General Structural Model Research Articles

Oncology from an HTA and Health Economic Perspective.

In this chapter, we will present and discuss the challenges of assessing oncology products from a health economic perspective. We will provide a brief introduction on the need for economic evaluation in health care and focus on cost-effectiveness and comparative aspects of the evaluation of oncology products, which are of paramount interest to HTA decision-making bodies using economic evaluation in their decision-making framework. As the burden of oncology is well-documented, we do not discuss it in detail here. Before we address the specific issue of oncology, we will briefly define the critical aspects of HTA assessment and also define what a cost-effectiveness analysis is and why economic modelling is the most appropriate tool to assess the cost-effectiveness of oncology products. We will touch upon the prices of oncology drugs and the questions that high prices raise regarding funding and availability. We then present an overview of the general structure of an oncology cost-effectiveness model. Usually, this is quite simple, representing response, progression, advanced-stage disease and death. Despite the relative simplicity of these models, some issues may render the evaluation more complex; we will touch upon these in this chapter: Issue with clinical inputs due to the design of randomised clinical trials (e.g. cross-over designs involving a treatment switch) Need for survival extrapolation and limitations of current parametric models Rare conditions with limited economic and comparative evidence available High pace of clinical development Finally, we will conclude with a discussion of the uncertainty around the evaluation of oncology products and the major evolution expected in health economics in oncology.

Developing New Technology Acceptance Model With Multi-Criteria Decision Technique: An Implementation Study

This study aims to develop and implement a new Technology Acceptance Model (TAM) to verify and explain users’ acceptance of SAP software. The structural equation model (SEM) technique is used with the AMOS program for the adoption process. General structural model, which includes Experience, Voluntariness, Perceived Enjoyment, Perceptions of External Control, Computer Self Efficacy, Output Quality and Job Relevance, is developed with Analytical Hierarchy Method (AHP) and Pareto Analysis by deciding variables from previously original developed models which are called TAM1, TAM2 and TAM3. New TAM is tested to a sample of 384 SAP users in a company that serves in metal production. Results illustrates that new TAM is an appropriate tool to explain and understand users’ acceptance of SAP besides of being flexible and easily implemented. SAP users’ job relevance and their perceptions of external control are the most important variables that affect perceived usefulness and perceived ease of use in the model.

A Unified Framework for Wavelet-Packet Identification of Dynamic Systems

This work proposes a unified framework for linear system identification in frequency subbands using wavelet packets. To that end, a previous wavelet-packet technique is extended to allow for the use of general model structures and standard identification procedures. The technique can be straightforwardly applied to the identification of models with different structures, such as subspace models, provided that their parameters can be identified using a least-squares approach. Models with autoregressive structures are used here for means of illustration. The proposed technique is illustrated with the examples of the longitudinal dynamics of a flexible aircraft and with the model of a cantilever beam. The proposed wavelet-packet approach is favorably compared with the technique on which it was based and with a recently proposed wavelet-based subspace identification technique.

Surface Morphology of Structural Plane and Effects of the Shear Strength Parameters

The surface morphology of a discontinuous structural plane in a natural rock mass is irregular. In studies on the shear strength of discontinuous structural planes, Barton introduced the joint roughness coefficient (JRC) to describe and calibrate the morphological characteristics of a rough structural plane. To describe Barton’s typical rough profile model, researchers commonly conduct a comparison based on a standard curve. This approach does not guarantee graphic precision. With subjective comparison reference drawing, the size and angle of a structural plane cannot be quantitatively controlled, and small bumps cannot be described. When the influence of the roughness coefficient of a rock surface is discussed, errors are caused by comparing Barton’s typical roughness profile model and a general structural surface model. This paper proposed a new method to simulate Barton’s typical structural surface. A numerical model of a complex structural plane is built automatically. The effects of cohesion and internal friction angle on the shear strength of the structural plane are discussed by changing the value of the shear strength parameters.

