trade companies are transforming and developing in the direction of cross‐border e‐commerce. However, due to the limitation of platform language processing and analysis technology, foreign trade companies encounter many bottlenecks in the process of transformation and upgrading. From the perspective of the semantic matching efficiency of e‐commerce platforms, this paper improves the logical and technical problems of cross‐border e‐commerce in the operation process and uses semantic matching efficiency as the research object to conduct experiments on the QQP dataset. We propose a graph network text semantic analysis model TextSGN based on semantic dependency analysis for the problem that the existing text semantic matching method does not consider the semantic dependency information between words in the text and requires a large amount of training data. The model first analyzes the semantic dependence of the text and performs word embedding and one‐hot encoding on the nodes (single words) and edges (dependencies) in the semantic dependence graph. On this basis, in order to quickly mine semantic dependencies, an SGN network block is proposed. The network block defines the way of information transmission from the structural level to update the nodes and edges in the graph, thereby quickly mining semantics dependent information allows the network to converge faster, train classification models on multiple public datasets, and perform classification tests. The experimental results show that the accuracy rate of TextSGN model in short text classification reaches 95.2%, which is 3.6% higher than the suboptimal classification method; the accuracy rate is 86.16%, the F1 value is 88.77%, and the result is better than other methods.
Developing a Recognition System for Diagnosing Melanoma Skin Lesions Using Artificial Intelligence Algorithms
Authors: Alsaade Fawaz Waselallah , Aldhyani Theyazn H. H. , Al-Adhaileh Mosleh Hmoud
Published: May 2021

In recent years, computerized biomedical imaging and analysis have become extremely promising, more interesting, and highly beneficial. They provide remarkable information in the diagnoses of skin lesions. There have been developments in modern diagnostic systems that can help detect melanoma in its early stages to save the lives of many people. There is also a significant growth in the design of computer-aided diagnosis (CAD) systems using advanced artificial intelligence. The purpose of the present research is to develop a system to diagnose skin cancer, one that will lead to a high level of detection of the skin cancer. The proposed system was developed using deep learning and traditional artificial intelligence machine learning algorithms. The dermoscopy images were collected from the PH2 and ISIC 2018 in order to examine the diagnose system. The developed system is divided into feature-based and deep leaning. The feature-based system was developed based on feature-extracting methods. In order to segment the lesion from dermoscopy images, the active contour method was proposed. These skin lesions were processed using hybrid feature extractions, namely, the Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) methods to extract the texture features. The obtained features were then processed using the artificial neural network (ANNs) algorithm. In the second system, the convolutional neural network (CNNs) algorithm was applied for the efficient classification of skin diseases; the CNNs were pretrained using large AlexNet and ResNet50 transfer learning models. The experimental results show that the proposed method outperformed the state-of-art methods for HP2 and ISIC 2018 datasets. Standard evaluation metrics like accuracy, specificity, sensitivity, precision, recall, and

F

-score were employed to evaluate the results of the two proposed systems. The ANN model achieved the highest accuracy for PH2 (97.50%) and ISIC 2018 (98.35%) compared with the CNN model. The evaluation and comparison, proposed systems for classification and detection of melanoma are presented.
The Role of Nasal Endoscopy in Allergic Rhinitis and House Dust Mite Sublingual Immunotherapy
Authors: Liu Beina , Feng Jiapeng , Hu Sunhong
Published: May 2021

Introduction: While there exists considerable evidence for efficacy of sublingual immunotherapy (SLIT), its impact on the improvement of nasal signs in allergic rhinitis (AR) patients remains quite unclear. In this study, the endoscopic examination and the modified Lund-Kennedy (MLK) scoring system were performed to describe and evaluate the therapeutic effect of SLIT. Methods: A total of 105 patients with AR induced by house dust mites were enrolled and treated with standardized Dermatophagoides farinae (D. farinae) drops for 1 year. The total nasal symptoms score (TNSS), total medication score (TMS), visual analog scale (VAS), and MLK scores were assessed at baseline and 6 and 12 months. The MLK score was also compared for its correlation with TNSS, TMS, and VAS. Results: The TNSS, TMS, and VAS scores statistically decreased after SLIT compared to baseline (all p < 0.05). After 12 months of treatment, the rates of well-controlled, partial controlled, and uncontrolled AR patients were 42, 49.5, and 8.5%, respectively. The nasal endoscopy findings showed significant improvement in nasal signs, which mainly included color change of turbinate mucosa, reduction of nasal secretions, and improvement of nasal edema. A significant decrease was observed in MLK scores, and there was a positive correlation between MLK and VAS scores. Conclusions: In addition to commonly utilized subjective assessments (TMS, TNSS, and VAS), our results of endoscopic examination and the MLK scores consistently confirmed that SLIT is an effective therapeutic modality for AR patients. The MLK scores might be considered as an auxiliary tool to evaluate efficacy of SLIT in clinical practice and outcomes research.
Prenatal Diagnosis of Snijders Blok-Campeau Syndrome in a Fetus with Macrocephaly
Authors: Malinger Gustavo , Hoffmann Chen , Achiron Reuven , Berkenstadt Michal
Published: May 2021

We present the prenatal imaging and whole exomics sequencing with the newly described Snijders Blok-Campeau macrocephaly syndrome.