bluuebunny/crossref_metadata_2025 huggingface dataset:
Construction and Application of Rehabilitation Training System Based on Intelligent Measurement and Control
Authors: Zhao Nana , Han Xiao , Chen Long
Published: May 2022

Rehabilitation training plays a very important role in the treatment of movement disorders. In order to meet the needs of modern rehabilitation training, this paper designs a rehabilitation training system based on intelligent measurement and control. The system realizes software and data construction through C/S architecture and B/S architecture. The intelligent measurement and control technology is used to identify and compare the patient’s movements, obtain the matching degree of the movements, correct the movements, and evaluate the training results. The rehabilitation system is constructed through the customized subsystem of rehabilitation specialist, patient training subsystem, and background management subsystem. Through experimental tests, the conclusions are drawn: first, the intelligent measurement and control algorithm has good convergence speed and network generalization ability. The average correct rate is 93.9% for the patient group and 94.5% for the healthy people, and the output results obtained are close to the ideal value. Second, the pressure test results show that the system can run stably when the concurrency is 100, 200, 500, and 1000. Third, the software design of the rehabilitation training system can meet the requirements in terms of usability, the accuracy rate is above 80%, and the user satisfaction is high. Fourth, compared with before training, the time for subjects to maintain balance during and after training increased significantly, indicating that the rehabilitation exercise training system has the ability to train standing static balance, and the training effect is significant. The system improves the effect and efficiency of rehabilitation training and has broad application prospects in the field of limb rehabilitation.
Finite-Buffer M/G/1 Queues with Time and Space Priorities
Authors: Kim Kilhwan
Published: May 2022

Many communication systems have finite buffers and service delay-sensitive and loss-sensitive types of traffic simultaneously. To meet the diverse QoS requirements of these heterogeneous types of traffic, it is desirable to offer delay-sensitive traffic time priority over loss-sensitive traffic, and loss-sensitive traffic space priority over delay-sensitive traffic. To analyze the performance of such systems, we study a finite-buffer M/G/1 priority queueing model where nonpreemptive time priority is given to delay-sensitive traffic and push-out space priority is given to loss-sensitive traffic. Compared to the previous study on finite-buffer M/M/1 priority queues with time and space priority, where service times are identical and exponentially distributed for both types of traffic, in our model we assume that service times are different and are generally distributed for different types of traffic. As a result, our model is more suitable for the performance analysis of communication systems accommodating multiple types of traffic with different service-time distributions. For the proposed queueing model, we derive the queue-length distributions, loss probabilities, and mean waiting times of both types of traffic, as well as the push-out probability of delay-sensitive traffic. With numerical examples, we also explore how the performance measures are affected by system parameters such as the buffer size, and the arrival rates and mean service times of both types of traffic for different service-time distributions.
A Novel Model to Detect and Classify Fresh and Damaged Fruits to Reduce Food Waste Using a Deep Learning Technique
Authors: Kumar T. Bharath , Prashar Deepak , Vaidya Gayatri , Kumar Vipin , Kumar S. Deva , Sammy F.
Published: May 2022

Due to a lack of efficient measures for dealing with food waste at many levels, including food supply chains, homes, and restaurants, the world’s food supply is shrinking at an alarming pace. In both homes and restaurants, overcooking and other factors are to be blamed for the majority of food that is wasted. Families are the primary source of food waste, and we sought to reduce this by identifying fresh and damaged food. In agriculture, the detection of rotting fruits becomes crucial. Despite the fact that people routinely classify healthy and rotten fruits, fruit growers find it ineffective. In contrast to humans, robots do not grow tired from doing the same thing again and again. Because of this, finding faults in fruits is a declared objective of the agricultural business in order to save labour, waste, manufacturing costs, and time spent on the process. An infected apple may infect a healthy one if the defects are not discovered. Food waste is more likely to occur as a consequence of this, which causes several problems. Input images are used to identify healthy and deteriorated fruits. Various fruits were employed in this study, including apples, bananas, and oranges. For classifying photographs into fresh and decaying fruits, softmax is used, while CNN obtains fruit image properties. A dataset from Kaggle was used to evaluate the suggested model’s performance, and it achieved a 97.14 percent accuracy rate. The suggested CNN model outperforms the current methods in terms of performance.
Application Research of Tooth Arrangement Based on Rotation Matrix Calculation and Resistance Detection in Oral
Authors: Wu Mingming
Published: May 2022

The goal of this research was to provide a new approach for analyzing orthodontic teeth arrangement inside oral depending on the rotation matrix computation and resistance detection. The present method includes the following operations within a certain therapy period: first three-dimensional positions of the tooth were evaluated with a pierced laser beam and a three-dimensional system of surface-scanning. Second, the three-dimensional shape data was automatically registered at maxillary 1st molars, and methods of coordinate had been normalized. Third, a translation vector and rotation matrix had been evaluated from automatic registration of two position data of a particular tooth. Fourth, the limited spiral axes of teeth had been measured as the zero rotational dislocation locus; and impressions for a model of the dental cast had been taken at five different points: shortly before and after device was fitted, and ten days, one month, and two months after the treatment started. The results showed that existing analysis approach could more quickly classify a specific tooth's movement by spinning all over and translating along a finite helical axis. It can provide statistical visual three-dimensional data on complex tooth arrangement throughout orthodontic therapy.
A Stock Selection Model of Image Classification Method Based on Convolutional Neural Network
Authors: Li Pengfei , Xu Jungang , Li Keyao
Published: May 2022

