scholarly journals Getting there and staying there: supporting and enabling persistent human life on Mars using synthetic natural rubber, self-healing materials, and biological batteries

2018 ◽  
Author(s):  
Nischal Acharya ◽  
Natalie Baker ◽  
Marilu Krystal Bravo ◽  
Katie Gu ◽  
Sierra Harken ◽  
...  
Keyword(s):  
Human Life ◽  
Planetary Exploration ◽  
Financial Strain ◽  
Self Healing ◽  
Renewable Materials ◽  
Life On Mars ◽  
Low Mass ◽  
Precautionary Measures ◽  
Power Shortages ◽  
Biological Solutions

AbstractPlanetary exploration requires a balance between preemptive planning and financial feasibility. The risk of mid-mission equipment failure, power shortages, or supply depletion incentivizes precautionary measures, but the financial strain of sending unnecessary mass into space limits this practice.To balance the two, our team explored the advantages of biological solutions, namely the self-sustaining abilities of low-mass organisms, to make planetary exploration more self-sufficient and economical. Prioritizing repair over replacement, we are developing self-healing materials embedded with Bacillus subtilis. For longer-lasting energy, we are designing a “biobactery” using linearly oriented Escherichia coli to generate power. For renewable materials, we are engineering bacteria to synthesize and degrade rubber. Individually, these projects offer sustainable alternatives for repair, power, and materials. But when combined, these consolidated insights can provide us with the power to get to Mars and resources to sustain us while we’re there.

scholarly journals Risk Assessment for Cardiac Arrest : A Deep Learning Approach With Channel Selection

2020 ◽  
Author(s):  
Atınç Yılmaz
Keyword(s):  
Risk Assessment ◽  
Cardiac Arrest ◽  
Deep Learning ◽  
Heart Attack ◽  
Human Life ◽  
Numerical Data ◽  
Channel Selection ◽  
Heart Attacks ◽  
Precautionary Measures ◽  
Feature Filters

Abstract Background: Risk of developing cardiovascular diseases, in the world, is increasing day by day. Accordingly, the number of deaths due to heart attacks is quite remarkable. Early risk assessment and diagnosis of heart disease are vital to prevent heart attacks by providing effective treatment planning and evaluation of outcomes. When a patient with high risk of heart attack is not treated correctly, chances of survival may reduce dramatically. For this reason, artificial intelligence-assisted systems can support the decision of doctors and it can anticipate risk without fatal consequences.Methods: In this study, individuals who has heart attack risks are predicted by using a proposed CNNs method. A set of medical data from patients with heart attacks and healthy individuals are provided from the UCI database. Reinforced deep learning and ANFIS architectures are also applied to the same problem in order to compare the results and put forth the efficiency of proposed method. In addition, ROC analysis and measurements of processing times for the applied methods were performed to reveal the performance, accuracy and efficiency of the study.Results: The proposed CNNs method and other methods are tested and evaluated. The accuracy performance of the methods were 94.34% for the proposed CNNs method, 91.58% for the ANFIS, and 92.66% for the deep multilayer neural network. Highest accuracy has been obtained by using the proposed CNNs method, which is 94.34%. The reasons why the proposed CNNs method is better than other methods is the use of channel selection layer, the number of convolution and pooling layers, the filter size used in these layers, and the functions used in the loss and activation layers.Conclusions: In the study, the channel selection formula is introduced in the proposed CNNs model to select the most discriminatory feature filters. Besides, the applicability of proposed CNNs method with images obtained from numerical data has been demonstrated. With the early prediction system proposed, it is now possible to take precautionary measures against possible cardiac arrest. In this study; a new method based on CNNs is proposed for early detection of possible heart attack, which is a great risk for human life. Different from studies in the literature, the channel selection formula is presented in the proposed CNNs method to select the most selective feature filters. Besides differently, it was used in the proposed CNNs method by converting all numerical data from dataset into 2D images. Afterwards, to show whether this the proposed method is applicable or not, the dataset which is numerical form was applied to other methods and compared.

