bluuebunny/crossref_metadata_2025 huggingface dataset:
Voting with the Stars: Analyzing Partisan Engagement between Celebrities and Politicians in India
Authors: Kommiya Mothilal Ramaravind , Mishra Dibyendu , Nishal Sachita , Lalani Faisal M. , Pal Joyojeet
Published: April 2022

Celebrity influencers are increasingly central to political discourse as they engage in, and get engaged with, on matters of electoral importance. In this paper, using Twitter data from 1432 sportspersons and entertainers and their engagement with the 1000 of the most followed ruling party and opposition politicians from India, we propose a new method to measure partisanship of celebrities along different modes of engagement. Our examination of polarization, through topical and retweet analyses, shows patterns related to both party incumbency and the level of internal organization. We find that the ruling BJP has been more effective than the opposition, the INC, in organized outreach to celebrities, by eschewing explicit party-based partisanship, and instead employing non-partisan narrative techniques, such as maintaining nationalism as the central theme in tweets. We find that while entertainers are equally engaged by both the ruling and opposition parties, sportspersons, who often enjoy a nationalist appeal by virtue of representing the country, tend to have a much more partisan relationship with the incumbent party.
Working for the Invisible Machines or Pumping Information into an Empty Void? An Exploration of Wikidata Contributors' Motivations
Authors: Zhang Charles Chuankai , Houtti Mo , Smith C. Estelle , Kong Ruoyan , Terveen Loren
Published: April 2022

Structured data peer production (SDPP) platforms like Wikidata play an important role in knowledge production. Compared to traditional peer production platforms like Wikipedia, Wikidata data is more structured and intended to be used by machines, not (directly) by people; end-user interactions with Wikidata often happen through intermediary "invisible machines." Given this distinction, we wanted to understand Wikidata contributor motivations and how they are affected by usage invisibility caused by the machine intermediaries. Through an inductive thematic analysis of 15 interviews, we find that: (i) Wikidata editors take on two archetypes---Architects who define the ontological infrastructure of Wikidata, and Masons who build the database through data entry and editing; (ii) the structured nature of Wikidata reveals novel editor motivations, such as an innate drive for organizational work; (iii) most Wikidata editors have little understanding of how their contributions are used, which may demotivate some. We synthesize these insights to help guide the future design of SDPP platforms in supporting the engagement of different types of editors.
XR-LIVE: Enhancing Asynchronous Shared-Space Demonstrations with Spatial-temporal Assistive Toolsets for Effective Learning in Immersive Virtual Laboratories
Authors: Thanyadit Santawat , Punpongsanon Parinya , Piumsomboon Thammathip , Pong Ting-Chuen
Published: April 2022

An immersive virtual laboratory (VL) could offer flexibility of time and space, as well as safety, for remote students to conduct laboratory activities through online experiential learning. Recording an instructor's demonstration inside a VL is an approach that allows students to learn directly from a demonstration. However, students have to learn from a recording while controlling the playback, which requires attention spent on additional spatial and temporal cues. This additional cognitive load could lead to errors during the laboratory procedure. To address these challenges, we have identified four design requirements to reduce attention load in VLs; namely, organized learning steps, improved student sense of co-presence, reduction of task-instructor split-attention, and learning independent of interpersonal distance. Based on these requirements, we have designed and implemented spatial-temporal assistive toolsets for laboratories in a virtual environment, namely XR-LIVE, to reduce cognitive load and enhance learning in an asynchronous shared-space demonstration, implemented based on the setup of a standard civil engineering laboratory. We also analyzed students' behavior in the VL demonstration to design guidelines applicable to generic VLs.
Benchmarking robots in smart cities
Authors: Studley Matthew , Presutti Valentina , Nardi Daniele , Carter Sarah
Published: April 2022

In order for robots to become integrated into society, we need to be able to prove that robots do their jobs reliably. Robot benchmarking competitions in smart cities offer a glimpse into our future.
Non-IID spatio-temporal prediction in smart cities
Authors: Ren Siyuan , Guo Bin , Wang Qinfen , Yu Zhiwen
Published: April 2022

Non-IID spatio-temporal prediction research points toward emerging directions and fundamental solutions to address various complexities from the perspective of both data couplings and heterogeneity. Delving into the non-IID challenge and opportunity of spatio-temporal prediction in smart cities, this article also addresses current solutions to bring some inspiration to future researchers.
Formal methods enhance deep learning for smart cities
Authors: Ma Meiyi
Published: April 2022

Rigorous approaches based on formal methods have the potential to fundamentally improve many aspects of deep learning. This article discusses the challenges and future directions of formal methods enhanced deep learning for smart cities.
The principle of least sensing
Authors: Wang Leye
Published: April 2022

With the worldwide emergence of data protection regulations, how to conduct law-regulated big data analytics becomes a challenging and fundamental problem. This article introduces the principle of least sensing, a promising sensing paradigm toward legally regulated big data analytics.
A Grid-Based Two-Stage Parallel Matching Framework for Bi-Objective Euclidean Traveling Salesman Problem
Authors: Lin Fandel , Hsieh Hsun-Ping
Published: April 2022

