Constraint based methods for allocation and scheduling of periodic applications

This work presents exact algorithms for the Resource Allocation and Cyclic Scheduling Problems (RA&CSPs). Cyclic Scheduling Problems arise in a number of application areas, such as in hoist scheduling, mass production, compiler design (implementing scheduling loops on parallel architectures), software pipelining, and in embedded system design. The RA&CS problem concerns time and resource assignment to a set of activities, to be indefinitely repeated, subject to precedence and resource capacity constraints. In this work we present two constraint programming frameworks facing two different types of cyclic problems. In first instance, we consider the disjunctive RA&CSP, where the allocation problem considers unary resources. Instances are described through the Synchronous Data-flow (SDF) Model of Computation. The key problem of finding a maximum-throughput allocation and scheduling of Synchronous Data-Flow graphs onto a multi-core architecture is NP-hard and has been traditionally solved by means of heuristic (incomplete) algorithms. We propose an exact (complete) algorithm for the computation of a maximum-throughput mapping of applications specified as SDFG onto multi-core architectures. Results show that the approach can handle realistic instances in terms of size and complexity. Next, we tackle the Cyclic Resource-Constrained Scheduling Problem (i.e. CRCSP). We propose a Constraint Programming approach based on modular arithmetic: in particular, we introduce a modular precedence constraint and a global cumulative constraint along with their filtering algorithms. Many traditional approaches to cyclic scheduling operate by fixing the period value and then solving a linear problem in a generate-and-test fashion. Conversely, our technique is based on a non-linear model and tackles the problem as a whole: the period value is inferred from the scheduling decisions. The proposed approaches have been tested on a number of non-trivial synthetic instances and on a set of realistic industrial instances achieving good results on practical size problem.

Inserimento eterofamigliare supportato di adulti sofferenti di disturbi psichici: Valutazione degli esiti

L’Inserimento Eterofamigliare Supportato di Adulti (IESA) sofferenti di disturbi psichici consiste nell’accogliere persone in cura presso i servizi psichiatrici territoriali, nel proprio domicilio, integrandole nelle proprie relazioni famigliari. Obiettivo è migliorare la qualità di vita dell’utente e favorirne l’integrazione nella comunità. Obiettivo. Valutare gli esiti dello IESA, con un disegno di ricerca longitudinale, considerando: psicopatologia, benessere psicologico, funzionamento sociale e familiare. Metodologia. 40 soggetti: 20 pazienti e 20 ospitanti. La valutazione clinica è stata effettuata all’inizio della convivenza e al follow-up di 1, 3, 6 e 12 mesi. Strumenti utilizzati: BPRS, VGF, PWB, SQ, FAD. Analisi statistica: Modello Lineare Generale (GLM) con l’Analisi della Varianza per prove ripetute e calcolo dell’effect-size. Risultati. 15 pazienti maschi e 5 femmine, 17 italiani. 11 soddisfano i criteri diagnostici (DSM-IV-TR) per schizofrenia e disturbi psicotici, 5 per i disturbi dell’umore e 4 per i disturbi di personalità. Dopo l’inserimento 3 sono stati i ricoveri e 4 le visite psichiatriche urgenti. 8 pazienti modificano/diminuiscono la terapia e 3 la sospendono. Aumenta il benessere psicologico (PWB); diminuiscono i sintomi psicopatologici (BPRS ed SQ) e migliora il funzionamento globale (VFG). Il gruppo dei famigliari composto da 11 uomini e 9 donne, 19 di nazionalità italiana; con età media di 55 anni. 8 sono coniugati, 6 celibi/nubili, 4 divorziati e 2 vedovi. 9 hanno figli, 11 lavorano e 8 sono pensionati. Nei famigliari aumenta il benessere psicologico (PWB), migliora il funzionamento famigliare (FAD) e la valutazione del funzionamento globale (VGF) rimane costante nel tempo. Discussioni e conclusioni. Il progetto IESA sembra migliorare la psicopatologia, con una diminuzione dei comportamenti maladattativi e un aumento delle capacità relazionali dell’ospite favorendone l’integrazione. Inoltre, lo IESA sembra diminuire i costi della cronicità psichiatrica: diminuzione degli accessi al Pronto Soccorso, delle visite psichiatriche urgenti e delle giornate di ricovero.

