bluuebunny/crossref_metadata_2025 huggingface dataset:
Study on the Mechanism of Cardiac Intensive Care after Thoracoscopic Surgery
Authors: Lin Min , Ye Maoting , Ren Jing
Published: March 2022

Objective. To explore the mechanism of intensive care of the heart after thoracoscopic surgery. Methods. 104 patients with severe cardiac disease were selected after thoracoscopic surgery in our hospital, received nursing care after surgery, and divided into control group (

n
=
53

) and research group (

n
=
51

) according to different nursing methods. Before nursing, the research group carried out targeted nursing and prevention of postoperative complications. The quality of life, complications, anxiety, depression and satisfaction scores, 6-minute walking distance, self-care ability scores, and cardiac function were compared between the two groups. Results. Patients’ quality of life scores improved significantly in both groups after treatment, but the increase was greater in the study group than in the control group (

P
<
0.05

); the incidence of complications was 18.9% and 5.9% in the study and control groups, respectively, and the incidence of complications was lower in the study group than in the control group (

P
<
0.05

); and the incidence of complications was lower in the study group than in the control group (

P
<
0.05

). After care, patients’ anxiety and depression scores were significantly lower, and satisfaction scores were significantly higher in both groups, with a greater change in the study group than in the control group (

P
<
0.05

); after care, patients’ 6-minute walking distance was significantly higher in both groups, with a greater change in the study group than in the control group (

P
<
0.05

); after care, LVEF indicators were significantly higher, and LVESD and LVED indicators were significantly higher, with a greater change in the study group than in the control group. After care, LVEF indexes increased significantly in both groups, while LVESD and LVED indexes decreased significantly in the study group, with a greater change than in the control group (

P
<
0.05

); after care, systolic blood pressure and heart rate increased significantly in both groups, with a greater increase in the study group than in the control group (

P
<
0.05

); after care, systolic blood pressure and heart rate increased significantly in both groups, with a more significant increase. Conclusion. Targeted nursing for patients with severe cardiac disease after thoracoscopic surgery has a significant effect, which can improve patients’ anxiety and depression, significantly improve patients’ self-care ability and quality of life, and at the same time improve patients’ cardiac function, heart rate, and blood pressure, with high patient satisfaction.
A Human Resource Demand Forecasting Method Based on Improved BP Algorithm
Authors: Lu Xingguang
Published: March 2022

Human resources are the first resource for enterprise development, and a reasonable human resource structure will increase the effectiveness of an enterprise’s human resource input and output. The reality is that even if an enterprise designs a human resource allocation plan in accordance with the corporate strategy, it is impossible for the enterprise to operate in full accordance with the plan during the operation process, so the human resource allocation plan only reflects the law of the enterprise’s human resource needs during the enterprise development process. Giving effective guidance to the specific work of human resources is difficult. It is impossible to carry out effective human resources structure adjustment to adapt to changes in human resources demand due to changes in corporate tactics, business, scale, and other factors, especially when the current domestic human resources market has not yet fully formed. This paper examines the impact of key factors such as the company’s business growth scale and production efficiency improvement on human resource needs with the goal of improving team structure, optimizing staff allocation, controlling labor costs, and improving efficiency and benefits. In this paper, we attempt to develop a human resource demand forecasting model based on business development and economic benefits and guided by intensive human resource development. We analyze and forecast the enterprise’s total human resource employment, personnel structure, and quality structure using this model. In light of this, this paper employs an improved BP neural network to construct a human resource demand forecasting system, resulting in a new quantitative forecasting method for human resource demand forecasting with strong theoretical significance. Simultaneously, the human resource demand forecasting system developed can enable enterprises to carry out personnel demand forecasting from the actual situation, making forecasting more applicable, flexible, and accurate, allowing enterprises to realize their strategies through reasonable human resource planning.
[Retracted] Econometric Analysis of the Hot Research of Marxist Theoretical Journals Based on Knowledge Map
Authors: Wang Lan , Wang Kai
Published: March 2022

In recent years, the research on Marxist theory has received a lot of attention. Many studies use quantitative research methods such as statistical keywords and constructing knowledge map to carry out Marxist theoretical analysis. In this paper, by constructing a self‐expanding Chinese word separation and self‐expanding address data knowledge graph, matching Marxist Chinese addresses based on the full‐text indexing knowledge graph, incorporating a weighted pinyin full‐text search mechanism to improve the matching accuracy of misspelled addresses, constructing a multiple matching mechanism for Marxist addresses by combining an online geographic parsing interface, and performing semisupervised matching for a small number of difficult addresses, a complete system of Marxist address matching methods is formed. By studying and analyzing the basic orientation and attention to academic hotspots, we can gain insight into the characteristics, rules, and problems of academic hotspots in Marxist theory journals.
Research on the Influence of Service Quality of Hotel Intelligent System on Customer Satisfaction Based on Artificial Intelligence Evaluation
Authors: Wang Yu
Published: March 2022

