The enterprise finance crisis forecast could provide alarm to managers and investors of the enterprise, many scholars advised different alarm models to explain and predict the enterprise is facing finance crisis or not. These models can be classified into three categories by analysis method, the first is single-variate model, it¡¦s easy to implement. The second is multi-variate model which need to fit some statistical assumption, and the third is Artificial Neural Network model which doesn¡¦t need to fit any statistical assumption. However, these models do not consider the industrial effect, different industry could have different finance crisis pattern. This study uses the advantage of Artificial Neural Network to build the process of the enterprise finance crisis forecast model, because it doesn¡¦t need to fit any statistical assumption. Finally, the study use reality finance data to prove the process, and compare with the other models. The result shows the model issued by this study is suitable in Taiwan Electronic Industry, but the performance in Taiwan architecture industry is not better than other models.

Advanced Traveler Information System (ATIS) is one component of Intelligent Transportation Systems (ITS), and a major component of ATIS is travel time information. The provision of timely and accurate transit travel time information is important because it attracts additional ridership and increases the satisfaction of transit users. The cost of electronics and components for ITS has been decreased, and ITS deployment is growing nationwide. Automatic Vehicle Location (AVL) Systems, which is a part of ITS, have been adopted by many transit agencies. These allow them to track their transit vehicles in real-time. The need for the model or technique to predict transit travel time using AVL data is increasing. While some research on this topic has been conducted, it has been shown that more research on this topic is required. The objectives of this research were 1) to develop and apply a model to predict bus arrival time using AVL data, 2) to identify the prediction interval of bus arrival time and the probabilty of a bus being on time. In this research, the travel time prediction model explicitly included dwell times, schedule adherence by time period, and traffic congestion which were critical to predict accurate bus arrival times. The test bed was a bus route running in the downtown of Houston, Texas. A historical based model, regression models, and artificial neural network (ANN) models were developed to predict bus arrival time. It was found that the artificial neural network models performed considerably better than either historical data based models or multi linear regression models. It was hypothesized that the ANN was able to identify the complex non-linear relationship between travel time and the independent variables and this led to superior results. Because variability in travel time (both waiting and on-board) is extremely important for transit choices, it would also be useful to extend the model to provide not only estimates of travel time but also prediction intervals. With the ANN models, the prediction intervals of bus arrival time were calculated. Because the ANN models are non parametric models, conventional techniques for prediction intervals can not be used. Consequently, a newly developed computer-intensive method, the bootstrap technique was used to obtain prediction intervals of bus arrival time. On-time performance of a bus is very important to transit operators to provide quality service to transit passengers. To measure the on-time performance, the probability of a bus being on time is required. In addition to the prediction interval of bus arrival time, the probability that a given bus is on time was calculated. The probability density function of schedule adherence seemed to be the gamma distribution or the normal distribution. To determine which distribution is the best fit for the schedule adherence, a chi-squared goodness-of-fit test was used. In brief, the normal distribution estimates well the schedule adherence. With the normal distribution, the probability of a bus being on time, being ahead schedule, and being behind schedule can be estimated.

Many engineering and scientific applications of flow in porous media are characterized by transport phenomena at multiple spatial scales, including pollutant transport, groundwater remediation, and acid injection to enhance well production. Carbon sequestration in particular is a multiscale problem, because the trapping and leakage mechanisms of CO2 in the subsurface occur from the sub-pore level to the basin scale. Quantitative and predictive pore-scale modeling has long shown to be a valuable tool for studying fluid-rock interactions in porous media. However, due to the size limitation of the pore-scale models (10-4-10-2m), it is impossible to model an entire reservoir at the pore scale. A straightforward multiscale approach would be to upscale macroscopic parameters (e.g. permeability) directly from pore-scale models and then input them into a continuum-scale simulator. However, it has been found that the large-scale models do not predict in many cases. One possible reason for the inaccuracies is oversimplified boundary conditions used in this direct upscaling approach. The hypothesis of this work is that pore-level flow and upscaled macroscopic parameters depends on surrounding flow behavior manifested in the form of boundary conditions. The detailed heterogeneity captured by the pore-scale models may be partially lost if oversimplified boundary conditions are employed in a direct upscaling approach. As a result, extracted macroscopic properties may be inaccurate. Coupling the model to surrounding media (using finite element mortars to ensure continuity between subdomains) would result in more realistic boundary conditions, and can thus improve the accuracy of the upscaled parameters. To test the hypothesis, mortar coupling is employed to couple pore-scale models and also couple pore-scale models to continuum models. Flow field derived from mortar coupling and direct upscaling are compared, preferably against a true solution if one exists. It is found in this dissertation that pore-scale flow and upscaled parameters can be significantly affected by the surrounding media. Therefore, using arbitrary boundary conditions such as constant pressure and no-flow boundaries may yield misleading results. Mortar coupling captures the detailed variation on the interface and imposes realistic boundary conditions, thus estimating more accurate upscaled values and flow fields. An advanced upscaling tool, a Super Permeability Tensor (SPT) is developed that contains pore-scale heterogeneity in greater detail than a conventional permeability tensor. Furthermore, a multiscale simulator is developed taking advantage of mortar coupling to substitute continuum grids directly with pore-scale models where needed. The findings from this dissertation can significantly benefit the understanding of fluid flow in porous media, and, in particular, CO2 storage in geological formations which requires accurate modeling across multiple scales. The fine-scale models are sensitive to the boundary conditions, and the large scale modeling of CO2 transport is sensitive to the CO2 behavior affected by the pore-scale heterogeneity. Using direct upscaling might cause significant errors in both the fine-scale and the large-scale model. The multiscale simulator developed in this dissertation could integrate modeling of CO2 physics at all relevant scales, which span the sub-pore or pore level to the basin scale, into one single simulator with effective and accurate communication between the scales. The multiscale simulator provides realistic boundary conditions for the fine scales, accurate upscaled information to continuum-scale, and allows for the distribution of computational power where needed, thus maintaining high accuracy with relatively low computational cost. / text

