A Case Based approach to assess Waiting Time Prediction at an Intensive Care Unity

Waiting time at an intensive care unity stands for a key feature in the assessment of healthcare quality. Nevertheless, its estimation is a difficult task, not only due to the different factors with intricate relations among them, but also with respect to the available data, which may be incomplete, self-contradictory or even unknown. However, its prediction not only improves the patients’ satisfaction but also enhance the quality of the healthcare being provided. To fulfill this goal, this work aims at the development of a decision support system that allows one to predict how long a patient should remain at an emergency unit, having into consideration all the remarks that were just stated above. It is built on top of a Logic Programming approach to knowledge representation and reasoning, complemented with a Case Base approach to computing.

Umbrella pine above ground biomass estimation with very high spatial resolution satellite images

Above ground biomass is frequently estimated with forest inventory data and an extrapolation method for the per unit area evaluations. This procedure is labour demanding and costly. In this study above ground biomass functions, whose independent variable is crown horizontal projection, were developed. Multi-resolution segmentation method and object-oriented classification, based on very high spatial resolution satellite images, were used to obtain the area of tree crown horizontal projection for umbrella pine (Pinus pinea L.). A set of inventory plots were measured and with existing allometric functions for this species above ground biomass per tree and per plot were calculated. The two data sets were used to fit linear functions both for individual plot and their cumulative values. The results show a good performance of the models. Errors smaller than 10% are obtained for stand areas greater than 1.4 ha. These functions have the advantages of estimating above ground biomass for all the area under study or surveillance, not requiring forest inventory; allow monitoring in short time periods; and are easily implemented in a geographical information system environment.

Estimation of the Traffic Intensity in a Piecewise-stationary Mt/Gt/1 Queue with Probing

We use a probing strategy to estimate the time dependent traffic intensity in an Mt/Gt/1 queue, where the arrival rate and the general service-time distribution change from one time interval to another, and derive statistical properties of the proposed estimator. We present a method to detect a switch from a stationary interval to another using a sequence of probes to improve the estimation. At the end, we compare our results with two estimators proposed in the literature for the M/G/1 queue.

Estimation of climatological water deficit in an experimental watershed in the brazilian cerrado.

ABSTRACT: This study aimed to estimate the probability of climatological water deficit in an experimental watershed in the Cerrado biome, located in the central plateau of Brazil. For that, it was used a time series of 31 years (1982?2012). The probable climatological water deficit was calculated by the difference between rainfall and probable reference evapotranspiration, on a decennial scale. The reference evapotranspiration (ET0) was estimated by the standard FAO-56 Penman-Monteith method. To estimate water deficit, it was used gamma distribution, time series of rainfall and reference evapotranspiration. The adherence of the estimated probabilities to the observed data was verified by the Kolmogorov-Smirnov nonparametric test, with significance level (a-0.05), which presented a good adjustment to the distribution models. It was observed a climatological water deficit, in greater or lesser intensity, between the annual decennials 2 and 32.

Wavelet analysis and hierarchical clustering of ofm phenotypic population's variations originated from southern america and europe and attempts of a first relation to molecular divergence.

In this work we compare Grapholita molesta Busck (Lepidoptera: Tortricidae) populations originated from Brazil, Chile, Spain, Italy and Greece using power spectral density and phylogenetic analysis to detect any similarities between the population macro- and the molecular micro-level. Log-transformed population data were normalized and AR(p) models were developed to generate for each case population time series of equal lengths. The time-frequency/scale properties of the population data were further analyzed using wavelet analysis to detect any population dynamics frequency changes and cluster the populations. Based on the power spectral of each population time series and the hierarchical clustering schemes, populations originated from Southern America (Brazil and Chile) exhibit similar rhythmic properties and are both closer related with populations originated from Greece. Populations from Spain and especially Italy, have higher distance by terms of periodic changes on their population dynamics. Moreover, the members within the same cluster share similar spectral information, therefore they are supposed to participate in the same temporally regulated population process. On the contrary, the phylogenetic approach revealed a less structured pattern that bears indications of panmixia, as the two clusters contain individuals from both Europe and South America. This preliminary outcome will be further assessed by incorporating more individuals and likely employed a second molecular marker.

