High-Level Security Dimension Evaluation of Blockchain BaaS System and Key Technology

In recent years,blockchain technology integration and application has gradually become an important driving force for new technological innovation and industrial transformation.While blockchain technology and applications are developing rapidly,the emerging security risks and obstacles have gradually become prominent.Attackers can still find security issues in blockchain systems and conduct attacks,causing increasing losses from network attacks every year.In response to the current demand for blockchain application security detection and assessment in all industries,and the insufficient coverage of existing detection technologies such as smart contract detectiontechnology,this paper proposes a blockchain core technology security assessment system model,and studies the relevant detection and assessment key technologies and systems.A security assessment scheme based on a smart contract and consensus mechanism detection scheme is designed.And the underlying blockchain architecture supports the traceability of detection results using super blockchains.Finally,the functionality and performance of the system were tested,and the test results show that the model and solutions proposed in this paper have good feasibility.

Mutation-associated transcripts reconstruct the prognostic features of oral tongue squamous cell carcinoma

Tongue squamous cell carcinoma is highly malignant and has a poor prognosis.In this study,we aimed to combine whole-genome sequencing,whole-genome methylation,and whole-transcriptome analyses to understand the molecular mechanisms of tongue squamous cell carcinoma better.Oral tongue squamous cell carcinoma and adjacent normal tissues from five patients with tongue squamous cell carcinoma were included as five paired samples.After multi-omics sequencing,differentially methylated intervals,methylated loop sites,methylated promoters,and transcripts were screened for variation in all paired samples.Correlations were analyzed to determine biological processes in tongue squamous cell carcinoma.We found five mutated methylation promoters that were significantly associated with mRNA and lncRNA expression levels.Functional annotation of these transcripts revealed their involvement in triggering the mitogen-activated protein kinase cascade,which is associated with cancer progression and the development of drug resistance during treatment.The prognostic signature models constructed based on WDR81 and HNRNPH1 and combined clinical phenotype-gene prognostic signature models showed high predictive efficacy and can be applied to predict patient prognostic risk in clinical settings.We identified biological processes in tongue squamous cell carcinoma that are initiated by mutations in the methylation promoter and are associated with the expression levels of specific mRNAs and lncRNAs.Collectively,changes in transcript levels affect the prognosis of tongue squamous cell carcinoma patients.

Explosive eye injuries: characteristics, traumatic mechanisms, and prognostic factors for poor visual outcomes

Background: Explosions can produce blast waves, high-speed medium, thermal radiation, and chemical spatter,leading to complex and compound eye injuries. However, few studies have comprehensively investigated the clinical features of different eye injury types or possible risk factors for poor prognosis.Methods: We retrospectively reviewed all consecutive records of explosive eye injuries(1449 eyes in 1115 inpatients)in 14 tertiary referral hospitals in China over 12 years(between January 1, 2008 and December 31, 2019). Data on demographics, eye injury types, ocular findings, treatments, and factors affecting visual prognosis were extracted from a standardized database of eye injuries and statistically analyzed.Results: Mechanical ocular trauma accounted for 94.00% of explosion-related eye injuries, among which intraocular foreign bodies(IOFBs) resulted in 55.17% of open globe injuries(OGIs) and contusion caused 60.22% of close globe injuries(CGIs). Proliferative vitreous retinopathy(PVR) was more common in perforating(47.06%) and IOFB(26.84%)than in penetrating(8.79%) injuries, and more common with laceration(24.25%) than rupture(9.22%, P<0.01).However, no difference was observed between rupture and contusion. Ultimately, 9.59% of eyes were removed and the final vision was ≤4/200 in 45.82% of patients. Poor presenting vision [odds ratio(OR)=5.789], full-thickness laceration of the eyeball ≥5 mm(OR=3.665), vitreous hemorrhage(OR=3.474), IOFB(OR=3.510), non-mechanical eye injury(NMEI, OR=2.622), rupture(OR=2.362), traumatic optic neuropathy(OR=2.102), retinal detachment(RD,OR=2.033), endophthalmitis(OR=3.281), contusion(OR=1.679), ciliary body detachment(OR=6.592), zone Ⅲ OGI(OR=1.940), and PVR(OR=1.615) were significant negative predictors for poor visual outcomes(P<0.05).Conclusions: Explosion ocular trauma has complex mechanisms, with multiple eyes involved and poor prognosis. In lethal level Ⅰ explosion injuries, eyeball rupture is a serious condition, whereas contusion is more likely to improve. In level Ⅱ injuries, IOFBs are more harmful than penetrating injuries, and level Ⅳ represents burn-related eye injuries.PVR is more associated with penetrating mechanisms than with OGI. Identifying the risk predictors for visual prognosis can guide clinicians in the evaluation and treatment of ocular blast injuries.

