Environmental Load Evaluation of Reuse Parts for Automobiles

Abstract Reuse parts are parts removed from scrap automobiles that can be still used. In general, reuse parts reduce not only the cost for replacement of failed parts but also the environmental load. This study quantitatively evaluates environmental loads, such as the amount of CO2 emission during the production of brand new parts, in order to quantify the beneficial effect of the reuse parts. The amount of CO2 emission can be calculated from the power consumption and operating time of each tool and machine employed. Reuse parts generate 0.62 kg of CO2 per automobile when produced, which corresponds to 1,212 kg per year. However, the amount of CO2 emitted from scrapping automobiles without producing new replacement parts is 3,063 kg per year. Therefore, the production of replacement parts emits three times less CO2 than scrapping

Association between skin diseases and severe bacterial infections in children: case-control study

BACKGROUND: Sepsis or bacteraemia, however rare, is a significant cause of high mortality and serious complications in children. In previous studies skin disease or skin infections were reported as risk factor. We hypothesize that children with sepsis or bacteraemia more often presented with skin diseases to the general practitioner (GP) than other children. If our hypothesis is true the GP could reduce the risk of sepsis or bacteraemia by managing skin diseases appropriately. METHODS: We performed a case-control study using data of children aged 0–17 years of the second Dutch national survey of general practice (2001) and the National Medical Registration of all hospital admissions in the Netherlands. Cases were defined as children who were hospitalized for sepsis or bacteraemia. We selected two control groups by matching each case with six controls. The first control group was randomly selected from the GP patient lists irrespective of hospital admission and GP consultation. The second control group was randomly sampled from those children who were hospitalized for other reasons than sepsis or bacteraemia. We calculated odds ratios and 95% confidence intervals (CI). A two-sided p-value less than 0.05 was considered significant in all tests. RESULTS: We found odds ratios for skin related GP consultations of 3.4 (95% CI: [1.1–10.8], p = 0.03) in cases versus GP controls and 1.4 (95% CI: [0.5–3.9], p = 0.44) in cases versus hospital controls. Children younger than three months had an odds ratio (cases/GP controls) of 9.2 (95% CI: [0.81–106.1], p = 0.07) and 4.0 (95% CI: [0.67–23.9], p = 0.12) among cases versus hospital controls. Although cases consulted the GP more often with skin diseases than their controls, the probability of a GP consultation for skin disease was only 5% among cases. CONCLUSION: There is evidence that children who were admitted due to sepsis or bacteraemia consulted the GP more often for skin diseases than other children, but the differences are not clinically relevant indicating that there is little opportunity for GPs to reduce the risk of sepsis and/or bacteraemia considerably by managing skin diseases appropriately

A comparison of probabilistic classifiers for sleep stage classification

\u3cp\u3eObjective: To compare conditional random fields (CRF), hidden Markov models (HMMs) and Bayesian linear discriminants (LDs) for cardiorespiratory sleep stage classification on a five-class sleep staging task (wake/N1/N2/N3/REM), to explore the benefits of incorporating time information in the classification and to evaluate the feasibility of sleep staging on obstructive sleep apnea (OSA) patients. Approach: The classifiers with and without time information were evaluated with 10-fold cross-validation on five-, four- (wake/N1 + N2/N3/REM) and three-class (wake/NREM/REM) classification tasks using a data set comprising 443 night-time polysomnography (PSG) recordings of 231 participants (180 healthy participants, 100 of which had a 'regular' sleep architecture, and 51 participants previously diagnosed with OSA). Main results: CRF with time information (CRFt) outperforms all other classifiers on all tasks, achieving a median accuracy and Cohen's κ for all participants of 62.8% and 0.44 for five classes, 68.8% and 0.47 for four classes, and 77.6% and 0.55 for three classes. An advantage was found in training classifiers, specifically for 'regular' and 'OSA' participants, achieving an improvement in classification performance for these groups. For 'regular' participants, CRFt achieved a median accuracy and Cohen's κ of 67.0% and 0.51, 70.8% and 0.53 and 81.3% and 0.62 for five-, four- and three-classes respectively, and for 'OSA' patients, of 59.9% and 0.40, 69.7% and 0.45, and 75.8% and 0.51 for five-, four- and three-classes respectively. Significance: The results suggest that CRFt is not only better at learning and predicting more complex and irregular sleep architectures, but that it also performs reasonably well in five-class classification - the standard for sleep scoring used in clinical PSG. Additionally, and albeit with a decrease in performance when compared with healthy participants, sleep stage classification in OSA patients using cardiorespiratory features and CRFt seems feasible with reasonable accuracy.\u3c/p\u3

Cardiorespiratory sleep stage detection using conditional random fields

\u3cp\u3eThis paper explores the probabilistic properties of sleep stage sequences and transitions to improve the performance of sleep stage detection using cardiorespiratory features. A new classifier, based on conditional random fields, is used in different sleep stage detection tasks (N3, NREM, REM, and wake) in night-time recordings of electrocardiogram and respiratory inductance plethysmography of healthy subjects. Using a dataset of 342 polysomnographic recordings of healthy subjects, among which 135 with regular sleep architecture, it outperforms hidden Markov models and Bayesian linear discriminants in all tasks, achieving an average accuracy of 87.38% and kappa of 0.41 (87.27% and 0.49 for regular subjects) for N3 detection, 78.71% and 0.55 (80.34% and 0.56 for regular subjects) for NREM detection, 88.49% and 0.51 (87.35% and 0.57 for regular subjects) for REM, and 85.69% and 0.51 (90.42% and 0.52 for regular subjects) for wake. In comparison with the state of the art, and having been tested on a much larger dataset, the classifier was found to outperform most of the work reported in the literature for some of the tasks, in particular for subjects with regular sleep architecture. It achieves a comparable accuracy for N3, higher accuracy and kappa for REM, and higher accuracy and comparable kappa for NREM than the best performing classifiers described in the literature.\u3c/p\u3

System and method for slow wave sleep detection

The present disclosure pertains to a system configured to detect slow wave sleep and/or non-slow wave sleep in a subject during a sleep session based on a predicted onset time of slow wave sleep and/or a predicted end time of slow wave sleep that is determined based on changes in cardiorespiratory parameters of the subject. Cardiorespiratory parameters in a subject typically begin to change before transitions between non-slow wave sleep and slow wave sleep. Predicting this time delay between the changes in the cardiorespiratory parameters and the onset and/or end of slow wave sleep facilitates better (e.g., more sensitive and/or more accurate) determination of slow wave sleep and/or non-slow wave sleep