Frequency domain analysis of stationary time series

This thesis provides a necessary and sufficient condition for asymptotic efficiency of a nonparametric estimator of the generalised autocovariance function of a Gaussian stationary random process. The generalised autocovariance function is the inverse Fourier transform of a power transformation of the spectral density, and encompasses the traditional and inverse autocovariance functions. Its nonparametric estimator is based on the inverse discrete Fourier transform of the same power transformation of the pooled periodogram. The general result is then applied to the class of Gaussian stationary ARMA processes and its implications are discussed. We illustrate that for a class of contrast functionals and spectral densities, the minimum contrast estimator of the spectral density satisfies a Yule-Walker system of equations in the generalised autocovariance estimator. Selection of the pooling parameter, which characterizes the nonparametric estimator of the generalised autocovariance, controlling its resolution, is addressed by using a multiplicative periodogram bootstrap to estimate the finite-sample distribution of the estimator. A multivariate extension of recently introduced spectral models for univariate time series is considered, and an algorithm for the coefficients of a power transformation of matrix polynomials is derived, which allows to obtain the Wold coefficients from the matrix coefficients characterizing the generalised matrix cepstral models. This algorithm also allows the definition of the matrix variance profile, providing important quantities for vector time series analysis. A nonparametric estimator based on a transformation of the smoothed periodogram is proposed for estimation of the matrix variance profile.

Deep Learning Architectures for time of arrival detection in Acoustic Emissions Monitoring

Acoustic Emission (AE) monitoring can be used to detect the presence of damage as well as determine its location in Structural Health Monitoring (SHM) applications. Information on the time difference of the signal generated by the damage event arriving at different sensors is essential in performing localization. This makes the time of arrival (ToA) an important piece of information to retrieve from the AE signal. Generally, this is determined using statistical methods such as the Akaike Information Criterion (AIC) which is particularly prone to errors in the presence of noise. And given that the structures of interest are surrounded with harsh environments, a way to accurately estimate the arrival time in such noisy scenarios is of particular interest. In this work, two new methods are presented to estimate the arrival times of AE signals which are based on Machine Learning. Inspired by great results in the field, two models are presented which are Deep Learning models - a subset of machine learning. They are based on Convolutional Neural Network (CNN) and Capsule Neural Network (CapsNet). The primary advantage of such models is that they do not require the user to pre-define selected features but only require raw data to be given and the models establish non-linear relationships between the inputs and outputs. The performance of the models is evaluated using AE signals generated by a custom ray-tracing algorithm by propagating them on an aluminium plate and compared to AIC. It was found that the relative error in estimation on the test set was < 5% for the models compared to around 45% of AIC. The testing process was further continued by preparing an experimental setup and acquiring real AE signals to test on. Similar performances were observed where the two models not only outperform AIC by more than a magnitude in their average errors but also they were shown to be a lot more robust as compared to AIC which fails in the presence of noise.

Small Area Estimation of Economic Security

The objective of this thesis is the small area estimation of an economic security indicator. Economic security is a complex concept that carries a variety of meanings. In the literature there is no a formal unambiguous definition for economic security and in this work we refer to the definition recently provided for its opposite, economic insecurity, as the “anxiety produced by the possible exposure to adverse economic events and by the anticipation of the difficulty to recover from them” (Bossert and D’Ambrosio, 2013). In the last decade interest for economic insecurity/security has grown constantly, especially since the financial crisis of 2008, but even more in the last year after the economic consequences due to the Covid-19 pandemic. In this research, economic security is measures through a longitudinal indicator that takes into account the income levels of Italian households, from 2014 to 2016. The target areas are groups of Italian provinces, for which the indicator is estimated using longitudinal data taken from EU-SILC survey. We notice that the sample size is too low to obtain reliable estimates for our target areas. Therefore we resort to some Small Area Estimation strategies to improve the reliability of the results. In particular we consider small area models specified at area level. Besides the basic Fay-Herriot area-level model, we propose to consider some longitudinal extensions, including time-specific random effects following an autoregressive processes of order 1 (AR1) and a moving average of order 1 (MA1). We found that all the small area models used show a significant efficiency gain, especially MA1 model.

