CFD Simulations on the Heating Capability in a Human Nasal Cavity

The air conditioning capability of the nose is dependent on the nasal mucosal temperature and the airflow dynamics caused by the airway geometry. A computational model of a human nasal cavity obtained through CT scans was produced and CFD techniques were applied to study the effects of morphological differences in the left and right nasal cavity on the airflow and heat transfer of inhaled air. A laminar steady flow of 10L/min was applied and two inhalation conditions were investigated: normal conditions, 25°C, 35% relative humidity and cold dry air conditions, 12°C, 13% relative humidity. It was found that the frontal regions of the nasal cavity exhibited greater secondary cross flows compared to the middle and back regions. The left cavity in the front region had a smaller cross-sectional area compared to the right which allowed greater heating as the heat source from the wall was closer to the bulk flow regions. Additionally it was found that the residence time of the inhaled air was important for the heating ability in laminar flows

Molecular Motor of Double-Walled Carbon Nanotube Driven by Temperature Variation

An elegant formula for coordinates of carbon atoms in a unit cell of a single-walled nanotube (SWNT) is presented and a new molecular motor of double-walled carbon nanotube whose inner tube is a long (8,4) SWNT and outer tube a short (14,8) SWNT is constructed. The interaction between inner an outer tubes is analytically derived by summing the Lennard-Jones potentials between atoms in inner and outer tubes. It is proved that the molecular motor in a thermal bath exhibits a directional motion with the temperature variation of the bath.Comment: 9 pages, 4 figures, revtex

Thermoelectric efficiency at maximum power in a quantum dot

We identify the operational conditions for maximum power of a nanothermoelectric engine consisting of a single quantum level embedded between two leads at different temperatures and chemical potentials. The corresponding thermodynamic efficiency agrees with the Curzon-Ahlborn expression up to quadratic terms in the gradients, supporting the thesis of universality beyond linear response.Comment: 4 pages, 3 figure

The struggle of a good friend getting old:cellular senescence in viral responses and therapy

Cellular senescence is a state of stable cell cycle arrest associated with macromolecular alterations and secretion of pro-inflammatory cytokines and molecules. Senescence-associated phenotypes restrict damage propagation and activate immune responses, two essential processes involved in response to viral infections. However, excessive accumulation and persistence of senescent cells can become detrimental and promote pathology and dysfunctions. Various pharmacological interventions, including antiviral therapies, lead to aberrant and premature senescence. Here, we review the molecular mechanisms by which viral infections and antiviral therapy induce senescence. We highlight the importance of these processes in attenuating viral dissemination and damage propagation, but also how prematurely induced senescent cells can promote detrimental adverse effects in humans. We describe which sequelae due to viral infections and treatment can be partly due to excessive and aberrant senescence. Finally, we propose that pharmacological strategies which eliminate senescent cells or suppress their secretory phenotype could mitigate side effects and alleviate the onset of additional morbidities. These strategies can become extremely beneficial in patients recovering from viral infections or undergoing antiviral therapy

Classical Poisson structures and r-matrices from constrained flows

We construct the classical Poisson structure and $r$-matrix for some finite dimensional integrable Hamiltonian systems obtained by constraining the flows of soliton equations in a certain way. This approach allows one to produce new kinds of classical, dynamical Yang-Baxter structures. To illustrate the method we present the $r$-matrices associated with the constrained flows of the Kaup-Newell, KdV, AKNS, WKI and TG hierarchies, all generated by a 2-dimensional eigenvalue problem. Some of the obtained $r$-matrices depend only on the spectral parameters, but others depend also on the dynamical variables. For consistency they have to obey a classical Yang-Baxter-type equation, possibly with dynamical extra terms.Comment: 16 pages in LaTe

Coefficient of performance at maximum figure of merit and its bounds for low-dissipation Carnot-like refrigerators