With the development of artificial intelligence technology, an increasing number of researchers try to apply different machine learning and deep learning methods to quantitative trading fields to obtain more stable and efficient trading models. As a typical quantitative trading strategy, stock selection has also attracted a lot of attention. There are many studies and applications on stock selection. However, the existing research and application cannot meet the continuous expansion of the scale and dimension of stock selection data set and cannot meet the needs in terms of efficiency and accuracy of stock selection. A convolutional neural network has been applied to image classification and achieved better results than the traditional methods. In this study, we first constructed a multifactor stock selection data set based on China’s stock market. Then, we apply the convolutional neural network model to analyze stock selection data and select stocks. The main contribution of this study is that we build a stock multifactor data set, construct a “factor picture,” and classify them by convolutional neural network to select stocks. This study also makes comparative experiments on the decision tree, support vector machine, and feedforward neural network in stock selection on the same data set constructed in this study. The results show that the stock selection method based on the convolutional neural network outperforms other methods in terms of the annual return, sharp ratio, and max drawdown.
Method and Application of Urban 3D Rapid Modeling of Geology Based on CAD Borehole Logs
Authors: Zhang Xuefeng , Chen Cheng , Xu Zifei , Li Hong
Published: May 2022

Urban 3D geological modeling is one of the key technologies of geological data visualization and spatial analysis. Aiming at the problems of cumbersome modeling process, low modeling efficiency, and accuracy of traditional modeling methods based on borehole data, in this paper, a fast 3D geological modeling method based on CAD borehole data is proposed. The method first designed a method for rapidly extracting borehole log information which is based on .Net API (c#). On this basis, knowledge-driven 3D geological modeling method based on profile data is employed. With these methods, the borehole data in CAD format can be converted to structured data, which can be directly used for data analysis, and the geological knowledge can be analyzed and integrated into the process of three-dimensional geological modeling. The feasibility of this method is verified using the CAD borehole data in the West District of Kaifeng City as an example. The results show that this method can not only realize the rapid construction of urban 3D geological model but also improve the accuracy of 3D geological model.
Clinicopathological and Prognostic Value of Necroptosis-Associated lncRNA Model in Patients with Kidney Renal Clear Cell Carcinoma
Authors: Gu Jun , He Zexi , Huang Yinglong , Luan Ting , Chen Zhenjie , Wang Jiansong , Ding Mingxia
Published: May 2022

Background. Necroptosis, a recently identified type of programmed necrotic cell death, is closely related to the tumorigenesis and development of cancer. However, it remains unclear whether necroptosis-associated long noncoding RNAs (lncRNAs) can be used to predict the prognosis of kidney renal clear cell carcinoma (KIRC). This work was designed to probe the possible prognostic worth of necroptosis-associated lncRNAs along with their impact on the tumor microenvironment (TME) in KIRC. Methods. The Cancer Genome Atlas (TCGA) database was used to extract KIRC gene expression and clinicopathological data. Pearson correlation analysis was used to evaluate necroptosis-associated lncRNAs against 159 known necroptosis-associated genes. To define molecular subtypes, researchers used univariate Cox regression analysis and consensus clustering, as well as clinical significance, TME, and tumor immune cells in each molecular subtype. We develop the necroptosis-associated lncRNA prognostic model using univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis. Patients were divided into high- and low-risk groups according to prognostic model. Moreover, comprehensive analyses, including prognostic value, gene set enrichment analysis (GSEA), immune infiltration, and immune checkpoint gene expression, were performed between the two risk groups. Finally, anticancer drug sensitivity analyses were employed for assessing associations for necroptosis-associated lncRNA expression profile and anticancer drug chemosensitivity. Results. Through univariate analysis, sixty-nine necroptosis-associated lncRNAs were found to have a significant relationship with KIRC prognosis. Two molecular clusters were identified, and significant differences were found with respect to clinicopathological features and prognosis. The segregation of patients into two risk groups was done by the constructed necroptosis-associated lncRNA model. The survival prognosis, clinical features, degree of immune cell infiltration, and expression of immune checkpoint genes of high-risk and low-risk groups were all shown to vary. Conclusions. Our study identified a model of necroptosis-associated lncRNA signature and revealed its prognostic role in KIRC. It is expected to provide a reference for the screening of KIRC prognostic markers and the evaluation of immune response.
Human Resource Management of Energy Companies Based on Big Data Analysis
Authors: Gui Peng , Zhang Min
Published: May 2022

