Container logistic innovations in forest-energy sector: Markets, future service concepts and technical improvements

The report presents the results of the commercialization project called the Container logistic services for forest bioenergy. The project promotes new business that is emerging around overall container logistic services in the bioenergy sector. The results assess the European markets of the container logistics for biomass, enablers for new business creation and required service bundles for the concept. We also demonstrate the customer value of the container logistic services for different market segments. The concept analysis is based on concept mapping, quality function deployment process (QFD) and business network analysis. The business network analysis assesses key shareholders and their mutual connections. The performance of the roadside chipping chain is analysed by the logistic cost simulation, RFID system demonstration and freezing tests. The EU has set the renewable energy target to 20 % in 2020 of which Biomass could account for two-thirds. In the Europe, the production of wood fuels was 132.9 million solid-m3 in 2012 and production of wood chips and particles was 69.0 million solidm3. The wood-based chips and particle flows are suitable for container transportation providing market of 180.6 million loose- m3 which mean 4.5 million container loads per year. The intermodal logistics of trucks and trains are promising for the composite containers because the biomass does not freeze onto the inner surfaces in the unloading situations. The overall service concept includes several packages: container rental, container maintenance, terminal services, RFID-tracking service, and simulation and ERP-integration service. The container rental and maintenance would provide transportation entrepreneurs a way to increase the capacity without high investment costs. The RFID-concept would lead to better work planning improving profitability throughout the logistic chain and simulation supports fuel supply optimization.

Comparison of three methods for the determination of baroreflex sensitivity in conscious rats

Baroreflex sensitivity was studied in the same group of conscious rats using vasoactive drugs (phenylephrine and sodium nitroprusside) administered by three different approaches: 1) bolus injection, 2) steady-state (blood pressure (BP) changes produced in steps), 3) ramp infusion (30 s, brief infusion). The heart rate (HR) responses were evaluated by the mean index (mean ratio of all HR changes and mean arterial pressure (MAP) changes), by linear regression and by the logistic method (maximum gain of the sigmoid curve by a logistic function). The experiments were performed on three consecutive days. Basal MAP and resting HR were similar on all days of the study. Bradycardic responses evaluated by the mean index (-1.5 ± 0.2, -2.1 ± 0.2 and -1.6 ± 0.2 bpm/mmHg) and linear regression (-1.8 ± 0.3, -1.4 ± 0.3 and -1.7 ± 0.2 bpm/mmHg) were similar for all three approaches used to change blood pressure. The tachycardic responses to decreases of MAP were similar when evaluated by linear regression (-3.9 ± 0.8, -2.1 ± 0.7 and -3.8 ± 0.4 bpm/mmHg). However, the tachycardic mean index (-3.1 ± 0.4, -6.6 ± 1 and -3.6 ± 0.5 bpm/mmHg) was higher when assessed by the steady-state method. The average gain evaluated by logistic function (-3.5 ± 0.6, -7.6 ± 1.3 and -3.8 ± 0.4 bpm/mmHg) was similar to the reflex tachycardic values, but different from the bradycardic values. Since different ways to change BP may alter the afferent baroreceptor function, the MAP changes obtained during short periods of time (up to 30 s: bolus and ramp infusion) are more appropriate to prevent the acute resetting. Assessment of the baroreflex sensitivity by mean index and linear regression permits a separate analysis of gain for reflex bradycardia and reflex tachycardia. Although two values of baroreflex sensitivity cannot be evaluated by a single symmetric logistic function, this method has the advantage of better comparing the baroreflex sensitivity of animals with different basal blood pressures.

