Avalanche noise characteristics of thin GaAs structures with distributed carrier generation

It is known that both pure electron and pure hole injection into thin GaAs multiplication regions gives rise to avalanche multiplication with noise lower than predicted by the local noise model. In this paper, it is shown that the noise from multiplication initiated by carriers generated throughout a 0.1 μm avalanche region is also lower than predicted by the local model but higher than that obtained with pure injection of either carrier type. This behavior is due to the effects of nonlocal ionization brought about by the dead space; the minimum distance a carrier has to travel in the electric field to initiate an ionization even

Gene transcription analysis during interaction between potato and Ralstonia solanacearum

Bacterial wilt (BW) caused by Ralstonia solanacearum (Rs) is an important quarantine disease that spreads worldwide and infects hundreds of plant species. The BW defense response of potato is a complicated continuous process, which involves transcription of a battery of genes. The molecular mechanisms of potato-Rs interactions are poorly understood. In this study, we combined suppression subtractive hybridization and macroarray hybridization to identify genes that are differentially expressed during the incompatible interaction between Rs and potato. In total, 302 differentially expressed genes were identified and classified into 12 groups according to their putative biological functions. Of 302 genes, 81 were considered as Rs resistance-related genes based on the homology to genes of known function, and they have putative roles in pathogen recognition, signal transduction, transcription factor functioning, hypersensitive response, systemic acquired resistance, and cell rescue and protection. Additionally, 50 out of 302 genes had no match or low similarity in the NCBI databases, and they may represent novel genes. Of seven interesting genes analyzed via RNA gel blot and semi-quantitative RT-PCR, six were induced, one was suppressed, and all had different transcription patterns. The results demonstrate that the response of potato against Rs is rapid and involves the induction of numerous various genes. The genes identified in this study add to our knowledge of potato resistance to Rs

Regulatory networks and connected components of the neutral space

The functioning of a living cell is largely determined by the structure of its regulatory network, comprising non-linear interactions between regulatory genes. An important factor for the stability and evolvability of such regulatory systems is neutrality - typically a large number of alternative network structures give rise to the necessary dynamics. Here we study the discretized regulatory dynamics of the yeast cell cycle [Li et al., PNAS, 2004] and the set of networks capable of reproducing it, which we call functional. Among these, the empirical yeast wildtype network is close to optimal with respect to sparse wiring. Under point mutations, which establish or delete single interactions, the neutral space of functional networks is fragmented into 4.7 * 10^8 components. One of the smaller ones contains the wildtype network. On average, functional networks reachable from the wildtype by mutations are sparser, have higher noise resilience and fewer fixed point attractors as compared with networks outside of this wildtype component.Comment: 6 pages, 5 figure

Crystal size induced reduction in thermal hysteresis of Ni-Ti-Nb shape memory thin films

Ni41.7Ti38.8Nb19.5 shape memory alloy films were sputter-deposited onto silicon substrates and annealed at various temperatures. A narrow thermal hysteresis was obtained in the Ni-Ti-Nb films with a grain size of less than 50 nm. The small grain size, or large amount of grain boundaries, facilitates the phase transformation, thus reduces the hysteresis. The corresponding less transformation friction and heat transfer during the shear process, as well as reduced spontaneous lattice distortion, are also responsible for this reduction of the thermal hysteresis

Dynamic behavior investigations and disturbance rejection predictive control of solvent-based post-combustion CO2 capture process

Increasing demand for flexible operation has posed significant challenges to the control system design of solvent-based post-combustion CO2 capture (PCC) process: 1) the capture system itself has very slow dynamics; 2) in the case of wide range of operation, dynamic behavior of the PCC process will change significantly at different operating points; and 3) the frequent variation of upstream flue gas flowrate will bring in strong disturbances to the capture system. For these reasons, this paper provides a comprehensive study on the dynamic characteristics of the PCC process. The system dynamics under different CO2 capture rates, re-boiler temperatures, and flue gas flow rates are analyzed and compared through step-response tests. Based on the in-depth understanding of the system behavior, a disturbance rejection predictive controller (DRPC) is proposed for the PCC process. The predictive controller can track the desired CO2 capture rate quickly and smoothly in a wide operating range while tightly maintaining the re-boiler temperature around the optimal value. Active disturbance rejection approach is used in the predictive control design to improve the control property in the presence of dynamic variations or disturbances. The measured disturbances, such as the flue gas flow rate, is considered as an additional input in the predictive model development, so that accurate model prediction and timely control adjustment can be made once the disturbance is detected. For unmeasured disturbances, including model mismatches, plant behavior variations, etc., a disturbance observer is designed to estimate the value of disturbances. The estimated signal is then used as a compensation to the predictive control signal to remove the influence of disturbances. Simulations on a monoethanolamine (MEA) based PCC system developed on gCCS demonstrates the excellent effect of the proposed controller

