Portfolio Strategy of Financial Market with Regime Switching Driven by Geometric Lévy Process

The problem of a portfolio strategy for financial market with regime switching driven by geometric Lévy process is investigated in this paper. The considered financial market includes one bond and multiple stocks which has few researches up to now. A new and general Black-Scholes (B-S) model is set up, in which the interest rate of the bond, the rate of return, and the volatility of the stocks vary as the market states switching and the stock prices are driven by geometric Lévy process. For the general B-S model of the financial market, a portfolio strategy which is determined by a partial differential equation (PDE) of parabolic type is given by using Itô formula. The PDE is an extension of existing result. The solvability of the PDE is researched by making use of variables transformation. An application of the solvability of the PDE on the European options with the final data is given finally

Predicting Anaerobic Membrane Bioreactor Performance Using Flow-Cytometry-Derived High and Low Nucleic Acid Content Cells

Having a tool to monitor the microbial abundances rapidly and to utilize the data to predict the reactor performance would facilitate the operation of an anaerobic membrane bioreactor (AnMBR). This study aims to achieve the aforementioned scenario by developing a linear regression model that incorporates a time-lagging mode. The model uses low nucleic acid (LNA) cell numbers and the ratio of high nucleic acid (HNA) to LNA cells as an input data set. First, the model was trained using data sets obtained from a 35 L pilot-scale AnMBR. The model was able to predict the chemical oxygen demand (COD) removal efficiency and methane production 3.5 days in advance. Subsequent validation of the model using flow cytometry (FCM)-derived data (at time t – 3.5 days) obtained from another biologically independent reactor did not exhibit any substantial difference between predicted and actual measurements of reactor performance at time t. Further cell sorting, 16S rRNA gene sequencing, and correlation analysis partly attributed this accurate prediction to HNA genera (e.g., Anaerovibrio and unclassified Bacteroidales) and LNA genera (e.g., Achromobacter, Ochrobactrum, and unclassified Anaerolineae). In summary, our findings suggest that HNA and LNA cell routine enumeration, along with the trained model, can derive a fast approach to predict the AnMBR performance

Stability and Synchronization Control of Stochastic Neural Networks

This book reports on the latest findings in the study of Stochastic Neural Networks (SNN). The book collects the novel model of the disturbance driven by Levy process, the research method of M-matrix, and the adaptive control method of the SNN in the context of stability and synchronization control. The book will be of interest to university researchers, graduate students in control science and engineering and neural networks who wish to learn the core principles, methods, algorithms and applications of SNN

Facial Expression Realization of Humanoid Robot Head and Strain-Based Anthropomorphic Evaluation of Robot Facial Expressions

The facial expressions of humanoid robots play a crucial role in human–computer information interactions. However, there is a lack of quantitative evaluation methods for the anthropomorphism of robot facial expressions. In this study, we designed and manufactured a humanoid robot head that was capable of successfully realizing six basic facial expressions. The driving force behind the mechanism was efficiently transmitted to the silicone skin through a rigid linkage drive and snap button connection, which improves both the driving efficiency and the lifespan of the silicone skin. We used human facial expressions as a basis for simulating and acquiring the movement parameters. Subsequently, we designed a control system for the humanoid robot head in order to achieve these facial expressions. Moreover, we used a flexible vertical graphene sensor to measure strain on both the human face and the silicone skin of the humanoid robot head. We then proposed a method to evaluate the anthropomorphic degree of the robot’s facial expressions by using the difference rate of strain. The feasibility of this method was confirmed through experiments in facial expression recognition. The evaluation results indicated a high degree of anthropomorphism for the six basic facial expressions which were achieved by the humanoid robot head. Moreover, this study also investigates factors affecting the reproduction of expressions. Finally, the impulse was calculated based on the strain curves of the energy consumption of the humanoid robot head to complete different facial expressions. This offers a reference for fellow researchers when designing humanoid robot heads, based on energy consumption ratios. To conclude, this paper offers data references for optimizing the mechanisms and selecting the drive components of the humanoid robot head. This was realized by considering the anthropomorphic degree and energy consumption of each part. Additionally, a new method for evaluating robot facial expressions is proposed

Multifunctional Textile Platform for Fiber Optic Wearable Temperature-Monitoring Application

Wearable sensing technologies have been developed rapidly in the last decades for physiological and biomechanical signal monitoring. Much attention has been paid to functions of wearable applications, but comfort parameters have been overlooked. This research presents a developed fabric temperature sensor by adopting fiber Bragg grating (FBG) sensors and processing via a textile platform. This FBG-based quasi-distributed sensing system demonstrated a sensitivity of 10.61 ± 0.08 pm/°C with high stability in various temperature environments. No obvious wavelength shift occurred under the curvatures varying from 0 to 50.48 m−1 and in different integration methods with textiles. The temperature distribution monitored by the developed textile sensor in a complex environment with multiple heat sources was deduced using MATLAB to present a real-time dynamic temperature distribution in the wearing environment. This novel fabric temperature sensor shows high sensitivity, stability, and usability with comfort textile properties that are of great potential in wearable applications