Sustainable improvement of water networks : to achieve best decisions for rehabilitation plans using advanced GIS and statistical techniques

Many of developing countries are facing crisis in water management due to increasing of population, water scarcity, water contaminations and effects of world economic crisis. Water distribution systems in developing countries are facing many challenges of efficient repair and rehabilitation since the information of water network is very limited, which makes the rehabilitation assessment plans very difficult. Sufficient information with high technology in developed countries makes the assessment for rehabilitation easy. Developing countries have many difficulties to assess the water network causing system failure, deterioration of mains and bad water quality in the network due to pipe corrosion and deterioration. The limited information brought into focus the urgent need to develop economical assessment for rehabilitation of water distribution systems adapted to water utilities. Gaza Strip is subject to a first case study, suffering from severe shortage in the water supply and environmental problems and contamination of underground water resources. This research focuses on improvement of water supply network to reduce the water losses in water network based on limited database using techniques of ArcGIS and commercial water network software (WaterCAD). A new approach for rehabilitation water pipes has been presented in Gaza city case study. Integrated rehabilitation assessment model has been developed for rehabilitation water pipes including three components; hydraulic assessment model, Physical assessment model and Structural assessment model. WaterCAD model has been developed with integrated in ArcGIS to produce the hydraulic assessment model for water network. The model have been designed based on pipe condition assessment with 100 score points as a maximum points for pipe condition. As results from this model, we can indicate that 40% of water pipeline have score points less than 50 points and about 10% of total pipes length have less than 30 score points. By using this model, the rehabilitation plans for each region in Gaza city can be achieved based on available budget and condition of pipes. The second case study is Kuala Lumpur Case from semi-developed countries, which has been used to develop an approach to improve the water network under crucial conditions using, advanced statistical and GIS techniques. Kuala Lumpur (KL) has water losses about 40% and high failure rate, which make severe problem. This case can represent cases in South Asia countries. Kuala Lumpur faced big challenges to reduce the water losses in water network during last 5 years. One of these challenges is high deterioration of asbestos cement (AC) pipes. They need to replace more than 6500 km of AC pipes, which need a huge budget to be achieved. Asbestos cement is subject to deterioration due to various chemical processes that either leach out the cement material or penetrate the concrete to form products that weaken the cement matrix. This case presents an approach for geo-statistical model for modelling pipe failures in a water distribution network. Database of Syabas Company (Kuala Lumpur water company) has been used in developing the model. The statistical models have been calibrated, verified and used to predict failures for both networks and individual pipes. The mathematical formulation developed for failure frequency in Kuala Lumpur was based on different pipeline characteristics, reflecting several factors such as pipe diameter, length, pressure and failure history. Generalized linear model have been applied to predict pipe failures based on District Meter Zone (DMZ) and individual pipe levels. Based on Kuala Lumpur case study, several outputs and implications have been achieved. Correlations between spatial and temporal intervals of pipe failures also have been done using ArcGIS software. Water Pipe Assessment Model (WPAM) has been developed using the analysis of historical pipe failure in Kuala Lumpur which prioritizing the pipe rehabilitation candidates based on ranking system. Frankfurt Water Network in Germany is the third main case study. This case makes an overview for Survival analysis and neural network methods used in water network. Rehabilitation strategies of water pipes have been developed for Frankfurt water network in cooperation with Mainova (Frankfurt Water Company). This thesis also presents a methodology of technical condition assessment of plastic pipes based on simple analysis. This thesis aims to make contribution to improve the prediction of pipe failures in water networks using Geographic Information System (GIS) and Decision Support System (DSS). The output from the technical condition assessment model can be used to estimate future budget needs for rehabilitation and to define pipes with high priority for replacement based on poor condition. rn

