Qualification Tool Research Articles

Sustainability qualification tool for Italian multi-family buildings

Sustainability qualification tool for Italian multi-family buildings

Silver nanostar films for surface-enhanced Raman spectroscopy (SERS) of the pesticide imidacloprid

Strategies for synthetic control of anisotropic metal nanostructures have grown in recent years in part due to their great potential for application as surface-enhanced Raman scattering (SERS) sensing substrates. It has been shown that SERS using silver substrates is a powerful tool for identification and qualification of trace chemical analysis on the basis of their unique molecular vibrations. In this work, we synthesized star-shaped silver nanostructures and fabricated SERS substrates to use the SERS enhancement of the Raman signal to detect neonicotinoid pesticides. These silver nanostar substrates were prepared by assembling the nanostar particles on a glass substrate surface using a self-assembly technique with various layers of silver nanostars film. The silver nanostar distribution on the solid substrate surface was found to have good reproducibility, reusability and were a stable SERS substrate giving SERS enhancements for pesticide detection at concentrations as low as 10−6 mg/ml. The distribution of these silver nanostars on the surface allowed excellent reproducibility of the detection with a low relative standard derivation (RSD) of SERS intensity of 8%. This work potentially builds a platform for an ultrasensitive detector where samples can be probed with little to no pre-processing and a range of pollutants can be detected at very low levels.

Image regression-based digital qualification for simulation-driven design processes, case study on curtain airbag

Today digital qualification tools are part of many design processes that make them dependent on long and expensive simulations, leading to limited ability in exploring design alternatives. Conventional surrogate modelling techniques depend on the parametric models and come short in addressing radical design changes. Existing data-driven models lack the ability in dealing with the geometrical complexities. Thus, to address the resulting long development lead time problem in the product development processes and to enable parameter-independent surrogate modelling, this paper proposes a method to use images as input for design evaluation. Using a case study on the curtain airbag design process, a database consisting of 60,000 configurations has been created and labelled using a method based on dynamic relaxation instead of finite element methods. The database is made available online for research benchmark purposes. A convolutional neural network with multiple layers is employed to map the input images to the simulation output. It was concluded that the showcased data-driven method could reduce digital testing and qualification time significantly and contribute to real-time analysis in product development. Designers can utilise images of geometrical information to build real-time prediction models with acceptable accuracy in the early conceptual phases for design space exploration purposes.

Using machine learning to predict dimensions and qualify diverse part designs across multiple additive machines and materials

One of the most critical decisions in a factory is whether to accept or reject manufactured parts, where the acceptance criteria usually includes part geometry and the presence of defects. Additive manufacturing (AM) produces parts having geometric defects that depend upon the part size and shape as well as manufacturing process parameters. There is a need to develop methods that can effectively predict these defects and aid the part qualification process. This study addresses two problems: first, the prediction of part geometry; and second, the use of these predictions to qualify parts. To mimic an AM production scheme, we produced 405 parts across nine builds, using two machines, three polymer materials, and two different arrangements of part designs within a build. For each of three part designs, we identified and measured five critical features per part for a total of 2025 feature measurements. We developed a data model that describes design information and manufacturing process information using both continuous and categorical variables and used this data to train a support vector regression (SVR) machine learning (ML) model to predict part geometry. The models are trained on a random selection of the 405 parts and predict the geometry of unsampled parts with high accuracy. The SVR models predict feature geometry to within 53 µm, which is close to the manufacturer reported process repeatability and much smaller than the 180 µm standard deviation of the data. An exploration of the data model shows that categorical information about the part design significantly improves the model prediction compared to models that do not capture this information. In the second part of the study, we leverage the same data model to develop a part qualification tool and explore three strategies for accepting or rejecting parts based on ML predictions. The strategies are based on: SVR feature geometry predictions and tunable acceptance thresholds, direct feature classifications with a part acceptance threshold, and direct part classifications. In all cases, the only model inputs are the manufacturing parameters and feature descriptors. The SVR-based part qualification strategy achieves the highest accuracy at 81% and is more data efficient than the other approaches. The research demonstrates the utility of ML in predicting AM part geometry and accurately classifying parts, and provides opportunities to better understand geometry variability in production AM.

Allocation of Pediatric Home Care Nursing Hours: The Minnesota Experience.

