scholarly journals Methodology for Data-Informed Process Improvement to Enable Automated Manufacturing in Current Manual Processes

2021 ◽  
Vol 11 (9) ◽  
pp. 3889
Author(s):  
Mumtahina Mahajabin Adrita ◽  
Alexander Brem ◽  
Dominic O’Sullivan ◽  
Eoin Allen ◽  
Ken Bruton
Keyword(s):  
Human Error ◽  
Lean Six Sigma ◽  
Manufacturing Industries ◽  
Data Conversion ◽  
Automatic Extraction ◽  
The Novel ◽  
Digital Formats ◽  
Using Data ◽  
Product Output ◽  
Data Informed

Manufacturing industries are constantly identifying ways to automate machinery and processes to reduce waste and increase profits. Machines that were previously handled manually in non-standardized manners can now be automated. Converting non-digital records to digital formats is called digitization. Data that are analyzed or entered manually are subject to human error. Digitization can remove human error, when dealing with data, via automatic extraction and data conversion. This paper presents methodology to identify automation opportunities and eliminate manual processes via digitized data analyses. The method uses a hybrid combination of Lean Six Sigma (LSS), CRISP-DM framework, and “pre-automation” sequence, which address the gaps in each individual methodology and enable the identification and analysis of processes for optimization, in terms of automation. The results from the use case validates the novel methodology, reducing the implant manufacturing process cycle time by 3.76%, with a 4.48% increase in product output per day, as a result of identification and removal of manual steps based on capability studies. This work can guide manufacturing industries in automating manual production processes using data digitization.

Robotic-Assisted Endoscopic Laminotomy: 2-Dimensional Operative Video

2021 ◽  
Author(s):  
Yingda Li ◽  
Michael Y Wang
Keyword(s):  
Spine Surgery ◽  
Human Error ◽  
Robotic Assistance ◽  
The Novel ◽  
Robotic Assisted ◽  
Accuracy And Precision ◽  
Surgical Ergonomics ◽  
Patient Consent ◽  
Potential Synergy ◽  
And Robotics

Abstract Endoscopy and robotics represent two emerging technologies within the field of spine surgery, the former an ultra-MIS approach minimizing the perioperative footprint and the latter leveraging accuracy and precision. Herein, we present the novel incorporation of robotic assistance into endoscopic laminotomy, applied to a 27-yr-old female with a large caudally migrated L4-5 disc herniation. Patient consent was obtained. Robotic guidance was deployed in (1) planning of a focussed laminotomy map, pivoting on a single skin entry point; (2) percutaneous targeting of the interlaminar window; and (3) execution of precision drilling, controlled for depth. Through this case, we illustrated the potential synergy between these 2 technologies in achieving precise bony removal tailored to the patient's unique pathoanatomy while simultaneously introducing safety mechanisms against human error and improving surgical ergonomics.1,2 The physicians consented to the publication of their images.

scholarly journals A Mathematical Model of Contact Tracing during the 2014–2016 West African Ebola Outbreak

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 608
Author(s):  
Danielle Burton ◽  
Suzanne Lenhart ◽  
Christina J. Edholm ◽  
Benjamin Levy ◽  
Michael L. Washington ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Disease Management ◽  
Ebola Virus ◽  
Virus Disease ◽  
Infected Individual ◽  
Vital Role ◽  
West African ◽  
Contact Tracing ◽  
The Novel ◽  
Using Data

The 2014–2016 West African outbreak of Ebola Virus Disease (EVD) was the largest and most deadly to date. Contact tracing, following up those who may have been infected through contact with an infected individual to prevent secondary spread, plays a vital role in controlling such outbreaks. Our aim in this work was to mechanistically represent the contact tracing process to illustrate potential areas of improvement in managing contact tracing efforts. We also explored the role contact tracing played in eventually ending the outbreak. We present a system of ordinary differential equations to model contact tracing in Sierra Leonne during the outbreak. Using data on cumulative cases and deaths, we estimate most of the parameters in our model. We include the novel features of counting the total number of people being traced and tying this directly to the number of tracers doing this work. Our work highlights the importance of incorporating changing behavior into one’s model as needed when indicated by the data and reported trends. Our results show that a larger contact tracing program would have reduced the death toll of the outbreak. Counting the total number of people being traced and including changes in behavior in our model led to better understanding of disease management.

scholarly journals Digital Tracing during the COVID-19 Pandemic: User Appraisal, Emotion, and Continuance Intention