In this paper, we analyze the service quality problems of hotels from the perspective of customer satisfaction and service, and propose corresponding solution strategies. This paper elaborates on the word vector of model input, and then focuses on the structure and calculation process of the two-channel RNN triplet block model proposed in this paper. According to the expression habits of daily emotional tendencies, the RNN ternary block structure is set up to capture the structure of emotional tendency expressions and strengthen the dependency relationship between emotional tendency expression words. Provide suggestions for the improvement of service quality of Guido Hotel based on the results of satisfaction analysis and customer questionnaire. By comparing the experimental results, the effectiveness of the two-channel RNN ternary block model in the hotel review sentiment tendency analysis task is verified.
A Comparison of Transcanalicular, Endonasal, and External Dacryocystorhinostomy in Functional Epiphora: A Minimum Two-Year Follow-Up Study
Authors: Ozturker Can , Purevdorj Bayasgalan , Karabulut Gamze Ozturk , Seif Gamal , Fazil Korhan , Khan Yasser Anwar , Kaynak Pelin
Published: March 2022

Purpose. To compare the outcomes of transcanalicular diode laser-assisted dacryocystorhinostomy (TCL-DCR), nonendoscopic endonasal dacryocystorhinostomy (NEN-DCR), and external dacryocystorhinostomy (EXT-DCR) as first-line treatments for functional epiphora. Methods. This multicenter, retrospective, case-control study included 135 eyes of 135 patients with functional epiphora (86 females and 49 males). Functional epiphora was diagnosed based on a patent lacrimal system with a delay in the fluorescein dye disappearance test (FDDT) or dacryoscintigraphy (DSG) and no ocular surface or eyelid abnormalities. The patients were treated with TCL-DCR (2008–2011) or Ext-DCR (2005–2008, 2011–2017) at Beyoglu Eye Research Hospital (Istanbul, Turkey) and NEN-DCR at Carrot Eye Surgery Clinic affiliated with the McMaster University (Hamilton, ON, Canada) (2010–2016). Success was defined as the absence of epiphora and the normalization of an earlier delayed FDDT after surgery. Results. The TCL-DCR, NEN-DCR, and EXT-DCR groups consisted of 38, 47, and 50 eyes with 25.9, 44.2, and 45.9 months of follow-up. The success rate for TCL-DCR was 65.8%, 70.2% for NEN-DCR, and 84.0% for EXT-DCR. During the follow-up period, 13.2% of TCL-DCR cases and 6.4% of NEN-DCR cases developed an anatomic obstruction of the lacrimal system. Conclusion. The EXT-DCR group had a higher success rate in the management of functional epiphora than the NEN-DCR and TCL-DCR groups and was significantly safer in terms of an iatrogenic anatomic block of the lacrimal system.
Blockchain and UAV-Enabled Signal Source Identification with Edge Computing and Wireless Signal-Aerial Image Fusion
Authors: Xiao Jian , Liu Peng , Lin Huaming , Fang Hangxiang , Xu Jiayi , Shan Hangguan , Hu Haoji , Huang Yi , Lu Huijuan
Published: March 2022

This paper proposes a novel signal source identification system composed of unmanned aerial vehicles (UAVs) and a blockchain, in which the identification method makes full use of binocular camera data and received signal strength. The UAV tasks are organized by using blockchain technology and smart contracts. To tackle the challenge that the transmit power of the object and the channel path loss coefficient are unknown to the UAV, a maximum likelihood estimation method is developed to estimate the parameters in the path loss log-normal shadowing model. Then, the mean squared error is used as the metric to distinguish the signalling object. The simulation results show that the proposed method can effectively complete the task. Also, a mobile edge computing- (MEC-) enabled UAV testbed system is designed and implemented in real environment. The system works accurately where the number of candidate objects is 3.
The Excited State and Charge Transfer of Two Nonfullerene Acceptors from First-Principles Many-Body Green’s Function Theory
Authors: Bai Xinyue , Liu Guokui , Li Yunzhi , Zhou Guangli , Leng Xia , Xia Qiying , Wei Yaoyao
Published: March 2022