Despite an increasing demand for pediatric home care nursing, there is no comprehensive or universal standard of care for prescribing pediatric home care nursing hours based on a child's medical complexity. Adoption of a qualification tool (QT) to allocate home care nursing hours based on the medical complexity of a child may mitigate inequality in access to care and improve the patient and family experience. A QT, developed in Minnesota, recommends home care nursing hours based on the level of medical complexity and need for skilled nursing interventions. Four hypothetical case studies demonstrate the use of the QT to calculate recommended nursing hours. To validate the tool, a survey of discharge planners found a percentage difference in calculated hours of 4.1, 5.7, 11.2, and 24.9 in the four case studies. Discharge planners rated the usability of the QT as favorable with a score of 3.6 on a Likert scale of 5. The recommended nursing hours prescribed for families, based on the QT, was perceived as meeting the needs of the child by 56% and 42% of surveyed parents and home care nurses (HCNs), respectively. The need for additional nursing hours was expressed by 33% and 50% of parents and nurses, respectively. In general, HCNs' assessment of allocated nursing hours paralleled that of parents. Further refinement and adoption of a standardized QT to allocate home care nursing hours may improve access and outcomes for children requiring home care nursing.

Data analysis to assess part quality in DED-LB/M based on in-situ process monitoring

In the field of Laser beam Direct Energy Deposition (DED-LB/M) for metal additive manufacturing, the implementation of qualification strategies parts from monitoring data and reduced order models is presently at low level of maturity. In this work, a methodology and a suite of novel data analysis tools targeting the joint analysis of multimodal data: process parameters, coaxial thermal imaging and part quality by Computer Tomography scans is presented. To demonstrate the proposed approach, a set of stainless-steel coupons were built with varying process parameters (power, process speed) and path planning strategies. Exploratory data analysis and feature engineering was performed on the dataset: process indicators, thermal and geometrical monitoring features are correlated to spatially resolved defects (mainly cracks) as well as the overall part quality obtained from the inspection phase paving the way for further implementation of in-situ process monitoring as a reliable tool for process optimization and qualification.

A Wide Frequency Range Relationship Between Transfer Impedance of Braided Cables and Shielding Effectiveness of Corresponding Plate Shields

Electromagnetic shielding properties of cables can be characterized by transfer impedance and planar shields by shielding effectiveness. Both properties are associated with measurement techniques, which have their own frequency limitations. The concept of sheet transfer impedance is proposed to allow the transition of complex cylindrical structures to planar configurations through a simple and efficient formula. Three-dimensional full-wave simulations of the periodic pattern of plate shields and planar braid shields are performed and compared to cylindrical analytic models. The proposed formula is validated by a good concordance between shielding effectiveness and transfer impedance values. Consequently, the industrial qualification tools such as transfer impedance and shielding effectiveness can then be linked to each other, and the two techniques can be used complementarily to cover a wide frequency range. Moreover, complex cylindrical structures can be more easily simulated using planar periodic patterns.

Advancing Laboratory Characterization and Qualification of Additives for Hydrate Slurry Flow in Multiphase Systems

In the flowlines of oil/gas production systems, the formation of gas hydrates, icelike crystalline compounds of water and gas that form at low temperature and high pressure, can disrupt stable flow, often resulting in solid blockages that necessitate remediation and pose safety issues. Low dosage hydrate inhibitor antiagglomerants (LDHI-AAs) are chemical additives used to manage the hydrate slurry flow at relatively low dosage (∼1% of water content) by dispersing hydrates in the continuous liquid hydrocarbon phase. A reliable assessment of LDHI-AA performance is thus required for their optimal use under a given field condition. Rocking cells have been extensively used in the oil/gas industry for LDHI-AA qualifications as it is easy to build and allows full visualization of the cell interior. However, a solid rolling ball, which is put into the conventional rocking cell for mixing, brings significant disturbances to the flow and dispersion of the phases and, in particular, by breaking up the hydrates formed and pushing them to accumulate at the end of the cell, giving a very conservative (worst-case) evaluation of LDHI-AAs. There is a large gap between the testing conditions of rocking cells and the actual field conditions. The recently developed rock-flow cell can provide a much closer representation of the shear, phase dispersion and thus the flow regime in flowlines. Here, we demonstrate the capability of the rock-flow cell as a more robust testing tool for LDHI-AA qualification. The impact of the cooling mode, degree of subcooling, and salinity are well characterized in terms of hydrate aggregation, deposition, and bedding. The test results demonstrate the advantages of the rock-flow cell over the conventional rocking cell to characterize the hydrate slurry by visualization and quantification of the hydrate formation and accumulation. As such, the rock-flow cell can be used as an effective testing tool for LDHI-AA qualification, which can also be applied to many other phase precipitation problems encountered in flow assurance.

Automated pipeline framework for processing of large-scale building energy time series data.

