Probabilistic Transgranular Stress Corrosion Cracking Analysis for Oil and Gas Pipelines

The crack morphology of transgranular stress corrosion cracking (TGSCC) suggests that the mechanism of growth and the condition at which TGSCC occur is different than that of intergranular stress corrosion cracking (IGSCC). Several attempts have been made to characterize IGSCC probabilistically; however, limited effort has been noticed for TGSCC. This paper attempts to analyze TGSCC probabilistically. The study includes assessment of probability of failure for low pH, TGSCC by R6 approach/BS 7910 approach/API 579 approach, which considers plastic yielding and linear elastic fracture mechanics, CSA Z 662-07 burst model approach and author's proposed strain-based approach. The paper observes that failure assessment diagram (FAD) based approaches (R6, BS 7910, and API 579) calculate least failure probability compared to CSA Z 662-07 burst model approach. The authors also noticed that their proposed hoop strain-based approach calculates closely to CSA Z 662-07 burst model approach. Finally, the authors justified the rationality of the results obtained by their approach

Financial threat, hardship and distress predict depression, anxiety and stress among the unemployed youths: a Bangladeshi multi-cities study

Introduction: Unemployment has a contributory role in the development of mental health problems and in Bangladesh there is increasing unemployment, particularly among youth. Consequently, the present study investigated depression, anxiety, and stress among recent graduates in a multi-city study across the country. Methods: A cross-sectional study was conducted among 988 Bangladeshi graduate jobseekers in six major cities of the country between August to November 2019. The measures included socio-demographics and life-style factors, study and job-related information, Economic Hardship Questionnaire, Financial Threat Scale, Financial Well-Being Scale, and Depression Anxiety Stress Scale-21. Results: Depression, anxiety and stress rates among the present sample were 81.1% (n=801), 61.5% (n=608) and 64.8% (n=640) respectively. Factors related to gender, age, socio-economic conditions, educational background, lack of extra-curricular activities, and high screen activity were significant risk factors of depression, anxiety, and stress. Structural equation modeling indicated that (while controlling for age, daily time spent on sleep study, and social media use), financial threat was moderately positively related to depression, anxiety, and stress. Financial hardship was weakly positively associated with depression, anxiety, and stress, whereas financial wellbeing was weakly negatively associated with depression, anxiety, and stress. Limitations: Due to the nature of the present study (i.e., cross-sectional study) and sampling method (i.e., convenience sampling), determining causality between the variables is not possible. Conclusions: The present results emphasized the important detrimental role of financial troubles on young people's mental health by showing that financial problems among unemployed youth predict elevated psychiatric distress in both men and women

Human Fall and Activity Detection, and Muscle Spasm Identification

While more and more inexpensive devices with embedded sensors are introduced to improve our living, the challenge is to process and analyze large datasets they collect for identification of vital events and activities. Datasets from wearable motion sensors are used to detect and monitor human fall and activities of daily life (ADLs). Existing methods for detection of fall and ADLs from motion datasets employ feature extraction and machine learning, but they have high classification errors. Thus, they produce false alarms for fall and wrong identifications of ADLs. Similar to motion dataset problem, detection of involuntary muscle activities from large EMG datasets (collected from spinal cord injured individual) is a challenging task. Recent studies have developed locations identification algorithms for spasms, motor units, and contractions on individual channel of the EMG datasets. It is important to know how and when repetitive muscle contractions happen in multiple muscles at the same time and is there any any reason for this involuntary co-activity. We demonstrate that the k-means clustering algorithm can semi-automatically extract training examples from motion data. We also propose one- and two- layer classification networks using neural networks and softmax regression. Moreover, we propose a distance measure, called Log-Sum Distance, for evaluating difference between two sequences of positive numbers. We use the proposed Log-Sum Distance measure to develop algorithms for recognition of human activities from motion data. The sequences of m positive numbers for Log-Sum Distance are residual sum of squares errors produced from modeling m motion time-series with multiple linear regression method. To reduce incorrect classification we define a threshold test and use it in our proposed novel algorithm. Log-Sum Distance measure also has been employed to identify the locations for repetitive muscle contractions in one or multiple channels of EMG recordings. We also propose a method to identify the muscle that triggers the first contraction in an identified region. We extract features from EMG data using wavelet filter and decomposing co-variance matrix for eigenvector. Experiments with fall detection, ADLs recognition and monitoring, and repetitive contractions identification methods proposed here show very high accuracy rates with different benchmark datasets. The proposed use of threshold values for classification of activities decreased incorrect classification rates. In summary, this work introduces novel methods and the state-of-the-art development and training of wearable devices for fall and ADLs recognition and monitoring. It also extends the involuntary muscle activities identification across multiple channels.</p