Nonfullerene acceptors (NFAs) have shown an outstanding performance upon producing highly efficient and sustainable organic solar cells (OSC). Recently, a growing group of researchers denoted to modify the structures of acceptor−donor−acceptor-type NFAs to raise the power conversion efficiencies (PCEs) when they are blended with a variety of polymer donors in OSC. In 2020, the ketone on the ending groups of BTP-IC were substituted for sulfonyl; the new NFA named BTP-IS was synthetized. The PCE of BTP-IS based OSC is 5.25% higher than that of the BTP-IC device. Based on this, the many-body Green’s function theory, combined with other quantum chemical methods, is conducted to study their ground electronic structures, excited states, and absorption spectra. The ground-state geometries, ionization energies, and the excited state energies are deeply sensitive to exchange-correlation functionals used in calculations. The lowest excited state energies calculated by full-BSE method using DFT-PBE as the starting point is 0.07~0.14 eV smaller than that by TDDFT-PBE method, which is more consistent with experimental data. This provided a methodology for future research on similar NFA systems. The first charge-transfer states and transfer mechanism of two molecules are proposed in this paper. Furthermore, we found that the reason for more efficient charge transport in BTP-IS-based OSC is the larger ionization energies and much weaker electron-hole interaction in BTP-IS. This finding is conducive to the better application of BTP-IS in OSC field.
Electrocardiograph Positioning on Intubation of Peripherally Inserted Central Catheter and Nursing for Patients with Multiple Myeloma under Deep Learning
Authors: Sun Xiumei , Li Zhiying , Xu Hongyan , Li Xianglan
Published: March 2022

This study was to explore the deep learning-based electrocardiograph (ECG) positioning for peripherally inserted central catheter (PICC) patients with multiple myeloma (MM) and provide theoretical guidance for clinical application. In this study, 70 patients with MM were selected as the research object and randomly divided into two groups, 35 cases in each group. The experimental duration was followed for one year. The efficiency of catheterization under the recurrent neural network (RNN) algorithm and the traditional method was evaluated through the operation time. The effects of catheterization were determined by the infection rates after catheterization. Results. The time required for PICC catheterization under the guidance of RNN algorithm was 25.6 ± 4.8 min. The time required for PICC catheter surgery under the traditional method was 66.2 ± 5.3 min, which was significantly different from the time required for surgery under the guidance of RNN algorithm (P < 0.05). After one year of tracking, under the guidance of RNN algorithm, the cumulative number of infected patients in every two months after PICC catheterization in 35 patients was 0, 0, 0, 0, 1, and 1, respectively. The number of infected patients in the other 35 patients under the traditional method was 0, 0, 0, 1, 2, and 2, respectively. In summary, PICC catheter surgery guided by artificial intelligence algorithm based on RNN neural network required less time and had lower risk of postoperative infection. According to the previous experience, we have summarized the nursing methods after catheterization. In this experiment, compared with the patients who did not follow the doctor’s advice, the 70 patients who followed the doctor’s advice obtained better therapeutic effects. Postoperative care can ensure the therapeutic effects.
Study on Design Optimization in Major Railway Projects with Fair Preference Based on Stackelberg Game
Authors: Liu Kai , Liu Yuming , Zhao Xi , Kou Yuanyuan
Published: March 2022

A contractor proposes design changes to achieve design optimization in the design stage of major railway projects, which can effectively solve problems such as project cost overruns. In order to promote cooperation between the construction party and the owner in the design stage, the fairness preference theory was introduced. Based on the relationship characteristics of project participants in a major railway project, a Stackelberg game model between the owner and contractor was established. The influence of fairness preference psychology on both parties’ decision-making behavior and utility changes was discussed. MATLAB software is used to perform numerical simulation analysis on the evolution results of the game model. The research results show the following: The project gain distribution coefficient of the owner and contractor will increase with the increase in their fairness perception strength. The acceptance rate of design changes will decrease with the increase in the contractor’s fairness perception strength, and the owner’s fairness perception intensity has no effect on the acceptance rate of design changes. When only one party has a fairness preference psychology, the project gain distribution coefficient will tilt towards that party. Fairness preference psychology can change the utility of the owner and contractor and narrow the utility gap between two parties.
A Fuzzy Comprehensive Evaluation and Random Forest Model for Financial Account Audit Early Warning
Authors: Zhu Danping
Published: March 2022

Financial risk is objective, developmental, and predictable and has an important impact on the development and operation of enterprises. China’s economy is currently facing major changes, and with the introduction of the “Made in China 2025” plan, deepening supply-side reform, and the rapid development of artificial intelligence, blockchain, and big data technology, the importance of enterprise financial early warning is becoming increasingly prominent. Therefore, by establishing and studying the changes in some financial indicators and establishing an effective enterprise financial early warning model, the signals of financial crisis can be found in time, to prevent and eliminate the hidden danger of enterprise financial crisis and ensure the financial security of the enterprise. The enterprise financial system and business management system are running well. Based on this, a financial early warning model is proposed in this study. First, financial early warning indicators are constructed, and the existing financial indicators are used to establish an early warning indicator system that can detect and identify the financial risks of the enterprise. Then, the financial early warning model based on the fuzzy comprehensive evaluation model and random forest algorithm for fuzzy comprehensive evaluation is constructed using the advantages and noise resistance of the integrated model of fuzzy comprehensive evaluation model and random forest algorithm. The financial data set is used to verify the model constructed in this study. The experimental results show that the model in this study not only is beneficial for enterprises to control financial crisis but also plays a financial early warning role.
Application Effect of Bladder Function Training Combined with Kangaiping Pills on Permanent Bladder Stoma after Radical Prostatectomy
Authors: Sha Kefu , Zhao Yue , Yang Deng , Song Zhaoxia , Zhao Mingjun , Gao Jieqing , Liu Tiejun
Published: March 2022

