bluuebunny/crossref_metadata_2025 huggingface dataset:
Design of Computer-Aided Translation System Based on Naive Bayesian Algorithm
Authors: Li Zhiqiang , Huang Juning , Zhong Weixuan
Published: September 2022

With the progress of society and the rapid development of science and technology, computer translation technology has become an important auxiliary tool in the fields of software localization and technical translation. This realistic demand has prompted translators to pay more attention to computer translation and have made some useful explorations on this basis. This paper aims to study and discuss computer-aided translation systems based on the fusion of naive Bayesian algorithms. This paper theoretically analyzes some key technologies in computer-aided translation. Computer-aided translation refers to helping translators to translate texts with a series of tools and then proposes a Bayesian classification algorithm. Translation memory technology can solve many practical problems, especially in the machinery manufacturing industry, processing some sentences in documents, which can reduce repetitive labor, unify vocabulary, and make translation styles more coordinated. The experimental results of this paper show that applying the naive Bayes method to the computer-aided translation system can better classify the documents in the translation system, thereby improving the ability of computer-aided translation. When the proportion of professional terms in the article reaches 85%, computer-aided translation has an auxiliary role for the translator. When the proportion of professional terms in the article reaches about 95%, computer-assisted translation can efficiently speed up the work speed and quality of translators. Due to the prosperity of computer translation systems, the duplication of labor for translators has been significantly reduced, and this ensures the consistency of terminology and translation style, so that the fruits of labor are fully utilized.
Spray: Streaming Log Parser for Real-Time Analysis
Authors: Zou Feng , Chen Xingshu , Luo Yonggang , Huang Tiemai , Liao Zhihong , Song Keer
Published: September 2022

Logs is an important source of data in the field of security analysis. Log messages characterized by unstructured text, however, pose extreme challenges to security analysis. To this end, the first issue to be addressed is how to efficiently parse logs into structured data in real-time. The existing log parsers mostly parse raw log files by batch processing and are not applicable to real-time security analysis. It is also difficult to parse large historical log sets with such parsers. Some streaming log parsers also have some demerits in accuracy and parsing performance. To realize automatic, accurate, and efficient real-time log parsing, we propose Spray, a streaming log parser for real-time analysis. Spray can automatically identify the template of a real-time incoming log and accurately match the log and its template for parsing based on the law of contrapositive. We also improve Spray’s parsing performance based on key partitioning and search tree strategies. We conducted extensive experiments from such aspects as accuracy and performance. Experimental results show that Spray is much more accurate in parsing a variety of public log sets and has higher performance for parsing large log sets.
An Approach for Verification of Secure Access Control Using Security Pattern
Authors: Gupta Charu , Singh Rakesh Kumar , Mohapatra Amar Kumar
Published: September 2022

According to OWASP-2021, more than 3,00,000 web applications have been detected for unauthenticated and unauthorised access leading to a breach of security trust. Security patterns are commonly used in web applications to address the problem of broken access. Web developers are not experts in implementing security patterns. Therefore, it is necessary to verify that the security pattern has been applied, specifying the original intent of the security pattern. In this paper, an approach has been proposed that analyses the behavioural aspect of security patterns to verify that it meets the security requirement of the web application. The proposed approach extracts the class diagram’s structural properties, relations, associations, and security-related constraints and verifies it using the first-order predicate logic. Experiments have been conducted using class diagrams of security patterns to detect instances of broken access control early in the design phase. The proposed approach will help minimise the risk of unauthenticated and unauthorised access to a web application.
Communication Channels for the Rule of Law and Environmental Sustainability: Reflections from a Green Economy Perspective
Authors: Wen Wen
Published: September 2022

To realize the sustainable development of the environment from the perspective of green economy, it is necessary to effectively utilize the communication channels of environmental sustainability under the rule of law. As a new driving force for economic growth and ecological environment, green economy is analyzed from the perspective of coordinating economic and environmental development. This paper fully analyzes the impact of green economy on economic growth and ecological environment. Based on the inherent relationship, the rule of law and environmental sustainability are conducive to promoting economic growth and also play a continuous role in environmental improvement. Areas with a high level of economic development also have a higher proportion of resource consumption. Driven by technological innovation, green economy can effectively reduce the impact of the environment, promote sustainability, and further promote the coordinated development of the economy and the environment. The results of the case analysis show that, in the proportion of the green economy with a large amount of investment, compared with the traditional policies and regulations, it greatly reduces the aggravation of environmental sustainability and has a positive role in promoting the long‐term development of the Chinese economy. It can not only effectively accelerate economic growth, but also realize the reflection from the perspective of green economy and promote the optimization of economic structure.
The Clinical Effect and Mechanism of Prostant on Urinary Retention and Anal Pain
Authors: Luo Wei-Min , Du Han , Jiang Hong-Liang , Deng Ying-Jun , Liang Xue , Qiu Ping , Cheng Yao
Published: September 2022

