Data Dependent Modeling of New Contamination Cases from Urban Historic Groundwater Records

Groundwater is the water located beneath the earth＇s surface in the soil pore spaces and in the fractures of rock formations. As one of the most important natural resources, groundwater is associated with the environment, public health, welfare, and long-term economic growth, which affects the daily activities of human beings. In modern urban areas, the primary contaminants of groundwater are artificial products, such as gasoline and diesel. To protect this important water resource, a series of efforts have been exerted, including enforcement and remedial actions. Each year, the TGPC （Texas Groundwater Protection Committee） in US publishes a ＂Joint Groundwater Monitoring and Contamination Report＂ to describe historic and new contamination cases in each county, which is an important data source for the design of prevention strategies. In this paper, a DDM （data dependent modeling） approach is proposed to predict county-level NCC （new contamination cases）. A case study with contamination information from Harris County in Texas was conducted to illustrate the modeling and prediction process with promising results. The one-step prediction error is 1.5%, while the two-step error is 12.1%. The established model can be used at the county-level, state-level, and even at the country-level. Besides, the prediction results could be a reference during decision-making processes.

Inference for accelerated bivariate dependent competing risks model based on Archimedean copulas under progressive censoring

Dependent competing risks model is a practical model in the analysis of lifetime and failure modes.The dependence can be captured using a statistical tool to explore the re-lationship among failure causes.In this paper,an Archimedean copula is chosen to describe the dependence in a constant-stress accelerated life test.We study the Archimedean copula based dependent competing risks model using parametric and nonparametric methods.The parametric likelihood inference is presented by deriving the general expression of likelihood function based on assumed survival Archimedean copula associated with the model parameter estimation.Combining the nonparametric estimation with progressive censoring and the non-parametric copula estimation,we introduce a nonparametric reliability estimation method given competing risks data.A simulation study and a real data analysis are conducted to show the performance of the estimation methods.

Structural Indexing for 3D Solid Digital Library

In a very large digital library that support computer aided collaborative design, an indexing process is crucial whenever the retrieval process has to select among many possible designs. In this paper, we address the problem of retrieving important design and engineering information by structural indexing. A design is represented by a model dependency graph, therefor, the indexing problem is to determine whether a graph is present or absent in a database of model dependency graphs. we present a novel graph indexing method using polynomial characterization of a model dependency graph and on hashing. Such an approach is able to create an high efficient 3D solid digital library for retrieving and extracting solid geometric model and engineering information.

Existence and Uniqueness of Positive Solutions for a System of Multi-order Fractional Differential Equations

In this paper, we study a class of ruin problems, in which premiums and claims are dependent. Under the assumption that premium income is a stochastic process, we raise the model that premiums and claims are dependent, give its numerical characteristics and the ruin probability of the individual risk model in the surplus process. In addition, we promote the number of insurance policies to a Poisson process with parameter λ, using martingale methods to obtain the upper bound of the ultimate ruin probability.

Theoretical Model Calculation for d+~8Li Reaction

Based on the theoretical models for light nuclei, the calculations of reaction cross sections and the angular distributions for d ＋^8Li reaction are performed. Since all of the particle emissions are from the compound nucleus to the discrete levels, the angular momentum coupling effect in pre-equilibrium mechanism is taken into account. The three- body break-up process and the recoil effect are involved. The theoretical calculated results are compared to existing experimental data.

A Probabilistic Estimation of Strong Earthquakes' Occurrence in Aeghion Area, Central Greece

The probability of occurrence of strong ( M W≥6 0) earthquakes in the area of Aeghion (Central Greece) is determined by Bayes statistics. A catalogue of strong shocks around the city of Aeghion since 1794 is used. For the purposes of our study two distributions of earthquakes’ occurrence are considered. In applying the Bayes approach, a Poisson distribution, which is a memoryless one, is assumed. In order to reinforce the result a time dependent model (normal distribution) is also used. An effort is made to find the probabilities of earthquake occurrence for successive decades are determined by both distributions. The estimated probability for a strong earthquake to occur during 1996～2005 in relation to the Bayes approach shows that the year 2004 is the most likely for this future event. A pattern is also revealed which suggests that the earthquakes in the examined area occurred in clusters (in time). The strong earthquakes in these clusters occurred in quadruplets.

