Health Informatics and Biomedical Engineering Applications最新文献

{"title":"PBC-ML: Predicting Breast Cancer in Humans using Machine Learning Approach","authors":"Damilola Oni, Satyam Mishra, Le Trung Thanh, Vu Minh Phuc, Linh C. Nguyen","doi":"10.54941/ahfe1003454","DOIUrl":"https://doi.org/10.54941/ahfe1003454","url":null,"abstract":"Cancer is a disease in which cells grow uncontrollably, potentially causing harm tosurrounding healthy tissue and organs. Breast cancer is a specific type of cancer that affectsthe breast and is the second most common cancer among women worldwide. Symptoms ofbreast cancer include a lump or tumour, swelling, nipple discharge, and swollen lymphnodes. Breast cancer is staged, with stage 0 being the earliest stage with minimal symptomsand stage 4 indicating the cancer has spread to other parts of the body. The future burdenof breast cancer is predicted to increase, with over 3 million new cases and 1 million deathsin 2040. Early detection is crucial for successful treatment and recovery, and machinelearning can be used to predict the likelihood of breast cancer based on symptoms. So wepropose in our research to use machine learning algorithms such as CART, SVM, NB, andKNN to analyse and build models for breast cancer detection. These findings offer asummary of relevant machine learning methods for breast cancer detection as it will help tocurb it and we got an accuracy of 98.2% compared to the state of art methods which hasaccuracy of 99%. It proves to be a valuable tool in the early detection of breast cancer andcan improve the accuracy of existing diagnostic methods.","PeriodicalId":107005,"journal":{"name":"Health Informatics and Biomedical Engineering Applications","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128103336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A pilot study of the effect of bathing time on thermal sensation to get a good night's sleep","authors":"T. Mitsuno, Sayaka Moriya","doi":"10.54941/ahfe1003455","DOIUrl":"https://doi.org/10.54941/ahfe1003455","url":null,"abstract":"In Japan, cold sensitivity is an indefinite complaint that affects many women. However, since it is not a life-threatening serious condition, it is difficult to treat or research the target. However, women who are aware of sensitivity to cold feel the cold most when they go to bed, and there are many people who suffer from the coldness that it is difficult to fall asleep even in a heated environment in winter. In this study, we investigated the effects of bathing, which is likely to raise body temperature, core temperature, skin temperature, and thermal sensation, and prevented coldness. The method of getting a good night's sleep was proposed without a cold sensation.The subject was a 21-year-old healthy woman who has heavy sensitivity to coldness. Experiment I: Room temperature and core temperature (oral temperature) were measured every 60 minutes from 7:00 to 23:00. Experiment II: After taking bath at 20:00, the room temperature, her core temperature, skin temperature of instep/toe, and the thermal sensation of the feet were measured every 20 minutes from 21:00 to 23:00. Experiment III: Two bath times were set from 20:00 or 22:00. The skin temperature of the instep/toe, core temperature, and thermal sensation were measured before and after bath time. Experiment IV: When she felt a cold sensation, core temperature, skin temperature of the instep/toe, and thermal sensation were measured before and after taking the 10 minutes foot bath at 23:00. Result I: From the correlation coefficient between body temperature and room temperature, which had a significant positive correlation, indicating that the higher the room temperature, the higher the body temperature. Room temperature increased significantly from morning to noon and decreased significantly in the evening. However, body temperature increased temporarily from evening to night and decreased significantly in the middle of the night. Result II: The oral temperature was significantly higher than before bathing until 21:20, and then significantly decreased from 22:00. Skin temperature of the instep was significantly higher until 22:00 and one of the toes was significantly higher until 21:40 than before bathing. The thermal sensation was significantly higher until 21:40 compared to before bathing and was evaluated as ``warm''. We calculated the relationship between the skin temperature of the instep/toe and the coldness sensation. The skin temperature (X1: instep, X2: toe) was explained by the equations: Y1 = 0.20X1-5.92 (R2 = 0.518) and Y2 = 0.14X2-3.67 (R2 = 0.667). The skin temperature of the instep/toe (X1/X2) was comfortable thermal sensations, Y1 = 0 (instep) and Y2 = 0 (toe), these skin temperatures were 29.6°C/26.2°C. Result III: Both oral and skin temperatures and their thermal sensations