Traveling salesman problem (TSP) is one of the most studied combinatorial optimization problems; several exact, heuristic or even learning-based strategies have been proposed to solve this challenging issue. Targeting on the research problem of bi-objective non-monotonic Euclidean TSP and based on the concept of the multi-agent-based approach, we propose a two-stage parallel matching approaching for solving TSP. Acting as a divide-and-conquer strategy, the merit lies in the simultaneously clustering and routing in the dividing process. Precisely, we first propose the Two-Stage Parallel Matching algorithm (TSPM) to deal with the bi-objective TSP. We then formulate the Grid-Based Two-Stage Parallel Matching (GRAPE) framework, which can synergize with TSPM, exact method, or other state-of-the-art TSP solvers, for solving large-scale Euclidean TSP. According to this framework, the original problem space is divided into smaller regions and then computed in parallel, which helps to tackle and derive solutions for larger-scale Euclidean TSP within reasonable computational resources. Preliminary evaluation based on TSPLIB testbed shows that our proposed GRAPE framework holds a decent quality of solutions in especially runtime for large-scale Euclidean TSP. Meanwhile, experiments conducted on two real-world datasets demonstrate the efficacy and adaptability of our proposed TSPM in solving the bi-objective non-monotonic TSP.
Algebra-Based Reasoning for Loop Synthesis
Authors: Humenberger Andreas , Amrollahi Daneshvar , Bjørner Nikolaj , Kovács Laura
Published: April 2022

Provably correct software is one of the key challenges of our software-driven society. Program synthesis—the task of constructing a program satisfying a given specification—is one strategy for achieving this. The result of this task is then a program that is correct by design. As in the domain of program verification, handling loops is one of the main ingredients to a successful synthesis procedure.

We present an algorithm for synthesizing loops satisfying a given polynomial loop invariant. The class of loops we are considering can be modeled by a system of algebraic recurrence equations with constant coefficients, thus encoding program loops with affine operations among program variables. We turn the task of loop synthesis into a polynomial constraint problem by precisely characterizing the set of all loops satisfying the given invariant. We prove soundness of our approach, as well as its completeness with respect to an
a priori
fixed upper bound on the number of program variables. Our work has applications toward synthesizing loops satisfying a given polynomial loop invariant—program verification—as well as generating number sequences from algebraic relations. To understand viability of the methodology and heuristics for synthesizing loops, we implement and evaluate the method using the
Absynth
tool.
Event Detection via Context Understanding Based on Multi-task Learning
Authors: Xia Jing , Li Xiaolong , Tan Yongbin , Zhang Wu , Li Dajun , Xiong Zhengkun
Published: April 2022

Event detection (ED)
aims to identify events of interest described in the text. With the current explosive growth of text data on the internet, ED is increasingly practical and has gained many researchers’ attention. The existing works usually design ED as a token-level multi-class classification task. In this setting, given a sentence, ED models’ prediction for each token is relatively independent and thus cannot fully utilize sentence-level information and the association relations between multiple events in this sentence. To handle these situations, this paper proposes a multi-task learning based event detection model, which introduces an event type oriented text classification as an auxiliary task to improve the model’s understanding of sentence-level information. In addition, this model utilizes a
Conditional Random Field (CRF)
to explore the correlations between various event types and constrain the model’s output space. Experimental comparisons with state-of-the-art baselines on DuEE dataset demonstrate the model’s effectiveness.
A New Nano-Design of a Fault-Tolerant Coplanar RAM with Set/Reset Ability Based on Quantum-Dots
Authors: Wei Xiong , Min Guo
Published: April 2022

Quantum Dot Cellular Automata (QCA) is a recent technology that has piqued researchers’ interest because of its small size and low energy consumption. With the help of quantum dots, the QCA technology delivers a new computational foundation for constructing digital circuits. Medical imaging and quantum computing are just a few applications for quantum dots. Quantum dots are nanocrystals that transmit data at the nano-scale. Since the memory is an important digital circuit, this work proposes a fault-tolerant loop-based coplanar Random Access Memory (RAM) with set/reset capability that uses the QCA rules. The memory cell’s operation is verified both physically and through simulations with the QCADesigner program. The quantum cost of the proposed memory cell shows that it has a negligible quantum cost. The proposed QCA-based memory circuit performs well in simulations, with 96 QCA cells and the output signal generated after 0.75 clock phases. The gates and wire in this design have around 85 percent better fault-tolerant capability than the best-presented memory systems. Furthermore, this circuit can tolerate most cell omission, displacement, misalignment, and deposition faults. This structure can be used to create high-performance higher-order fault-tolerant memory structures.
Research on Handicraft Design Based on Artificial Intelligence Technology in Complex Environments
Authors: Xiang Shishi , Niu Zhiyuan , Wu Yuzhou
Published: April 2022

In this twenty-first century, research is taking new dimension with the unpredictable development in information technology. With the aid of this technology, handicraft designs can be classified and made by utilizing artificial intelligence techniques. The Chinese handicraft designs will have unique style in the presentation. Using the artificial intelligence techniques, the arc styles of the handicraft design can be identified and classified. In complex environment like epidemic or pandemic situations, every user has to focus on the minor variations in financial aspects. Implementation of artificial intelligent technology will aid the user to get updated with the market strategy for financial allocation to resources. In the market, each arc style may have different demands and the charges will improve the profit for the company and also the country. In this study, the intelligent computer-aided art and craft design (ICAACD) method is implemented on the financial dataset of China and analysis is performed. This proposed system is compared with the existing algorithms such as GNNA, RNNA, and CNNA, and it is observed that the proposed ICAACD method outperforms with the accuracy of 82%.
Prognosis and Characterization of Immune Microenvironment in Head and Neck Squamous Cell Carcinoma through a Pyroptosis-Related Signature
Authors: Lu Lin , Zhang Peiling , Cao Xiaofei , Guan Mingmei
Published: April 2022

