Simulation of Neural Behavior

The brain is an organ that takes the central role in advanced information processing. There exist great many neurons in our brain, which build complicated neural networks. All information processing in the brain is accomplished by neural activity in the form of neural oscillations. In order to understand the mechanisms of information processing, it is necessary to clarify functions of neurons and neural networks. Although the current progress of experiment technology is remarkable, only experiments by themselves cannot uncover the behavior of only a single neuron. Computational neuroscience is a research field, which fills up the deficiency in experiments. By modeling the essential features of a neuron or a neural network, we can analyze their fundamental properties by computer simulation. In this chapter, one aspect of computational neuroscience is described. At the first, the cell membrane and a neuron can be modeled by using an RC circuit. Next, the Hodgkin-Huxley model is introduced, which has the function of generation of action potentials. Furthermore, many neurons show the subthreshold resonance phenomena, and the cell membrane is necessary to be modeled by an RLC circuit. Finally, some simulation results are shown, and properties of such neuronal behaviors are discussed

Mixing-vessel composting system at a large swine finishing farm

Reviewed by Joseph Heywood Harrison, Professor, Nutrient Management Specialist, Washington State University, andLide Chen, Associate Professor, Nutrient Management Specialist, University of Idaho."On-farm manure treatment can be challenging for many animal feeding operations, especially for those who have limited nearby fields for manure land application. To date, very few large-scale animal farms utilize composting as a long-term treatment for liquid manure. Composting is a biological process in which microorganisms convert organic materials into soillike material, which can effectively convert animal manure into value-added products. Compost has been well-documented and proven to be an excellent soil conditioner, which could add organic matter, improve soil structure, reduce fertilizer requirements, and reduce soil erosion potential."--First page.Written by Zonggang Li (Post Doctoral fellow, Agricultural Systems Technology), Gilbert J. Miito (Post Doctoral fellow, Agricultural Systems Technology), Teng Teeh Lim (Extension Professor, Agricultural Systems Technology). Reviewed by Joseph Heywood Harrison, Professor, Nutrient Management Specialist, Washington State University, andLide Chen, Associate Professor, Nutrient Management Specialist, University of Idaho.Includes bibliographical reference

Geochemical characteristics and implications of shale gas from the Longmaxi Formation, Sichuan Basin, China

AbstractGas geochemical analysis was conducted on the shale gas from the Longmaxi Formation in the Weiyuan-Changning areas, Sichuan Basin, China. Chemical composition was measured using an integrated method of gas chromatography combined with mass spectrometry. The results show that the Longmaxi shale gas, after hydraulic fracturing, is primarily dominated by methane (94.0%–98.6%) with low humidity (0.3%–0.6%) and minor non-hydrocarbon gasses which are primarily comprised of CO2, N2, as well as trace He. δ13CCO2 = −2.5‰−6.0‰3He/4He = 0.01–0.03Ra.The shale gas in the Weiyuan and Changning areas display carbon isotopes reversal pattern with a carbon number (δ13C1 > δ13C2) and distinct carbon isotopic composition. The shale gas from the Weiyuan pilot has heavier carbon isotopic compositions for methane (δ13C1: from −34.5‰ to −36.8‰), ethane (δ13C2: −37.6‰ to −41.9‰), and CO2 (δ13CCO2: −4.5‰ to −6.0‰) than those in the Changning pilot (δ13C1: −27.2‰ to −27.3‰, δ13C2: −33.7‰ to −34.1‰, δ13CCO2: −2.5‰ to −4.6‰). The Longmaxi shale was thermally high and the organic matter was in over mature stage with good sealing conditions. The shale gas, after hydraulic fracturing, could possibly originate from the thermal decomposition of kerogen and the secondary cracking of liquid hydrocarbons which caused the reversal pattern of carbon isotopes. Some CO2 could be derived from the decomposition of carbonate. The difference in carbon isotopes between the Weiyuan and Changning areas could be derived from the different mixing proportion of gas from the secondary cracking of liquid hydrocarbons caused by specific geological and geochemical conditions

The Devil is the Classifier: Investigating Long Tail Relation Classification with Decoupling Analysis

Long-tailed relation classification is a challenging problem as the head classes may dominate the training phase, thereby leading to the deterioration of the tail performance. Existing solutions usually address this issue via class-balancing strategies, e.g., data re-sampling and loss re-weighting, but all these methods adhere to the schema of entangling learning of the representation and classifier. In this study, we conduct an in-depth empirical investigation into the long-tailed problem and found that pre-trained models with instance-balanced sampling already capture the well-learned representations for all classes; moreover, it is possible to achieve better long-tailed classification ability at low cost by only adjusting the classifier. Inspired by this observation, we propose a robust classifier with attentive relation routing, which assigns soft weights by automatically aggregating the relations. Extensive experiments on two datasets demonstrate the effectiveness of our proposed approach. Code and datasets are available in https://github.com/zjunlp/deepke