Germline and somatic variations influence the somatic mutational signatures of esophageal squamous cell carcinomas in a Chinese population

Abstract Background Esophageal squamous cell carcinomas (ESCC) is the fourth most lethal cancer in China. Previous studies reveal several highly conserved mutational processes in ESCC. However, it remains unclear what are the true regulators of the mutational processes. Results We analyzed the somatic mutational signatures in 302 paired whole-exome sequencing data of ESCC in a Chinese population for potential regulators of the mutational processes. We identified three conserved subtypes based on the mutational signatures with significantly different clinical outcomes. Our results show that patients of different subpopulations of Chinese differ significantly in the activity of the “NpCpG” signature (FDR = 0.00188). In addition, we report ZNF750 and CDC27, of which the somatic statuses and the genetic burdens consistently influence the activities of specific mutational signatures in ESCC: the somatic ZNF750 status is associated with the AID/APOBEC-related mutational process (FDR = 0.0637); the somatic CDC27 copy-number is associated with the “NpCpG” (FDR = 0.00615) and the AID/APOBEC-related mutational processes (FDR = 8.69 × 10− 4). The burdens of germline variants in the two genes also significantly influence the activities of the same somatic mutational signatures (FDR < 0.1). Conclusions We report multiple factors that influence the mutational processes in ESCC including: the subpopulations of Chinese; the germline and somatic statuses of ZNF750 and CDC27 and exposure to alcohol and tobacco. Our findings based on the evidences from both germline and somatic levels reveal potential genetic regulators of the somatic mutational processes and provide insights into the biology of esophageal carcinogenesis

Optimization and decision making of guide vane closing law for pumped storage hydropower system to improve adaptability under complex conditions

The pumped storage hydropower system (PSHS) is considered a high-quality peaking and frequency regulation energy source due to its operational flexibility and fast response. However, its frequent regulation leads to complex operating conditions with potential harm to the stability of the system. This paper focuses on analyzing and improving the adaptability of guide vane closing law under complex conditions. This is obtained by proposing a refined numerical model of PSHS considering non-linear factors and analyzing the effects of the guide vane closing law and initial operating conditions on the load rejection. The results revealed that a suitable two-stage guide vane closing law effectively reduces the risk of load rejection. In addition, when the initial load of two units is different, it is beneficial to improve the load rejection characteristics when the unit with the smaller load rejects the load first. Finally, three groups of parameters for the optimal guide vane closing law (the Pareto solution sets) are obtained by multi-objective sparrow search algorithm (MOSSA) under the rated, maximum water head, and maximum rotational speed conditions. The obtained Pareto solution and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) are used for scoring the solutions and obtain an optimal suitable for complex operating conditions. The water head and rotational speed are reduced by an average of 7.76 % and 3.74 % for the different operating conditions compared to the model validation results, respectively. These results provide a theoretical basis for the selection of the optimal guide vane closing laws and improve the safety during load rejection under complex practical operating conditions

Determination of Methamphetamine by High-Performance Liquid Chromatography in Odor-Adsorbent Material Used for Training Drug-Detection Animals

The objective of the present report was to develop and validate a simple, sensitive, and selective analytical method for the determination of methamphetamine in an odor-adsorbent material (gauze) which was used to improve and standardize the training method used for drug-detection animals. High-performance liquid chromatography (HPLC) was performed using a Spherisorb ODS2 C18 column (200 mm × 4.6 mm, 5 μm), with a mobile phase consisting of a 0.25% methanol/triethylamine aqueous solution (V:V = 20:80), the pH of which was adjusted to 3.1 using glacial acetic acid, at a flow rate of 1.0 mL/min. The column temperature was 25 °C, and the detection of the analytes was performed at a wavelength of 260 nm. Methamphetamine showed good linearity (R2 = 0.9999) in the range of 4.2~83.2 mg/mL. The stability of the test material was good over 24 h. The precision of the method was good, with an average spiked recovery of 86.2% and an RSD of 2.9%. The methamphetamine content in the gauze sample was determined to be 7.8 ± 2.2 μg/sample. A high-performance liquid chromatography (HPLC) method was optimized and validated for the determination of methamphetamine in adsorbent materials (gauze). Validation data in terms of specificity, linearity, the limit of detection and the limit of quantification, reproducibility, precision, stability, and recovery indicated that the method is suitable for the routine analysis of methamphetamine in adsorbent materials (gauze) and provided a basis for training drug-detection animals