Human resource management mode refers to a comprehensive summary of management objectives, processes, content, methods, and other elements. The more common two modes are control mode and commitment mode. The enterprise human resource management model has many different types. The generation pair promotes the development of enterprise human resource management from the traditional model to the platform model, processing complex data with the help of data-based technical means, and realizing the integration and sharing of resource data. This paper takes an energy company as an example to carry out a detailed study. The article takes the big data as the background and the company as the research object. From the perspective of human resource management, this paper tries to find out the performance management, compensation and benefits management, and other issues of the company in human resource management under the background of the big data era and puts forward corresponding solutions for the current problems. In particular, the company gave certain opinions on how to build a human resource management system in the context of the current big data era. By conducting field research on the company and issuing questionnaires, this paper finds out the current problems of the company in human resource management and proposes corresponding solutions for these problems.
Extracellular Vesicles Carrying miR-887-3p Promote Breast Cancer Cell Drug Resistance by Targeting BTBD7 and Activating the Notch1/Hes1 Signaling Pathway
Authors: Wang Bing , Wang Yueping , Wang Xuedong , Gu Juan , Wu Wenyong , Wu Huaiguo , Wang Qingping , Zhou Daoping
Published: May 2022

Objective. Chemoresistance remains the primary reason threatening the prognosis of breast cancer (BC) patients. Extracellular vesicles (EVs) contribute to chemoresistance by carrying microRNAs (miRNAs). This study investigated the mechanism of miR-887-3p mediated by EVs in BC cell drug resistance. Methods. MDA-MB-231-derived EVs were extracted and identified. BC cells were treated with different concentrations of doxorubicin, cisplatin, and fulvestrant, and the cell survival was evaluated. PKH26-labeled EVs were cocultured with BC cells, and the uptake of EVs was observed. miR-887-3p expression in BC cells and EVs was detected. After silencing miR-887-3p in MDA-MB-231 cells, BC cells were treated with EV-inhi to observe drug resistance. The target gene of miR-887-3p was predicted and verified. The levels of downstream Notch1/Hes1 pathway were detected. Xenograft experiment was conducted to evaluate the effect of EVs on the growth and drug resistance in vivo. Results. MDA-MB-231-derived EVs enhanced the drug resistance of BC cells. EVs carried miR-887-3p into BC cells. miR-887-3p expression was elevated in BC cells and EVs. miR-887-3p inhibition reduced the drug resistance of BC cells. miR-887-3p targeted BTBD7. Overexpression of BTBD7 partially reversed the drug resistance of BC cells caused by EV treatment. EV treatment increased the level of Notch1/Hes1, and overexpression of BTBD7 decreased the level of Notch1/Hes1. In vivo experiments further validated the results of in vitro experiments. Conclusion. EVs carrying miR-887-3p could target BTBD7 and activate the Notch1/Hes1 signaling pathway, thereby promoting BC cell drug resistance. This study may offer novel insights into BC treatment.
Research on the Development Model of Rural Tourism Based on Multiobjective Planning and Intelligent Optimization Algorithm
Authors: Yang Lu , Dong Xiaoying
Published: May 2022

In order to improve the effect of rural tourism development, this study combines multiobjective planning and intelligent optimization algorithms to analyze the development mode of rural tourism and further deals with the problem of multisensor target tracking under unknown input interference conditions by designing a tourism network consensus algorithm. The algorithm adopts a distributed multisensor fusion structure combined with consensus estimation, and each sensor first performs two-level information filtering estimation and unknown input parameter estimation locally. The experimental research results show that the rural tourism development model based on multiobjective planning and intelligent optimization algorithm proposed in this study can play an important role in the development of rural tourism.
Analysis of Eccentricity-Based Topological Invariants with Zero-Divisor Graphs
Authors: Hui Zhi-hao , Rauf Abdul , Abbas Muhammad Mohsin , Aslam Adnan
Published: May 2022

Let

R
=

Z

♭

1

♭

2

♭

3

×

Z

q

2

be a commutative ring, where

♭

1

,

♭

2

,

♭

3

are distinct primes, and

q

is any prime integer. A zero divisor graph

J

R

of ring

R

is a graph with vertex set consist of zero divisors elements of

R

and any two vertices

a
,
b

are adjacent if and only if

a
b
=
0

. A topological index is a numerical number associated with the graph and may be helpful to correlate the graph with certain of its physical/chemical properties. In this paper, we have computed some eccentricity based topological indices of

J

R

, namely, atom-bond connectivity index (

AB

C

5

), eccentricity-based harmonic index of fourth type (

H

4

J

), geometric-arithmetic eccentricity index (

G

A

4

J

), eccentricity-based third Zagreb index, and eccentricity-based first Zagreb index.
Similar Classification Algorithm for Educational and Teaching Knowledge Based on Machine Learning
Authors: Li Yuping
Published: May 2022

From ancient times, machines did adhere to the commands that a human or a user prepared. According to the program, the machines are controlled by implementing machine learning (ML). It plays a significant part in the development of information technology (IT) companies and the rise of the education system. Using stored memories, people learn new things, making them feel better than before. Machines are pretty different from human knowledge. Instead of using memory power, they use statistical comparison to analyze the data. Here, the amount of data is stored in a database, and according to the reaction received from the user, it gets additional data to create new data. For example, once a person hears music using the application, they will hear repeated music before further entry. In this case, the application is working based on the machine learning algorithm. First, it collects the information from the user, and then, it uses the same information (data) to make the user’s work more efficient when they return. The existing system like Support Vector Machine (SVM) and learning management system approaches the necessity and development of the higher education system using machine learning algorithms. This proposed system focuses on classifying education and teaching knowledge by implementing the machine learning-based similar classification algorithm (ML-SCA). ML-SCA focuses on classifying similar teaching videos and the recommendations to improve the teaching and academic knowledge for the teachers and the students. ML-SCA is compared with the existing neural network and