Inspiratory flow-volume curve in snoring patients with and without obstructive sleep apnea

We analyzed the flow-volume curves of 50 patients with complaints of snoring and daytime sleepiness in treatment at the Pneumology Unit of the University Hospital of Brasília. The total group was divided into snorers without obstructive sleep apnea (OSA) (N = 19) and snorers with OSA (N = 31); the patients with OSA were subdivided into two groups according to the apnea/hypopnea index (AHI): AHI<20/h (N = 14) and AHI>20/h (N = 17). The control group (N = 10) consisted of nonsmoking subjects without complaints of snoring, daytime sleepiness or pulmonary diseases. The population studied (control and patients) consisted of males of similar age, height and body mass index (BMI); spirometric data were also similar in the four groups. There was no significative difference in the ratio of forced expiratory and inspiratory flows (FEF50%/FIF50%) in any group: control, 0.89; snorers, 1.11; snorers with OSA (AHI<20/h), 1.42, and snorers with OSA (AHI>20/h), 1.64. The FIF at 50% of vital capacity (FIF50%) of snoring patients with or without OSA was lower than the FIF50% of the control group (P<0.05): snorers 4.30 l/s; snorers with OSA (AHI<20/h) 3.69 l/s; snorers with OSA (AHI>20/h) 3.17 l/s and control group 5.48 l/s. The FIF50% of patients with severe OSA (AHI>20/h) was lower than the FIF50% of snorers without OSA (P<0.05): 3.17 l/s and 4.30 l/s, respectively. We conclude that 1) the FEF50%/FIF50% ratio is not useful for predicting OSA, and 2) FIF50% is decreased in snoring patients with and without OSA, suggesting that these patients have increased upper airway resistance (UAR).

In vitro-induced antibody production in chronic hepatitis C virus infection

The objectives of the present study were to assess the in vitro-induced anti-hepatitis C virus (HCV) antibody production (IVIAP) in relation to the clinical, biochemical, virologic and histologic variables of patients with HCV infection. The study included 57 patients (60% males) with HCV infection (anti-HCV and HCV-RNA positive). Alanine aminotransferase (ALT) was elevated in 89% of the patients. Mean viral load was 542,241 copies/ml and histology of the liver showed chronic hepatitis in 27/52 (52%) and cirrhosis in 11/52 (21%) patients. IVIAP levels were determined by immunoenzymatic assay at median absorbance of 0.781 at 450 nm. IVIAP was negative in 14% of the patients. When groups with IVIAP levels above and below the median were compared, high IVIAP levels were associated with the male sex, elevated ALT levels and more advanced disease stage. After logistic regression analysis, advanced histologic damage to the liver remained as the only independent variable associated with elevated IVIAP levels. Using a receiver operator characteristic curve, the best cut-off level for IVIAP was established (= 1.540), with 71% sensitivity and 94% specificity for the detection of more advanced disease stages (grades 3 and 4). These findings are consistent with the participation of immunological mechanisms in the genesis of the hepatic lesions induced by HCV and indicate that the IVIAP test may be useful as a noninvasive marker of liver damage either alone or in combination with other markers.

Standard-curve competitive RT-PCR quantification of myogenic regulatory factors in chicken embryos

The reverse transcription-polymerase chain reaction (RT-PCR) is the most sensitive method used to evaluate gene expression. Although many advances have been made since quantitative RT-PCR was first described, few reports deal with the mathematical bases of this technique. The aim of the present study was to develop and standardize a competitive PCR method using standard-curves to quantify transcripts of the myogenic regulatory factors MyoD, Myf-5, Myogenin and MRF4 in chicken embryos. Competitor cDNA molecules were constructed for each gene under study using deletion primers, which were designed to maintain the anchorage sites for the primers used to amplify target cDNAs. Standard-curves were prepared by co-amplification of different amounts of target cDNA with a constant amount of competitor. The content of specific mRNAs in embryo cDNAs was determined after PCR with a known amount of competitor and comparison to standard-curves. Transcripts of the housekeeping ß-actin gene were measured to normalize the results. As predicted by the model, most of the standard-curves showed a slope close to 1, while intercepts varied depending on the relative efficiency of competitor amplification. The sensitivity of the RT-PCR method permitted the detection of as few as 60 MyoD/Myf-5 molecules per reaction but approximately 600 molecules of MRF4/Myogenin mRNAS were necessary to produce a measurable signal. A coefficient of variation of 6 to 19% was estimated for the different genes analyzed (6 to 9 repetitions). The competitive RT-PCR assay described here is sensitive, precise and allows quantification of up to 9 transcripts from a single cDNA sample.