Pyroptosis, as a novel identified programmed cell death, is closely correlated with tumor immunity and shows potential roles in cancer treatment. Discerning a pyroptosis-related gene signature and its correlations with tumor immune microenvironment is critical in head and neck squamous cell carcinoma (HNSCC). Transcriptome data and corresponding clinical data were downloaded from TCGA and GEO databases. Tumor mutation burden (TMB) data were obtained from TCGA database. Firstly, univariate and least absolute shrinkage and selection operator (LASSO) regression analyses were used to construct a six pyroptosis-related gene signature. Kaplan–Meier analysis, receiver operating characteristic (ROC) curves, and principal component analysis (PCA) results verified that the risk model has good performance in predicting the survival. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed that the pyroptosis-related gene signature was immune related. Finally, the immune landscape and immunotherapy sensitivity prediction capabilities of the risk model were further explored. There were close correlations between the overall survival (OS) and various immune cells and immune functions. Single-sample gene set enrichment analysis (ssGSEA) showed that high risk group had decreased expression of various immune cells and lower activities of immune functions. Meanwhile, tumor mutation burden (TMB) data combining risk score could well predict the OS of HNSCC patients. However, tumor immune dysfunction and exclusion (TIDE) analysis revealed that there was no significant difference in the sensitivity to immunotherapies between high and low risk groups. Finally, a nomogram based on risk score and clinicopathological parameters was constructed. And, the risk model demonstrated better sensitivity and specificity than TIDE scores and T-cell-inflamed signature (TIS). In conclusion, although the risk model could not well predict the immune escape and response to immunotherapies, the signature established by pyroptosis-related genes, with better sensitivity and specificity than TIDE scores and TIS signature, could be used for predicting prognosis and immune status of HNSCC patients.
Antifungal Strategy in Patients with Invasive Fungal Disease Associated with Hematological Malignancies Based on Risk Stratification
Authors: Chen Lijin , Luo Luting , Chen Yanxin , Wang Yinzhou , Li Jing , Zheng Xiaoyun , Yang Ting , Hu Jianda
Published: April 2022

Patients with hematological malignancies (HM) often develop the invasive fungal disease (IFD), causing important morbidity/mortality. While treatment guidelines are available, risk stratification models for optimizing antifungal therapy strategies are few. Clinical records from 458 HM patients with IFD were retrospectively analyzed. Following Chinese treatment guidelines, patients received empirical (n = 239) or diagnostic-driven therapy (n = 219). The effectiveness rate was 87.9% for the empirical and 81.7% for the diagnostic-driven therapy groups (

P
≥
0.05

). The incidence of adverse reactions was 18.4% and 16.9%, respectively (

P
≥
0.05

). All risk factors of IFD in HM patients were estimated in the univariate analyses and multivariate analyses by the chi-square test and logistic regression model. Duration ≥14 days (OR = 18.340,

P
=
0.011

), relapsed/refractory disease (OR = 11.670,

P
=
0.005

), IFD history (OR = 5.270,

P
=
0.021

), and diabetes (OR = 3.120,

P
=
0.035

) were significantly associated with IFD in the multivariate analysis. Patients with more than 3 of these factors have a significant difference in effective rates between the empirical (85.7%) and diagnostic-driven (41.6%) therapy (

P
=
0.008

). Empirical and diagnostic-driven therapy effective rates were 80.6% and 70.9% in the patients with two risk factors (

P
>
0.05

) and 85.1% and 85.4% in the patients with one risk factor (

P
>
0.05

). Thus, there was no significant difference in effectiveness in patients with one or two risk factors. The abovementioned risk stratification can guide clinical antifungal therapy. The patients with 3 or more risk factors benefit from empirical therapy.
Effect of Thermophoresis on Heat Diffusion in Isobutane/Copper‐Oxide Nanofluid under Pool Boiling Condition: Numerical Investigation
Authors: Feroskhan M. , Venugopal T. , Almakayeel Naif Mana , Khan T. M. Yunus , Khadar Shaik Dawood Abdul , Gobinath N.
Published: April 2022

