The purpose of this Thesis is to determine which set of private law rules ought to apply to the use and storage of bodily material. I recommend that the most appropriate legal approach is through a combination of property rights and duties of confidentiality. The suggestion is that where a healthcare institution obtains possession of bodily material, their possession of the material may give rise to property rights in the material. In addition, where an individual retains entitlements in bodily material that is held by a healthcare institution, the entitlements of the individual ought to be protected through the imposition of duties on the healthcare institution that are akin to duties of confidentiality. This recommendation is the product of two main inquires. The first inquiry concerns which entitlements individuals and institutions ought to be able to exercise in separated bodily material. This involves an investigation into which aspects of the relationship between a person and their body can also be found in the relationship between a person and their separated bodily material. It also involves an assessment as to which societal interests can be served through allocating entitlements in bodily material to healthcare institutions, and how to resolve the conflict between individual and societal interests in the use and storage of bodily material. The second main inquiry concerns the way in which different branches of private law are able to protect entitlements in things. I identify that property rights, rights of bodily integrity and privacy are similar insofar as they protect entitlements through the exclusion of others. Property rights are nonetheless distinct as property law concerns rights than can exist independently of the rights-holder. The recommended approach follows from connecting the different entitlements in bodily material that ought to obtain legal protection with different ways an entitlement may be afforded legal protection.

Ekonomikos nuosmukio sąlygomis kiekvienai įmonei labai svarbu teisingas investavimo sprendimas, efektyviai valdyti ilgalaikį materialųjį turtą, disponuoti teisingai ir laiku pateikiama apskaitos informacija, leidžiančia atspindėti organizacijos finansinius rezultatus. Ilgalaikį materialųjį turtą turi ir naudoja visos įmonės. Daugelyje įmonių šis turtas sudaro didžiąją dalį disponuojamo turto. Todėl ilgalaikio materialiojo turto apskaita ir analizė yra labai aktuali tema magistro baigiamajam darbui. Siekiant pateikti tikslią ir teisingą informaciją apie įmonės būklę ir jos veiklos rezultatus įmonės finansininkams ir vadovams svarbu pasirinkti tinkamiausius ilgalaikio materialiojo turto įtraukimo į apskaitą ir nusidėvėjimo būdus. Pagrindinė ilgalaikio materialiojo turto apskaitos problema nagrinėjama darbe yra ta, kad neteisingai suprasti įstatymai ar jų prieštaravimas iškreipia finansinius rezultatus, kas leidžia apgauti finansinių ataskaitų vartotojus. Tyrimo objektas: Ilgalaikio materialiojo turto apskaita ir analizė UAB „Skuodo šiluma“. Darbo tikslas. Ištirti teorinius ir praktinius ilgalaikio materialiojo turto apskaitos principus, analizės klausimus ir pateikti pasiūlymus kaip efektyviau, atskleisti ilgalaikio materialiojo turto panaudojimo rezervus. Darbe iškeltos hipotezės: tinkamai pasirinkta ilgalaikio materialiojo turto apskaitos metodika turi didelį poveikį įmonės veiklos rezultatams; UAB „Skuodo šiluma“ racionaliai valdo ilgalaikį materialųjį turtą, o tai turi... [toliau žr. visą tekstą] / In the conditions of economic depression it is very important for each organization to make correct decisions regarding financing and investments, to effectively manage the assets, and to command the timely and correct accounting information that allows reflecting financial results of the organization. Tangible assets are used and the total businesses. In many companies, this property makes up most of the available assets. Therefore, fixed assets accounting and analysis is a very hot topic master thesis. In order to provide accurate and truthful information about the company's status and performance of the company accountants and managers is important to choose the most appropriate long-term tangible assets in the accounting and depreciation methods. The main tangible fixed assets accounting issue discussed in the paper is that it misunderstood the law or conflict distorts financial results, which makes it possible to deceive financial statement users. The object of research: tangible asset accounting and analysis of JSC "Skuodas siluma“ Objective. To investigate the theoretical and practical aspects of fixed asset accounting principles, analysis of issues and make proposals on how better to reveal the use of property, plant and reserves. Hypothesis: the proper choice of fixed asset accounting methodology has a significant impact on the performance of companies JSC "Skuodas siluma" rationally manage fixed assets, which has a positive impact on overall company performance. The... [to full text]

