bluuebunny/crossref_metadata_2025 huggingface dataset:
Rich specifications for Ethereum smart contract verification
Authors: Bräm Christian , Eilers Marco , Müller Peter , Sierra Robin , Summers Alexander J.
Published: October 2021

Smart contracts are programs that execute in blockchains such as Ethereum to manipulate digital assets. Since bugs in smart contracts may lead to substantial financial losses, there is considerable interest in formally proving their correctness. However, the specification and verification of smart contracts faces challenges that rarely arise in other application domains. Smart contracts frequently interact with unverified, potentially adversarial outside code, which substantially weakens the assumptions that formal analyses can (soundly) make. Moreover, the core functionality of smart contracts is to manipulate and transfer resources; describing this functionality concisely requires dedicated specification support. Current reasoning techniques do not fully address these challenges, being restricted in their scope or expressiveness (in particular, in the presence of re-entrant calls), and offering limited means of expressing the resource transfers a contract performs.

In this paper, we present a novel specification methodology tailored to the domain of smart contracts. Our specifications and associated reasoning technique are the first to enable: (1) sound and precise reasoning in the presence of unverified code and arbitrary re-entrancy, (2) modular reasoning about collaborating smart contracts, and (3) domain-specific specifications for resources and resource transfers, expressing a contract's behaviour in intuitive and concise ways and excluding typical errors by default. We have implemented our approach in 2vyper, an SMT-based automated verification tool for Ethereum smart contracts written in Vyper, and demonstrated its effectiveness for verifying strong correctness guarantees for real-world contracts.
How statically-typed functional programmers write code
Authors: Lubin Justin , Chasins Sarah E.
Published: October 2021

How working statically-typed functional programmers write code is largely understudied. And yet, a better understanding of developer practices could pave the way for the design of more useful and usable tooling, more ergonomic languages, and more effective on-ramps into programming communities. The goal of this work is to address this knowledge gap: to better understand the high-level authoring patterns that statically-typed functional programmers employ. We conducted a grounded theory analysis of 30 programming sessions of practicing statically-typed functional programmers, 15 of which also included a semi-structured interview. The theory we developed gives insight into how the specific affordances of statically-typed functional programming affect domain modeling, type construction, focusing techniques, exploratory and reasoning strategies, and expressions of intent. We conducted a set of quantitative lab experiments to validate our findings, including that statically-typed functional programmers often iterate between editing types and expressions, that they often run their compiler on code even when they know it will not successfully compile, and that they make textual program edits that reliably signal future edits that they intend to make. Lastly, we outline the implications of our findings for language and tool design. The success of this approach in revealing program authorship patterns suggests that the same methodology could be used to study other understudied programmer populations.
APIfix: output-oriented program synthesis for combating breaking changes in libraries
Authors: Gao Xiang , Radhakrishna Arjun , Soares Gustavo , Shariffdeen Ridwan , Gulwani Sumit , Roychoudhury Abhik
Published: October 2021

Use of third-party libraries is extremely common in application software. The libraries evolve to accommodate new features or mitigate security vulnerabilities, thereby breaking the Application Programming Interface(API) used by the software. Such breaking changes in the libraries may discourage client code from using the new library versions thereby keeping the application vulnerable and not up-to-date. We propose a novel output-oriented program synthesis algorithm to automate API usage adaptations via program transformation. Our aim is not only to rely on the few example human adaptations of the clients from the old library version to the new library version, since this can lead to over-fitting transformation rules. Instead, we also rely on example usages of the new updated library in clients, which provide valuable context for synthesizing and applying the transformation rules. Our tool APIFix provides an automated mechanism to transform application code using the old library versions to code using the new library versions - thereby achieving automated API usage adaptation to fix the effect of breaking changes. Our evaluation shows that the transformation rules inferred by APIFix achieve 98.7% precision and 91.5% recall. By comparing our approach to state-of-the-art program synthesis approaches, we show that our approach significantly reduces over-fitting while synthesizing transformation rules for API usage adaptations.
The reads-from equivalence for the TSO and PSO memory models
Authors: Bui Truc Lam , Chatterjee Krishnendu , Gautam Tushar , Pavlogiannis Andreas , Toman Viktor
Published: October 2021

The verification of concurrent programs remains an open challenge due to the non-determinism in inter-process communication. One recurring algorithmic problem in this challenge is the consistency verification of concurrent executions. In particular, consistency verification under a reads-from map allows to compute the
reads-from (RF) equivalence
between concurrent traces, with direct applications to areas such as Stateless Model Checking (SMC). Importantly, the RF equivalence was recently shown to be coarser than the standard Mazurkiewicz equivalence, leading to impressive scalability improvements for SMC under SC (sequential consistency). However, for the
relaxed memory
models of TSO and PSO (total/partial store order), the algorithmic problem of deciding the RF equivalence, as well as its impact on SMC, has been elusive.

