Tracking Space and Time Changes of Physical Properties in Complex Geological Media

An important issue in seismology concerns the characterization of the propagation medium, aiming to analyze the behavior of rocks in relation to the generation of earthquakes (both natural and human-made). The basic idea is that seismic waves can be used to image the medium’s physical properties. In this context we placed our research project, concerning the reconstruction of the spatial and temporal changes of physical properties (velocity, attenuation, rock parameters) in complex geological media. In the first part of this thesis we present a detailed description of known and new methodologies useful to track the seismicity, the propagation medium’s features and their temporal variation. In particular, a new rock modelling approach is constructed, allowing the conversion of velocity and attenuation values in rock micro-parameters; and a new equalization procedure for the 4D tomography is developed, allowing at once to optimize the choice of time-windows in the case of massive data-sets and to completely handle seismic tomography issues. In the second part, we show the results obtained by applying this methodologies to three complex areas: the Irpinia fault zones, The Geysers geothermal area and the Solfatara volcano. The relevance of these three areas lies not only in their different physical nature, but also in their different dimension. The obtained results show how the described methodologies can be used in seismogenic and volcanic areas to improve the knowledge of the medium’s properties, in order to mitigate the risk associated to destructive events, and in geothermal areas, to monitor the induced seismicity through the tracking of the medium properties’ temporal variation. Therefore, this thesis represents a useful tool for the characterization of the propagation medium, by providing a compendium of different methodologies and by showing the results of their application to three complex areas characterized by different physical nature and dimensional scale

STRUCTURAL DETERMINISTIC MODELING DESIGN AND FABRICATION OF ELECTROSPUN SCAFFOLDS FOR SOFT TISSUE ENGINEERING

The research fields of tissue engineering, biomechanics and regenerative medicine continue to evolve in response to the ever growing need for tissue replacement options. These fields aim to restore, maintain, or improve tissue or whole organ function. This doctoral studies focus on the development and experimental validation of a structural deterministic modeling strategy to: A) guide tissue engineering scaffold design, B) provide a better understanding of cellular mechanical and metabolic response to local micro-structural deformations. Targeted clinical application was the pulmonary heart valve. Electrospinning was selected as the optimal platform technology to implement, validate and test the presented designing strategy. An innovative custom made software was developed and tested on Electrospun poly (ester urethane) urea scaffolds (ES-PEUU), decellularized native tissues and collagen gels to fully characterized engineered constructs morphology. These structural information were adopted to feed and assist the mechanical modeling Two previously unevaluated fabrication modalities were investigated throughout both mechanical testing and image analysis in order to explore further how the electrospinning fabrication process can alter the structure and mechanical response: variation of mandrel translation velocity and concurrent electrospraying of cell culture medium with or without cells or rigid particulates. These fabrication parameters were studied to enrich control in the electrospinning process. 8 The detected material topology and mechanical equi-biaxial data were adopted to generate statistically equivalent scaffold mechanical models. The structural determinist approach was applied to ES-PEUU scaffolds, validated and mechanical response at organ and cell level was produced through FEM simulation. Prediction included: membrane tension vs. stretch relation, elasticity moduli, Nuclear Aspect Ratio vs. stretch relation for the cells micro-integrated into the scaffold. A three weeks in vivo - study on an ovine model was performed to demonstrated the feasibility of the adoption of ES-PEUU for TEHVs and more generally this material potential for soft tissue regeneration. Explants analysis showed surgical feasibility and acceptable valve functionality. The developed design strategies combining image analysis and structural deterministic modeling enabled the material topology to be both quantified and reproduced. Material fabrication parameters were related to material micro-architecture Similarly, the micro-architecture was related to macro scale mechanical responses such as in-plane reactions or flexural rigidity, and complex meso – micro scales mechanisms such NAR – stretch relation. In conclusion, the modeling approach introduced in this work bridges for the first time the scaffold fabrication parameters with the mechanical response at different scale length. The developed paradigm will be utilized to identify the optimal scaffold for a given soft tissue engineering application

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed

Effects of errorless learning on the acquisition of velopharyngeal movement control