K

-means algorithms. Based on the efficiency results, it is observed that the proposed ML-SCA has achieved 92% higher than the existing algorithms.
Blueberry Anthocyanins Extract Attenuates Acrylamide-Induced Oxidative Stress and Neuroinflammation in Rats
Authors: Fang Zizhuang , Luo Yinghua , Ma Chen , Dong Li , Chen Fang
Published: May 2022

Acrylamide (AA) is a widespread environmental and dietary-derived neurotoxin, which can induce oxidative stress and associated inflammation in the brain. Anthocyanins widely occur as natural antioxidant and anti-inflammatory phytochemicals. Herein, the protective effects of blueberry anthocyanins extract (BAE) against AA-induced neurotoxicity were investigated in rats. The rats were pretreated with BAE (175 mg/kg body weight/day) by oral gavage for the first 7 days, followed by the co-administration of BAE and AA (35 mg/kg body weight/day) by oral gavage for the next 12 days. Results showed that BAE significantly decreased the malondialdehyde (MDA) production, and increased glutathione (GSH) and antioxidant enzyme levels; and it also suppressed microglial activation, astrocytic reaction, and pro-inflammatory cytokine expressions. Furthermore, BAE elevated the extracellular signal-related kinase (ERK)/cAMP response elements binding protein (CREB)/brain-derived neurotrophic factor (BDNF) pathway, and relieved the accumulation of amyloid beta (Aβ) 1-42 and 1-40 after AA exposure. Consequently, AA-induced neuronal necrosis and downregulation of synaptosomal-associated protein 25 (SNAP-25) were attenuated by BAE in the hippocampus and cerebral cortex. In conclusion, BAE can exert a protective function on neurons and synapses against AA-induced oxidative stress and neuroinflammation.
Study on Coupling and Coordinated Development of Ecological Environment-Energy Consumption-Regional Economic Growth in Greater Bay Area around Hangzhou Bay
Authors: Huo Yan
Published: May 2022

Environment, energy, and economy often constitute an interrelated and contradictory ternary system. In this study, Greater Bay Area around Hangzhou Bay Area is taken as the main research object, and Guangdong-Hong Kong-Macao Greater Bay Area is taken as the reference object. By constructing the index system of 3E system coordination evaluation model, the coordination degree and coordination status of ecological environment, energy consumption, and economic growth subsystems of the two areas are compared and analyzed, and the coupling relationship between 2E and 3E systems of Greater Bay Area around Hangzhou Bay and Guangdong-Hong Kong-Macao Greater Bay Area is further studied. The results show that the eco-environmental coordination degree and energy consumption coordination degree of the cities in Greater Bay Area around Hangzhou Bay are between 0.4248–0.5668 and 0.4528–0.5874, respectively, which are in the state of weak coordination to primary coordination, while the economic growth coordination degree is between 0.5022 and 0.6878, which is in the state of primary coordination to intermediate coordination. The coupling of 2E system in Greater Bay Area around Hangzhou Bay and Guangdong-Hong Kong-Macao Greater Bay Area shows linear growth and nonlinear growth, respectively (R2 = 0.997), presenting a good coupling development overall. The coupling levels of ecological environment-energy consumption, ecological environment-economic growth, and energy consumption-economic growth in Greater Bay Area around Hangzhou Bay increase by 18.94, 86.72, and 98.20% respectively. The coupling of 3E systems between Greater Bay Area around Hangzhou Bay and Guangdong-Hong Kong-Macao Greater Bay Area presents a nonlinear development trend that is increasing year by year (R2 > 0.99), but the coupling level of 3E systems between the two is still low, both of which are in the primary/weak coordination state, while the overall growth rate of 3E system coupling in Greater Bay Area around Hangzhou Bay is higher than that of Guangdong-Hong Kong-Macao Greater Bay Area.
An AOA and Orientation Angle-Based Localization Algorithm for Passive RFID Tag Array
Authors: Xie Liangbo , Ren Yan , Wang Yong , Nie Wei , Zhou Mu
Published: May 2022

This paper proposes an indoor localization algorithm based on AOA (Arrive of Angle) and orientation angle (OA) for passive UHF RFID (Ultra-High Frequency Radio Frequency Identification) tag array. By utilizing a uniform linear tag array (ULA), the AOA, which is defined as the angle of the reader relative to the tag array, can be estimated by the Multiple Signal Classification (MUSIC) algorithm. The relationship between AOA, OA of the tag array, and the direction angle (DA) between each reader antenna to the geometric center of the tag array is analyzed, and an OA retrieval method based on rotating the coordinate system is proposed to calculate DA. The tag array localization is finally acquired by the DA of each antenna. Simulation and experiment results show that the proposed algorithm can achieve decimeter-level localization accuracy. The proposed algorithm achieves a mean accuracy of 0.216 m, which is a significant improvement compared with the traditional AOA localization algorithm.
Application of Internet of Things Technology in Student Management Evaluation System
Authors: Zhu Jiaming , Li Zhi , Fu Jing , He Fangcheng , Mou Xiaoling , Wu Pengjv
Published: May 2022