2021 ◽  
Vol 13 (2) ◽  
pp. 608
Author(s):  
Ayoung Suh ◽  
Mengjun Li
Keyword(s):  
Mobile Apps ◽  
Contact Tracing ◽  
The Novel ◽  
Personal Privacy ◽  
Continuance Intention ◽  
Infectious Disease Outbreak ◽  
Using Data ◽  
Novel Coronavirus ◽  
Technological Affordances ◽  
Method Approach

This study explores how people appraise the use of contact tracing apps during the novel coronavirus (COVID-19) pandemic in South Korea. Despite increasing attention paid to digital tracing for health disasters, few studies have empirically examined user appraisal, emotion, and their continuance intention to use contact tracing apps for disaster management during an infectious disease outbreak. A mixed-method approach combining qualitative and quantitative inquiries was employed. In the qualitative study, by conducting interviews with 25 people who have used mobile apps for contact tracing, the way users appraise contact tracing apps for COVID-19 was explored. In the quantitative study, using data collected from 506 users of the apps, the interplay among cognitive appraisal (threats and opportunities) and its association with user emotion, and continuance intention was examined. The findings indicate that once users experience loss emotions, such as anger, frustration, and disgust, they are not willing to continue using the apps. App designers should consider providing technological affordances that enable users to have a sense of control over the technology so that they do not experience loss emotions. Public policymakers should also consider developing measures that can balance public health and personal privacy.

scholarly journals Investigation of the genetic variation in ACE2 on the structural recognition by the novel coronavirus (SARS-CoV-2)

2020 ◽  
Author(s):  
Xingyi Guo ◽  
Zhishan Chen ◽  
Yumin Xia ◽  
Weiqiang Lin ◽  
Hongzhi Li
Keyword(s):  
Genetic Variation ◽  
The Novel ◽  
S Protein ◽  
Angiotensin Converting Enzyme 2 ◽  
Missense Variants ◽  
Catalytic Metal ◽  
Using Data ◽  
Novel Coronavirus ◽  
Insight Into

Abstract Background: The outbreak of coronavirus disease (COVID-19) was caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), through its surface spike glycoprotein (S-protein) recognition on the receptor Angiotensin-converting enzyme 2 (ACE2) in humans. However, it remains unclear how genetic variations in ACE2 may affect its function and structure, and consequently alter the recognition by SARS-CoV-2. Methods: We have systemically characterized missense variants in the gene ACE2 using data from the Genome Aggregation Database (gnomAD; N = 141,456). To investigate the putative deleterious role of missense variants, six existing functional prediction tools were applied to evaluate their impact. We further analyzed the structural flexibility of ACE2 and its protein-protein interface with the S-protein of SARS-CoV-2 using our developed Legion Interfaces Analysis (LiAn) program.Results: Here, we characterized a total of 12 ACE2 putative deleterious missense variants. Of those 12 variants, we further showed that p.His378Arg could directly weaken the binding of catalytic metal atom to decrease ACE2 activity and p.Ser19Pro could distort the most important helix to the S-protein. Another seven missense variants may affect secondary structures (i.e. p.Gly211Arg; p.Asp206Gly; p.Arg219Cys; p.Arg219His, p.Lys341Arg, p.Ile468Val, and p.Ser547Cys), whereas p.Ile468Val with AF = 0.01 is only present in Asian.Conclusions: We provide strong evidence of putative deleterious missense variants in ACE2 that are present in specific populations, which could disrupt the function and structure of ACE2. These findings provide novel insight into the genetic variation in ACE2 which may affect the SARS-CoV-2 recognition and infection, and COVID-19 susceptibility and treatment.

scholarly journals Forecasting COVID-19 cases in Algeria using logistic growth and polynomial regression models

2021 ◽  
Author(s):  
Mohamed LOUNIS ◽  
Babu Malavika
Keyword(s):  
Regression Model ◽  
Logistic Model ◽  
Polynomial Regression ◽  
Logistic Growth ◽  
The Novel ◽  
Logistic Growth Model ◽  
Polynomial Regression Models ◽  
Using Data ◽  
Novel Coronavirus ◽  
Polynomial Regression Model