While there have been many recently developed Elastic Network Models (ENM) to calculate the fluctuation dynamics of proteins, e.g., Gaussian Network Model (GNM), Anisotropic Network Model (ANM), Distance Network Model (DNM), the concept of loading these models to study the molecular mechanics and constitutive behavior of structural proteins has remained relatively untouched, until very recently. This work entails using the ANM as the framework for developing a finite element model of a 9–monomer strand of actin. Critical input parameters to the model, such as the cutoff radius, r[subscript c], and spring constant, k, are generated by matching the all-atom steered molecular dynamics (SMD) residue displacements to that of the ANM. The parameters yielding the best match between the SMD and structural ENM (SENM) simulations will then be input into the finite element model (FEM) for a more in depth analysis. The finite element model incorporates a 9–monomer strand of actin. The F–actin strand is subjected axial and torsional loads comparable to those seen in vivo. Key areas of interest in the protein are examined, such as the nucleotide binding pocket (NBP) and the DNase I binding loop, to demonstrate how loading affects the protein’s conformation. Local residue displacements are tracked in an effort to garner a better understanding of how various loads are transmitted through F–actin during key events. Insights and conclusions are discussed along with the implications of this work. / text

<p>Natural steroidal estrogen hormones in swine wastes generated from Concentrated Animal Feeding Operations (CAFOs) have become a potential pollutant to many aquatic environments due to their adverse impacts on the reproductive biology of aquatic organisms. In North Carolina, the swine CAFO industry is a major agricultural economic enterprise that is responsible for the generation of large volumes of waste. However, there is limited scientific understanding regarding the concentration, fate, and transport of the estrogenic compounds from these swine facilities into terrestrial and aquatic environments. To address this issue, my research involved the development and application of integrated Bayesian networks (BNs) models that can be used to better characterize and assess the generation, fate, and transport of site-specific swine CAFO-derived estrogen compounds. The developed model can be used as decision support tool towards estrogen risk assessment. Modularized and melded BN approaches were used to capture the predictive and casual relationships of the estrogen budget and its movement within and between the three major systems of a swine farrowing CAFO. These systems include the animal barns, the anaerobic waste lagoon, and the spray fields. For the animal barn system, a facility-wide estrogen budget was developed to assess the operation-specific estrogen excretion, using an object-oriented BN (OOBN) approach. The developed OOBN model provides a means to estimate and predict estrogen fluxes from the whole swine facility in the context of both estrogen type and animal operating unit. It also accounts for the uncertainties in the data and in our understanding of the system. Next, mass balance melding BN models were developed to predict the natural estrogen fates and budgets in two lagoon compartments, the slurry and the sludge storage. This involved utilizing mass balance equations to account for the mechanisms of flushing, sorption, transformation, settling, and burial reactions of estrogen compounds in the slurry and sludge storages. As an alternative approach, a regression based BN melding approach was developed to both characterize estrogen fate and budgets as a result of the sequential transformation processes between natural estrogen compounds and to assess the seasonal effects on the estrogen budgets in the two different lagoon compartments. Finally, a dynamic BN model was developed to characterize rainfall-driven estrogen runoff processes from the spray fields. The dynamic BN models were used to assess the potential risk of estrogen runoff to adjacent waterways. In addition, the dynamic model was used to quantify the effects of manure application rates, rainfall frequency, the time of rainfall and irrigation, crop types, on-farm best management practices, seasonal variability, and successive rainfall and manure application events on estrogen runoff. </p><p>The model results indicate that the farrowing barn is the biggest contributor of total estrogen as compared to the breeding and gestation operating barns. Once the estrogen reaches the anaerobic lagoon, settling and burial reactions were shown to be the most significant factors influencing estrogen levels in the slurry and sludge, respectively. The estrogen budgets in the lagoon were also found to vary by season, with higher slurry and sludge estrogen levels in the spring as compared to the summer. The risk of estrogen runoff was predicted to be lower in the summer as compared to the spring, primarily due to the spray field crop management plans adopted. The results also indicated that Bermuda grass performed more favorably when compared to soybean, when it came to retaining surface water runoff in the field. Model predictions indicated that there is a low risk of estrogen runoff losses from the spray fields under multiple irrigation and rainfall events, unless the time interval between irrigation was less than 10 days and/or in the event of a prolonged high magnitude rainstorm event. Overall, the estrone was the most persistent form of natural estrogens in the three major systems of the swine farrowing CAFO.</p> / Dissertation