Commercial buildings account for one third of the total electricity consumption in the United States and a significant amount of this energy is wasted. Therefore, there is a need for "virtual" energy audits, to identify energy inefficiencies and their associated savings opportunities using methods that can be non-intrusive and automated for application to large populations of buildings. Here we demonstrate virtual energy audits applied to large populations of buildings' time-series smart-meter data using a systematic approach and a fully automated Building Energy Analytics (BEA) Pipeline that unifies, cleans, stores and analyzes building energy datasets in a non-relational data warehouse for efficient insights and results. This BEA pipeline is based on a custom compute job scheduler for a high performance computing cluster to enable parallel processing of Slurm jobs. Within the analytics pipeline, we introduced a data qualification tool that enhances data quality by fixing common errors, while also detecting abnormalities in a building's daily operation using hierarchical clustering. We analyze the HVAC scheduling of a population of 816 buildings, using this analytics pipeline, as part of a cross-sectional study. With our approach, this sample of 816 buildings is improved in data quality and is efficiently analyzed in 34 minutes, which is 85 times faster than the time taken by a sequential processing. The analytical results for the HVAC operational hours of these buildings show that among 10 building use types, food sales buildings with 17.75 hours of daily HVAC cooling operation are decent targets for HVAC savings. Overall, this analytics pipeline enables the identification of statistically significant results from population based studies of large numbers of building energy time-series datasets with robust results. These types of BEA studies can explore numerous factors impacting building energy efficiency and virtual building energy audits. This approach enables a new generation of data-driven buildings energy analysis at scale.

An ICP-MS-based assay for characterization of gold nanoparticles with potential biomedical use

Most of potential diagnostic and therapeutic nanoparticles fail to reach clinical trials because assessment of their ‘drug-like’ properties is often overlooked during the discovery stage. This compromises the results of cell culture and animal experiments, making them insufficient to evaluate the lead candidates for testing on patients. In this study, we demonstrate the potential of high-resolution inductively coupled plasma mass spectrometry (ICP-MS) as a nanoparticle qualification tool. Using novel gold nanoparticles stabilized by N-heterocyclic carbenes as test nanoparticles, it was shown that important prerequisites for biomedical applications, such as resistance to the action of human serum milieu or reactivity toward serum biomolecules, can be reliably assessed by recording the signals of gold or sulfur isotopes. Implemented during the screening stage, the method would provide benefits in shortening timelines and reducing cost for selection and initial testing of medicinal nanoparticle candidates.

Performance of Roche qualitative HEV assay on the cobas 6800 platform for quantitative measurement of HEV RNA.

Hepatitis E virus (HEV) infection is an increasingly recognized cause of acute and chronic hepatitis in high-income countries and is the most frequent cause of acute viral hepatitis in many European countries. Appropriate tools to detect and quantify HEV RNA are needed. This study aimed to evaluate the performance of the Roche cobas® HEV assay and compare it with the Fast Track Diagnostics (FTD) Hepatitis E RNA assay. HEV viral load determination and lower limit of detection (LOD, defined as the lowest amount of viral copies that could be detected in 95 % of repeats) were assessed using a WHO standard dilution panel, testing 240 samples of various concentrations. Reproducibility was tested at three different concentration levels, for different genotypes, and with different sample types (serum, plasma) in 30 samples. Sample stability was analyzed after three freeze/thaw cycles in 25 samples. Cobas HEV assay showed a strong linear relationship between log of HEV WHO dilution series and Ct values over the reportable range from 200-5000 IU/mL HEV RNA copies. The amplification efficiency was higher than 92 %. LOD was 22 IU/mL (95 % CI: 17.4-31.8) and reproducibility tests showed a 100 % nucleic acid test (NAT) reactivity of cobas HEV for WHO dilution series (range 200-5000 IU/mL, n = 90). Cobas HEV assay detected all different HEV genotypes from biobank samples irrespective of the sample type. NAT reactivity of cobas HEV was not affected by three freeze/thaw cycles. Roche cobas HEV assay is a powerful NAT tool in terms of robustness, reproducibility and linearity. It is a feasible alternative for high-volume testing.

Basic and Clinical Pharmaco-Therapeutics of SGLT2 Inhibitors: A Contemporary Update.

