A simple mass-action model for the eukaryotic heat shock response and its mathematical validation

The heat shock response is a primordial defense mechanism against cell stress and protein misfolding. It proceeds with the minimum number of mechanisms that any regulatory network must include, a stress-induced activation and a feedback regulation, and can thus be regarded as the archetype for a cellular regulatory process. We propose here a simple mechanistic model for the eukaryotic heat shock response, including its mathematical validation. Based on numerical predictions of the model and on its sensitivity analysis, we minimize the model by identifying the reactions with marginal contribution to the heat shock response. As the heat shock response is a very basic and conserved regulatory network, our analysis of the network provides a useful foundation for modeling strategies of more complex cellular processes.     

Numerical study of shock propagation and attenuation in narrow tubes including friction and heat losses

Numerical simulations of shock propagation and attenuation in narrow tubes are carried out using a one-dimensional approach. The discretization of the convective terms is based on the fifth-order weighted essentially non-oscillatory interpolation. The influence of the dissipative processes such as momentum and heat losses is investigated. Viscosity as well as heat losses are found to play a key role in the attenuation of the shock speed as well as the shock intensity in the long-time evolution, demonstrating the transition from a hyperbolic behavior towards a diffusive regime. Specifically, when only strong heat exchanges are considered, numerical tests, corroborated by a simple asymptotic analysis, showed a transition from a hyperbolic adiabatic regime to an isothermal regime. Furthermore, the influence of the scaling parameter ReD/4L_s, through the variation of the tube diameter, D, the viscous length scale, L_s, and the Reynolds number on the shock propagation behavior is examined.     

基于神经网络的供热生产􀀂 过程优化控制的研究

针对我国热力行业生产现状,以供热生产的换热过程为研究对象,提出了应用软测量技术的径向基函数神经网络提取生产过程特征参数的方法和Fletcher＆Reeves共轭梯度法优化模型.为供热生产过程的最小参数模糊故障诊断和测控平台的实现提供了保证.     

Performance assessment of heat exchanger using intelligent decision making tools

Process and manufacturing industries today are under pressure to deliver high quality outputs at lowest cost. The need for industry is therefore to implement cost savings measures immediately, in order to remain competitive. Organizations are making strenuous efforts to conserve energy and explore alternatives. This paper explores the development of an intelligent system to identify the degradation of heat exchanger system and to improve the energy performance through online monitoring system. The various stages adopted to achieve energy performance assessment are through experimentation, design of experiments and online monitoring system. Experiments are conducted as per full factorial design of experiments and the results are used to develop artificial neural network models. The predictive models are used to predict the overall heat transfer coefficient of clean/design heat exchanger. Fouled/real system value is computed with online measured data. Overall heat transfer coefficient of clean/design system is compared with the fouled/real system and reported. It is found that neural net work model trained with particle swarm optimization technique performs better comparable to other developed neural network models. The developed model is used to assess the performance of heat exchanger with the real/fouled system. The performance degradation is expressed using fouling factor, which is derived from the overall heat transfer coefficient of design system and real system. It supports the system to improve the performance by asset utilization, energy efficient and cost reduction in terms of production loss. This proposed online energy performance system is implemented into the real system and the adoptability is validated.     

Numerical simulation of radially converging shock waves in a bubble cavity

This paper presents a numerical technique investigating the final stage of focusing a radially converging nonspherical shock wave in the neighborhood of center of the axially symmetric cavitation bubble subjected to strong compression. Hydrodynamic model used includes liquid compressibility, heat conductivity of vapor and liquid, as well as evaporation and condensation on the interphase surface; the realistic wide-range equations of state are used. The calculation is performed on moving grids with explicit accentuation of the bubble surface. This technique is based on the TVD modification of the Godunov second order accuracy scheme in space and time. Its efficiency is due to an allowance for the special features of the problem in the final stage of focusing of nonspherical shock wave in the central part of the bubble. After the value of deformation of the shock exceeds the threshold (i.e., when the shock wave becomes largely nonspherical) in the central field of the bubble the curvilinear radially diverging grid is changed by the rectilinear oblique-angled grid close to Cartesian. At the same moment, the spherical immovable system of the reference frame is changed to a cylindrical system. The recalculation of the cell parameters from grid to grid is made by the method of conservative interpolation. The efficiency of the proposed approach is shown by the test problem’s calculation results and an illustrative example.     