Anal pain and urinary retention are the two most outstanding complications of the procedure for prolapse and hemorrhoids (PPH) surgery. This study intended to assess the clinical effect and mechanism of Prostant on urinary retention and anal pain after the PPH. Here, 30 patients received PPH surgery. The role and mechanism of Prostant in patients and mice with urinary retention and anal pain were evaluated. ANOVA tests were executed and differences between groups were regarded as statistically significant when

p
<
0.05

. Prostant effectively improved the urination status, lower abdomen symptoms, time to urinate and score of VAS, and the reduction of TNF-α and IL-6. Similarly, Prostant can ameliorate the outcome of urodynamics in urinary retention mice. Mechanically, Prostant reversed the urinary retention-elevated the serum level of hs-CRP and TNF-α, reduction of IL-2, imbalance of Treg/Th17, and level of JAK2 and phosphorylated STAT3. Besides, Prostant ameliorated the pain as shown by the reduction of writhing response, and the elevation of threshold of pain and degree of swelling. Moreover, Prostant antagonized the pain-induced dysregulation of Treg/Th17. Therefore, Prostant can treat patients and mice with anal pain and urinary retention by modulating the balance of Th17/Treg to regulate the secretion and production of inflammatory factors. We hope our results can establish a scientific treatment approach for solving anal pain and urinary retention after PPH surgery of mixed hemorrhoids.
Promoting Immortalized Adipose-Derived Stem Cell Transdifferentiation and Proliferation into Neuronal-Like Cells through Consecutive 525 nm and 825 nm Photobiomodulation
Authors: Jansen van Rensburg Madeleen , Crous Anine , Abrahamse Heidi
Published: September 2022

Neuronal cells can be generated from adipose-derived stem cells (ADSCs) through biological or chemical inducers. Research has shown that this process may be optimized by the introduction of laser irradiation in the form of photobiomodulation (PBM) to cells. This in vitro study is aimed at generating neuronal-like cells with inducers, chemical or biological, and at furthermore treating these transdifferentiating cells with consecutive PBM of a 525 nm green (G) laser and 825 nm near-infrared (NIR) laser light with a fluence of 10 J/cm2. Cells were exposed to induction type 1 (IT1): 3-isobutyl-1-methylxanthine (IBMX) (0.5 mM)+indomethacin (200 μM)+insulin (5 μg/ml) for 14 days, preinduced with β-mercaptoethanol (BME) (1 mM) for two days, and then incubated with IT2: β-hydroxyanisole (BHA) (100 μM)+retinoic acid (RA) (10-6 M)+epidermal growth factor (EGF) (10 ng/ml)+basic fibroblast growth factor (bFGF) (10 ng/ml) for 14 days and preinduced with β-mercaptoethanol (BME) (1 mM) for two days and then incubated with indomethacin (200 μM)+RA (1 μM)+forskolin (10 μM) for 14 days. The results were evaluated through morphological observations, viability, proliferation, and migration studies, 24 h, 48 h, and 7 days post-PBM. The protein detection of an early neuronal marker, neuron-specific enolase (NSE), and late, ciliary neurotrophic factor (CNTF), was determined with enzyme-linked immunosorbent assays (ELISAs). The genetic expression was also explored through real-time PCR. Results indicated differentiation in all experimental groups; however, cells that were preinduced showed higher proliferation and a higher differentiation rate than the group that was not preinduced. Within the preinduced groups, results indicated that cells treated with IT2 and consecutive PBM upregulated differentiation the most morphologically and physiologically.
First-Aid System for Marathon Athletes Based on Telemedicine Technology
Authors: Chen Caixia , Liu Shuai
Published: September 2022

With the emphasis on sports in the region and the improvement of people’s living standards, more and more people participate in various sports and activities including marathon, so that more and more cities are hosting or even vying for it. With the marathon, not only the joy of successfully holding the event, but also the pain of the contestants and even the sadness of sudden death. In order to reduce the occurrence of such problems, this paper studies the emergency system of marathon runners based on telemedicine technology. The experimental results show that the research on first-aid bears for athletes based on telemedicine technology can save a lot of time and managers, improve the efficiency of first aid by 3.45%, and provide first-aid services for injured athletes at the first time, so as to ensure the health of athletes, ensuring the confidence and well-being of athletes participating in the marathon.
An Improved Apriori Association Rule for the Identification of Acupoints Combination in Treating COVID-19 Patients
Authors: Zheng Yanyan
Published: September 2022

This work presents a data-driven method for identifying the potential core acupoint combination in COVID-19 treatment through mining the association rules from the retrieved scientific literature and guidelines for prevention and treatment of COVID-19 published all over China. It is based on the representation of the acupoint data in a binary form, the use of a novel association rule mining algorithm properly tailored for discovering the relationship of acupoint groups among combinations of different descriptions. The proposed method is applied to the real database of acupoint descriptions collected from published literature and guidelines. The obtained results show the effectiveness of the proposed method.
Authenticated Wireless Links between a Drone and Sensors Using a Blockchain: Case of Smart Farming
Authors: Alqarni Kahlid S. , Almalki Faris A. , Soufiene Ben Othman , Ali Obaid , Albalwy Faisal
Published: September 2022