Objective. To investigate the application effect of bladder function training combined with Kangaiping pills on permanent bladder stoma after radical prostatectomy (RP). Methods. The clinical data of 80 patients with a permanent bladder stoma after RP in our hospital from December 2018 to December 2019 were retrospectively analyzed, and they were equally split into the experimental group (EG) and control group (CG) according to the odd and even hospitalization numbers. EG received bladder function training combined with Kangaiping pills while CG received routine nursing for permanent bladder stomas to compare the urodynamic indexes and quality of life (QOL) scores after intervention between the two groups. Results. Compared with CG, EG after intervention achieved an obviously higher number of patients with bladder function grade I (

∗

), higher urodynamic indexes (

P
<
0.001

), a higher SF-36 score (

P
<
0.001

), a lower LUTS score (

P
<
0.001

), and a lower total incidence of postoperative adverse reactions (

P
<
0.05

). Conclusion. Bladder function training combined with Kangaiping pills is a reliable method to improve the bladder function of patients with a permanent bladder stoma after RP. This intervention method greatly enhances the QOL of patients and reduces the risk of postoperative adverse reactions, which is recommended for clinical application.
User Model-Based Personalized Recommendation Algorithm for News Media Education Resources
Authors: Shilin Zhu
Published: March 2022

Traditional recommendations for news and media education resources usually ignore the importance of sequential patterns in user check-in behavior and fail to effectively capture the complex and dynamically changing interests of users. As a result, this study provides a recommendation model for news and media education materials based on a user model. To capture changes in users’ interests, the model can represent and fuse short-term and long-term preferences separately. For short-term preferences, a long- and short-term memory network incorporating spatiotemporal contextual information is proposed to learn complex sequential transfer patterns in users’ check-in behaviors and further extract short-term preferences accurately through a goal-based attention mechanism. A user attention-based approach is utilized to capture fine-grained links between users and interest points for long-term preferences. Finally, experimental simulations are conducted on two datasets, Foursquare and Gowalla. The results show that the proposed user model-based recommendation model for news media education resources has better performance compared with the mainstream recommendation methods on different evaluation criteria, which validates the effectiveness of the proposed model.
Collocation Features in Translated Texts Based on English Analogy Corpus
Authors: Liu Bin , Wang Jing
Published: March 2022

The aim of this paper is to explore the characteristics of the use of verbal collocations in English, to compare the use of verbal collocations in the English translation and the original English text, and then to compare and analyse the characteristics of the choice of verbal collocations in the English text. In this paper, we take bilateral marked causative complex sentences as the object of study and use deep learning methods to automatically explore the implied features of complex sentences while incorporating the significant knowledge of relational words in linguistic research. The experimental results achieved an F1 value of 92.13%, which is better than that of the existing comparison models, demonstrating the effectiveness of the method.
[Retracted] A Novel Approach to Automate Complex Software Modularization Using a Fact Extraction System
Authors: Khan Muhammad Zakir , Naseem Rashid , Anwar Aamir , Haq Ijaz Ul , Alturki Ahmad , Ullah Syed Sajid , Al-Hadhrami Suheer A.
Published: March 2022

Complex software systems that support organizations are updated regularly, which can erode system architectures. Moreover, documentation is rarely synchronized with the changes to the software system. This creates a slew of issues for future software maintenance. To this goal, information extraction tools use exact approaches to extract entities and their corresponding relationships from source code. Such exact approaches extract all features, including those that are less prominent and may not be significant for modularization. In order to resolve the issue, this work proposes an enhanced approximate information extraction approach, namely, fact extractor system for Java applications (FESJA) that aims to automate software modularization using a fact extraction system. The proposed FESJA technique extracts all the entities along with their corresponding more dominant formal and informal relationships from a Java source code. Results demonstrate the improved performance of FESJA, by extracting 74 (classes), 43 (interfaces), and 31 (enumeration), in comparison with eminent information extraction techniques.
Analysis on the Differences of Combination Effects of Science and Technology Innovation Policies
Authors: Zhou Meirong , Wei Ping , Zeng Xi , Deng Lianbing
Published: March 2022