Prediction-error identification of LPV systems: A nonparametric Gaussian regression approach

In this paper, a Bayesian nonparametric approach is introduced to estimate multi-input multi-output (MIMO) linear parameter-varying (LPV) models under the general noise model structure of Box–Jenkins (BJ) type. The approach is based on the estimation of the one-step-ahead predictor of general LPV-BJ structures. Parts of the predictors associated with the input and output signals are modeled as asymptotically stable infinite impulse response (IIR) models. Then, these IIR models are identified in a completely nonparametric sense: not only the coefficients are estimated as functions, but also the whole time evolution of the impulse response w.r.t. the scheduling signal of the LPV system. In this Bayesian setting, the estimate of the one-step-ahead predictor is a realization from a zero-mean Gaussian random field, where the covariance function is a multidimensional Gaussian kernel that encodes both the possible structural dependencies and the stability of the predictor. Two different kernel formulations are presented for the LPV setting, namely a diagonal (DI) like and tuned/correlated (TC) like kernels, where the TC-like kernel is able to describe the correlation between coefficient functions associated with different time indices. The unknown hyperparameters that parameterize the DI or TC kernel are tuned by maximizing the marginal likelihood w.r.t. the observed data. Moreover, we provide a nonparametric realization scheme to recover the original process and noise IIRs from the identified one-step-ahead predictor. The performance of the presented identification approach is tested on a MIMO LPV-BJ simulation example by means of an extensive Monte-Carlo study.

Evolution of a generic, dynamic and multicomponent tumbling mill model structure incorporating a wide-range 4D appearance function

This work aims to build a generic dynamic model structure, which can accommodate interchangeable sub-models of each sub-process, making it amenable to continuous upgrade without the need for redevelopment, for multicomponent tumbling grinding mills. The Generic Tumbling Mill Model Structure (GTMMS) is based on a population mass balance framework which incorporates breakage characteristics, transport, classification along the mill, a discharge function, and energy consumption incorporated in a dynamic mill model structure. Version III builds on two earlier versions by incorporating energy distributions derived from discrete element modelling, an updated version of the 4D breakage appearance function which applies to a broader size range, and addresses multi-component ore breakage via the probability distribution of energy split based on material stiffness. The model has been tested against multi-component plant survey data. GTMMS III suggests a mechanistic insight into mixture prediction through component analysis and is a step forward towards the unified comminution model (UCM) with its mechanistic, generic, and dynamic prediction capability.

Concept of Lattice Mismatch and Emergence of Surface States in Two-dimensional Hybrid Perovskite Quantum Wells

Surface states are ubiquitous to semiconductors and significantly impact the physical properties and, consequently, the performance of optoelectronic devices. Moreover, surface effects are strongly amplified in lower dimensional systems such as quantum wells and nanostructures. Layered halide perovskites (LHPs) are two-dimensional solution-processed natural quantum wells where optoelectronic properties can be tuned by varying the perovskite layer thickness n, i.e., the number of octahedra spanning the layer. They are efficient semiconductors with technologically relevant stability. Here, a generic elastic model and electronic structure modeling are applied to LHPs heterostructures with various layer thickness. We show that the relaxation of the interface strain is triggered by perovskite layers above a critical thickness. This leads to the release of the mechanical energy arising from the lattice mismatch, which nucleates the surface reorganization and may potentially induce the formation of previously observed lower energy edge states. These states, which are absent in three-dimensional perovskites are anticipated to play a crucial role in the design of LHPs for optoelectronic systems.

Parametric analysis of external and internal factors influence on building energy performance using non-linear multivariate regression models

The energy efficiency is one of the most important issues nowadays; the problem with the buildings heating is especially relevant for Ukraine. The aim of the paper is to develop a convenient tool building energy performance analysis based on regression model for internal air temperature prediction, depending on a number of internal and external influential factors. The external climatic factors, such as outside air temperature, wind speed and direction, solar heat gains depending on building fenestration surfaces orientation, are considered. Internal factors include heating load, number of floors, air exchange rate etc. In order to achieve the goal, a room dynamic simulation model is created in the EnergyPlus software. A number of simulations are carried out based on the created building energy model. The individual and aggregate selected factors influence on inside air temperature change is considered. The general structure of the multivariate nonlinear regression model for inside air temperature determination is analyzed and selected. Constant coefficients are obtained for each selected influencing factor, and verification of the received nonlinear regression model is performed based on simulation data using January climatic data from the IWEC file. The adequacy of the obtained regression model is estimated by the corrected determination coefficient (R2 = 0.981) and Fisher's criterion (F = 1324.3), which indicates the high accuracy of the obtained multivariate nonlinear regression. The proposed approach for regression model creation can be used for other architectural and thermal properties of building envelope.

