Technical Challenges for Compressors and Steam Turbines for Efficient and Sustainable Operation in Mega Ethylene Plants

TutorialTutorial 9: Changing markets, industry demands for increased efficiency and long term operation, and availability of new technology have all contributed in development of more efficient and reliable turbomachines. The authors present ethylene plants trends, demonstrate the challenges faced by the turbomachinery equipment manufacturers and highlight various advancements in the turbomachinery technology. History maps are introduced for design advancements, verification tests, and application results in terms of transient fluid dynamics, thermodynamics, rotor dynamics, and blade vibration strength evaluation. In addition, after recognizing the need for long term operation and related typical damage and deterioration modes, the authors explain various practical technologies (such as effective on-line washing, flow path surface treatment, combination of anti-corrosion and erosion prevention, stage performance enhancement by partial component replacement, NDE techniques for both compressors and steam turbines, and a unique casing replacement technique on the same footprint for increasing capacity) used to provide more efficient and reliable machines

OpenPLC based control system testbed for PLC whitelisting system

This paper proposes a security testbed system for industrial control systems. In control systems, controllers are final fortresses to continue the operation of field systems. Then, we need countermeasures of controllers. The whitelisting function is efficient in controller security. The whitelisting function registers normal operations in a list and detects unregistered operations as abnormal. We need a testbed system to check whether the whitelist function does not affect other functions of the controller. The industrial controller and its engineering tool are relatively expensive, and are customized with respect to controller vendors. To enhance the whitelist development, this study proposes a testbed system using OpenPLC which is an open-source software. This system is independent of controller vendors and is applicable for controller programming languages. We implement a whitelist based anomaly detection method for the testbed system and validate that the anomaly detection method operates correctly

Reduced serum level of leukocyte cell-derived chemotaxin 2 is associated with the presence of diabetic retinopathy

AbstractBackgroundVascular endothelial growth factor (VEGF) signaling is an important pathway in the development of diabetic retinopathy (DR). A recent report showed that leukocyte cell-derived chemotaxin 2 (LECT2) suppresses the VEGF signaling in endothelial cells. However, the clinical relevance of LECT2 in DR is unknown. This study aimed to investigate serum LECT2 levels and the presence of DR.MethodsThe study included 230 people with type 2 diabetes mellitus (DM), 95 with DR and 135 without DR. Serum LECT2 levels were measured using an enzyme-linked immunosorbent assay. Data were evaluated using Spearman's rank correlation, univariate and multivariate logistic regression.ResultsSerum LECT2 levels were significantly lower in participants with DM having DR than in those not having DR (35.6±14.9ng/ml vs. 44.5±17.6ng/ml, P<0.001). Spearman's rank correlation analysis revealed a significant association between serum LECT2 levels and the presence of DR (P<0.001). Multiple regression analysis revealed that serum LECT2 levels were independently related to DR (P<0.001).ConclusionsThese findings indicated that serum LECT2 level is negatively associated with the presence of DR and suggest that low circulating LECT2 level is a risk factor for DR

Association between interstitial lung abnormality and mortality in patients with esophageal cancer

The version of record of this article, first published in Japanese Journal of Radiology, is available online at Publisher’s website: https://doi.org/10.1007/s11604-024-01563-x.Purpose: To investigate the relationship between interstitial lung abnormalities (ILAs) and mortality in patients with esophageal cancer and the cause of mortality. Materials and methods: This retrospective study investigated patients with esophageal cancer from January 2011 to December 2015. ILAs were visually scored on baseline CT using a 3-point scale (0 = non-ILA, 1 = indeterminate for ILA, and 2 = ILA). ILAs were classified into subcategories of non-subpleural, subpleural non-fibrotic, and subpleural fibrotic. Five-year overall survival (OS) was compared between patients with and without ILAs using the multivariable Cox proportional hazards model. Subgroup analyses were performed based on cancer stage and ILA subcategories. The prevalences of treatment complications and death due to esophageal cancer and pneumonia/respiratory failure were analyzed using Fisher’s exact test. Results: A total of 478 patients with esophageal cancer (age, 66.8 years ± 8.6 [standard deviation]; 64 women) were evaluated in this study. Among them, 267 patients showed no ILAs, 125 patients were indeterminate for ILAs, and 86 patients showed ILAs. ILAs were a significant factor for shorter OS (hazard ratio [HR] = 1.68, 95% confidence interval [CI] 1.10–2.55, P = 0.016) in the multivariable Cox proportional hazards model adjusting for age, sex, smoking history, clinical stage, and histology. On subgroup analysis using patients with clinical stage IVB, the presence of ILAs was a significant factor (HR = 3.78, 95% CI 1.67–8.54, P = 0.001). Subpleural fibrotic ILAs were significantly associated with shorter OS (HR = 2.22, 95% CI 1.25–3.93, P = 0.006). There was no significant difference in treatment complications. Patients with ILAs showed a higher prevalence of death due to pneumonia/respiratory failure than those without ILAs (non-ILA, 2/95 [2%]; ILA, 5/39 [13%]; P = 0.022). The prevalence of death due to esophageal cancer was similar in patients with and without ILA (non-ILA, 82/95 [86%]; ILA 32/39 [82%]; P = 0.596). Conclusion: ILAs were significantly associated with shorter survival in patients with esophageal cancer

Development of fully automated and ultrasensitive assays for urinary adiponectin and their application as novel biomarkers for diabetic kidney disease

