Imperfect Classification Research Articles

Deep learning-based subtyping of gastric cancer histology predicts clinical outcome: a multi-institutional retrospective study

IntroductionThe Laurén classification is widely used for Gastric Cancer (GC) histology subtyping. However, this classification is prone to interobserver variability and its prognostic value remains controversial. Deep Learning (DL)-based assessment of hematoxylin and eosin (H&E) stained slides is a potentially useful tool to provide an additional layer of clinically relevant information, but has not been systematically assessed in GC.ObjectiveWe aimed to train, test and externally validate a deep learning-based classifier for GC histology subtyping using routine H&E stained tissue sections from gastric adenocarcinomas and to assess its potential prognostic utility.MethodsWe trained a binary classifier on intestinal and diffuse type GC whole slide images for a subset of the TCGA cohort (N = 166) using attention-based multiple instance learning. The ground truth of 166 GC was obtained by two expert pathologists. We deployed the model on two external GC patient cohorts, one from Europe (N = 322) and one from Japan (N = 243). We assessed classification performance using the Area Under the Receiver Operating Characteristic Curve (AUROC) and prognostic value (overall, cancer specific and disease free survival) of the DL-based classifier with uni- and multivariate Cox proportional hazard models and Kaplan–Meier curves with log-rank test statistics.ResultsInternal validation using the TCGA GC cohort using five-fold cross-validation achieved a mean AUROC of 0.93 ± 0.07. External validation showed that the DL-based classifier can better stratify GC patients' 5-year survival compared to pathologist-based Laurén classification for all survival endpoints, despite frequently divergent model-pathologist classifications. Univariate overall survival Hazard Ratios (HRs) of pathologist-based Laurén classification (diffuse type versus intestinal type) were 1.14 (95% Confidence Interval (CI) 0.66–1.44, p-value = 0.51) and 1.23 (95% CI 0.96–1.43, p-value = 0.09) in the Japanese and European cohorts, respectively. DL-based histology classification resulted in HR of 1.46 (95% CI 1.18–1.65, p-value < 0.005) and 1.41 (95% CI 1.20–1.57, p-value < 0.005), in the Japanese and European cohorts, respectively. In diffuse type GC (as defined by the pathologist), classifying patients using the DL diffuse and intestinal classifications provided a superior survival stratification, and demonstrated statistically significant survival stratification when combined with pathologist classification for both the Asian (overall survival log-rank test p-value < 0.005, HR 1.43 (95% CI 1.05–1.66, p-value = 0.03) and European cohorts (overall survival log-rank test p-value < 0.005, HR 1.56 (95% CI 1.16–1.76, p-value < 0.005)).ConclusionOur study shows that gastric adenocarcinoma subtyping using pathologist’s Laurén classification as ground truth can be performed using current state of the art DL techniques. Patient survival stratification seems to be better by DL-based histology typing compared with expert pathologist histology typing. DL-based GC histology typing has potential as an aid in subtyping. Further investigations are warranted to fully understand the underlying biological mechanisms for the improved survival stratification despite apparent imperfect classification by the DL algorithm.

Experimental analysis of lithium-ion cell procurement: Quality differences, correlations, and importance of cell characterization

Since every production process exhibits slight variances, lithium-ion cells are usually tested for their function and quality by the manufacturer during end-of-line testing, whereby specific tolerance limits are generally not communicated to end users. In addition, commercial cells are usually sold through various distribution channels and intermediaries, creating a black box regarding the actual cell properties delivered, unless a clear requirement specification supplier commitment has been defined. In this work, 250 cylindrical 18650 cells from the same manufacturer and model were obtained via two distribution channels and quality grading categories and extensively characterized using established methods of cell characterization. The relationship between the various measurements and cell characteristics was evaluated and visualized to reveal interrelated factors which remain hidden in aggregate observations. The results show that the choice of vendor can have a significant impact on the performance characteristics obtained for the supposedly same product, with strong scatter being evident both between and within the three batches. In addition, the given grading classes do not correlate with the measured cell performance in terms of capacity, internal resistance, cycling performance or a combination of the aforementioned criteria. This could be caused by an imperfect classification by the manufacturer or a mixing of quality classes at the vendor. Hence, the investigation encourages the implementation of appropriate cell quality categorization depending on the application and a profound exchange of data between vendors and customers, in order to prevent the utilization of under-performing cells.