Dynamics and control issues of multi-rotor platforms

This thesis deals with the analytic study of dynamics of Multi--Rotor Unmanned Aerial Vehicles. It is conceived to give a set of mathematical instruments apt to the theoretical study and design of these flying machines. The entire work is organized in analogy with classical academic texts about airplane flight dynamics. First, the non--linear equations of motion are defined and all the external actions are modeled, with particular attention to rotors aerodynamics. All the equations are provided in a form, and with personal expedients, to be directly exploitable in a simulation environment. This has requited an answer to questions like the trim of such mathematical systems. All the treatment is developed aiming at the description of different multi--rotor configurations. Then, the linearized equations of motion are derived. The computation of the stability and control derivatives of the linear model is carried out. The study of static and dynamic stability characteristics is, thus, addressed, showing the influence of the various geometric and aerodynamic parameters of the machine and in particular of the rotors. All the theoretic results are finally utilized in two interesting cases. One concerns the design of control systems for attitude stabilization. The linear model permits the tuning of linear controllers gains and the non--linear model allows the numerical testing. The other case is the study of the performances of an innovative configuration of quad--rotor aircraft. With the non--linear model the feasibility of maneuvers impossible for a traditional quad--rotor is assessed. The linear model is applied to the controllability analysis of such an aircraft in case of actuator block.

Body mass index, hormone replacement therapy, and endometrial cancer risk: a meta-analysis

Background: Body mass index (BMI) is a risk factor for endometrial cancer. We quantified the risk and investigated whether the association differed by use of hormone replacement therapy (HRT), menopausal status, and histologic type. Methods: We searched MEDLINE and EMBASE (1966 to December 2009) to identify prospective studies of BMI and incident endometrial cancer. We did random-effects meta-analyses, meta-regressions, and generalized least square regressions for trend estimations assuming linear, and piecewise linear, relationships. Results: Twenty-four studies (17,710 cases) were analyzed; 9 studies contributed to analyses by HRT, menopausal status, or histologic type, all published since 2003. In the linear model, the overall risk ratio (RR) per 5 kg/m2 increase in BMI was 1.60 (95% CI, 1.52–1.68), P < 0.0001. In the piecewise model, RRs compared with a normal BMI were 1.22 (1.19–1.24), 2.09 (1.94–2.26), 4.36 (3.75–5.10), and 9.11 (7.26–11.51) for BMIs of 27, 32, 37, and 42 kg/m2, respectively. The association was stronger in never HRT users than in ever users: RRs were 1.90 (1.57–2.31) and 1.18 (95% CI, 1.06–1.31) with P for interaction ¼ 0.003. In the piecewise model, the RR in never users was 20.70 (8.28–51.84) at BMI 42 kg/m2, compared with never users at normal BMI. The association was not affected by menopausal status (P ¼ 0.34) or histologic type (P ¼ 0.26). Conclusions: HRT use modifies the BMI-endometrial cancer risk association. Impact: These findings support the hypothesis that hyperestrogenia is an important mechanism underlying the BMI-endometrial cancer association, whilst the presence of residual risk in HRT users points to the role of additional systems. Cancer Epidemiol Biomarkers Prev; 19(12); 3119–30.

Alterations of white matter integrity related to motor activity in schizophrenia

Altered structural connectivity is a key finding in schizophrenia, but the meaning of white matter alterations for behavior is rarely studied. In healthy subjects, motor activity correlated with white matter integrity in motor tracts. To explore the relation of motor activity and fractional anisotropy (FA) in schizophrenia, we investigated 19 schizophrenia patients and 24 healthy control subjects using Diffusion Tensor Imaging (DTI) and actigraphy on the same day. Schizophrenia patients had lower activity levels (AL). In both groups linear relations of AL and FA were detected in several brain regions. Schizophrenia patients had lower FA values in prefrontal and left temporal clusters. Furthermore, using a general linear model, we found linear negative associations of FA and AL underneath the right supplemental motor area (SMA), the right precentral gyrus and posterior cingulum in patients. This effect within the SMA was not seen in controls. This association in schizophrenia patients may contribute to the well known dysfunctions of motor control. Thus, structural disconnectivity could lead to disturbed motor behavior in schizophrenia.