Trajectory planning of single and dual-arm robots for time-optimal handling of liquids and objects

This Thesis studies the optimal control problem of single-arm and dual-arm serial robots to achieve the time-optimal handling of liquids and objects. The first topic deals with the planning of time-optimal anti-sloshing trajectories of an industrial robot carrying a cylindrical container filled with a liquid, considering 1-dimensional and 2-dimensional planar motions. A technique for the estimation of the sloshing height is presented, together with its extension to 3-dimensional motions. An experimental validation campaign is provided and discussed to assess the thoroughness of such a technique. As far as anti-sloshing trajectories are concerned, 2-dimensional paths are considered and, for each one of them, three constrained optimizations with different values of the sloshing-height thresholds are solved. Experimental results are presented to compare optimized and non-optimized motions. The second part focuses on the time-optimal trajectory planning for dual-arm object handling, employing two collaborative robots (cobots) and adopting an admittance-control strategy. The chosen manipulation approach, known as cooperative grasping, is based on unilateral contact between the cobots and the object, and it may lead to slipping during motion if an internal prestress along the contact-normal direction is not prescribed. Thus, a virtual penetration is considered, aimed at generating the necessary internal prestress. The stability of cooperative grasping is ensured as long as the exerted forces on the object remain inside the static-friction cone. Constrained-optimization problems are solved for 3-dimensional paths: the virtual penetration is chosen among the control inputs of the problem and friction-cone conditions are treated as inequality constraints. Also in this case experiments are presented in order to prove evidence of the firm handling of the object, even for fast motions.

Novel methods of state estimation and parameter identification for high-performance motorcycles

This research activity aims at providing a reliable estimation of particular state variables or parameters concerning the dynamics and performance optimization of a MotoGP-class motorcycle, integrating the classical model-based approach with new methodologies involving artificial intelligence. The first topic of the research focuses on the estimation of the thermal behavior of the MotoGP carbon braking system. Numerical tools are developed to assess the instantaneous surface temperature distribution in the motorcycle's front brake discs. Within this application other important brake parameters are identified using Kalman filters, such as the disc convection coefficient and the power distribution in the disc-pads contact region. Subsequently, a physical model of the brake is built to estimate the instantaneous braking torque. However, the results obtained with this approach are highly limited by the knowledge of the friction coefficient (μ) between the disc rotor and the pads. Since the value of μ is a highly nonlinear function of many variables (namely temperature, pressure and angular velocity of the disc), an analytical model for the friction coefficient estimation appears impractical to establish. To overcome this challenge, an innovative hybrid solution is implemented, combining the benefit of artificial intelligence (AI) with classical model-based approach. Indeed, the disc temperature estimated through the thermal model previously implemented is processed by a machine learning algorithm that outputs the actual value of the friction coefficient thus improving the braking torque computation performed by the physical model of the brake. Finally, the last topic of this research activity regards the development of an AI algorithm to estimate the current sideslip angle of the motorcycle's front tire. While a single-track motorcycle kinematic model and IMU accelerometer signals theoretically enable sideslip calculation, the presence of accelerometer noise leads to a significant drift over time. To address this issue, a long short-term memory (LSTM) network is implemented.

Back-in-time void finder: dynamical detection of cosmic voids for precision cosmology

Cosmic voids are vast and underdense regions emerging between the elements of the cosmic web and dominating the large-scale structure of the Universe. Void number counts and density profiles have been demonstrated to provide powerful cosmological probes. Indeed, thanks to their low-density nature and they very large sizes, voids represent natural laboratories to test alternative dark energy scenarios, modifications of gravity and the presence of massive neutrinos. Despite the increasing use of cosmic voids in Cosmology, a commonly accepted definition for these objects has not yet been reached. For this reason, different void finding algorithms have been proposed during the years. Voids finder algorithms based on density or geometrical criteria are affected by intrinsic uncertainties. In recent years, new solutions have been explored to face these issues. The most interesting is based on the idea of identify void positions through the dynamics of the mass tracers, without performing any direct reconstruction of the density field. The goal of this Thesis is to provide a performing void finder algorithm based on dynamical criteria. The Back-in-time void finder (BitVF) we present use tracers as test particles and their orbits are reconstructed from their actual clustered configuration to an homogeneous and isotropic distribution, expected for the Universe early epoch. Once the displacement field is reconstructed, the density field is computed as its divergence. Consequently, void centres are identified as local minima of the field. In this Thesis work we applied the developed void finding algorithm to simulations. From the resulting void samples we computed different void statistics, comparing the results to those obtained with VIDE, the most popular void finder. BitVF proved to be able to produce a more reliable void samples than the VIDE ones. The BitVF algorithm will be a fundamental tool for precision cosmology, especially with upcoming galaxy-survey.