The Mechanism of Interaction of Natural Physical Factors and the Integral Organism Forms the Basis of the General Theory of Physiotherapy

Optimal combination of drug therapy and physical factors increases the efficacy of treatment and medical rehabilitation of patients. It is regrettable that the search for methods to improve the individual dosing of the applied energies with an approach to the cellular level of dynamicity (constant correction of parameters of the applied physical factors during the whole period of the treatment) and comprehensive (in the presence of several diseases) treatment are clearly insufficient for the present time. Perhaps such a search is difficult due to the current lack of a general theory of physiotherapy and imperfect classification of physical factors. Thus, it became necessary to develop principles and methods for a comprehensive study of the effects produced by the physical methods in a clinical setting, and the requirements for testing new devices for physiotherapy sharply increased. The author of this doctrine was an outstanding scientist Vladimir Sergeyevich Ulashchik. In his opinion, the creation of a general theory of physiotherapy is one of the promising tasks of the scientific physiotherapy. It is known that since the end of the XIX century, the principles of nervism and reflex theory have been the theoretical basis of domestic physiotherapy. Influence of humoral and endocrine changes was further added to the reflex theory of the mechanism of the therapeutic effect of the external environment (natural and preformed physical factors), but still with the leading principle of nervism. It should be noted that there have always been doubts about the exhaustive explanation of the reflex theory of all causes and consequences of the interaction of external physical factors with the human body.

The Mechanism of Interaction Between Natural Physical Factors and the Whole Organism Forms the Basis of the General Theory of Physiotherapy

The optimal combination of drug therapy and physical factors increases the effectiveness of treatment and medical rehabilitation of patients. It is regrettable that the search for methods to improve the individual dosage of applied energies with an approach to the cellular level of dynamism (constant correction of the parameters of the applied physical factors during the entire treatment period) and complex (in the presence of several diseases) treatment is clearly not enough today. Perhaps this search is difficult due to the current lack of a general theory of physiotherapy and imperfect classification of physical factors. In this regard, there is a need to develop principles and methods for a comprehensive study of the effects of physical methods in clinical settings, and the requirements for testing new devices for physiotherapy have sharply increased. The author of this direction was an outstanding scientist Vladimir Sergeyev ich Ulashchik. In his opinion, the creation of a general theory of physiotherapy is one of the promising tasks of scientific physiotherapy. It is known that from the end XIX The basic principles of nervousness and reflex theory are the theoretical basis of Russian physiotherapy. Subsequently, the influence of humoral and endocrine changes was added to the reflex theory of the mechanism of therapeutic action of the external environment (natural and preformed physical factors), but still with the leading principle of nervism. It should be noted that there have always been doubts about the comprehensive explanation of the reflex theory of all the causes and consequences of the interaction of external physical factors with the human body. On this basis, “permanent” problems remained unresolved: • general and specific in the action of physical factors; • selectivity of their actions; • the ratio of nervous and humoral, local and general, functional and morphological with a single and course application of physiotherapy; • inability to determine the “dose-effect”; • nature of the body’s primary reactions to environmental influences; • orientation of processes that determine the final result of interaction between external physical factors and the whole organism; • realization of all potential opportunities for obtaining the maximum desired positive clinical effect during physical therapy. Thanks to the great success of quantum mechanics( optics), chemistry, biology, supramolecular chemistry, and fibre-optic physics, it became necessary to review the mechanism of action of the physical factor and the response of the whole organism in order to diagnose, prevent, treat, and rehabilitate patients. Quantum therapy originated before our era, when people began to consciously apply “natural factors” for therapeutic purposes with a governing “ verbal accompaniment and explanation.” With the subsequent development of scientific, technical and humanitarian disciplines (mathematics, mechanics, physics, chemistry, biology, etc.), the philosophy of using natural factors for therapeutic purposes changed. At the same time, the culture of external influence on the whole organism of any forces and means changed and improved, i.e. the use of” natural factors “for therapeutic purposes became systemic, with the subsequent emergence of a new scientific discipline in medicine “physiotherapy”. Its fundamental nature is that the entire universe, the Cosmos and the World around us consists of elementary particles (quanta), devoid of repetitions and existing in a three-dimensional dimension

