Mechanistic Leak-Detection Modeling for Single Gas-Phase Pipelines: Lessons Learned from Fit to Field-Scale Experimental Data

The use of pipelines is one of the most popular ways of delivering gas phases as shown by numerous examples in hydrocarbon transportation systems in Arctic regions, oil and gas production facilities in onshore and offshore wells, and municipal gas distribution systems in urban areas. A gas leak from pipelines can cause serious problems not only because of the financial losses associated but also its social and environmental impacts. Therefore, establishing an early leak detection model is vital to safe and secure operations of such pipeline systems.A leak detection model for a single gas phase is presented in this study by using material balance and pressure traverse calculations. The comparison between two steady states, with and without leak, makes it possible to quantify the magnitude of disturbance in two leak detection indicators such as the change in inlet pressure (ΔPin) and the change in outlet flow rate (Δqout) in a broad range of leak locations (xleak) and leak opening sizes (dleak). The results from the fit to large-scale experimental data of Scott and Yi (1998) show that the value of leak coefficient (CD), which is shown to be the single-most important but largely unknown parameter, ranges from 0.55 to 4.11, and should be a function of Reynolds number (NRe) which is related to leak characteristics such as leak location (xleak), leak opening size (dleak), leak rate (qleak) and system pressure. Further investigations show that between the two leak detection indicators, the change in outlet flow rate (Δqout) is superior to the change in inlet pressure (ΔPin) because of larger disturbance, if the pressure drop along the pipeline is relatively small compared to the outlet pressure; otherwise, the change in inlet pressure (ΔPin) is superior to the change in outlet flow rate (Δqout).Key words: Leak; Leak detection modeling; Pipeline; Leak coefficient; Gas flow in pip

E-shopping and in-store shopping status in Tehran: Can e-shopping reduce traffic in the future?

Nowadays Information and Communication Technology effects on all aspects of human activities, such as teleworking, electronic commerce, electronic banking, electronic learning, etc. the most of these services can prevent unnecessary travels in cities especially in rush hour. The aim of this study is to explore the frequency of electronic shopping and in-store shopping in Tehran, according to gender, educational background and employment status in order to obtain some information about the behavior of online and traditional shoppers in Tehran. For this purpose, 510 questionnaires were collected and the shoppers were categorized and analyzed in some groups, using SPSS23. The findings showed that the people often tend to buy their favorable products traditionally. As expected, the percentage of people that never experience electronic shopping is high but the development of online shops and mobile apps can attract people to this way of modern shopping. A good strategy for improving online marketing will reduce traffic congestion, travel time, energy consumption, and air pollution and so on

Phylovar: toward scalable phylogeny-aware inference of single-nucleotide variations from single-cell DNA sequencing data

Motivation: Single-nucleotide variants (SNVs) are the most common variations in the human genome. Recently developed methods for SNV detection from single-cell DNA sequencing data, such as SCI and scVILP, leverage the evolutionary history of the cells to overcome the technical errors associated with single-cell sequencing protocols. Despite being accurate, these methods are not scalable to the extensive genomic breadth of single-cell whole-genome (scWGS) and whole-exome sequencing (scWES) data. Results: Here, we report on a new scalable method, Phylovar, which extends the phylogeny-guided variant calling approach to sequencing datasets containing millions of loci. Through benchmarking on simulated datasets under different settings, we show that, Phylovar outperforms SCI in terms of running time while being more accurate than Monovar (which is not phylogeny-aware) in terms of SNV detection. Furthermore, we applied Phylovar to two real biological datasets: an scWES triple-negative breast cancer data consisting of 32 cells and 3375 loci as well as an scWGS data of neuron cells from a normal human brain containing 16 cells and approximately 2.5 million loci. For the cancer data, Phylovar detected somatic SNVs with high or moderate functional impact that were also supported by bulk sequencing dataset and for the neuron dataset, Phylovar identified 5745 SNVs with non-synonymous effects some of which were associated with neurodegenerative diseases. Availability and implementation: Phylovar is implemented in Python and is publicly available at https://github.com/NakhlehLab/Phylovar.National Science Foundation | Ref. IIS-1812822National Science Foundation | Ref. IIS-210683

Association between depression, anxiety, and insomnia with musculoskeletal pain source: a multi-center study

Background: Musculoskeletal pain syndrome (MPS) is one of the modern diseases. Musculoskeletal pain (MP) may develop at any age and impact physical and mental health. This study aimed to evaluate the association between anxiety, depression, and insomnia with musculoskeletal pain source. This cross-sectional study was conducted on 450 patients with musculoskeletal pain. Goldberg depression (GB), Beck Anxiety Inventory (BAI), and Morin Insomnia Severity Index (ISI) questionnaires were used to collect data. Participants have divided into two groups: individuals with unknown musculoskeletal pain sources and individuals with known musculoskeletal pain sources. Anxiety, depression, and insomnia scores were compared between the two groups. For statistical analysis of data mean (SD), frequency (), Chi-square, Mann-Whitney test, and Logistic regression models were used. All analysis was performed using SPSS 26. Results: In this study, 39.4 of the participants were in severe depression, 31.1 in severe anxiety, 34.7 in the no clinically significant, and 32.9 in the sub-threshold insomnia group. There was a significant difference between the severity of anxiety and insomnia in the two groups with the known and unknown pain sources (p < 0.05). However, the score of depression (OR = 1.00, 95 CI 0.99�1.01), anxiety (OR = 1.00, 95 CI 0.99�1.02), and insomnia (OR = 1.01, 95 CI 0.98�1.03) was not related to the pain source. Conclusion: There was a statistically significant relationship between anxiety and insomnia severity with musculoskeletal pain source. According to the high prevalence of depression, anxiety, and depression in both groups with known and unknown musculoskeletal pain sources, the cooperation of orthopedists, rheumatologists, and physical therapists with psychiatrist can be useful in improving the condition of patients. © 2021, The Author(s)

Anti-inflammatory Components from Functional Foods for Obesity

Obesity, defined as excessive fat accumulation that may impair health, has been described throughout human history, but it has now reached epidemic proportions with the WHO estimating that 39% of the world’s adults over 18 years of age were overweight or obese in 2016. Obesity is a chronic low-grade inflammatory state leading to organ damage with an increased risk of common diseases including cardiovascular and metabolic disease, non-alcoholic fatty liver disease, osteo-arthritis and some cancers. This inflammatory state may be influenced by adipose tissue hypoxia and changes in the gut microbiota. There has been an increasing focus on functional foods and nutraceuticals as treatment options for obesity as drug treatments are limited in efficacy. This chapter summarises the importance of anthocyanin-containing fruits and vegetables, coffee and its components, tropical fruit and food waste as sources of phytochemicals for obesity treatment. We emphasise that preclinical studies can form the basis for clinical trials to determine the effectiveness of these treatments in humans