Latent Errors and Visible Earned Value: How the Evolutionary Model Integrates Earned Value Metrics with Project System Dynamics

A project model is presented that weaves together ideas from earned value project management and systems dynamics. It is able to adjust to increasingly unhealthy actual project behaviors in ways that preserve the signature pattern of the staffing histograms observed in the real world and provide a tool for managers to correct projects that are not meeting the plan. Starting from the planned staffing histogram and the project performance baseline, the model captures the delay and cost of experience dilution, includes the unplanned-for effort that is revealed in the typical pattern of the Cost Performance Index, assesses progress using the actual cost to date and the earned value to date, and adjusts staffing, scope, or both, to complete the project on schedule. A new method of approximating work remaining, called project-to-date, is shown to track the planned staffing histogram better than the commonly used fraction-complete method.

Machine learning analysis of volatolomic profiles in breath can identify non-invasive biomarkers of liver disease: A pilot study

Disease-related effects on hepatic metabolism can alter the composition of chemicals in the circulation and subsequently in breath. The presence of disease related alterations in exhaled volatile organic compounds could therefore provide a basis for non-invasive biomarkers of hepatic disease. This study examined the feasibility of using global volatolomic profiles from breath analysis in combination with supervised machine learning to develop signature pattern-based biomarkers for cirrhosis. Breath samples were analyzed using thermal desorption-gas chromatography-field asymmetric ion mobility spectroscopy to generate breathomic profiles. A standardized collection protocol and analysis pipeline was used to collect samples from 35 persons with cirrhosis, 4 with non-cirrhotic portal hypertension, and 11 healthy participants. Molecular features of interest were identified to determine their ability to classify cirrhosis or portal hypertension. A molecular feature score was derived that increased with the stage of cirrhosis and had an AUC of 0.78 for detection. Chromatographic breath profiles were utilized to generate machine learning-based classifiers. Algorithmic models could discriminate presence or stage of cirrhosis with a sensitivity of 88–92% and specificity of 75%. These results demonstrate the feasibility of volatolomic profiling to classify clinical phenotypes using global breath output. These studies will pave the way for the development of non-invasive biomarkers of liver disease based on volatolomic signatures found in breath.

Leveraging on Machine Learning Solution for Pioneering Wells Augmented Stuck Pipe Indicator in Real Time Centre

Stuck pipe incidents remain as one of the major problems in the drilling industry. The incidents will lead to expensive loss time in daily spread cost, bottom hole assembly cost, sidetracking cost as well as fishing cost. The Wells Augmented Stuck Pipe (WASP) Indicator, a state-of-the-art machine learning technology that seamlessly integrates with PETRONAS existing technologies, is introduced as the stuck pipe prevention detection system for the company. Historical real-time drilling data and stuck pipe incidents reports between 2007 and 2019 are used for the development of machine learning models. The models utilize key drilling parameters such as hookload and equivalent circulating density (ECD) to predict and analyze trends to detect any signature pattern anomalies for various stuck pipe events. The prediction and alarm are displayed in real-time monitoring software to trigger the operation team for prompt intervention. The WASP solution has demonstrated proven outcomes using historical and live well with high confidence in detecting stuck pipe incidents due to differential sticking, hole cleaning, and wellbore geometry. The WASP Indicator is envisaged to provide the company with cutting edge advantages in the industry. It is expected that the system will reduce the identification period and improve the reaction time of the monitoring specialists in recognizing the stuck pipe symptoms and highlighting potential incidents. The system is also bringing value to the company via non-productive time (NPT) cost avoidance and identification of early onset of various stuck pipe events based on distinct mechanisms. With the system, the existing portfolio value can be enhanced via setting dynamic trends and models into historical experiences context. The WASP Indicator is aspired to be the forefront innovation that will leap through the norm and lead the region in a greater plan of drilling automation system.

Signature Identification Menggunakan Metode Template Matching dan Fuzzy K-Nearest Neighbor

— Signature is the result of the process of writing a person of a particular nature as a symbolic substance, which means a symbol or mark. Signature is usually used as an identifying mark of a person, each person must have his own signature in a different pattern. Because it's used as a person's identifying badge, Signatures now become particularly susceptible to counterfeiting and abuse that require check with a signature pattern recognition. This research has created a signature pattern recognition system using methods Template Matching and Fuzzy K-Nearest Neighbor to help recognize a person's signature pattern. The number of signatures used is 110 in two categories: the original signature with 100 data and the false signature with 10 data, and there were 10 classes taken using smartphone cameras. From this research, it was found that the best value from the image size of 200x200 pixels was 92% of the class that owned the signature legible, Positive Predictive Value (PPV) 88% and False Rejection Rate (FRR) 12%, with a k=3 on the original signature, and 90% of the class that owned the signature legible, Negative Predictive Value (NPV) 90% dan False Acceptance Rate (FAR) 10% with a k=9 on the false signature. From these results, it could be concluded that methods Template Matching and Fuzzy K-Nearest Neighbor could be used for signature pattern recognition.