The mechanism of interaction between natural physical factors and the holistic organism forms the basis of the general theory of physiotherapy

Optimal combination of drug therapy and physical factors increases the effectiveness of treatment and medical rehabilitation of patients. It is regrettable that search for methods of improvement of individual dosing of applied energies with approximation to the cellular level of dynamism (constant correction of parameters of applied physical factors during the whole period of treatment) and complex (in the presence of several diseases) treatment is conducted today obviously insufficiently. Perhaps this search is hampered by the current lack of a general theory of physiotherapy and imperfect classification of physical factors. In connection with this there appeared a necessity of development of principles and methods of comprehensive study of the action of physical methods in clinical conditions, and requirements for testing new apparatuses for physiotherapy increased sharply. The author of this direction was the outstanding scientist Vladimir Sergeyevich Ulashchik. In his opinion, creation of general theory of physiotherapy is one of the perspective tasks of scientific physiotherapy. It is known that since the end of XIX century the principles of neurovism and reflex theory have been the theoretical basis of domestic physiotherapy. Subsequently, to the reflex theory of the mechanism of healing action of the International Physical Medicine &amp; Rehabilitation Journal Review Article Open Access external environment (natural and preformed physical factors) the influence of humoral and endocrine changes was added, but still with the leading principle of neurovism. It should be noted that there have always been doubts that the reflexive theory can fully explain all causes and consequences of the interaction of external physical factors with the human body. On this basis, “permanent” problems remained unresolved: • common and specific in the action of physical factors; • the selectivity of their action; • correlation of nervous and humoral, local and general, functional and morphologic3al at single and course application of physiotherapeutic influence; • inability to determine “dose-effect”; • the nature of the organism’s primary reactions to environmental influences; • the direction of the processes that predetermine the final result of the interaction between external physical factors and the whole organism; • realization of all potential possibilities of obtaining the maximum desired positive clinical effect in physiotherapy. Thanks to the tremendous advances in quantum mechanics (optics), chemistry, biology, supramolecular chemistry, and fiber-optic physics, it became necessary to review the mechanism of action of a physical factor and the response of the whole body in order to diagnose, prevent, treat, and rehabilitate patients. Quantum therapy emerged before our era, when man began to consciously apply “natural factors” for therapeutic purposes with controlling “verbal support and explanation”. With the subsequent development of scientific, technical and humanitarian disciplines (mathematics, mechanics, physics, chemistry, biology, etc.) the philosophy of using natural factors for therapeutic purposes changed. In parallel, the culture of influence from outside on the holistic organism of any forces and means changed and improved, i.e. application of “natural factors” with therapeutic purpose acquired systemic character with subsequent emergence of new scientific discipline in medicine “physiotherapy”.Its fundamentality is that all Universe, Space and surrounding world consists of elementary particles (quanta), devoid of repetitions and existing in three-dimensional dimension.

Data Quality in Electronic Health Record Research: An Approach for Validation and Quantitative Bias Analysis for Imperfectly Ascertained Health Outcomes Via Diagnostic Codes.

It is incumbent upon all researchers who use the electronic health record (EHR), including data scientists, to understand the quality of such data. EHR data may be subject to measurement error or misclassification that have the potential to bias results, unless one applies the available computational techniques specifically created for this problem. In this article, we begin with a discussion of data-quality issues in the EHR focusing on health outcomes. We review the concepts of sensitivity, specificity, positive and negative predictive values, and demonstrate how the imperfect classification of a dichotomous outcome variable can bias an analysis, both in terms of prevalence of the outcome, and relative risk of the outcome under one treatment regime (aka exposure) compared to another. This is then followed by a description of a generalizable approach to probabilistic (quantitative) bias analysis using a combination of regression estimation of the parameters that relate the true and observed data and application of these estimates to adjust the prevalence and relative risk that may have existed if there was no misclassification. We describe bias analysis that accounts for both random and systematic errors and highlight its limitations. We then motivate a case study with the goal of validating the accuracy of a health outcome, chronic infection with hepatitis C virus, derived from a diagnostic code in the EHR. Finally, we demonstrate our approaches on the case study and conclude by summarizing the literature on outcome misclassification and quantitative bias analysis.