In this work we solve the algorithmic problem of consistency verification for the TSO and PSO memory models given a reads-from map, denoted VTSO-rf and VPSO-rf, respectively. For an execution of
n
events over
k
threads and
d
variables, we establish novel bounds that scale as
n

k
+1

for TSO and as
n

k
+1

· min(
n

k
2

, 2

k
·
d

) for PSO. Moreover, based on our solution to these problems, we develop an SMC algorithm under TSO and PSO that uses the RF equivalence. The algorithm is
exploration-optimal
, in the sense that it is guaranteed to explore each class of the RF partitioning exactly once, and spends polynomial time per class when
k
is bounded. Finally, we implement all our algorithms in the SMC tool Nidhugg, and perform a large number of experiments over benchmarks from existing literature. Our experimental results show that our algorithms for VTSO-rf and VPSO-rf provide significant scalability improvements over standard alternatives. Moreover, when used for SMC, the RF partitioning is often much coarser than the standard Shasha-Snir partitioning for TSO/PSO, which yields a significant speedup in the model checking task.
JavaDL: automatically incrementalizing Java bug pattern detection
Authors: Dura Alexandru , Reichenbach Christoph , Söderberg Emma
Published: October 2021

Static checker frameworks support software developers by automatically discovering bugs that fit general-purpose bug patterns. These frameworks ship with hundreds of detectors for such patterns and allow developers to add custom detectors for their own projects. However, existing frameworks generally encode detectors in imperative specifications, with extensive details of not only
what
to detect but also
how
. These details complicate detector maintenance and evolution, and also interfere with the framework’s ability to change how detection is done, for instance, to make the detectors incremental.

In this paper, we present JavaDL, a Datalog-based declarative specification language for bug pattern detection in Java code. JavaDL seamlessly supports both exhaustive and incremental evaluation from the same detector specification. This specification allows developers to describe local detector components via
syntactic pattern matching
, and nonlocal (e.g., interprocedural) reasoning via
Datalog-style logical rules
.

We compare our approach against the well-established SpotBugs and Error Prone tools by re-implementing several of their detectors in JavaDL. We find that our implementations are substantially smaller and similarly effective at detecting bugs on the Defects4J benchmark suite, and run with competitive runtime performance. In our experiments, neither incremental nor exhaustive analysis can consistently outperform the other, which highlights the value of our ability to transparently switch execution modes. We argue that our approach showcases the potential of
clear-box static checker frameworks
that constrain the bug detector specification language to enable the framework to adapt and enhance the detectors.
Generalizable synthesis through unification
Authors: Ji Ruyi , Xia Jingtao , Xiong Yingfei , Hu Zhenjiang
Published: October 2021

The generalizability of PBE solvers is the key to the empirical synthesis performance. Despite the importance of generalizability, related studies on PBE solvers are still limited. In theory, few existing solvers provide theoretical guarantees on generalizability, and in practice, there is a lack of PBE solvers with satisfactory generalizability on important domains such as conditional linear integer arithmetic (CLIA). In this paper, we adopt a concept from the computational learning theory, Occam learning, and perform a comprehensive study on the framework of synthesis through unification (STUN), a state-of-the-art framework for synthesizing programs with nested if-then-else operators. We prove that Eusolver, a state-of-the-art STUN solver, does not satisfy the condition of Occam learning, and then we design a novel STUN solver, PolyGen, of which the generalizability is theoretically guaranteed by Occam learning. We evaluate PolyGen on the domains of CLIA and demonstrate that PolyGen significantly outperforms two state-of-the-art PBE solvers on CLIA, Eusolver and Euphony, on both generalizability and efficiency.
Building Machine Learning-based Threat Hunting System from Scratch
Authors: Chen Chung-Kuan , Lin Si-Chen , Huang Szu-Chun , Chu Yung-Tien , Lei Chin-Laung , Huang Chun-Ying
Published: October 2021

Machine learning has been widely used for solving challenging problems in diverse areas. However, to the best of our knowledge, seldom literature has discussed in-depth how machine learning approaches can be used effectively to “hunt” (identify) threats, especially
advanced persistent threats (APTs)
, in a monitored environment. In this study, we share our past experiences in building machine learning-based threat-hunting models. Several challenges must be considered when a security team attempts to build such models. These challenges include (1) weak signal, (2) imbalanced data sets, (3) lack of high-quality labels, and (4) no storyline. In this study, we propose Fuchikoma and APTEmu to demonstrate how we tackle the above-mentioned challenges. The former is a proof of concept system for demonstrating the ideas behind autonomous threat-hunting. It is a machine learning-based anomaly detection and threat hunting system which leverages
natural language processing (NLP)
and graph algorithms. The latter is an APT emulator, which emulates the behavior of a well-known APT called APT3, which is the target used in the first round of MITRE ATT&CK Evaluations. APTEmu generates attacks on Windows machines in a virtualized environment, and the captured system events can be further used to train and enhance Fuchikoma’s capabilities. We illustrate the steps and experiments we used to build the models, discuss each model’s effectiveness and limitations of each model, and propose countermeasures and solutions to improve the models. Our evaluation results show that machine learning algorithms can effectively assist threat hunting processes and significantly reduce security analysts’ efforts. Fuchikoma correctly identifies malicious commands and achieves high performance in terms of over 80% True Positive Rate and True Negative Rate and over 60% F3. We believe our proposed approaches provide valuable experiences in the area and shed light on automated threat-hunting research.
Active and Passive Collection of SSH Key Material for Cyber Threat Intelligence
Authors: Dulaunoy Alexandre , Huynen Jean-Louis , Thirion Aurelien
Published: October 2021