Modeling the pressure inactivation dynamics of Escherichia coli

Escherichia coli, as a model microorganism, was treated in phosphate-buffered saline under high hydrostatic pressure between 100 and 300 MPa, and the inactivation dynamics was investigated from the viewpoint of predictive microbiology. Inactivation data were curve fitted by typical predictive models: logistic, Gompertz and Weibull functions. Weibull function described the inactivation curve the best. Two parameters of Weibull function were calculated for each holding pressure and their dependence on holding pressure was obtained by interpolation. With the interpolated parameters, inactivation curves were simulated and compared with the experimental data sets.

Stability of relative oxygen pulse curve during repeated maximal cardiopulmonary testing in professional soccer players

During cardiopulmonary exercise testing (CPET), stroke volume can be indirectly assessed by O2 pulse profile. However, for a valid interpretation, the stability of this variable over time should be known. The objective was to analyze the stability of the O2 pulse curve relative to body mass in elite athletes. VO2, heart rate (HR), and relative O2 pulse were compared at every 10% of the running time in two maximal CPETs, from 2005 to 2010, of 49 soccer players. Maximal values of VO2 (63.4 ± 0.9 vs 63.5 ± 0.9 mL O2•kg-1•min-1), HR (190 ± 1 vs188 ± 1 bpm) and relative O2 pulse (32.9 ± 0.6 vs 32.6 ± 0.6 mL O2•beat-1•kg-1) were similar for the two CPETs (P > 0.05), while the final treadmill velocity increased from 18.5 ± 0.9 to 18.9 ± 1.0 km/h (P < 0.01). Relative O2 pulse increased linearly and similarly in both evaluations (r² = 0.64 and 0.63) up to 90% of the running time. Between 90 and 100% of the running time, the values were less stable, with up to 50% of the players showing a tendency to a plateau in the relative O2 pulse. In young healthy men in good to excellent aerobic condition, the morphology of the relative O2 pulse curve is consistent up to close to the peak effort for a CPET repeated within a 1-year period. No increase in relative O2pulse at peak effort could represent a physiologic stroke volume limitation in these athletes.

A dose-response curve for biodosimetry from a 6 MV electron linear accelerator

Biological dosimetry (biodosimetry) is based on the investigation of radiation-induced biological effects (biomarkers), mainly dicentric chromosomes, in order to correlate them with radiation dose. To interpret the dicentric score in terms of absorbed dose, a calibration curve is needed. Each curve should be constructed with respect to basic physical parameters, such as the type of ionizing radiation characterized by low or high linear energy transfer (LET) and dose rate. This study was designed to obtain dose calibration curves by scoring of dicentric chromosomes in peripheral blood lymphocytes irradiated in vitro with a 6 MV electron linear accelerator (Mevatron M, Siemens, USA). Two software programs, CABAS (Chromosomal Aberration Calculation Software) and Dose Estimate, were used to generate the curve. The two software programs are discussed; the results obtained were compared with each other and with other published low LET radiation curves. Both software programs resulted in identical linear and quadratic terms for the curve presented here, which was in good agreement with published curves for similar radiation quality and dose rates.

Forecasting the Yield Curve of Government Bonds: A Comparative Study

For the past 20 years, researchers have applied the Kalman filter to the modeling and forecasting the term structure of interest rates. Despite its impressive performance in in-sample fitting yield curves, little research has focused on the out-of-sample forecast of yield curves using the Kalman filter. The goal of this thesis is to develop a unified dynamic model based on Diebold and Li (2006) and Nelson and Siegel’s (1987) three-factor model, and estimate this dynamic model using the Kalman filter. We compare both in-sample and out-of-sample performance of our dynamic methods with various other models in the literature. We find that our dynamic model dominates existing models in medium- and long-horizon yield curve predictions. However, the dynamic model should be used with caution when forecasting short maturity yields