Distributed Data Storage and Fusion for Collective Perception in Resource-Limited Mobile Robot Swarms

In this letter, we propose an approach to the distributed storage and fusion of data for collective perception in resource-limited robot swarms. We demonstrate our approach in a distributed semantic classification scenario. We consider a team of mobile robots, in which each robot runs a pre-trained classifier of known accuracy to annotate objects in the environment. We provide two main contributions: (i) a decentralized, shared data structure for efficient storage and retrieval of the semantic annotations, specifically designed for low-resource mobile robots; and (ii) a voting-based, decentralized algorithm to reduce the variance of the calculated annotations in presence of imperfect classification. We discuss theory and implementation of both contributions, and perform an extensive set of realistic simulated experiments to evaluate the performance of our approach.

An analysis of the imperfections and defects inside composite bipolar plates using X-Ray computer tomography and resistivity simulations

With the increasing mass production, the quality control of bipolar plates (BPPs) becomes more important. A classification and understanding of imperfections and defects is necessary for the design of quality assurance measures within the manufacturing process. In this paper, a combined X-Ray computer tomography (CT) and resistivity simulation approach is used to investigate graphite composite BPPs. With this non-destructive approach, the morphology and composition of a detailed BPP section, as well as an entire flow field, can be analyzed for its characteristics and defects. The different detected imperfections occurring in injection-molded and compression-molded BPP samples include cracks, air bubbles and agglomerations of voids and foreign particles. The investigated flow field geometry has a major impact and increases the electrical resistance by about 19% compared to a bulk material body. The investigated imperfections inside the BPP material have a minimal impact on the electrical resistance of the BPP.

An analysis of online quality control by attributes with an imperfect classification system and inspections with samples of size n

In this article we propose a new online control system aiming to lower the instants in which the production process migrates from being in- to out-of-control state, which generates an increase in the non-conformity rates. As shifts from in- to out-of-control are non-deterministic, a sample of size n is collected, for each m or L units produced, and each element from the sample is imprecisely classified as conform or non-conform (that is, there may be classification errors). If the amount of conform units from the sample is equal or greater than a, the process would not be adjusted and another sample would be collected after m units produced. If the quantity of conform units is inferior to a, the process would be adjusted and another sample would be collected after L units produced, given that L > m. A genetic algorithm is proposed to approximately find the values of a, n, m, and L that minimize all costs involved in the process being controlled. All procedures are illustrated through a detailed numerical example that attests the efficacy and efficiency of the proposed online control system.

Research on Application of Classification Model Based on Stack Generalization in Staging of Cervical Tissue Pathological Images

Cervical cancer is a malignant tumor that threatens women's health and life. Cervical pathology examination, as the gold standard for cervical cancer diagnosis, provides an important basis for the surgical plan and postoperative follow-up strategy for cervical cancer. Cervical biopsy diagnosis includes normal, low-grade squamous intraepithelial lesion (LSIL), high-grade squamous intraepithelial lesion (HSIL) and squamous cell carcinoma (SCC). At present, cervical pathology examination still relies on the doctor's personal clinical experience and subjective judgment, which is time-consuming and may cause misdiagnosis or missed diagnosis. In addition, the current intelligent classification of cervical pathological images still has disadvantages such as imperfect classification system and low classification accuracy. Therefore, this experiment uses the ResNet50 model of the convolutional neural network as the feature extractor, and selects the K-Nearest Neighbour (KNN), Random Forest (RF), Support Vector Machine (SVM) classifiers in Machine Learning to perform cervical tissue pathological images Discrimination, the accuracy of the classification results were 85.83%, 80.33%, and 86.67%. In order to further improve the accuracy of the model and enhance the applicability and stability of the model, this experiment proposes the Stacked Generalization (SK) classification model. The first-layer base learner of the SK classification model selects CNN-KNN, CNN-RF, CNN-SVM, and the second-layer classifier selects Multilayer Perceptron (MLP). Among them, MLP makes the final result by learning the classification performance of the base learner for label discrimination, the accuracy of the classification model after ensemble learning is 90.00%. In addition, this experiment uses the Synthetic Minority Oversampling Technique (SMOTE) algorithm to amplify the training samples, and the amplified data set has a classification accuracy of 89.17% under the training of the SK classification model. The results show that the SK classification model in this experiment has a high classification ability for cervical histopathological images, and has good generalization ability and robustness.