Low Mass SCoRe-S Designs for Affordable Planetary Exploration

2007 ◽  
Author(s):  
Steven A. Hatton ◽  
Mohamed S. El-Genk
Keyword(s):  
Planetary Exploration ◽  
Low Mass

scholarly journals Early detection of COVID-19 pandemic: evidence from Baidu Index

2020 ◽  
Author(s):  
Bizhi Tu ◽  
Laifu Wei ◽  
Yaya Jia ◽  
Jun Qian
Keyword(s):  
Search Engine ◽  
Human Life ◽  
Systems Science ◽  
Shortness Of Breath ◽  
Daily Growth ◽  
Global Pandemic ◽  
Sputum Production ◽  
Precautionary Measures ◽  
Index Value ◽  
Search Index

Abstract Background: New coronavirus disease 2019 (COVID-19) poses a severe threat to human life, and causes a global pandemic. The purpose of current research is to explore the onset and progress of the pandemic with a novel perspective using Baidu Index.Methods: We collected the confirmed data of COVID-19 infection between January 11, 2020, and April 22, 2020, from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). Based on known literature, we obtained the search index values of the most common symptoms of COVID-19, including fever, cough, fatigue, sputum production, and shortness of breath. Spearman's correlation analysis was used to analyze the association between the Baidu index values for each COVID-19-related symptoms and the number of confirmed cases. Regional differences among 34 provinces/ regions were also analyzed. Results: Daily growth of confirmed cases and Baidu index values for each symptoms presented a robust positive correlation during the outbreak (fever: rs=0.705, p=9.623×10-6; cough: rs=0.592, p=4.485×10-4; fatigue: rs=0.629, p=1.494×10-4; sputum production: rs=0.648, p=8.206×10-5; shortness of breath: rs=0.656, p=6.182×10-5). The average search-to-confirmed interval is 19.8 days in China (fever: 22 days, cough: 19 days, fatigue: 20 days, sputum production: 19 days, and shortness of breath: 19 days). We discovered similar results in the top 10 provinces/regions, which had the highest cumulative cases. Conclusion: Search terms of COVID-19- related symptoms on the Baidu search engine can be used to early warn the outbreak of the epidemic. Relevant departments need to pay more attention to areas with high search index and take precautionary measures to prevent these potentially infected persons from spreading further. Baidu search engine can reflect the public's attention to the pandemic and regional epidemics of viruses. Based on changes in the Baidu index value, we can predict the arrival of the peak confirmed cases. The clinical characteristics related to COVID-19- including fever, cough, fatigue, shortness of breath, deserve more attention during the pandemic.

scholarly journals Studying Unemployment Effects on Mental Health: Social Media versus Traditional Approach

2020 ◽  
Author(s):  
Samara Ahmed ◽  
Adil E. Rajput ◽  
Akil Sarirete ◽  
Asmaa Aljaberi ◽  
Ohoud Alghanem ◽  
...  
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Traditional Approach ◽  
Human Life ◽  
Financial Strain ◽  
Involuntary Unemployment ◽  
Social Exchanges ◽  
Medical Practitioners ◽  
Health Social Media ◽  
Insight Into

Social media, traditionally reserved for social exchanges on the net, has been increasingly used by researchers to gain insight into different facets of human life. Unemployment is an area that has gained attention by researchers in various fields. Medical practitioners especially in the area of mental health have traditionally monitored the effects of involuntary unemployment with great interest. In this work, we compare the feedback gathered from social media using crowdsourcing techniques to results obtained prior to the advent of Big Data. We find that the results are consistent in terms of 1) financial strain is the biggest stressor and concern, 2) onslaught of depression is typical and 3) possible interventions including reemployment and support from friends and family is crucial in minimizing the effects of involuntary unemployment. Lastly, we could not find enough evidence to study effects on physical health and somatization in this work.

Criminal Protection of the Environment in Jordanian Legislation

2020 ◽  
Vol 11 (1) ◽  
pp. 6
Author(s):  
Abdullah ALKSEILAT ◽  
Hamzeh ABU ISSA ◽  
Ayman AL-REFOU
Keyword(s):  
Environmental Protection ◽  
Analytical Approach ◽  
Human Life ◽  
Legal Protection ◽  
The Other ◽  
Precautionary Measures ◽  
Criminal Protection ◽  
Free Environment ◽  
Shed Light ◽  
Environmental Crimes

Having a clean and pollution-free environment is one of the most important rights of humans. Such a right has been recognized by national and international covenants, so laws have been devoted to protecting them and preventing infringement of their components and elements, because of the importance they constitute at the level of human life. As a result of the increasing assault on the environment and the gravity of the infringement of its elements, the legislator intervened in all countries (including the Jordanian legislator), to lay down the rules regulating the protection thereof and punishing the perpetrators of environmental crimes through deterrent measures and appropriate penalties. Therefore, this research will shed light on the criminal provisions decided by the Jordanian legislator in the Environmental Protection Law No. 6 of 2017, especially in terms of the penalties decided by the legislator on the perpetrators of environmental crimes, with the aim of embodying the criminal and legal protection of the environment. As the aim of this article is to clarify and clarify the position of the Jordanian legislator regarding the penal protection of the environment, we have used the descriptive and analytical approach to reach the results. The article concluded that the Environmental Protection Law stipulated several crimes to protect the environment in its various forms, but the legislator did not put in place precautionary measures prior to the occurrence of the crime, and on the other hand, the provisions laid down by the legislator are still scattered and need a general rooting for all environmental crimes.  