Associations Between Personal Cancer History and Lung Cancer Risk

The study aim was to investigate the relationship between factors related to personal cancer history and lung cancer risk as well as assess their predictive utility. Characteristics of interest included the number, anatomical site(s), and age of onset of previous cancer(s). Data from the Prostate, Lung, Colorectal and Ovarian Screening (PLCO) Cancer Screening Trial (N = 154,901) and National Lung Screening Trial (N = 53,452) were analysed. Logistic regression models were used to assess the relationships between each variable of interest and 6-year lung cancer risk. Predictive utility was assessed through changes in area-under-the-curve (AUC) after substitution into the PLCOall2014 lung cancer risk prediction model. Previous lung, uterine and oral cancers were strongly and significantly associated with elevated 6-year lung cancer risk after controlling for confounders. None of these refined measures of personal cancer history offered more predictive utility than the simple (yes/no) measure already included in the PLCOall2014 model.

Inflation dynamics and the New Keynesian Phillips Curve: an identification robust econometric analysis

In this paper, we use identification-robust methods to assess the empirical adequacy of a New Keynesian Phillips Curve (NKPC) equation. We focus on the Gali and Gertler’s (1999) specification, on both U.S. and Canadian data. Two variants of the model are studied: one based on a rationalexpectations assumption, and a modification to the latter which consists in using survey data on inflation expectations. The results based on these two specifications exhibit sharp differences concerning: (i) identification difficulties, (ii) backward-looking behavior, and (ii) the frequency of price adjustments. Overall, we find that there is some support for the hybrid NKPC for the U.S., whereas the model is not suited to Canada. Our findings underscore the need for employing identificationrobust inference methods in the estimation of expectations-based dynamic macroeconomic relations.

Estimation des facteurs de risque de la progression de la scoliose idiopathique de l’adolescence

Cette étude a pour but de tester si l’ajout de variables biomécaniques, telles que celles associées à la morphologie, la posture et l’équilibre, permet d’améliorer l’efficacité à dissocier 29 sujets ayant une scoliose progressive de 45 sujets ayant une scoliose non progressive. Dans une étude rétrospective, un groupe d’apprentissage (Cobb: 27,1±10,6°) a été utilisé avec cinq modèles faisant intervenir des variables cliniques, morphologiques, posturales et d’équilibre et la progression de la scoliose. Un groupe test (Cobb: 14,2±8,3°) a ensuite servit à évaluer les modèles dans une étude prospective. Afin d’établir l’efficacité de l’ajout de variables biomécaniques, le modèle de Lonstein et Carlson (1984) a été utilisé à titre d’étalon de mesures. Le groupe d’apprentissage a été utilisé pour développer quatre modèles de classification. Le modèle sans réduction fut composé de 35 variables tirées de la littérature. Dans le modèle avec réduction, une ANCOVA a servit de méthode de réduction pour passer de 35 à 8 variables et l’analyse par composantes principales a été utilisée pour passer de 35 à 7 variables. Le modèle expert fut composé de huit variables sélectionnées d’après l’expérience clinque. L’analyse discriminante, la régression logistique et l’analyse par composantes principales ont été appliquées afin de classer les sujets comme progressifs ou non progressifs. La régression logistique utilisée avec le modèle sans réduction a présenté l’efficience la plus élevée (0,94), tandis que l’analyse discriminante utilisée avec le modèle expert a montré l’efficience la plus faible (0,87). Ces résultats montrent un lien direct entre un ensemble de paramètres cliniques et biomécaniques et la progression de la scoliose idiopathique. Le groupe test a été utilisé pour appliquer les modèles développés à partir du groupe d’apprentissage. L’efficience la plus élevée (0,89) fut obtenue en utilisant l’analyse discriminante et la régression logistique avec le modèle sans réduction, alors que la plus faible (0,78) fut obtenue en utilisant le modèle de Lonstein et Carlson (1984). Ces valeurs permettent d’avancer que l’ajout de variables biomécaniques aux données cliniques améliore l’efficacité de la dissociation entre des sujets scoliotiques progressifs et non progressifs. Afin de vérifier la précision des modèles, les aires sous les courbes ROC ont été calculées. L’aire sous la courbe ROC la plus importante (0,93) fut obtenue avec l’analyse discriminante utilisée avec le modèle sans réduction, tandis que la plus faible (0,63) fut obtenue avec le modèle de Lonstein et Carlson (1984). Le modèle de Lonstein et Carlson (1984) n’a pu séparer les cas positifs des cas négatifs avec autant de précision que les modèles biomécaniques. L’ajout de variables biomécaniques aux données cliniques a permit d’améliorer l’efficacité de la dissociation entre des sujets scoliotiques progressifs et non progressifs. Ces résultats permettent d’avancer qu’il existe d’autres facteurs que les paramètres cliniques pour identifier les patients à risque de progresser. Une approche basée sur plusieurs types de paramètres tient compte de la nature multifactorielle de la scoliose idiopathique et s’avère probablement mieux adaptée pour en prédire la progression.