Visual simulation of heat shimmering and mirage

We provide a physically-based framework for simulating the natural phenomena related to heat interaction between objects and the surrounding air. We introduce a heat transfer model between the heat source objects and the ambient flow environment, which includes conduction, convection, and radiation. The heat distribution of the objects is represented by a novel temperature texture. We simulate the thermal flow dynamics that models the air flow interacting with the heat by a hybrid thermal lattice Boltzmann model (HTLBM). The computational approach couples a multiple-relaxation-time LBM (MRTLBM) with a finite difference discretization of a standard advection-diffusion equation for temperature. In heat shimmering and mirage, the changes in the index of refraction of the surrounding air are attributed to temperature variation. A nonlinear ray tracing method is used for rendering. Interactive performance is achieved by accelerating the computation of both the MRTLBM and the heat transfer, as well as the rendering on contemporary graphics hardware (GPU)     

Model based control of compact heat exchangers independent of the heat transfer behavior

Compact heat exchangers have a wide range of applications where standard control strategies typically rely on the knowledge of the heat transfer model and thus on the overall heat transfer coefficient. In particular for compact plate heat exchangers, the overall heat transfer coefficient strongly varies with the manufacturer's plate design and has to be identified by means of extensive measurements. This paper presents an alternative approach for the control of compact heat exchangers which can be implemented without the knowledge of the heat transfer behavior and is robust against changes in the coolant supply system. For this, a model based control strategy is presented which relies on the total thermal energy stored in the fluids of the heat exchanger as control variable instead of the outlet temperature. Furthermore, two methods are developed in order to estimate the total thermal energy, one based on a Kalman Filter and the other one on quasi-static considerations. Finally, the proposed control and estimation strategies are validated by means of simulation and measurement results on an industrial plate heat exchanger.     

Physics-based model of a stroke-dependent shock absorber

Multibody system simulation has become a mature analysis tool as part of industrial suspension design processes. However, when applied to a specific problem, it turns out that not kinematics or elasto-kinematics is the major issue, but force elements like shock absorbers. Classical linear or nonlinear characteristics are no longer applicable to modern stroke- or frequency-dependent shock absorbers, especially regarding transient, non-stationary analyses. Therefore, more emphasis has to be put on understanding and modeling this kind of elements. The paper will show a systematic modeling and identification process for a stroke-dependent, single tube shock absorber starting from basic physics laws for gas dynamics and hydraulics. Valve characteristics being the determining element of damper performance are modeled by rather flexible Hermite splines which are identified on a test rig in a two-step procedure. Simulations of VDA (Association of German Automotive Industry) tests and randomly excited vibrations of the identified damper model show high agreement with experimental results, and thus the high potential of the proposed modeling approach.     

Commitment and dispatch of heat and power units via affinely adjustable robust optimization

The joint management of heat and power systems is believed to be key to the integration of renewables into energy systems with a large penetration of district heating. Determining the day-ahead unit commitment and production schedules for these systems is an optimization problem subject to uncertainty stemming from the unpredictability of demand and prices for heat and electricity. Furthermore, owing to the dynamic features of production and heat storage units as well as to the length and granularity of the optimization horizon (e.g., one whole day with hourly resolution), this problem is in essence a multi-stage one. We propose a formulation based on robust optimization where recourse decisions are approximated as linear or piecewise-linear functions of the uncertain parameters. This approach allows for a rigorous modeling of the uncertainty in multi-stage decision-making without compromising computational tractability. We perform an extensive numerical study based on data from the Copenhagen area in Denmark, which highlights important features of the proposed model. Firstly, we illustrate commitment and dispatch choices that increase conservativeness in the robust optimization approach. Secondly, we appraise the gain obtained by switching from linear to piecewise-linear decision rules within robust optimization. Furthermore, we give directions for selecting the parameters defining the uncertainty set (size, budget) and assess the resulting trade-off between average profit and conservativeness of the solution. Finally, we perform a thorough comparison with competing models based on deterministic optimization and stochastic programming.     

Geospatial relation captioning for high-spatial-resolution images by using an attention-based neural network

High-spatial-resolution (HSR) remote sensing images serve as carriers of geographic information. Exploring geo-objects and their geospatial relations is fundamental in understanding HSR remote sensing images. To this end, this study proposes an intelligent semantic understanding method for HSR remote sensing images via geospatial relation captions. Firstly, we propose a method of geospatial relation expression to convey the topological, directional and distance relations of geo-objects in HSR images. Secondly, on the basis of images and their geospatial relation captions, an image dataset is constructed for model training. Finally, geospatial relation captioning is implemented for HSR images by using an attention-based deep neural network model. Experimental results demonstrate that the proposed captioning method can effectively provide geospatial semantics for HSR image understanding.     