This article describes a system for storing historical forensic artifacts collected from SSH connections. This system exposes a REST API in a similar fashion as passive DNS databases, malware hash registries, and SSL notaries with the goal of supporting incident investigations and monitoring of infrastructure.
SAIBERSOC
: A Methodology and Tool for Experimenting with Security Operation Centers
Authors: Rosso Martin , Campobasso Michele , Gankhuyag Ganduulga , Allodi Luca
Published: October 2021

In this article, we introduce
SAIBERSOC
(Synthetic Attack Injection to Benchmark and Evaluate the Performance of Security Operation Centers), a tool and methodology enabling security researchers and operators to evaluate the performance of deployed and operational Security Operation Centers (SOC)—or any other security monitoring infrastructure. The methodology relies on the MITRE ATT&CK Framework to define a procedure to generate and automatically inject synthetic attacks in an operational SOC to evaluate any output metric of interest (e.g., detection accuracy, time-to-investigation). To evaluate the effectiveness of the proposed methodology, we devise an experiment with

n=124

students playing the role of SOC analysts. The experiment relies on a real SOC infrastructure and assigns students to either a
BADSOC
or a
GOODSOC
experimental condition. Our results show that the proposed methodology is effective in identifying variations in SOC performance caused by (minimal) changes in SOC configuration. We release the
SAIBERSOC
tool implementation as free and open source software.
Drug Repurposing for Alzheimer’s Disease Based on Protein‐Protein Interaction Network
Authors: Soleimani Zakeri Negar Sadat , Pashazadeh Saeid , MotieGhader Habib
Published: October 2021

Alzheimer’s disease (AD) is known as a critical neurodegenerative disorder. It worsens as symptoms concerning dementia grow severe over the years. Due to the globalization of Alzheimer’s disease, its prevention and treatment are vital. This study proposes a method to extract substantial gene complexes and then introduces potential drugs in Alzheimer’s disease. To this end, a protein‐protein interaction (PPI) network was utilized to extract five meaningful gene complexes functionally interconnected. An enrichment analysis to introduce the most important biological processes and pathways was accomplished on the obtained genes. The next step is extracting the drugs related to AD and introducing some new drugs which may be helpful for this disease. Finally, a complete network including all the genes associated with each gene complex group and genes’ target drug was illustrated. For validating the proposed potential drugs, Connectivity Map (CMAP) analysis was accomplished to determine target genes that are up‐ or downregulated by proposed drugs. Medical studies and publications were analyzed thoroughly to introduce AD‐related drugs. This analysis proves the accuracy of the proposed method in this study. Then, new drugs were introduced that can be experimentally examined as future work. Raloxifene and gentian violet are two new drugs, which have not been introduced as AD‐related drugs in previous scientific and medical studies, recommended by the method of this study. Besides the primary goal, five bipartite networks representing the genes of each group and their target miRNAs were constructed to introduce target miRNAs.
Construction of the Rule of Law System of Marine Ecological Environment Protection under the Background of Wireless Network Information Fusion
Authors: Xu Lijuan , Zhang Lihong
Published: October 2021

With the rapid development of my country’s marine economy, human society is demanding more and more marine resources and space, resulting in serious damage to the marine ecological environment. In order to protect the marine ecology and improve the sustainable development of marine resources, this paper studies the legal system of marine ecological environmental protection based on the background of wireless network information fusion. By studying the status quo of several well‐known marine ports and ecological environment in China, building a wireless network information fusion platform, integrating wireless network information into marine ecological surveys, collecting data through WSN nodes, and comparing wireless network information fusion with traditional methods, the efficiency of marine ecological environmental protection and the identification of marine pollution methods in different periods of time coordinate the rapid economic development and build a sustainable marine ecology. The results of the study found that the monitoring efficiency based on wireless network information fusion is more than 25% higher than the traditional monitoring method, and the recognition efficiency after 3 months is still more than 20% higher than the traditional method. This shows that information fusion in wireless networks can play a key role in the construction of a legal system for marine ecological and environmental protection.
Intracavitary Electrocardiogram Guidance Aids Excavation of Rhythm Abnormalities in Patients with Occult Heart Disease
Authors: Wei Yanli , Zhu Ying , Wen Xin , Rui Qing , Hu Wei
Published: October 2021