Mechanism and Prediction of Post-Operative Atrial Fibrillation Based on Atrial Electrograms

La fibrillation auriculaire (FA) est une arythmie touchant les oreillettes. En FA, la contraction auriculaire est rapide et irrégulière. Le remplissage des ventricules devient incomplet, ce qui réduit le débit cardiaque. La FA peut entraîner des palpitations, des évanouissements, des douleurs thoraciques ou l’insuffisance cardiaque. Elle augmente aussi le risque d'accident vasculaire. Le pontage coronarien est une intervention chirurgicale réalisée pour restaurer le flux sanguin dans les cas de maladie coronarienne sévère. 10% à 65% des patients qui n'ont jamais subi de FA, en sont victime le plus souvent lors du deuxième ou troisième jour postopératoire. La FA est particulièrement fréquente après une chirurgie de la valve mitrale, survenant alors dans environ 64% des patients. L'apparition de la FA postopératoire est associée à une augmentation de la morbidité, de la durée et des coûts d'hospitalisation. Les mécanismes responsables de la FA postopératoire ne sont pas bien compris. L'identification des patients à haut risque de FA après un pontage coronarien serait utile pour sa prévention. Le présent projet est basé sur l'analyse d’électrogrammes cardiaques enregistrées chez les patients après pontage un aorte-coronaire. Le premier objectif de la recherche est d'étudier si les enregistrements affichent des changements typiques avant l'apparition de la FA. Le deuxième objectif est d'identifier des facteurs prédictifs permettant d’identifier les patients qui vont développer une FA. Les enregistrements ont été réalisés par l'équipe du Dr Pierre Pagé sur 137 patients traités par pontage coronarien. Trois électrodes unipolaires ont été suturées sur l'épicarde des oreillettes pour enregistrer en continu pendant les 4 premiers jours postopératoires. La première tâche était de développer un algorithme pour détecter et distinguer les activations auriculaires et ventriculaires sur chaque canal, et pour combiner les activations des trois canaux appartenant à un même événement cardiaque. L'algorithme a été développé et optimisé sur un premier ensemble de marqueurs, et sa performance évaluée sur un second ensemble. Un logiciel de validation a été développé pour préparer ces deux ensembles et pour corriger les détections sur tous les enregistrements qui ont été utilisés plus tard dans les analyses. Il a été complété par des outils pour former, étiqueter et valider les battements sinusaux normaux, les activations auriculaires et ventriculaires prématurées (PAA, PVA), ainsi que les épisodes d'arythmie. Les données cliniques préopératoires ont ensuite été analysées pour établir le risque préopératoire de FA. L’âge, le niveau de créatinine sérique et un diagnostic d'infarctus du myocarde se sont révélés être les plus importants facteurs de prédiction. Bien que le niveau du risque préopératoire puisse dans une certaine mesure prédire qui développera la FA, il n'était pas corrélé avec le temps de l'apparition de la FA postopératoire. Pour l'ensemble des patients ayant eu au moins un épisode de FA d’une durée de 10 minutes ou plus, les deux heures précédant la première FA prolongée ont été analysées. Cette première FA prolongée était toujours déclenchée par un PAA dont l’origine était le plus souvent sur l'oreillette gauche. Cependant, au cours des deux heures pré-FA, la distribution des PAA et de la fraction de ceux-ci provenant de l'oreillette gauche était large et inhomogène parmi les patients. Le nombre de PAA, la durée des arythmies transitoires, le rythme cardiaque sinusal, la portion basse fréquence de la variabilité du rythme cardiaque (LF portion) montraient des changements significatifs dans la dernière heure avant le début de la FA. La dernière étape consistait à comparer les patients avec et sans FA prolongée pour trouver des facteurs permettant de discriminer les deux groupes. Cinq types de modèles de régression logistique ont été comparés. Ils avaient une sensibilité, une spécificité et une courbe opérateur-receveur similaires, et tous avaient un niveau de prédiction des patients sans FA très faible. Une méthode de moyenne glissante a été proposée pour améliorer la discrimination, surtout pour les patients sans FA. Deux modèles ont été retenus, sélectionnés sur les critères de robustesse, de précision, et d’applicabilité. Autour 70% patients sans FA et 75% de patients avec FA ont été correctement identifiés dans la dernière heure avant la FA. Le taux de PAA, la fraction des PAA initiés dans l'oreillette gauche, le pNN50, le temps de conduction auriculo-ventriculaire, et la corrélation entre ce dernier et le rythme cardiaque étaient les variables de prédiction communes à ces deux modèles.