Online-review analysis based large-scale group decision-making for determining passenger demands and evaluating passenger satisfaction: Case study of high-speed rail system in China

High-speed rail (HSR) has become an essential mode of public transportation in China and is likely to remain so for the foreseeable future. To promote the development of the HSR industry, a high level of passenger satisfaction must be ensured, which means that passenger satisfaction must be assured. Focusing on HSR in-cabin factors that affect the travel experience of HSR passengers, this study aims to determine passenger demands (PDs) and to evaluate passenger satisfaction by using a combination of online review analysis and large-scale group decision-making (LSGDM). By using web crawler technology, online reviews related to HSR were harvested from a microblogging platform to extract PD data and information. The six PDs that reflect the most frequent concerns of passengers were identified by analyzing the online reviews. The level of satisfaction of passengers with respect to these PDs was analyzed based on the online responses from 100 HSR passengers and by adopting the interval-valued two-tuple linguistic representation model. The final degrees of satisfaction and rankings of the PDs were then determined by using the LSGDM approach with the k-means clustering method and a consensus-reaching process. This research thus constructs an index system of HSR passenger satisfaction evaluation based on online-review analysis and evaluates the process by using LSGDM approaches. The conclusions provide insights into the improvements desired by HSR passengers for in-cabin services and to improve passenger satisfaction.     

Receding horizon boundary control of nonlinear conservation laws with shock avoidance

This paper deals with the boundary control for scalar non-linear hyperbolic systems of conservation laws. One of the issues arising from these systems is the occurrence of singularities (called shocks) in the domain. We will show that to avoid the shock, a set of constraints on both the control and the state at the boundary has to be fulfilled. Then, a proof of the exponential stability of the system is established provided that there is no shock and that the state at the boundary is exponentially stable. These conditions are shown to be achieved by the Receding Horizon Optimal Control approach. A simulation is finally carried out with the freeway traffic model to demonstrate the effectiveness of the here-proposed control.     

Optimal setpoint control of complex heat exchanger systems

This paper presents an approach for calculating the best way of distributing the streams following through a certain class of complex heat exchanger systems in order to achieve maximum heat recovery within the system. A computer code has been developed by which the described method is demonstrated, off-line, for two real cases. This program can be readily integrated into an over-all, on-line computer control system for any complex process consisting of an exchanger system of this class. Using an accurate and detailed heat exchanger model, the exit temperatures of each exchanger are calculated by a simple mathematical procedure based on Gilmour's design method. This procedure has been included in a general model for the complete scheme of the system. The scheme is made up of a series of heat exchanger groups with parallel paths in each group. The optimal distribution of the streams within a group is found by the direct search method of Hooke and Jeeves, modified to include constraints; while the overall optimization of the system is achieved via dynamic programming.     

热集成精馏序列综合的MINLP模型

将夹点分析法应用于热集成精馏序列综合的换热网络设计中,建立一个以分离序列和操作压力为变量的混合整数非线性规划模型,然后运用改进模拟退火算法进行求解,避免了以换热网络结构作为独立变量,提高了用随机搜索方法获得最优解的可能性,同时提高了计算速度。该模型既允许一个塔的冷凝器和多个塔的再沸器进行热匹配,以及一个塔的再沸器和多个塔的冷凝器进行热匹配,还允许一个塔的冷凝器和再沸器同时与其它塔进行热匹配,使可回收利用的能量大大增加。算例的计算结果表明该方法是求解多组分热集成精馏序列综合问题的有效方法。     

A mobility constraint model to infer sensor behaviour in forest fire risk monitoring

Wireless sensor networks (WSNs) play an important role in forest fire risk monitoring. Various applications are in operation. However, the use of mobile sensors in forest risk monitoring remains largely unexplored. Our research contributes to fill this gap by designing a model which abstracts mobility constraints within different types of contexts for the inference of mobile sensor behaviour. This behaviour is focused on achieving a suitable spatial coverage of the WSN when monitoring forest fire risk. The proposed mobility constraint model makes use of a Bayesian network approach and consists of three components: (1) a context typology describing different contexts in which a WSN monitors a dynamic phenomenon; (2) a context graph encoding probabilistic dependencies among variables of interest; and (3) contextual rules encoding expert knowledge and application requirements needed for the inference of sensor behaviour. As an illustration, the model is used to simulate the behaviour of a mobile WSN to obtain a suitable spatial coverage in low and high fire risk scenarios. It is shown that the implemented Bayesian network within the mobility constraint model can successfully infer behaviour such as sleeping sensors, moving sensors, or deploying more sensors to enhance spatial coverage. Furthermore, the mobility constraint model contributes towards mobile sensing in which the mobile sensor behaviour is driven by constraints on the state of the phenomenon and the sensing system.     