HIV-associated neurocognitive disorder(HAND):a review

Objective:According to the World Health Organization,the number of infected people with human immunodeficiency virus(HIV)in 2019 was about 38 million.Using combinational antiretroviral therapy in recent years has increased life expectancy in these people and HIV has been changed from a deadly disease to a chronic one.HIV-associated neurological disorders(HAND)include asymptomatic neurological disorders,Motor neurological disorders,and HIV-associated dementia.Methods:We searched 3 databases(PubMed,Science Direct and Scopus databases)from January 2015 to March 2021 for the keywords;HIV associated with a neurocognitive disorder,HAND and combinational antiretroviral therapy to provide careful consideration of various aspects of these disorders,including;risk factors,their diagnostic methods,their epidemiology,and finally the treatment of HAND.Results:In the initial search,we came across 1351 articles.Three authors did the title-abstract screening,and 270 articles were entered into full-text screening from the previous step.Four authors did the full-text screening,and the screening outcome was 21 articles.We categorized the results of our study into sub-categories,including:risk factors,pathogenicity,clinical manifestations,diagnosis,and epidemiology of HAND.Conclusions:Although the HAND subheading has made great strides,many questions about the disease remain unanswered,and many of the diagnosing established methods are not 100%accurate and as a result of that,this epidemy can’t be fully endured yet.We hope to find and apply further advances and more definitive solutions in the coming decades.

Efficient Cloud Image Retrieval System Using Weighted-Inverted Index and Database Filtering Algorithms

With the advance of multimedia technology and communications, images and videos become the major streaming information through the Internet. How to fast retrieve desired similar images precisely from the Internet scale image/video databases is the most important retrieval control target. In this paper, a cloud based content-based image retrieval （CBIR） scheme is presented. Database-categorizing based on weighted-inverted index （DCWII） and database f＇dtering algorithm （DFA） is used to speed up the features matching process. In the DCWII, the weights are assigned to discrete cosine transform （DCT） coefficients histograms and the database is categorized by weighted features. In addition, the DFA filters out the irrelevant image in the database to reduce unnecessary computation loading for features matching. Experiments show that the proposed CBIR scheme outperforms previous work in the precision-recall performance and maintains mean average precision （mAP） about 0.678 in the large-scale database comprising one million images. Our scheme also can reduce about 50% to 85% retrieval time by pre-filtering the database, which helps to improve the efficiency of retrieval systems.

Efficiency Evaluation for the Annual Equipment Maintenance Based on Set Pair Analysis

Most traditional assessment methods, which have complicated mathematic formulas,are difficult for calculation and application in system efficiency evaluation. A new approach to annual equipment maintenance system efficiency assessment,the set pair analysis method( SPAM),has been proposed based on the principle of set pair analysis( SPA). Firstly,the evaluation index system of the annual equipment maintenance is proposed. Secondly,the evaluation model of the annual equipment maintenance efficiency based on the SPAM is built. Then the application processes of the SPAM are introduced. At the end of the paper,a real application example is given. The results of the application indicate that the proposed model is feasible and effective. It can give strong supports to improve the evaluation effect of the unit equipment maintenance support.

Chaotic movement of vortices in superconductors of high-T_c superconducting maglev system at a high speed

Vortex motion in superconductors of high-To superconducting maglev system is studied by a computational simulation. The vortex system is treated in a similar fashion, as a system where defects are arranged in periodic arrays and driven by an alternating current （AC） field. Computational simulation reveals that due to the high nonlinearity of the vortex-defect interaction, the system shows typical chaotic characters. Effects of the vortex-vortex coupling, the driving frequency, and the vortex viscosity on the vortex motion have been studied to reveal the interaction between the spatial and temporal chaos. It is found that the mean-field approach is a good approximation to describe the vortex motion in one dimensional vortex system under an AC driving field. The vortex motion under the damping mode is a kind of self-organized motion. Lastly, the spatial chaos can dominate the chaotic behavior of the system.

Predict the Future Hospitalized Patients Number Based on Patient’s Temporal and Spatial Fluctuations Using a Hybrid ARIMA and Wavelet Transform Model

Relative to hospitalized patient information, outpatient admission information is relatively simple. It only includes the patient admission time, place of residence and other information. Traditionally, the excavation of this information is not sufficient. However, when a large number of patients admitted time and residence information combined to consider, and add some data mining technology, some of the previously ignored regular information is likely to be found. Using 5 years of data mining research and admission data from a paediatric department at a large women’s and children’s hospital in China, we found important fluctuation rules regarding admissions using wavelet analysis on hospital admission data among different scales of cyclical fluctuations. Method: Seasonal distribution of patient number was analysed based on Haar wavelet transformation, and level 3 and level 2 of wavelets were extracted out to fit the data. The distribution function of hospitalized patients was visualized by kernel density estimation. Using linear regression and ARIMA (autoregressive integrated moving average model) predict the seasonally number of patients in the future. Results: The data analysis demonstrates the total surge of inpatients was decomposed into one mother wavelet and five small wavelets, each of which represents different time frequency. Besides, as distance from hospital increases, the number of patients decreased exponentially. The seasonal factors are the largest time factor influencing the number changes of patients. Conclusion: By wavelet analysis and the improved prediction model, we could make forecast on the future inpatient number trend and prove factors such as geographic position is influential on inpatient amount. Additionally, the concept of data mining based on spatial distribution and spectral analysis could be applied to other aspects of social management.