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<p>Hot-melt adhesives have been commercially available for a long time and they are used in a wide range of applications. The adhesive performance is governed by the adhesive material property as well as the application conditions for each type of substrate. In order to achieve a good bond between the adhesive and the designated substrate, both wetting ability and open time of the adhesive material have to be considered. Three commercial hot-melts were used in this study in order to examine the relationship between the material property and the adhesive performance. The thermal properties of the materials were obtained through Differential Scanning Calorimetry while Dynamic Analysis (DA) described their viscoelastic behaviour, and the hysteresis loop helped to characterize the flow regime from which the application conditions for the adhesive could be chosen. The adhesive performance was evaluated in term of the force required to break the bond between the adhesive and the substrate through a series of standardized pull-off tests. The effect of the time-temperature trade-off on the adhesive performance by varying the application temperature as well as prolonging the available bond-formation time was also examined. In most cases, the adhesive performance improved with extended open time. However, improved adhesive performance was also shown to be the response of shorter Maxwell characteristic time which was evaluated from the DA data. By providing the characteristic time as a linkage, a relationship between the adhesive performance and the material properties could be established. These results also offer a basis for the formulation of adhesives using structure-property parameters derived from DA.</p> / Master of Applied Science (MASc)

"Material property estimation method is developed with 1-D heat conduction model and bounding exercise for Fire Dynamics Simulator (FDS) analysis. The purpose of this study is to develop an unsophisticated tool to convert small scale cone calorimeter data into input data that can be used in computational fluid dynamics (CFD) models to predict flame spread. Specific interests of input data for FDS in this study include thermal conductivity, specific heat, pre exponential factor, activation energy, heat of vaporization. The tool consists of two objects; 1-D model and bounding exercise. Main structure of the model is based on one of the thermal boundary conditions in the FDS, named as “Pyrolysis Model, Thermally-Thick Solid”, in which pyrolysis flux occurs on the surface of the object under radiant heat flux. This boundary condition is adopted because it has the best characteristics in the dynamics of modeling which are subject to our interests. The structure of the model is simple and concise. For engineering point of view, a practical model ought to have such simplicity that saves time and effort. Pyrolysis model in FDS meets this requirement. It is also a part of reason that this study is to develop a computational model which converts a set of data from the cone calorimeter test to a set of input data for FDS. A pyrolysis term on a surface of an object in this boundary condition will be playing an important role regarding a surface temperature and a mass loss rate of the object. Bounding exercise is introduced to guide proper outcome out of the modeling. Prediction of the material properties from the simulation is confirmed by the experimental data in terms of surface temperature history and mass loss rate under the bounding exercise procedure. For the cone calorimeter, thirteen different materials are tested. Test materials vary with their material composition such as thermoplastics, fiber reinforced plastics (FRP), and a wood. Throughout the modeling fed by a set of the cone calorimeter test data, estimated material properties are provided. So called “Bounding Exercise” is introduced here to draw the estimated material properties. Bounding exercise is a tool in order to guide the material property estimation procedure. Three sets of properties (Upper, Standard and Lower) are derived from the boundary exercise as recommended material properties. From the modeling results, PMMA shows the best agreement regarding the estimated material properties compared with already known results from the references. Wood indicates, however, somewhat different results, in which the mass loss rate takes a peak around the ignition and decreases sharply. This burning behavior can not be predicted using the “Pyrolysis Model”. The model in this study does not account so called “Charring Behavior” that a charring layer toward a surface or difference between a charred density in a charring layer and a normal density in a virgin layer of a wood. These factors result in a discrepancy of the estimated material properties with the reference data. Unlike PMMA and wood, FRP materials show a unique ignition characteristic. Mass loss rate history from some FRP materials indicate more a thermoplastic burning behavior and other materials tend to char. In addition there are few known material property data for theses materials and it is difficult to verify the results from this study with pre-existing data. Some plastic samples also indicate difficulties of the modeling. Because some samples melt and disfigure during the test, one dimensional heat transfer boundary condition is no longer applicable. Each bounding exercise results are fully examined and analyze in Chapter 6. Some of limitations contain model’s structural limitation, in which the model is too simple for certain cases, as well as limitations of bounding exercise. Finally, recommendations are made for future work including upgraded model accountable for the pyrolysis of charring material and FRP materials, data comparison with FDS results, and improved bounding exercise method."