Online non-affine nonlinear system identification based on state-space neuro-fuzzy models

This paper proposes a new general recurrent state-space neuro-fuzzy model structure. Three topologies are under assessment, including the state-input recurrent neuro-fuzzy system, the series-parallel recurrent neuro-fuzzy system and the parallel recurrent neuro-fuzzy system. Moreover, the underlying generalised state-space Takagi–Sugeno system is proven to be a universal approximator, and some stability conditions derived for this system. The online training is carried out based on a constrained unscented Kalman filter, where weights, membership functions and consequents are recursively updated. Results from experiments on a benchmark MIMO system demonstrate the applicability and flexibility of the proposed system identification approach.

Minimal models for dark matter and the muon g \u2212 2 anomaly

We construct models with minimal field content that can simultaneously explain the muon g − 2 anomaly and give the correct dark matter relic abundance. These models fall into two general classes, whether or not the new fields couple to the Higgs. For the general structure of models without new Higgs couplings, we provide analytical expressions that only depend on the SU(2)L representation. These results allow to demonstrate that only few models in this class can simultaneously explain (g − 2)μ and account for the relic abundance. The experimental constraints and perturbativity considerations exclude all such models, apart from a few fine-tuned regions in the parameter space, with new states in the few 100 GeV range. In the models with new Higgs couplings, the new states can be parametrically heavier by a factor sqrt{1/{y}_{mu }} , with yμ the muon Yukawa coupling, resulting in masses for the new states in the TeV regime. At present these models are not well constrained experimentally, which we illustrate on two representative examples.

A novel trust evolution algorithm based on a quantum-like model of computational trust

Trust models play an important role in decision support systems and computational environments in general. The common goal of the existing trust models is to provide a representation as close as possible to the social phenomenon of trust in computational domains. In recent years, the field of quantum decision making has been significantly developed. Researchers have shown that the irrationalities, subjective biases, and common paradoxes of human decision making can be better described based on a quantum theoretic model. These decision and cognitive theoretic formulations that use the mathematical toolbox of quantum theory (i.e., quantum probabilities) are referred to by researchers as quantum-like modeling approaches. Based on the general structure of a quantum-like computational trust model, in this paper, we demonstrate that a quantum-like model of trust can define a powerful and flexible trust evolution (i.e., updating) mechanism. After the introduction of the general scheme of the proposed model, the main focus of the paper would be on the proposition of an amplitude amplification-based approach to trust evolution. By performing four different experimental evaluations, it is shown that the proposed trust evolution algorithm inspired by the Grover’s quantum search algorithm is an effective and accurate mechanism for trust updating compared to other commonly used classical approaches.

Intracellular Hepatitis C Virus Modeling Predicts Infection Dynamics and Viral Protein Mechanisms.

Hepatitis C virus (HCV) infection is a global health problem, with nearly 2 million new infections occurring every year and up to 85% of these infections becoming chronic infections that pose serious long-term health risks. To effectively reduce the prevalence of HCV infection and associated diseases, it is important to understand the intracellular dynamics of the viral life cycle. Here, we present a detailed mathematical model that represents the full hepatitis C virus life cycle. It is the first full HCV model to be fit to acute intracellular infection data and the first to explore the functions of distinct viral proteins, probing multiple hypotheses of cis- and trans-acting mechanisms to provide insights for drug targeting. Model parameters were derived from the literature, experiments, and fitting to experimental intracellular viral RNA, extracellular viral titer, and HCV core and NS3 protein kinetic data from viral inoculation to steady state. Our model predicts higher rates for protein translation and polyprotein cleavage than previous replicon models and demonstrates that the processes of translation and synthesis of viral RNA have the most influence on the levels of the species we tracked in experiments. Overall, our experimental data and the resulting mathematical infection model reveal information about the regulation of core protein during infection, produce specific insights into the roles of the viral core, NS5A, and NS5B proteins, and demonstrate the sensitivities of viral proteins and RNA to distinct reactions within the life cycle.IMPORTANCE We have designed a model for the full life cycle of hepatitis C virus. Past efforts have largely focused on modeling hepatitis C virus replicon systems, in which transfected subgenomic HCV RNA maintains autonomous replication in the absence of virion production or spread. We started with the general structure of these previous replicon models and expanded it to create a model that incorporates the full virus life cycle as well as additional intracellular mechanistic detail. We compared several different hypotheses that have been proposed for different parts of the life cycle and applied the corresponding model variations to infection data to determine which hypotheses are most consistent with the empirical kinetic data. Because the infection data we have collected for this study are a more physiologically relevant representation of a viral life cycle than data obtained from a replicon system, our model can make more accurate predictions about clinical hepatitis C virus infections.

