The remanufacturing evaluation for feasibility and comprehensive benefit of retired grinding machine.
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ABSTRACT:
SUBMITTER: Ling T
PROVIDER: S-EPMC7302915 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:This Series has been retired because it described mouse data, but the underlying data were from rat.
2004-10-14 | GSE1829 | GEO
Project description:Symptoms of pulmonary injury following lung irradiation may not manifest clinically in childhood. We performed comprehensive pulmonary evaluation of patients who had received lung irradiation for treatment of cancer.Patients underwent a focused history and physical examination, computed tomography of the chest, pulmonary function test, and cardiopulmonary exercise stress test (CPET). Health-related Quality of Life was also measured.Fourteen patients were recruited with median age of 16 years (range, 6 to 21 y). Median time from pulmonary radiation to testing was 5 years (range, 2 to 11 y). Five patients reported pulmonary symptoms. Twelve patients (85.7%) had at least 1 pulmonary function test abnormality. Nine patients demonstrated CPET abnormalities; 7 patients had abnormal pulmonary limitation to exercise, and 5 patients had exercise-induced bronchospasm. The pulmonary limitations included abnormal ventilatory response to exercise in 5 patients, and gas exchange abnormalities in 4 patients. Chest computed tomography demonstrated grade 1-2 radiation-induced lung changes in 4 patients, and grade 3 abnormalities in 1 patient.Significant pulmonary dysfunction was observed in childhood cancer survivors who had received lung irradiation. CPET is feasible in childhood cancer survivors and can be valuable for assessment of pulmonary function and exercise capacity.
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Project description:Despite the extensive evidence on the benefits of physical activity (PA) in older adults, including reduced risk of disease, mortality, falls, and cognitive and functional decline, most do not attain sufficient PA levels. Theoretical work suggests that behavioral change interventions are most effective during life transitions, and as such, a theory-based, online intervention tailored for recently retired and empty nest individuals could lend support for increasing levels of PA. The aim of this study is to examine the feasibility of the intervention and study procedures for a future controlled trial. This study has a randomized controlled trial design with an embedded qualitative and quantitative process evaluation. Participants are randomized at 1:1 between the intervention and waitlist controls. Potential participants are within six months of their final child leaving the familial home or within six months of retiring (self-defined), currently not meeting the Canadian PA guidelines, have no serious contraindications to exercise, and are residing in Victoria, British Columbia, Canada. Participants are recruited by online and print flyers as well as in-person at community events. The study aims to recruit 40 empty nest and 40 retired participants; half of each group received the intervention during the study period. The internet-delivered intervention is delivered over a 10-week period, comprising 10 modules addressing behavior change techniques associated with PA. Primary outcomes relate to recruitment, attrition, data collection, intervention delivery, and acceptability. Secondary behavioral outcomes are measured at baseline and post-treatment (10 weeks). Intervention-selected participants are invited to an optional qualitative exit interview. The results of this feasibility study will inform the planning of a randomized effectiveness trial, that will examine the behavior change, health-related fitness, and well-being outcomes by exploring how reflexive processes of habit and identity may bridge adoption and maintenance in behavioral adherence.
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Project description:Unsorted retired batteries with varied cathode materials hinder the adoption of direct recycling due to their cathode-specific nature. The surge in retired batteries necessitates precise sorting for effective direct recycling, but challenges arise from varying operational histories, diverse manufacturers, and data privacy concerns of recycling collaborators (data owners). Here we show, from a unique dataset of 130 lithium-ion batteries spanning 5 cathode materials and 7 manufacturers, a federated machine learning approach can classify these retired batteries without relying on past operational data, safeguarding the data privacy of recycling collaborators. By utilizing the features extracted from the end-of-life charge-discharge cycle, our model exhibits 1% and 3% cathode sorting errors under homogeneous and heterogeneous battery recycling settings respectively, attributed to our innovative Wasserstein-distance voting strategy. Economically, the proposed method underscores the value of precise battery sorting for a prosperous and sustainable recycling industry. This study heralds a new paradigm of using privacy-sensitive data from diverse sources, facilitating collaborative and privacy-respecting decision-making for distributed systems.