This thesis presents a finite element method based comparative study of linear and geometrically non-linear progressive failure analysis of thin walled composite aerospace structures, which are typically subjected to combined in-plane and out-of-plane loadings. Different ply and constituent based failure criteria and material property degradation schemes have been included in a PCL code to be executed in MSC Nastran. As case studies, progressive failure analyses of sample composite laminates with cut-outs under combined loading are executed to study the effect of geometric non-linearity on the first ply failure and progression of failure. Ply and constituent based failure criteria and different material property degradation schemes are also compared in terms of predicting the first ply failure and failure progression. For mode independent failure criteria, a method is proposed for the determination of separate material property degradation factors for fiber and matrix failures which are assumed to occur simultaneously. The results of the present study show that under combined out-of-plane and in-plane loading, linear analysis can significantly underestimate or overestimate the failure progression compared to geometrically non-linear analysis even at low levels of out-of-plane loading.

Intermittent contact mode atomic force microscopy has been widely employed for simultaneous topography imaging and material characterization. The work in the literature includes both qualitative and quantitative methods. Regular AFM cantilevers are generally used in these methods, yet these cantilevers come with certain limitations. These limitations result from the very nature of cantilever probes. They are passive force sensors with insufficient damping. This prevents having active and complete control on tip-sample forces, causing sample damage and inaccurate topography measurement. Ideally, an AFM probe should offer high bandwidth to resolve interaction forces, active control capability for small interaction force and stable operation, and sufficient damping to avoid transient ringing which causes undesired forces on the sample. Force sensing integrated readout and active tip (FIRAT) probe offers these properties. A special imaging mode, time-resolved interaction force (TRIF) mode imaging can be performed using FIRAT probe for simultaneous topography and material property imaging. The accuracy of topography measurement of samples with variations in elastic and adhesive properties is investigated via numerical simulations and experiments. Results indicate that employing FIRAT probe's active tip control (ATC) capability during TRIF mode imaging provides significant level of control over the tip-sample forces. This improves the accuracy of topography measurement during simultaneous material property imaging, compared to conventional methods. Moreover, Active tip control (ATC) preserves constant contact time during force control for stable contact while preventing loss of material property information such as elasticity and adhesive forces.

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Tire parameters play a very important role in tire performance. Depending on the driving conditions for which a given tire is designed, its parameters must be chosen appropriately (e.g., the radius of the tire, the width of the tire, material properties of different sections). Among tire characteristics, the material properties of the rubber compounds have a vital role in tire behavior. Previous studies show that the material properties of the rubber are highly dependent on temperature. Thus, a comprehensive study on the effect of the material properties of the rubber on tire performance for different temperatures as well as different road conditions is required. In this study, a theoretical model has been developed for tire-ice interaction. The temperature changes obtained from the model are used to calculate the height of the water film created by the heat generated due to the friction force. Next, the viscous friction coefficient at the contact patch is obtained. By using the thermal balance equation at the contact patch, dry friction is obtained. Knowing the friction coefficients for the dry and wet regions, the equivalent friction coefficient is calculated. The model has been validated using experimental results for three similar tires with different rubber compounds properties. For the experimental part of this study, four tires have been selected for testing. Three of them have identical tire geometry and structure but different rubber tread compounds. Several tests were conducted for the chosen tires in three modes: free-rolling, braking, and traction. The tests were performed for two different normal loads (4 kN and 5.6 kN), two different inflation pressures (21 psi (144.8 kPa) and 28 psi (193 kPa)), and three tire temperatures levels (-10°C, -5°C, and -1 °C). The Terramechanics Rig at TMVS at Virginia Tech has been used for conducting the tests. The results from this study show the sensitivity of the magnitude of the tractive force with respect to parameters such as tire temperature, normal load, etc. The results also indicate that the tire with the lowest value of the Young modulus has the highest traction among all four tires used in this study. The model developed can be used to predict the temperature changes at the contact patch, the tire friction force, the areas of wet and dry regions, the height of the water film for different ice temperatures, different normal loads, etc. The results from this study coincide with the obtained results