Agriculture is confronted with several significant difficulties, such as rising air temperatures and population growth, causing the implementation of smart farming operations as an optimum solution. This research aims to contribute to the growing knowledge of the potential role of blockchain technology in promoting the concept of smart farming by enhancing the efficiency of farming operations by boosting agricultural production, lowering environmental impact, and automating the work of farmers. It proposes a secure blockchain-based framework to establish trust among smart farming users. The framework utilizes asymmetric key exchange mechanism using an ECC authentication algorithm and SHA-256 hash function cryptography to secure communication between sensors and drones in the farm field. The SHA-256 hashing function ensures data integrity as attempts to tamper with data result in a different hash value, breaking the chain of blocks. To demonstrate the feasibility of the proposed framework, a proof-of-concept implementation was developed on the Ethereum blockchain, in which smart contracts were used to model the framework operations. The proof of concept’s performance was examined using Hyperledger Caliper for latency, throughput, and transaction success rate. The findings clearly indicate that blockchain technology can provide an efficient and scalable mechanism to advance smart farming and address some of the barriers that inhibit smart farming, particularly regarding to data integrity and availability.
Irisin Ameliorates Oxidative Stress-Induced Injury in Pancreatic Beta-Cells by Inhibiting Txnip and Inducing Stat3-Trx2 Pathway Activation
Authors: Liu Chongxiao , Zhou Jianhua , Xu Yanhong , Gong Sa , Zhu Yi , Zhang Hongli , Dong Yan , Zhao Bingxia , Li Xiaohua
Published: September 2022

Lipotoxicity can lead to beta-cell dysfunction and apoptosis because it induces oxidative stress. Recent studies have found that Irisin prevents pancreatic beta-cell dysfunction induced by palmitic acid (PA). However, an association between the protection against oxidative stress conferred by Irisin and beta-cell dysfunction has not been fully elucidated. In this study, we observed that Irisin treatment prevented INS-1 cell apoptosis induced by PA treatment and preserved the insulin-secreting function of INS-1 cells in vitro. These effects probably resulted from the Irisin-induced decrease in intracellular ROS levels triggered by PA treatment. In addition, PA treatment induced oxidative stress partially by inhibiting the activation of thioredoxin 2 (Trx2) through its increase of thioredoxin-interacting protein (Txnip) expression. However, Irisin administration blocked the increase in Txnip expression, which reversed the PA-induced inactivation of Trx2. Irisin also increased the nuclear translocation of Stat3, and the inhibition of Stat3 by siRNAs blocked Irisin-induced Trx2 expression, indicating that both Txnip and Stat3 are involved in Irisin-induced activation of Trx2. Furthermore, blockade of Stat3 by siRNAs led to the decreased gene expression of MafA and Ins and to cessation of glucose-induced insulin secretion that had been enhanced by Irisin. In vivo, HFD treatment led to reduced glucose tolerance and an increase in the level of the oxidative marker malondialdehyde (MDA) compared to that in the control group. However, these effects were ameliorated by Irisin injection due to the inhibition of beta-cell apoptosis and the activation of Trx2, probably through Txnip inhibition and Stat3 activation. In conclusion, our results reveal a possible mechanism for Irisin-induced beta-cell protection, which is mediated through Txnip inhibition and activation of the Stat3-Trx2 pathway.
Clove Flower Extract (Syzygium aromaticum) Has Anticancer Potential Effect Analyzed by Molecular Docking and Brine Shrimp Lethality Test (BSLT)
Authors: Aksono Eduardus Bimo , Latifah Aprilia Cahya , Suwanti Lucia Tri , Haq Kautsar Ul , Pertiwi Herinda
Published: September 2022

Various anticancer medications have been discovered due to advances in the health care industry, but they have undesirable side effects. On the other hand, anticancer drugs derived from natural sources have low side effects, making them excellent for cancer therapy. This study aims to evaluate the effect of clove flower extract (Syzygium aromaticum) as a potential anticancer agent by determining grid-score values using molecular docking and LC50 values using the brine shrimp lethality test (BSLT) technique. As animal models, three hundred larvae of Artemia salina leach were divided into six groups. Each group has ten larvae that have undergone five replications. The clove flower extract concentration in the treatment media was 50 ppm (T1), 250 ppm (T2), 500 ppm (T3), 750 ppm (T4), 1000 ppm (T5), and 0 ppm (seawater) as the control. The probit analysis of Artemia Salina leach mortality percentage data. The results indicated that the clove flower extract (Syzygium aromaticum) is harmful to larvae with LC50 values of 227,1 g/ml or in the equation y = 2,8636 x – 1,7466 with an R2 value of 0.9062. According to molecular docking, eugenol acetate (grid-score −42.120834) has a close relationship with the cognate enzyme nitric oxide synthase (3E7G) based on its proximity to the grid score value (grid-score −61.271812). Therefore, clove flower extract has the potential to act as an anticancer medication. Based on the grid-score proximity, eugenol acetate is close to the homologous enzyme nitric oxide synthase (3E7G). Inhibition of nitric oxide synthase also shows a reduction in cancer cell proliferation.
Protein Tyrosine Phosphatase Receptor Type R (PTPRR) Reduces AChR Clustering by Dephosphorylating MuSK
Authors: Chen Yanxun , Guan Maohao , Yu Fengqiang , Yang Zhongshan , Yi Weiqiang , Huang Xuan , Qiu Ruiqin , Lai Fancai
Published: September 2022

