bluuebunny/crossref_metadata_2025 huggingface dataset:
Identification of Potential Therapeutic Target Genes in Osteoarthritis
Authors: Hu Yang , Wu Yinteng , Gan Fu , Jiang Mingyang , Chen Dongxu , Xie Mingjing , Jike Yiji , Bo Zhandong
Published: August 2022

Objective. Osteoarthritis (OA), also known as joint failure, is characterized by joint pain and, in severe cases, can lead to loss of joint function in patients. Immune-related genes and immune cell infiltration play a crucial role in OA development. We used bioinformatics approaches to detect potential diagnostic markers and available drugs for OA while initially exploring the immune mechanisms of OA. Methods. The training set GSE55235 and validation set GSE51588 and GSE55457 were obtained from the Gene Expression Omnibus (GEO) database and differentially expressed genes (DEGs) were identified by the limma package. Gene set enrichment analysis (GSEA) was performed on the GSE55235 dataset using the cluster profiler package. At the same time, DEGs were analyzed by gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). In addition, protein-protein interaction (PPI) analysis was performed on the common DEGs of the three datasets using the STRING database. Proteins with direct linkage were identified as hub genes, and the relation of hub genes was subsequently analyzed using the GOSemSim package. Hub genes’ expression profiles and diagnostic capabilities (ROC curves) were analyzed and validated using three datasets. In addition, we performed RT-qPCR to validate the levels of hub genes. The immune microenvironment was analyzed using the CIBERSORT package, and the relationship between hub genes and immune cells was evaluated. In addition, we used a linkage map (CMAP) database to identify available drug candidates. Finally, the GSEA of hub genes was used to decipher the potential pathways corresponding to hub genes. Results. Three hub genes (CX3CR1, MYC, and TLR7) were identified. CX3CR1 and TLR7 were highly expressed in patients with OA, whereas the expression of MYC was low. The results of RT-qPCR validation were consistent with those obtained using datasets. Among these genes, CX3CR1 and TLR7 can be used as diagnostic markers. It was found that CX3CR1, MYC, and TLR7 affect the immune microenvironment of OA via different immune cells. In addition, we identified a potential drug for the treatment of OA. Altogether, CX3CR1, MYC, and TLR7 affect the immune response of OA through multiple pathways. Conclusion. CX3CR1, MYC, and TLR7 are associated with various immune cells and are the potential diagnostic markers and therapeutic targets for OA.
Vestibular Autorotation Test: The Differences in Peripheral and Central Acute Vestibular Syndrome
Authors: Guo Nan , Zhou Lijing , Zhang Yu , Fan Xiuyu
Published: August 2022

Objective. To evaluate the difference between the vestibular autorotation test (VAT) in the peripheral and central acute vestibular syndrome (AVS). Methodology. Patients with AVS diagnosed by clinical manifestation admitted to the third affiliated hospital of Qiqihar Medical College from January 2019 to January 2021 were enrolled and divided into peripheral AVS (peripheral group) and central AVS (central group) according to the results of the MRI examination. Results. A total of 332 patients with AVS were recruited, including 282 patients in the peripheral group and 50 patients in the central group. The horizontal gain of both groups showed a downward trend at 2–6 Hz. There was no significant change in the horizontal phase between the two groups at 2–6 Hz. The horizontal gain of the two groups was stable at 2–6 Hz with no significant changes in the horizontal phase between 2–6 Hz in both groups. The central group showed a significantly lower proportion of gain increase coupled with loss and a strikingly higher proportion of gain increase without a loss than in the peripheral group (all

P
<
0.001

). Conclusion. The increased horizontal and vertical gain of VAT in patients with AVS is of high value in the diagnosis of ACS. Significant differences in the results of VAT in patients with central and peripheral AVS could provide a reference for diagnosis.
A Robust and Lightweight Detector for Ship Target with Complex Background in SAR Image
Authors: Wang Hengtao , Zhang Shang
Published: August 2022

Accurate target detection technology on ships can improve the comprehensive perception ability of weapon equipment. For SAR ship target detection in complex environments, false and missing alarms are serious. We design a new real-time ship target detection algorithm 3S-YOLO in SAR images. Firstly, reconstruct the network structure, adjust the relationship between receptive field and multiscale fusion, and realize the lightweight processing of feature extraction network and feature fusion network. Then, the network is pruned and compressed by the FPGM pruning algorithm to accelerate the reasoning speed. Finally, the Varifocal-EIoU loss function is designed to balance the positive and negative samples and overlapping losses and highlight the contribution of positive samples. To verify the effectiveness of the 3S-YOLO algorithm, verification is carried out in public datasets SSDD and HRSID. The results show that the accuracy of the model can be improved to 99.2% and 95.6%, respectively, after optimization. After pruning, the model volume decreased significantly and could be compressed to 190 KB. Model reasoning time can be reduced to less than 3 ms. Compared with the current mainstream algorithms, 3S-YOLO has achieved good results in all aspects to meet the real-time ship target detection in SAR images.
Decomposition Methods for Solving Finite‐Horizon Large MDPs
Authors: el Akraoui Bouchra , Daoui Cherki , Larach Abdelhadi , Rahhali khalid
Published: August 2022