Clinical relevance of sodium/glucose cotransporter 2 (SGLT2) inhibitors has been rapidly evolving across several therapy areas, apart from type 2 diabetes mellitus. While some of these developments are based on recognized scientific explanations, unexpected study findings have also shaped much of our present understanding. As the role of these agents evolves in various facets of cardiology, nephrology, hepatology and endocrinology, their optimum clinical value propositions should be realized in line with the principles of personalized medicine. An updated pharmaco-ergonomic qualification tool, based on the present evidence with these agents, would be a step in this direction. This review describes the present evidence on diverse pharmacological and therapeutic aspects for various SGLT2 inhibitors, as an attempt to provide useful guidance for optimum application in clinical practice.

Feasibility of modified Edmonton Obesity Staging System in bariatriccenter.

The number of bariatric procedures is growing worldwide annually. While there is no doubt that a high body mass index is fundamental in qualification for surgical intervention, the Edmonton Obesity Staging System (EOSS) developed in 2009 may be an additional tool for identifying patients for whom surgery should be performed and those for whom the surgery should be postponed. Assessment of the feasibility of modified EOSS as a qualification tool for surgery for obesity and related diseases and its association with morbidity. University Hospital, Jagiellonian University Medical College, Cracow, Poland. A retrospective study of prospectively collected data. All patients were assigned an EOSS score based on their medical record. We only considered the medical aspect, so the achieved results may be underpowered. Analyzed outcomes involved specific and overall complication rate, length of hospital stay, and weight loss. Regression models were created to assess the influence of EOSS on length of stay and complications. We included 761 patients who were operated on between 2009 and 2018 in our department. Of these, 505 patients received sleeve gastrectomy and 256 patients received Roux-en-Y gastric bypass. The median EOSS score was 2, which was assigned to 80% of the patients. The analysis showed no influence of modified EOSS on peri- or postoperative outcomes. Additionally, there were no significant differences in the number of patients who achieved bariatric success. The follow-up rate at 1 year was 70%. Our study suggests that modified EOSS score has no influence on the risk of developing complications and prolonged hospital stay. Patients with an EOSS score of ≥3 have higher major complication rate.

Low-Frequency Relation Between Transfer Impedance and Shielding Effectiveness of Braided Cables and Grid Shields

Metal textile fabrics used in electromagnetic shielding are often complex structures, and manufacturers need approximate models to predict their performance. In addition, different physical phenomena make their behavior varying according to the frequency bands. We propose to develop a model to determine the response of these materials in different frequency bands, and this article presents the low-frequency part of the model. A simple method of modeling braids and metallic grids at low frequency is proposed. Because of their periodic nature, these textile structures can be reduced to an elementary pattern that constitutes them. The calculus of the shielding effectiveness (SE) is then possible for complex structures. The concept of sheet transfer impedance has been defined to allow the passage of complex cylindrical structures to planar forms through a simple and efficient formula. The industrial qualification tools such as transfer impedance and SE can then be linked to each other. Full-wave three-dimensional simulations of periodic pattern of braids and grids show a very good agreement (around 0.1 dB) with the proposed analytical model in the low-frequency band up to 100 kHz and an acceptable approximation up to 1 MHz.

Living Learning Environments

Learning factories and innovation laboratories are current approaches for the qualification of participants from the field of Industrial Engineering. The focus here is on a transfer of knowledge from research to the manufacturing industry. For more than 10 years different learning concepts and scenarios in the working context close to value creation have been developed and tested in learning factories. The participants can experience new situations through different scenarios and learn from them. These concepts are already being used in vocational education and training courses. In innovation laboratories various technologies and scenarios are being investigated and tested. As an example, a well-known innovation lab in Stuttgart focuses on the working environment of the future. Various demonstrators are used to simulate and present specific scenarios from the manufacturing industry, integrating the respective stakeholders. In this paper, two learning environments are compared and evaluated with regard to their potential applications for qualification. For the analysis, evaluation sheets of training events in the Learning Factory advanced Industrial Engineering (LF aIE) and an exemplary innovation lab in Stuttgart have been used. It assess the different learning environments in terms of their suitability as a qualification tool. Based on this, it will be examined how the current learning systems meet the requirements of a value-added integrated competence development.

Vibration-based Damage Detection in Bridges via Machine Learning

Environmental corrosion and external loads degrade the performance of a bridge over the course of its service life. Although dynamic fingerprints are damage-sensitive, they are rarely applied to bridges in-situ due to environmental noise. Machine learning techniques can facilitate effective structural damage detection. This paper proposes a detection method based on dynamic fingerprints and machine learning techniques for multi-damage problems in bridges. Vibration analysis is conducted to acquire the dynamic fingerprints, then the Bayesian fusion is used to integrate these features and preliminarily locate the damage. The RSNB method, which combines Rough Set theory and the Naive-Bayes classifier, is introduced as a robust classification tool for damage qualification. A continuous bridge is numerically simulated to validate the effectiveness of the proposed method. The RSNB method is compared with back propagation neural network, support vector machine, and decision tree techniques, it is found that the RSNB outperforms other three methods in terms of transparency, accuracy, efficiency, noise robustness, and stability.