scholarly journals Using Baidu search values to monitor and predict the confirmed cases of COVID-19 in China: – evidence from Baidu Index 

2020 ◽  
Author(s):  
Bizhi Tu ◽  
Laifu Wei ◽  
Yaya Jia ◽  
Jun Qian
Keyword(s):  
Search Engine ◽  
Human Life ◽  
Systems Science ◽  
Optimal Time ◽  
Time Lags ◽  
Shortness Of Breath ◽  
Daily Growth ◽  
Global Pandemic ◽  
Sputum Production ◽  
Precautionary Measures

Abstract Background: New coronavirus disease 2019 (COVID-19) poses a severe threat to human life and causes a global pandemic. The purpose of current research is to explore whether the search-engine query patterns could serve as a potential tool for monitoring the outbreak of COVID-19.Methods: We collected the number of COVID-19 confirmed cases between January 11, 2020, and c, from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). The search index values of the most common symptoms of COVID-19 (e.g., fever, cough, fatigue) were retrieved from Baidu Index. Spearman's correlation analysis was used to analyze the association between the Baidu index values for each COVID-19-related symptom and the number of confirmed cases. Regional distributions among 34 provinces/ regions in China were also analyzed. Results: Daily growth of confirmed cases and Baidu index values for each COVID-19 related symptoms presented a robust positive correlation during the outbreak (fever: rs=0.705, p=9.623×10-6; cough: rs=0.592, p=4.485×10-4; fatigue: rs=0.629, p=1.494×10-4; sputum production: rs=0.648, p=8.206×10-5; shortness of breath: rs=0.656, p=6.182×10-5). The average search-to-confirmed interval is 19.8 days in China. The daily Baidu Index value's optimal time lags were the fourth day for cough, third day for fatigue, firth day for sputum production, firth day for shortness of breath, and 0 days for fever. Conclusion: Search terms of COVID-19-related symptoms on the Baidu search engine have significant correlations with confirmed cases. Since the Baidu search engine can reflect the Public's attention to the pandemic and regional epidemics of viruses, relevant departments need to pay more attention to areas with high searches of COVID-19-related symptoms and take precautionary measures to prevent these potentially infected persons from further spreading.

scholarly journals Using Baidu search values to monitor and predict the confirmed cases of COVID-19 in China - evidence from Baidu Index

2021 ◽  
Author(s):  
Bizhi Tu ◽  
Laifu Wei ◽  
Yaya Jia ◽  
Jun Qian
Keyword(s):  
Search Engine ◽  
Human Life ◽  
Systems Science ◽  
Optimal Time ◽  
Time Lags ◽  
Shortness Of Breath ◽  
Daily Growth ◽  
Sputum Production ◽  
Related Symptom ◽  
Precautionary Measures

Abstract Background: New coronavirus disease 2019 (COVID-19) has posed a severe threat to human life and caused a global pandemic. The current research aimed to explore whether the search-engine query patterns could serve as a potential tool for monitoring the outbreak of COVID-19.Methods: We collected the number of COVID-19 confirmed cases between January 11, 2020, and April 22, 2020, from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). The search index values of the most common symptoms of COVID-19 (e.g., fever, cough, fatigue) were retrieved from the Baidu Index. Spearman's correlation analysis was used to analyze the association between the Baidu index values for each COVID-19-related symptom and the number of confirmed cases. Regional distributions among 34 provinces/ regions in China were also analyzed.Results: Daily growth of confirmed cases and Baidu index values for each COVID-19-related symptom presented robust positive correlations during the outbreak (fever: rs=0.705, p=9.623×10-6; cough: rs=0.592, p=4.485×10-4; fatigue: rs=0.629, p=1.494×10-4; sputum production: rs=0.648, p=8.206×10-5; shortness of breath: rs=0.656, p=6.182×10-5). The average search-to-confirmed interval (STCI) was 19.8 days in China. The daily Baidu Index value's optimal time lags were the four days for cough, two days for fatigue, three days for sputum production, one day for shortness of breath, and 0 days for fever.Conclusion: The searches of COVID-19-related symptoms on the Baidu search engine were significantly correlated to the number of confirmed cases. Since the Baidu search engine could reflect the public's attention to the pandemic and the regional epidemics of viruses, relevant departments need to pay more attention to areas with high searches of COVID-19-related symptoms and take precautionary measures to prevent these potentially infected persons from further spreading.