An Embedded In-Cabin Lightweight High-Performance 3D Gaze Estimation System

Gaze estimation has gained interest in recent years for being an important cue to obtain information about the internal cognitive state of humans. Regardless of whether it is the 3D gaze vector or the point of gaze (PoG), gaze estimation has been applied in various fields, such as: human robot interaction, augmented reality, medicine, aviation and automotive. In the latter field, as part of Advanced Driver-Assistance Systems (ADAS), it allows the development of cutting-edge systems capable of mitigating road accidents by monitoring driver distraction. Gaze estimation can be also used to enhance the driving experience, for instance, autonomous driving. It also can improve comfort with augmented reality components capable of being commanded by the driver's eyes. Although, several high-performance real-time inference works already exist, just a few are capable of working with only a RGB camera on computationally constrained devices, such as a microcontroller. This work aims to develop a low-cost, efficient and high-performance embedded system capable of estimating the driver's gaze using deep learning and a RGB camera. The proposed system has achieved near-SOTA performances with about 90% less memory footprint. The capabilities to generalize in unseen environments have been evaluated through a live demonstration, where high performance and near real-time inference were obtained using a webcam and a Raspberry Pi4.

Estimated Disability-adjusted Life Years Averted By Long-term Provision Of Long Acting Contraceptive Methods In A Brazilian Clinic.

What is the contribution of the provision, at no cost for users, of long acting reversible contraceptive methods (LARC; copper intrauterine device [IUD], the levonorgestrel-releasing intrauterine system [LNG-IUS], contraceptive implants and depot-medroxyprogesterone [DMPA] injection) towards the disability-adjusted life years (DALY) averted through a Brazilian university-based clinic established over 30 years ago. Over the last 10 years of evaluation, provision of LARC methods and DMPA by the clinic are estimated to have contributed to DALY averted by between 37 and 60 maternal deaths, 315-424 child mortalities, 634-853 combined maternal morbidity and mortality and child mortality, and 1056-1412 unsafe abortions averted. LARC methods are associated with a high contraceptive effectiveness when compared with contraceptive methods which need frequent attention; perhaps because LARC methods are independent of individual or couple compliance. However, in general previous studies have evaluated contraceptive methods during clinical studies over a short period of time, or not more than 10 years. Furthermore, information regarding the estimation of the DALY averted is scarce. We reviewed 50 004 medical charts from women who consulted for the first time looking for a contraceptive method over the period from 2 January 1980 through 31 December 2012. Women who consulted at the Department of Obstetrics and Gynaecology, University of Campinas, Brazil were new users and users switching contraceptive, including the copper IUD (n = 13 826), the LNG-IUS (n = 1525), implants (n = 277) and DMPA (n = 9387). Estimation of the DALY averted included maternal morbidity and mortality, child mortality and unsafe abortions averted. We obtained 29 416 contraceptive segments of use including 25 009 contraceptive segments of use from 20 821 new users or switchers to any LARC method or DMPA with at least 1 year of follow-up. The mean (± SD) age of the women at first consultation ranged from 25.3 ± 5.7 (range 12-47) years in the 1980s, to 31.9 ± 7.4 (range 16-50) years in 2010-2011. The most common contraceptive chosen at the first consultation was copper IUD (48.3, 74.5 and 64.7% in the 1980s, 1990s and 2000s, respectively). For an evaluation over 20 years, the cumulative pregnancy rates (SEM) were 0.4 (0.2), 2.8 (2.1), 4.0 (0.4) and 1.3 (0.4) for the LNG-IUS, the implants, copper IUD and DMPA, respectively and cumulative continuation rates (SEM) were 15.1 (3.7), 3.9 (1.4), 14.1 (0.6) and 7.3 (1.7) for the LNG-IUS, implants, copper IUD and DMPA, respectively (P < 0.001). Over the last 10 years of evaluation, the estimation of the contribution of the clinic through the provision of LARC methods and DMPA to DALY averted was 37-60 maternal deaths; between 315 and 424 child mortalities; combined maternal morbidity and mortality and child mortality of between 634 and 853, and 1056-1412 unsafe abortions averted. The main limitations are the number of women who never returned to the clinic (overall 14% among the four methods under evaluation); consequently the pregnancy rate could be different. Other limitations include the analysis of two kinds of copper IUD and two kinds of contraceptive implants as the same IUD or implant, and the low number of users of implants. In addition, the DALY calculation relies on a number of estimates, which may vary in different parts of the world. LARC methods and DMPA are highly effective and women who were well-counselled used these methods for a long time. The benefit of averting maternal morbidity and mortality, child mortality, and unsafe abortions is an example to health policy makers to implement more family planning programmes and to offer contraceptive methods, mainly LARC and DMPA, at no cost or at affordable cost for the underprivileged population. This study received partial financial support from the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP), grant # 2012/12810-4 and from the National Research Council (CNPq), grant #573747/2008-3. B.F.B., M.P.G., and V.M.C. were fellows from the scientific initiation programme from FAPESP. Since the year 2001, all the TCu380A IUD were donated by Injeflex, São Paulo, Brazil, and from the year 2006 all the LNG-IUS were donated by the International Contraceptive Access Foundation (ICA), Turku, Finland. Both donations are as unrestricted grants. The authors declare that there are no conflicts of interest associated with this study.