Neuromuscular junction (NMJ) formation and maintenance depend on the proper localization and concentration of various molecules at synaptic contact sites. Acetylcholine receptor (AChR) clustering on the postsynaptic membrane is a cardinal event in NMJ formation. Muscle-specific tyrosine kinase (MuSK), which functions depending on its phosphorylation, plays an essential role in AChR clustering. In the present study, we used plasmid-based biochemical screening and determined that protein tyrosine phosphatase receptor type R (PTPRR) is responsible for dephosphorylating MuSK on tyrosine residue 754. Furthermore, we showed that PTPRR significantly reduced MuSK-dependent AChR clustering in C2C12 myotubes. Collectively, these data illustrate a negative regulation function of PTPRR in AChR clustering.
Attribute-Based Policy Evaluation Using Constraints Specification Language and Conflict Detections
Authors: Sun Wei
Published: September 2022

Attribute-based access control (ABAC) has attracted widespread interest and has become an ideal mechanism due to its flexibility characteristic and the powerful expressiveness for various security policies, such as the separation-of-duty constraint and cardinality constraint. The formulation of appropriate ABAC policies is critical for ensuring system security and robustness. However, conflicts occur frequently in existing state-of-the-art systems. Most conventional detection methods either lack the evaluation of the policy quality or consider no constraint. To resolve these problems, a novel method for the ABAC policy evaluation is proposed in this study. First, to meet diverse organizational requirements, we use the attribute-based constraints specification language to uniformly formulate and specify the conflict relations among attributes and present the satisfiability of conflict relations. Second, to comprehensively detect the conflict problems, we present the evaluation criteria for conflicts on attributes and rules and propose a novel algorithm for detecting conflicts. Last, we validate the effectiveness and efficiency of the proposal through experiments, which demonstrate that it not only improves the policy quality but also reduces the conflicting number and conflicting probability.
Impact of an Abdominal Compression Bandage on the Completion of Colonoscopy for Obese Adults: A Prospective Randomized Controlled Trial
Authors: Liu Ting-Ting , Meng Yi-Teng , Xiong Feng , Wei Cheng , Luo Su , Zhan Sheng-Gang , Song Yang , Li Ying-Xue , Shi Rui-Yue , Yao Jun , Wang Li-Sheng , Li De-Feng
Published: September 2022

Background and Aim. Obesity has been linked to incomplete colonoscopy. In the present study, we aimed to determine whether an abdominal compression bandage could improve the complete colonoscopy rate (CCR) of obese patients. Methods. Eligible patients were randomly allocated into the abdominal bandage and conventional groups during a routine colonoscopy. The primary outcome was CCR. Results. A total of 250 eligible patients were randomly assigned to the abdominal bandage and conventional groups from January 2021 to April 2021. Eleven patients (five in the abdominal bandage group and six in the conventional group) were excluded due to schedule cancellation after randomization, and 239 patients were eventually included in the final analysis. There were no significant differences between the two groups regarding baseline characteristics (P > 0.05). Furthermore, no significant differences were observed in terms of advanced adenoma detection rate (AADR), polyp detection rate (PDR), bowel preparation scale (BBPS), bubble scale (BS), and withdrawal time between the two groups (P > 0.05). However, compared with the conventional group, the cecal insertion time (CIT) of the abdominal bandage group was significantly shortened (279.00 (234.50–305.75) vs. 421.00 (327.00–485.00), P < 0.001), and the CCR (96.7% vs. 88.2%, P = 0.01) and adenoma detection rate (ADR) (47.5% vs. 32.8%, P < 0.001) were improved. Besides, logistic regression analysis showed that body mass index (BMI) and abdominal compression bandage were associated with CCR. Conclusions. Abdominal compression bandages could effectively shorten CIT and improve CCR and ADR for obese patients during a routine colonoscopy. This trial is registered with the Chinese Clinical Trial Registry (No. ChiCTR2100043556).
Product Optimization and Antimicrobial Efficiency of Starch-Based Active Packaging Film Prepared Using Amira (Plumbago Zeylanica) Root Extract
Authors: Muche Melak , Zegeye Adamu , Lemma Hirut , Gisila Tilahun
Published: September 2022

This study aimed to develop food packaging from cornstarch combined with methanol extract of Plumbago zeylanica root as antimicrobial agents. The antimicrobial activity of the extract was tested against three bacterial strains, namely, Staphylococcus aureus, Salmonella typhi, and Escherichia coli and one fungal strain called Aspergilla niger. Microorganism growth inhibition was examined by employing a zone of inhibition check on solid media. Two hundred microliters of assorted concentrations (200 mg/ml, 100 mg/ml, 50 mg/ml, and 25 mg/ml) of plant extracts were added into an eight-millimeter hole diameter employing a micropipette. Hence, 200 µl of 200 mg/ml extract inhibited 13.5, 8.75, 10.5, and 27 mm diameter for S. typhi, E. coli, S. aureus, and A. niger, respectively. The minimum inhibitory concentrations 1.25 mg/ml, 2.5 mg/ml, 1.25 mg/ml, and 0.625 mg/ml of crude extract utterly suppressed the growth of S. aureus, E. coli, S. typhi, and A. niger, respectively. Later on, the antimicrobial agent films and the control media were prepared by the casting method. The prepared antimicrobial packaging films exhibited inhibitory zones (a wide clear zone on solid media) for S. typhi and A. niger growth inhibition; however, inhibitions for E. coli and S. aureus were not effective. Antimicrobial activity decreased while decreasing the solubility of the film and antimicrobial concentration with increasing cornstarch content in the solution. The tensile strength of the films increased with increasing antimicrobial content in the solution. Higher antimicrobial inhibition activity was found at a 1.5 glycerol/starch ratio, 1.5 gram content of P. zeylanica, and 0.5 mm film thickness. Generally, the plasticized cornstarch antimicrobial packaging film combined with P. zeylanica extract had a significant effect on the growth of each selected microorganism and the physical properties of the material (