Development, validation and effectiveness of diagnostic prediction tools for colorectal cancer in primary care: a systematic review

BackgroundTools based on diagnostic prediction models are available to help general practitioners (GP) diagnose colorectal cancer. It is unclear how well they perform and whether they lead to increased or quicker diagnoses and ultimately impact on patient quality of life and/or survival. The aim of this systematic review is to evaluate the development, validation, effectiveness, and cost-effectiveness, of cancer diagnostic tools for colorectal cancer in primary care.MethodsElectronic databases including Medline and Web of Science were searched in May 2017 (updated October 2019). Two reviewers independently screened titles, abstracts and full-texts. Studies were included if they reported the development, validation or accuracy of a prediction model, or assessed the effectiveness or cost-effectiveness of diagnostic tools based on prediction models to aid GP decision-making for symptomatic patients presenting with features potentially indicative of colorectal cancer. Data extraction and risk of bias were completed by one reviewer and checked by a second. A narrative synthesis was conducted.ResultsEleven thousand one hundred thirteen records were screened and 23 studies met the inclusion criteria. Twenty-studies reported on the development, validation and/or accuracy of 13 prediction models: eight for colorectal cancer, five for cancer areas/types that include colorectal cancer. The Qcancer models were generally the best performing.Three impact studies met the inclusion criteria. Two (an RCT and a pre-post study) assessed tools based on the RAT prediction model. The third study looked at the impact of GP practices having access to RAT or Qcancer.Although the pre-post study reported a positive impact of the tools on outcomes, the results of the RCT and cross-sectional survey found no evidence that use of, or access to, the tools was associated with better outcomes. No study evaluated cost effectiveness.ConclusionsMany prediction models have been developed but none have been fully validated. Evidence demonstrating improved patient outcome of introducing the tools is the main deficiency and is essential given the imperfect classification achieved by all tools. This need is emphasised by the equivocal results of the small number of impact studies done so far.

Hyperspectral Image Classification With Context-Aware Dynamic Graph Convolutional Network

In hyperspectral image (HSI) classification, spatial context has demonstrated its significance in achieving promising performance. However, conventional spatial context-based methods simply assume that spatially neighboring pixels should correspond to the same land-cover class, so they often fail to correctly discover the contextual relations among pixels in complex situations, and thus leading to imperfect classification results on some irregular or inhomogeneous regions such as class boundaries. To address this deficiency, we develop a new HSI classification method based on the recently proposed Graph Convolutional Network (GCN), as it can flexibly encode the relations among arbitrarily structured non-Euclidean data. Different from traditional GCN, there are two novel strategies adopted by our method to further exploit the contextual relations for accurate HSI classification. First, since the receptive field of traditional GCN is often limited to fairly small neighborhood, we proposed to capture long range contextual relations in HSI by performing successive graph convolutions on a learned region-induced graph which is transformed from the original 2D image grids. Second, we refine the graph edge weight and the connective relationships among image regions by learning the improved adjacency matrix and the 'edge filter', so that the graph can be gradually refined to adapt to the representations generated by each graph convolutional layer. Such updated graph will in turn result in accurate region representations, and vice versa. The experiments carried out on three real-world benchmark datasets demonstrate that the proposed method yields significant improvement in the classification performance when compared with some state-of-the-art approaches.

The Influence of Selected Local Phenomena in CFRP Laminate on Global Characteristics of Bolted Joints.