Identification Of Corynebacterium Spp. Isolated From Bovine Intramammary Infections By Matrix-assisted Laser Desorption Ionization-time Of Flight Mass Spectrometry.

Corynebacterium species (spp.) are among the most frequently isolated pathogens associated with subclinical mastitis in dairy cows. However, simple, fast, and reliable methods for the identification of species of the genus Corynebacterium are not currently available. This study aimed to evaluate the usefulness of matrix-assisted laser desorption ionization/mass spectrometry (MALDI-TOF MS) for identifying Corynebacterium spp. isolated from the mammary glands of dairy cows. Corynebacterium spp. were isolated from milk samples via microbiological culture (n=180) and were analyzed by MALDI-TOF MS and 16S rRNA gene sequencing. Using MALDI-TOF MS methodology, 161 Corynebacterium spp. isolates (89.4%) were correctly identified at the species level, whereas 12 isolates (6.7%) were identified at the genus level. Most isolates that were identified at the species level with 16 S rRNA gene sequencing were identified as Corynebacterium bovis (n=156; 86.7%) were also identified as C. bovis with MALDI-TOF MS. Five Corynebacterium spp. isolates (2.8%) were not correctly identified at the species level with MALDI-TOF MS and 2 isolates (1.1%) were considered unidentified because despite having MALDI-TOF MS scores >2, only the genus level was correctly identified. Therefore, MALDI-TOF MS could serve as an alternative method for species-level diagnoses of bovine intramammary infections caused by Corynebacterium spp.

A Metabolomic Protocol For Plant Systematics By Matrix-assisted Laser-desorption/ionization Time-of Flight Mass Spectrometry.

Matrix-assisted laser desorption/ionization time-of flight mass spectrometry (MALDI-TOF MS) has been widely used for the identification and classification of microorganisms based on their proteomic fingerprints. However, the use of MALDI-TOF MS in plant research has been very limited. In the present study, a first protocol is proposed for metabolic fingerprinting by MALDI-TOF MS using three different MALDI matrices with subsequent multivariate data analysis by in-house algorithms implemented in the R environment for the taxonomic classification of plants from different genera, families and orders. By merging the data acquired with different matrices, different ionization modes and using careful algorithms and parameter selection, we demonstrate that a close taxonomic classification can be achieved based on plant metabolic fingerprints, with 92% similarity to the taxonomic classifications found in literature. The present work therefore highlights the great potential of applying MALDI-TOF MS for the taxonomic classification of plants and, furthermore, provides a preliminary foundation for future research.