syedzayyan/paper_mill_benchmark huggingface dataset:
Analysis of Psychological Changes and Intervention Mechanism of Elderly Groups Based on Deep Learning Analysis Technology

The elderly group is a unique social phenomenon in china.This study analyzes the typology of psychological changes in the elderly group based on the analysis of deep learning techniques and also combines crisis intervention theory to study the intervention strategies of social workers in different stages of emotional changes in the elderly group.A questionnaire survey of the elderly was conducted using the survey method, in which 10,948 valid questionnaires were screened from the psychological condition self-check scale and 11,040 valid questionnaires were screened from the mental health survey questionnaire for the elderly.The degree of negative emotions of the elderly group in public emergencies was not related to age, but significantly correlated with age ( p value &lt; 0.05), and there was a tendency that the higher the age, the deeper the degree; in addition, elderly people of different professions also showed significant differences ( p &lt; 0.05); elderly people of different regions also showed significant differences ( p &lt; 0.05).In crisis intervention, social workers mainly provide services such as initial diagnosis, primary intervention, secondary intervention, and assessment for the caseworkers.The practical study found that social workers need to use strategies such as short-term focal solutions, avoiding guiding clients with their own values; crisis intervention programmes should be flexible, proactively helping clients to rebuild their support network, developing clients' self-solving skills, and implementing interprofessional teamwork and whole-person rehabilitation services.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;
Analysis of Inducing Factors of Chronic Pulmonary Heart Disease Caused by Chronic Obstructive Pulmonary Disease at High Altitude through Epidemiological Investigation under Intelligent Medicine and Big Data

This study explores the risk factors of chronic pulmonary heart disease (cphd) induced by plateau chronic obstructive pulmonary disease (copd) based on intelligent medical treatment and big data of electrocardiogram (ecg) signal.Based on gpu, a wavelet algorithm is introduced to extract features of ecg signal, and it was combined with generalized regression neural network (grnn) to improve classification accuracy.From june 2018 to december 2020, 10,185 patients diagnosed with copd in the plateau area by pulmonary function testing, ecg, and chest x-ray at x hospital are taken as the research objects to evaluate the distribution of cphd incidence at different ages and altitudes.The running time of gtx780ti is about 15 times shorter than that of cpu.The accuracy of n detection based on the gpu-accelerated neural network model reached 98.06%.Accuracy (acc), sensitivity (se), specificity (sp), and positive rate (pr) of v were 99.03%, 89.17%, 98.92%, and 93.18%, respectively.The acc, se, sp, and pr of s were 99.54%, 86.22%, 99.74%, and 92.56%, respectively.The grnn classification accuracy was up to 98%.19% of copd patients were diagnosed with cphd, including 1,409 males (72.82%) and 526 females (36.24%).The highest prevalence of cphd was 64.60% when the altitude was 1,900–2,499 m, and the prevalence was only 2.43% when the altitude was ≥3,500 m.The highest prevalence of cphd was 63.77% at the age of 61–70 years, and the lowest prevalence at the age of 15∼20 years was only 0.26%.Therefore, the gpu-based neural network model improved the classification accuracy of ecg signals.Age and altitude were risk factors for cphd induced by high-altitude copd, which provided a reference for the prevention, diagnosis, and treatment of cphd in high-altitude areas.

Journal: Journal of Healthcare Engineering (2022)
Publication types: Research Article;
An ML-Enabled Internet of Things Framework for Early Detection of Heart Disease

Healthcare occupies a central role in sustainable societies and has an undeniable impact on the well‐being of individuals.However, over the years, various diseases have adversely affected the growth and sustainability of these societies.Among them, heart disease is escalating rapidly in both economically settled and undeveloped nations and leads to fatalities around the globe.To reduce the death ratio caused by this disease, there is a need for a framework to continuously monitor a patient’s heart status, essentially doing early detection and prediction of heart disease.This paper proposes a scalable machine learning (ml) and internet of things‐(io t‐) based three‐layer architecture to store and process a large amount of clinical data continuously, which is needed for the early detection and monitoring of heart disease.Layer 1 of the proposed framework is used to collect data from io t wearable/implanted smart sensor nodes, which includes various physiological measures that have significant impact on the deterioration of heart status.Layer 2 stores and processes the patient data on a local web server using various ml classification algorithms.Finally, layer 3 is used to store the critical data of patients on the cloud.The doctor and other caregivers can access the patient health conditions via an android application, provide services to the patient, and inhibit him/her from further damage.Various performance evaluation measures such as accuracy, sensitivity, specificity, f1‐measure, mcc‐score, and roc curve are used to check the efficiency of our proposed io t‐based heart disease prediction framework.It is anticipated that this system will assist the healthcare sector and the doctors in diagnosing heart patients in the initial phases.