High specific mechanical properties of composites are the reason for their use in various fields, e.g., the aerospace industry. Mechanical joints are still used in the aerospace industry to assembly large aircraft structures. The properties of laminate around the hole can be, however, weakened, compared to their nominal values as a result of a drilling process or cyclic loading. This paper aims at the classification and analysis of imperfections affecting mechanically fastened joints in a laminate structure. A method of modeling the hole vicinity, a gradient material model, as well as the numerical and experimental estimation of laminate deterioration in this area, were proposed and analyzed. Comparative analysis of numerical and experimental results based on displacements of the testing machine grip and the extensometer length confirmed the aforementioned results as consistent in linear ranges. Therefore, joint characteristics obtained based upon measurement of the grip displacement and the ratio of stiffness in linear ranges are sufficient to determine the parameters of a gradient material model. Some imperfections resulting from, e.g., asymmetry, were included in the gradient material model; thus, the obtained weakening of laminate properties in the hole vicinity can be overestimated. Therefore, further analyses of the gradient material model for laminate structures are necessary.

Suspended sediment source tracing at the Juksan Weir in the Yeongsan River using composite fingerprints

Since 2008, the Yeongsan River in South Korea has experienced significant environmental changes, such as the dredging of its riverbed and the construction of large weirs. The quantity and quality of the fine sediments that provide adsorptive surfaces for pollutants has an influence on water quality, human health, and aquatic organisms. To determine the dominant contributors and seasonal variation in suspended sediment at the Juksan Weir in the Yeongsan River, reservoir sediments were obtained at monthly intervals using a time-integrated suspended sediment sampler. We applied a fingerprinting procedure combined with a multivariate mixing model to discriminate between different types of land use and to apportion sediment sources. Cropland topsoils, forest topsoils and channel bank materials were selected as end-members, and fallout radionuclides, geochemical elemental concentrations and the magnetic properties of each source material were used as diagnostic tracer properties. According to the mixing model results, channel bank materials were the dominant source of reservoir sediment during the entire monitoring period. In winter, there was a slight increase in the contribution from forest topsoils, which could have been influenced by freeze–thaw processes. Despite the high proportion of agricultural land use in the study catchment and its proximity to the river, there was a small sediment contribution from the cropland topsoils. There were uncertainties resulting from an imperfect classification due to the spatial distributions of forest and cropland and the combination of the mixing model with the fingerprinting procedure. The results provide the basis for developing strategies for sediment control and reservoir management.

Dynamics of fine grinding in a fluidized bed opposed jet mill

Today the modeling of the process dynamics and the complex two-phase flows in mills still remain a challenging task. In this study the dynamic comminution process in a fluidized bed opposed jet mill is investigated. For this purpose, well-defined model glass beads were ground in a lab-scale mill. The particle size distributions (PSDs) inside the mill and at the outlet, solid concentrations and the holdup of the mill were recorded. By comparing the morphology of the ground material with that of particles examined in single particle stressing experiments, we conclude that the predominant stressing mechanism in the mill is determined by a high number of low intensity stressing events. Furthermore, the grinding kinetics is strongly influenced by the solid holdup within the mill and the initial PSD of the feed material. A higher holdup leads to a higher product mass flow rate with slightly finer particles. The determined high solid concentration in the transport section of the mill indicates a high loading of the classifier which increases with the holdup. Our study shows that the internal recirculation is crucial for the overall performance of the mill:Due to imperfect classification, particles accumulate inside the mill which is further enhanced by a high holdup of fine particles. Our results clearly show that the classification and transport processes must be studied in detail for any in-depth understanding of dry fine grinding in fluidized bed opposed jet mills.

Enhancement of Network Attack Classification using Particle Swarm Optimization and Multi Layer-Perceptron

Network intrusion detection systems (NIDSs) give classification for all data passing during these systems and produce an alarm report whether these data are normal or abnormal. Many researchers have used various techniques to solve classification problems in IDSs but these techniques still have some vulnerability by getting imperfect classification for attacks. In this study, a proposed system has been developed that achieves classification technique by using hybrid soft computing technique which is Multi Layer-Perceptron (MLP) with Particle Swarm Optimization (PSO). The PSO has been used to improve the learning capability of the MLP by setting up the linkage weights in an attempt to enhance classification accuracy of the MLP. Simulation results conducted over three forms of experiments show that the proposed system gives high classification compared with other methods. The results show also that the percentages of classification has been reached to (98.9%) with (1.1) false alarm. General Terms Networks Security, Intrusion Detection, Neural Networks Applications