Conventional algorithms for solving Markov decision processes (MDPs) become intractable for a large finite state and action spaces. Several studies have been devoted to this issue, but most of them only treat infinite‐horizon MDPs. This paper is one of the first works to deal with non‐stationary finite‐horizon MDPs by proposing a new decomposition approach, which consists in partitioning the problem into smaller restricted finite‐horizon MDPs, each restricted MDP is solved independently, in a specific order, using the proposed hierarchical backward induction (HBI) algorithm based on the backward induction (BI) algorithm. Next, the sub‐local solutions are combined to obtain a global solution. An example of racetrack problems shows the performance of the proposal decomposition technique.
Privacy Preservation Learning with Deep Cooperative Method for Multimedia Data Analysis
Authors: Si Wen , Liu Cong
Published: August 2022

In parallel with the rapid adoption of deep learning to multimedia data analysis, there has been growing awareness and concerns about data security and privacy. The recent advancement of federated learning enables many network clients to collaboratively train a model under the orchestration of a central server while preserving clients’ privacy. However, the standard assumption of independent and identical distribution (IID) may be broken under the federated learning because data label preferences may vary across clients. Recent efforts address this issue either by adapting a strong global model for each local model, respectively, or by training individual local models for similar clients together. However, both strategies degrade in highly non-IID scenarios. This work introduces a novel method, deep cooperative learning (DCL), to address this problem. It leverages the reciprocal structure between deep learning tasks in different clients to obtain effective feedback signals to enhance the learning process of personalized local models. To the best of our knowledge, this is the first time the non-IID is addressed under the principle of task interactions. We demonstrated the effectiveness of DCL on the two tasks of medical multimedia data analysis. The results show that our method presents a significant performance improvement compared with the standard federated learning method. In conclusion, this work developed a method for addressing non-IID problems in deep-learning-based privacy preservation learning. It allows the highly non-IID data to be used to improve the local model performance.
DSA Image Analysis of Clinical Features and Nursing Care of Cerebral Aneurysm Patients Based on the Deep Learning Algorithm
Authors: Wang Jian , Ti Lin , Sun Xiaorui , Yang Ruping , Zhang Nafei , Sun Kejuan
Published: August 2022

Objective. A deep learning algorithm was developed for automatic detection and localization of intracranial aneurysms in DSA, and its clinical characteristics were analyzed, and targeted nursing measures were formulated. Methods. Using a retrospective multicenter study method based on radiology reports, DSA images of aneurysms were randomly divided into 75 cases in the training set, 20 cases in the internal test set, and 35 cases in the external test set. Using a computer-aided detection method based on the three-dimensional U-Net (3D U-Net), after preprocessing DSA images, automatic segmentation of intracranial blood vessels is performed to obtain regions of interest, and based on the segmentation results, physicians’ annotations are introduced. The 3D U-Net network model is trained and adjusted, and the obtained model is used to automatically detect the cerebral aneurysm area. Results. Fivefold cross-validation was used for the training set and the internal test set, and a sensitivity of

94.4
±
1.1

%

was obtained. Automatic detection of aneurysms was performed on the external test set, and the average false positive rate was 0.86 FPs/case (false positives/case). The resulting sensitivity was 82.9%. The classification comparison of external test sets showed that the sensitivity of the method for detecting aneurysms with sizes of 5.00~<10.00 mm and ≥10.00 mm (88.2% and 100.0%) was higher than that for aneurysms with sizes of <3.00 mm and 3.00~<5.00 mm (50.0% and 72.7%). The sensitivity of patients aged 50-60 years and >60 years (90.0% and 87.5%) was higher than that of patients aged <50 years (66.7%), and there was little difference between different genders (84.6% in males and 81.8% in females). Conclusion. The deep learning algorithm has high diagnostic performance in detecting intracranial aneurysms, which is verified by external datasets.
Credit Risk Evaluation in Enterprise Financial Management by Using Convolutional Neural Network under the Construction of Smart City
Authors: Zeng Hui
Published: August 2022

Smart cities are the forward goals of future cities, and the development of small- and medium-sized enterprises (SMEs) plays a key role in it. To solve the financing difficulties of SMEs, it is necessary to scientifically, objectively, and accurately assess the credit risk of SMEs. Based on the current research and analysis of scholars in related fields, taking corporate financial data as the object, the SMEs’ credit risk is assessed through an adaptive and self-learning convolutional neural network (CNN) method. The GoogleNet method is further improved to reconstruct the credit risk evaluation model of SMEs. Finally, the influence of the convolution kernel depth on the accuracy is discussed through the optimization of the structure and the selection of the initial value of the learning rate. The results are verified by using the data in the 2016–2021 SME sector in Shanghai and Shenzhen in the wind database. It can be found that when the learning rate is finally set to 0.4 and the convolution kernel depth is set to 8 and 32 in turn, the evaluation accuracy of the test dataset is the highest, with a total accuracy of 96.8%. Therefore, it is believed that the newly constructed model has higher accuracy and more practical value than the other three typical risk assessment models.
Application of Blockchain Technology in Water Rights Trading in the Irrigation Area under the Internet-of-Things Environment
Authors: Li Yang , Xie Jiancang , Yang Jianghua , Ren Jian , Zhai Nan
Published: August 2022