Understanding thermophoresis in nanorefrigerants stands challenging for a long period despite the phenomenon is attributed for the motion dynamics of particles in nanofluids. Influence of thermophoretic mobility of copper‐oxide nanoparticles on the heat transfer behavior of isobutane (R600a) refrigerant is reported in this work. Pool boiling of the isobutane/copper‐oxide nanofluid is numerically simulated in computational fluid dynamics utilizing both single and two‐phase approaches. Mobility of particles in the saturated refrigerant is studied, and the time scales associated with (Brownian and momentum) diffusions are numerically solved. The temperature contour of the liquid pool is validated with the experimental data, and the properties of the nanorefrigerant such as thermal conductivity, viscosity, and specific heat are estimated in ANSYS Fluent. 5% increase in thermal conductivity and marginal reduction in specific heat of the nanorefrigerant for 0.01% volume fraction of copper‐oxide nanoparticles is witnessed. Mobility of particles due to temperature gradient is found higher near the heater; however, thermophoretic velocity is predicted to be lower because of the narrow temperature gradient. Numerical results showed that the time scale for momentum diffusion is shorter (in the order of e+03) than Brownian diffusion, and hence the momentum diffusion is found to be the predominant mode of heat transfer in nanorefrigerant.
Optical Active Thermal Stable Nanocomposites Using Polybutadiene-Based Polyurethane and Graphene Quantum Dot-MnO2
Authors: Rahmatpanah Zahra , Alavi Nikje Mir Mohammad , Dargahi Maryam
Published: April 2022

Synthesis and characterization of new polybutadiene-based polyurethane, graphene quantum dot-MnO2 nanoparticles, and relative nanocomposites were set as the aim of current artwork. For this purpose, a one-pot polymerization approach was employed in preparation of polyurethane through the reaction of amine polyol and toluene diisocyanate (TDI) in presence of DBTDL catalyst. Nanocomposites were synthesized using 1 to 3 incorporation percent of graphene quantum dot-MnO2 nanoparticles in polymer matrix. 1H-NMR and FT-IR spectroscopies confirmed successful synthesis of reaction products including graphene quantum dot-MnO2, polyurethane, and nanocomposites. UV-vis and PL spectrophotometry techniques were applied for achieving optical information of samples. Optical properties of nanocomposites were reserved properly with no great quenching. Thermal stabilities, degradation rates, and thermal characteristics of polyurethane and nanocomposites were investigated using TGA/DTG and DSC analysis. Thermal stability showed direct relationship to nanoparticle content, and 3%wt nanocomposite showed improved thermal behaviour in comparison with pure PU. SEM, XRD, and AFM techniques proved successful nanocomposite synthesis with detecting nanoparticle species and fine nanoparticle dispersion with improved topographic and morphologic characteristics making GQD-MnO2 polyurethane nanocomposites a good candidate for using in optical active and thermal stable coatings.
Sequence Analysis and Feature Extraction of Sports Images Using Recurrent Neural Network
Authors: Xu Ju
Published: April 2022

Image sequence analysis is attracting significant attention at present, but its principles and techniques have rarely been applied to the field of sports biomechanics. As far as the technology of automatic recognition of joint points by computers is concerned, it is still in the experimental stage. The purpose of this paper is to study and analyze the sequence analysis and feature extraction of sports images based on cyclic neural network. This paper puts forward the basic concepts of sports image sequence analysis and feature extraction and analyzes the importance of sports in this context. As the experimental results demonstrates, the application rate of detecting human motion by using template matching technology detection is between 15% and 47%, while the accuracy of image sequence analysis method has increased from 17% to about 65%. Generally speaking, although the template matching technology detection method is more popular than the image sequence analysis method at the beginning, the popularity of the image sequence analysis method is significantly higher than that of the template matching technology detection method after time precipitation. Therefore, it is very important to study the sequence analysis and feature extraction of sports image based on cyclic neural network.
Data on the Impact of Epidemic on Nursing Staff’s Mental Health in the Context of Wireless Network
Authors: Guo Dan , Guo Yi , Xing YanJi
Published: April 2022

The research was aimed to analyze the impact of epidemic pneumonia on nursing personnel’s mental health under wireless network background and to improve the selection of random forest classification (RFC) algorithm parameters by the whale optimization algorithm (WOA). Besides, a total of 148 in-service nursing personnel were selected as the research objects, and 148 questionnaires were recycled effectively. The collected data were analyzed by the improved RFC algorithm. In addition, the research investigated the impacts of demographic factors on nursing personnel’s mental health by the one-way variance method. The results demonstrated that the accuracy of the improved algorithm in training samples and test samples reached 83.3% and 81.6%, respectively, both of which were obviously higher than those of support vector machine (SVM) (80.1% and 79.3%, respectively) and back-propagation neural network (BPNN) (78.23% and 77.9%, respectively), and the differences showed statistical meanings

P
<
0.05

. The Patient Health Questionnaire-9 (PHQ-9) showed that the depression levels of 9.46% of the included personnel were above moderate. The Generalized Anxiety Disorder (GAD-7) demonstrated that the anxiety levels of 3.38% of the included personnel were above moderate. The insomnia severity index (ISI) indicated that the insomnia levels of 3.38% of the included personnel were above moderate. The average score of male personnel (3.65) was obviously lower than that of female personnel (3.71). Besides, the average scale score of married personnel (3.78) was significantly higher than that of unmarried personnel (3.65). The average scale scores of personnel with bachelor’s (3.66) and master’s degrees (3.62) were obviously lower than those of personnel with junior college (3.77) and technical secondary school (3.75) diplomas. The average scale score of personnel with over 5-year work experience (3.68) was significantly lower than that of personnel working for less than five years (3.72). The average scale score of personnel with experience in responding to public emergencies (3.65) was obviously lower than that of personnel without related experience (3.74). The differences all showed statistical meaning