Scoliosis curve type classification from 3D trunk image

Adolescent idiopathic scoliosis (AIS) is a deformity of the spine manifested by asymmetry and deformities of the external surface of the trunk. Classification of scoliosis deformities according to curve type is used to plan management of scoliosis patients. Currently, scoliosis curve type is determined based on X-ray exam. However, cumulative exposure to X-rays radiation significantly increases the risk for certain cancer. In this paper, we propose a robust system that can classify the scoliosis curve type from non invasive acquisition of 3D trunk surface of the patients. The 3D image of the trunk is divided into patches and local geometric descriptors characterizing the surface of the back are computed from each patch and forming the features. We perform the reduction of the dimensionality by using Principal Component Analysis and 53 components were retained. In this work a multi-class classifier is built with Least-squares support vector machine (LS-SVM) which is a kernel classifier. For this study, a new kernel was designed in order to achieve a robust classifier in comparison with polynomial and Gaussian kernel. The proposed system was validated using data of 103 patients with different scoliosis curve types diagnosed and classified by an orthopedic surgeon from the X-ray images. The average rate of successful classification was 93.3% with a better rate of prediction for the major thoracic and lumbar/thoracolumbar types.

Non invasive classification system of scoliosis curve types using least-squares support vector machines

Objective To determine scoliosis curve types using non invasive surface acquisition, without prior knowledge from X-ray data. Methods Classification of scoliosis deformities according to curve type is used in the clinical management of scoliotic patients. In this work, we propose a robust system that can determine the scoliosis curve type from non invasive acquisition of the 3D back surface of the patients. The 3D image of the surface of the trunk is divided into patches and local geometric descriptors characterizing the back surface are computed from each patch and constitute the features. We reduce the dimensionality by using principal component analysis and retain 53 components using an overlap criterion combined with the total variance in the observed variables. In this work, a multi-class classifier is built with least-squares support vector machines (LS-SVM). The original LS-SVM formulation was modified by weighting the positive and negative samples differently and a new kernel was designed in order to achieve a robust classifier. The proposed system is validated using data from 165 patients with different scoliosis curve types. The results of our non invasive classification were compared with those obtained by an expert using X-ray images. Results The average rate of successful classification was computed using a leave-one-out cross-validation procedure. The overall accuracy of the system was 95%. As for the correct classification rates per class, we obtained 96%, 84% and 97% for the thoracic, double major and lumbar/thoracolumbar curve types, respectively. Conclusion This study shows that it is possible to find a relationship between the internal deformity and the back surface deformity in scoliosis with machine learning methods. The proposed system uses non invasive surface acquisition, which is safe for the patient as it involves no radiation. Also, the design of a specific kernel improved classification performance.