Reinforced coordinated control of coal-fired power plant retrofitted with solvent based CO2 capture using model predictive controls

Solvent-based post-combustion CO2 capture (PCC) provides a promising technology for the CO2 removal of coal-fired power plant (CFPP). However, there are strong interactions between the CFPP and the PCC system, which makes it challenging to attain a good control for the integrated plant. The PCC system requires extraction of large amounts of steam from the intermediate/low pressure steam turbine to provide heat for solvent regeneration, which will reduce power generation. Wide-range load variation of power plant will cause strong fluctuation of the flue gas flow and brings in a significant impact on the PCC system. To overcome these issues, this paper presents a reinforced coordinated control scheme for the integrated CFPP-PCC system based on the investigation of the overall plant dynamic behavior. Two model predictive controllers are developed for the CFPP and PCC plants respectively, in which the steam flow rate to re-boiler and the flue-gas flow rate are considered as feed-forward signals to link the two systems together. Three operating modes are considered for designing the coordinated control system, which are: (1) normal operating mode; (2) rapid power load change mode; and (3) strict carbon capture mode. The proposed coordinated controller can enhance the overall performance of the CFPP-PCC plant and achieve a flexible trade-off between power generation and CO2 reduction. Simulation results on a small-scale subcritical CFPP-PCC plant developed on gCCS demonstrates the effectiveness of the proposed controller

Commodity prices rise sharply at turning points

Commodity prices depend on supply and demand. With an uneven distribution of resources, prices are high at locations starved of commodity and low where it is abundant. We introduce an agent-based model in which agents set their prices to maximize profit. At steady state, the market self-organizes into three groups: excess producers, consumers, and balanced agents. When resources are scarce, prices rise sharply at a turning point due to the disappearance of excess producers. Market data of commodities provide evidence of turning points for essential commodities, as well as a yield point for non-essential ones

Effect of CO2 laser cutting process parameters on edge quality and operating cost of AISI316L

Laser cutting is a popular manufacturing process utilized to cut various types of materials economically. The width of laser cut or kerf, quality of the cut edges and the operating cost are affected by laser power, cutting speed, assist gas pressure, nozzle diameter and focus point position as well as the work-piece material. In this paper CO2 laser cutting of stainless steel of medical grade AISI316L has been investigated. Design of experiment (DOE) was implemented by applying Box-Behnken design to develop the experiment lay-out. The aim of this work is to relate the cutting edge quality parameters namely: upper kerf, lower kerf, the ratio between them, cut section roughness and operating cost to the process parameters mentioned above. Then, an overall optimization routine was applied to find out the optimal cutting setting that would enhance the quality or minimize the operating cost. Mathematical models were developed to determine the relationship between the process parameters and the edge quality features. Also, process parameters effects on the quality features have been defined. Finally, the optimal laser cutting conditions have been found at which the highest quality or minimum cost can be achieved

Variational approximation for mixtures of linear mixed models

Mixtures of linear mixed models (MLMMs) are useful for clustering grouped data and can be estimated by likelihood maximization through the EM algorithm. The conventional approach to determining a suitable number of components is to compare different mixture models using penalized log-likelihood criteria such as BIC.We propose fitting MLMMs with variational methods which can perform parameter estimation and model selection simultaneously. A variational approximation is described where the variational lower bound and parameter updates are in closed form, allowing fast evaluation. A new variational greedy algorithm is developed for model selection and learning of the mixture components. This approach allows an automatic initialization of the algorithm and returns a plausible number of mixture components automatically. In cases of weak identifiability of certain model parameters, we use hierarchical centering to reparametrize the model and show empirically that there is a gain in efficiency by variational algorithms similar to that in MCMC algorithms. Related to this, we prove that the approximate rate of convergence of variational algorithms by Gaussian approximation is equal to that of the corresponding Gibbs sampler which suggests that reparametrizations can lead to improved convergence in variational algorithms as well.Comment: 36 pages, 5 figures, 2 tables, submitted to JCG