P
<
0.05

. The results of this research revealed that the accuracy of the improved RFC algorithm was remarkably higher than that of the SVM and BPNN algorithms. Furthermore, many nursing personnel suffered from mental diseases at different levels with the impact of the epidemic. Gender, marital status, education level, and experience in responding to public emergencies were the main factors affecting nursing personnel’s mental health.
Uncertainty Measure for Multisource Intuitionistic Fuzzy Information System
Authors: Wang Hong , Li Hong
Published: April 2022

Multisource information systems and multigranulation intuitionistic fuzzy rough sets are important extended types of Pawlak’s classical rough set model. Multigranulation intuitionistic fuzzy rough sets have been investigated in depth in recent years. However, few studies have considered this combination of multisource information systems and intuitionistic fuzzy rough sets. In this paper, we give the uncertainty measure for multisource intuitionistic fuzzy information system. Against the background of multisource intuitionistic fuzzy information system, each information source is regarded as a granularity level. Considering the different importance of information sources, we assign different weights to them. Firstly, the paper proposes an optimal source selection method. Secondly, we study the weighted generalized, weighted optimistic, and weighted pessimistic multigranularity intuitionistic fuzzy rough set models and uncertainty measurement methods in the multisource intuitionistic fuzzy information system, and we further study the relationship between the three models and related properties. Finally, an example is given to verify the validity of the models and methods.
Chronic Musculoskeletal Pain Moderates the Association between Sleep Quality and Dorsostriatal-Sensorimotor Resting State Functional Connectivity in Community-Dwelling Older Adults
Authors: Montesino-Goicolea Soamy , Valdes-Hernandez Pedro A. , Cruz-Almeida Yenisel
Published: April 2022

Aging is associated with poor sleep quality and greater chronic pain prevalence, with age-related changes in brain function as potential underlying mechanisms. Objective. The following cross-sectional study aimed to determine whether self-reported chronic musculoskeletal pain in community-dwelling older adults moderates the association between sleep quality and resting state functional brain connectivity (rsFC). Methods. Community-dwelling older individuals (mean age = 73.29 years) part of the NEPAL study who completed the Pittsburg Sleep Quality Index (PSQI) and a rsFC scan were included (n = 48) in the present investigation. To that end, we tested the effect of chronic pain-by-PSQI interaction on rsFC among atlas-based brain regions-of-interest, controlling for age and sex. Results and Discussion. A significant network connecting the bilateral putamen and left caudate with bilateral precentral gyrus, postcentral gyrus, and juxtapositional lobule cortex, survived global multiple comparisons (FDR; q < 0.05) and threshold-free network-based-statistics. Greater PSQI scores were significantly associated with greater dorsostriatal-sensorimotor rsFC in the no-pain group, suggesting that a state of somatomotor hyperarousal may be associated with poorer sleep quality in this group. However, in the pain group, greater PSQI scores were associated with less dorsostriatal-sensorimotor rsFC, possibly due to a shift of striatal functions toward regulation sensorimotor aspects of the pain experience, and/or aberrant cortico-striatal loops in the presence of chronic pain. This preliminary investigation advances knowledge about the neurobiology underlying the associations between chronic pain and sleep in community-dwelling older adults that may contribute to the development of effective therapies to decrease disability in geriatric populations.
Impact of Yiqi Huoxue Decoction on the Relationship between Remodeling of Cardiac Nerves and Macrophages after Myocardial Infarction in Rats
Authors: Liu Yunke , Guo Shunwen , Zhang Yuqin , Wang Hui , Wu Jiani , Lin Wang’ou , Zhang Binyue , Chen Jie , Li Yufei , Zhang Yunshu , Wei Fanyu , Du Tianhui
Published: April 2022

Sympathetic nerve remodeling after myocardial infarction (MI) has an indispensable role in cardiac remodeling. Numerous works have shown that sympathetic nerve remodeling can be delayed by inhibition of inflammatory response. Earlier studies have shown improvement in ventricular remodeling and inhibited chronic stage neural remodeling by Yiqi Huoxue decoction (YQHX). Therefore, the current study looked at the inhibitory effect of YQHX prescription on proinflammatory mediators and macrophages and the effect on neural remodeling at 3 and 7 days after MI. YQHX inhibited the expression of Toll-like receptor 4 (TLR4) and nuclear factor kappa B (NF-κB) proteins and macrophage infiltration within 7 days after myocardial infarction. YQHX could decrease Th-positive nerve fiber density in the area around infarction and reduce the expression of growth-associated protein 43 (GAP43), nerve growth factor (NGF), and tyrosine hydroxylase (TH) proteins, which was associated with the remodeling of sympathetic nerves. Thus, the nerve remodeling inhibition after MI due to YQHX may be through its anti-inflammatory action. These data provide direct evidence for the potential application of traditional Chinese medicine (TCM) in the remodeling of sympathetic nerves after MI.
A Simple and Sensitive HPLC-MS/MS Assay for the Quantitation of Blonanserin and N-Desethyl Blonanserin in Rat Plasma and Its Application to Pharmacokinetic Study
Authors: Huang Shan-qing , Li Xiao-lin , Wang Zhan-zhang , Lu Hao-yang , Xiao Tao , Ni Xiao-jia , Liu Shu-jing , Zhang Ming , Shang De-wei , Wen Yu-guan
Published: April 2022