were increased significantly after bathing at 20:00 or 22:00. Although the increasing rate of core temperature at 20:00 bath time was significantly higher than at 22:00, however, there was no difference ","PeriodicalId":107005,"journal":{"name":"Health Informatics and Biomedical Engineering Applications","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134103679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Humanitude: first step towards the creation of a voice-bot companion for persons with dementia","authors":"Daniel Isaac Ruiz Cruz, Sergio Navarro Tuch, Ariel Lopez Aguilar, Rogelio Bustamante-Bello, Lili Marlene Camacho Bustamante","doi":"10.54941/ahfe1003464","DOIUrl":"https://doi.org/10.54941/ahfe1003464","url":null,"abstract":"During the last decade, the life span of the world has been incremented year by year, which comes with a higher probability of suffering from an illness related to aging. An example of this is dementia or Alzheimer’s disease, which causes a progressive decline in various cognitive functions. The costs of living with these types of diseases can destroy a family’s economy if there is no early treatment and detection. This article will aim to develop a protocol to obtain data from interviewing people with cognitive-related questions. This data will form a training corpus to help the development of a neural network that can give a dementia pre-diagnosis. Subsequently, the neural network can be used to program an adaptative companion voice-bot for people with dementia. This will focus on the conversation pillar of Humanitude, a care methodology that can be applied to persons with dementia. The development of this protocol would lead to the creation of one of the few Spanish-based training corpus available for detecting dementia. It is an important step toward developing new tools that can make an early pre-diagnosis or serve as an alternative caregiving solution for people with this condition. Furthermore, this project can lead to the creation of different research whose solutions use Spanish as its main language.","PeriodicalId":107005,"journal":{"name":"Health Informatics and Biomedical Engineering Applications","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132093081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detecting Stroke in Human Beings using Machine Learning","authors":"Damilola Oni, S. Mishra, Le Trung Thanh, Vu Minh Phuc, Y. Pham","doi":"10.54941/ahfe1003460","DOIUrl":"https://doi.org/10.54941/ahfe1003460","url":null,"abstract":"In developing and underdeveloped nations, stroke is a leading cause of mortality and disability. Stroke is a life-threatening condition that develops when there is a lack of blood flow to the brain from the carotid arteries and vertebral arteries. Because the brain suffers damage and can quickly expire without oxygen, stroke frequently results in death and can occasionally affect nearby body parts if the patient is not given prompt medical attention. Spasticity, contractures, paralysis, and death are among the effects. According to the World Health Organization, stroke accounts for over 137,000 fatalities per year in the United States alone and over 451,000 deaths per year in Africa. Today, stroke is a medical illness that affects people in practically every region of the world, including industrialized, developing, and undeveloped nations. In general, 1 in 4 adults over 25 will experience a stroke at some point in their lives. This year, 12.2 million people are predicted to experience their first stroke, and 6.5 million of them will pass away as a result. The number of stroke victims worldwide exceeds 110 million. What if this global endemic could be stopped? The world will be safer and life expectancy will rise if accurate stroke prediction technology is developed. We have proposed our research study to develop a solution to predict strokes in people using machine learning. We have employed four models/classifiers to check the accuracy on each of them with same dataset of people and we have achieved great results. The two models gave 98% and 98.29% successful accuracy results which is very close to state-of-the-art methods (99%).","PeriodicalId":107005,"journal":{"name":"Health Informatics and Biomedical Engineering Applications","volume":"189 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116104506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detecting and monitoring wandering in AD patients with an integrated device based on Humanitude","authors":"Gabriela Cervantes Alarcón, Sergio Navarro Tuch, Ariel Lopez Aguilar, Lili Marlene Camacho Bustamante, Rogelio Bustamante-Bello","doi":"10.54941/ahfe1003463","DOIUrl":"https://doi.org/10.54941/ahfe1003463","url":null,"abstract":"Alzheimer is a neurodegenerative type of dementia, that has progressive impairment of cognitive