In modern society, the rapid development of the knowledge economy makes education become the core resource of a country’s economic and social modernization development. Under this circumstance, student management has been paid more and more attention by people. This paper aims to study how to use the internet of things technology to study the student management evaluation system. This paper proposes a data mining algorithm based on the internet of things technology and proposes a decision tree method and an association rule algorithm based on the data mining algorithm. The experimental results of this paper show that the student growth rate in 2018 increased from 10% to 33% in 2019 and then from 21% in 2020 to 35% in 2021. It can be seen that with the development of the economy, the enrollment rate is also increasing, and the number of people who can go to college has increased rapidly. This also increases the difficulty of student management, making teachers’ work more arduous, and the complexity of the amount of information leads to a drop in the efficiency of student management. However, the student management evaluation system based on the internet of things technology is beneficial to solving this problem.
Attribute Reduction Algorithm on Concept Lattice and Application in Smart City Energy Consumption Analysis
Authors: Wang Can , Bo Yu , Xu Chunming
Published: May 2022

With the economic development in recent years, smart cities have also risen. Smart city is based on the next generation of innovation in the knowledge society, and the use of new generation information technology in all areas of the city is conducive to the improvement of the quality of urbanization and the quality of citizens’ lives. This article aims to study the significance of the reduction algorithm in the energy consumption analysis of smart cities. This article puts forward the problem of how to solve the problem of huge energy consumption in smart cities. In smart cities, the related objects that generate energy consumption mainly include industry, agriculture, and transportation construction. People’s lives are ubiquitous in the generation of energy consumption. This paper analyzes the energy consumption of smart cities through the reduction algorithm and puts forward a plan on how to reduce the energy consumption of smart cities, which is meaningful for the rational use of resources and reducing the cost of smart cities.
A Globe Calibration Method for Optical Multisensor in 3D Complex Surface Measurement System
Authors: Guo Yanyan , Che Xiangqian , Meng Xiangli , Bian Li
Published: May 2022

There are few existing omnipotent sensors that handle a complex surface inspection task in an accurate and effective way. The prevailing solution is integrating multiple sensors and taking advantage of their strengths. One key task is the extrinsic parameter calibration (global calibration) of the multiple sensors before measurement. This paper proposes a method of optimal extrinsic calibration for a structured light sensor (SLS) and conoscopic holography sensor (CHS). In adopting this method, a common planar calibration board is placed with different poses in front of the multisensory system, and the extrinsic calibration problem is solved through a three-dimensional reconstruction of the calibration board and using geometric constraints of the views from the SLS and CHS. This calibration method, which uses only the plane calibration board, is simple. Physical experiments demonstrate that the proposed method is robust and accurate in the calibration of multiple inhomogeneous optical sensors for the measurement of a complex surface.
Terminal Complement Pathway Deficiency in an Adult Patient with Meningococcal Sepsis
Authors: Staels F. , Meersseman W. , Stordeur P. , Willekens K. , Van Loo S. , Corveleyn A. , Meyts I. , Meyfroidt G. , Schrijvers R.
Published: May 2022

The complement system is an essential part of our innate immune system. Three enzymatic activation pathways are described, all converging into a common terminal pathway which causes lysis of the target cell. Late complement deficiencies (LCDs) are typically diagnosed in children or adolescents with invasive meningococcal disease (IMD). However, IMD can also be a first manifestation in adulthood and should prompt for the evaluation of the LCD. We report the case of a young adult with IMD who was found to have a LCD, caused by a compound heterozygous mutation in C6. His vaccination status was optimized and prophylactic antibiotic treatment was initiated. By means of this case, we would like to raise awareness of underlying LCD in (young) adults presenting with IMD by N. meningitidis. Screening for complement deficiencies after IMD, followed by genetic testing, can be lifesaving and allows for genetic counselling. In addition, we discuss the diagnosis and treatment of LCD.
Supervised Computer-Aided Diagnosis (CAD) Methods for Classifying Alzheimer’s Disease-Based Neurodegenerative Disorders
Authors: Gupta Suneet , Saravanan V. , Choudhury Amarendranath , Alqahtani Abdullah , Abonazel Mohamed R. , Babu K. Suresh
Published: May 2022

Alzheimer’s disease is incurable at the moment. If it can be appropriately diagnosed, the correct treatment can postpone the patient’s illness. To aid in the diagnosis of Alzheimer’s disease and to minimize the time and expense associated with manual diagnosis, a machine learning technique is employed, and a transfer learning method based on 3D MRI data is proposed. Machine learning algorithms can dramatically reduce the time and effort required for human treatment of Alzheimer’s disease. This approach extracts bottleneck features from the M-Net migration network and then adds a top layer to supervised training to further decrease the dimensionality and delete portions. As a consequence, the transfer network presented in this study has several advantages in terms of computational efficiency and training time savings when used as a machine learning approach for AD-assisted diagnosis. Finally, the properties of all subject slices are combined and trained in the classification layer, completing the categorization of Alzheimer’s disease symptoms and standard control. The results show that this strategy has a 1.5 percentage point better classification accuracy than the one that relies exclusively on VGG16 to extract bottleneck features. This strategy could cut the time it takes for the network to learn and improve its ability to classify things. The experiment shows that the method works by using data from OASIS. A typical transfer learning network’s classification accuracy is about 8% better with this method than with a typical network, and it takes about 1/60 of the time with this method.
Autoregressive Linguistic Steganography Based on BERT and Consistency Coding
Authors: Zheng Xiaoyan , Wu Hanzhou
Published: May 2022