A general structured model of a hermaphrodite population

Both empirical and theoretical studies, have dealt with the question how to best optimize reproductive fitness for hermaphrodites, using models such as game theory or complicated energetic costs and benefits of mating displays. However, hermaphrodites exhibit a broad spectrum of sexual behaviors like simultaneous, sequential or lifetime gonochorist that cannot be explained using a unique formalism.A possible explanation of this diversity relies on the way these species maximize their fitness: Does the individual hermaphrodite split its time between strategies maximizing its instantaneous reproductive fitness or its evolutionary fitness?Here, we compare these two points of view and extend a game theoretical formalism to a sex allocation model that underlies all sexual behaviors as a result of a dynamic game whose payoff depends on the costs and benefits of sexual reproduction. Using this formalism, we prove that a simultaneous hermaphrodites strategy is stable even for high values of sex changing costs. Moreover, we prove that the stability of a sequential hermaphrodite is linked to the average energy allocated to the pure female strategy.

Statistical forecast of seasonal discharge in Central Asia using observational records: development of a generic linear modelling tool for operational water resource management

Abstract. The semi-arid regions of Central Asia crucially depend on the water resources supplied by the mountainous areas of the Tien Shan and Pamir and Altai mountains. During the summer months the snow-melt- and glacier-melt-dominated river discharge originating in the mountains provides the main water resource available for agricultural production, but also for storage in reservoirs for energy generation during the winter months. Thus a reliable seasonal forecast of the water resources is crucial for sustainable management and planning of water resources. In fact, seasonal forecasts are mandatory tasks of all national hydro-meteorological services in the region. In order to support the operational seasonal forecast procedures of hydro-meteorological services, this study aims to develop a generic tool for deriving statistical forecast models of seasonal river discharge based solely on observational records. The generic model structure is kept as simple as possible in order to be driven by meteorological and hydrological data readily available at the hydro-meteorological services, and to be applicable for all catchments in the region. As snow melt dominates summer runoff, the main meteorological predictors for the forecast models are monthly values of winter precipitation and temperature, satellite-based snow cover data, and antecedent discharge. This basic predictor set was further extended by multi-monthly means of the individual predictors, as well as composites of the predictors. Forecast models are derived based on these predictors as linear combinations of up to four predictors. A user-selectable number of the best models is extracted automatically by the developed model fitting algorithm, which includes a test for robustness by a leave-one-out cross-validation. Based on the cross-validation the predictive uncertainty was quantified for every prediction model. Forecasts of the mean seasonal discharge of the period April to September are derived every month from January until June. The application of the model for several catchments in Central Asia – ranging from small to the largest rivers (240 to 290 000 km2 catchment area) – for the period 2000–2015 provided skilful forecasts for most catchments already in January, with adjusted R2 values of the best model in the range of 0.6–0.8 for most of the catchments. The skill of the prediction increased every following month, i.e. with reduced lead time, with adjusted R2 values usually in the range 0.8–0.9 for the best and 0.7–0.8 on average for the set of models in April just before the prediction period. The later forecasts in May and June improve further due to the high predictive power of the discharge in the first 2 months of the snow melt period. The improved skill of the set of forecast models with decreasing lead time resulted in narrow predictive uncertainty bands at the beginning of the snow melt period. In summary, the proposed generic automatic forecast model development tool provides robust predictions for seasonal water availability in Central Asia, which will be tested against the official forecasts in the upcoming years, with the vision of operational implementation.