Risk-based authentication is an increasingly popular component in the security architecture deployed by many organizations in mitigating online identity threat. Risk-based authentication uses contextual and historical information extracted from online communications to build a risk profile for the user that can be used to make accordingly authentication and authorization decisions. Existing risk-based authentication systems rely on basic web communication information such as the source IP address or the velocity of transactions performed by a specific account, or originating from a certain IP address. Such information can easily be spoofed and as such put in question the robustness and reliability of the proposed systems. In this thesis, we propose in this work an online risk-based authentication system which provides more robust user identity information by combining mouse dynamics, keystroke dynamics biometrics, and user site actions in a multimodal framework. We propose a Bayesian network model for analyzing free keystrokes and mouse movements involved in web sessions. Experimental evaluation of our proposed model with 24 participants yields an Equal Error Rate of 6.91%. This is encouraging considering that we are dealing with free text and mouse movements and the fact that many web sessions tend to be short. / Graduate

This thesis presents an investigation of reactive transport of species in porous media, with the aim to understand better and predict the fate of radionuclide in engineered and natural barriers of future deep geological disposal facilities for nuclear waste. The work involves developments of several pore-scale models for simulating reactive transport by coupling convective, adsorptive and diffusive processes. Pore network models (PNM) are amongst the appealing approaches that provide a suitable description for dealing with mutable pore space structures. Such models have been used to describe conservative as well as reactive transport in saturated and unsaturated porous media. In the present thesis, pore network models based on a regular tessellation of truncated octahedral cells are proposed and developed to simulate mass transport in porous media with incomplete pore space information due to limitation of existing characterisation techniques. Bentonite and Opalinus Clay are selected to illustrate the methodology. The micro- and meso-structure of these clays and their effects on the transport behaviour are investigated. The research shows that the clays are anisotropic and heterogeneous with fast diffusion parallel to the bedding plane and slow diffusion perpendicular to the bedding plane. In addition, different types of species have different accessible porosity and macroscopic diffusion coefficients. The anisotropy and heterogeneity of clays are achieved by different length scales and percentage of pores in different directions in the pore network models. The transport behaviour of various species, including sorption and anion exclusion, is simulated and analyzed. The effect of sorption is simulated via changing the pore radii by a coarse grained mathematical formula or by a formula directly in each pore. The results are in good agreement with experimentally measured macroscopic (bulk) diffusivities for the materials studied, including anisotropic diffusion coefficients. This lends strong support to the physical realism of the proposed models. The developed methodology can be used for any micro and meso-porous material with known distribution of pore sizes. It can be extended to other pore space changing mechanisms, in addition to sorption, to derive mechanism-based evolution laws for the transport parameters of porous media.