The link between visual exploration and neuronal activity: A multi-modal study combining eye tracking, functional magnetic resonance imaging and transcranial magnetic stimulation

In the present multi-modal study we aimed to investigate the role of visual exploration in relation to the neuronal activity and performance during visuospatial processing. To this end, event related functional magnetic resonance imaging er-fMRI was combined with simultaneous eye tracking recording and transcranial magnetic stimulation (TMS). Two groups of twenty healthy subjects each performed an angle discrimination task with different levels of difficulty during er-fMRI. The number of fixations as a measure of visual exploration effort was chosen to predict blood oxygen level-dependent (BOLD) signal changes using the general linear model (GLM). Without TMS, a positive linear relationship between the visual exploration effort and the BOLD signal was found in a bilateral fronto-parietal cortical network, indicating that these regions reflect the increased number of fixations and the higher brain activity due to higher task demands. Furthermore, the relationship found between the number of fixations and the performance demonstrates the relevance of visual exploration for visuospatial task solving. In the TMS group, offline theta bursts TMS (TBS) was applied over the right posterior parietal cortex (PPC) before the fMRI experiment started. Compared to controls, TBS led to a reduced correlation between visual exploration and BOLD signal change in regions of the fronto-parietal network of the right hemisphere, indicating a disruption of the network. In contrast, an increased correlation was found in regions of the left hemisphere, suggesting an intent to compensate functionality of the disturbed areas. TBS led to fewer fixations and faster response time while keeping accuracy at the same level, indicating that subjects explored more than actually needed.

Investigating human audio-visual object perception with a combination of hypothesis-generating and hypothesis-testing fMRI analysis tools

Primate multisensory object perception involves distributed brain regions. To investigate the network character of these regions of the human brain, we applied data-driven group spatial independent component analysis (ICA) to a functional magnetic resonance imaging (fMRI) data set acquired during a passive audio-visual (AV) experiment with common object stimuli. We labeled three group-level independent component (IC) maps as auditory (A), visual (V), and AV, based on their spatial layouts and activation time courses. The overlap between these IC maps served as definition of a distributed network of multisensory candidate regions including superior temporal, ventral occipito-temporal, posterior parietal and prefrontal regions. During an independent second fMRI experiment, we explicitly tested their involvement in AV integration. Activations in nine out of these twelve regions met the max-criterion (A < AV > V) for multisensory integration. Comparison of this approach with a general linear model-based region-of-interest definition revealed its complementary value for multisensory neuroimaging. In conclusion, we estimated functional networks of uni- and multisensory functional connectivity from one dataset and validated their functional roles in an independent dataset. These findings demonstrate the particular value of ICA for multisensory neuroimaging research and using independent datasets to test hypotheses generated from a data-driven analysis.

Regional mRNA expression of key gluconeogenic enzymes in the liver of dairy cows

Liver tissue was collected from eight random dairy cows at a slaughterhouse to test if gene expression of pyruvate carboxylase (PC), mitochondrial phosphoenolpyruvate carboxykinase (PEPCKm) and cytosolic phosphoenolpyruvate carboxykinase (PEPCKc) is different at different locations in the liver. Obtained liver samples were analysed for mRNA expression levels of PC, PEPCKc and PEPCKm and subjected to the MIXED procedure of SAS to test for the sampled locations with cow liver as repeated subject. Additionally, the general linear model procedure (GLM) for analysis of variance was applied to test for significant differences for mRNA abundance of PEPCKm, PEPCKc and bPC between the livers. In conclusion, this study demonstrated that mRNA abundance of PC, PEPCKc and PEPCKm is not different between locations in the liver but may differ between individual cows.

Impact of weather and climate on the incidence of acute coronary syndromes

BACKGROUND: First investigations of the interactions between weather and the incidence of acute myocardial infarctions date back to 1938. The early observation of a higher incidence of myocardial infarctions in the cold season could be confirmed in very different geographical regions and cohorts. While the influence of seasonal variations on the incidence of myocardial infarctions has been extensively documented, the impact of individual meteorological parameters on the disease has so far not been investigated systematically. Hence the present study intended to assess the impact of the essential variables of weather and climate on the incidence of myocardial infarctions. METHODS: The daily incidence of myocardial infarctions was calculated from a national hospitalization survey. The hourly weather and climate data were provided by the database of the national weather forecast. The epidemiological and meteorological data were correlated by multivariate analysis based on a generalized linear model assuming a log-link-function and a Poisson distribution. RESULTS: High ambient pressure, high pressure gradients, and heavy wind activity were associated with an increase in the incidence of the totally 6560 hospitalizations for myocardial infarction irrespective of the geographical region. Snow- and rainfall had inconsistent effects. Temperature, Foehn, and lightning showed no statistically significant impact. CONCLUSIONS: Ambient pressure, pressure gradient, and wind activity had a statistical impact on the incidence of myocardial infarctions in Switzerland from 1990 to 1994. To establish a cause-and-effect relationship more data are needed on the interaction between the pathophysiological mechanisms of the acute coronary syndrome and weather and climate variables.

