Ground penetrating radar early-time technique for soil electromagnetic parameters estimation

In recent years, thanks to the technological advances, electromagnetic methods for non-invasive shallow subsurface characterization have been increasingly used in many areas of environmental and geoscience applications. Among all the geophysical electromagnetic methods, the Ground Penetrating Radar (GPR) has received unprecedented attention over the last few decades due to its capability to obtain, spatially and temporally, high-resolution electromagnetic parameter information thanks to its versatility, its handling, its non-invasive nature, its high resolving power, and its fast implementation. The main focus of this thesis is to perform a dielectric site characterization in an efficient and accurate way studying in-depth a physical phenomenon behind a recent developed GPR approach, the so-called early-time technique, which infers the electrical properties of the soil in the proximity of the antennas. In particular, the early-time approach is based on the amplitude analysis of the early-time portion of the GPR waveform using a fixed-offset ground-coupled antenna configuration where the separation between the transmitting and receiving antenna is on the order of the dominant pulse-wavelength. Amplitude information can be extracted from the early-time signal through complex trace analysis, computing the instantaneous-amplitude attributes over a selected time-duration of the early-time signal. Basically, if the acquired GPR signals are considered to represent the real part of a complex trace, and the imaginary part is the quadrature component obtained by applying a Hilbert transform to the GPR trace, the amplitude envelope is the absolute value of the resulting complex trace (also known as the instantaneous-amplitude). Analysing laboratory information, numerical simulations and natural field conditions, and summarising the overall results embodied in this thesis, it is possible to suggest the early-time GPR technique as an effective method to estimate physical properties of the soil in a fast and non-invasive way.

A new method for the estimation of the reverberation time from measured room impulse responses with application to Italian opera houses

The Schroeder's backward integration method is the most used method to extract the decay curve of an acoustic impulse response and to calculate the reverberation time from this curve. In the literature the limits and the possible improvements of this method are widely discussed. In this work a new method is proposed for the evaluation of the energy decay curve. The new method has been implemented in a Matlab toolbox. Its performance has been tested versus the most accredited literature method. The values of EDT and reverberation time extracted from the energy decay curves calculated with both methods have been compared in terms of the values themselves and in terms of their statistical representativeness. The main case study consists of nine Italian historical theatres in which acoustical measurements were performed. The comparison of the two extraction methods has also been applied to a critical case, i.e. the structural impulse responses of some building elements. The comparison underlines that both methods return a comparable value of the T30. Decreasing the range of evaluation, they reveal increasing differences; in particular, the main differences are in the first part of the decay, where the EDT is evaluated. This is a consequence of the fact that the new method returns a “locally" defined energy decay curve, whereas the Schroeder's method accumulates energy from the tail to the beginning of the impulse response. Another characteristic of the new method for the energy decay extraction curve is its independence on the background noise estimation. Finally, a statistical analysis is performed on the T30 and EDT values calculated from the impulse responses measurements in the Italian historical theatres. The aim of this evaluation is to know whether a subset of measurements could be considered representative for a complete characterization of these opera houses.

The XENON1T experiment: Monte Carlo background estimation and sensitivity curves study

Despite the scientific achievement of the last decades in the astrophysical and cosmological fields, the majority of the Universe energy content is still unknown. A potential solution to the “missing mass problem” is the existence of dark matter in the form of WIMPs. Due to the very small cross section for WIMP-nuleon interactions, the number of expected events is very limited (about 1 ev/tonne/year), thus requiring detectors with large target mass and low background level. The aim of the XENON1T experiment, the first tonne-scale LXe based detector, is to be sensitive to WIMP-nucleon cross section as low as 10^-47 cm^2. To investigate the possibility of such a detector to reach its goal, Monte Carlo simulations are mandatory to estimate the background. To this aim, the GEANT4 toolkit has been used to implement the detector geometry and to simulate the decays from the various background sources: electromagnetic and nuclear. From the analysis of the simulations, the level of background has been found totally acceptable for the experiment purposes: about 1 background event in a 2 tonne-years exposure. Indeed, using the Maximum Gap method, the XENON1T sensitivity has been evaluated and the minimum for the WIMP-nucleon cross sections has been found at 1.87 x 10^-47 cm^2, at 90% CL, for a WIMP mass of 45 GeV/c^2. The results have been independently cross checked by using the Likelihood Ratio method that confirmed such results with an agreement within less than a factor two. Such a result is completely acceptable considering the intrinsic differences between the two statistical methods. Thus, in the PhD thesis it has been proven that the XENON1T detector will be able to reach the designed sensitivity, thus lowering the limits on the WIMP-nucleon cross section by about 2 orders of magnitude with respect to the current experiments.