Scene classification based on a hierarchical convolutional sparse auto-encoder for high spatial resolution imagery

Efficiently representing and recognizing the semantic classes of the subregions of large-scale high spatial resolution (HSR) remote-sensing images are challenging and critical problems. Most of the existing scene classification methods concentrate on the feature coding approach with handcrafted low-level features or the low-level unsupervised feature learning approaches, which essentially prevent them from better recognizing the semantic categories of the scene due to their limited mid-level feature representation ability. In this article, to overcome the inadequate mid-level representation, a patch-based spatial-spectral hierarchical convolutional sparse auto-encoder (HCSAE) algorithm, based on deep learning, is proposed for HSR remote-sensing imagery scene classification. The HCSAE framework uses an unsupervised hierarchical network based on a sparse auto-encoder (SAE) model. In contrast to the single-level SAE, the HCSAE framework utilizes the significant features from the single-level algorithm in a feedforward and full connection approach to the maximum extent, which adequately represents the scene semantics in the high level of the HCSAE. To ensure robust feature learning and extraction during the SAE feature extraction procedure, a ‘dropout’ strategy is also introduced. The experimental results using the UC Merced data set with 21 classes and a Google Earth data set with 12 classes demonstrate that the proposed HCSAE framework can provide better accuracy than the traditional scene classification methods and the single-level convolutional sparse auto-encoder (CSAE) algorithm.     

Modeling and analysis of preloaded liquid spring/damper shock absorbers

Preloaded liquid spring/damper based shock isolation systems are suitable for heavy load military applications. In this paper, mathematical models are developed for passive liquid spring shock absorbers. The preloading is achieved by mounting the load between two liquid spring/dampers. Dynamics of such shock absorbers involve coupled hydrodynamic and thermodynamic phenomena. The energy dissipated through orifice due to hydrodynamic losses heats up the working fluid and consequently the heat is dissipated to environment. Such multi-energy domain interaction is well represented in this paper by using bond graph models. Moreover, the developed model accounts for the strain-rate dependent damping offered by the compressible working fluid in the liquid spring. The results show that proper choice of preloading and geometric parameters (spring dimensions and orifice sizes) can, respectively, reduce the thermodynamic and strain-rate dependent damping phenomena.     

Modelling chaotic behaviour of SIP retransmission mechanism

The Session Initiation Protocol (SIP) retransmission mechanism can cause SIP network collapse with short-term overload. In this paper, we investigate with a fluid modelling approach the chaotic behaviour of the SIP retransmission mechanism in SIP networks. We capture the complex correlation structure in SIP systems through a detailed and novel queuing analysis. To dimension a buffer size which can avoid unnecessary message drop in a SIP server, we develop a sufficient condition for a stable SIP system analytically based on our fluid model. We also apply our fluid model to the simulation of a complex SIP system. We compare the simulation results achieved through our fluid models with those based on OPNET^® event-driven approach to demonstrate the validity of our approach.     

异构无线传感器网络中一种可扩展的代码分发技术

代码分发一直是无线传感器网络研究的热点问题.目前的研究工作主要集中在同构场景下的代码分发,广播是这些研究工作中最常用的手段.而对于异构场景下的代码分发问题,研究工作则相对较少,传统的基于广播的方法很难直接适用.文中针对异构网络下的代码分发问题,把该问题归约为最小非叶节点MNN(minimum nonleaf nodes)Steiner树问题,并设计了一种基于多播的代码分发协议HSR(heterogeneous sensor networks scalable reprogramming protocol).该协议利用组件化的思想,为不同类型节点(或代码模块)建立了多棵最优代码分发多播树.并证明了在解决MNN问题时,HSR达到了理论最优近似率ln|R|(R为目标节点数),有效的降低了异构网络下代码分发过程中的通信开销和能耗.在此基础上,文中还设计了两种压缩编码机制:特殊路由日志机制SRL(special routinglog)和跳步受限的局部广播机制HLB(hops-restricted local broadcast),使得多播树的信息可以被无损压缩,增强了HSR协议的可扩展性.在实时性方面,提出了基于多播树的3阶段流水线调度方法,有效缓解了隐藏终端和干扰问题.仿真结果证明了协议的正确性和有效性.