In this work, we use a mathematical model of the property listing task dynamics and test its ability to predict processing time in semantic and lexical decision tasks. The study aims at exploring the temporal dynamics of semantic access in these tasks and showing that the mathematical model captures essential aspects of semantic access, beyond the original task for which it was developed. In two studies using the semantic and lexical decision tasks, we used the mathematical model’s coefficients to predict reaction times. Results showed that the model was able to predict processing time in both tasks, accounting for an independent portion of the total variance, relative to variance predicted by traditional psycholinguistic variables (i.e., frequency, familiarity, concreteness imageability). Overall, this study provides evidence of the mathematical model’s validity and generality, and offers insights regarding the characterization of concrete and abstract words.

A mathematical model is proposed for a revolute joint mechanism with an n-degree of freedom (DOF). The matrix approach is used for finding the relation between two consecutive links to determine desired link parameters such as position, velocity and acceleration using the forward kinematic approach. The matrix approach was confirmed for a proposed 10 DOF revolute type (R-type) human upper limb model with servo motors at each joint. Two DOFs are considered each at shoulder, elbow and wrist joint, followed by four DOF for the fingers. Two DOFs were considered for metacarpophalangeal (mcp) and one DOF each for proximal interphalangeal (pip) and distal interphalangeal (dip) joints. MATLAB script function was used to evaluate the mathematical model for determining kinematic parameters for all the proposed human upper limb model joints. The simplified method for kinematic analysis proposed in this paper will further simplify the dynamic modeling of any mechanism for determining joint torques and hence, easy to design control system for joint movements.

Event data provide high-resolution and high-volume information about political events and have supported a variety of research efforts across fields within and beyond political science. While these datasets are machine coded from vast amounts of raw text input, the necessary dictionaries require substantial prior knowledge and human effort to produce and update, effectively limiting the application of automated event-coding solutions to those domains for which dictionaries already exist. I introduce a novel method for generating dictionaries appropriate for event coding given only a small sample dictionary. This technique leverages recent advances in natural language processing and machine learning to reduce the prior knowledge and researcher-hours required to go from defining a new domain-of-interest to producing structured event data that describe that domain. I evaluate the method with the production of a novel event dataset on cybersecurity incidents.

In the previous works (Rozanov et al., 2013; 2015) we have performed one-dimensional (1D) numerical simulations of the target compression and burning at the absorbed energy of ~1.5 MJ. As a result, the target was chosen to have a low initial aspect ratio in order to be less sensitive to the influence of such parameters as laser pulse duration, total laser energy, and equations of state model. The simulation results demonstrated a higher probability of ignition and effective burning of such a system. In the present work we discuss the impact of irradiation asymmetry on this baseline target implosion. The details of the 1D compression and a possible influence of 2D and 3D effects due to the hydrodynamic instability and mixing have been described. In accordance with the 2D calculations the target is still ignited, however, the symmetry analysis of 3D ones gives reasons to further reduce the efficiency of conversion of kinetic energy into potential energy.

In this work we propose a method for analysis of postsurgical haemodynamics after femoralartery treatment of occlusive vascular disease. Patient specific reconstruction algorithmof 1D core network based on MRI data is proposed as a tool for such analysis. Along withpresurgical ultrasound data fitting it provides effective personalizing predictive methodthat is validated with clinical observations.

In this paper we develop and study numerically a model to describe some aspects of soundpropagation in the human lung, considered as a deformable and viscoelastic porous medium(the parenchyma) with millions of alveoli filled with air. Transmission of sound throughthe lung above 1 kHz is known to be highly frequency-dependent. We pursue the key ideathat the viscoelastic parenchyma structure is highly heterogeneous on the small scaleε and use two-scale homogenization techniques to derive effectiveacoustic equations for asymptotically small ε. This process turns out tointroduce new memory effects. The effective material parameters are determined from thesolution of frequency-dependent micro-structure cell problems. We propose a numericalapproach to investigate the sound propagation in the homogenized parenchyma using aDiscontinuous Galerkin formulation. Numerical examples are presented.

Objectives: Establishing containment measures against the potential spread of the smallpox virus has become a major issue in the public health field since the 2001 anthrax attacks in the United States. The primary objective of the study was to investigate the relationship between the level of activity of public health agencies and the voluntary cooperation of residents with ring-vaccination measures against a smallpox epidemic.

Methods: A discrete-time, stochastic, individual-based model was used to simulate the spread of a smallpox epidemic that has become a more pressing topic due to 9/11 and to assess the effectiveness of and required resources for ring-vaccination measures in a closed community. In the simulation, we related sensitive tracing to the level of activity of the public health agency and strict isolation to the level of voluntary cooperation from residents.