In this paper, the analysis of intracavitary electrocardiograms is used to guide the mining of abnormal cardiac rhythms in patients with hidden heart disease, and the algorithm is improved to address the data imbalance problem existing in the abnormal electrocardiogram signals, and a weight-based automatic classification algorithm for deep convolutional neural network electrocardiogram signals is proposed. By preprocessing the electrocardiogram data from the MIT-BIH arrhythmia database, the experimental dataset training algorithm model is obtained, and the algorithm model is migrated into the project. In terms of system design and implementation, by comparing the advantages and disadvantages of the electrocardiogram monitoring system platform, the overall design of the system was carried out in terms of functional and performance requirements according to the system realization goal, and a mobile platform system capable of classifying common abnormal electrocardiogram signals was developed. The system is capable of long-term monitoring and can invoke the automatic classification algorithm model of electrocardiogram signals for analysis. In this paper, the functional logic test and performance test were conducted on the main functional modules of the system. The test results show that the system can run stably and monitor electrocardiogram signals for a long time and can correctly call the deep convolutional neural network-based automatic electrocardiogram signal classification algorithm to analyze the electrocardiogram signals and achieve the requirements of displaying the electrocardiogram signal waveform, analyzing the heartbeat type, and calculating the average heart rate, which achieves the goal of real-time continuous monitoring and analysis of the electrocardiogram signals.
Stressors of Nursing Interns and Their Influencing Factors: A Cross-Sectional Study
Authors: Wang Xiaoqing , Han Yu , Chai Runyu , Chai Rong
Published: October 2021

Objective. It is still unknown whether the stress level and stressors in Chinese nursing interns are influenced by teacher-related factors. This research was carried out for better understanding of the stress in nursing interns and distribution of stressors during their clinical practice and targeted measures to unwind the stress of nursing interns. Methods. A questionnaire survey, titled Questionnaire on Stressors of Nursing Interns during Clinical Practice, was conducted on nursing interns at a 3A Grade Hospital in Shandong Province. Characteristics of the nursing interns and stressors of nursing interns were collected. A multiple-linear regression model was used to explore the influencing factors of nursing interns’ scores. Results. A total of 132 nursing interns were investigated in this study, and the overall stress scores were calculated. The stressors during the internship include the nature and content of the job, role orientation, workload, conflict between study and work, practice preparation, and interpersonal relationships. Gender, education level, instructor encouragement, and parents engaged in the medical industry were adjusted in the multiple-linear regression model as covariates. All of these factors had significant impacts on the scores of stressors (

P

 < 0.05), with the partial regression coefficient values of 13.38, −10.52, −5.02, 3.4, −9.89, −14.77, and −15.83 for factors like female, undergraduates, graduate students, never obtained encouragement from teachers, obtained encouragement from teachers occasionally, obtained encouragement from teachers frequently, and parents engaged in the medical industry, respectively. Conclusion. The stressors of nursing interns are mostly work-wise, and teachers’ encouragement is an important protective factor for nursing interns to reduce stress. Therefore, clinical instructors should take targeted measures in teaching methods and work arrangements according to the needs of interns.
[Retracted] Design and Research of Mathematics Teaching Intelligent Classroom Based on PCA‐NN Algorithm
Authors: Li Baozhen , Zhang Lina
Published: October 2021

With the increasing importance of mathematics in basic education, how to evaluate and analyze the intelligent effect of mathematics teaching classroom through scientific methods has become one of the indicators to evaluate the intelligent classroom. This paper studies the design and application of mathematics teaching intelligent classroom based on the PCA‐NN (principal component analysis‐neural network) algorithm. Firstly, this paper briefly describes the current research status of mathematics teaching intelligent classroom design and PCA‐NN algorithm. Secondly, combined with the key factors of mathematics teaching, it formulates specific standards and puts forward an adaptive strategy of intelligent and personalized intelligent mathematics teaching classroom. Finally, the algorithm is verified by experiments. The results show that, for students with different mathematics basic levels, the mathematics teaching intelligent classroom based on the PCA‐NN algorithm can effectively improve the quality of mathematics classroom teaching. Through the research on the factors such as teaching quality, effect, and interaction mode involved in the process of mathematics teaching classroom design, the intelligent classroom design factors affecting teaching quality are determined. This paper analyzes and studies the system from different angles. The research results provide some help for the current quality evaluation of classroom teaching and use the PCA‐NN algorithm to make quantitative analysis and multivariate verification of mathematics classroom teaching effect.
Vitamin D Deficiency in Dengue Hemorrhagic Fever and Dengue Shock Syndrome among Sri Lankan Children: A Case-Control Study
Authors: Dissanayake Sharmila , Tennekoon Sureshi , Gaffoor Sharmila , Liyanage Guwani
Published: October 2021