The figure of merit for refrigerators performing finite-time Carnot-like cycles between two reservoirs at temperature $T_h$ and $T_c$ ($<T_h$) is optimized. It is found that the coefficient of performance at maximum figure of merit is bounded between 0 and $(\sqrt{9+8\varepsilon_c}-3)/2$ for the low-dissipation refrigerators, where $\varepsilon_c =T_c/(T_h-T_c)$ is the Carnot coefficient of performance for reversible refrigerators. These bounds can be reached for extremely asymmetric low-dissipation cases when the ratio between the dissipation constants of the processes in contact with the cold and hot reservoirs approaches to zero or infinity, respectively. The observed coefficients of performance for real refrigerators are located in the region between the lower and upper bounds, which is in good agreement with our theoretical estimation.Comment: 5 journal pages, 3 figure

The value of what’s to come: Neural mechanisms coupling prediction error and the utility of anticipation

Having something to look forward to is a keystone of well-being. Anticipation of future reward, such as an upcoming vacation, can often be more gratifying than the experience itself. Theories suggest the utility of anticipation underpins various behaviors, ranging from beneficial information-seeking to harmful addiction. However, how neural systems compute anticipatory utility remains unclear. We analyzed the brain activity of human participants as they performed a task involving choosing whether to receive information predictive of future pleasant outcomes. Using a computational model, we show three brain regions orchestrate anticipatory utility. Specifically, ventromedial prefrontal cortex tracks the value of anticipatory utility, dopaminergic midbrain correlates with information that enhances anticipation, while sustained hippocampal activity mediates a functional coupling between these regions. Our findings suggest a previously unidentified neural underpinning for anticipation’s influence over decision-making and unify a range of phenomena associated with risk and time-delay preference

qq-analogue of modified KP hierarchy and its quasi-classical limit

A $q$-analogue of the tau function of the modified KP hierarchy is defined by a change of independent variables. This tau function satisfies a system of bilinear $q$-difference equations. These bilinear equations are translated to the language of wave functions, which turn out to satisfy a system of linear $q$-difference equations. These linear $q$-difference equations are used to formulate the Lax formalism and the description of quasi-classical limit. These results can be generalized to a $q$-analogue of the Toda hierarchy. The results on the $q$-analogue of the Toda hierarchy might have an application to the random partition calculus in gauge theories and topological strings.Comment: latex2e, a4 paper 15 pages, no figure; (v2) a few references are adde

Efficiency at maximum power of minimally nonlinear irreversible heat engines

We propose the minimally nonlinear irreversible heat engine as a new general theoretical model to study the efficiency at the maximum power $\eta^*$ of heat engines operating between the hot heat reservoir at the temperature $T_h$ and the cold one at $T_c$ ($T_c \le T_h$). Our model is based on the extended Onsager relations with a new nonlinear term meaning the power dissipation. In this model, we show that $\eta^*$ is bounded from the upper side by a function of the Carnot efficiency $\eta_C\equiv 1-T_c/T_h$ as $\eta^*\le \eta_C/(2-\eta_C)$. We demonstrate the validity of our theory by showing that the low-dissipation Carnot engine can easily be described by our theory.Comment: 6 pages, 1 figur

Optical Flow Estimation in the Deep Learning Age

Akin to many subareas of computer vision, the recent advances in deep learning have also significantly influenced the literature on optical flow. Previously, the literature had been dominated by classical energy-based models, which formulate optical flow estimation as an energy minimization problem. However, as the practical benefits of Convolutional Neural Networks (CNNs) over conventional methods have become apparent in numerous areas of computer vision and beyond, they have also seen increased adoption in the context of motion estimation to the point where the current state of the art in terms of accuracy is set by CNN approaches. We first review this transition as well as the developments from early work to the current state of CNNs for optical flow estimation. Alongside, we discuss some of their technical details and compare them to recapitulate which technical contribution led to the most significant accuracy improvements. Then we provide an overview of the various optical flow approaches introduced in the deep learning age, including those based on alternative learning paradigms (e.g., unsupervised and semi-supervised methods) as well as the extension to the multi-frame case, which is able to yield further accuracy improvements.Comment: To appear as a book chapter in Modelling Human Motion, N. Noceti, A. Sciutti and F. Rea, Eds., Springer, 202