Glomerular filtration rate (GFR) and urinary albumin excretion rate (UAER) are used to diagnose and classify the severity of chronic kidney disease. Total adiponectin (T-AN) and high molecular weight adiponectin (H-AN) assays were developed using the fully automated immunoassay system, HI-1000 and their significance over conventional biomarkers were investigated. The T-AN and H-AN assays had high reproducibility, good linearity, and sufficient sensitivity to detect trace amounts of adiponectin in the urine. Urine samples after gel filtration were analyzed for the presence of different molecular isoforms. Low molecular weight (LMW) forms and monomers were the major components (93%) of adiponectin in the urine from a diabetic patient with normoalbuminuria. Urine from a microalbuminuria patient contained both high molecular weight (HMW) (11%) and middle molecular weight (MMW) (28%) adiponectin, although the LMW level was still high (52%). The amount of HMW (32%) and MMW (42%) were more abundant than that of LMW (24%) in a diabetic patient with macroalbuminuria. T-AN (r = − 0.43) and H-AN (r = − 0.38) levels showed higher correlation with estimated GFR (eGFR) than UAER (r = − 0.23). Urinary levels of both T-AN and H-AN negatively correlated with renal function in diabetic patients and they may serve as new biomarkers for diabetic kidney disease

Radiological prediction of tumor invasiveness of lung adenocarcinoma on thin-section CT

To evaluate thin-section computed tomography (CT) (TSCT) features that differentiate adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IVA), and to determine the size of solid portion on CT that correlates to pathological invasive components. Forty-eight patients were included. Nodules were classified into ground-glass nodule (GGN), part-solid, solid, and heterogeneous. Visual density of GGNs was subjectively evaluated using reference standard images: faint GGN (Ga), −400 HU; and mixed (Ga + Gb, Ga + Gc, and Gb + Gc). The evaluated TSCT findings included margin of nodule, distribution of solid portion, distribution of air bronchiologram, and pleural indentation. The longest diameters of the solid portion and the entire tumor were measured. Invasive diameters were measured in pathological specimens. Twenty-two AISs (16 GGNs [7 Ga, 5 Gb, 2 Gc, 1 Ga + Gc, 1 Gb + Gc], 4 part-solids, and 2 heterogeneous), 6 MIAs (1 GGN [Gb + Gc], 3 part-solids, and 2 solids), and 20 IVAs (1 GGN [Gb], 3 part-solids, and 16 solid) were found. The longest diameter (mean ± standard deviation) of the solid portion and total tumor were 9.7 ± 9.7 and 18.9 ± 5.6 mm, respectively. Significant differences in TSCT findings between AIS and IVA were margin of nodule (Pearson chi-squared test, P = 0.004), distribution of air bronchiologram (P = 0.0148), and pleural indentation (P = 0.0067). A solid portion >5.3 mm on TSCT indicated MIA or IVA, and >7.3 mm indicated IVA (receiver operating characteristic analysis, P 7.3 mm on TSCT indicates IVA

Application of deep learning (3-dimensional convolutional neural network) for the prediction of pathological invasiveness in lung adenocarcinoma

To compare results for radiological prediction of pathological invasiveness in lung adenocarcinoma between radiologists and a deep learning (DL) system.Ninety patients (50 men, 40 women; mean age, 66 years; range, 40-88 years) who underwent pre-operative chest computed tomography (CT) with 0.625-mm slice thickness were included in this retrospective study. Twenty-four cases of adenocarcinoma in situ (AIS), 20 cases of minimally invasive adenocarcinoma (MIA), and 46 cases of invasive adenocarcinoma (IVA) were pathologically diagnosed. Three radiologists of different levels of experience diagnosed each nodule by using previously documented CT findings to predict pathological invasiveness. DL was structured using a 3-dimensional (3D) convolutional neural network (3D-CNN) constructed with 2 successive pairs of convolution and max-pooling layers, and 2 fully connected layers. The output layer comprises 3 nodes to recognize the 3 conditions of adenocarcinoma (AIS, MIA, and IVA) or 2 nodes for 2 conditions (AIS and MIA/IVA). Results from DL and the 3 radiologists were statistically compared.No significant differences in pathological diagnostic accuracy rates were seen between DL and the 3 radiologists (P>. 11). Receiver operating characteristic analysis demonstrated that area under the curve for DL (0.712) was almost the same as that for the radiologist with extensive experience (0.714; P=. 98). Compared with the consensus results from radiologists, DL offered significantly inferior sensitivity (P=. 0005), but significantly superior specificity (P=. 02).Despite the small training data set, diagnostic performance of DL was almost the same as the radiologist with extensive experience. In particular, DL provided higher specificity than radiologists

Impact Analysis of Granularity Levels on Feature Location Technique

APRES 2014 : Asia Pacific Requirements Engineering Symposium, April 28-29, 2014, Auckland, New ZealandDue to the increasing of software requirements and software features, modern software systems continue to grow in size and complexity. Locating source code entities that required to implement a feature in millions lines of code is labor and cost intensive for developers. To this end, several studies have proposed the use of Information Retrieval (IR) to rank source code entities based on their textual similarity to an issue report. The ranked source code entities could be at a class or function granularity level. Source code entities at the class-level are usually large in size and might contain a lot of functions that are not implemented for the feature. Hence, we conjecture that the class-level feature location technique requires more effort than function-level feature location technique. In this paper, we investigate the impact of granularity levels on a feature location technique. We also presented a new evaluation method using effort-based evaluation. The results indicated that function-level feature location technique outperforms class-level feature location technique. Moreover, function-level feature location technique also required 7 times less effort than class-level feature location technique to localize the first relevant source code entity. Therefore, we conclude that feature location technique at the function-level of program elements is effective in practice