Water rights trading is an important way to solve the shortage of water resources. Combined with the characteristics of water resources and the requirements of the market economy, the organic combination of the government and the market should be strengthened. Through the establishment of a water rights system and a water rights trading market, the implementation of water rights trading will be promoted. Due to the decentralized and distributed structure, blockchain technology greatly reduces its intermediary cost in the application process. The tamper-resistant timestamp feature can realize data tracking and information anticounterfeiting, and flexible programming attributes can promote the specification of market order. Firstly, the reasons for water rights trading are studied, and the water rights trading system is analyzed. It is found that the system has the problems of the cumbersome transaction process and high transaction costs. According to the characteristics of water rights trading, the alliance chain Hyperledger Fabric is selected as the underlying network system of the trading system. Secondly, combined with water rights trading, Hyperledger Fabric multichannel technology is used to design a three-channel accounting method, and the chain code for water rights trading is designed and implemented based on the Go language. CouchDB is adopted to store key-value data, and Ubuntu, Docker, and Docker Compose are used to construct an operating environment of the chain code. Finally, the proposed system is tested. The test findings reveal that the distributed water volume of experimental area 1 is 41.8368 million m3, the current water volume is 23.831 million m3, and the quota management water volume is 20.432 million m3. The distributed water volume in area 2 is 40.4605 million m3, the current water volume is 4317.64 m3, and the quota management water volume is 77.4795 million m3. The distributed water volume in area 3 is 65.6928 million m3, the current water volume is 77.472 million m3, and the quota management water volume is 64.412 million m3. The water rights trading system based on Hyperledger Fabric can record each transaction data, which can be stored in each node, and the data stored in each node are the same. The proposed scheme provides a certain reference for the application of blockchain technology in water rights trading in irrigation areas.
The Establishment of Risk Evaluation Index System for Small- and Medium-Sized Agency Bookkeeping Companies
Authors: Wu Qingfu , Zhang Xiaoqian
Published: August 2022

In recent years, China’s economy has developed rapidly; many small companies have risen rapidly; and the tax system has become more and more standardized. Because many small businesses cannot afford to hire full-time accountants, they opt to outsource accounting services, giving small- and medium-sized bookkeeping firms a large market space. However, these opportunities also bring huge operational risks to small- and medium-sized bookkeeping companies. The purpose of this research is to help such enterprises carry out risk management and reduce operational risks. This study uses an analytic hierarchy process and a fuzzy comprehensive assessment approach to successfully combine quantitative and qualitative analysis and create a multilevel analysis structure model of the risk management evaluation index system of small- and medium-sized agency accounting firms. The structural model is verified by a case, the specific risk score of each factor is calculated through the scores of 20 experts, and the importance of risk is judged according to the size of the score, indicating that the structural model is feasible.
Evaluation of Provincial Tourism Competitiveness in the Yellow River Basin Based on Clustering Algorithm
Authors: Geng Nana , Shao Xiuying
Published: August 2022

Tourism is an important part of China’s national economy. Promoting the development of regional tourism and improving the level of tourism competitiveness can promote the development of regional economy, society, and people's livelihood. Therefore, in the context of the HQDEV of the YRB, it is of great significance to study the evaluation and promotion strategies of tourism competitiveness along the Yellow River for promoting their HQDEV in terms of ecology, economy, and tourism. Taking the provinces as the research area, this article has constructed an evaluation index system of tourism competitiveness in the provinces and used the entropy weigh, the Topsis, and the natural breakpoint classification method. The economic and tourism industry subsystems and the overall tourism competitiveness level are comprehensively evaluated and analyzed, and on this basis, the promotion strategy of tourism competitiveness of the nine provinces along the Yellow River is proposed. According to the weight of the evaluation index system, the index weight of the ecological environment competitiveness subsystem is 0.3891, of which the index weight of the ecological protection level is 0.3057, and the environmental quality level is 0.0834. The index weight of the subsystem is 0.2691, of which the index weight of the economic innovation level is 0.1695, and the social and people's livelihood level is 0.0996. For the tourism competitiveness, the index weight of the ecological environment competitiveness subsystem is 0.3891, the index weight of the socioeconomic competitiveness subsystem is 0.2691, and the index weight of the tourism industry competitiveness subsystem is 0.3418. The results show that the level of tourism competitiveness along the nine provinces maintains a relatively stable form, but the overall level is not high and the intraregional differences are small.
An Overview of Composite Standard Elastic‐Net Distribution Based on Complex Wavelet Coefficients
Authors: Aloafi Tahani A. , Aljohani Hassan M.
Published: August 2022