Science and technology innovation (STI) policy is a strategic principle to guide the whole cause of STI. The study on STI policy and its effect is particularly important. Most of the existing studies on the effect of STI policy focus on the effect of a single policy, and the studies on the effect of policy combination and its differences need to be further enriched and improved. This study proposes a method combining system simulation experiment and analysis of variance (ANOVA) to study the differences of combination effects of STI policies. The results show that there are significant effect differences in the combination of STI policies as a whole, but when it comes to different combinations of STI policies, not all policy combinations have significant differences. This study not only points out whether there are significant differences in a certain effect among which combinations of STI policies but also points out whether there are significant differences in all effects among which combinations of STI policies at the same time. This study has theoretical and practical significance for realizing scientific policy-making and sustainable development.
Evaluation of the Engineering Geology Practice Route Based on Multifactor Reliability Analysis
Authors: Zhao Xiaoming , Yang Binbin , Tang Dongqi
Published: March 2022

Engineering geological practice and investigation are important measures for geological fluid and engineering geological research; the practice route has an important impact on the achievement of research objectives. In this study, the practice route is mainly affected by four primary indicators: abundance of geological resources, rationality of the practice route, effect of practice, and the accommodation conditions. The four primary indicators include 13 secondary indicators, and each secondary indicator can be divided into positive and negative random variables, which is the specific content of the evaluation. In order to collect the feedback data of participants on the practice route, a questionnaire was constructed based on indicators at all levels. According to the results of the questionnaire, the mean and standard deviation of each random variable are counted and the distribution law of probability density is fitted. A performance function is constructed to represent the approval status of participants for each indicator. Considering the influence of 26 random variables, a multifactor evaluation method for the engineering geology practice route is proposed based on the reliability analysis method. This method can be used to analyze the approval status of random factors, search for the shortcomings of the route, and provide an effective method to improve the rationality of the route. The conclusion shows that this study provides a solution to evaluate fuzzy problems in engineering geological practice, especially if the problems are difficult to be quantified through experiment or measurement.
Effective Bots’ Detection for Online Smartphone Game Using Multilayer Perceptron Neural Networks
Authors: Tsaur Woei-Jiunn , Tseng Chinyang Henry , Chen Chin-Ling
Published: March 2022

Online smartphone game bots can cause unfair behaviors and even shorten the game’s life cycle. The random forest algorithm in machine learning is a widely used solution to identify game bots through behavioral features. Although the random forest algorithm can exactly detect more definite game bot players, some players belonging to the gray area cannot be detected accurately. Therefore, this study collects players’ data and extracts the features to build the multilayer perceptron, neural network model, for effectively detecting online smartphone game bots. This approach calculates each player’s abnormal rate to judge game bots and is evaluated on the famous mobile online game. Based on these abnormal rates, we then use K means to cluster players and further define the gray area. In the experimental evaluation, the results demonstrate the proposed learning model has better performance, not only increasing the accuracy but also reducing the error rate as compared with the random forest model in the same players’ dataset. Accordingly, the proposed learning model can detect bot players more accurately and is feasible for real online smartphone games.
A Terrain Elevation Map Generation Method Based on Self-Attention Mechanism and Multifeature Sketch
Authors: Cai Xingquan , Xi Mengyao , Yu Nu , Yang Zhe , Sun Haiyan
Published: March 2022

To address the issues of low efficiency in manual terrain feature map annotating and poor realism in terrain elevation map generation, this paper proposes a terrain elevation map generation method based on self-attention mechanism and multifeature sketch. Firstly, the proposed method extracts features from a terrain elevation map using an adaptive feature enhancement method. Afterwards, our method adds a self-attention mechanism to the generator and discriminator of conditional generative adversarial network to capture the global spatial features and generates a realistic terrain elevation map. Finally, a level of detail method is used to visualize the three-dimensional terrain, and an interactive terrain editing tool for roaming interaction is implemented. Experimental data show that the proposed method performs well in subjective visual performance and objective criteria and has obvious advantages over other current typical methods.
Early Warning of Regional Landslide Disaster and Development of Rural Ecological Industrialization Based on IoT Sensor
Authors: Chen Xianjun
Published: March 2022

Regional landslide disaster is actually the instability caused by the movement of ground or slopes, etc. If a landslide occurs in a habitat area where people live, it can cause a great deal of damage. So, in order to improve the early warning effect of regional geological landslide disaster, the study abandoned the conventional geological probe data, directly used the tilt photography data provided by the fixed camera, introduced the camera clock synchronization control system supported by the high-sensitivity atomic clock timing function, and used the data warehouse hardware and computing host hardware. Combined with the spatial convolution neural network, fuzzy neural network (logarithmic depth iterative regression neural network, polynomial depth iterative regression neural network, transfinite learning machine, and binary neural network) and other technologies finally realize high geological disaster early warning sensitivity and long early warning advance. The system will become an alternative to the previous geological disaster early warning system based on the direct data of geological embedded probes.
Application and Comparison of Multiple Machine Learning Models in Finance
Authors: Jiang Yali
Published: March 2022

