Aggregation and caking processes of granular materials:continuum model and simulation with application to sugar

Aggregation and caking of particles are common severe problems in many operations and processing of granular materials, where granulated sugar is an important example. Prevention of aggregation and caking of granular materials requires a good understanding of moisture migration and caking mechanisms. In this paper, the modeling of solid bridge formation between particles is introduced, based on moisture migration of atmospheric moisture into containers packed with granular materials through vapor evaporation and condensation. A model for the caking process is then developed, based on the growth of liquid bridges (during condensation), and their hardening and subsequent creation of solid bridges (during evaporation). The predicted caking strengths agree well with some available experimental data on granulated sugar under storage conditions.

Almost Separable Data Aggregation by Layers of Formal Neurons

Information extraction or knowledge discovery from large data sets should be linked to data aggregation process. Data aggregation process can result in a new data representation with decreased number of objects of a given set. A deterministic approach to separable data aggregation means a lesser number of objects without mixing of objects from different categories. A statistical approach is less restrictive and allows for almost separable data aggregation with a low level of mixing of objects from different categories. Layers of formal neurons can be designed for the purpose of data aggregation both in the case of deterministic and statistical approach. The proposed designing method is based on minimization of the of the convex and piecewise linear (CPL) criterion functions.

Laminar distribution of β-amyloid (Aβ) peptide deposits in the frontal lobe in familial and sporadic Alzheimer's disease

To determine whether genetic factors influence frontal lobe degeneration in Alzheimer's disease (AD), the laminar distributions of diffuse, primitive, and classic β-amyloid (Aβ) peptide deposits were compared in early-onset familial AD (EO-FAD) linked to mutations of the amyloid precursor protein (APP) or presenilin 1 (PSEN1) gene, late-onset familial AD (LO-FAD), and sporadic AD (SAD). The influence of apolipoprotein E (Apo E) genotype on laminar distribution was also studied. In the majority of FAD and SAD cases, maximum density of the diffuse and primitive Aβ deposits occurred in the upper cortical layers, whereas the distribution of the classic Aβ deposits was more variable, either occurring in the lower layers, or a double-peaked (bimodal) distribution was present, density peaks occurring in upper and lower layers. The cortical layer at which maximum density of Aβ deposits occurred and maximum density were similar in EO-FAD, LO-FAD and SAD. In addition, there were no significant differences in distributions in cases expressing Apo E ε4 alleles compared with cases expressing the ε2 or ε3 alleles. These results suggest that gene expression had relatively little effect on the laminar distribution of Aβ deposits in the frontal lobe of the AD cases studied. Hence, the pattern of frontal lobe degeneration in AD is similar regardless of whether it is associated with APP and PSEN1, mutation, allelic variation in Apo E, or with SAD.

An Approach to Variable Aggregation in Efficiency Analysis

In the nonparametric framework of Data Envelopment Analysis the statistical properties of its estimators have been investigated and only asymptotic results are available. For DEA estimators results of practical use have been proved only for the case of one input and one output. However, in the real world problems the production process is usually well described by many variables. In this paper a machine learning approach to variable aggregation based on Canonical Correlation Analysis is presented. This approach is applied for efficiency estimation of all the farms in Terceira Island of the Azorean archipelago.

A comparison of spatial patterns of TDP-43 cellular inclusions in familial and sporadic frontotemporal lobar degeneration with TDP-43 proteinopathy

Abnormal protein aggregates of transactive response (TAR) DNA-binding protein (TDP-43) in the form of neuronal cytoplasmic inclusions (NCI), oligodendroglial inclusions (GI), neuronal internuclear inclusions (NII), and dystrophic neurites (DN) are the pathological hallmark of frontotemporal lobar degeneration with TDP-43 proteinopathy (FTLD-TDP). To investigate the role of phosphorylated TDP-43 (pTDP-43) in neurodegeneration in FTLD-TDP, the spatial patterns of the pTDP-43-immunoreactive NCI, GI, NII, and DN were studied in frontal and temporal cortex in three groups of cases: (1) familial FTLD-TDP caused by progranulin (GRN) mutation, (2) a miscellaneous group of familial cases containing cases caused by valosin-containing protein (VCP) mutation, ubiquitin associated protein 1 (UBAP1) mutation, and cases not associated with currently known genes, and (3) sporadic FTLD-TDP. In a significant number of brain regions, the pTDP-43-immunoreactive inclusions developed in clusters and the clusters were distributed regularly parallel to the tissue boundary. The spatial patterns of the inclusions were similar to those revealed by a phosphorylation-independent anti-TDP-43 antibody. The spatial patterns and cluster sizes of the pTDP-43-immunoreactive inclusions were similar in GRN mutation cases, remaining familial cases, and in sporadic FTLD-TDP. Hence, pathological changes initiated by different genetic factors in familial cases and by unknown causes in sporadic FTLD-TDP appear to follow a parallel course resulting in very similar patterns of degeneration of frontal and temporal lobes.

Familial adrenal hypoplasia syndromes

The adrenal cortex secretes steroid hormones, including glucocorticoids and mineralocorticoids. Glucocorticoids control body homeostasis, stress, and immune responses, while mineralocorticoids regulate the water and electrolyte balance. A spectrum of genetic defects can disrupt the normal adrenal development, causing adrenal hypoplasia and various forms of adrenal insufficiency, which usually present in infancy or childhood with or without mineralocorticoid deficiency and with or without gonadal dysfunction. The genetic causes of adrenal hypoplasia can be broadly categorized into adrenal hypoplasia due to adrenocorticotropic hormone resistance syndromes (i.e., familial glucocorticoid deficiency and triple A syndrome) and adrenal hypoplasia due to primary defects in the development of the adrenal glands (i.e., X-linked adrenal hypoplasia congenita and primary adrenal hypoplasia caused by steroidogenic factor 1 mutations).