Rheological Behaviour for Polymer Melts and Concentrated Solutions Part Ⅲ: A New Multiple Entanglement Model to Predict the Dependence of Linear Viscoelastic Function (η_0, Ψ_(10)~0,η_(ext)~0) on the Ranges of Primary Molecular Weights and the Species of Polymers

It is shown theoretically that the viscoelasticity of polymer melts is determined by three combining factorst they are the primary molecular weight and its distribution, the number of entanglement sites on polymer chain and the sequence distribution of constituent chains in entanglement spacings. A unified quantity for the three combing factors is the average constrained dimensional number of constituent chains in the long entanglement spacings (v). A new relation of v to the primary molecular weight and the number of testing polymers were derived from the multiple entanglement and reptation model, and a new method for determining v was proposed. The dependences of linear viscoelastic functions on the primary molecular weight and its distribution were derived by the statistical method. When Mn=6Me to 18 Me, the values of (v) can range from 3.33 to 3.70. Their values are in a good agreement with the experiment data, and it can slightjy vary with the different species of polymers and the different ranges of molecular weight of polymers

Fault evolution-test dependency modeling for mechanical systems

Tracking the process of fault growth in mechanical systems using a range of tests is important to avoid catastrophic failures, So, it is necessary to study the design for testability （DFT）. In this paper, to improve the testability performance of me- chanical systems for tracking fault growth, a fault evolution-test dependency model （FETDM） is proposed to implement DFT. A testability analysis method that considers fault trackability and predictability is developed to quantify the testability performance of mechanical systems. Results from experiments on a centrifugal pump show that the proposed FETDM and testability analysis method can provide guidance to engineers to improve the testability level of mechanical systems.

A novel AC turning on behavior model based on survival analysis

Occupant control behavior is a key factor affecting the energy consumption of building air-conditioners(ACs).The operating behavior of ACs and their models in office buildings have been investigated extensively.However,although the thermal sensation of occupants is affected by their previous thermal experience,few researchers have attempted to incorporate this effect quantitatively in models of AC turning on behavior.Not considering the cumulative effect may result in inaccurate predictions.Therefore,in this study,a survival model is proposed to describe AC turning on behavior in office buildings under the cumulative dimension of time.Based on a dataset containing environmental parameters and occupant behavior information,as well as considering occupants entering a room as the starting event and turning on an air-conditioner as the end event,the endurance time before an AC is turned on is investigated,and a survival model is used to predict the probability of the AC turning on due to environmental factors.Based on a switch curve,confusion matrix,and tolerance–time curve,the prediction results of the survival model are analyzed and validated.The results show that a tolerance temperature of 29℃and a tolerance duration setting of 1 h can effectively model the turning on behavior of the AC.In addition,based on comparison results of different models,the survival model presents a more stable switching curve,a higher F1 score,and a tolerance curve that is more similar to reality.Different tolerance durations,as well as static and dynamic tolerance temperature settings,are considered to optimize the model.Furthermore,the AC energy consumption is calculated under the survival model and the traditional Weibull model.Simulation results were compared with measurement,and the survival model verified the improvement effect of prediction accuracy by 8%than the Weibull model.By considering the time-transformed accumulation of physical environmental factors,the accuracy of AC turning on models can be improved,thus providing an effective reference for future building energy consumption simulations.

Analysis of SIRVI model with time dependent coefficients and the effect of vaccination on the transmission rate and COVID-19 epidemic waves

COVID-19 epidemic models with constant transmission rate cannot capture the patterns of the infection data in the presence of pharmaceutical and non-pharmaceutical interventions during a pandemic.Because of this,a new modification of SIR model that contain the vaccination compartment with time dependent coefficients and weak/lossimmunity is explored.Literature review confirms that the effect of vaccination on the time dependent transmission rate is still an open problem.This study answers this open problem.In this study,we first prove the well-posedness and investigate the model dynamics to show their continuous dependence on the model parameters.We then provide an algorithm to derive the time-dependent transmission function for the epidemiologic model and the data of the infected cases.The derived coupled nonlinear differential equations show the effect of vaccination on the transmission rate.Unlike previous studies,we first filter the published data and solve the nonlinear coupled differential equations using the finite difference technique,where the coefficient of the coupled nonlinear differential equations is a function of given data.We then show that time-dependent transmission function can be represented by linear combinations of Gaussian radial base function.We then validate the prediction of our models using numerical simulations,where we used the published data of COVID-19 confirmed cases by the Ministries of Health in Saudi Arabia and Poland.Finally,the numerical solutions of a SIRVI model with time dependent transmission rate show that the waves for currently active cases are in good agreement with the data of Saudi Arabia and Poland.

Model-based and Fuzzy Logic Approaches to Condition Monitoring of Operational Wind Turbines

It is common for wind turbines to be installed in remote locations on land or offshore, leading to difficulties in routine inspection and maintenance. Further, wind turbines in these locations are often subject to harsh operating conditions. These challenges mean there is a requirement for a high degree of maintenance. The data generated by monitoring systems can be used to obtain models of wind turbines operating under different conditions, and hence predict output signals based on known inputs. A model-based condition monitoring system can be implemented by comparing output data obtained from operational turbines with those predicted by the models, so as to detect changes that could be due to the presence of faults. This paper discusses several techniques for model-based condition monitoring systems: linear models, artificial neural networks, and state dependent parameter "pseudo" transfer functions.The models are identified using supervisory control and data acquisition(SCADA) data acquired from an operational wind firm. It is found that the multiple-input single-output state dependent parameter method outperforms both multivariate linear and artificial neural network-based approaches. Subsequently, state dependent parameter models are used to develop adaptive thresholds for critical output signals. In order to provide an early warning of a developing fault, it is necessary to interpret the amount by which the threshold is exceeded, together with the period of time over which this occurs. In this regard, a fuzzy logic-based inference system is proposed and demonstrated to be practically feasible.