Introduction. Dengue fever is a vector-borne disease associated with a significant public health impact. The clinical picture ranges from undifferentiated fever to more severe forms such as dengue hemorrhagic fever (DHF) and dengue shock syndrome (DSS). Compared to healthy controls, we explored the likelihood of having vitamin D deficiency (VDD) among children with severe dengue infection. Methods. This case-control study compared hospitalized children (2 months to 12 years) with DHF and DSS with radiologically confirmed plasma leak with age-matched healthy controls. The association of 25-hydroxy vitamin D [25(OH)D] level, age, sex, and socioeconomic status with DHF/DSS was assessed using univariate and multivariate logistic regression. Results. Forty children with DHF/DSS were compared with 52 healthy controls. Mean (SD) age was 8.8 (2.9) years and 7.9 (3.7) years among cases and controls, respectively. Most (n = 28, 70%) had DHF. In multivariate logistic regression, the likelihood of having VDD [25(OH)D < 20 ng/mL] was 3.6 times higher in cases compared to controls (Odds Ratio (OR): 3.65, 95% Confidence Interval (CI): 1.461, 9.102,

p
=
0.006

). When serum 25(OH)D was used as a continuous independent variable, the strength of the association between DHF/DSS and serum 25(OH)D was weak but statistically significant; the likelihood of having DHF/DSS is 0.94 times less with 1 ng/mL increase in serum 25(OH)D (OR: 0.940, 95% CI: 0.887, 0.995,

p
=
0.03

). Conclusion. The present study suggests that the likelihood of having VDD among children with DHF/DSS is higher than that in their healthy counterparts. Thus, further studies are critical in confirming whether vitamin D repletion is beneficial in preventing severe forms of dengue in the quest to reduce the morbidity and mortality associated with dengue infection.
Estimation of the Uncertainty due to Each Step of Simulating the Photovoltaic Conversion under Real Operating Conditions
Authors: Dubois Anne Migan , Badosa Jordi , Bourdin Vincent , Torres Aguilar Moira I. , Bonnassieux Yvan
Published: October 2021

The simulation of photovoltaic installations is a major issue for their sizing, their smart grid operation, and their fault detection and diagnosis. In this article, we study in detail every step of the simulation chain, either from the global horizontal irradiance and the ambient temperature (i.e., 4 steps of simulation) or considering the global in-plane irradiance and the module operating temperature (i.e., 1 step of simulation). The average quality estimation of the models is made through the calculations of average annual error between estimations and measurements, from 2016 to 2020. We have shown that the most uncertain step is the conversion of the global irradiance in its diffuse and direct components (17.2%, 2 models tested). If the model goes up to the in-plane irradiance, the average annual error decreases to 5.3% (6 models tested). The photovoltaic module temperature calculation induces an error of less than 2Â°C (4 models tested with 2 configurations). Meanwhile, the photoelectrical conversion shows a 3.5% error, similar to the measurement uncertainties, considering as input, the modules temperature, and the in-plane irradiance. If the simulation goes from the global irradiance and the ambient temperature measured locally, the estimation leads to a 6.7% average annual error. If the local measurements are not available, we can use the closest meteorological station’s records (13 for our study), and the error becomes 12.1%. Finally, we can also use the satellite images that lead to a 15.2% error, for average per year. The impact of available input shows that modeling the DC photovoltaic production, using global horizontal irradiance and ambient temperature, gives rise to an error of 6.6% for local measurements, 12.1% for weather station measurements, and 15.2% for satellite images estimations. This article thus draws up a review of the existing models, allowing to calculate the DC production of a photovoltaic module, depending on the atmospheric conditions, and highlights the most precise or most critical steps, considering in situ and weather station ground-based measurements, and also estimation from satellite images.
Fabrication and Characterization of Chitosan-Polyethylene Glycol (Ch-Peg) Based Hydrogels and Evaluation of Their Potency in Rat Skin Wound Model
Authors: Hasan Md Mahmudul , Uddin Md Forhad , Zabin Nayera , Shakil Md Salman , Alam Morshed , Achal Fahima Jahan , Ara Begum Most. Hosney , Hossen Md Sakib , Hasan Md Ashraful , Morshed Md Mahbubul
Published: October 2021

Thermal burns are a major cause of death and suffering around the globe. They can cause debilitating, life-altering injuries as well as lead to significant psychological and financial consequences. Several research works have been conducted in attempt to find a wound healing therapy that is successful. At present, hydrogels have been widely used in cutting-edge research for this purpose because they have suitable properties. This study aimed to see how therapy with chitosan-polyethylene glycol (Ch-Peg) based hydrogels affected the healing of burn wounds in rats. With the concern of public health, xanthan gum (X), boric acid (B), gelatin (Ge), polyethylene glycol (Peg), chitosan (Ch), glutaraldehyde (G), and HPLC-grade water were prepared using X : Ge : G, X : Ge : Peg : G, X : Ge : Ch : G, X : Ge : Peg : Ch : G, X : Ge : B : Ch : G, X : Ge : B : Peg : G, and X : Ge : B : Peg : Ch : G. The produced composite hydrogels were examined for swelling ability, biodegradability, rheological characteristics, and porosity. The 3D structure of the hydrogel was revealed by scanning electron microscopy (SEM). After that, the structural characterization technique named Fourier-transform infrared spectroscopy (FTIR) was used to describe the composites (SEM). Lastly, in a rat skin wound model, the efficacy of the produced hydrogels was studied. Swelling ability, biodegradability, rheological properties, and porosity were all demonstrated in composite hydrogels that contained over 90% water. Hydrogels with good polymeric networks and porosity were observed using SEM. The existence of bound water and free, intra- and intermolecule hydrogen-linked OH and NH in the hydrogels was confirmed using FTIR. In a secondary burned rat model, all hydrogels showed significant wound healing effectiveness when compared to controls. When compared to other composite hydrogels, wounds treated with X : Ge : Peg : Ch : G, X : Ge : B : Peg : G, and X : Ge : B : Peg : Ch:G recovered faster after 28 days. In conclusion, this research suggests that X : Ge : Peg : Ch : G, X : Ge : B : Peg : G, and X : Ge : B : Peg : Ch : G could be used to treat skin injuries in the clinic.
Prevalence of Posttranscatheter Aortic Valve Implantation Vascular Complications in Real Life
Authors: Matta Anthony , Canitrot Ronan , Nader Vanessa , Bouisset Frederic , Lhermusier Thibault , Campelo-Parada Francisco , Grunenwald Etienne , Marcheix Bertrand , Elbaz Meyer , Carrie Didier , Roncalli Jerome
Published: October 2021