Living with COVID-19 pandemic – Emerging challenges for ultrasound physicians and their suggested solution

2020 ◽  
Vol 24 (Supp-1) ◽  
pp. 99-107
Author(s):  
Shagufta Malik ◽  
Musab Riaz
Keyword(s):  
Human Life ◽  
Medical Community ◽  
Health Concern ◽  
Gear Up ◽  
Patient Screening ◽  
Coronavirus Infection ◽  
Multi Level ◽  
Precautionary Measures ◽  
Cleaning And Disinfection ◽  
Examination Room

Background: The recent coronavirus disease (COVID-19) pandemic is a serious health concern with far-reaching implications in every facet of human life. New challenges have emerged for ultrasound physicians engaged in diagnostic ultrasound examinations. Methods: Based on a comprehensive literature review the author has suggested a few precautionary measures that should be incorporated by the ultrasound physicians in their practice against the spread of coronavirus disease. Suggestions: Multi-level safeguard checks before, during, and after the ultrasound examination are suggested to protect ultrasound physicians, staff, and patients from COVID-19. The importance of triage for patient screening is stressed.  Also, limiting patients by deferring non-urgent cases and cancelling aerosol-generating procedures is recommended. The need of counselling of patients and staff is stressed regarding the importance of facemasks, hand hygiene, and safe distancing. Incorporating different types of barriers against the virus such as facemasks, face-shields, personnel protective suits for ultrasound physicians and staff, and shielding the equipment and transducer with disposable or wipeable plastic sheets is suggested. Besides, the significance of cleaning and disinfection of the examination room and equipment by suitable disinfectants after each patient and at the end of the day is highlighted.  Conclusion: Learning to live with the COVID-19 pandemic is the need of the day both for the general public and medical community. Being members of the medical community ultrasound physicians should gear up to the emerging challenges of COVID-19 to protect themselves, their patients, and allied healthcare staff from coronavirus infection.

A rapid self-healing hydrogel based on PVA and sodium alginate with conductive and cold-resistant properties

Soft Matter ◽  
2020 ◽  
Vol 16 (13) ◽  
pp. 3319-3324 ◽  
Author(s):  
Guangfeng Wu ◽  
Kaiyun Jin ◽  
Li Liu ◽  
Huixuan Zhang
Keyword(s):  
Sodium Alginate ◽  
Self Healing ◽  
Renewable Materials ◽  
Significant Attention

Self-healing hydrogels as renewable materials have attracted significant attention recently.

scholarly journals Using Baidu search values to monitor and predict the confirmed cases of COVID-19 in China: – evidence from Baidu index

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Bizhi Tu ◽  
Laifu Wei ◽  
Yaya Jia ◽  
Jun Qian
Keyword(s):  
Search Engine ◽  
Human Life ◽  
Systems Science ◽  
Optimal Time ◽  
Time Lags ◽  
Shortness Of Breath ◽  
Daily Growth ◽  
Sputum Production ◽  
Related Symptom ◽  
Precautionary Measures

Abstract Background New coronavirus disease 2019 (COVID-19) has posed a severe threat to human life and caused a global pandemic. The current research aimed to explore whether the search-engine query patterns could serve as a potential tool for monitoring the outbreak of COVID-19. Methods We collected the number of COVID-19 confirmed cases between January 11, 2020, and April 22, 2020, from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). The search index values of the most common symptoms of COVID-19 (e.g., fever, cough, fatigue) were retrieved from the Baidu Index. Spearman’s correlation analysis was used to analyze the association between the Baidu index values for each COVID-19-related symptom and the number of confirmed cases. Regional distributions among 34 provinces/ regions in China were also analyzed. Results Daily growth of confirmed cases and Baidu index values for each COVID-19-related symptom presented robust positive correlations during the outbreak (fever: rs=0.705, p=9.623× 10− 6; cough: rs=0.592, p=4.485× 10− 4; fatigue: rs=0.629, p=1.494× 10− 4; sputum production: rs=0.648, p=8.206× 10− 5; shortness of breath: rs=0.656, p=6.182× 10–5). The average search-to-confirmed interval (STCI) was 19.8 days in China. The daily Baidu Index value’s optimal time lags were the 4 days for cough, 2 days for fatigue, 3 days for sputum production, 1 day for shortness of breath, and 0 days for fever. Conclusion The searches of COVID-19-related symptoms on the Baidu search engine were significantly correlated to the number of confirmed cases. Since the Baidu search engine could reflect the public’s attention to the pandemic and the regional epidemics of viruses, relevant departments need to pay more attention to areas with high searches of COVID-19-related symptoms and take precautionary measures to prevent these potentially infected persons from further spreading.

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