Sequence Length of HIV-1 Subtype B Increases over Time: Analysis of a Cohort of Patients with Hemophilia over 30 Years

We aimed to investigate whether the sequence length of HIV-1 increases over time. We performed a longitudinal analysis of full-length coding region sequences (FLs) during an HIV-1 outbreak among patients with hemophilia and local controls infected with the Korean subclade B of HIV-1 (KSB). Genes were amplified by overlapping RT-PCR or nested PCR and subjected to direct sequencing. Overall, 141 FLs were sequentially determined over 30 years in 62 KSB-infected patients. Phylogenetic analysis indicated that within KSB, two FLs from plasma donors O and P comprised two clusters, together with 8 and 12 patients with hemophilia, respectively. Signature pattern analysis of the KSB of HIV-1 revealed 91 signature nucleotide residues (1.1%). In total, 48 and 43 signature nucleotides originated from clusters O and P, respectively. Six positions contained 100% specific nucleotide(s) in clusters O and P. In-depth FL analysis for over 30 years indicated that the KSB FL significantly increased over time before combination antiretroviral therapy (cART) and decreased with cART. This increase occurred due to the significant increase in env and nef genes, originating in the variable regions of both genes. The increase in sequence length of HIV-1 over time suggests an evolutionary direction.

Sequence Length of HIV-1 Subtype B Increases Over Time: Analysis of a Cohort of Patients With Hemophilia Over 30 Years

We aimed to investigate whether the sequence length of HIV-1 increases over time. A longitudinal analysis of full-length coding region sequences (FLs) during an HIV-1 outbreak among pa-tients with hemophilia and local controls infected with the Korean subclade B of HIV-1 (KSB) was performed. Genes were amplified by overlapping RT-PCR or nested PCR and subjected to direct sequencing. Overall, 141 FLs were sequentially determined over 30 years in 62 KSB-infected patients. Phylogenetic analysis indicated that within KSB, two FLs from plasma donors O and P comprised two clusters together with 8 and 12 patients with hemophilia, respectively. Signature pattern analysis for the KSB of HIV-1 revealed 91 signature nucleotide residues (1.05%). In total, 48 and 43 signature nucleotides originated from clusters O and P, respectively. Only six positions contained 100% specific nucleotide(s) in clusters O and P. Additionally, in-depth FL analysis over 30 years indicates that the KSB FL significantly increased over time before combined antiretroviral therapy (cART) and decreased with cART. The increase occurred due to a significant increase in env and nef genes, originating in the variable regions of both genes. The increase in the sequence length of HIV-1 over time suggests that it has an evolutionary direction.

A Medical-Inspired Framework to Classify Downhole Shocks Waveforms While Drilling

This paper presents a medical approach to classify shock waveforms acquired at 31,250 hertz downhole. The shock signals are treated as drilling electrocardiogram (D-ECG). The D-ECGs are processed using clustering algorithms and merged with drilling incidents to identify an arrhythmic signature pattern that can lead to catastrophic failures. In medicine, the analysis of heartbeat cycles in an electrocardiogram signal is very important for monitoring heart patients. In the drilling industry, downhole shocks are present most of the time. They are present so often that the authors introduce the concept of drilling electrocardiogram (D-ECG) based on shock waveforms acquired at high frequency. The shock module was implemented in hardware using a field programmable gate array (FPGA) and run inside the control unit of an RSS to complement the navigation systems composed. The shock acquisition and processing are performed at 31,250 Hz, providing enough bandwidth to fully reconstruct high-frequency events. A novel methodology combining field incidents with machine learning clustering algorithms is proposed to identify arrhythmic shocks signatures and whirl and bit bounce in real time, preventing failures to the BHA.

Sequence Length of HIV-1 Subtype B Increases Over Time: Analyzing a Cohort of Patients with Hemophilia Over 30 Years

The objective of this study is to investigate whether the sequence length of HIV-1 increases over time. A longitudinal analysis of full-length coding region sequences (FLs) in an outbreak of HIV-1 infection among patients with hemophilia and local controls identified as infected with the Korean subclade B of HIV-1 (KSB). Genes amplified by overlapping RT-PCR or nested PCR were subjected to direct sequencing. In total, 141 FLs were sequentially determined over 30 years in 62 KSB-infected patients. Non-KSB sequences were retrieved from the Los Alamos National Laboratory HIV Database. Phylogenetic analysis indicated that within KSB, 2 FLs from plasma donors O and P comprised two clusters together with 8 and 12 patients with hemophilia, respectively. Signature pattern analysis for the KSB of HIV-1 revealed signature nucleotide residues at 1.05%, compared with local controls. Additionally, in-depth FLs sequence analysis over 30 years in KSB indicates that the KSB FL significantly increases over time before combined antiretroviral therapy (cART) and decreases on cART. Furthermore, the increase in FLs over time significantly occurred in the subtypes B, C and G, but, there was no increase in the subtypes D, A, and F1. Consequently, subtypes F1 and D had the shortest sequence length. Our analysis was extended to compare HIV-1 with HIV-2 and SIVs. Essentially, the longer the sequence length (SIVsm > HIV-2 > SIVcpz > HIV-1), the longer the survival period. The increase in the length of the HIV-1 sequence over time suggests that it might be an evolutionary direction toward attenuated pathogenicity.