Background. Vascular complications (VCs) are commonly observed after transfemoral transcatheter aortic valve implantation (TAVI) procedures. Closure devices for the access site were developed to reduce their incidence. We aim to evaluate the prevalence, predictors, and outcomes of the occurrence of post-TAVI VCs. Materials and Methods. A retrospective study was conducted on 1336 consecutive patients who underwent TAVI at the University Hospital of Toulouse, France, between January 2016 and March 2020. All included procedures were performed through the common femoral artery, and ProGlide® was the used closure device. The studied population was divided into two groups depending on the occurrence of VCs defined according to Valve Academic Research Consortium-2 criteria. Results. The mean age of the studied population was 84.4 ± 6.9, and 48% were male. 90% of TAVI interventions were performed through the right femoral artery. The prevalence of VCs was 18.8%, and 3.7% were major. Prolonged procedure duration was an independent predictor of VCs. Using the right access site and smaller introducer size (14 Fr) were preventive factors. No significant difference in mortality rate was detected between the two groups. Conclusion. This study showed a low prevalence for post-TAVI VCs, especially for the major type. An increase in bleeding events and prolonged cardiac care unit stay were the common adverse outcomes.
An Algorithm of Scene Information Collection in General Football Matches Based on Web Documents
Authors: Li Bin , Zhang Ting
Published: October 2021

In order to obtain the scene information of the ordinary football game more comprehensively, an algorithm of collecting the scene information of the ordinary football game based on web documents is proposed. The commonly used T-graph web crawler model is used to collect the sample nodes of a specific topic in the football game scene information and then collect the edge document information of the football game scene information topic after the crawling stage of the web crawler. Using the feature item extraction algorithm of semantic analysis, according to the similarity of the feature items, the feature items of the football game scene information are extracted to form a web document. By constructing a complex network and introducing the local contribution and overlap coefficient of the community discovery feature selection algorithm, the features of the web document are selected to realize the collection of football game scene information. Experimental results show that the algorithm has high topic collection capabilities and low computational cost, the average accuracy of equilibrium is always around 98%, and it has strong quantification capabilities for web crawlers and communities.
The Effect of Educational Intervention on the Improvement of Nontechnical Skills in Circulating Nurses
Authors: Kalantari Reza , Zamanian Zahra , Hasanshahi Mehdi , Faghihi Seyed Aliakbar , Jamali Jamshid , Niakan Hadi
Published: October 2021

Background. Nontechnical skills are necessary for clinicians’ safe performance and prevention of errors in the operating room. Educational intervention is a useful way to improve these skills, which are a vital area for improvement. Circulating nurses are surgical team members whose work depends heavily on using nontechnical skills. This study is aimed at assessing the effect of an educational intervention on the improvement of circulating nurses’ nontechnical skills. Methods. This semiexperimental study was conducted on 300 circulating nurses divided into the intervention and no intervention groups each containing 150 participants. The nontechnical skills were assessed using the circulating practitioners’ list of nontechnical skills. Then, the intervention group received training regarding these skills, and the two groups were evaluated again. After all, the data were entered into the SPSS 24 software and were analyzed using descriptive statistics and Wilcoxon and Mann–Whitney tests. Furthermore, Kendall’s tau, independent sample t‐test, and one‐way ANOVA were used for assessment of relationship between median scores and demographics. Results. The results revealed a significant improvement in the scores of all domains of nontechnical skills in the intervention group (p < 0.05). The highest and lowest improvements were observed in teamwork (42%) and situational awareness (16.7%), respectively. After the intervention, the scores of some of the behaviors were still below the average level or were not improved significantly. Conclusions. Circulating nurses’ nontechnical skills can be improved by educational interventions. However, regarding the low scores or no improvements in the scores of some behaviors, other intervention types such as policymaking and correcting the existing hierarchies in the operating room can be useful to complete the educational interventions.
Study on Announcement Effect of Stock Repurchase from the Perspective of Configuration Analysis
Authors: Wang Hong Bing , Jin Ai Hua , Yu Hai Yun
Published: October 2021