General equilibrium trade modelling with Canada–US transportation costs

ABSTRACTTransportation costs are an important topic in international trade, but seldom have researchers paid attention to general equilibrium trade modelling with transportation costs and explored their relevant effects. This paper uses numerical general equilibrium trade model structures to simulate the impacts of transportation costs on welfare and trade for a Canada–US country pair case. We compare two groups of model structures: Armington assumption models and homogeneous goods models. Within these two groups of models, we also compare balanced trade structures to trade imbalance structures and production function transportation costs to iceberg transportation costs. Armington goods models generate more absolute welfare gains from transportation cost elimination than homogeneous goods models. Welfare gains under balanced trade structures are larger in production function transportation cost scenarios than in iceberg transportation cost scenarios, but under trade imbalance structures, welfare gains are greater under iceberg transportation cost scenarios. Canada's welfare gains in the iceberg transportation cost scenario are significantly larger than gains in the production function transportation cost scenario. On trade effects, homogeneous goods models generate more export and import gains, balanced trade structures have more trade variations, and iceberg transportation costs generate more trade effects.

Vibration Decoupled Modeling and Robust Control of Redundant Cable-Driven Parallel Robots

Trajectory tracking and vibration control of redundant cable-driven parallel robots (CDPR) attracted the attention of different researchers; however, the effects of the CDPRs end-effectors disturbing force/moment on the undesired vibrations and minimization of such effects has received few attention. A new general dynamic modeling and robust control structure for redundant CDPR is proposed in this paper, which evaluates such factors and tries to minimize their effects on the performance of CDPRs. Considering the effects of redundant cables on the CDPRs’ stiffness improvement, we neglect the second and higher order terms of motion errors that helps to separate the moving platform's undesired vibration equation from its desired (nominal) equation of motion. The obtained vibration model forms a linear parametric variable (LPV) dynamic system that lets us investigate the effects of external disturbances on the undesired vibration in different directions, and use a wide class of well-established robust and optimal vibration control techniques. Consequently, in order to minimize the effect of disturbances on the moving platform trajectory tracking performance, a robust and optimal vibration compensator is designed using the $LPV-H_\infty$ control design techniques. Based on the platform's desired trajectory, the proposed control scheme calculates the cables’ nominal tension required for trajectory tracking and further generates the auxiliary signals to augment these tensions to counteract the undesirable vibrations. Based on the obtained dynamic model, the developed approach is simplified for kinematically-constrained redundant CDPRs as a new actuation method with restricted rotational degrees of freedom. Via numerical analysis and experimental tests of a planar kinematically-constrained CDPR, the proposed modeling and robust control design approach are examined and compared with other common control design methodologies. Moreover, the effectiveness of kinematically-constrained actuation method in vibration elimination and control design simplification of CDPRs is demonstrated.

In silico, Synthesis and Biological Investigations of Pyrrolo[3,4-C]Pyrrole Hydroxamic Acid Derivatives as Potential Anticancer Agents

Based in a general structural pharmacophore model of suberoylanilide hydroxamic acid (commercially known as Vorinostat©), we synthesized a series of new pyrrolo[3,4-c]pyrrole hydroxamic acid derivatives, 9a-c, to be tested as candidates for anti-cancer drugs. The evaluation of their possible biological activity was assessed in two ways: a) computational characterization from molecular calculations and quantum reactivity descriptors and b) biological assays. Molecular docking and density functional theory calculations were performed to assess the binding properties of our newly synthesized pyrrolo[3,4-c] pyrrole hydroxamic acid derivatives, employing as the biological target the histone deacetylase isoforms available in the protein data bank. Furthermore, to characterize the effect of changing the functional groups that we varied while designing our drug model, and to improve the assessment of the binding energy, conceptual density functional theory reactivity descriptors were calculated to rationalize the capability of the new drugs to interact with the histones active site. Our findings show that the newly synthesized derivative, 9c, display the best energetic coupling with the biological target and the more favorable values of the density functional theory descriptors to interact with the active site. The biological assay of the anti-cancer drug candidates was done using three different techniques: i) anti-proliferative activity on two breast cancer cell lines; ii) Histone H3 acetylation; and iii) DNA damage. Docking studies were performed on histone deacetylase enzymes. The biological function of these enzymes is the deacetylation of histones. We analyze the level of histone acetylation in two cell lines. The computational findings are in good agreement with the biological evaluation. Our main contribution is that one of our newly synthesized derivatives, 9c, performs better than the commercial reference suberoylanilide hydroxamic acid.