Estimation of the risks of thermal stress due to the microclimate for manual fruit and vegetable harvesters in central Italy

Agricultural workers are exposed to various risks, including chemical agents, noise, and many other factors. One of the most characteristic and least known risk factors is constituted by the microclimatic conditions in the different phases of work (in field, in greenhouse, etc). A typical condition is thermal stress due to high temperatures during harvesting operations in open fields or in greenhouses. In Italy, harvesting is carried out for many hours during the day, mainly in the summer, with temperatures often higher than 30 degrees C. According to ISO 7243, these conditions can be considered dangerous for workers' health. The aim of this study is to assess the risks of exposure to microclimatic conditions (heat) for fruit and vegetable harvesters in central Italy by applying methods established by international standards. In order to estimate the risk for workers, the air temperature, radiative temperature, and air speed were measured using instruments in conformity with ISO 7726. Thermodynamic parameters and two more subjective parameters, clothing and the metabolic heat production rate related to the worker's physical activity, were used to calculate the predicted heat strain (PHS) for the exposed workers in conformity with ISO 7933. Environmental and subjective parameters were also measured for greenhouse workers, according to ISO 7243, in order to calculate the wet-bulb globe temperature (WBGT). The results show a slight risk for workers during manual harvesting in the field. On the other hand, the data collected in the greenhouses show that the risk for workers must not be underestimated. The results of the study show that, for manual harvesting work in climates similar to central Italy, it is essential to provide plenty of drinking water and acclimatization for the workers in order to reduce health risks. Moreover, the study emphasizes that the possible health risks for greenhouse workers increase from the month of April through July.

Post-mortem virtual estimation of free abdominal blood volume

PURPOSE: The purpose of this retrospective study was to examine the reliability of virtually estimated abdominal blood volume using segmentation from postmortem computed tomography (PMCT) data. MATERIALS AND METHODS: Twenty-one cases with free abdominal blood were investigated by PMCT and autopsy. The volume of the blood was estimated using a manual segmentation technique (Amira, Visage Imaging, Germany) and the results were compared to autopsy data. Six of 21 cases had undergone additional post-mortem computed tomographic angiography (PMCTA). RESULTS: The virtually estimated abdominal blood volumes did not differ significantly from those measured at autopsy. Additional PMCTA did not bias data significantly. CONCLUSION: Virtual estimation of abdominal blood volume is a reliable technique. The virtual blood volume estimation is a useful tool to deliver additional information in cases where autopsy is not performed or in cases where a postmortem angiography is performed.

Estimation of the postmortem interval by means of ¹H MRS of decomposing brain tissue: influence of ambient temperature

Standard methods for the estimation of the postmortem interval (PMI, time since death), based on the cooling of the corpse, are limited to about 48 h after death. As an alternative, noninvasive postmortem observation of alterations of brain metabolites by means of (1)H MRS has been suggested for an estimation of the PMI at room temperature, so far without including the effect of other ambient temperatures. In order to study the temperature effect, localized (1)H MRS was used to follow brain decomposition in a sheep brain model at four different temperatures between 4 and 26°C with repeated measurements up to 2100 h postmortem. The simultaneous determination of 25 different biochemical compounds at each measurement allowed the time courses of concentration changes to be followed. A sudden and almost simultaneous change of the concentrations of seven compounds was observed after a time span that decreased exponentially from 700 h at 4°C to 30 h at 26°C ambient temperature. As this represents, most probably, the onset of highly variable bacterial decomposition, and thus defines the upper limit for a reliable PMI estimation, data were analyzed only up to this start of bacterial decomposition. As 13 compounds showed unequivocal, reproducible concentration changes during this period while eight showed a linear increase with a slope that was unambiguously related to ambient temperature. Therefore, a single analytical function with PMI and temperature as variables can describe the time courses of metabolite concentrations. Using the inverse of this function, metabolite concentrations determined from a single MR spectrum can be used, together with known ambient temperatures, to calculate the PMI of a corpse. It is concluded that the effect of ambient temperature can be reliably included in the PMI determination by (1)H MRS.

CT based volume measurement and estimation in cases of pericardial effusion

The measurement of fluid volumes in cases of pericardial effusion is a necessary procedure during autopsy. With the increased use of virtual autopsy methods in forensics, the need for a quick volume measurement method on computed tomography (CT) data arises, especially since methods such as CT angiography can potentially alter the fluid content in the pericardium. We retrospectively selected 15 cases with hemopericardium, which underwent post-mortem imaging and autopsy. Based on CT data, the pericardial blood volume was estimated using segmentation techniques and downsampling of CT datasets. Additionally, a variety of measures (distances, areas and 3D approximations of the effusion) were examined to find a quick and easy way of estimating the effusion volume. Segmentation of CT images as shown in the present study is a feasible method to measure the pericardial fluid amount accurately. Downsampling of a dataset significantly increases the speed of segmentation without losing too much accuracy. Some of the other methods examined might be used to quickly estimate the severity of the effusion volumes.

Chances and limits of method restriction: a detailed analysis of suicide methods in Switzerland

The objective of this study was to estimate the potential of method restriction as a public health strategy in suicide prevention. Data from the Swiss Federal Statistical Office and the Swiss Institutes of Forensic Medicine from 2004 were gathered and categorized into suicide submethods according to accessibility to restriction of means. Of suicides in Switzerland, 39.2% are accessible to method restriction. The highest proportions were found in private weapons (13.2%), army weapons (10.4%), and jumps from hot-spots (4.6%). The presented method permits the estimation of the suicide prevention potential of a country by method restriction and the comparison of restriction potentials between suicide methods. In Switzerland, reduction of firearm suicides has the highest potential to reduce the total number of suicides.