Isospin splitting of nucleon effective mass and symmetry energy in isotopic nuclear reactions

Within an isospin and momentum dependent transport model, the dynamics of isospin particles（nucleons and light clusters） in Fermi-energy heavy-ion collisions are investigated for constraining the isospin splitting of nucleon effective mass and the symmetry energy at subsaturation densities. The impacts of the isoscalar and isovector parts of the momentum dependent interaction on the emissions of isospin particles are explored, i.e., the mass splittings of m_n^＊=m_p^＊ and m_n^＊〉 m_p^＊（m_n^＊〈 m_p^＊）. The single and double neutron to proton ratios of free nucleons and light particles are thoroughly investigated in the isotopic nuclear reactions of ^112Sn＋^112Sn and ^124Sn＋^124Sn at incident energies of 50 and 120 MeV/nucleon, respectively. It is found that both the effective mass splitting and symmetry energy impact the kinetic energy spectra of the single ratios, in particular at the high energy tail（larger than 20 Me V）. The isospin splitting of nucleon effective mass slightly impacts the double ratio spectra at the energy of 50 MeV/nucleon. A soft symmetry energy with stiffness coefficient of γ_s =0.5 is constrained from the experimental data with the Fermi-energy heavy-ion collisions.

Human stem cell model to study signal transduction and molecular regulation mechanisms in CML

Abstract:Objective To develop a primary human hematopoietic stem/progenitor cell model for chronic myeloid leukemia (CML) and study signal transduction and molecular regulation mechanisms in CML. Methods We developed a human model of p210BCR/ABL positive CML by transducing normal human umbilical cord blood CD34+ cells with a retroviral vector containing the b3a2 bcr/abl cDNA. We also examined whether this model recreated the cellular phenotype of CML by assessing cell adhesion, cell migration, cell proliferation and cell survival. Results We found that significantly more myeloid colony forming units grew from p210BCR/ABL expressing cells, adhesion of p210BCR/ABL expressing CD34+ cells to fibronectin was decreased but migration over fibronectin was enhanced compared with mock transduced CD34+ cells. In this model, we showed that the presence of p210BCR/ABL leads to elevated levels of p27kip in p210BCR/ABL expressing CD34+ cells. We also showed that multidrug resistance-1 (MDR-1) Pgp was upregulated in the p210BCR/ABL expressing cells which correlates with the expression of p210BCR/ABL. Conclusion This primary human CML model recreates most of the features of CML and provides a useful tool to study signal transduction and downstream molecular regulation drived by the p210BCR/ABL oncogene in normal CD34+ cells.

Penalized least squares estimation with weakly dependent data

In statistics and machine learning communities, the last fifteen years have witnessed a surge of high-dimensional models backed by penalized methods and other state-of-the-art variable selection techniques.The high-dimensional models we refer to differ from conventional models in that the number of all parameters p and number of significant parameters s are both allowed to grow with the sample size T. When the field-specific knowledge is preliminary and in view of recent and potential affluence of data from genetics, finance and on-line social networks, etc., such（s, T, p）-triply diverging models enjoy ultimate flexibility in terms of modeling, and they can be used as a data-guided first step of investigation. However, model selection consistency and other theoretical properties were addressed only for independent data, leaving time series largely uncovered. On a simple linear regression model endowed with a weakly dependent sequence, this paper applies a penalized least squares（PLS） approach. Under regularity conditions, we show sign consistency, derive finite sample bound with high probability for estimation error, and prove that PLS estimate is consistent in L_2 norm with rate （s log s/T）～1/2.

Criterion of Semi-Markov Dependent Risk Model

A rigorous definition of semi-Markov dependent risk model is given. This model is a generalization of the Markov dependent risk model. A criterion and necessary conditions of semi- Markov dependent risk model are obtained. The results clarify relations between elements among semi-Markov dependent risk model more clear and are applicable for Markov dependent risk model.

MODELLING AND NUMERICAL SOLUTIONS OF A GAUGE PERIODIC TIME DEPENDENT GINZBURG-LANDAU MODEL FOR TYPE-Ⅱ SUPERCONDUCTORS

In this paper we seek the solutions of the time dependent Ginzburg-Landau model for type-Ⅱ superconductors such that the associated physical observables are spatially periodic with respect to some lattice whose basic lattice cell is not necessarily rectangular. After appropriately foring the gange, the model can be formulated as a system of nonlinear parabolic partial differential equations with quasi-periodic boundary conditions. We first give some results concerning the existence, uniqueness and regularity of solutions and then we propose a semiimplicit finite element scheme solving the system of nonlinear partial dmerential equations and show the optimal error estimates both in the L2 and energy norm.We also report on some numerical results at the end of the paper.