Journal: BioMed Research International (2022)
Publication types: Research Article;
An Intervention Study on Children’s Healthy Joint Attention Skills Based on a Mixed Instructional Approach of DTT and PRT

Joint attention is an important element that influences children’s early development of communication and sociality, and joint attention is more often than not the earliest incipient of their prosocial behavior.Joint attention skills are one of the core deficits of children with autism, and identifying and remediating the core problems of autism is a popular area of interest, with joint attention being the focus of attention.The aim of this study was to investigate whether the combined orientation model of discrete trial teaching (dtt) and pivotal response training (prt) could improve the joint attention skills of children with autism.This study used a cross-behavioral multitest design in a single-subject study with two preschool children with autism as subjects, with the independent variable being joint attention teaching and the dependent variable being the three joint attention skills (eye gaze, following directions, and active display).After the instructional intervention, children with autism showed a significant increase in the correctness of “eye alternation,” “following directions,” and “moving displays.”

Journal: Journal of Healthcare Engineering (2022)
Publication types: Research Article;
An Intelligent Collection System of Big Data in Medical and Health Education Based on the Internet of Things

The internet of medical things has developed rapidly as an important direction in the field of internet of things, especially through the use of the new generation of information technology, theoretical and applied research on medical io t intelligent health management that provides “full-service.” it has become a research hotspot of many universities and research institutions.Therefore, conducting research on intelligent health management in the network of medical things is of great engineering importance and theoretical guidance to improve the level of medical information.For health management in the network of medical things to conduct research around the goal of health management “everything full spectrum for everyone,” analyzed the lack of sharing of health information in current health management, lack of continuous monitoring and management of health indicators, etc., a new “individual-family-community-hospital” four-level intelligent health management service model is proposed, the hardware architecture of intelligent healthcare management and the software maintenance system have been built.Through methods such as real-time multi-source data collection, mobile sensing, cloud computing, multi-network fusion technology, continuous monitoring and intelligent management of health data is realized convenient, fast and efficient.It solves the problems that the existing medical system cannot meet the multi-level health needs, personal data security and privacy protection, etc., it has achieved the goal of real-time interactive health management of regionalization, multi-level and multi-center, and whole-person, whole-process and all-round.

Journal: Journal of Healthcare Engineering (2022)
Publication types: Research Article;
An Empirical Study on the Influencing Factors of the Returning Intention of Overseas Talents

Many scholars have carried out a large number of studies on the factors influencing the intention of returning overseas talents based on different theories.This study integrates two theoretical perspectives at macro and individual levels, puts forward a new theoretical framework for analyzing the intention of returning overseas talents, and constructs a theoretical model including the intention of returning overseas talents, talent ecological environment of the host country, prospect expectation of home country, talent growth expectation, and perceived opportunity.Based on 302 survey samples, we used correlation analysis, confirmatory factor analysis, structural equation, and other methods, and the results show that four variables, including talent ecological environment of the host country, prospect expectation of home country, talent growth expectation, and perceived opportunity, have a significant impact on the intention of returning overseas talents.The research also found that talent growth expectation and perceived opportunity had mediating effects, and the prospect expectation and perceived opportunity of origin had moderating effects, which provides a new research idea for the research on the factors influencing the return intention of overseas talents.

Journal: Discrete Dynamics in Nature and Society (2022)
Publication types: Research Article;
A Training Method for a Sensor-Based Exercise Rehabilitation Robot

In order to solve the problem that the traditional mirror therapy did not take into account the real-time recovery of the affected limb and the training effect was limited, a training method of sports rehabilitation robot based on sensor was proposed.A mirror active rehabilitation training system was proposed, which was composed of four steps including trajectory acquisition of the limb inertial measurement unit (imu), fuzzy adaptive proportion differentiation (pd) control in closed-loop variable domain, muscle force estimation of the surface electromyographic signal (s emg) of the affected limb, and power compensation of the outer ring of the affected limb.The experimental results showed that the sagittal forward flexion angle of the healthy shoulder increased from 0° to 128° at a relatively uniform speed, and the sagittal forward flexion angle of the shoulder was basically consistent with that of the healthy limb after the adaptive power compensation of the affected limb.The calculated trajectory tracking error of the healthy limb controlled by the fuzzy adaptive pd controller in the variable domain was 0.21 ± 1.35 ° .The horizontal backward extension angle of the healthy shoulder joint increased from 0° to 43°, and the following trajectory of the affected limb was roughly consistent with the movement trajectory of the healthy limb.The calculated tracking error of the healthy limb trajectory was 0.39 ± 1.45 ° .It was concluded that the control system could provide the real-time power compensation according to the recovery of the affected limb, give full play to the training initiative of the affected limb, and make the affected limb achieve a better rehabilitation training effect.