Statistical tools have changed significantly in the past decade; when the maximum likelihood method is usually applied, it provides an inaccurate solution due to its unsuitable properties and causes problems in fit. However, the current Ordinary Least Square (OLS) Model is more reliable in specific situations where the estimation is based on the slope only. On the other hand, some methods depend on the slope, and the intercept is recommended. These methods can be described as thresholding rules. Another often preferred method is the posterior mean (PM) technique. This procedure depends on two parts, the first part is the likelihood, and the other is the prior distribution, where the second part plays a significant role in the estimation. In this article, the standard elastic‐net distribution is assumed as the prior part, which consists of two parts, the first being the normal distribution and the second being the double exponential distribution. The reason why this model is used is that wavelet tools have different levels of resolution. Thus, this model may provide a more accurate estimation for the wavelet coefficients, which might be estimated using a normal or double exponential distribution. In the past, some properties of elastic‐net penalized were introduced and discussed. However, more properties are introduced for this distribution. In addition, two models based on the elastic‐net method are demonstrated, involving the point mass prior. The first model combines normal as likelihood, and elastic‐net distributions as prior, while the other combines the double exponential distribution as likelihood with the elastic‐net distribution as prior. Moreover, the level‐dependent components are estimated at each resolutions. A simulated investigation is studied using the Markov Chain Monte Carlo (MCMC) tool to estimate the underlying features, where real data are involved and modelled using the proposed methods. A stationary wavelet basis is also applied. As a result, the proposed procedure reduces noise levels which may be helpful since noise levels often corrupt real data, usually a significant cause of most numerical estimation problems.
Volleyball Sports Teaching Based on Augmented Reality and Wireless Communication Assistance
Authors: Ding Wei , Li Hao , Wang Xi
Published: August 2022

The 21st century is the information age. The ever-changing information technology not only affects the development of the global economy, but also has an important impact on the field of education and personnel training. Under the influence of information technology, the way people acquire, impart, and evaluate knowledge has changed greatly. The educational concepts, models, and methods of the new era are fundamentally different from traditional education, which provides new opportunities and new challenges for the education and teaching work of contemporary teachers. This paper aims to discuss the application of augmented reality technology in volleyball teaching, and give an effective method for AR to be applied to volleyball teaching. In this paper, the existing AR teaching system has the problem of poor transmission rate and cannot cooperate with volleyball teaching well. Therefore, a wireless communication auxiliary algorithm based on intelligent reflective surface is proposed. And the current situation of volleyball teaching is investigated and analyzed. And in response to the survey results, 78% of the students supported teachers in applying AR technology to assist teaching in volleyball teaching classes. This fully shows that there is still a lot of application space for AR technology in current volleyball teaching.
Big Data’s Analysis and Prediction Method of Art Education Based on the BP Neural Network
Authors: Zhu Xinya
Published: August 2022

The traditional art education analysis and prediction are mainly based on the evaluation of human supervisors, and the evaluation results given by different evaluators vary greatly. The traditional analysis and prediction is difficult to reflect the objectivity and fairness of art education evaluation. This paper proposes a new idea of using the BP neural network model to analyze and forecast art education to improve the objectivity and impartiality of art education evaluation by big data. The work first constructed the art education evaluation index system covering the content of art education comprehensively and extensively. Then the BP neural network of art education analysis and prediction model was established. According to the content of art education evaluation system, the BP neural network’s input vector contains 30 evaluation indexes that affect art education, and its output vector is the art education evaluation results. The BP neural network with the structure of 30 × 10 × 1 was trained using the collected data. Finally, the work verified the scientificity of the evaluation model. The results of empirical analysis show that the established art education evaluation index system is reasonable and can reflect the artistic level of students, and the model of BP neural network has reliability in the analysis and prediction of art education big data and can objectively evaluate the level of art education.
Grading Evaluation of Goaf Stability Based on Entropy and Normal Cloud Model
Authors: Ma Tianxing , Lin Yun , Zhou Xiaobin , Zhang Mingzhi
Published: August 2022

Aiming at the fuzziness and randomness of goaf stability classification, to obtain goaf stability classification more objectively, an entropy weight‐normal cloud model for goaf stability classification is proposed. Based on the geological conditions and engineering conditions, 14 indexes that affect the stability of a goaf are selected to establish an evaluation index system, and the weight of each index is determined using the entropy weight method, which makes the weight distribution more objective. Based on the cloud model theory, the cloud numerical characteristics of each evaluation index belonging to goaf stability level are calculated, and a corresponding cloud model is generated. Combined with the entropy weight, the comprehensive certainty degree is calculated, and the evaluation results are obtained. Taking 25 mined‐out areas in Xishan mine of Shandong Gold Mining and the Dabaoshan mine as examples, the model is used for stability evaluation, and the evaluation results are basically consistent with the actual situation, which proves the feasibility of the method and provides a new and effective method for stability evaluation of mined‐out areas.
Clinical Effect of Feldspar Glass Ceramics and Lithium Disilicate Reinforced Glass Ceramics
Authors: Liu Chang , Yang Suming , Chu Xiaoyu
Published: August 2022