A questionnaire survey on reading and public library utilization of the disabled in Beijing

To investigate the reading activities and library use of the disabled, the authors conducted a questionnaire-based survey among 306 disabled people in four districts of Beijing. The result shows that the disabled mainly take reading as a way of self-education and leisure. They have interest in reading but spend little time on it. Moreover, their age and education level have certain influence on their reading time and effect. Libraries play an important role in the reading activities of the disabled, who have a very strong interes in using libraries, but are limited by such factors as long distance between their home and the libraries, their misperception of libraries, and lack of barrier-free facilities in the libraries. Therefore, public libraries are highly recommended to improve their layout resources, and facilities to provide better services for the disabled.

Intercultural Trust in Global Contexts:Synthesizing a Western Nomological Approach with a Chinese Systems Approach

Intercultural trust in global contexts plays a central role in helping people from different cultures to communicate comfortably,which is essential for cooperation.Attempting to construct a framework that might foster international cooperation,and thus be helpful for coping with global emergencies,we relate a Western nomological approach to an Eastern systems approach to analyse intercultural trust in global contexts.Considering cultural impacts on intercultural trust and the nomological framework of cultural differences,we propose an intercultural trust model to interpret how cultural differences influence trust.A qualitative study of Chinese-Irish interactions was conducted to interpret this model.We organized 10 seminars on intercultural trust,and interviewed 16 people to further explore the respondents'deeper feelings and experiences about intercultural trust in global contexts.Through this study,we have identified factors impacting on intercultural trust,and found that intercultural trust can be developed and improved in various ways.To llustrate these ways,we have provided tactics and methods for building intercultural trust in global contexts.Implications are highlighted for organizations to avoid cultural clashes and relevant political or economic risks.

Post-discharge follow-up of patients with spine trauma in the National Spinal Cord Injury Registry of Iran during the COVID-19 pandemic:Challenges and lessons learned

Purpose:The purpose of the National Spinal Cord Injury Registry of Iran(NSCIR-IR)is to create an infrastructure to assess the quality of care for spine trauma and in this study,we aim to investigate whether the NSCIR-IR successfully provides necessary post-discharge follow-up data for these patients.Methods:An observational prospective study was conducted from April 11,2021 to April 22,2022 in 8 centers enrolled in NSCIR-IR,respectively Arak,Rasht,Urmia,Shahroud,Yazd,Kashan,Tabriz,and Tehran.Patients were classified into 3 groups based on their need for care resources,respectively:(1)non-spinal cord injury(SCI)patients without surgery(group 1),(2)non-SCI patients with surgery(group 2),and(3)SCI patients(group 3).The assessment tool was a self-designed questionnaire to evaluate the care quality in 3 phases:pre-hospital,in-hospital,and post-hospital.The data from the first 2 phases were collected through the registry.The post-hospital data were collected by conducting follow-up assessments.Telephone follow-ups were conducted for groups 1 and 2(non-SCI patients),while group 3(SCI patients)had a face-to-face visit.This study took place during the COVID-19 pandemic.Data on age and time interval from injury to follow-up were expressed as mean±standard deviation(SD)and response rate and follow-up loss as a percentage.Results:Altogether 1538 telephone follow-up records related to 1292 patients were registered in the NSCIR-IR.Of the total calls,918(71.05%)were related to successful follow-ups,but 38 cases died and thus were excluded from data analysis.In the end,post-hospital data from 880 patients alive were gathered.The success rate of follow-ups by telephone for groups 1 and 2 was 73.38%and 67.05%respectively,compared to 66.67%by face-to-face visits for group 3,which was very hard during the COVID-19 pandemic.The data completion rate after discharge ranged from 48%-100%,22%-100%and 29%-100%for groups 1-3.Conclusions:To improve patient accessibility,NSCIR-IR should take measures during data gathering to increase the accuracy of registered contact information.Regarding the loss to follow-ups of SCI patients,NSCIR-IR should find strategies for remote assessment or motivate them to participate in follow-ups through,for example,providing transportation facilities or financial support.