Accurate and effective financial data analysis is very important for investors to avoid risks and formulate profitable investment strategies. Therefore, the analysis of financial data has important research significance. However, the financial market is a complex nonlinear dynamic system affected by many factors. It is very challenging to analyze the financial data according to the obtained information. Among them, stock selection is the most typical financial data mining problem. The core of stock selection is to design a systematic scoring mechanism to quantitatively score stocks so as to more intuitively reflect the investment value of stocks. The scoring mechanism is based on the assumption that stocks with higher scores have higher investment value and stocks with lower scores have lower investment value. The stock selection model proposed in this paper mainly includes two steps: stock prediction and stock scoring. First, construct stock predictors and use machine learning forecasting methods to predict the future price of each stock. Second, construct a stock scoring mechanism to evaluate each stock through the predictive factors and financial factors in the previous step. Finally, select high-scoring stocks and make equal-weight investments. This paper applies the model to the empirical study of the A-share market, verifies its feasibility and effectiveness, and makes a systematic comparison with other benchmark models.
The type material of spider wasps (Hymenoptera, Pompilidae) deposited in the Federal Scientific Center of the East Asia Terrestrial Biodiversity, Russian Academy of Sciences, Vladivostok, Russia
Authors: LOKTIONOV VALERY M.
Published: March 2022

The type material of spider wasps deposited in the Federal Scientific Center of the East Asia Terrestrial Biodiversity, Far Eastern Branch of the Russian Academy of Sciences, Vladivostok, Russia is catalogued. In total, this includes 305 primary type specimens (31 holotypes and 274 paratypes) belonging to 62 species and 1 subspecies, described between 1962 and 2019 from Russia, Tajikistan, China, South Korea, Japan, Laos, Indonesia, and Sri Lanka. All 31 holotypes are illustrated with color photographs and black-and-white drawings. Photographs of the habitus, head, hypopygium and genitalia of three holotypes and photographs of the habitus and head of 23 holotypes are presented for the first time. References to the original description, type localities, current status, and distribution of taxa are given. Data on the labels of holotypes and paratypes with clarifications and English translations are provided.
A Nonstationary Standardized Precipitation Index (NSPI) Using Bayesian Splines
Authors: Stagge James H. , Sung Kyungmin
Published: March 2022

The standardized precipitation index (SPI) measures meteorological drought relative to historical climatology by normalizing accumulated precipitation. Longer record lengths improve parameter estimates, but these longer records may include signals of anthropogenic climate change and multidecadal natural climate fluctuations. Historically, climate nonstationarity has either been ignored or incorporated into the SPI using a quasi-stationary reference period, such as the WMO 30-yr period. This study introduces and evaluates a novel nonstationary SPI model based on Bayesian splines, designed to both improve parameter estimates for stationary climates and to explicitly incorporate nonstationarity. Using synthetically generated precipitation, this study directly compares the proposed Bayesian SPI model with existing SPI approaches based on maximum likelihood estimation for stationary and nonstationary climates. The proposed model not only reproduced the performance of existing SPI models but improved upon them in several key areas: reducing parameter uncertainty and noise, simultaneously modeling the likelihood of zero and positive precipitation, and capturing nonlinear trends and seasonal shifts across all parameters. Further, the fully Bayesian approach ensures all parameters have uncertainty estimates, including zero precipitation likelihood. The study notes that the zero precipitation parameter is too sensitive and could be improved in future iterations. The study concludes with an application of the proposed Bayesian nonstationary SPI model for nine gauges across a range of hydroclimate zones in the United States. Results of this experiment show that the model is stable and reproduces nonstationary patterns identified in prior studies, while also indicating new findings, particularly for the shape and zero precipitation parameters.

Significance Statement

We typically measure how bad a drought is by comparing it with the historical record. With long-term changes in climate or other factors, however, a typical drought today may not have been typical in the recent past. The purpose of this study is to build a model that measures drought relative to a changing climate. Our results confirm that the model is accurate and captures previously noted climate change patterns—a drier western United States, a wetter eastern United States, earlier summer weather, and more extreme wet seasons. This is significant because this model can improve drought measurement and identify recent changes in drought.
Political changemakers in Norway: The strategies and political ideas of welfare providers
Authors: Segaard Signe Bock
Published: March 2022