The clinical effect of feldspar ceramics and lithium disilicate reinforced glass ceramics was studied. 57 affected teeth with complete root canal treatment and computer aided design/computer aided manufacturing (CAD/CAM) endocrown restoration were reviewed 24‐36 months aftercompletion of the restoration. These dimensions including anatomical morphology, proximal contact , color matching, edge integrity, edge coloring, secondary caries, kinesthetic occlusion relationship, and periodontal health of the affected teeth by the modified clinical scoringcriteriafor United States Public Health Service (USPHS) were evaluated, and the patients’ satisfaction was recorded. The 57 affected teeth were divided into feldspar group and lithium disilicate reinforced ceramic group according to the different repair materials, and the statistical results are analyzed. The experimental results show medullary fixation crown can be used to repair a large area of dental defects after root canal treatment.
Basic characteristics of postural balance of professional hockey players and novices
Authors: Leonov Sergey V. , Kruchinina Anna P. , Bugrii Grigoriy S. , Bulaeva Natalia I. , Polikanova Irina S.
Published: August 2022

Background. Stance (or posture) is the fundamental posture of a hockey player, allowing him to perform various movements during training and playing processes. However, studies of postural balance of hockey players are scarce. Objective. Ye main aim of the study was to track a hockey player’s posture as well as to compare static postural characteristics of professional hockey players and novices. Sample. Ye study involved 22 male subjects (mean age 20±2.3 years), including 13 hockey players and 9 subjects who were new to hockey (mainly freestyle wrestlers). Ye professional level was assessed by the number of years of experience and by the presence of sports ranks. Ye average experience was 14.18±3.8 years. Ye sports ranks ranged from third junior category to Candidate Master. Method. A method for tracking the posture of a hockey player using the DTrack2 and SteamVR Tracking 2.0 systems was tested. Ye obtained results made it possible to compare the static characteristics of the stance in professional hockey players and novices based on the analysis of changes in the knee and hip joint angles, as well as changes in the position of the head during performing the task of maintaining the stand for 5 minutes (Leonov, Polikanova, Kruchinina et al., 2021). Results. Analysis of movements with the DTrack2 system and the SteamVR Tracking 2.0 system showed identical results, allowing further use of the SteamVR Tracking 2.0 system. Analysis of the positional tracking data showed that professional hockey players were characterized by a greater amplitude of oscillation (1–2 degrees) than novices (no more than 1 degree) when maintaining their stance. Professional hockey players are also characterized by a signiZcantly smaller amplitude of oscillation of the head position during performance of tasks. Qualitative analysis of changes in angles of knee and hip joints showed that professional hockey players, in contrast to beginners, have a higher and more stable amplitude of movement parameters [uctuations during the whole study, which in turn allows to optimize energy expenditure and provide better posture stabilization. Conclusion. Yus, the results of the study indicate the importance of correct head positioning in stabilizing the postural balance of a hockey player. For posture maintenance, the hip strategy of postural balance maintenance, associated with a greater amplitude of hip joint oscillation, is the most optimal.
Surgical vs Transcatheter Aortic Valve Replacement in Patients With a Low Ejection Fraction
Authors: Arafat Amr A. , Alawami Murtadha H. , Hassan Essam , Alshammari Ahmad , AlFayez Latifa A. , Albabtain Monirah A. , Ismail Huda H. , Adam Adam I. , Pragliola Claudio , Algarni Khaled D.
Published: August 2022

Currently, there is no preference for surgical (SAVR) vs transcatheter (TAVR) aortic valve replacement in patients with low ejection fraction (EF). The present study retrospectively compared the outcomes of SAVR vs TAVR in patients with EF ≤40% (70 SAVR and 117 TAVR patients). Study outcomes were survival and the composite endpoint of stroke, aortic valve reintervention, and heart failure readmission. The patients who had TAVR were older (median: 75 (25-75th percentiles: 69-81) vs 51 (39-66) years old; P < .001) with higher EuroSCORE II (4.95 (2.99-9.85) vs 2 (1.5-3.25); P < .001). Postoperative renal impairment was more common with SAVR (8 (12.5%) vs 4 (3.42%); P = .03), and they had longer hospital stay [9 (7-15) vs 4 (2-8) days; P < .001). There was no difference between groups in stroke, reintervention, and readmission (Sub-distributional Hazard ratio: .95 (.37-2.45); P = .92). Survival at 1 and 5 years was 95% and 91% with SAVR and 89% and 63% with TAVR. Adjusted survival was comparable between groups. EF improved significantly (β: .28 (.23-.33); P < 0.001) with no difference between groups ( P = .85). In conclusion, TAVR could be as safe as SAVR in patients with low EF.
What Prospective Applicants Look for on Otolaryngology Residency Websites
Authors: DiNardo Lauren A. , Reese Alyssa D. , Seeley Jeffrey , Carr Michele M.
Published: August 2022

Objective:

To determine what prospective residency applicants look for on otolaryngology residency program websites.