from the experiments. According to the data available, tire B with the smallest value of Young modulus and the smallest value of the specific heat parameter was shown to have the highest friction coefficient in both simulation and experiment. After validating the results using experimentally collected data, the model was used to perform a sensitivity analysis on the tire performance with respect to six material properties of the tread rubber: thermal conductivity, rubber density, Young's modulus, specific heat, roughness parameter of the rubber, and radii of spherical asperities of the rubber. The results from this study show the sensitivity of the magnitude of the friction coefficient to the rubber material properties. The friction coefficient has a direct relationship with the density of the rubber and has an inverse relationship with Young's modulus, specific heat, and roughness parameter. / Doctor of Philosophy / In order to decrease the number of deaths and injuries caused by driving on icy roads and increase the safety of the vehicle, it is important to improve the tire performance on ice. To this, understanding the effects of different tire and road parameters such as material properties of the rubber, loading condition, and temperature on the tire-ice performance is required. Tire parameters play a very important role in tire performance. Depending on the driving conditions for which a given tire is designed, its parameters must be chosen appropriately In this project, the effects of different tire and terrain parameters such as rubber material properties on tire performance on ice using an experimental and modeling approach have been studied. For the experimental part of this study, several tests were conducted for more than 30 tires with different material properties. The results of this study show what are the most important material properties of the rubber for designing a tire with the best performance on ice. For the modeling part of this study, a semi-analytical model was developed. The model was validated using collected experimental data and was used to predict the performance of the tire by having information about its material and physical properties. The developed model called ATIIM2.0 has several advantages. First, it is a unique model for a complete tire (not a rubber block) that can be used to predict the performance of the tire by using its material properties. In addition, this model can be connected to vehicle models to improve the performance of the vehicle in general. The model developed can be used to predict the temperature changes at the contact patch, the tire friction force, the areas of wet and dry regions, the height of the water film for different ice temperatures, different normal loads, etc. The results from this study coincide with the obtained results from the experiments.

Cellular mechanical metamaterials (CMMs) are a special class of materials that consist of microstructural architectures of macroscopic hierarchical frameworks that can have extraordinary properties. These properties largely depend on the topology and arrangement of the unit cells constituting the microstructure. The material hierarchy facilitates the synthesis and design of CMMs on the micro-scale to achieve enhanced properties (i.e., improved strength, toughness, low density) on the component (macro)-scale. However, designing on-demand cellular metamaterials usually requires solving a challenging inverse problem to explore the complex structure-property relations. The first part of this study (Ch. 3) proposes an experience-free and systematic design methodology for microstructures of CMMs using an advanced stochastic searching algorithm called micro-genetic algorithm (μGA). Locally, this algorithm minimizes the computational expense of the genetic algorithm (GA) with a small population size and a conditionally reduced parameter space. Globally, the algorithm employs a new search strategy to avoid local convergence induced by the small population size and the complexity of the parameter space. What's more, inspired by natural evolution in the GA, this study applies the inverse design method with the standard GA (sGA) as a sampling algorithm for intuitively mapping material-property spaces of CMMs, which requires the selection of objective properties and stochastic search of property points within the property space. The mapping methodology utilizing the sGA is proposed in the second part of the study (Ch. 4). This methodology involves a robust strategy that is shown to identify more comprehensive property spaces than traditional mapping approaches. The resulting property space allows designers to acknowledge the limitations of material performance, and select an appropriate class of CMMs based on the difficulty of the realization and fabrication of their microstructures. During the fabrication process, manufacturing defects cause uncertainty in the microstructures, and thus the structural properties. The third part of the study (Ch. 5) investigates the effects of the uncertainty stemming from manufacturing defects on the material property space. To accelerate the uncertainty quantification (UQ) via the Monte Carlo method, this study utilizes a machine learning technique to bypass the expensive simulations to compute properties. In addition to reducing the computational expense of the simulations, the deep learning method has been proven to be practical to accomplish non-intuitive design tasks. Due to the numerous combinations of properties and complex underlying geometries of metamaterials, it is numerically intractable