P

 < 0.05). A preprint of this research has previously been uploaded to the Addis Ababa University database (Yirdaw et al. 2018). In this section, only the summary and updated content of the research findings are presented.
New Search Method for Sbox-Related Impossible Differentials
Authors: Zhang Yi , Shen Xuan , Liu Guoqiang , Li Chao
Published: September 2022

The impossible differential attack is one of the most fundamental tools of cryptanalysis and has been successfully applied to a large variety of block ciphers. In a typical impossible differential attack, the foundation and first step is to construct an impossible differential. Nowadays, two kinds of most commonly used approaches in impossible differential construction are the matrix-based and tool-aided automatic search methods. In this paper, we proposed a new search method combining an early-abort strategy with the guess-and-determine technique to find longer impossible differentials. Compared with the previous matrix-based search methods, ours has taken the details of Sbox into consideration, while compared with the tool-aided methods, ours is independent of a third-party solver and is applicable to ciphers with large (≥8 bits) Sboxes, which could be tough work for tool-aided methods to cover. Therefore, more accurate results could be obtained. To prove the effectiveness of our method, it is applied to CSA and FOX64. For CSA, 23/24/25-round impossible differentials were found for CSA-BC. This improved the longest impossible differential distinguisher so far by 1/2/3 rounds. And 25-round key recovery attacks are performed against CSA-BC and CSA, which are the longest impossible differential attack by now. For FOX64, we proved that an impossible differential for its round function always implies an impossible differential for 4-round FOX64, and new types of impossible differentials were able to be found. Our method has its own advantage in dealing with large Sboxes, and it may be a possible direction to search for Sbox-related impossible differentials.
Nonpolynomial Spline Interpolation for Solving Fractional Subdiffusion Equations
Authors: Emadifar Homan , Hamasalh Faraidun K.
Published: September 2022

The nonpolynomial spline interpolation is proposed to distinguish numerical analysis from the senes boundary conditions, accurance error estimations. The idea used in this article is readily applicable to obtain numerical solution of nonpolynomial spline interpolation. These analyze the methods that are suitable for the numerical solution of subdiffusion equation. The method has been shown to be stable by using von Neumann technique. The accuracy and efficiency of the scheme are checked by several examples to obtain numerical tests.
Obstetrics and Gynecology Emergency Department Activity during Lockdown in a Teaching Hospital, Hub Center, for COVID-19
Authors: Amadori R. , Buscemi R. , Desando A. , Grillo F. , Remorgida V. , Surico D.
Published: September 2022

Background. The lockdown related to the SARS-CoV-2 pandemic has imposed profound changes in the interaction of the population with hospitals and emergency departments. The main aim of this research was to evaluate the impact of lockdown on the activity of obstetrics and gynecology emergency department (OGED) in a teaching hospital, hub center, for COVID-19. Methods. The study considers all visits to the OGED with their different triage color codes that represent the clinical severity of each case (from the most severe to the least one: red, yellow, green, white). Data were selected through the “PSNet” triage program and collected anonymously. We analyzed frequency distributions of the variables separately for each woman and calculated mean and standard deviations for continuous variables. We then analyzed the association between factors and outcomes for categorical variables (expressed as a number and percentage of the total) using the chi-square test (χ2). The level of significance was established withp<0.05. Statistical analysis was performed using SPSS Statistics V20.0. Given the fact that the study has a retrospective observational nature and it is based on an anonymous routine database, approval by the Local Ethics Committee was not necessary. Results. The relative decrease of patients presenting to OGED in 2020 was −50.96%. The percentage of nonpregnant women was significantly lower in 2020 compared to 2019 (p≤0.0001; Δ = −79.46%). Regarding the obstetric group, we saw an important decrease of visits in 2020 compared to 2019 (p<0.0001; Δ = −40%). The prevalence of yellow codes was significantly higher in 2020 (Δ = +29.72%), while that of white (Δ = −61.58%) and green (Δ = −52.22%) codes was significantly lower (p≤0.0001). Comparing the diagnoses at discharge, we could highlight significant reductions in 2020 for more than one diagnosis: bleeding (p≤0.0001; Δ = −70.42%), pain (p≤0.0001; Δ = −81.22%), urinary diseases (p=0.004; Δ = −75.64%), and gastrointestinal diseases (p≤0.0001; Δ = −87.50%). Conclusions. An evident change emerged in relation to the dynamics between the local obstetrical and gynecological population, and OGED resources. The COVID‐19 lockdown greatly reduced the rate of admission to OGED without time-related obstetric and gynecological complications. The reduction of admissions suggests a more appropriate use of the ED by patients that may inspire future policies for the implementation of emergency services.
Financial Early Warning Model for Listed Companies Based on the Smart Sensor Data Network
Authors: Wang Yao
Published: September 2022

