•Convolution networks can predict bankruptcy by inputting financial ratios as an image.•Predictive accuracy improves with correlated financial ratios placed in the vicinity.•Deeper network ...configuration improves predictive accuracy.•Creating artificial financial data does not ensure the same effect as using real data.•Convolution-network-based bankruptcy prediction outperforms traditional methods.
Convolutional neural networks are being applied to identification problems in a variety of fields, and in some areas are showing higher discrimination accuracies than conventional methods. However, applications of convolutional neural networks to financial analyses have only been reported in a small number of studies on the prediction of stock price movements. The reason for this seems to be that convolutional neural networks are more suitable for application to images and less suitable for general numerical data including financial statements. Hence, in this research, an attempt is made to apply a convolutional neural network to the prediction of corporate bankruptcy, which in most cases is treated as a two-class classification problem. We use the financial statements (balance sheets and profit-and-loss statements) of 102 companies that have been delisted from the Japanese stock market due to de facto bankruptcy as well as the financial statements of 2062 currently listed companies over four financial periods. In our proposed method, a set of financial ratios are derived from the financial statements and represented as a grayscale image. The image generated by this process is utilized for training and testing a convolutional neural network. Moreover, the size of the dataset is increased using the weighted averages to create synthetic data points. A total of 7520 images for the bankrupt and continuing enterprises classes are used for training the convolutional neural network based on GoogLeNet. Bankruptcy predictions through the trained network are shown to have a higher performance compared to methods using decision trees, linear discriminant analysis, support vector machines, multi-layer perceptron, AdaBoost, or Altman’s Z′′-score.
•Water consumption behavior of a lead-acid battery during microcycling is analyzed.•Gas evolution starts immediately after starting charge even at PSoC.•Gassing is greater during charge at PSoC than ...during overcharge at the same voltage.•Ratio of released H2 to O2 can significantly differ from 2:1 during charge at PSOC.
Water electrolysis behavior of a 12 V lead-acid battery for vehicles equipped with idling stop system under vehicle operational conditions is investigated. The behavior of water electrolysis during a microcycling test at 60°C is analyzed by means of in-situ gas analyses and electrochemical measurements. During charge phases under partial state of charge conditions, rates at which gas is released out of the battery container are always higher than those at the steady states during static overcharge conditions at the same voltage. The volume ratio of hydrogen to oxygen released during the charge under partial state of charge conditions is significantly different from 2, which is the stoichiometric ratio of water electrolysis products. This gas evolution behavior can be ascribed to the different potentials of each positive and negative electrode under the two different charge conditions. Reducing the difference between the potentials under the two conditions will be a key to reduce the water electrolysis during microcycling operations.
This paper presents an evaluation of the system-level integrated conceptual information of a major complex for a small-scale network containing two loops in accordance with the integrated information ...theory 3.0 framework. We focus on the following parameters characterizing the system model: (1) number of nodes in the loop, (2) frustration of the loop, and (3) temperature controlling the stochastic fluctuation of the state transition. Effects of these parameters on the integrated conceptual information and conditions for major complexes formed by a single loop, rather than the entire network, are investigated. Our first finding is that parity of the number of nodes forming a loop has a strong effect on the integrated conceptual information. For loops with an even number of nodes, the number of concepts tends to decrease, and the integrated conceptual information becomes smaller. Our second finding is that a major complex is more likely to be formed by a small number of nodes under small stochastic fluctuations. On the other hand, the entire network can easily become a major complex under larger stochastic fluctuations, and this tendency can be reinforced by frustration. It is also shown that, although counterintuitive, the integrated conceptual information can be maximized in the presence of stochastic fluctuations. These results suggest that even when several small subnetworks are connected by only a few connections, such as a bridge, the entire network may become a major complex by introducing some stochastic fluctuations and by frustrating loops with an even number of nodes.
•Parity of the number of nodes in a loop strongly affects the integrated information.•Frustration in a loop can generate a major complex formed by the entire network.•Small stochastic fluctuations cause a major complex formed by a small number of nodes.•Moderate stochastic fluctuations can increase the integrated information.
Genome sequencing of
Streptomyces
, myxobacteria, and fungi showed that although each strain contains genes that encode the enzymes to synthesize a plethora of potential secondary metabolites, only a ...fraction are expressed during fermentation. Interest has therefore grown in the activation of these cryptic pathways. We review current progress on this topic, describing concepts for activating silent genes, utilization of “natural” mutant-type RNA polymerases and rare earth elements, and the applicability of ribosome engineering to myxobacteria and fungi, the microbial groups known as excellent searching sources, as well as actinomycetes, for secondary metabolites.
Celotno besedilo
Dostopno za:
CEKLJ, DOBA, EMUNI, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SBMB, SBNM, UILJ, UKNU, UL, UM, UPUK
PDF