Results: Our results suggest that early and intensive case detection and contact tracing by public health agencies can reduce the scale of an epidemic and use fewer total resources. In contrast, voluntary reporting by the traced contacts of symptom onset after vaccination had little impact on the scale of epidemic in our model. However, it reduced the total required resources, indicating that citizens' voluntary cooperation would contribute to reducing the burden on public health agencies.

Conclusions: We conclude that a combined effort on the part of public health agencies and residents in performing containment measures is essential to quickly ending a smallpox epidemic.

(Disaster Med Public Health Preparedness. 2012;6:270–276)

In this paper, a non-linear mathematical model is proposed and analysed to study the role of technology in combating social crimes in a dynamic population by considering immigration and emigration rates of susceptible population and criminals. The problem is modelled by considering five interacting variables, namely the density of susceptible population, the density of criminals, the density of removed (isolated) criminals, the density of crime burden and the level of technology used to control crime. The proposed model is analysed by using the stability theory of differential equation and simulation. The model analysis shows that the crime burden decreases considerably as the level of technology increases. It is noted that the crime in a society can be controlled almost completely if criminals from the general population are removed by intensive use of technology.

Myasthenia gravis (MG) is an autoimmune disorder in which patients experience muscular fatigability due to the presence of anti-acetylcholine receptor (AChR) antibodies which inhibit signal transduction across the neuro-muscular junction. Like all complex disorders, disease is caused by an interaction between genetic and environmental factors. Although several genes have been identified which appear to be associated with MG, both classic twin studies and current multi-gene models are insufficient to explain either disease pathogenesis or inheritance. We examined the literature on MG to determine both mono- and dizygotic twin concordance rates, and used this data to (1) estimate the proportion of the population with underlying genetic predisposition to MG and the frequency of the environmental component and (2) derive the number of inherited genetic regions that are required to confer predisposition to MG. Using a MZ twin concordance rate of 35.5%, and a dizygotic rate of approximately 4–5% (based on family data), the probability of encountering environmental components necessary to develop MG in an individual with genetic predisposition is approximately 52.4%, making the frequency of predisposition (1:5240) roughly twice the rate of incidence. Furthermore, the number of genetic regions co-inherited between affected individuals is between two and four, which may be large haplotypes with interacting activity. Determining these haplotypes, by fully sequencing associated regions in cases and controls to identify mutations present, may therefore be a practically step toward the understanding of complex disease.

National policies regarding the BCG vaccine for tuberculosis vary greatly throughout the international community and several countries are currently considering discontinuing universal vaccination. Detractors of BCG point to its uncertain effectiveness and its interference with the detection and treatment of latent tuberculosis infection (LTBI).

In order to quantify the trade-off between vaccination and treatment of LTBI, a mathematical model was designed and calibrated to data from Brazil, Ghana, Germany, India, Mexico, Romania, the United Kingdom and the United States. Country-specific thresholds for when LTBI treatment outperforms mass vaccination were found and the consequences of policy changes were estimated.

Our results suggest that vaccination outperforms LTBI treatment in all settings but with greatly reduced efficiency in low incidence countries. While national policy statements emphasize BCG’s interference with LTBI detection, we find that reinfection should be more determinant of a country’s proper policy choice.

Excessive sedimentation of fine particles on stream beds has been recognized as a major threat to running-water ecosystems. Deposition of fine sediments often affects hyporheic zone (HZ) functioning by (1) reducing hydrological exchanges at the water–sediment interface and by (2) increasing the organic matter (OM) content of surface sediments. These two factors usually occur concurrently to control biogeochemical processes in sediments. In the present study, experimental and modelling approaches were coupled to evaluate the contribution of these factors on the biogeochemical functioning of the HZ. We used a one-dimensional (1D) vertical model taking into account the hydrodynamic properties, the vertical distribution of the OM and the main microbial processes involved in OM processing (aerobic respiration, denitrification, nitrification and sulphate reduction). This Mobile-Immobile Model for Organic Matter (MIM-OM) model was calibrated and validated using experimental data (conservative tracer, dissolved oxygen and nitrate concentrations) obtained in filtration columns filled with a porous sedimentary matrix. Simulations showed that organic carbon content and Darcy velocity acted in concert to shape biogeochemical processes in stream sediments. The use of the MIM-OM model on data obtained in filtration columns impacted by fine sediment deposition indicated that the biodegradability of the OM (modified through the degradation parameter kPOC) also played a key role on biogeochemical processes occurring in sediments. In conclusion, the MIM-OM model appears as an efficient simulation tool to evaluate biogeochemical functioning in river sediments under different conditions (granulometry, quality of surface water and clogging).

The immune system is able to protect the host from tumor onset, and immune deficienciesare accompanied by an increased risk of cancer. Immunology is one of the fields in biologywhere the role of computational and mathematical modeling and analysis were recognized theearliest, beginning from 60s of the last century. We introduce the two most common methodsin simulating the competition among the immune system, cancers and tumor immunologystrategies: differential equations and rule-based models. Several specific implementationsare presented, describing in details how they work and how they advance or contribute thefield of tumor immunology.