Pricing Zero-Coupon Catastrophe Bonds Using EVT with Doubly Stochastic Poisson Arrivals

The frequency and severity of climate abnormal change displays an irregular upward cycle as global warming intensifies. Therefore, this paper employs a doubly stochastic Poisson process with Black Derman Toy (BDT) intensity to describe the catastrophic characteristics. By using the Property Claim Services (PCS) loss index data from 2001 to 2010 provided by the US Insurance Services Office (ISO), the empirical result reveals that the BDT arrival rate process is superior to the nonhomogeneous Poisson and lognormal intensity process due to its smaller RMSE, MAE, MRPE, and U and larger E and d. Secondly, to depict extreme features of catastrophic risks, this paper adopts the Peak Over Threshold (POT) in extreme value theory (EVT) to characterize the tail characteristics of catastrophic loss distribution. And then the loss distribution is analyzed and assessed using a quantile-quantile (QQ) plot to visually check whether the PCS index observations meet the generalized Pareto distribution (GPD) assumption. Furthermore, this paper derives a pricing formula for zero-coupon catastrophe bonds with a stochastic interest rate environment and aggregate losses generated by a compound doubly stochastic Poisson process under the forward measure. Finally, simulation results verify pricing model predictions and show how catastrophic risks and interest rate risk affect the prices of zero-coupon catastrophe bonds

A New Anthracene Derivative from Marine Streptomyces sp. W007 Exhibiting Highly and Selectively Cytotoxic Activities

A new anthracene derivative, 3-hydroxy-1-keto-3-methyl-8-methoxy-1,2,3, 4-tetrahydro-benz[α]anthracene, was isolated from the marine strain Streptomyces sp. W007, and its structure was established by spectroscopic analysis including mass spectra, 1D- and 2D-NMR (1H–1H COSY, HMBC, HSQC and NOESY) experiments. 3-hydroxy-1-keto-3-methyl-8-methoxy-1,2,3,4-tetrahydro-benz[α]anthracene showed cytotoxicity against human lung adenocarcinoma cell line A549

Benchmarking knowledge-driven zero-shot learning

External knowledge (a.k.a. side information) plays a critical role in zero-shot learning (ZSL) which aims to predict with unseen classes that have never appeared in training data. Several kinds of external knowledge, such as text and attribute, have been widely investigated, but they alone are limited with incomplete semantics. Some very recent studies thus propose to use Knowledge Graph (KG) due to its high expressivity and compatibility for representing kinds of knowledge. However, the ZSL community is still in short of standard benchmarks for studying and comparing different external knowledge settings and different KG-based ZSL methods. In this paper, we proposed six resources covering three tasks, i.e., zero-shot image classification (ZS-IMGC), zero-shot relation extraction (ZS-RE), and zero-shot KG completion (ZS-KGC). Each resource has a normal ZSL benchmark and a KG containing semantics ranging from text to attribute, from relational knowledge to logical expressions. We have clearly presented these resources including their construction, statistics, data formats and usage cases w.r.t. different ZSL methods. More importantly, we have conducted a comprehensive benchmarking study, with two general and state-of-the-art methods, two setting-specific methods and one interpretable method. We discussed and compared different ZSL paradigms w.r.t. different external knowledge settings, and found that our resources have great potential for developing more advanced ZSL methods and more solutions for applying KGs for augmenting machine learning. All the resources are available at https://github.com/China-UK-ZSL/Resources_for_KZSL.Comment: Published in Journal of Web Semantics, 2022. Final version please refer to our Github repository

Identification of initial fault time for bearing based on monitoring indicator, WEMD and Infogram

Rolling element bearing is a core component in the rotating machine. The performance of the whole machine is mainly dominated by the performance condition of the rolling element bearing. The Initial Fault Time (IFT) is a beginning landmark of the unhealthy condition of bearings. In order to identify accurately and rapidly the IFT under the weak fault signatures and heavy background noise, an identification method of the IFT is proposed by the monitoring indicator and envelope analysis with Weighted Empirical Mode Decomposition (WEMD) and Infogram. The monitoring indicator is constructed by the variation coefficient of the summation of the multiple standardized statistical features of the vibration signal. The approximate IFT can be obtained by the minimum before the early stage of the continuous increase in the monitoring indicator. Whereafter, a more accurate IFT can be detected by envelope analysis with WEMD and Infogram based on interval-halving backtracking strategy. The proposed method is verified by the tested dataset provided by Intelligent Maintenance System (IMS). The results show that the proposed method is efficient, rapid and simple for identifying the IFT