Study Design:

Survey.

Setting:

The survey was distributed in a virtual format through Google Forms. A link to the survey was given at the University at Buffalo Otolaryngology Chautauqua, sent to Otolaryngology program directors with requests to forward to PGY1 residents and Otolaryngology interest groups, sent to medical school Otolaryngology interest group leaders, posted on Twitter through several accounts, and on otomatch.com

Methods:

A 45 item survey was given to fourth year medical students applying to otolaryngology, re-applicants applying to otolaryngology, and first year otolaryngology residents who had recently matched. Data collected included participant’s age, sex, race and ethnicity, current status in school/residency program, and their type of medical degree (MD or DO). Participants were then asked to mark the components listed that they wished to see. Components were separated into 5 categories: Application/Selection Criteria, Employment Aspects, Program Features, Social/Community, and Faculty/Contact Information. Data was analyzed using Mann-Whitney and Kruskal Wallis tests when appropriate. P value <.05 was considered statistically significant.

Results:

One hundred one surveys were collected. Sixty-four (63.4%) participants were fourth-year medical students, 32 (31.7%) were first-year residents, and 5 (5.0%) were re-applicants. Thirteen of the items on the survey were sought by 80% or more of respondents. Ninety-nine (98.0%) of the participants wanted to see a current resident list with photos and 97 (96.0%) wanted to see the intern year schedule online. Eighty-five percent agreed that salary and benefits information would be valuable on residency websites. Women expressed significantly more interest in salary ( P = .034), benefits ( P = .026), and parking information ( P = .048).

Conclusion:

Our study provides insight on key items to include on residency program websites. Websites that contain information that applicants want to use can be helpful for application decisions in the future.
Distinguishing apathy and depression in dementia: A longitudinal study
Authors: Connors Michael H , Teixeira-Pinto Armando , Ames David , Woodward Michael , Brodaty Henry
Published: August 2022

Objectives:

Apathy is a common symptom in dementia, though can be difficult to distinguish from depression due to shared features and frequent co-occurrence. As such, a significant limitation of much previous research on apathy is the failure to control for depression. The current study sought to address this by examining the trajectory and clinical correlates of apathy after controlling for depression.

Methods:

Seven hundred and seventy-nine patients with dementia were recruited from nine memory clinics around Australia. Measures of dementia severity, cognition, functional ability, neuropsychiatric symptoms, caregiver burden and medication use were completed at baseline and at regular intervals over a 3-year period. Driving and institutionalisation data were obtained throughout the study. Mortality data were obtained from state registries 8 years after baseline.

Results:

Of the 662 patients with completed measures of neuropsychiatric symptoms, 342 (51.7%) had apathy and 332 (50.2%) had depression at baseline, while 212 (32.0%) had both. Whereas apathy increased over time, depression remained relatively stable. Apathy, but not depression, was associated with greater dementia severity, poorer cognition and function, driving cessation and mortality. Both apathy and depression were associated with greater neuropsychiatric symptoms, psychosis, caregiver burden and institutionalisation.

Conclusions:

Apathy increases over the course of dementia and is associated with worse clinical outcomes independent of depression. Distinguishing apathy and depression appears important given their different implications for prognosis and management.
Sentencing social psychology: Scientific deviance and the diffusion of statistical rules
Authors: Larregue Julien
Published: August 2022

This article investigates the inquiries and sanctions that followed accusations of fraud directed toward Dutch social psychologist Diederik Stapel in the early 2010s. Relying on the public reports published by the investigative committees, as well as on interviews conducted with committee members and Stapel’s former students and collaborators, we propose to analyze how this case facilitated the diffusion, in social psychology, of statistical rules that were hitherto unenforced in this field. The Stapel case thus illustrates the regulative role played by statistics in the contemporary scientific field while also demonstrating the appeal of legal modes of dealing with misconduct when it comes to the treatment of scientific deviance. More generally, this article shows how the study of scientific deviance can serve to bring to light symbolic hierarchies that are habitually kept tacit, thus serving as a magnifying glass for the scientific field’s inner processes.
Detecting Rating Scale Malfunctioning With the Partial Credit Model and Generalized Partial Credit Model
Authors: Wind Stefanie A.
Published: August 2022