and behavioral functions, most commonly presented above 65 years old and it is divided in three stages. Wandering is the most frightening symptom for familiars and caregivers in the mild stage (second stage of the disease) because it can cause from a minor damage to death. Detecting and monitoring wandering is a complex task and has become a strong research line for several research projects. This paper focuses in proposing, developing and testing a support system for monitoring trajectories to identify direction changes alterations and positioning. The proposed system is called Motion acquisition system + Global positioning system(SAM + GPS). The system integrates an accelerometer, magnetometer, gyroscope and a GPS module. To address this challenge, after the development of the device, a pilot test was conducted with 9 young adult healthy subjects instrumented with the SAM + GPS. Each subject needed to route 2 specific trajectories to prove that the integrated device was able to measure different direction changes and map an accurate trajectory for future wandering detection. It was found that the SAM + GPS had an acceptable error of 12.29% measuring the direction changes in the first trajectory and a 27.44% critical error in the second trajectory. In addition, the device was able to map most of the trajectories with high similarities to an ideal pattern traced with the expected horizontal accuracy of 2.5m of the GPS Neo 6m module. It is worth mentioning that the device had a better performance in outdoor environments than in indoor environments, so in future work, more tests are considered with a larger population sample with the integration of an indoor positioning system and Humanitude methodologyprinciples.","PeriodicalId":107005,"journal":{"name":"Health Informatics and Biomedical Engineering Applications","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123887034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"VIS-NLP: Vaccination Inventory System for justified user using Natural Language Processing","authors":"Minh Phuc Vu, Satyam Mishra, Le Trung Thanh, Damilola Oni","doi":"10.54941/ahfe1003459","DOIUrl":"https://doi.org/10.54941/ahfe1003459","url":null,"abstract":"In the healthcare industry, especially the Covid-19 pandemic in 2020, produced huge problems with isolate patient and patient heath. Thus, created large amount of data that has been generated every day for the patient heath, in this case is to justify the vaccination of users from social network Twitter. Processing such large volume of the data involves high computation overhead. Good health and well-being; to ensure healthy lives and promote well-being for all at all ages is United Nations 3rd Sustainable Development Goal and we want to align our study with it as well. It is crucial to create an application that is beneficial for humanity health. When we get large datasets from pandemics like Covid-19, for large scale datasets, we presented a solution to verify the user if they are vaccinated or not vaccinated by using Natural Language Processing methods to build an accuracy result, we tried to reduce the computation overhead by storing the data in distributed environment. After processing data, training the data, used pad_sequences, Keras, NLP to build the model. Through multiple epochs we have got an accuracy towards 90 to 91% (which is closer to state-of-the-art methods i.e., 95%). And since our accuracy is higher, we can further utilize it to increase for higher number of epochs. We hope scientists can further develop it and use it in real world applications so that more precious human being lives can be saved. By implementation of its successful results, it also aligns with one of the United Nations Sustainable Development Goals i.e., 3rd: Good Health and Well-Being.","PeriodicalId":107005,"journal":{"name":"Health Informatics and Biomedical Engineering Applications","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115309477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mathematical analysis of daily ECG in assessing the effectiveness of obesity treatment in young patients","authors":"I. Kurnikova, Sh. Gulova, N. Danilina, I. Mokhammed, A. Yurovsky","doi":"10.54941/ahfe1003461","DOIUrl":"https://doi.org/10.54941/ahfe1003461","url":null,"abstract":"Mathematical analysis of the ECG in medicine has been used for a long time, since in 1932 Fleisch and Beckmann first applied the mathematical assessment of the heart rate using the standard deviation of the R-R intervals to assess fluctuations. Mathematical analysis technologies are constantly developing and improving and are the method of choice in the analysis of heart rate variability (HRV). HRV analysis is based on the measurement of time intervals between adjacent ECG R-waves with the construction of a dynamic series - a cardiorhythmogram (CRG). Evaluation of HRV allows to obtain data not only on the functioning of the patient's cardiovascular system, but also on the tension (or exhaustion) of regulatory mechanisms (the state of autonomic regulation), and hence on the preservation of adaptation reserves and rehabilitation capabilities of the body. And this opens opportunities for predicting and monitoring the effectiveness of therapy. At present, the leading direction of research is the development of practical aspects of applying the results of daily HRV analysis.Purpose. To evaluate the possibilities of HRV analysis in monitoring the effectiveness of treatment in young patients with obesity.Materials and methods. Patients with exogenous constitutional obesity underwent 24-hour monitoring of heart rate (Holter monitoring - HM) with software computer analysis of the wave spectrum of the obtained data and selection of frequencies - 0.004–0.08 Hz (very low frequencies - VLF); 0.09-0.16 Hz (low frequencies - LF); 0.17-0.5 Hz (high frequencies - HF); more than 0.5 Hz (Ultra Low Frequency Waves - ULF). Two coefficients were calculated - LF/HF (coefficient of vagosympathetic balance) - the ratio of the power of low frequency waves (LF) to the power of high frequency waves (HF) and the index of centralization (CI) - the ratio of the activity of the central circuit of regulation to the autonomous one (LF+VLF/HF).Results. In total, 14 young patients (from 17 to 26 years old) who were admitted to the medical center with a diagnosis of exogenous constitutional obesity were examined. The survey complex included an analysis of HRV. The initial indicator of autonomic balance was determined by the coefficient LF/HF and the degree of tension of regulatory systems by the index of centralization (CI). In young people, parasympathetic activity prevailed in the wave spectrum - % of high frequency waves (HF) characterizing parasympathetic activity exceeded % of low frequency waves (LF) characterizing sympathetic activity. The follow-up period ranged from 8 months to 1.5 years.In the examined patients, at the beginning of the observation, the predominance of parasympathetics was noted in all, and the value of the coefficient below 0.7 was found in 14 people. (85.7%), which confirmed the predominance of parasympathetics with a significant violation of the autonomic balance (in healthy individuals, the ratio of sympathetic/parasympathetic in terms of LF/HF is from ","PeriodicalId":107005,"journal":{"name":"Health Informatics and Biomedical Engineering Applications","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114928317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Home Healthcare System Application Design for COVID-19 Preventive Management","authors":"Yukun Xia, Yanfang Gan, Zijie Ding, Shanyu Ge, YongKang Wu","doi":"10.54941/ahfe1003453","DOIUrl":"https://doi.org/10.54941/ahfe1003453","url":null,"abstract":"COVID-19 is an infectious disease now known as a \"global pandemic\" and is reported to be transmitted directly, aerosolized and by contact, and is highly contagious when in contact with patients. Fever, dry cough, and malaise are the most common symptoms of COVID-19. And at this stage, there is still no comprehensive solution for the containment of COVID-19 from a microbiological and curative point of view. Therefore, we need a more independent environment and a smarter medical system for detection and transient isolation before and after social events. And IoT is a popular and proven management technology that can support a variety of human behavior management programs.In this paper, we used interview method, follow-up method, questionnaire method, and literature search method for research verification, process profiling of multiple usage scenarios, and proposed an APP（Application） program design of home medical system for different users' behavioral habits, with functions including risk assessment of planned activity locations and self detection after social activities (via close objects such as masks), etc. The application consists of four main modules: detection, planning, recording, and communication, tracking and warning of epidemic risk sites via wearable devices to reduce the risk of infection for users, and integration of software functions with smart home systems via IoT technology to improve the effectiveness of preventive management for users.","PeriodicalId":107005,"journal":{"name":"Health Informatics and Biomedical Engineering Applications","volume":"286 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124560280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital Informed Consent for Older Adults in Emergency Department Research","authors":"C. Edwards, F. Abujarad","doi":"10.54941/ahfe1003449","DOIUrl":"https://doi.org/10.54941/ahfe1003449","url":null,"abstract":"The objective of