Bicep2. III. INSTRUMENTAL SYSTEMATICS

In a companion paper we have reported a $>5\sigma$ detection of degree scale $B $-mode polarization at 150 GHz by the BICEP2 experiment. Here we provide a detailed study of potential instrumental systematic contamination to that measurement. We focus extensively on spurious polarization that can potentially arise from beam imperfections. We present a heuristic classification of beam imperfections according to their symmetries and uniformities, and discuss how resulting contamination adds or cancels in maps that combine observations made at multiple orientations of the telescope about its boresight axis. We introduce a technique, which we call "deprojection", for filtering the leading order beam-induced contamination from time ordered data, and show that it removes power from BICEP2's $BB$ spectrum consistent with predictions using high signal-to-noise beam shape measurements. We detail the simulation pipeline that we use to directly simulate instrumental systematics and the calibration data used as input to that pipeline. Finally, we present the constraints on $BB$ contamination from individual sources of potential systematics. We find that systematics contribute $BB$ power that is a factor $\sim10\times$ below BICEP2's 3-year statistical uncertainty, and negligible compared to the observed $BB$ signal. The contribution to the best-fit tensor/scalar ratio is at a level equivalent to $r=(3-6)\times10^{-3}$.

Caring for the patient, caring for the record: an ethnographic study of 'back office' work in upholding quality of care in general practice.

BackgroundThe quality of information recorded about patient care is considered key to improving the overall quality, safety and efficiency of patient care. Assigning codes to patients’ records is an important aspect of this documentation. Current interest in large datasets in which individual patient data are collated (e.g. proposed NHS care.data project) pays little attention to the details of how ‘data’ get onto the record. This paper explores the work of summarising and coding records, focusing on ‘back office’ practices, identifying contributors and barriers to quality of care.MethodsEthnographic observation (187 hours) of clinical, management and administrative staff in two UK general practices with contrasting organisational characteristics. This involved observation of working practices, including shadowing, recording detailed field notes, naturalistic interviews and analysis of key documents relating to summarising and coding. Ethnographic analysis drew on key sensitizing concepts to build a ‘thick description’ of coding practices, drawing these together in a narrative synthesis.ResultsCoding and summarising electronic patient records is complex work. It depends crucially on nuanced judgements made by administrators who combine their understanding of: clinical diagnostics; classification systems; how healthcare is organised; particular working practices of individual colleagues; current health policy. Working with imperfect classification systems, diagnostic uncertainty and a range of local practical constraints, they manage a moral tension between their idealised aspiration of a ‘gold standard’ record and a pragmatic recognition that this is rarely achievable in practice. Adopting a range of practical workarounds, administrators position themselves as both formally accountable to their employers (general practitioners), and informally accountability to individual patients, in a coding process which is shaped not only by the ‘facts’ of the case, but by ongoing working relationships which are co-constructed alongside the patient’s summary.ConclusionData coding is usually conceptualised as either a technical task, or as mundane, routine work, and usually remains invisible. This study offers a characterisation of coding as a socially complex site of moral work through which new lines of accountability are enacted in the workplace, and casts new light on the meaning of coded data as conceptualised in the ‘quality of care’ discourse.Electronic supplementary materialThe online version of this article (doi:10.1186/s12913-015-0774-7) contains supplementary material, which is available to authorized users.

System for the Evaluation and Classification of Imperfections in Auto Bodywork

Quality aspects are more important every day, since they have a major impact on the final product. The automobile industry is not unaware of this fact, being an important issue of the car’s sheet quality analysis. Nowadays, most of the systems for quality analysis are implemented outside the production line and are performed manually. In this work, an autonomous system is proposed in order to enable the automatic quantification and classification of the imperfections produced in the sheets composing the auto bodywork due to the squeezing process. The system consists of a motorized system to capture the image, an algorithm to extract the geometrical information of the image obtained, and a system based on fuzzy logic to classify the imperfections measured from the geometrical parameters.

New Classification of Defects and Imperfections for Aluminum Alloy Castings

In recent years, aluminum alloys have become more and more relevant because of their low density, coupled with good mechanical and corrosion properties. Different processes are available for the production of aluminum alloy components, such as rolling, extrusion, and powder metallurgy, but a significant role is played by foundry processes. Defects and imperfections are physiologically generated by different casting techniques as a result of the process stages, alloy properties and die or mold design.