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In this paper, our focus is on ABA trilayer graphene nanoribbon (TGN), in which the middle layer is horizontally shifted from the top and bottom layers. The conductance model of TGN as a FET channel is presented based on Landauer formula. Besides the good reported agreement with experimental study lending support to our model, the presented model demonstrates that minimum conductivity increases dramatically by temperature. It also draws parallels between TGN and bilayer graphene nanoribbon, in which similar thermal behavior is observed. Maxwell–Boltzmann approximation is employed to form the conductance of TGN near the neutrality point. Analytical model in degenerate regime in comparison with reported data proves that TGN-based transistor will operate in degenerate regime like what we expect in conventional semiconductors. Moreover, our model confirms that in similar condition, the conductivity of TGN is less than bilayer graphene nanoribbon as reported in some experiments.

A new model for efficiency droop in InGaN/GaN light-emitting diodes (LEDs) is proposed, where the primary nonradiative recombination mechanisms, including Shockley–Read–Hall (SRH), Auger and carrier leakage, are considered. A room-temperature external quantum efficiency (EQE) measurement was performed on our designed samples and analyzed by the new model. Owing to advantages over the common “$ABC+f(n)$ model”, the “new model” is able to effectively extract recombination coefficients and calculate the leakage currents of the hole and electron. From this new model, we also found that hole leakage is distinct at low injection, while it disappears at high injection, which is contributed to the weak blocking effect of electron in quantum wells (QWs) at low injection.

An extended lattice hydrodynamic model with time delay is proposed under non-lane discipline. We try to grasp the impacts of the non-lane discipline of the considered lattice sites. Linear stability analysis of the proposed model is executed and the stability criterion is obtained. Using the reductive perturbation method, we investigate nonlinear analysis of the proposed model and derive the mKdV equation and its solution, which could reveal the propagation of density waves. We analyze the effect of time delay, the ratio of lane deviation and the control coefficient on the stability of traffic flow via numerical experiments. We find that those indices play an important role in the stability of traffic flow. The longer the time delay, the more unstable the system becomes. Also, the ratio of lane deviation and the control coefficient is able to more quickly dissipate the traffic congestions occurring in traffic flow.

Nonlinear dynamical models are frequently used to approximate and predict observed physical, biological and economic systems. Such models will be subject to errors both in the model dynamics, and the observations of the underlying system. In order to improve models, it is necessary to understand the causes of error growth. A complication with chaotic models is that small errors may be amplified by the model dynamics. This paper proposes a technique for estimating levels of both dynamical and observational noise, based on the model drift. The method is demonstrated for a number of models, for cases with both stochastic and nonstochastic dynamical errors. The effect of smoothing or treating the observations is also considered. It is shown that use of variational smoothing techniques in the presence of dynamical model errors can lead to potentially deceptive patterns of error growth.

The analysis of delay dynamics (DD) is the basic big picture in networked control systems (NCS) research since the knowledge of its behavior may improve the design of more robust controllers, and consequently, the system performance. However, the extreme complexity of modern communications and networks, coupled with their traffic characteristics, makes the characterization of their performance through analytical models a difficult task. Relying on fractional calculus (FC), this paper studies the dynamics of IP delays and attempts to clarify the most important features of network traffic, providing the reader some connections between traffic in communication networks and FC. Likewise, a fractional order model of DD is presented based on a survey of current network traffic models. Some simulations are given to validate the proposed model.

We study the kinetics of specific adhesion of giant vesicles on complemetary (bilayer-decorated) solid surfaces. Tensed vesicles exhibit a single adhesion zone that grows slowly. Floppy vesicles adhere via many small adhesion spots that grow and fuse, merging finally into a large adhesive zone. Using approaches derived from Ref. 12, we show how the progressive mobilization of adhesive molecules (diffusing toward the patch) can explain our experimental observations.

Androgen deprivation therapy is a common treatment for advanced or metastatic prostate cancer. Like the normal prostate, most tumors depend on androgens for proliferation and survival but often develop treatment resistance. Hormonal treatment causes many undesirable side effects which significantly decrease the quality of life for patients. Intermittently applying androgen deprivation in cycles reduces the total duration with these negative effects and may reduce selective pressure for resistance. We extend an existing model which used measurements of patient testosterone levels to accurately fit measured serum prostate specific antigen (PSA) levels. We test the model's predictive accuracy, using only a subset of the data to find parameter values. The results are compared with those of an existing piecewise linear model which does not use testosterone as an input. Since actual treatment protocol is to re-apply therapy when PSA levels recover beyond some threshold value, we develop a second method for predicting the PSA levels. Based on a small set of data from seven patients, our results showed that the piecewise linear model produced slightly more accurate results while the two predictive methods are comparable. This suggests that a simpler model may be more beneficial for a predictive use compared to a more biologically insightful model, although further research is needed in this field prior to implementing mathematical models as a predictive method in a clinical setting. Nevertheless, both models are an important step in this direction.

A model to describe the mechanism of conformational dynamics in secondary protein based on matter interactions is proposed. The approach deploys the lagrangian method by imposing certain symmetry breaking. The protein backbone is initially assumed to be nonlinear and represented by the Sine-Gordon equation, while the nonlinear external bosonic sources is represented by ϕ⁴ interaction. It is argued that the nonlinear source induces the folding pathway in a different way than the previous work with initially linear backbone. Also, the nonlinearity of protein backbone decreases the folding speed.

We study the behavior of the number of votes cast for different electoral subjects in majority elections, and in particular, the Albanian elections of the last 10 years, as well as the British, Russian, and Canadian elections. We report the frequency of obtaining a certain percentage (fraction) of votes versus this fraction for the parliamentary elections. In the distribution of votes cast in majority elections we identify two regimes. In the low percentiles we see a power law distribution, with exponent about -1.7. In the power law regime we find over 80% of the data points, while they relate to 20% of the votes cast. Votes of the small electoral subjects are found in this regime. The other regime includes percentiles above 20%, and has Gaussian distribution. It corresponds to large electoral subjects. A similar pattern is observed in other first past the post (FPP) elections, such as British and Canadian, but here the Gaussian is reduced to an exponential. Finally we show that this distribution can not be reproduced by a modified "word of mouth" model of opinion formation. This behavior can be reproduced by a model that comprises different number of zealots, as well as different campaign strengths for different electoral subjects, in presence of preferential attachment of voters to candidates.

A new approach to model the biomatter dynamics based on the field theory is presented. It is shown that some well known tools in field theory can be utilized to describe the physical phenomena in life matters, in particular at elementary biomatters like DNA and proteins. In this approach, the biomatter dynamics are represented as results of interactions among its elementary matters in the form of lagrangian. Starting from the lagrangian would provide stronger underlying theoretical consideration for further extension. Moreover, it also enables us to acquire rich physical observables using statistical mechanics instead of relying on the space-time dynamics from certain equation of motions which is not solvable due to its nonlinearities. Few examples from previous results are given and explained briefly.