Linguistic steganography (LS) conceals the presence of communication by embedding secret information into a text. How to generate a high-quality text carrying secret information is a key problem. With the widespread application of deep learning in natural language processing, recent algorithms use a language model (LM) to generate the steganographic text, which provides a higher payload compared with many previous arts. However, the security still needs to be enhanced. To tackle this problem, we propose a novel autoregressive LS algorithm based on BERT and consistency coding, which achieves a better trade-off between embedding payload and system security. In the proposed work, based on the introduction of the masked LM, given a text, we use consistency coding to make up for the shortcomings of block coding used in the previous work so that we can encode arbitrary-size candidate token set and take advantage of the probability distribution for information hiding. The masked positions to be embedded are filled with tokens determined by an autoregressive manner to enhance the connection between contexts and therefore maintain the quality of the text. Experimental results have shown that compared with related works, the proposed work improves the fluency of the steganographic text while guaranteeing security and also increases the embedding payload to a certain extent.
Relative Prevalence and Associated Factors of Complex Regional Pain Syndrome Type I in Patients with Radial Head Fractures Treated with Open Reduction and Internal Fixation: A Cross-Sectional Study
Authors: Jia Siming , Ren Chuan , Shi Xiaoying , Shi Tailong , Sun Dacheng , Zhang Yuqin , Ding Kai , Du Hao , Zhu Yanbin , Chen Wei
Published: May 2022

Objective. This cross-sectional study aimed to examine the incidence and associated factors of complex regional pain syndrome type I (CRPS I) in patients who underwent open reduction and internal fixation (ORIF) for radial head fractures. Methods. The study enrolled 601 radial head fracture patients treated with ORIF, 523 of which completed the 1-year follow-up. The incidence of CRPS I in those patients was assessed using the Budapest criteria. Patients were then divided into 2 groups: patients with CRPS I (n = 28) and patients without CRPS I (n = 495). The patients’ demographic and clinical data before the operation were prospectively collected by our team. Independent t-tests and χ2 tests were used as univariate analyses to compare the demographic and clinical data between the two groups. Meanwhile, multivariate regression analysis was conducted to identify the associated risk factors for CRPS I. Results. The incidence of CRPS I in patients with radial head fractures treated with ORIF was 5.5% during the first year following surgery. Significant differences were observed in age, gender, type of trauma, modified Mason Classification, and depressive personality disorders. The logistic regression analysis revealed that the female gender, modified Mason type III fractures, and depressive patients were significantly more likely to develop CRPS I (

p
=
0.021

, 0.023, and 0.025, respectively). Conclusions. The incidence of CRPS I among radial head fracture patients undergoing ORIF was 5.5%. In addition, early detection of CRPS I and providing adequate intervention will likely result in greater benefits for those patients.
Analysis and Simulation of Fractional Order Smoking Epidemic Model
Authors: Ahmad Aqeel , Farman Muhammad , Ghafar Abdul , Inc Mustafa , Ahmad Mohammad Ozair , Sene Ndolane
Published: May 2022

In recent years, there are many new definitions that were proposed related to fractional derivatives, and with the help of these definitions, mathematical models were established to overcome the various real-life problems. The true purpose of the current work is to develop and analyze Atangana-Baleanu (AB) with Mittag-Leffler kernel and Atangana-Toufik method (ATM) of fractional derivative model for the Smoking epidemic. Qualitative analysis has been made to `verify the steady state. Stability analysis has been made using self-mapping and Banach space as well as fractional system is analyzed locally and globally by using first derivative of Lyapunov. Also derive a unique solution for fractional-order model which is a new approach for such type of biological models. A few numerical simulations are done by using the given method of fractional order to explain and support the theoretical results.
Eight-Year Depressive Symptom Trajectories and Incident Stroke: A 10-Year Follow-Up of the HRS (Health and Retirement Study)
Authors: Soh Yenee , Tiemeier Henning , Kawachi Ichiro , Berkman Lisa F. , Kubzansky Laura D.
Published: May 2022

Background:

Evidence suggests a link between depressive symptoms and risk of subsequent stroke. However, most studies assess depressive symptoms at only one timepoint, with few examining this relationship using repeatedly measured depressive symptoms. This study aimed to examine the relationship between depressive symptom trajectories and risk of incident stroke.