This paper takes the stock repurchase cases in 2018-2019 after the promulgation of the New Company Law as the research object. Using the qualitative comparative analysis method and PSO-ICA-GARCH model, we analyze the influencing factors of the stock repurchase announcement market reaction. Through the analysis of the degree of premium (DP), repurchase ratio (RR), natural logarithm of total assets (LAS), return on net assets (ROE), top 10 shareholder equity ratio (CR10), and executive shareholding ratio (MSL), it is found that the paths of high CAR stock repurchase effect include repurchase clause-oriented type and centralization-proportional matching type; the path of nonhigh CAR stock repurchase effect includes size-premium limit type, profit-proportion limit type, and repurchase clause limit type. Compared with GARCH model, it has higher separation accuracy and more accurate model prediction effect.
Efficacy of Posterior Cervical Laminectomy and Decompression plus Lateral Mass Screw-Rod Internal Fixation in the Treatment of Multisegment Cervical Spinal Canal Stenosis and Effects on Cervical Curvature and Range of Motion Parameters
Authors: Liu Bo , Wang Yufei , Zhang Yaning
Published: October 2021

Objective. To investigate the clinical effects of posterior laminectomy and decompression plus lateral mass screw-rod internal fixation for the treatment of multisegment cervical spinal canal stenosis and the improvement of cervical curvature and range of motion in patients. Methods. A total of 68 patients with multisegment cervical spinal stenosis who were treated in our hospital from January 2019 to June 2020 were selected and randomly divided into the control group and the observation group according to the random number table, with 34 patients in each group. The patients in the control group were treated with traditional posterior cervical open-door laminoplasty with silk suture fixation, while those in the observation group were treated with posterior cervical laminectomy and decompression plus lateral mass screw-rod internal fixation. The perioperative index of patients in the two groups was recorded, and the clinical efficacy of patients was evaluated. The patient’s JOA score, cervical physiological curvature, and cervical range of motion were evaluated. The occurrence of complications was recorded during follow-up. Results. The amount of intraoperative bleeding and postoperative rehabilitation training time in the observation group was less than that in the control group (

P
<
0.05

). There was no significant difference in operation time between the two groups (

P
>
0.05

). The total effective rate of the observation group was significantly higher than that of the control group (

P
<
0.05

). The JOA scores at 1 week, 6 months, and 12 months after operation in the observation group were higher than those in the control group (

P
<
0.05

). The physiological curvature of cervical spine in the observation group at 1 week, 6 months, and 12 months after operation was higher than that in the control group (

P
<
0.05

). The cervical range of motion at 12 months after operation in the observation group was significantly higher than that in the control group (

P
<
0.05

). The incidence of postoperative complications in patients of the observation group was significantly lower than that of the control group (

P
<
0.05

). Conclusion. Posterior laminectomy and decompression plus lateral mass screw-rod internal fixation can help patients to improve various clinical symptoms caused by nerve compression and obtain better improvement of cervical curvature and range of motion. It is an ideal surgical method for the treatment of multisegment cervical spinal canal stenosis, and it is conducive to improving the clinical efficacy of patients.
Large-Scale Image Retrieval of Tourist Attractions Based on Multiple Linear Regression Equations
Authors: Song Yinping
Published: October 2021

This paper presents an in-depth study and analysis of large-scale tourist attraction image retrieval using multiple linear regression equation approaches. This feature extraction method often relies on the partitioning of the grid and is only effective when the overall similarity of different images is high. The BOF model is borrowed from the method for text retrieval, which generally extracts the local features of an image by the scale-invariant feature transform algorithm and clusters them using

k

-means to obtain a low-dimensional visual dictionary and characterizes the image features with a histogram vector based on the visual dictionary. However, when there are many kinds of images, the dimensionality of the visual dictionary will be large and it is not convenient to construct the BOF model. The last fully connected layer is taken as the image feature, and it is dimensionalized by the principal component analysis method, and then, the low-dimensional feature index structure is constructed using the locality-sensitive hashing- (LSH-) based approximate nearest neighbor algorithm. The accuracy of our graph retrieval has increased by 8%. The advantages of feature extraction by a convolutional neural network and the high efficiency of a hash index structure in retrieval are used to solve the shortcomings of traditional methods in terms of accuracy and other aspects in image retrieval. The results show that compared with the above two algorithms, for most of the attractions, the method has a relatively obvious advantage in the accuracy of retrieval, and when there are few similar images of a particular attraction in the attraction image library, the accuracy of the query results is not much different from the first two methods.
Providing Data Insights to Suppliers for Product Development: Incentive Analysis of an E-Tailer
Authors: Dai Bingqin , Liu Nian , Jian Zhaoquan
Published: October 2021