abstract: This thesis explores a wide array of topics related to the protein folding problem, ranging from the folding mechanism, ab initio structure prediction and protein design, to the mechanism of protein functional evolution, using multi-scale approaches. To investigate the role of native topology on folding mechanism, the native topology is dissected into non-local and local contacts. The number of non-local contacts and non-local contact orders are both negatively correlated with folding rates, suggesting that the non-local contacts dominate the barrier-crossing process. However, local contact orders show positive correlation with folding rates, indicating the role of a diffusive search in the denatured basin. Additionally, the folding rate distribution of E. coli and Yeast proteomes are predicted from native topology. The distribution is fitted well by a diffusion-drift population model and also directly compared with experimentally measured half life. The results indicate that proteome folding kinetics is limited by protein half life. The crucial role of local contacts in protein folding is further explored by the simulations of WW domains using Zipping and Assembly Method. The correct formation of N-terminal &beta;-turn turns out important for the folding of WW domains. A classification model based on contact probabilities of five critical local contacts is constructed to predict the foldability of WW domains with 81% accuracy. By introducing mutations to stabilize those critical local contacts, a new protein design approach is developed to re-design the unfoldable WW domains and make them foldable. After folding, proteins exhibit inherent conformational dynamics to be functional. Using molecular dynamics simulations in conjunction with Perturbation Response Scanning, it is demonstrated that the divergence of functions can occur through the modification of conformational dynamics within existing fold for &beta;-lactmases and GFP-like proteins: i) the modern TEM-1 lactamase shows a comparatively rigid active-site region, likely reflecting adaptation for efficient degradation of a specific substrate, while the resurrected ancient lactamases indicate enhanced active-site flexibility, which likely allows for the binding and subsequent degradation of different antibiotic molecules; ii) the chromophore and attached peptides of photocoversion-competent GFP-like protein exhibits higher flexibility than the photocoversion-incompetent one, consistent with the evolution of photocoversion capacity. / Dissertation/Thesis / Ph.D. Physics 2014

A caracterização das flutuações dos resíduos da proteína em torno do seu estado nativo é essencial para estudar mudanças conformacionais, interação proteína-proteína e interação proteína-ligante. Tal caracterização pode ser capturada pelo modelo de rede gaussiana (GNM). Este modelo tem sido modificado e novas propostas têm surgido nos últimos anos. Nesta Tese, apresentamos um estudo sobre como melhorar o GNM e exploramos o seu desempenho em predizer os fatores-B experimentais. Modelos de redes elásticas são construídos a partir das coordenadas experimentais dos levando em consideração pares de átomos de C? distantes entre si até um dado raio de corte Rc . Estes modelos descrevem as interações entre os atómos por molas com a mesma constante de força. Desenvolvemos um método baseado em simulações numéricas com um campo de forças simplificado para atribuir pesos a estas constantes de mola. Este método considera o tempo em que dois átomos de C? permanecem conectados na rede durante o desenovelamento parcial, estabelecendo assim uma forma de medir a intensidade de cada ligação. Examinamos dois diferentes campos de forças simplificados e exploramos o cálculo desses pesos a partir do desenovelamento das estruturas nativas. Nós comparamos o seu desempenho na predição dos fatores-B com outros modelos de rede elástica. Avaliamos tal desempenho utilizando o coeficiente de correlação entre os fatores-B preditos e experimentais. Mostramos como o nosso modelo pode descrever melhor os fatores-B / The characterization of the fluctuations in protein residues around its native state is essential to study conformational changes, protein binding interaction and protein-protein interaction. Such characterization can be captured by simple elastic network models as the Gaussian Network Model (GNM). This model has been modified and new proposals have emerged in recent years. In this Thesis we propose an extended version of GNM, namely wGNM. Elastic network models are built on the experimental C? coordinates,and they only take the pairs of C? atoms within a given cutoff distance Rc into account. These models describe the interactions by elastic springs with the same force constant to predicted the experimental B-factors, providing insights into the structure-function properties of proteins. We have developed a method based on numerical simulations with a simple coarse-grained force field, to attribute weights to these spring constants. This method considers the time that two C? atoms remain connected in the network during partial unfolding, establishing a means of measuring the strength of each link. We examined two different coarse-grained force fields and explored the computation of these weights by unfolding native structures. We compare the B-factors predicted by different elastic network models with the experimental ones employing the correlation coefficient between these two quantities. We show that wGNM performs better and consequently provides better evaluation of the B-factors

Nowadays, the improvement of factors, such as transportation, communication, and technological advancement, is allowing SMEs to move towards international markets faster than before. Combining those factors with knowledge gained inside firms, software SMEs are able to follow the trend of internationalizing their activities. Depending only on one side of knowledge to expand abroad, will probably inhibit SMEs to survive in the foreign atmosphere. Moreover, the insufficiency of market 5  knowledge and the lack of international experience prevent SMEs to meet the basic requirements to establish relationships outside the home country. The dependence only on the domination of internal knowledge and the uniqueness of the product, may provoke software firms for an excessive ambition to internationalize - which can lead to tangling the way to establish a network abroad.