Raising Energy Efficiency of High-Head Drinking Water Pumping Schemes in Hilly India – Massive Potential, Complex Challenges

Investigations of energy efficiency of 25 pumps showed wire-to-water efficiencies ranging from 30% to 60%, with an average of 47%. Raising the efficiency of just 7 pumps to the realistic target of 60% would require an initial investment of 126 k€ and represent a net present value (profit) of 446 k€ over a 10-year pump lifetime, saving 8.6 kt of CO2 emissions. The primary measures for raising efficiency are in order of priority: 1) improving pre-filtration of raw water to prevent rapid mechanical wear due to suspended particles during monsoon, 2) providing training, improved working conditions, and better tools and spare parts among pump operators and 3) replacing aging, oversized pumps with properly sized pumps operating close to peak efficiency. As of January 2014 the results have been confirmed by a Bureau of Energy Efficiency-certified energy auditor and the extent and funding of efficiency measures implementation is in planning

Federated Learning for Predictive Maintenance and Quality Inspection in Industrial Applications

Data-driven machine learning is playing a crucial role in the advancements of Industry 4.0, specifically in enhancing predictive maintenance and quality inspection. Federated learning (FL) enables multiple participants to develop a machine learning model without compromising the privacy and confidentiality of their data. In this paper, we evaluate the performance of different FL aggregation methods and compare them to central and local training approaches. Our study is based on four datasets with varying data distributions. The results indicate that the performance of FL is highly dependent on the data and its distribution among clients. In some scenarios, FL can be an effective alternative to traditional central or local training methods. Additionally, we introduce a new federated learning dataset from a real-world quality inspection setting

Moderate physical activity may prevent cartilage loss in women with knee osteoarthritis : data from the Osteoarthritis Initiative

All authors have completed the ICMJE uniform disclosure form at http://www.icmje.org/coi_disclosure.pdf and declare: data acquisition in this study was funded by the Osteoarthritis Initiative, a public–private partnership comprised of five contracts (N01-AR-2-2258; N01-AR-2-2259;N01-AR-2-2260; N01-AR-2-2261; N01-AR-2-2262) funded by the National Institutes of Health, a branch of the Department of Health and Human Services, and conducted by the Osteoarthritis Initiative study Investigators. Private funding partners of the OAI include Merck Research Laboratories, Novartis Pharmaceuticals Corporation, GlaxoSmithKline, and Pfizer, Inc. Private sector funding for the Osteoarthritis Initiative is managed by the Foundation for the National Institutes of Health. The image analysis in this study was partly funded by the FNIH OA Biomarkers Consortium, with grants, direct and in -kind contributions, provided by: AbbVie; Amgen Inc.; Arthritis Foundation; Bioiberica S.A.; DePuy Mitek, Inc.; Flexion Therapeutics, Inc.; GlaxoSmithKline; Merck KGaA; Rottapharm | Madaus; Sanofi; and Stryker. Other parts of funding were provided by a direct grant from Merck KGaA, by a contract with the University of Pittsburgh (Pivotal OAI MRI Analyses [POMA]: NIH/NHLBI Contract No. HHSN2682010000 21C), by a vendor contract from the OAI coordinating center at University of California, San Francisco (N01-AR-2-2258), and by an ancillary study to the OAI held by the Division of Rheumatology, Feinberg School of Medicine, Northwestern University (R01 AR52918). This research has also received funding from the European Union Seventh Framework Programme (FP7-PEOPLE-2013-ITN; KNEEMO) under grant agreement number 607510. AGC is supported by a National Health and Medical Research Council (NHMRC) of Australia Early Career Fellowship (Neil Hamilton Fairley Clinical Fellowship No.1121173). The sponsors were not involved in the design and conduct of this particular study, in the analysis and interpretation of the data, and in the preparation, review, or approval of the manuscript.Peer reviewedPostprin

Comparison of Clustering Algorithms for Statistical Features of Vibration Data Sets