This paper uses intelligent sensors to build a data network, collects information about listed enterprises in all aspects, performs frequency statistics and semantic analysis based on the financial domain lexicon on information related to listed enterprises, and introduces big data variables into a nonlinear support vector machine early warning model of enterprises combined with financial indicators. This paper introduces online reviews as big data indicators based on financial early warning theories and methods. Suitable financial indicators and big data indicators are selected for the financial early warning model to filter the available indicators. The prediction results of only financial indicators are compared with the prediction results of incorporating big data indicators. Quantify the comment information related to enterprises on the platform through sentiment classification and statistics on the number of comment information posted. The cost-sensitive support vector machine is used as the base classifier of the improved AdaBoost algorithm to build a dynamic imbalance warning model. To address the problems of large complexity and computation of unbalanced big data classification, high reliance on a priori knowledge, and classification performance to be improved, the classification and detection method of the intelligent sensor data network is proposed. By introducing migration learning, the problem of knowledge acquisition and training efficiency of high-dimensional complex data feature extraction in a big data environment is effectively solved, and the network performance is optimized by a conjugate gradient descent algorithm. Through simulation experiments, the prediction accuracy of the model for positive class samples is significantly improved after the nonequilibrium processing, the recall rate of the network model reaches 84.15%, and the prediction accuracy of the network model under different time steps reaches more than 90%. The experiment proves that the model can send out financial alert information more quickly and efficiently and accurately when a financial crisis may occur, relative to the traditional financial forecasting methods.
Mechanisms of Intervertebral Disc Degeneration Treatment with Deer Antlers Based on Network Pharmacology and Molecular Docking
Authors: Weng Rui , Lin Hongheng , Li Zhuoyao , Chen Daman , Lin Xiaoxiao , Zhang Zhenyu , Chen Qiqi , Yao Yiqi , Li Wenchao
Published: September 2022

Background. With the aging of the population, the prevalence of IVDD increases preoperatively. How to better treat IVDD has become an important clinical issue. Deer antlers proved to have a great effect on the treatment of IVDD in many studies, but the molecular mechanism has not been clarified. Objective. To investigate the molecular mechanism and target of deer antlers in the treatment of IVDD. Methods. Compounds from deer antlers were collected and targets were predicted using HERB, TCMSP, TCMID, SwissADME, and SwissTargetPrediction. Collection of disease targets for IVDD was done using GeneCards, TTD, DrugBank, DisGeNET, and OMIM. Cytoscape 3.7.2, AutoDock Vina (v1.1.2), and R software were used for data analysis and the construction of network diagrams. Results. A total of 5 active compounds from deer antlers were screened and 104 therapeutic targets were predicted. A total of 1023 IVDD disease targets were collected. Subsequently, PPI network prediction analysis was performed for disease and treatment targets, and 112 core targets were collected after screening. After obtaining the core target, we used the clusterProfiler software package of R software to carry out GO and KEGG enrichment analyses for the core target and plot the bubble maps. According to the GO enrichment results, the main biological processes of IVDD treatment by deer antlers lie in the rhythmic process, mRNA catabolic process, and G1/S transition of the mitotic cell cycle. KEGG results were mainly related to the PI3K-Akt signaling pathway, thyroid hormone signaling pathway, and Notch signaling pathway. Molecular docking results showed that estrone had the best docking results on ESR1. Conclusion. Deer antlers are rich in various compounds that can prevent the development of IVDD by upregulating the PI3K-Akt signaling pathway and Notch signaling pathway. Its key compounds estradiol and estrone can reduce the inflammatory response and oxidative stress in tissues and organs, thus slowing down the progression of IVDD. Estrone, the active compound in deer antlers, was found by molecular docking to have good results against ESR1, the target of the disease, which may be a potential site for drug therapy.
Week-One Anaemia was Associated with Increased One-Year Mortality in Critically Ill Surgical Patients
Authors: Wu Feng-Hsu , Wong Li-Ting , Wu Chieh-Liang , Chao Wen-Cheng
Published: September 2022