Log-Sum Distance Measures and Its Application to Human-Activity Monitoring and Recognition Using Data From Motion Sensors

For the detection of human activities using motion data many techniques employ feature extraction and machine learning. But detection rates and incorrect classification rates require further increase and decrease, respectively. We address both the problems. We propose a novel distance measure, called log-sum distance, for evaluating difference between two sequences of positive numbers. We use the proposed log-sum distance measure to develop algorithms for recognition of human activities from the motion data. The sequences of m positive numbers are residual sum of squares errors produced from modeling m motion time-series with the multiple linear regression method. To reduce incorrect classification, we define a threshold test and use it in our proposed novel algorithm. We have defined an optimization function and used it for computing optimal threshold values. Extensive evaluation of our activity detection algorithm with two different sets of data sets show increased activity recognition rates and decreased incorrect classification rates compared with other existing methods. In one data set, the proposed algorithm detects all activities with 100% accuracy and in the another data set, it detects all activities with 99% or higher accuracy. The proposed use of threshold values for classification of activities decreased incorrect classification rates

Detection of Good and Bad Sensor Nodes in the Presence of Malicious Attacks and Its Application to Data Aggregation

Most of the sensor nodes have multiple inexpensive and unreliable sensors embedded in them. For many applications, readings from multiple sensors are aggregated. However, the presence of malicious attacks adds a challenge to sensor data aggregation. Detection of those compromised and unreliable sensors and sensor nodes is important for robust data aggregation as well as their management and maintenance. In this work, we develop a method for identification of good and bad sensor nodes and apply it for secure data aggregation algorithms. We consider altered/unreliable readings as outliers and identify them using an augmented and modified version of a local outlier factor computation method. We use the outlier detection algorithm for reliable and unreliable sensor detection and use the results from this algorithm for an unreliable sensor-node identification algorithm. We show its usefulness for secure data aggregation algorithms. Extensive evaluations of the proposed algorithm show that it identifies good and bad nodes and estimates true sensor value efficiently

Semi-Automatic Extraction of Training Examples From Sensor Readings for Fall Detection and Posture Monitoring

While inexpensive wearable motion-sensing devices have shown great promise for fall detection and posture monitoring, two major problems still exist and have to be solved: a framework for the development of firmware and software to make intelligent decisions. We address both the problems. We propose a generic framework for developing the firmware. We also demonstrate that the k-means clustering algorithm can semi-automatically extract training examples from motion data. Moreover, we trained and evaluated several one- and two-level classification networks to monitor non-fall activities and to detect fall events. The proposed classification networks are the combinations of neural networks and softmax regression. These networks are trained offline with examples extracted by our proposed method. The cross-validation of trained two-level networks shows 100% accuracy for non-fall activities and fall events. The data sets for training and testing have been collected using the devices we assembled with four off-the-shelf components. We have programmed them using a prototype of our proposed framework. The data sets include seven types of non-fall activities and four types of fall events. This paper advances the state of the art for the development and training of wearable devices for monitoring non-fall activities and detecting fall events