Incorporação do protocolo de lesão por pressão na gerência do cuidado: Teoria Fundamentada nos Dados

Aim: Understand the significance of the incorporation of the pressure injury protocol in the care management performed by nurses. Method: Qualitative research using a methodological framework based on the Grounded Theory. Data were collected through a semi-structured interview, with 22 nurses from a university hospital, from July to September 2014. Results: The central phenomenon "Making the Pressure Injury Protocol meaningful as a Nursing Qualification Tool for Managerial Care", composed of the category "Qualifying the professionals for the management of care" and its four subcategories, which are: "Perceiving the professional qualification from the protocol", "Considering the protocol a teaching tool", "Perceiving the protocol as an instrument of education in service", and "Evidencing the importance of protocols in patient safety". Conclusion: The study showed that the protocol is a tool of care, as well as an instrument of education in service, which makes management of care safer and also promotes professional qualification.

Linear discriminant analysis, partial least squares discriminant analysis, and soft independent modeling of class analogy of experimental and simulated near‐infrared spectra of a cultivation medium for mammalian cells

AbstractCurrently, the qualification and control of medium formulations are performed based on simple methods (eg, pH and osmolality measurement of medium solutions), expensive and time‐consuming cell culture tests, and the quantification of certain critical compounds by liquid chromatography. In addition to traditional medium qualification tools, relatively new spectroscopic techniques, such as fluorescence spectroscopy, nuclear magnetic resonance, Raman and near‐infrared spectroscopies, and combinations of these techniques are increasingly being applied to medium powder investigation. A chemically defined medium powder for Chinese hamster ovary cell cultivation was investigated in this study to determine its response to heat treatments at different temperatures (30°C, 50°C, and 70°C). Because the low availability and high costs of medium powders limit the sample sizes for such experiments, 5 groups of simulated data sets were generated based on the experimental spectra to compare the efficiencies of 3 classification methods: linear discriminant analysis (LDA) based on principal component analysis (PCA), partial least squares discriminant analysis (PLS‐DA), and soft independent modeling of class analogy (SIMCA). In case of these data sets, PCA‐LDA showed better results for the classification of experimental spectra than PLS‐DA and SIMCA. Moreover, the PLS‐DA and SIMCA models yielded different results for different training set groups, while the PCA‐LDA model yielded similar results for all training sets.

Characterization of topology optimized Ti-6Al-4V components using electron beam powder bed fusion

The use of manufacturing to generate topology optimized components shows promise for designers. However, designers who assume that additive manufacturing follows traditional manufacturing techniques may be misled due to the nuances in specific techniques. Since commercial topology optimization software tools are neither designed to consider orientation of the parts nor large variations in properties, the goal of this research is to evaluate the limitations of an existing commercial topology optimization software (i.e. Inspire®) using electron beam powder bed fusion (i.e. Arcam®) to produce optimized Ti-6Al-4V alloy components. Emerging qualification tools from Oak Ridge National Laboratory including in-situ near-infrared imaging and log file data analysis were used to rationalize the final performance of components. While the weight savings of each optimized part exceeded the initial criteria, the failure loads and locations proved instrumental in providing insight to additive manufacturing with topology optimization. This research has shown the need for a comprehensive understanding of correlations between geometry, additive manufacturing processing conditions, defect generation, and microstructure for characterization of complex components such as those designed by topology optimization.

Perfil dos gerentes da Atenção Primária: uma revisão integrativa

Objetivo: Caracterizar o perfil da gerência local de saúde da Atenção Primária. Metodologia: Revisão integrativa de literatura a partir da BVS e da BDENF, utilizando as palavras-chaves “Gerência de Serviços de Saúde” AND “Atenção Primária à Saúde”. Foram incluídos artigos publicados em todos os idiomas, de 2006 a 2015, resultando em 14 estudos. Resultados: Observou-se o predomínio do enfermeiro como gestor local de saúde, e a importância de habilidades como liderança, comunicação, organização, planejamento, bom relacionamento, conhecimento, criatividade, e capacidade de motivação. A formação continuada foi evidenciada como importante ferramenta para qualificação da gestão. Conclusão: A definição das competências do gestor local de saúde e discussões sobre a gestão de tais serviços de saúde é essencial para a qualificação da rede de assistência no SUS