Background. Anaemia has a deleterious effect on surgical patients, but the long-term impact of anaemia in critically ill surgical patients remains unclear. Methods. We enrolled consecutive patients who were admitted to surgical intensive care units (ICUs) at a tertiary referral centre in central Taiwan between 2015 and 2020. We used both Cox proportional hazards analysis and propensity score-based analyses, including propensity score matching (PSM), inverse probability of treatment weighting (IPTW), and covariate balancing propensity score (CBPS) to determine hazard ratios (HRs) and 95% confidence intervals (CIs) for one-year mortality. Results. A total of 7,623 critically ill surgical patients were enrolled, and 29.9% (2,280/7,623) of them had week-one anaemia (haemoglobin <10 g/dL). We found that anaemia was independently associated with an increased risk of one-year mortality after adjustment for relevant covariates (aHR, 1.170; 95% CI, 1.045–1.310). We further identified a consistent strength of association between anaemia and one-year mortality in propensity score-based analyses, with the adjusted HRs in the PSM, IPTW, and CBPS were 1.164 (95% CI 1.025–1.322), 1.179 (95% CI 1.030–1.348), and 1.181 (1.034–1.349), respectively. Conclusions. We identified the impact on one-year mortality of anaemia in critically ill surgical patients, and more studies are needed to validate our findings.
DBN Neural Network Model Combined with Meta-Analysis on the Curative Effect of Acupuncture and Massage
Authors: Wang Xiujun
Published: September 2022

Acupuncture and massage are among the oldest medical treatments in China. During the acupuncture process, as well as the subsequent needle extraction process, there are differences in the acupuncture intensity, treatment duration, and acupuncture depth. For both medical treatments of acupuncture and massage, this article learns and analyzes a large amount of literature and applies DBN neural network method to build a human skeletal model to simulate and identify medical professional steps such as acupuncture therapy. The research results show that the recognition rate of DBN reaches 92.1% after the training of 1000 samples. After learning all the training samples, the DBN model achieved feature recognition accuracy of 96.4%, 97.68%, 96.66%, and 92.27% for the test samples of mixed needling process, needle insertion operation, needle extraction operation, and rotary needle handling process, respectively. The research in this article can contribute to the modernization of Chinese medicine by maximizing the simulation of the force on the human body when receiving needling and tui-na, as well as the clinical treatment effect.
Construction of a Prediction Model for Distance Education Quality Assessment Based on Convolutional Neural Network
Authors: Wang Peizhang
Published: September 2022

This paper introduces the principles and operation steps of convolution and pooling of convolutional neural networks in detail. In view of the shortcomings of fixed sampling points and single receptive field in traditional convolution and pooling forms, deformable convolution and deformable pooling are introduced to enhance the network’s ability to adapt to image details and large displacement problems. The concepts of warp, loop optimization, and network stack are introduced. In order to improve the optimization performance of the algorithm, three subnetwork structures and stack models are designed, and various methods are used to improve the prediction accuracy of distance education quality assessment. In order to improve the accuracy and timeliness of education quality assessment, this paper proposes a distance education quality assessment model based on mining algorithms. The prediction index is selected by the improved BP neural network. It is required to establish the input layer node as the input vector based on the number of data sources since the input layer is used for data input. The neural network is trained with a quarter of the mining data, and the mining algorithm is further trained with network error trials. A fuzzy relationship matrix is created based on the assessment of teaching quality’s hierarchical structure. This leads to the conclusion of the fuzzy thorough evaluation of the effectiveness of distant learning. Experiments show that the proposed model has an average accuracy of 96%, the average teaching quality modeling time is 25.44 ms, and the evaluation speed is fast.
Machine-Learning-Based Human Fall Detection Using Contact- and Noncontact-Based Sensors
Authors: Chandak Ayush , Chaturvedi Nitin , Dhiraj
Published: September 2022

Automated human fall detection is an essential area of research due to its health implications in day-to-day life. Detecting and timely reporting of human falls may lead to saving human life. In this paper, fall detection has been targeted using machine-learning-based approaches from two perspectives regarding data sources, that is, contact-based and noncontact-based sensors. In both of these cases, various methods based on deep learning and machine learning techniques have been attempted, and their performances were compared. The approaches analyze data in fixed time windows and extract features in the time domain or spatial domain which obtain relative information between consecutive data samples. After experimentation, it was found that the proposed noncontact-based sensor techniques outperformed the contact-based sensor techniques by a margin of 1.82%. After this, it was also found that the noncontact-based sensor techniques outperformed the state of the art of noncontact-based sensor results by a margin of 3.15%. To better suit these techniques for real-world applications, embedded board implementation and privacy preservation of subject by using advanced methods such as compressive sensing and feature encoding need to be attempted.
Optimization of Thumb Prosthesis Design by Using Five Performance Criteria
Authors: Tsamo Nestor , Tcheukam Toko Denis , Talla Pierre Kisito
Published: September 2022

The thumb prosthesis mechanism is optimally designed by using five performance criteria including the following: least square structural error, mechanical manufacturing imprecision error, driving optimal torque, mechanical strength reliability, and production cost of the thumb mechanism. This paper was devoted to the optimization of the thumb prosthesis’s mechanism by taking into consideration the manufacturing cost model based on machining cost theory which took into detail the shape of the workpieces and the strength reliability of all the parts composing the entire mechanism. Every optimization problem displays a particular set of an independent vector of optimal parameters, showing the impact of each objective function on the configuration of the prosthetic device. The multiobjective optimization showed that the mechanical reliability and the production cost included in any combination of the simultaneous optimization enabled the achievement of the same optimum variables design, though with some exceptions. With the inclusion of the labor charges, the depreciation rate of the equipment, and production assets in the mathematical’s manufacturing cost model, the optimal manufacturing cost generated from the numerical simulation was 501.0021 USD. Therefore, the global manufacturing cost and the mechanical strength reliability of the whole prosthesis mechanism have a real impact on the customization of the structure, due to the stochastic nature of the trajectory of the cutting tools during the manufacturing processes.
Research on the Impact of Optimal Configuration of University Library Layout on Students’ Academic Literacy Development Based on 5G
Authors: Xiao Jing
Published: September 2022