There are a number of interesting applications where modeling elastic and/or viscoelastic materials is fundamental, including uses in civil engineering, the food industry, land mine detection and ultrasonic imaging. Here we provide an overview of the subject for both elastic and viscoelastic materials in order to understand the behavior of these materials. We begin with a brief introduction of some basic terminology and relationships in continuum mechanics, and a review of equations of motion in a continuum in both Lagrangian and Eulerian forms. To complete the set of equations, we then proceed to present and discuss a number of specific forms for the constitutive relationships between stress and strain proposed in the literature for both elastic and viscoelastic materials. In addition, we discuss some applications for these constitutive equations. Finally, we give a computational example describing the motion of soil experiencing dynamic loading by incorporating a specific form of constitutive equation into the equation of motion.

Systems biology is a rapidly expanding field of research and is applied in a number of biological disciplines. In animal sciences, omics approaches are increasingly used, yielding vast amounts of data, but systems biology approaches to extract understanding from these data of biological processes and animal traits are not yet frequently used. This paper aims to explain what systems biology is and which areas of animal sciences could benefit from systems biology approaches. Systems biology aims to understand whole biological systems working as a unit, rather than investigating their individual components. Therefore, systems biology can be considered a holistic approach, as opposed to reductionism. The recently developed ‘omics’ technologies enable biological sciences to characterize the molecular components of life with ever increasing speed, yielding vast amounts of data. However, biological functions do not follow from the simple addition of the properties of system components, but rather arise from the dynamic interactions of these components. Systems biology combines statistics, bioinformatics and mathematical modeling to integrate and analyze large amounts of data in order to extract a better understanding of the biology from these huge data sets and to predict the behavior of biological systems. A ‘system’ approach and mathematical modeling in biological sciences are not new in itself, as they were used in biochemistry, physiology and genetics long before the name systems biology was coined. However, the present combination of mass biological data and of computational and modeling tools is unprecedented and truly represents a major paradigm shift in biology. Significant advances have been made using systems biology approaches, especially in the field of bacterial and eukaryotic cells and in human medicine. Similarly, progress is being made with ‘system approaches’ in animal sciences, providing exciting opportunities to predict and modulate animal traits.

Linear oscillators are used for modeling a diverse array of natural systems, for instance acoustics, materials science, and chemical spectroscopy. In this paper I describe simple models of structural interactions in biological molecules, known as elastic network models, as a useful topic for undergraduate biology instruction in mathematical modeling. These models use coupled linear oscillators to model the fluctuations of molecular structures around the equilibrium state. I present many learning activities associated with building and understanding these models, ranging from analytical to computational. I provide a number of web resources where students can obtain structural data, perform calculations, and suggest research directions for independent projects.

A growing body of literature testifies to the importance of quantitative reasoning skillsin the 21st-century biology curriculum, and to the learning benefits associated withactive pedagogies. The process of modeling a biological system provides an approach thatintegrates mathematical skills and higher-order thinking with existing course contentknowledge. We describe a general strategy for teaching model-building in an introductorybiology course, using the example of a model of an infectious disease outbreak.Preliminary assessment data suggest that working through the formal process of modelconstruction may help students develop their scientific reasoning and communicationskills.

Infectious diseases ranging from the common cold to cholera affect our society physically, emotionally, ecologically, and economically. Yet despite their importance and impact, there remains a lack of effective teaching materials for epidemiology and disease ecology in K-12, undergraduate, and graduate curricula [2]. To address this deficit, we’ve developed a classroom lesson with three instructional goals: (1) Familiarize students on basic concepts of infectious disease ecology; (2) Introduce students to a classic compartmental model and its applications in epidemiology; (3) Demonstrate the application and importance of mathematical modeling as a tool in biology. The instructional strategy uses a game-based mathematical manipulative designed to engage students in the concepts of infectious disease spread. It has the potential to be modified for target audiences ranging from Kindergarten to professional schools in science, public health, policy, medical, and veterinarian programs. In addition, we’ve provided variations of the activity to enhance the transfer of fundamental concepts covered in the initial lesson to more complex concepts associated with vaccination and waning immunity. While 10 variations are presented here, the true number of directions in which the game might extend will only be limited by the imagination of its students [6].

We present the fiber-spring elastic model of the arterial wall with atheroscleroticplaque composed of a lipid pool and a fibrous cap. This model allows us to reproducepressure to cross-sectional area relationship along the diseased vessel which is used inthe network model of global blood circulation. Atherosclerosis attacks a region ofsystemic arterial network. Our approach allows us to examine the impact of the diseasedregion onto global haemodynamics.