to obtain optimal material designs that satisfy multiple user-defined performance criteria at the same time. Nevertheless, a deep learning method called conditional generative adversarial networks (CGANs) is capable of solving this many-to-many inverse problem. The fourth part of the study (Ch. 6) proposes a new inverse design framework using CGANs to overcome this challenge. Given combinations of target properties, the framework can generate a group of geometric patterns providing these target properties. Therefore, the proposed strategy provides alternative solutions to satisfy on-demand requirements while increasing the freedom in the fabrication process. Besides, with the advances in additive manufacturing (AM), the design space of an engineering material can be further enlarged by multi-scale topology optimization. As the interplay between microstructure and macrostructure drives the overall mechanical performance of engineering materials, it is necessary to develop a multi-scale design framework to optimize structural features in these two scales simultaneously. The final part of the study (Ch. 7) presents a concurrent multi-scale topology optimization method of CMMs. Structures in micro and macro scales are optimized concurrently by utilizing sequential quadratic programming (SQP) with the Solid Isotropic Material with Penalization (SIMP) method and a numerical homogenization approach. / Doctor of Philosophy / Cellular materials widely exist in natural biological systems such as honeycombs, bones, and wood. Recent advances in additive manufacturing have enabled us to fabricate these materials with high precision. Inspired by architectures in nature, cellular mechanical metamaterials (CMMs) have been introduced recently as a new class of architected systems. The materials are formed by hierarchical microstructural topologies, which have a decisive influence on the structural performance at the macro-scale. Therefore, the design of these materials primarily focuses on the geometric arrangement of their microstructures rather than the chemical composition of their base material. Tailoring the microstructures of these materials can lead to several outstanding features, such as high stiffness and strength, low density, and high energy absorption. However, it is challenging to design microstructures that satisfy user-defined requirements for properties and material costs. This is mainly due to the trade-off between the accuracy and computing times of the optimization process. In the first part of this study (Ch. 3), a design framework is proposed to overcome this issue. The framework employs a global search algorithm called the genetic algorithm (GA). With a newly designed search algorithm, the framework reduces errors between target and optimized material properties while improving computational efficiency. Inspired by the algorithm behind the GA, the second part of the study (Ch. 4) employs a similar algorithm to identify a material property chart demonstrating all possible combinations of mechanical properties of CMMs. Each axis of the material property chart corresponds to a selected mechanical property, such as Young's modulus or Poisson's ratio, along different directions. The boundary of the property space helps designers understand material performance limitations and make informed decisions in engineering practices. In the fabrication process, unexpected material properties might be achieved due to defects and tolerances in additive manufacturing (AM), such as uneven surfaces, shrinkage of pores, etc. The third part of the study (Ch. 5) investigates the uncertainty propagation on mechanical properties as a result of these manufacturing defects. To investigate the uncertainty propagation problem efficiently, the study uses a deep learning method to predict the variations (stochasticity) of properties. Consequently, the material property space boundary also varies with the uncertainty of properties. In addition to their computational efficiency, deep learning methods are beneficial for solving many-to-many inverse design problems. Traditionally, the global and local search/optimization methods retrieve alternative optimal solutions in their Pareto front set, where each solution is considered to be equally good. A deep learning method called conditional generative adversarial networks (CGANs) can bypass the property calculation to accelerate the simulation process while obtaining a group of candidates with on-demand properties. The fourth part of the study (Ch. 6) employs CGANs to build a new inverse design framework to increase flexibility in the fabrication process by generating alternative solutions for the microstructures of CMMs. Besides, as fabrication technologies have advanced, designing engineering systems has become increasingly complex. Material design is now not only focused on meeting micro-scale requirements but also addressing needs at multiple scales. The interaction between the microstructure (small-scale) and macrostructure (large-scale) significantly influences the overall performance of engineering systems. To optimize structures effectively, there is a need for a design framework that considers these two scales simultaneously. Thus, the final part of the study (Ch. 7) introduces a method called concurrent multi-scale topology optimization. To obtain the extreme performance of a multi-scale structure, this approach optimizes its structure at both micro- and macro-scales concurrently, using gradient-based optimization algorithms with density-based property determination methods in the two scales.