Mathematical modeling of falling liquid film evaporation process

A mathematical model of evaporation process from a laminar falling liquid film on a vertical plate of constant temperature is presented. The model is developed with and without interfacial shear stress due to the vapor flow at the liquid film surface. The vapor pressure drop, vapor exit velocity and cooling rate are calculated for different liquid mass flow values. It is shown that lower liquid mass flow produces higher cooling rate. The results also show that the interfacial shear stress has a considerable negative effect on the cooling rate. It is proved that there exists an optimum distance between the plates, which gives the maximum volumetric cooling rate.     

Makespan estimation and order acceptance in batch process industries when processing times are uncertain

Batch process industries are characterized by complex precedence relationships between operations, which renders the estimation of an acceptable workload very difficult. A detailed schedule based model can be used for this purpose, but for large problems this may require a prohibitive large amount of computation time. We propose a regression based model to estimate the makespan of a set of jobs. We extend earlier work based on deterministic processing times by considering Erlang-distributed processing times in our model. This regression-based model is used to support customer order acceptance. Three order acceptance policies are compared by means of simulation experiments: a scheduling policy, a workload policy and a regression policy. The results indicate that the performance of the regression policy can compete with the performance of the scheduling policy in situations with high variety in the job mix and high uncertainty in the processing times. Correspondence to: C.V. Ivanescu     

Tunneling spectroscopy of palladium-barium emitters

The surface of a palladium-barium cathode was studied using scanning probe microscopy techniques. Data from the tunneling spectroscopy showed the pattern of the electron work function distribution over a microscopic region on the emitter surface. This pattern is compared to the distribution of various phases in the palladium-barium alloy employed.     

Nanocell logic gates for molecular computing

Molecular electronics seeks to build electrical devices to implement computation - logic and memory - using individual or small collections of molecules. These devices have the potential to reduce device size and fabrication costs, by several orders of magnitude, relative to conventional CMOS. However, the construction of a practical molecular computer will require the molecular switches and their related interconnect technologies to behave as large-scale diverse logic, with input/output wires scaled to molecular dimensions. It is unclear whether it is necessary or even. possible to control the precise regular placement and interconnection of these diminutive molecular systems. This paper describes genetic algorithm-based simulations of molecular device structures in a nanocell where placement and connectivity of the internal molecular switches are not specifically directed and the internal topology is generally disordered. With some simplifying assumptions, these results show that it is possible to use easily fabricated nanocells as logic devices by setting the internal molecular switch states after the topological molecular assembly is complete. Simulated logic devices include an inverter, a NAND gate, an XOR gate and a 1-bit adder. Issues of defect and fault tolerance are addressed.     

Cluster analysis in industrial market segmentation through artificial neural network

Market segmentation has commonly applied cluster analysis. This study intends to make the comparison of conventional two-stage method with proposed two-stage method through the simulated data. The proposed two-stage method is the combination of self-organizing feature maps and K-means method. The simulation results show that the proposed scheme is better than the conventional two-stage method based on the rate of misclassification.     

Transformed Estimates of Densities of Heavy-Tailed Distributions and Classification

Nonparametric estimation of the density of a heavy-tailed probability distribution is investigated. The initial data are transformed to a bounded interval and the distribution density is determined by an inverse transformation of the distribution density estimate of transformed data. An adaptive data transformation is studied, in which the order of decay of the tail of the true distribution density is preserved and stable estimation of the deviation in tail index estimates is guaranteed. In classification, the empirical risk of erroneous classification by the Bayes empirical classifier is used as a measure for the quality of distribution density estimates.