A high-performance liquid chromatographic method coupled with triple quadrupole mass spectrometry (LC-MS/MS) for the analysis of blonanserin and its active metabolite, N-desethyl blonanserin, in rat plasma has been developed and validated. The biological samples were treated by simple direct protein precipitation before separation on an Agilent Eclipse Plus C18 column (4.6 × 100 mm, 3.5 μm) with a column temperature of 35°C at a flow rate of 0.5 mL/min. The mobile phase A is a mixture of methanol and water (75 : 25, v/v, 5 mM ammonium formate), and the mobile phase B is acetonitrile containing 0.1% formic acid. The ratio of mobile phase A to mobile phase B is 15 : 85. Electrospray ionization (ESI) multiple reaction monitoring modes are used for detection, which are m/z 368.10 ⟶ 296.90 (blonanserin), m/z 340.15 ⟶ 297.05(N-desethyl blonanserin), and m/z 348.15⟶ 302.05 (N-desethyl blonanserin-d8). The linear response range was 0.1–100.0 ng/mL for blonanserin and N-desethyl blonanserin. The lower limit of quantification (LLOQ), calibration curves, carryover, and matrix effects were sufficiently accurate and precise according to the National Medical Products Administration (NMPA) guidelines for bioanalytical method validation. This analytical method was successfully applied in a blonanserin-poloxamer thermosensitive gel pharmacokinetic study in rats.
Application of Intelligent Archives Management Based on Data Mining in Hospital Archives Management
Authors: Li Juan
Published: April 2022

Data mining belongs to knowledge discovery, which is the process of revealing implicit, unknown, and valuable information from a large amount of fuzzy application data. The potential information revealed by data mining can help decision makers adjust market strategies and reduce market risks. The information excavated must be real and not universally known, and it can be the discovery of a specific problem. Data mining algorithms mainly include the neural network method, decision tree method, genetic algorithm, rough set method, fuzzy set method, association rule method, and so on. Archives management, also known as archive work, is the general term for various business works, in which archives directly manage archive entities and archive information and provide utilization services. It is also the most basic part of national archives. Hospital archives are an important part of hospital management, and hospital archives are the accumulation of work experience and one of the important elements for building a modern hospital. Hospital archives are documents, work records, charts, audio recordings, videos, photos, and other types of documents, audio-visual materials, and physical materials, such as certificates, trophies, and medals obtained by hospitals, departments, and individuals. The purpose of this paper is to study the application of intelligent archives management based on data mining in hospital archives management, expecting to use the existing data mining technology to improve the current hospital archives management. This paper investigates the age and educational background of hospital archives management workers and explores the relationship between them and the quality of archives management. Based on the decision number algorithm, on the basis of the database, the hospital data is classified and analyzed, and the hospital file data is classified and processed through the decision number algorithm to improve the system data processing capability. The experimental results of this paper show that among the staff working in the archives management department of the hospital, 20-to-30-year-olds account for 46.2% of the total group. According to the data, the staff in the archives management department of the hospital also tends to be younger. Among the staff under the age of 30, the file pass rate was 98.3% and the failure rate was 1.7%. Among the staff over 50 years old, the file pass rate was 99.9% and the failure rate was 0.1%. According to the data, the job is related to the experience of the employee.
Construction and Knowledge Mining of Traditional Chinese Medicine Ancient Books Bibliographic Abstracts Database Based on Genetic Algorithm and BP Neural Network
Authors: Wang Yongmei , Ren Shujun , Song Li , Zhang Jiang
Published: April 2022

With the rapid development of modern science technology and Internet technology, the establishment of a unified and standardized bibliographic summary database to realize the exchange and resource sharing of ancient Chinese medicine bibliography, is the inevitable trend of ancient Chinese medicine bibliography digital service. Firstly, this paper formulates the bibliographic metadata specification of traditional Chinese medicine ancient books (TCMAB), extracts each cataloging file into an XML document in line with the bibliographic metadata specification of TCMAB. Secondly, this paper realizes the unified description of ancient book resources in the database system of TCMAB, uses the native XML database eXist to store and manage the XML documents of all traditional Chinese medicine ancient book resources, and integrates the multimedia data with XML data. Finally, genetic algorithm and BP neural network are used for knowledge mining and discovery, the overall model design of TCMAB bibliographic abstracts database system is proposed based on the construction process of knowledge map. The system platform adopts B/S mode, eXist database management system, PowerSSP streaming media and video server for audio video processing.
Progesterone Changes the Pregnancy-Induced Adaptation of Cardiomyocyte Kv2.1 Channels via MicroRNA-29b
Authors: Liang Shuang , Sun Yu-Shuang , Li Lu , Long Yao , Wang Meng , Yang Hou-Zhi , Li Chun-Di , Wang Yan , Li Shan-Shan , Chen Xu , Jin Xin
Published: April 2022