OTDR-based optical fiber bending and tensile loss analysis

When optical fiber is deployed in practical engineering,bending and stretching of fiber optics is inevitable,which will affect optical communication.The fiber losses of different bending radii are simulated by COMSOL software.In order to verify the accuracy of simulation results,an experiment was designed to measure the losses of single-mode fiber under different bending radii and tension forces.The results show that the sensitive bending diameter of fiber loss is between 5 mm and 10 mm.The tensile effect has little influence on the fiber loss,but when the tensile force is greater than 160 N,the fiber breaks.This study provides an important reference for fiber layout in practical engineering.

Applying data mining techniques to classify patients with suspected hepatitis C virus infection

Background Hepatitis C virus(HCV)has a high prevalence worldwide,and the progression of the disease can cause irreversible damage to severe liver damage or even death.Therefore,developing prediction models using machine learning techniques is beneficial.This study was conducted to classify suspected patients with HCV infection using different classification models.Methods The study was conducted using a dataset derived from the University of California,Irvine(UCI)Ma-chine Learning Repository.Since the HCV dataset was imbalanced,the synthetic minority oversampling technique(SMOTE)was applied to balance the dataset.After cleaning the dataset,it was divided into training and test data for developing six classification models.These six algorithms included the support vector machine(SVM),Gaus-sian Naïve Bayes(NB),decision tree(DT),random forest(RF),logistic regression(LR),and K-nearest neighbors(KNN)algorithm.The Python programming language was used to develop the classifiers.Receiver operating characteristic curve analysis and other metrics were used to evaluate the performance of the proposed models.Results After the evaluation of the models using different metrics,the RF classifier had the best performance among the six methods.The accuracy of the RF classifier was 97.29%.Accordingly,the area under the curve(AUC)for LR,KNN,DT,SVM,Gaussian NB,and RF models were 0.921,0.963,0.953,0.972,0.896,and 0.998,respectively,RF showing the best predictive performance.Conclusion Various machine learning techniques for classifying healthy and unhealthy patients were used in this study.Additionally,the developed models might identify the stage of HCV based on trained data.

Identification of electrocardiogram signals using internet of things based on combinatory classification

Combination of computer sciences and electronics has resulted in one of the most remarkable technologies of the recent years called internet of things,considered as a challenge in electronic health systems for taking care of patients.Internet of things presents a promising paradigm for management of digital identification in the form of service customization.The effect of internet of things on healthcare is still in its preliminary stages and requires a substantial development.Various equipment and services are developed and utilized for health systems by providing different things to establish communication and information provision to users at any conditions or places.In this paper,attempts have been made to detect electrocardiogram(ECG)signal through a wireless simple sensing network of body using internet of things operating based on classification and feature extraction.

Using modified Delphi method to propose and validate the components of a child injury surveillance system for Iran

Purpose:Child injuries are a public health concern globally.Injury surveillance systems(ISSs)have beneficial impact on child injury prevention.There is a need for evidence-based consensus on frameworks to establish child ISSs.This research aims to investigate the key components of a child ISS for Iran and to propose a framework for implementation.Methods:Data were gathered through interview with experts using unstructured questions from January 2017 to December 2018 to identify child ISS functional components.Qualitative data were analyzed using content analysis method.Then,modified Delphi method was used to validate the functional components.Based on the outcomes of the content analysis,a questionnaire with closed questions was developed and presented to a group of experts.Consensus was achieved in two rounds.Results:In round I,117 items reached consensus.In round II,5 items reached consensus and were incorporated into final framework.Consensus was reached for 122 items comprising the final framework and representing 7 key components:goals of the system,data sources,data set,coalition of stakeholders,data collection,data analysis and data distribution.Each component consisted of several subcomponents and respective elements.Conclusion:This agreed framework will assist in standardizing data collection,analysis and distribution,which help to detect child injury problems and provide evidence for preventive measures.

Reasons for delayed spinal cord decompression in individuals with traumatic spinal cord injuries in Iran:A qualitative study from the perspective of neurosurgeons

Purpose:The median time from the event leading to the spinal cord injury(SCI)to the time of decompressive surgery is estimated to be 6.9 days in Iran,which is much longer than the proposed ideal time(less than 24 h)in published guidelines.The current qualitative study aimed to determine the reasons for the observed decompression surgery delay in Iran from the perspective of neurosurgeons.Methods:This qualitative study is designed to perform content analysis on the gathered data from face-to-face semi-structured interviews with 12 Iranian neurosurgeons.Results:The findings of the current study suggest that patient-related factors constitute more than half of the codes extracted from the interviews.Overall,the type of injury,presence of polytrauma,and surgeons’’wrong attitude are the main factors causing delayed spinal cord decompression in Iranian patients from the perspective of neurosurgeons.Other notable factors include delay in transferring patients to the trauma center,delay in availability of necessary equipment,and scarce medical personnel.Conclusion:In the perspective of neurosurgeons,the type of injury,presence of polytrauma,and surgeons’’wrong attitude are the leading reasons for delayed decompressive surgery of individuals with SCI in Iran.