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Project description:Due to their multiple beneficial effects, antioxidant peptides have attracted increasing interest. Currently, the screening and identification of bioactive peptides, including antioxidative peptides based on wet-chemistry methods are time-consuming and highly rely on many advanced instruments and trained personnel. Quantitative structure-activity relationship (QSAR) analysis as an in silico method can be more efficient and cost-effective. However, model performance of QSAR studies on antioxidant peptides was still poor due to limited attempts in model development approaches. The objective of this study was to compare popular machine learning methods for antioxidant activity modeling and screening of tripeptides and identify the critical amino acid features that determine the antioxidant activity. 533 numerical indices of amino acids were adopted to characterize 130 tripeptides with known antioxidant activity from the published literature, and then 7 feature selection strategies plus pairwise correlation were used to screen the most important indices for antioxidant activity and model building. 14 machine learning methods were used to build models based on the feature selection strategies, respectively. Among the 98 models, non-linear regression methods tended to perform better, and the best model with an R 2 Test of 0.847 and RMSETest of 0.627 for tripeptide antioxidants was obtained by combining random forest for feature selection and tree-based extreme gradient boost regression for model development. Based on the predicted antioxidant values of 7870 unknown tripeptides, potentially high antioxidant activity tripeptides all have a tyrosine, tryptophan, or cysteine residue at the C-terminal position. Furthermore, the predicted antioxidant activity of six synthesized tripeptides was confirmed through experimental determination, and for the first time, the cysteine or tyrosine residue at the C-terminal was found to be critical to the antioxidant activity based on both QSAR models and experimental observations.
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Project description:Brazed monolayer diamond grinding wheels have advantages of a high abrasive bonding strength, high protrusion, and a large chip disposal space. However, it is difficult to prepare ordered and fine-grained brazed diamond grinding wheels. This study presents a new method for grain-arranged, brazed diamond grinding wheels with microtextures with similar performance to ordered and fine-grained brazed diamond grinding wheels. First, coarse diamond grains (18/20 mesh) were orderly brazed to fabricate the end grinding wheels. Next, a series of microtextures were ablated on the diamond grains using a pulsed laser, and two types of textured end grinding wheels-TG-G (ablated microgrooves only) and TG-GH (ablated microgrooves and microholes)-were prepared. Then, an experiment involving the grinding of alumina ceramics was performed, and the grinding characteristics and grinding mechanism were analyzed. The results indicated that compared with untextured diamond end grinding wheels (TG), the textured diamond grinding wheels (TG-G and TG-GH) significantly reduced the grinding force and the roughness of the machined surface. The local stress concentration at the microtextures promoted the formation of microcracks in the diamond grains of TG-G and TG-GH, and the self-sharpness of the grinding wheel was significantly improved. The brittle fracture mode of ceramic materials in grinding included intergranular fracture and transgranular fracture. Ironing pressure action was a key material-removal mechanism. It had an important influence on the cutting force and plasticity characteristics of the TG machined surface. For the surfaces processed by TG-G and TG-GH, the effect of ironing was weakened, while shearing played a more important role. The TG-GH grinding wheel ablated with microgrooves and microholes was superior to the TG-G grinding wheel ablated with only microgrooves, with regard to the grinding force, roughness, and self-sharpening.