the informed consent (IC) process is to inform potential participants about the purpose, procedures, risks, and benefits associated with clinical research and medical procedures. Traditional paper consent processes are generally long and confusing, especially in busy settings for research such as the emergency department (ED). We describe how we used a tablet-based digital IC process to recruit (N=1,002) older adults for an elder mistreatment study in the ED. Methods: The Virtual Multimedia Interactive Informed Consent (VIC) consent tool was previously developed and tested in an AHRQ-funded R21 study and was found to be usable, acceptable, and it enhanced participants’ comprehension and satisfaction when compared to a traditional paper-based IC process (Abujarad et al., 2021a). VIC was developed using a user-centered design (UCD) approach, incorporating digital coaching, multimedia features such as animated videos to explain research procedures, automated text-to-speech audio, and automated teach-back to emphasize key concepts. The VIC digital consent tool was used to recruit patients for an NIA-funded R01 study evaluating the feasibility of the VOICES Elder Mistreatment Intervention, a self-administered digital health intervention to increase identification of elder mistreatment in ED settings. Due to the complexities of elder mistreatment identification, we recognized the need for an IC process that ensures participant privacy, autonomy, and comprehension, with particular focus on the risks and benefits of recognizing and disclosing mistreatment. A total of 1,002 participants ages 60 and older were consented and enrolled during their visit in the ED. Results: A total of 1,204 of eligible participants agreed to participate in the study and started the consent, of whom 1,012 (84%) participants completed the consent process and enrolled in the VOICES study. Of the 192 (16%) participants who were not enrolled in the study: 158 (13%) did not complete the IC process for varying reasons, the most common reason being due to pain, and 34 (3%) completed the IC fully and chose not to participate in VOICES study. Of the consented participants, 99% fully completed the VOICES study and filled all surveys. Consented participants included older adults from 60 to 102 years old with a mean age of 73.5. Most participants were female, white, and high school educated or higher.Discussion: We believe that the use of digital IC process benefitted the participants who were able to complete the IC process on their own and with minimal help from the study coordinators. We received a high study completion rate among consented participants, and we believe that emphasizing key concepts and using multimedia to explain the more complicated research topics helped better educate potential participants to make a true informed decision about their participation in the VOICES study. It is likely that research participants who have a better understanding of the na","PeriodicalId":107005,"journal":{"name":"Health Informatics and Biomedical Engineering Applications","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133635450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sitting posture recognition for smart chair","authors":"Ren-jieh Kuo, Chih-Wen Shih, Chong-Hao Wang","doi":"10.54941/ahfe1003458","DOIUrl":"https://doi.org/10.54941/ahfe1003458","url":null,"abstract":"In recent years, the relationship between sitting posture and health has been paid attention to by researchers, since a person spends about 90% of a day sitting except for sleeping time, and the prolonged sitting is one of the important causes of musculoskeletal diseases. Basically, the different sitting postures caused by sitting for a long time will cause different pressure problems on the spine. Thus, this study intends to accurately predict sitting posture to reduce the damage caused by sitting posture using random forest. A smart chair with eight pressure sensors provided by a case company in Taiwan is applied to collect pressure data of various sitting postures in order to develop a prediction model to predict the sitting posture. Since random forest also owns the capability of feature extraction, it is also employed to find unnecessary sensors to reduce the cost of smart chair and further achieve higher prediction accuracy. The results showed that random forest can yield better results for the current problem compared with other methods. In addition, after the feature extraction via random forest, it can be known that there is indeed a sensor that can be eliminated. The accuracy can be enhanced from 90.70% to 91.36%.","PeriodicalId":107005,"journal":{"name":"Health Informatics and Biomedical Engineering Applications","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132777695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}