Vibration-based condition monitoring systems are receiving increasing attention due to their ability to accurately identify different conditions by capturing dynamic features over a broad frequency range. However, there is little research on clustering approaches in vibration data and the resulting solutions are often optimized for a single data set. In this work, we present an extensive comparison of the clustering algorithms K-means clustering, OPTICS, and Gaussian mixture model clustering (GMM) applied to statistical features extracted from the time and frequency domains of vibration data sets. Furthermore, we investigate the influence of feature combinations, feature selection using principal component analysis (PCA), and the specified number of clusters on the performance of the clustering algorithms. We conducted this comparison in terms of a grid search using three different benchmark data sets. Our work showed that averaging (Mean, Median) and variance-based features (Standard Deviation, Interquartile Range) performed significantly better than shape-based features (Skewness, Kurtosis). In addition, K-means outperformed GMM slightly for these data sets, whereas OPTICS performed significantly worse. We were also able to show that feature combinations as well as PCA feature selection did not result in any significant performance improvements. With an increase in the specified number of clusters, clustering algorithms performed better, although there were some specific algorithmic restrictions.Comment: 12 pages, 10 figures, Proceedings of the 5th International Data Science Conference iDSC202

Accuracy and longitudinal reproducibility of quantitative femorotibial cartilage measures derived from automated U-Net-based segmentation of two different MRI contrasts: data from the osteoarthritis initiative healthy reference cohort

Objective To evaluate the agreement, accuracy, and longitudinal reproducibility of quantitative cartilage morphometry from 2D U-Net-based automated segmentations for 3T coronal fast low angle shot (corFLASH) and sagittal double echo at steady-state (sagDESS) MRI. Methods 2D U-Nets were trained using manual, quality-controlled femorotibial cartilage segmentations available for 92 Osteoarthritis Initiative healthy reference cohort participants from both corFLASH and sagDESS (n = 50/21/21 training/validation/test-set). Cartilage morphometry was computed from automated and manual segmentations for knees from the test-set. Agreement and accuracy were evaluated from baseline visits (dice similarity coefficient: DSC, correlation analysis, systematic offset). The longitudinal reproducibility was assessed from year-1 and -2 follow-up visits (root-mean-squared coefficient of variation, RMSCV%). Results Automated segmentations showed high agreement (DSC 0.89-0.92) and high correlations (r >= 0.92) with manual ground truth for both corFLASH and sagDESS and only small systematic offsets (<= 10.1%). The automated measurements showed a similar test-retest reproducibility over 1 year (RMSCV% 1.0-4.5%) as manual measurements (RMSCV% 0.5-2.5%). Discussion The 2D U-Net-based automated segmentation method yielded high agreement compared with manual segmentation and also demonstrated high accuracy and longitudinal test-retest reproducibility for morphometric analysis of articular cartilage derived from it, using both corFLASH and sagDESS.ISSN:0968-5243ISSN:1352-866

Clinical evaluation of fully automated thigh muscle and adipose tissue segmentation using a U-Net deep learning architecture in context of osteoarthritic knee pain

Objective Segmentation of thigh muscle and adipose tissue is important for the understanding of musculoskeletal diseases such as osteoarthritis. Therefore, the purpose of this work is (a) to evaluate whether a fully automated approach provides accurate segmentation of muscles and adipose tissue cross-sectional areas (CSA) compared with manual segmentation and (b) to evaluate the validity of this method based on a previous clinical study. Materials and methods The segmentation method is based on U-Net architecture trained on 250 manually segmented thighs from the Osteoarthritis Initiative (OAI). The clinical evaluation is performed on a hold-out test set bilateral thighs of 48 subjects with unilateral knee pain. Results The segmentation time of the method is < 1 s and demonstrated high agreement with the manual method (dice similarity coeffcient: 0.96 ± 0.01). In the clinical study, the automated method shows that similar to manual segmentation (− 5.7 ± 7.9%, p < 0.001, effect size: 0.69), painful knees display significantly lower quadriceps CSAs than contralateral painless knees (− 5.6 ± 7.6%, p < 0.001, effect size: 0.73). Discussion Automated segmentation of thigh muscle and adipose tissues has high agreement with manual segmentations and can replicate the effect size seen in a clinical study on osteoarthritic pain.ISSN:0968-5243ISSN:1352-866