Journal: Journal of Sensors (2022)
Publication types: Research Article;
A Three-Dimensional Cement Quantification Method for Decision Prediction of Vertebral Recompression after Vertebroplasty

Proposing parameters to quantify cement distribution and increasing accuracy for decision prediction of vertebroplasty postoperative complication.Methods.Finite element analysis was used to biomechanically assess vertebral mechanics ( n = 51 ) after percutaneous vertebroplasty (pvp) or kyphoplasty (pkp).The vertebral space was divided into 27 portions.The numbers of cement occupied portions and numbers of cement-endplate contact portions were defined as overall distribution number (o dn) and overall endplate contact number (o ep), respectively.And cement distribution was parametrized by o dn and o ep.The determination coefficients of vertebral mechanics and parameters ( r 2 ) can validate the correlation of proposed parameters with vertebral mechanics.Results.O dn and o ep were mainly correlated with failure load ( r 2 = 0.729 ) and stiffness ( r 2 = 0.684 ), respectively.O dn, o ep, failure load, and stiffness had obvious difference between the pvp group and the pkp group ( p &lt; 0.05 ).The regional endplate contact number in the front column is most correlated with vertebral stiffness ( r 2 = 0.59 ) among all regional parameters.Cement volume and volume fraction are not dominant factors of vertebral augmentation, and they are not suitable for postoperative fracture risk prediction.Conclusions.Proposed parameters with high correlation on vertebral mechanics are promising for clinical utility.The o dn and o ep can strongly affect augmented vertebral mechanics thus is suitable for postoperative fracture risk prediction.The parameters are beneficial for decision-making process of revision surgery necessity.Parametrized methods are also favorable for surgeon’s preoperative planning.The methods can be inspirational for clinical image recognition development and auxiliary diagnosis.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
A Study Using Power Cycling on the Affective Responses of a Low-Volume High-Intensity Interval Training to Male Subjects with Type 2 Diabetes in Different Physical Activity Status

High-intensity interval training (hiit) has been shown in studies to enhance glucose management and cardiovascular well-being in patients with type 2 diabetes.In this study, we used power cycling to assess the physical activity levels of men with type 2 diabetes during a single low-volume hiit session.First, fifty-six men with type 2 diabetes volunteered to take part in the study, and they were split into two groups based on the international physical activity scale short form (ipa).To the first 1–4 labor bouts, both the sufficiently physically active and insufficiently physically active groups exhibited equal positive emotional reactions ( p &gt; 0.05 ).However, over time (about 5–10 times), both of them showed reduced emotional reactions, with a significant difference ( p &lt; 0.01 ).The insufficiently physically active group had lower mean emotional response, lowest effective response, and maximum effective response values than the sufficiently physically active group ( p &lt; 0.001 ), while the difference in rpe between the two groups was not statistically significant ( p &gt; 0.05 ).From the standpoint of emotional response, the proposed model shows that hiit or reduced volume hiit exercise prescriptions should be utilized with caution in physical activity programs for novices and less active and chronically sick persons.The frequency, intensity, and effects of low-volume hiit on individuals’ emotional reactions and health conditions in the t2dm group are also investigated.Furthermore, this low-volume hiit program can be successfully applied in the real-world setting of people who are not physically active enough or who are chronically unwell.

Journal: Journal of Healthcare Engineering (2021)
Publication types: Research Article;
A Real-Time Crowd Monitoring and Management System for Social Distance Classification and Healthcare Using Deep Learning

Coronavirus born covid-19 disease has spread its roots in the whole world.It is primarily spread by physical contact.As a preventive measure, proper crowd monitoring and management systems are required to be installed in public places to limit sudden outbreaks and impart improved healthcare.The number of new infections can be significantly reduced by adopting social distancing measures earlier.Motivated by this notion, a real-time crowd monitoring and management system for social distance classification is proposed in this research paper.In the proposed system, people are segregated from the background using the yolo v4 object detection technique, and then the detected people are tracked by bounding boxes using the deepsort technique.This system significantly helps in covid-19 prevention by social distance detection and classification in public places using surveillance images and videos captured by the cameras installed in these places.The performance of this system has been assessed using mean average precision (m ap) and frames per second (fps) metrics.It has also been evaluated by deploying it on jetson nano, a low-cost embedded system.The observed results show its suitability for real-time deployment in public places for covid-19 prevention by social distance monitoring and classification.