The cardiovascular system adaptation occurs during pregnancy to ensure adequate maternal circulation. Progesterone (P4) is widely used in hormone therapy to support pregnancy, but little is known about its effects on maternal cardiac function. In this study, we investigated the cardiac repolarization and ion channel expression in pregnant subjects and mice models and studied the effects of P4 administrations on these pregnancy-mediated adaptations. P4 administrations shortened the prolongation of QTC intervals and action potential duration (APD) that occurred during pregnancy, which was mainly attributable to the reduction in the voltage-gated potassium (Kv) current under basal conditions. In vitro studies indicated that P4 regulated the Kv2.1 channel in a bidirectional manner. At a low dose (1 μM), P4 induced upregulation of Kv2.1 through P4 receptor, while at a higher dose (5 μM), P4 downregulated Kv2.1 by targeting microRNA-29b (miR-29b). Our data showed that P4 modulated maternal cardiac repolarization by regulating Kv2.1 channel activity during pregnancy. Kv2.1, as well as miR-29b, might be used as potential therapeutic targets for adaptations of the maternal cardiovascular system or evaluation of progesterone medication during pregnancy.
SAD: Website Fingerprinting Defense Based on Adversarial Examples
Authors: Tang Renzhi , Shen Guowei , Guo Chun , Cui Yunhe
Published: April 2022

Website fingerprinting (WF) attacks can infer website names from encrypted network traffic when the victim is browsing the website. Inherent defenses of anonymous communication systems such as The Onion Router(Tor) cannot compete with current WF attacks. The state-of-the-art attack based on deep learning can gain over 98% accuracy in Tor. Most of the defenses have excellent defensive capabilities, but it will bring a relatively high bandwidth overhead, which will seriously affect the user’s network experience. And some defense methods have less impact on the latest website fingerprinting attacks. Defense-based adversarial examples have excellent defense capabilities and low bandwidth overhead, but they need to get the complete website traffic to generate defense data, which is obviously impractical. In this article, based on adversarial examples, we propose segmented adversary defense (SAD) for deep learning-based WF attacks. In SAD, sequence data are divided into multiple segments to ensure that SAD is feasible in real scenarios. Then, the adversarial examples for each segment of data can be generated by SAD. Finally, dummy packets are inserted after each segment original data. We also found that setting different head rates, that is, end points for the segments, will get better results. Experimentally, our results show that SAD can effectively reduce the accuracy of WF attacks. The technique drops the accuracy of the state-of-the-art attack hardened from 96% to 3% while incurring only 40% bandwidth overhead. Compared with the existing proposed defense named Deep Fingerprinting Defender (DFD), the defense effect of SAD is better under the same bandwidth overhead.
Primary Extra Nodal Diffuse Large B-Cell Lymphoma of the Maxillary Sinus with Symptoms of Acute Pulpitis
Authors: Cui Jian-tong , Zhang Shao-qing , He Hui-xia
Published: April 2022

Diffuse large B-cell lymphoma not otherwise specified (DLBCL-NOS) is a subtype of large B-cell non-Hodgkin lymphoma with various clinical and pathological manifestations. DLBCL-NOS which primarily arises from maxillary sinus is rare and hard to diagnose due to unique anatomy. Here, we present a case of DLBCL-NOS that developed in the left maxillary sinus of a 72-year-old male, who presented with severe toothache that resembled acute pulpitis. The lesion was diagnosed and treated based on radiographs, histological, immunohistological examinations, and PET-CT analysis. Despite its rare incidence, DLBCL-NOS should still be included in differential diagnoses to rule out malignancy in cases of endodontic disease.
Analyzing Driving Safety Using Vehicle-Water-Filled Rutting Dynamics Model and Simulation
Authors: Zhang Yandi , Yuan Bobo , Chou Yukun
Published: April 2022

Rutting is one of the major asphalt pavement distresses that could cause hydroplaning and lead to roadway safety concerns on a rainy day. However, there is still no theoretical methodology with simulation to evaluate and predict the driving safety caused by rut-induced hydroplaning using vehicle dynamics. This paper proposes a methodology, based on a new developed “vehicle-water-filled rutting” open-loop vehicle dynamics model analyzed by CarSim (a professional vehicle dynamic software), to simulate and compute the impact of unbalanced water-filled rutting on driving stability/safety with a special focus on the vehicle’s lateral dynamic stability, including lateral offset and lateral acceleration. Analysis results show the following: (1) The unbalanced water depths in left rutting and right rutting lead to the different friction between left rutting and right rutting and make the vehicle wander left and right uncontrollably along the roadway. (2) When the vehicle speed is greater than 80 km/h and the rutting width exceeds 0.7 m, the unbalanced water-filled rutting begins to affect the vehicle’s lateral stability apparently and very likely threaten the driver’s life. (3) By computing the vehicle’s lateral offset and acceleration in different water widths and lengths in the hydroplaning situation, this paper proposes the rutting width and length thresholds for vehicle oversteer and instability, based on which the vehicle risk level is proposed, RISK I is the situation where vehicle’s lateral offset exceeds 1.025 mm which likely causes vehicle running into the adjacent lane (the danger can be avoided if there is no vehicle in the adjacent lane), RISK II is the situation where the vehicle’s tires begin to work into the nonlinear zone (the vehicle’s lateral acceleration exceeds 0.4 g) with the increase of rutting width and length, which makes the vehicle lose controls irreversibly for drivers, and this situation should arouse more concerns for transportation agencies. The proposed methodology would enable transportation agencies to predict the security risk of rutting and play a vital role for transportation agencies to scientifically establish the relevant specifications (e.g., rutting maintenance) that proactively trigger the maintenance and rehabilitation to prevent potential safety concerns from happening. Recommendations on future researches, including taking braking stability and real rutting conditions of the proposed model into consideration, are also continued in the future study.
Analysis on Regional Characteristics of Economic Growth Based on Total Factor Productivity
Authors: Ma Weidong , Wu Cheng Chung , Tang Deshan
Published: April 2022