This article contributes to the knowledge of interest groups’ political power. It examines how interest groups shape political debates and decision-making, and what strategies are most successful to this end, through an in-depth case study analysis of the Norwegian transposition of the revised EU policy on public procurement. The case is unique as it illustrates a policy process that changed direction at the eleventh hour, embodies an ideational fight between different views of a good society, and—surprisingly—concludes in favour of non-profit interests. Based on hearing statements, media items, transcripts from the final parliamentary debate on the matter, and interviews with leaders of interest groups, the article demonstrates that the ability to coordinate action and frame and control the public and political debate was a vital power resource for non-profit interests, who did so using a broad range of both direct and indirect strategies. The political ideas advanced through these two strategies were nearly identical; however, the indirect strategy was more personified and strongly emphasised normative conflict.
Factors Associated With Parental COVID-19 Vaccination Acceptance
Authors: Letterie Mia C. , Patrick Stephen W. , Halvorson Alese E. , Dupont William D. , Carroll Kecia N. , Zickafoose Joseph S. , Williams Sarah E.
Published: March 2022

As the coronavirus pandemic continues to impact families and children, understanding parental attitudes and likely acceptance of the COVID-19 vaccine is essential. We conducted a statewide survey with a representative sample of parents in Tennessee focused on COVID-19 and influenza vaccine acceptance and perspectives. Data from 1066 parents were analyzed using weighted survey methods to generalize results to the state of Tennessee. About 53% of parents reported a likelihood to vaccinate their children against COVID-19, and 45% were likely to vaccinate their child against COVID-19 and influenza. Female parents were less likely to vaccinate their children against COVID-19, but the strongest predictor of likely COVID-19 vaccine acceptance was influenza vaccine acceptance (adjusted odds ratio = 5.46; 95% confidence interval: 3.20-9.30). Parental acceptance of COVID-19 vaccines for children is closely tied to influenza vaccine acceptance. Public health approaches to maximize vaccine uptake could focus on children who have not been receiving influenza vaccines.
Team Self-Maintenance during Long-Duration Space Exploration: A Conceptual Framework
Authors: Wildman Jessica L. , Fedele Dominic , Wilder Anderson , Curtis Michael T. , DiazGranados Deborah
Published: March 2022

Objective

We developed a conceptual framework of Team Self-Maintenance (TSM) within long-duration space exploration (LDSE), which we define as the process of monitoring, adjusting, and maintaining the psychological well-being of a team in the absence of external support.

Background

Specific to LDSE and isolated, confined, and extreme (ICE) environments, periods of routine can have a debilitating effect on the crew’s well-being and performance, and TSM is a critical process for avoiding these detrimental effects.

Method

Based on themes drawn from nine subject matter expert interviews combined with an extensive literature review on related concepts, we developed an integrative conceptual framework of the key inputs, processes, and outputs involved in TSM within LDSE contexts.

Results

Our TSM framework suggests team well-being as a key outcome that must be maintained during LDSE and information sharing, self-regulation, resource recovery, and emotional support as the key processes that enable team well-being. We also identify several contextual inputs that can serve as intervention points for enabling effective TSM.

Conclusion

Our framework suggests that future research and practice aimed at effective LDSE should emphasize team well-being, rather than just performance, and that there are many open questions in terms of how teams will manage their own socio-emotional needs (e.g., conflict, recovery activities, and boredom) without external systems and support.

Application

This conceptual framework describes the primary inputs, processes, and outcomes involved in the team self-maintenance process. This framework reflects context-specific theorizing most likely to be applicable only to LDSE contexts.
Strength and failure mechanism of single-lap magnesium-basalt fiber metal laminate adhesively bonded joints: Experimental and numerical assessments
Authors: Mottaghian Fatemeh , Taheri Farid
Published: March 2022

A quick literature search reveals the significant lack of data and information concerning magnesium-to-magnesium bonded joints as well as fiber-metal laminates (FMLs) made with magnesium alloys. Therefore, a systematic series of experimental and numerical investigations are carried out to assess the performance of single-lap joints mating FML adherends. The primary goal is to better understand the effects of geometrical and material parameters that influence the performance of magnesium-to-magnesium joints. The FML adherends used in this study consist of basalt natural fiber-epoxy laminate sandwiched in between thin sheets of magnesium alloys, which were subsequently adhesively bonded using a room-cured epoxy resin. The effects of two types of surface treatments, namely, “sandblasting” and “sandblasting with resin coating” on the bond strength and failure mechanism of the adhesively bonded joints (ABJs) are investigated. A 3D numerical model developed to simulate the response of the joints subjected to quasi-static lap-shear tests. This model, which accounts for the material and geometrical nonlinearity in the joints, is used to perform a parametric analysis for establishing the optimal overlap bond length. The distributions of the shear and peel stresses in the overlap region and the effects of adhesive thickness on the performance of the joints are systematically examined. The comparison of the experimental data and numerical results confirms the robustness and cost-effectiveness of the numerical model in predicting the response of such single-lap ABJs.
The Effects of Communication Strategy Training on Speaking Task Performance
Authors: Milliner Brett , Dimoski Blagoja
Published: March 2022