Many suppliers invest in product development for market expansion via product development efforts. However, not all suppliers have accumulated market information, which is a critical factor for efficient product development that helps suppliers know consumer preferences better. Being aware of this, some e-tailers (e.g., JD.com) provide market information and data insights for their suppliers, which improves the latter’s product development efficiency. Supported by the market information, the market potential of suppliers’ products can be enlarged and their pricing power may be strengthened, which affects e-tailers from both positive and negative aspects. Based on these observations, we investigate an e-tailer’s incentive to share market information to its suppliers by formulating the tradeoffs among market expansion, product development efficiency, and suppliers’ pricing decisions. We find that the e-tailer prefers sharing market information with its suppliers when the latter’s product development efficiency is high, or the product development efficiency is high but the efficiency improvement rate is high.
Cathepsin C Is Involved in Macrophage M1 Polarization via p38/MAPK Pathway in Sudden Cardiac Death
Authors: Dai Jialin , Liu Jiangjin , Zhang Qiong , An Yang , Xia Bing , Wan Changwu , Zhang Yuanyuan , Yu Yanni , Wang Jie
Published: October 2021

This study was aimed at identifying molecular markers associated with the pathogenesis of sudden cardiac death (SCD). It provides a proteomic analysis of human left anterior descending coronary artery from subjects diagnosed with SCD through histological examination and cases of nondisease accidental deaths through autopsy. A total of 2784 proteins were obtained from label-free quantitative proteomic analysis. This included a total of 265 differential proteins which were involved in SCD-related processes, such as inflammation, muscle system process regulation, metal ion transport, and lysosomal pathway. Western blotting was carried out to measure the expressions of cathepsin C (CTSC), focal adhesion kinase (FAK), p-FAK, and proteins related to the p38/MAPK signaling pathway, whereas immunohistochemistry was performed to determine the localization and expression of CTSC, TNF-α, and CD206 in arterial tissues. It was found that CTSC were the most expressed proteins with a significant upward trend in SCD cases. Besides, CTSC regulated macrophage polarization to M1 through the FAK-induced p38/MAPK signaling pathway. This promoted the release of inflammatory factors and eventually increased the inflammatory response. In conclusion, this study implies that CTSC may be one of the key molecular targets for promoting macrophage M1 polarization in SCD, which may provide new therapeutic insights into the treatment of inflammatory diseases.
CST: A Multitask Learning Framework for Colorectal Cancer Region Mining Based on Transformer
Authors: Sui Dong , Zhang Kang , Liu Weifeng , Chen Jing , Ma Xiaoxuan , Tian Zhaofeng
Published: October 2021

Colorectal cancer is a high death rate cancer until now; from the clinical view, the diagnosis of the tumour region is critical for the doctors. But with data accumulation, this task takes lots of time and labor with large variances between different doctors. With the development of computer vision, detection and segmentation of the colorectal cancer region from CT or MRI image series are a great challenge in the past decades, and there still have great demands on automatic diagnosis. In this paper, we proposed a novel transfer learning protocol, called CST, that is, a union framework for colorectal cancer region detection and segmentation task based on the transformer model, which effectively constructs the cancer region detection and its segmentation jointly. To make a higher detection accuracy, we incorporate an autoencoder‐based image‐level decision approach that leverages the image‐level decision of a cancer slice. We also compared our framework with one‐stage and two‐stage object detection methods; the results show that our proposed method achieves better results on detection and segmentation tasks. And this proposed framework will give another pathway for colorectal cancer screen by way of artificial intelligence.
Assessment of Mechanical Stress Induced by Radiofrequency Currents on Skin Interfaces
Authors: Kruglikov Ilja L.
Published: October 2021

The epidermal‐dermal (ED) and dermal‐subcutaneous (DS) junctions are the most prominent skin interfaces, which are known to be of primary importance in different dermatological and aesthetic conditions. These interfaces are strongly modified in aging skin, and their effective targeting can lead to improvement of skin appearance in aging and by cellulite. Application of radiofrequency (RF) currents to the skin can selectively produce mechanical stress on these interfaces. Here, we assess the stresses induced by RF currents of different frequencies on EDJ and DSJ and discuss possible applications of the interfacial therapy in aesthetic medicine.
Research on Prediction Algorithm of Employees’ Psychological Stress Based on Multifeature Fusion
Authors: Chang Lijun
Published: October 2021

A multifeature fusion-based enterprise employee psychological stress prediction algorithm is suggested to address the concerns of low prediction accuracy, long duration, and poor results in current psychological stress prediction approaches. Examine ECG signal generation and properties, as well as the notion and causes of heart rate variability. The ECG signal is gathered according to the psychological stress reaction mechanism, and the digital filter is utilized to filter and preprocess the noise interference of the ECG signal. The linear discriminant analysis algorithm extracts the time domain linear features, frequency domain linear features, and nonlinear features of the ECG signal and then selects the ECG signal characteristics. D-S evidence theory is used to fuse the time domain linear characteristics, frequency domain linear characteristics, and nonlinear characteristics of the ECG signal, construct the psychological stress prediction model, obtain the final result of psychological stress state prediction, and realize the psychological stress prediction of enterprise employees, all based on multifeature fusion technology. The results of the experiments reveal that the suggested algorithm has a greater predictive effect on employee psychological stress, allowing it to enhance forecast accuracy and reduce prediction time.