In this paper, we adopt stochastic frontier model of translog production function to measure the total factor productivity growth rate of 13 cities in Jiangsu province from the year 2000 to 2020 and study the characteristics of the contribution rates of capital, labor, and total factor productivity in driving economic growth. The study shows that, firstly, the explanatory power of the model could be increased by 35.9% after 6 effect variables, such as urban economic development level, have been taken into consideration when two‐step method has been adopted to explore the model parameters of stochastic frontier; secondly, there is an obviously positive correlation between the 4 variables, urban economic development level, urban size, level of opening up to the outside world and infrastructure construction, and production efficiency, while there is a negative correlation between economic structure and production efficiency, and the research input intensity has an uncertain effect on production efficiency; thirdly, the TFP growth trend of urban economy of the province shows a similar “W” trend, technical progress becomes the first element in affecting total factor productivity, and the negative growth in technical efficiency and scale efficiency result in a dramatic decline in growth rate of TFP; fourthly, among all the elements contributing to urban economic growth, capital has the greatest contribution, TFP takes the second place, and labor element takes the last. The double difference between capital and TFP appears to be the major cause in forming regional difference of urban economy. The regional differences of TFP concerning infrastructure advantage, position advantage, factor advantage, and policy advantage are the key factors in urban economic gap. At the end of this study, we propose some policy suggestions.
Two Cases of Duodenal Ulcers That Developed after Transcatheter Procedures for Unruptured Visceral Artery Aneurysms
Authors: Iwamuro Masaya , Kawai Yusuke , Uka Mayu , Matsui Yusuke , Hiraki Takao , Kawahara Yoshiro , Okada Hiroyuki
Published: April 2022

Herein, we report two cases of duodenal ulcers that developed after transcatheter procedures for the treatment of unruptured artery aneurysms. Both patients recovered after the administration of nothing by mouth, intravenous fluids, and proton-pump inhibitors. Notably, the duodenal ulcer was unchanged in one patient six days after endovascular treatment and improved in the other patient 13 days after angiography. These cases suggest that conservative treatment is acceptable in patients with duodenal ischemia that develops as an adverse effect of endovascular procedures. The usefulness of esophagogastroduodenoscopy in such patients has also been highlighted.
Who Is at Risk of Death after Renal Trauma? An Analysis of Thirty-Day Mortality after 1,799 Cases of Renal Trauma
Authors: Baboudjian Michael , Lebacle Cedric , Gondran-Tellier Bastien , Hutin Marine , Olivier Jonathan , Ruggiero Marina , Panayotopoulos Paul , Dominique Ines , Millet Clémentine , Bergerat Sébastien , Freton Lucas , Betari Reem , Matillon Xavier , Chebbi Ala , Caes Thomas , Patard Pierre-Marie , Szabla Nicolas , Sabourin Laura , Dariane Charles , Rizk Jerome , Madec Francois-Xavier , Nouhaud Francois-Xavier , Rod Xavier , Fiard Gaelle , Pradere Benjamin , Peyronnet Benoit ,   TRAUMAFUF Collaborative
Published: April 2022

Introduction: The aim of the study was to report the 30-day mortality (30DM) after renal trauma and identify the risk factors associated with death. Methods: The TRAUMAFUF project was a retrospective multi-institutional study including all patients with renal trauma admitted to 17 French hospitals between 2005 and 2015. The included population focused on patients of all age groups who underwent renal trauma during the study period. The primary outcome was death within 30 days following trauma. The multivariate logistic regression model with a stepwise backward elimination was used to identify predictive factors of 30DM. Results: Data on 1,799 renal trauma were recorded over the 10-year period. There were 59 deaths within 30 days of renal trauma, conferring a 30DM rate of 3.27%. Renal trauma was directly involved in 5 deaths (8.5% of all deaths, 0.3% of all renal trauma). Multivariate stepwise logistic regression analysis revealed that age >40 years (odds ratio [OR] 2.18; 95% confidence interval [CI]: 1.20–3.99; p = 0.01), hemodynamic instability (OR 4.67; 95% CI: 2.49–9; p < 0.001), anemia (OR 3.89; 95% CI: 1.94–8.37; p < 0.001), bilateral renal trauma (OR 6.77; 95% CI: 2.83–15.61; p < 0.001), arterial contrast extravasation (OR 2.09; 95% CI: 1.09–3.96; p = 0.02), and concomitant visceral and bone injuries (OR 6.57; 95% CI: 2.41–23.14; p < 0.001) were independent predictors of 30DM. Conclusion: Our large multi-institutional study supports that the 30DM of 3.27% after renal trauma is due to the high degree of associated injuries and was rarely a consequence of renal trauma alone. Age >40 years, hemodynamic instability, anemia, bilateral renal trauma, arterial contrast extravasation, and concomitant visceral and bone lesions were predictors of death. These results can help clinicians to identify high-risk patients.