Noninvasive estimation of organ weights by postmortem magnetic resonance imaging and multislice computed tomography

OBJECTIVE: Computed tomography (CT) and magnetic resonance imaging (MRI) are introduced as an alternative to traditional autopsy. The purpose of this study was to investigate their accuracy in mass estimation of liver and spleen. METHODS: In 44 cases, the weights of spleen and liver were estimated based on MRI and CT data using a volume-analysis software and a postmortem tissue-specific density factor. In a blinded approach, the results were compared with the weights noted at autopsy. RESULTS: Excellent correlation between estimated and real weights (r = 0.997 for MRI, r = 0.997 for CT) was found. Putrefaction gas and venous air embolism led to an overestimation. Venous congestion and drowning caused higher estimated weights. CONCLUSION: Postmortem weights of liver and spleen can accurately be assessed by nondestructive imaging. Multislice CT overcomes the limitation of putrefaction and venous air embolism by the possibility to exclude gas. Congestion seems to be even better assessed.

Smooth Hazard Function Estimation for Interval Censored Data with Time Varying Covariates

In this paper we propose methods for smooth hazard estimation of a time variable where that variable is interval censored. These methods allow one to model the transformed hazard in terms of either smooth (smoothing splines) or linear functions of time and other relevant time varying predictor variables. We illustrate the use of this method on a dataset of hemophiliacs where the outcome, time to seroconversion for HIV, is interval censored and left-truncated.

Deadspace estimation from CO2 versus molar mass measurements in infants

BACKGROUND: Estimation of respiratory deadspace is often based on the CO2 expirogram, however presence of the CO2 sensor increases equipment deadspace, which in turn influences breathing pattern and calculation of lung volume. In addition, it is necessary to correct for the delay between the sensor and flow signals. We propose a new method for estimation of effective deadspace using the molar mass (MM) signal from an ultrasonic flowmeter device, which does not require delay correction. We hypothesize that this estimation is correlated with that calculated from the CO2 signal using the Fowler method. METHODS: Breath-by-breath CO2, MM and flow measurements were made in a group of 77 term-born healthy infants. Fowler deadspace (Vd,Fowler) was calculated after correcting for the flow-dependent delay in the CO2 signal. Deadspace estimated from the MM signal (Vd,MM) was defined as the volume passing through the flowhead between start of expiration and the 10% rise point in MM. RESULTS: Correlation (r = 0.456, P < 0.0001) was found between Vd,MM and Vd,Fowler averaged over all measurements, with a mean difference of -1.4% (95% CI -4.1 to 1.3%). Vd,MM ranged from 6.6 to 11.4 ml between subjects, while Vd,Fowler ranged from 5.9 to 12.0 ml. Mean intra-measurement CV over 5-10 breaths was 7.8 +/- 5.6% for Vd,MM and 7.8 +/- 3.7% for Vd,Fowler. Mean intra-subject CV was 6.0 +/- 4.5% for Vd,MM and 8.3 +/- 5.9% for Vd,Fowler. Correcting for the CO2 signal delay resulted in a 12% difference (P = 0.022) in Vd,Fowler. Vd,MM could be obtained more frequently than Vd,Fowler in infants with CLD, with a high variability. CONCLUSIONS: Use of the MM signal provides a feasible estimate of Fowler deadspace without introducing additional equipment deadspace. The simple calculation without need for delay correction makes individual adjustment for deadspace in FRC measurements possible. This is especially important given the relative large range of deadspace seen in this homogeneous group of infants.

Checking Assumptions in Latent Class Regression Models via a Markov Chain Monte Carlo Estimation Approach: An Application to Depression and Socio-Economic Status

Latent class regression models are useful tools for assessing associations between covariates and latent variables. However, evaluation of key model assumptions cannot be performed using methods from standard regression models due to the unobserved nature of latent outcome variables. This paper presents graphical diagnostic tools to evaluate whether or not latent class regression models adhere to standard assumptions of the model: conditional independence and non-differential measurement. An integral part of these methods is the use of a Markov Chain Monte Carlo estimation procedure. Unlike standard maximum likelihood implementations for latent class regression model estimation, the MCMC approach allows us to calculate posterior distributions and point estimates of any functions of parameters. It is this convenience that allows us to provide the diagnostic methods that we introduce. As a motivating example we present an analysis focusing on the association between depression and socioeconomic status, using data from the Epidemiologic Catchment Area study. We consider a latent class regression analysis investigating the association between depression and socioeconomic status measures, where the latent variable depression is regressed on education and income indicators, in addition to age, gender, and marital status variables. While the fitted latent class regression model yields interesting results, the model parameters are found to be invalid due to the violation of model assumptions. The violation of these assumptions is clearly identified by the presented diagnostic plots. These methods can be applied to standard latent class and latent class regression models, and the general principle can be extended to evaluate model assumptions in other types of models.