Journal: Journal of Healthcare Engineering (2022)
Publication types: Research Article;
A Novel Biomarker, FKBP10, for Poor Prognosis Prediction in Patients with Clear Cell Renal Cell Carcinoma

To screen genes associated with poor prognosis of clear cell renal cell carcinoma (cc rcc) from the public databases hpa (human protein atlas), ualcan, and gepia (gene expression profiling interactive analysis) and to investigate the expression of fkbp10 in cc rcc and the effect on prognosis of the patients and the biological behavior of cc rcc cells.Methods.The tumor tissues and adjacent noncancerous tissues of 42 patients with cc rcc diagnosed and treated in our hospital were collected, and the general information of the patients was recorded.Fkbp10 expression in the tissues was determined by q rt-pcr and western blot, and its relationship with general information and prognosis of patients was analyzed.Knockdown or overexpression experiments were carried out with the human proximal tubule epithelial cell line hk-2 and cc rcc cell lines caki-1, 786-o, achn, and a498 to verify the relationship between fkbp10 expression and cell proliferation and adhesion ability using mtt assay and fibronectin adhesion assay, respectively.Western blot was utilized to examine the protein expression level of c-myc, cyclin d1, and bcl-2 in the cells.Results.Fkbp10 was highly expressed in cc rcc tissues and cells and was correlated with poor prognosis.In addition, fkbp10 expression was positively correlated with cc rcc tumor size and staging and negatively correlated with tumor differentiation.Moreover, knockdown of fkbp10 significantly inhibited the proliferation of cc rcc cells, notably declined the protein expression of c-myc, cyclin d1, and bcl-2, and promoted cell adhesion.Conclusion.Fkbp10 is highly expressed in cc rcc tissues and cells and is associated with poor prognosis in patients.Fkbp10 participated in the occurrence and development of cc rcc by promoting cell proliferation and inhibiting apoptosis and adhesion.

Journal: Evidence-Based Complementary and Alternative Medicine (2022)
Publication types: Research Article;
A New Face Image Recognition Algorithm Based on Cerebellum-Basal Ganglia Mechanism

Face recognition is one of the popular areas of research in the field of computer vision.It is mainly used for identification and security system.One of the major challenges in face recognition is identification under numerous illumination environments by changing the direction of light or modifying the lighting magnitude.Exacting illumination invariant features is an effective approach to solve this problem.Conventional face recognition algorithms based on nonsubsampled contourlet transform (nsct) and bionic mode are not capable enough to recognize the similar faces with great accuracy.Hence, in this paper, an attempt is made to propose an enhanced cerebellum-basal ganglia mechanism (cbgm) for face recognition.The integral projection and geometric feature assortment method are used to acquire the facial image features.The cognition model is deployed which is based on the cerebellum-basal ganglia mechanism and is applied for extraction of features from the face image to achieve greater accuracy for recognition of face images.The experimental results reveal that the enhanced cbgm algorithm can effectively recognize face images with greater accuracy.The recognition rate of 100 ar face images has been found to be 96.9%.The high recognition accuracy rate has been achieved by the proposed cbgm technique.

Journal: Journal of Healthcare Engineering (2021)
Publication types: Research Article;
A Hybrid Model for Assessing the Performance of Medical Tourism: Integration of Bayesian BWM and Grey PROMETHEE-AL

Medical tourism (mt) is the activity of traveling domestically or abroad to receive medical services.The scope of medical treatments covers dentistry, surgery, antiaging procedures, preventive medicine, and even some health-related treatments (meditation, physiotherapy, psychotherapy, addiction treatment, psychiatry, etc.).Due to the global boom in mt, governments are actively promoting mt packages to capture this huge business opportunity.However, what are the key factors that make mt development successful or unsuccessful?How can the performance of current mt operators be evaluated?And, how can the performance of underperforming operators be improved?This paper addresses these questions by proposing a mt assessment framework that summarizes the potential key factors of mt.In addition, this study proposes a model that integrates the bayesian best-worst method (bayesian bwm) and grey preference ranking organization method for enrichment evaluations based on aspiration level (grey promethee-al) to assess the performance of assessed mt operators.The bayesian bwm not only aggregates the judgments of multiple experts but also generates a set of objective group criteria weights.Besides, the modified promethee incorporates the grey theory and aspiration level concept to increase the usefulness of the original promethee.The results of the analysis show that the two most critical criteria for mt are “the operators have cloud computing systems to analyze the travelers’ sensor data in real-time and accurately to provide customized medical services” and “multilingualism and communication skills of medical travel-related personnel.” poor performers in the travel industry can be improved by prioritizing the criteria in order of importance.The management implications of this study can be used as a basis for performance evaluation by operators and government health care organizations.

Journal: Journal of Function Spaces (2022)
Publication types: Research Article;