Detection of Differences in Longitudinal Cartilage Thickness Loss Using a Deep-Learning Automated Segmentation Algorithm: Data From the Foundation for the National Institutes of Health Biomarkers Study of the Osteoarthritis Initiative

Objective To study the longitudinal performance of fully automated cartilage segmentation in knees with radiographic osteoarthritis (OA), we evaluated the sensitivity to change in progressor knees from the Foundation for the National Institutes of Health OA Biomarkers Consortium between the automated and previously reported manual expert segmentation, and we determined whether differences in progression rates between predefined cohorts can be detected by the fully automated approach. Methods The OA Initiative Biomarker Consortium was a nested case-control study. Progressor knees had both medial tibiofemoral radiographic joint space width loss (>= 0.7 mm) and a persistent increase in Western Ontario and McMaster Universities Osteoarthritis Index pain scores (>= 9 on a 0-100 scale) after 2 years from baseline (n = 194), whereas non-progressor knees did not have either of both (n = 200). Deep-learning automated algorithms trained on radiographic OA knees or knees of a healthy reference cohort (HRC) were used to automatically segment medial femorotibial compartment (MFTC) and lateral femorotibial cartilage on baseline and 2-year follow-up magnetic resonance imaging. Findings were compared with previously published manual expert segmentation. Results The mean +/- SD MFTC cartilage loss in the progressor cohort was -181 +/- 245 mu m by manual segmentation (standardized response mean [SRM] -0.74), -144 +/- 200 mu m by the radiographic OA-based model (SRM -0.72), and -69 +/- 231 mu m by HRC-based model segmentation (SRM -0.30). Cohen's d for rates of progression between progressor versus the non-progressor cohort was -0.84 (P < 0.001) for manual, -0.68 (P < 0.001) for the automated radiographic OA model, and -0.14 (P = 0.18) for automated HRC model segmentation. Conclusion A fully automated deep-learning segmentation approach not only displays similar sensitivity to change of longitudinal cartilage thickness loss in knee OA as did manual expert segmentation but also effectively differentiates longitudinal rates of loss of cartilage thickness between cohorts with different progression profiles

Mesenchymal iron deposition is associated with adverse long-term outcome in non-alcoholic fatty liver disease

BACKGROUND & AIMS Approximately one third of patients with non-alcoholic fatty liver disease (NAFLD) show signs of mild to moderate iron overload. The impact of histological iron deposition on the clinical course of patients with NAFLD has not been established. METHODS & RESULTS For this retrospective study, 299 consecutive patients with biopsy-proven NAFLD and a mean follow-up of 8.4 (±4.1; range: 0.3-18.0) years were allocated to one of four groups according to presence of hepatic iron in the reticuloendothelial system (RES) and/or hepatocytes (HC): 156 subjects (52%) showed no stainable iron (NONE), 58 (19%) exclusively reticuloendothelial (xRES), 19 (6%) exclusively hepatocellular (xHC), and 66 (22%) showed a mixed (HC/RES) pattern of iron deposition. A long-term analysis for overall survival, hepatic, cardiovascular or extrahepatic-malignant events was conducted. Based on multivariate Cox proportional hazards models any reticuloendothelial iron was associated with fatal and non-fatal hepatic events. Specifically, xRES showed a cause-specific hazard ratio (csHR) of 2.4 (95%-CI, 1.0-5.8; p=0.048) for hepatic as well as cardiovascular fatal and non-fatal events combined (csHR 3.2; 95%-CI, 1.2-8.2; p=0.015). Further, the mixed HC/RES iron pattern showed a higher rate of combined hepatic fatal and non-fatal events (csHR 3.6; 95%-CI, 1.4-9.5; p=0.010), while xHC iron deposition was not associated with any defined events. CONCLUSIONS The presence of reticuloendothelial-accentuated hepatic iron distribution patterns is associated with detrimental long-term outcomes reflected in a higher rate of both liver-related and cardiovascular fatal and non-fatal events