This paper provides an in-depth study and analysis of the relevance of adopting the idea of 5G for the optimal configuration of university library layout to the development of students’ academic literacy. It also analyzes the benefits of 5G on the digital governance of libraries together with the convenience that the application of 5G can provide to smart libraries and helps to realize the research on the service innovation model of smart libraries under 5G. The survey mainly focuses on four indicators: the development of physical space services in university libraries; facilities and equipment for physical space services; the content and form of physical space services; and it presents the current problems of physical space services, including the overall convergence of physical space services in university libraries. When different libraries are co-constructed and shared, there will be incompatibility problems, and if the library is required to develop its own management system, it will waste resources. The library space layout is arranged by highlighting the elements of the cognitive map to improve the lending efficiency; using a more continuous and transparent interface in terms of color and material to increase the experience of spaciousness; using more wood elements and green to add a natural atmosphere to achieve the purpose of relaxing the readers’ heart; and finally increasing the light source in the reading area to complement the illumination in the shaping of the light environment. In terms of spatial functions, more attention is paid to the design of public interaction and leisure spaces so that readers can rejuvenate themselves and devote themselves to the next learning tasks. The interior design is considered in terms of the relationship between arousal theory and task performance, and a moderately complex interface is designed to improve readers’ efficiency in the space. Finally, the paper concludes with a summary of what has not been thoroughly researched and an outlook on the future of university library interior design.
Reduced PU.1 Expression Collaborates withTet2Loss to Trigger Myeloid Leukemogenesis
Authors: Schleicher Wolfgang E. , Pietras Eric M.
Published: September 2022

The leukemic transformation of hematopoietic stem and progenitor cells in the setting of Tet2 deficiency is driven by PU.1 gene network loss through complementary reduction in PU.1 expression and hypermethylation of ETS loci at the enhancers of PU.1 target genes.

See related article by Aivalioti et al., p. 444 (6).
Abstract IA39: Pathology of AYA lymphomas: Creating a roadmap for translational research
Authors: Lim Megan S.
Published: September 2022

Mature lymphomas of the adolescent and young adult population (AYA) are rare and clinically and pathobiologically distinct from those in the adult population. The pathobiologic aspects of lymphomas in the AYA population are largely unknown as most studies do not specify age groups and use diagnostic criteria that have been established for lymphomas in the adult population. B-cell lymphomas make up the majority of lymphomas in the AYA population and many are aggressive including Burkitt lymphoma, large/high grade B-cell lymphoma with 11q aberrations and diffuse large B-cell lymphoma (DLBCL), NOS. Most DLBCL, NOS arise as de novo lymphomas. They may occur as a secondary neoplasm after childhood cancer or in cancer predisposition syndromes, primary immunodeficiencies or other inherited genetic diseases (inborn error of immunity-associated lymphoid proliferations and lymphomas). Most DLBCL, NOS in the AYA population are of germinal center derivation, exhibit high proliferation index and while comprehensive studies are lacking, they are biologically distinct from adult diffuse large B-cell lymphomas with less cytogenetic complexity. Mature T-cell lymphomas are extremely rare in the AYA population and composed primarily of those driven by a dominant oncogenic tyrosine kinase (ALK+ anaplastic large cell lymphoma) or Epstein-Barr virus and most arise from T cells of the innate immune system. T-cell lymphomas arising from the adaptive immune system (nodal T follicular helper (TFH) cell lymphoma, peripheral T cell lymphoma, NOS) which are common in the adult population are rare in AYA. The most recent WHO Classification of Paediatric Tumors recognizes specific entities that are enriched in the AYA population with distinct genetic features including indolent B-cell lymphoma including pediatric-type follicular lymphomas, pediatric nodal marginal zone lymphoma and large B-cell lymphomas with IRF4 rearrangement, and EBV-associated T cell lymphoma such as systemic EBV-positive T-cell lymphoma of childhood, systemic chronic active EBV-positive disease and hydroa vacciniforme lymphoproliferative disorder. The distinct clinical and pathobiologic features of lymphomas in the AYA population provide unique opportunities for translational discoveries leveraging innovative technologies such as single cell sequencing, B and T cell immune repertoire analyses and circulating tumor DNA, that will provide insights into the interplay between the immune system, epigenetics, germline predisposition and viruses. These studies are poised to yield actionable information that impacts therapies and improve patient outcomes.

Citation Format: Megan S. Lim. Pathology of AYA lymphomas: Creating a roadmap for translational research [abstract]. In: Proceedings of the Third AACR International Meeting: Advances in Malignant Lymphoma: Maximizing the Basic-Translational Interface for Clinical Application; 2022 Jun 23-26; Boston, MA. Philadelphia (PA): AACR; Blood Cancer Discov 2022;3(5_Suppl):Abstract nr IA39.