Increased sensitivity in mapping task demand in visuospatial processing using reaction-time-dependent hemodynamic response predictors in rapid event-related fMRI

Searching for the neural correlates of visuospatial processing using functional magnetic resonance imaging (fMRI) is usually done in an event-related framework of cognitive subtraction, applying a paradigm comprising visuospatial cognitive components and a corresponding control task. Besides methodological caveats of the cognitive subtraction approach, the standard general linear model with fixed hemodynamic response predictors bears the risk of being underspecified. It does not take into account the variability of the blood oxygen level-dependent signal response due to variable task demand and performance on the level of each single trial. This underspecification may result in reduced sensitivity regarding the identification of task-related brain regions. In a rapid event-related fMRI study, we used an extended general linear model including single-trial reaction-time-dependent hemodynamic response predictors for the analysis of an angle discrimination task. In addition to the already known regions in superior and inferior parietal lobule, mapping the reaction-time-dependent hemodynamic response predictor revealed a more specific network including task demand-dependent regions not being detectable using the cognitive subtraction method, such as bilateral caudate nucleus and insula, right inferior frontal gyrus and left precentral gyrus.

Classification Using Generalized Partial Least Squares

The advances in computational biology have made simultaneous monitoring of thousands of features possible. The high throughput technologies not only bring about a much richer information context in which to study various aspects of gene functions but they also present challenge of analyzing data with large number of covariates and few samples. As an integral part of machine learning, classification of samples into two or more categories is almost always of interest to scientists. In this paper, we address the question of classification in this setting by extending partial least squares (PLS), a popular dimension reduction tool in chemometrics, in the context of generalized linear regression based on a previous approach, Iteratively ReWeighted Partial Least Squares, i.e. IRWPLS (Marx, 1996). We compare our results with two-stage PLS (Nguyen and Rocke, 2002A; Nguyen and Rocke, 2002B) and other classifiers. We show that by phrasing the problem in a generalized linear model setting and by applying bias correction to the likelihood to avoid (quasi)separation, we often get lower classification error rates.

Multiple Testing With an Empirical Alternative Hypothesis

An optimal multiple testing procedure is identified for linear hypotheses under the general linear model, maximizing the expected number of false null hypotheses rejected at any significance level. The optimal procedure depends on the unknown data-generating distribution, but can be consistently estimated. Drawing information together across many hypotheses, the estimated optimal procedure provides an empirical alternative hypothesis by adapting to underlying patterns of departure from the null. Proposed multiple testing procedures based on the empirical alternative are evaluated through simulations and an application to gene expression microarray data. Compared to a standard multiple testing procedure, it is not unusual for use of an empirical alternative hypothesis to increase by 50% or more the number of true positives identified at a given significance level.

DECOMPOSITION OF REGRESSION ESTIMATORS TO EXPLORE THE INFLUENCE OF "UNMEASURED" TIME-VARYING CONFOUNDERS

In environmental epidemiology, exposure X and health outcome Y vary in space and time. We present a method to diagnose the possible influence of unmeasured confounders U on the estimated effect of X on Y and to propose several approaches to robust estimation. The idea is to use space and time as proxy measures for the unmeasured factors U. We start with the time series case where X and Y are continuous variables at equally-spaced times and assume a linear model. We define matching estimator b(u)s that correspond to pairs of observations with specific lag u. Controlling for a smooth function of time, St, using a kernel estimator is roughly equivalent to estimating the association with a linear combination of the b(u)s with weights that involve two components: the assumptions about the smoothness of St and the normalized variogram of the X process. When an unmeasured confounder U exists, but the model otherwise correctly controls for measured confounders, the excess variation in b(u)s is evidence of confounding by U. We use the plot of b(u)s versus lag u, lagged-estimator-plot (LEP), to diagnose the influence of U on the effect of X on Y. We use appropriate linear combination of b(u)s or extrapolate to b(0) to obtain novel estimators that are more robust to the influence of smooth U. The methods are extended to time series log-linear models and to spatial analyses. The LEP plot gives us a direct view of the magnitude of the estimators for each lag u and provides evidence when models did not adequately describe the data.