Abstract The novel Coronavirus respiratory disease 2019 (COVID-19) is still expanding through the world since it started in Wuhan (China) on December 2019 reporting a number of more than 84.4 millions cases and 1.8 millions deaths on January 3rd 2021.In this work and to forecast the COVID-19 cases in Algeria, we used two models: the logistic growth model and the polynomial regression model using data of COVID-19 cases reported by the Algerian ministry of health from February 25th to December 2nd, 2020. Results showed that the polynomial regression model fitted better the data of COVID-19 in Algeria the Logistic model. The first model estimated the number of cases on January, 19th 2021 at 387673 cases. This model could help the Algerian authorities in the fighting against this disease.

scholarly journals DESIGN AND IMPLEMENTATION OF INTELLIGENT SEAWATER AUTOMATIC ON-LINE MONITORING SYSTEM BASED ON BIG DATA

2018 ◽  
Vol 48 (3) ◽  
pp. 157-162
Author(s):  
L. Y. LI ◽  
J. YANG ◽  
Y. LEI ◽  
K. H. XIONG ◽  
W. H. CHEN ◽  
...  
Keyword(s):  
Monitoring System ◽  
Online Monitoring ◽  
Large Data ◽  
Test System ◽  
Data Conversion ◽  
Detection Technology ◽  
Large Data Analysis ◽  
Using Data ◽  
Integration Data ◽  
Sea Area

Based on large data analysis method and automatic detection technology, this paper designs a test system, which can realize intelligent online monitoring of seawater. Based on the theory of large data, the data preprocessing method of large data is applied by relying on the information transmitted by integrated sensors. Using data cleaning, data integration, data conversion and data reduction technology, a large number of data collected by marine monitoring devices are processed accurately. An automatic seawater monitoring system is designed on a software platform. Finally, combined with the experimental data of a certain sea area, the test results are analyzed, which proves the feasibility and effectiveness of the designed seawater online monitoring system. It has achieved the effect of seawater environmental analysis and early warning.

scholarly journals Development of a Road Condition Information System using Augmented Reality Technology on Smartphones

2020 ◽  
Vol 10 (2) ◽  
pp. 49-55
Keyword(s):  
Information System ◽  
Augmented Reality ◽  
High Speed ◽  
Human Error ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Data Conversion ◽  
The Road ◽  
Road Condition ◽  
Augmented Reality Technology

Recently, accidents involving ground transportations are getting worse and more serious. Indonesian State Police (Korlantas POLRI) recorded the number of accidents in 2018 as many as 109,215 accidents. The number has incresed 4.69 percent compared to 2017 as many as 104,327 events. Road traffic accidents are caused by human error, the driver in this case. The driver's mistake is influenced by several factors, one of them is they cannot expect the road condition when they drive a vehicle at high speed. To solve this problem, drivers need information that can show road conditions. So, we present a new approach for detecting damaged roads by applying augmented reality technology. This research produces a road condition information system to help drivers get information about road conditions via smartphone. This system uses augmented reality technology with a markerless GPS Based Tracking method. The development of this system requires several stages such as collecting the data, data conversion, data classification, and views road condition. The researchers gathered the road condition data from the Public Work Department Semarang. This department itself undertakes a task to control the road condition in Semarang The trial of this system includes all drivers in Semarang city. Based on the results of the questionnaire responded to by 93 respondents, this test obtained an average value of 68%. So this system gets a pretty good response from the driver. Through this system, all drivers can avoid the damaged road condition which can cause traffic-congested and accident.

scholarly journals Machine Learning for the Diagnosis of Orthodontic Extractions: A Computational Analysis Using Ensemble Learning

2020 ◽  
Vol 7 (2) ◽  
pp. 55
Author(s):  
Yasir Suhail ◽  
Madhur Upadhyay ◽  
Aditya Chhibber ◽  
Kshitiz
Keyword(s):  
Machine Learning ◽  
Human Error ◽  
Ensemble Methods ◽  
Treatment Decision ◽  
Learning Models ◽  
Error Training ◽  
Treatment Plans ◽  
Suitable Treatment ◽  
Using Data ◽  
Machine Learning Models

Extraction of teeth is an important treatment decision in orthodontic practice. An expert system that is able to arrive at suitable treatment decisions can be valuable to clinicians for verifying treatment plans, minimizing human error, training orthodontists, and improving reliability. In this work, we train a number of machine learning models for this prediction task using data for 287 patients, evaluated independently by five different orthodontists. We demonstrate why ensemble methods are particularly suited for this task. We evaluate the performance of the machine learning models and interpret the training behavior. We show that the results for our model are close to the level of agreement between different orthodontists.

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