Rating scale analysis techniques provide researchers with practical tools for examining the degree to which ordinal rating scales (e.g., Likert-type scales or performance assessment rating scales) function in psychometrically useful ways. When rating scales function as expected, researchers can interpret ratings in the intended direction (i.e., lower ratings mean “less” of a construct than higher ratings), distinguish between categories in the scale (i.e., each category reflects a unique level of the construct), and compare ratings across elements of the measurement instrument, such as individual items. Although researchers have used these techniques in a variety of contexts, studies are limited that systematically explore their sensitivity to problematic rating scale characteristics (i.e., “rating scale malfunctioning”). I used a real data analysis and a simulation study to systematically explore the sensitivity of rating scale analysis techniques based on two popular polytomous item response theory (IRT) models: the partial credit model (PCM) and the generalized partial credit model (GPCM). Overall, results indicated that both models provide valuable information about rating scale threshold ordering and precision that can help researchers understand how their rating scales are functioning and identify areas for further investigation or revision. However, there were some differences between models in their sensitivity to rating scale malfunctioning in certain conditions. Implications for research and practice are discussed.
Exploration of the Stacking Ensemble Machine Learning Algorithm for Cheating Detection in Large-Scale Assessment
Authors: Zhou Todd , Jiao Hong
Published: August 2022

Cheating detection in large-scale assessment received considerable attention in the extant literature. However, none of the previous studies in this line of research investigated the stacking ensemble machine learning algorithm for cheating detection. Furthermore, no study addressed the issue of class imbalance using resampling. This study explored the application of the stacking ensemble machine learning algorithm to analyze the item response, response time, and augmented data of test-takers to detect cheating behaviors. The performance of the stacking method was compared with that of two other ensemble methods (bagging and boosting) as well as six base non-ensemble machine learning algorithms. Issues related to class imbalance and input features were addressed. The study results indicated that stacking, resampling, and feature sets including augmented summary data generally performed better than its counterparts in cheating detection. Compared with other competing machine learning algorithms investigated in this study, the meta-model from stacking using discriminant analysis based on the top two base models—Gradient Boosting and Random Forest—generally performed the best when item responses and the augmented summary statistics were used as the input features with an under-sampling ratio of 10:1 among all the study conditions.
*Relationship Between Creativity and Intelligence: A Multimethod Investigation of Alternative Theoretical Assumptions in Two Samples of Secondary School Students
Authors: Breit Moritz , Preuß Julian , Scherrer Vsevolod , Moors Tobias , Preckel Franzis
Published: August 2022

The threshold hypothesis and the necessary-but-not-sufficient hypothesis represent popular views on the relationship between intelligence and creativity. However, most studies investigating these hypotheses used suboptimal or even inappropriate statistical methods, calling into question the robustness of the available evidence. The ability differentiation hypothesis presents a third theoretical view on the relationship, but ability differentiation studies including creativity measures are scarce. In this study, the relationship between fluid intelligence as a core element of intelligence and divergent thinking as an important indicator of creativity was investigated in two large samples of secondary school students in Germany ( N = 1,328, Mage = 14.47; N = 524, Mage = 13.77). Four different statistical approaches were applied (i.e., test for heteroscedasticity, segmented regression analysis, local structural equation modeling, and necessary-but-not-sufficient condition analysis). The results did not support the threshold hypothesis or a nonlinear relationship as predicted by ability differentiation hypothesis and only partially supported the necessary-but-not-sufficient hypothesis.
A fractional-order Duhem model of rate-dependent hysteresis for piezoelectric actuators
Authors: Yang Liu , Zhong Ruobing , Li Dongjie , Li Zhan
Published: August 2022

In this paper, a Fractional-Order Duhem Model (FODuhem) is proposed to describe the rate-dependent hysteresis nonlinearity of piezoelectric actuators (PEAs). A fractional-order operator is introduced on the basis of the traditional Duhem model, and the unique nonlocal memory property of the fractional-order operator makes it possible to describe the memory effect inherent in hysteresis. A differential evolutionary algorithm was used to identify the parameters of the FODuhem model. Finally, experimental results clearly show that the FODuhem model can better describe the rate-dependent hysteresis behavior of piezoelectric actuators compared with the conventional Duhem model.
Risk control of metal raw materials based on deep learning
Authors: Zhao Cheng , Wang Yexin , Cen Yuefeng , Wu Lebin , Zhou Jie
Published: August 2022

Stabilizing the purchase cost of metal raw materials is of great significance to the metal manufacturing industry. Most enterprises use futures hedging strategies to cope with the risks arising from fluctuations in the prices of metal raw materials. However, the difference between spot and futures prices of metals makes it impossible to fully control the risk. In order to further improve the efficiency of hedging and controlling corporate risks, it is necessary to accurately predict futures prices. However, the decomposition algorithms in traditional mixed models are prone to modal aliasing and have limited ability to extract nonlinear features from futures prices. Therefore, this paper proposes a variational modal decomposition-sample entropy-Cascaded Long Short-Term Memory Neural Network Model (VMD-SE-CLSTM). This paper proposes SE combined with VMD algorithm to determine the decomposition number to suppress the aliasing phenomenon of subsequence patterns, and introduces CLSTM network to improve the extraction ability of nonlinear features in futures data. The experiments are compared with 10 mainstream model methods and the method proposed in this paper. The experimental results show that the model reduces the prediction error and improves the prediction accuracy of the model, which is of great significance for enterprises to improve hedging efficiency, reduce operating risks and control production costs.
Executive Functions Training for 7- to 10-Year-Old Students With Mathematics Difficulty: Instant Effects and 6-Month Sustained Effects
Authors: Cai Dan , Zhao Jing , Chen Zhijun , Liu Di
Published: August 2022