Methods:

This prospective cohort included 12 520 US individuals aged ≥50 years enrolled in the Health and Retirement Study, free of stroke at study baseline (1998). We used the 8-item Center for Epidemiologic Studies Depression scale to assess depressive symptoms (high defined as ≥3 symptoms; low <3 symptoms) at 4 consecutive, biennial timepoints from 1998 to 2004. We assigned individuals to 5 predefined trajectories based on their scores at each timepoint (consistently low, decreasing, fluctuating, increasing, and consistently high). Using self-reported doctors’ diagnoses, we assessed incident stroke over a subsequent 10-year period from 2006 to 2016. Cox regression models estimated the association of depressive symptom trajectories with risk of incident stroke, adjusting for demographics, health behaviors, and health conditions.

Results:

During follow-up, 1434 incident strokes occurred. Compared with individuals with consistently low symptoms, individuals with consistently high depressive symptoms (adjusted hazard ratio, 1.18 [95% CI, 1.02–1.36]), increasing symptoms (adjusted hazard ratio, 1.31 [95% CI, 1.10–1.57]), and fluctuating symptoms (adjusted hazard ratio, 1.21 [95% CI, 1.01–1.46]) all had higher hazards of stroke onset. Individuals in the decreasing symptom trajectory group did not show increased stroke risk.

Conclusions:

Depressive symptom trajectories characterized by high symptoms at multiple timepoints were associated with increased stroke risk. However, a trajectory with depressive symptoms that started high but decreased over time was not associated with higher stroke risk. Given the remitting-relapsing nature of depressive symptoms, it is important to understand the relationship between depressive symptoms and stroke risk over time through repeated assessments.
Characterizing Reasons for Stroke Thrombectomy Ineligibility Among Potential Candidates Transferred in a Hub‐and‐Spoke Network
Authors: Regenhardt Robert W. , Awad Amine , Kraft Andrew W. , Rosenthal Joseph A. , Dmytriw Adam A. , Vranic Justin E. , Bonkhoff Anna K. , Bretzner Martin , Etherton Mark R. , Hirsch Joshua A. , Rabinov James D. , Singhal Aneesh B. , Rost Natalia S. , Stapleton Christopher J. , Leslie‐Mazwi Thabele M. , Patel Aman B.
Published: May 2022

Background

Access to endovascular thrombectomy (EVT) is relatively limited. Hub‐and‐spoke networks seek to transfer appropriate large‐vessel occlusion stroke candidates to EVT‐capable hubs. However, some patients are ineligible upon hub arrival, and factors that drive transfer inefficiencies are not well described. We sought to quantify EVT transfer efficiency and identify reasons for EVT ineligibility.

Methods

Consecutive EVT candidates presenting to 25 spokes from 2018 to 2020 with pretransfer computed tomography angiography‐defined large‐vessel occlusion and Alberta Stroke Program Early Computed Tomography Score of ≥6 were identified from a prospectively maintained database. Outcomes of interest included hub EVT, reasons for EVT ineligibility, and 90‐day modified Rankin scale score of ≤2.

Results

Among 258 patients, the median age was 70 years (interquartile range, 60–81 years); 50% were women. A total of 56% were ineligible for EVT after hub arrival. Cited reasons were large established infarct (49%), mild symptoms (33%), recanalization (6%), distal occlusion (5%), subocclusive lesion (3%), and goals of care (3%). Late window patients (last known well >6 hours) were more likely to be ineligible (67% versus 43%;
P
<0.0001). EVT‐ineligible patients were older (73 versus 68 years;
P
=0.04), had lower National Institutes of Health Stroke Scale score (10 versus 16;
P
<0.0001), had longer last known well‐to‐hub arrival time (8.4 versus 4.6 hours;
P
<0.0001), had longer spoke Telestroke consult‐to‐hub arrival time (2.8 versus 2.2 hours;
P
<0.0001), and received less intravenous thrombolysis (32% versus 45%;
P
=0.04) compared with eligible patients. EVT ineligibility independently reduced the odds of 90‐day modified Rankin scale score of ≤2 (adjusted odds ratio, 0.26; 95% CI, 0.12–0.56;
P
=0.001) when controlling for age, National Institutes of Health Stroke Scale score, and last known well‐to‐hub arrival time.

Conclusions

Among patients transferred for EVT, there are multiple reasons for ineligibility upon hub arrival, with most excluded for infarct growth and mild symptoms. Understanding factors that drive transfer inefficiencies is important to improve EVT access and outcomes.
Hippocampal Contribution to Probabilistic Feedback Learning: Modeling Observation- and Reinforcement-based Processes
Authors: Patt Virginie M. , Palombo Daniela J. , Esterman Michael , Verfaellie Mieke
Published: May 2022