Improving energy efficiency in a wireless sensor network by combining cooperative MIMO with data aggregation

In wireless sensor networks where nodes are powered by batteries, it is critical to prolong the network lifetime by minimizing the energy consumption of each node. In this paper, the cooperative multiple-input-multiple-output (MIMO) and data-aggregation techniques are jointly adopted to reduce the energy consumption per bit in wireless sensor networks by reducing the amount of data for transmission and better using network resources through cooperative communication. For this purpose, we derive a new energy model that considers the correlation between data generated by nodes and the distance between them for a cluster-based sensor network by employing the combined techniques. Using this model, the effect of the cluster size on the average energy consumption per node can be analyzed. It is shown that the energy efficiency of the network can significantly be enhanced in cooperative MIMO systems with data aggregation, compared with either cooperative MIMO systems without data aggregation or data-aggregation systems without cooperative MIMO, if sensor nodes are properly clusterized. Both centralized and distributed data-aggregation schemes for the cooperating nodes to exchange and compress their data are also proposed and appraised, which lead to diverse impacts of data correlation on the energy performance of the integrated cooperative MIMO and data-aggregation systems.

Additive and multiplicative properties of scoring methods for preference aggregation

The paper reviews some additive and multiplicative properties of ranking procedures used for generalized tournaments with missing values and multiple comparisons. The methods analysed are the score, generalised row sum and least squares as well as fair bets and its variants. It is argued that generalised row sum should be applied not with a fixed parameter, but a variable one proportional to the number of known comparisons. It is shown that a natural additive property has strong links to independence of irrelevant matches, an axiom judged unfavourable when players have different opponents.

On the additivity of preference aggregation methods

The paper reviews some axioms of additivity concerning ranking methods used for generalized tournaments with possible missing values and multiple comparisons. It is shown that one of the most natural properties, called consistency, has strong links to independence of irrelevant comparisons, an axiom judged unfavourable when players have different opponents. Therefore some directions of weakening consistency are suggested, and several ranking methods, the score, generalized row sum and least squares as well as fair bets and its two variants (one of them entirely new) are analysed whether they satisfy the properties discussed. It turns out that least squares and generalized row sum with an appropriate parameter choice preserve the relative ranking of two objects if the ranking problems added have the same comparison structure.

Mitigating congestion by integrating time forecasting and realtime information aggregation in cellular networks

An iterative travel time forecasting scheme, named the Advanced Multilane Prediction based Real-time Fastest Path (AMPRFP) algorithm, is presented in this dissertation. This scheme is derived from the conventional kernel estimator based prediction model by the association of real-time nonlinear impacts that caused by neighboring arcs’ traffic patterns with the historical traffic behaviors. The AMPRFP algorithm is evaluated by prediction of the travel time of congested arcs in the urban area of Jacksonville City. Experiment results illustrate that the proposed scheme is able to significantly reduce both the relative mean error (RME) and the root-mean-squared error (RMSE) of the predicted travel time. To obtain high quality real-time traffic information, which is essential to the performance of the AMPRFP algorithm, a data clean scheme enhanced empirical learning (DCSEEL) algorithm is also introduced. This novel method investigates the correlation between distance and direction in the geometrical map, which is not considered in existing fingerprint localization methods. Specifically, empirical learning methods are applied to minimize the error that exists in the estimated distance. A direction filter is developed to clean joints that have negative influence to the localization accuracy. Synthetic experiments in urban, suburban and rural environments are designed to evaluate the performance of DCSEEL algorithm in determining the cellular probe’s position. The results show that the cellular probe’s localization accuracy can be notably improved by the DCSEEL algorithm. Additionally, a new fast correlation technique for overcoming the time efficiency problem of the existing correlation algorithm based floating car data (FCD) technique is developed. The matching process is transformed into a 1-dimensional (1-D) curve matching problem and the Fast Normalized Cross-Correlation (FNCC) algorithm is introduced to supersede the Pearson product Moment Correlation Co-efficient (PMCC) algorithm in order to achieve the real-time requirement of the FCD method. The fast correlation technique shows a significant improvement in reducing the computational cost without affecting the accuracy of the matching process.

High-performance liquid chromatography measurement of carotenoids in human serum, etoposide in pig cerebrospinal fluid and measurement of the aggregation of xanthophylls in aqueous solutions

The present study measured a chemotherapy drug, etoposide, in pig cerebrospinal fluid after intraventricular administrations were made directly into the fourth ventricle of the brain; cytotoxic concentrations for a twenty-four hour period after infusions. The analytical method developed validates the potential treatment of malignant brain tumors. The increase in serum carotenoid concentration in 30 healthy individuals was measured after supplementation with lutein. HPLC analysis of serum levels of carotenoids showed an increase in the concentration of lutein and a constant concentration of other major serum carotenoids. An initial attempt to measure the enthalpy of aggregation of xanthophylls was conducted by using ultraviolet-visible spectroscopy. The enthalpy of lutein aggregation and AH range of zeaxanthin disordering of aggregation are reported. Monomethyl ether of lutein did not aggregate in any of the aqueous solutions.

Fractal analysis of the effect of particle aggregation distribution on thermal conductivity of nanofluids

This project was supported by the National Natural Science Foundation of China (No. 41572116), the Fundamental Research Funds for the Central Universities, China University of Geosciences, Wuhan) (No. CUG160602).

L'environnement familial des adolescents agresseurs sexuels

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Effets d'un suivi postnatal intensif et précoce auprès de mères adolescentes sur le niveau de soutien perçu et la structure de leur réseau familial et communautaire

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.