Executive function (EF) training has shown promise for remedying general EF deficiencies faced by students with mathematics difficulty (MD) and for improving their performance. However, latest research also suggests that the instant and sustained effects of EF training remain inconsistent. In this study, 32 Chinese students with MD, age 7 to 10 years, were recruited and randomly divided into two groups: the training group ( n = 16, 25 training sessions) and the control group ( n = 16). Both groups took a pretest, a posttest, and a follow-up test (after 6 months) on EF, fluid intelligence, and mathematics skills. In the posttest, the training group’s performance significantly improved in 2-back, number shifting, letter shifting, calculation fluency, and mathematics problem-solving tasks, but not in Stroop, Flanker, 1-back, numerical operations, and colored progressive matrices tasks. In the follow-up test after 6 months, the effects of training only on the 2-back and letter shifting tasks were sustained. The effect on the numerical operations task appeared; however, the effects on number shifting, calculation fluency, and mathematics problem-solving tasks disappeared. The results of this study show that EF training has instant effects of improving EF and mathematics skills of students with MD, and 6-month sustained effects on some of the improved skills. However, for fluid intelligence, the effects may be very limited.
Longitudinal Stability and Change in Young Students’ Mastery-Approach Goals for Running
Authors: Xiang Ping , Liu Jiling , Li Weidong , Guan Jianmin
Published: August 2022

In the present study, we examined the longitudinal stability and change in mastery-approach goals for running as offered in regularly scheduled physical education (PE) and athletics classes in the United States. There were five waves of data collection from 806 students (431 boys; 375 girls) who were tracked from fourth to eighth grade while participating in running activities in PE or athletics classes. We assessed the participants’ mastery-approach goals using four items on a 5-point scale. We found acceptable longitudinal construct validity, measurement invariance, and scale reliability for the scores of mastery-approach goals, and we found these goals to have moderate stability across this 5-year period. Latent growth modeling revealed a linear decline in mean mastery-approach goal scores over the five school years. We concluded that the assessment of mastery-approach goals over time was both valid and reliable and that these running mastery-approach goals were stable in most respects but became less intense over time.
Subjective Sensitivity to Exteroceptive and Interoceptive processing in Highly Sensitive Person
Authors: Ujiie Yuta , Takahashi Kohske
Published: August 2022

A highly sensitive person is known to have greater levels of sensory processing sensitivity (SPS) referring to a personality trait to exhibit high stimulation and arousal while processing subtle sensory signals. However, how SPS levels reflect the profile of sensitivity in exteroceptive and interoceptive sensory processing remains inadequately understood. Thus, we collected data using the Highly Sensitive Person Scale (HSPS), the Glasgow Sensory Questionnaire (GSQ), and the Body Perception Questionnaire–Short Form (BPQ-SF) from 600 Japanese adults, and examined their relationships. The results revealed that SPS levels, assessed by the HSPS, were significantly, positively correlated with hypersensitivity scores of the GSQ in several exteroceptive sensory domains. Further, SPS levels were positively correlated with sensitivity in interoceptive awareness assessed by the BPQ-SF; however, it does so scarcely. Our findings suggest that SPS levels reflect a subjective sensitivity to exteroceptive sensory processing regardless of sensory domains and narrowly to the interoceptive sensory processing.
Telehealth Assessment in Rehabilitation Counseling During the COVID-19 Pandemic
Authors: Pebdani Roxanna N. , Zeidan Adriana M. , Fearn-Smith Erin M. , Matthews Lynda R.
Published: August 2022

Research has shown that using telehealth for rehabilitation assessment can be an effective approach. The COVID-19 pandemic and subsequent lockdowns led to many rehabilitation counselors pivoting to telehealth assessment with their clients. This study explores rehabilitation counselors’ use of rehabilitation assessments and telehealth since the beginning of the COVID-19 pandemic. Using a mixed-methods approach, the researchers analyzed data from 41 rehabilitation counselors across Australia. Participants were asked which measures they used prior to the pandemic, how their use of the measures changed during telehealth, and how their work changed due to the COVID-19 pandemic. The Depression Anxiety Stress Scales and the Occupational Search Inventory were the most commonly used tests. Theoretical analysis demonstrated that participants utilized tests based on their usefulness in comprehensive assessment and rehabilitation planning, for engaging the client in the assessment process, out of necessity (mandated tests), and due to attributes of the test the counselor valued. Participants described the impact of COVID-19 on assessment practice, demonstrating that despite challenges to telehealth, there were also benefits and that assessment measures could be altered for use in telehealth. Although telehealth had an impact on how rehabilitation counselors provided assessments, many found ways to make it work at a distance.