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Project description:Most approaches to machine learning from electronic health data can only predict a single endpoint. The ability to simultaneously simulate dozens of patient characteristics is a crucial step towards personalized medicine for Alzheimer's Disease. Here, we use an unsupervised machine learning model called a Conditional Restricted Boltzmann Machine (CRBM) to simulate detailed patient trajectories. We use data comprising 18-month trajectories of 44 clinical variables from 1909 patients with Mild Cognitive Impairment or Alzheimer's Disease to train a model for personalized forecasting of disease progression. We simulate synthetic patient data including the evolution of each sub-component of cognitive exams, laboratory tests, and their associations with baseline clinical characteristics. Synthetic patient data generated by the CRBM accurately reflect the means, standard deviations, and correlations of each variable over time to the extent that synthetic data cannot be distinguished from actual data by a logistic regression. Moreover, our unsupervised model predicts changes in total ADAS-Cog scores with the same accuracy as specifically trained supervised models, additionally capturing the correlation structure in the components of ADAS-Cog, and identifies sub-components associated with word recall as predictive of progression.
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Project description:BackgroundTo select the most complete, continuous, and accurate assembly for an organism of interest, comprehensive quality assessment of assemblies is necessary. We present a novel tool, called Evaluation of De Novo Assemblies (EvalDNA), which uses supervised machine learning for the quality scoring of genome assemblies and does not require an existing reference genome for accuracy assessment.ResultsEvalDNA calculates a list of quality metrics from an assembled sequence and applies a model created from supervised machine learning methods to integrate various metrics into a comprehensive quality score. A well-tested, accurate model for scoring mammalian genome sequences is provided as part of EvalDNA. This random forest regression model evaluates an assembled sequence based on continuity, completeness, and accuracy, and was able to explain 86% of the variation in reference-based quality scores within the testing data. EvalDNA was applied to human chromosome 14 assemblies from the GAGE study to rank genome assemblers and to compare EvalDNA to two other quality evaluation tools. In addition, EvalDNA was used to evaluate several genome assemblies of the Chinese hamster genome to help establish a better reference genome for the biopharmaceutical manufacturing community. EvalDNA was also used to assess more recent human assemblies from the QUAST-LG study completed in 2018, and its ability to score bacterial genomes was examined through application on bacterial assemblies from the GAGE-B study.ConclusionsEvalDNA enables scientists to easily identify the best available genome assembly for their organism of interest without requiring a reference assembly. EvalDNA sets itself apart from other quality assessment tools by producing a quality score that enables direct comparison among assemblies from different species.
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Project description:The ability to predict the grinding force for hard and brittle materials is important to optimize and control the grinding process. However, it is a difficult task to establish a comprehensive grinding force model that takes into account the brittle fracture, grinding conditions, and random distribution of the grinding wheel topography. Therefore, this study developed a new grinding force model for micro-grinding of reaction-bonded silicon carbide (RB-SiC) ceramics. First, the grinding force components and grinding trajectory were analysed based on the critical depth of rubbing, ploughing, and brittle fracture. Afterwards, the corresponding individual grain force were established and the total grinding force was derived through incorporating the single grain force with dynamic cutting grains. Finally, a series of calibration and validation experiments were conducted to obtain the empirical coefficient and verify the accuracy of the model. It was found that ploughing and fracture were the dominate removal modes, which illustrate that the force components decomposed are correct. Furthermore, the values predicted according to the proposed model are consistent with the experimental data, with the average deviation of 6.793% and 8.926% for the normal and tangential force, respectively. This suggests that the proposed model is acceptable and can be used to simulate the grinding force for RB-SiC ceramics in practice.
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Project description:BACKGROUND: A comprehensive evaluation and remediation program is an essential component of any residency program. The evaluation system should identify problems accurately and early and allow residents with problems to be assigned to a remediation program that effectively deals with them. Elements of a proactive remediation program include a process for outlining deficiencies, providing resources for improvement, communicating clear goals for acceptable performance, and reevaluating performance against these goals. INTERVENTION: In recognition of the importance of early detection and prompt remediation of the struggling resident, we sought to develop a multifaceted approach to resident evaluation with the aim of early identification and prompt remediation of difficulties. This article describes our comprehensive evaluation program and remediation program, which uses resources within our radiology department and institutional graduate medical education office. DISCUSSION: An effective evaluation system should identify problems accurately and early, whereas a proactive remediation program should effectively deal with issues once they are identified.
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