This classroom study evaluates the effectiveness of pre-teaching communication strategies before learners undertake an information-gap speaking task. A convenience sample of 67 first-year students taking mandatory English classes at a private Japanese university was subject to one of two instructional approaches. The experimental group (n=37) undertook a pre-task communication strategy awareness activity coupled with a video modeling the ensuing information-gap task and communication strategies, while the control group (n=30) received no pre-task training. An analysis of speaking times, task worksheets and audio recordings of conversations indicated that the experimental group significantly outperformed the control group in task accuracy and communicative efficiency. In addition, a review of experimental group conversations indicated that most participants implemented the six targeted communicative strategies multiple times throughout the speaking task. Major implications for foreign language teachers include the value of pre-task modeling and communication strategies awareness-raising to help learners overcome disconnects in communicative tasks. Further, this study demonstrates the utility of information-gap speaking tasks for: (a) practicing communicative strategies; and (b) developing learners’ communicative competencies for real-world English as a lingua franca contexts.
Liturgy as Essentially Poietic
Authors: Cassingena-Trévedy François
Published: March 2022

The Christian liturgy is far more than a simple mobilization of a diverse range of artistic expressions in service of its ordinary performance; in fact, the liturgy is in essence a poiesis of the faith. Nor does this poiesis function as a system in harmony with Christian dogma and ethics, because in many regards it transcends these other two approaches. In the midst of the contemporary questioning concerning faith and life, believing and living, the centuries-old poiesis represents an invaluable resource. Truth alone cannot be imposed, nor should it be. More than ever, the symbolic translation of the faith, or to put it in other words, its aesthetic mediation, as expressed in its communitarian practice, will prove itself worthy of faith.
How Often do Medical Specialties Question the Practices that They Perform? An Empirical, Cross-Sectional Analysis of the Published Literature
Authors: Haslam Alyson , Powell Kerrington , Prasad Vinay
Published: March 2022

An aspect of overuse is who decides which practices are evaluated for overuse and which of the studies on overuse are published in the medical literature. We sought to examine the frequency with which studies in medical journals questioned an established practice. As a secondary objective, we sought to determine if there was variance among medical specialties. We conducted a retrospective, cross-sectional review of the published literature in 14 medical specialty journals. We included studies from one issue in three high-impact journals (November/December 2020) for each specialty. We assessed whether the study reported on a medical practice, whether it reported on an existing practice, whether the author expressed uncertainty regarding the practice, whether the study was a randomized design, and if the authors encouraged further testing in randomized studies. For all medical specialties combined, we found that 37% (n = 98) questioned existing practices, and 15% (n = 40) either tested the practice in a randomized trial or encouraged future randomized testing of the practice. The medical specialties that questioned their practices the most were gastroenterology (61%; n = 10/18), obstetrics/gynecology (52%; n = 11/21), and cardiovascular (50%; n = 5/10). These findings indicate that, although research is being conducted to examine current medical practices, few studies advocate for randomized testing of these practices, and even fewer actually test them in a randomized fashion. Additionally, the variation across medical specialties suggests areas in which to look for potential practices that are low-value, duplicative, and/or wasteful.
Impact of Online Classes on Parents: Case of Parents of Primary Class Students
Authors: Longjam Gyaneshwari
Published: March 2022

With all other responsibilities like work from home and house hold chores, assisting the children with online classes and other related things like online tests and exams, online learning activities, homework etc. has made the life of parents very difficult. Thus, the present research has focused on assessing the stress and anxiety level of parents owing to online classes of their children. The study used quantitative analysis conducted on primary data collected via quantitative questionnaire. The study found parents to experience mild to moderate level of anxiety wherein most of the respondent parents in the study stated that they feel anxious “a large part of their time”. When it comes to stress, the parents stated that they feel stressed sometimes owing to online classes of their children. This study can be used by policy makers of the corporate houses to update their policies of work life balance for working parents. Further, this study can also be used by education department of the country to assess and accordingly formulate the syllabus, in a pandemic like situation, in such away that both parents and teachers have equal role to play.
The ever –present need for biopsy to diagnose coeliac disease safely
Authors: Rai Anuradha , Mittal Santosh , Taneja Kanchan , Mahajan Nidhi
Published: March 2022

Coeliac disease (CD) is an autoimmune enteropathy which occurs in genetically predisposed individuals on exposure to gluten. The recommended diagnostic approach includes serological screening by Anti-tissue Transglutaminase antibody (anti-tTG) followed by a small intestinal biopsy. As high anti-tTG antibody is expected to be significantly associated with MARSH III histopathological changes in the small intestine, it has now become the basis for a biopsy-free approach to diagnose CD. However, we report cases of giardiasis mimicking CD both clinically and serologically; differentiation demands small intestinal biopsy.