Session 1pSC - Speech Communication: Cross-Linguistic Studies of Speech Sound Learning of the Languages of Hong Kong (Poster Session)The implicit motor learning literature suggests a benefit for learning if errors are minimized during practice. This study investigated whether the same principle holds for learning velopharyngeal movement control. Normal speaking participants learned to produce hypernasal speech in either an errorless learning condition (in which the possibility for errors was limited) or an errorful learning condition (in which the possibility for errors was not limited). Nasality level of the participants’ speech was measured by nasometer and reflected by nasalance scores (in %). Errorless learners practiced producing hypernasal speech with a threshold nasalance score of 10% at the beginning, which gradually increased to a threshold of 50% at the end. The same set of threshold targets were presented to errorful learners but in a reversed order. Errors were defined by the proportion of speech with a nasalance score below the threshold. The results showed that, relative to errorful learners, errorless learners displayed fewer errors (50.7% vs. 17.7%) and a higher mean nasalance score (31.3% vs. 46.7%) during the acquisition phase. Furthermore, errorless learners outperformed errorful learners in both retention and novel transfer tests. Acknowledgment: Supported by The University of Hong Kong Strategic Research Theme for Sciences of Learning © 2012 Acoustical Society of Americapublished_or_final_versio

Experimental study on the hydro-mechanical behavior of soils improved using the CSM technology

Deep Mixing Methods (DMMs) can be regarded as constantly evolving technologies for improving soil properties in order to satisfy predefined design requirements. Their applications are very common in geotechnical engineering and, in some cases, they can be conveniently selected instead of more traditional techniques. Despite DMMs are customarily used to strengthen soft soils like peats, clays, and silts, they can also be used very effectively in various subsoil configurations for several purposes, as, for instance, in the case of soil liquefaction prevention or cut-off/retaining walls. Even if soil mixing practice has become very consolidated in geotechnical engineering and numerous researchers in the past have tried to develop predictive equations taking into account the more relevant factors affecting the strength of DM constructions, i.e. influence of binder, soil, mixing and curing conditions, there is still no widely applicable formula for the estimation of the field strength characterized by a reasonable level of accuracy. Predictions are normally based on the mechanical behaviour of laboratory prepared mixtures, which, most of the time, significantly differ from in-situ treated soils due to the specific mixing, curing, and subsoil conditions encountered at the site. Technical standards were recently developed to provide general guidelines for the production of good quality laboratory mixed soil samples. Similarly, other codes concerning the critical deep mixing site construction aspects were introduced in several counties in order to improve the quality assurance and quality control (QA/QC) programmes conceived to verify the treatment effectiveness. However, a direct correlation between laboratory and field mixing performance is still far from being described, probably owing to the lack of a sufficient number of well documented case histories. In this research, a comparison tool between laboratory and field procedures has been tentatively deduced from energetic considerations depending on mixing efforts transferred to the soil to be treated using different devices. This thesis mainly focuses on the results of a comprehensive experimental investigation carried out on treated soil mixtures collected from several worldwide jobsites in which the Cutter Soil Mixing (CSM) technology was used. CSM, launched since 2003, is a recent and efficient system that, besides other DMMs, has the advantage of a high level of process control providing detailed information regarding the in-situ mixing method. The elaboration of these data, which significantly support the usual QA/QC procedures, has been used to define a new easily determinable site parameter closely related to the mixing efficacy, which, in turn, greatly influences the performance attained. As other DM methods, CSM produces some amount of spoil material, which is deemed to contain part of the binder introduced into the soil to activate hydration reactions once combined with both water and minerals in the ground. Since no estimation methods are available to evaluate the binder loss, an approximate amount of binding material is customarily added and mixed with the natural soil, hampering the performance prediction. To remedy this situation, a new formulation has been proposed to estimate the binder loss and to compute a more proper cement content. During the research activity, mechanical, hydraulic, mineralogical, and micro-structural tests were carried out in order to describe in detail the behaviour of the CSM treated material from different points of view and to acquire a reliable picture of the main factors affecting the relevant properties of stabilized soils. The obtained test results allowed to develop a new mathematical model for the evolution of the mechanical strength of granular and cohesive soils treated with the CSM technique as a function of the specific site conditions. The defined procedure has proved to be very effective in the major part of the case histories considered in this work

Advanced Sensors for Real-Time Monitoring Applications

It is impossible to imagine the modern world without sensors, or without real-time information about almost everything—from local temperature to material composition and health parameters. We sense, measure, and process data and act accordingly all the time. In fact, real-time monitoring and information is key to a successful business, an assistant in life-saving decisions that healthcare professionals make, and a tool in research that could revolutionize the future. To ensure that sensors address the rapidly developing needs of various areas of our lives and activities, scientists, researchers, manufacturers, and end-users have established an efficient dialogue so that the newest technological achievements in all aspects of real-time sensing can be implemented for the benefit of the wider community. This book documents some of the results of such a dialogue and reports on advances in sensors and sensor systems for existing and emerging real-time monitoring applications