Simple probabilistic reinforcement learning is recognized as a striatum-based learning system, but in recent years, has also been associated with hippocampal involvement. This study examined whether such involvement may be attributed to observation-based learning (OL) processes, running in parallel to striatum-based reinforcement learning. A computational model of OL, mirroring classic models of reinforcement-based learning (RL), was constructed and applied to the neuroimaging data set of Palombo, Hayes, Reid, and Verfaellie [2019. Hippocampal contributions to value-based learning: Converging evidence from fMRI and amnesia. Cognitive, Affective & Behavioral Neuroscience, 19(3), 523–536]. Results suggested that OL processes may indeed take place concomitantly to reinforcement learning and involve activation of the hippocampus and central orbitofrontal cortex. However, rather than independent mechanisms running in parallel, the brain correlates of the OL and RL prediction errors indicated collaboration between systems, with direct implication of the hippocampus in computations of the discrepancy between the expected and actual reinforcing values of actions. These findings are consistent with previous accounts of a role for the hippocampus in encoding the strength of observed stimulus–outcome associations, with updating of such associations through striatal reinforcement-based computations. In addition, enhanced negative RL prediction error signaling was found in the anterior insula with greater use of OL over RL processes. This result may suggest an additional mode of collaboration between the OL and RL systems, implicating the error monitoring network.
A natural cortical axis connecting the outside and inside of the human brain
Authors: Hilgetag Claus C. , Goulas Alexandros , Changeux Jean-Pierre
Published: May 2022

What structural and connectivity features of the human brain help to explain the extraordinary human cognitive abilities? We recently proposed a set of relevant connectomic fundamentals, some of which arise from the size scaling of the human brain relative to other primate brains, while others of these fundamentals may be uniquely human. In particular, we suggested that the remarkable increase of the size of the human brain due to its prolonged prenatal development has brought with it an increased sparsification, hierarchical modularization, as well as increased depth and cytoarchitectonic differentiation of brain networks. These characteristic features are complemented by a shift of projection origins to the upper layers of many cortical areas as well as the significantly prolonged postnatal development and plasticity of the upper cortical layers. Another fundamental aspect of cortical organization that has emerged in recent research is the alignment of diverse features of evolution, development, cytoarchitectonics, function, and plasticity along a principal, natural cortical axis from sensory (“outside”) to association (“inside”) areas. Here we highlight how this natural axis is integrated in the characteristic organization of the human brain. In particular, the human brain displays a developmental expansion of outside areas and a stretching of the natural axis such that outside areas are more widely separated from each other and from inside areas than in other species. We outline some functional implications of this characteristic arrangement.
Μέσος ὑμῶν ἕστηκεν
Authors: Tolan Daniel J.
Published: May 2022

This article lays out Origen’s anthropological application of John 1:26. In particular, it examines the way in which Origen pairs the phrase “one whom you do not know has stood in your midst (μέσος ὑμῶν)” with the Stoic terminus technicus “governing faculty” (ἡγεμονικόν) through an identification of μέσος with soul. This identification assumes that μέσος refers to the soul and that references to the soul apply to the governing faculty. The former stands upon Origen’s four-fold interpretation of μέσος; the latter is an assumption based in Stoic psychology. This article begins with an examination of how Origen connects μέσος to soul and, subsequently, soul to the governing faculty. Next, it examines Origen’s engagement with John 1:26 in his Commentary on John. Finally, the article discusses the various ways in which Origen interprets this verse in his other works.
Trivariate Analysis of Changes in Drought Characteristics in the CMIP6 Multimodel Ensemble at Global Warming Levels of 1.5°, 2°, and 3°C
Authors: Tabari Hossein , Willems Patrick
Published: May 2022

Drought is a major natural hazard with far-reaching social, economic, and environmental impacts whose characteristics are highly interdependent across different spatial and temporal scales. Traditional global warming impact assessments on drought at the global scale have, however, taken into account only one drought characteristic at a time, likely leading to an underestimation of the overall impact. Here, we perform a trivariate analysis of changes in drought conditions at 1.5°, 2°, and 3°C global warming levels using 25 CMIP6 GCMs. Drought properties are characterized by the Standardized Soil Moisture Index (SSI). The future joint return periods of droughts historically associated with 10-, 20-, and 30-yr return periods are computed under the warming levels using copula functions considering drought duration, peak, and severity. Our comparative assessments of global warming impact on drought properties between univariate and trivariate analyses corroborate the substantial underestimation of the impact by the univariate analysis. The trivariate analysis shows that around 63%–91% of the global land will be subject to more recurrent droughts, while the percentage of the land reduces to 41%–56% for the univariate analysis. The difference between the univariate and trivariate analyses enlarges with global warming levels and the extremity of drought events. Based on the trivariate analysis, a 30-yr drought would become at least threefold more recurrent in 11%, 15%, and 20% of the global land at 1.5°, 2°, and 3°C warming levels, respectively, but the univariate analysis could not reach such large increases in drought conditions.
Storytelling and Deliberative Play in the Oregon Citizens’ Assembly Online Pilot on COVID-19 Recovery
Authors: Black Laura W. , Wolfe Anna W. , Han Soo-Hye
Published: May 2022

This article draws on the deliberative play framework to examine empirical examples of storytelling in an online deliberative forum: The Oregon Citizen Assembly (ORCA) Pilot on COVID-19 Recovery. ORCA engaged 36 citizens in deliberation about state policy through an online deliberative process spanning seven weeks. Drawing on literature on small stories in deliberation, we trace stories related to a policy proposal about paying parents to educate children at home. Our analysis demonstrates that storytelling activities accomplish aspects of deliberative play through introducing uncertainty, resisting premature closure, and promoting an “as if” frame that allows groups to explore the scope and implications of proposals. Forum design influences interaction and our analysis suggests that technology use and timing are key design features that can facilitate or inhibit deliberative play.