Five Factor Model personality disorder scales: An introduction to a special section on assessment of maladaptive variants of the five factor model.

The Five-Factor Model (FFM) is a dimensional model of general personality structure, consisting of the domains of neuroticism (or emotional instability), extraversion versus introversion, openness (or unconventionality), agreeableness versus antagonism, and conscientiousness (or constraint). The FFM is arguably the most commonly researched dimensional model of general personality structure. However, a notable limitation of existing measures of the FFM has been a lack of coverage of its maladaptive variants. A series of self-report inventories has been developed to assess for the maladaptive personality traits that define Diagnostic and Statistical Manual of Mental Disorders (fifth edition; DSM-5) Section II personality disorders (American Psychiatric Association [APA], 2013) from the perspective of the FFM. In this paper, we provide an introduction to this Special Section, presenting the rationale and empirical support for these measures and placing them in the historical context of the recent revision to the APA diagnostic manual. This introduction is followed by 5 papers that provide further empirical support for these measures and address current issues within the personality assessment literature. (PsycINFO Database Record

THE RELATIONSHIP AMONG STRATEGIC ORIENTATIONS, ORGANIZATIONAL INNOVATIVENESS, AND BUSINESS PERFORMANCE

Today’s complex and competitive business environment restricts the managers to plan their futures strategically. Thus, strategic orientation approach is taking the place of traditional approach to strategic management by spreading strategic thinking to the employees like a corporate culture. The aims of this study are to explore the mutual relationships among market orientation, technology orientation, and organizational innovativeness, and to examine the joint effects of those key drivers on firm performance, and to advance the understanding of the role of the strategic orientations of the firms. Due to the general structure of the research model, a questionnaire survey on 161 manufacturing firms has been concluded. According to analysed data a strong relationship between strategic orientations and the firm performance is indicated. Foremost, the results show that product innovation can significantly assist a competitor-oriented firm in improving its financial performance, while a technology-oriented firm improving its growth and market performance.

Effect of Time-Dependent Selective Serotonin Reuptake Inhibitor Antidepressants During Pregnancy on Behavioral, Emotional, and Social Development in Preschool-Aged Children

ObjectiveTo evaluate the effect of prenatal exposure to selective serotonin reuptake inhibitors (SSRIs) on children’s behavioral, emotional, and social development by age 5 years, and over time since age 1.5 years.MethodThe prospective Norwegian Mother and Child Cohort Study was linked to the Medical Birth Registry of Norway. We included women who reported depressive/anxiety disorders before and/or during pregnancy. Children born to women who used SSRIs in early (weeks 0−16), mid- (weeks 17−28), or late (> week 29) pregnancy were compared to those who were unexposed. Children’s internalizing and externalizing behaviors (Child Behavior Checklist) and temperament traits (Emotionality, Activity and Shyness Temperament Questionnaire) were measured at 1.5, 3, and 5 years. Mean scores were calculated and standardized. General linear marginal structural models were fitted to account for time-varying exposure and confounders, and censoring; 3-level growth-curve models were used.ResultsA total of 8,359 mother–child dyads were included, and 4,128 children had complete outcome data at age 5 years. Children exposed to SSRIs in late pregnancy had an increased risk of anxious/depressed behaviors by age 5 years compared with unexposed children (adjusted β = 0.50, 95% CI = 0.04, 0.96). Such risk was not evident for earlier timings of exposure. There was no evidence for a substantial prenatal SSRI effect on externalizing, social, and emotional problems.ConclusionThese findings suggest no substantial increased risk for externalizing, emotional, or social problems in preschool-aged children following prenatal SSRI exposure. Although the role of chance and potential unmeasured confounding cannot be ruled out, late-pregnancy SSRI exposure was associated with greater anxious/depressed behaviors in the offspring.