A Fast Numerical Scheme for Black-Scholes Option Pricing Model

Faculty adviser: Bilyk DmytroThe exact solution of the Black-Scholes equation involves stochastic term, which made it time-consuming to calculate. Therefore, I try to find a way to solve the Black-Scholes equation numerically to avoid evaluating the stochastic term. In this paper, I use forward difference, backward difference, and Crank-Nicolson method to discretize the equation and Jacobi method, Gauss-Seidel method and Succesive Over Relaxation (SOR) Method are used to speed up the matrix operation process.This research was supported by the Undergraduate Research Opportunities Program (UROP)

Prospect of detecting magnetic fields from strong-magnetized binary neutron stars

Binary neutron star mergers are unique sources of gravitational waves in multi-messenger astronomy. The inspiral phase of binary neutron stars can emit gravitational waves as chirp signals. The present waveform models of gravitational wave only considered the gravitational interaction. In this paper, we derive the waveform of the gravitational wave signal taking into account the presence of magnetic fields. We found that the electromagnetic interaction and radiation can introduce different frequency-dependent power laws for both amplitude and frequency of the gravitational wave. We show from the results of Fisher information matrix that the third-generation observation may detect magnetic dipole moments if the magnetic field is around 10^17 G

Modeling and simulation of extended ant colony labor division for benefit distribution of the all-for-one tourism supply chain with front and back decoupling

This paper takes the supply chain alliance under the decoupling of the front and back of the all-for-one tourism as the research object. Considering the three behavior stimuli of self-benefit, altruism, and invariance, this article resets the attributes such as environmental stimuli and response threshold of ants based on the characteristics of the all-for-one tourism supply chain with shared services as the core under the decoupling of the front and back. Moreover, it introduces dual intervention factors to coordinate the benefit distribution process of different member companies, takes fairness as the main goal of benefit distribution, introduces relative deprivation as the measure index of fairness, and establishes a dynamic all-for-one tourism supply chain alliance benefit distribution model. The experimental results show that the extended model has good flexibility of benefit distribution and realizes the fair distribution of supply chain benefits

Autonomous concrete crack semantic segmentation using deep fully convolutional encoder-decoder network in concrete structures inspection

Structure health inspection is the way to ensure that structures stay in optimum condition. Traditional inspection work has many disadvantages in dealing with the large workload despite using remote image-capturing devices. This research focuses on image-based concrete crack pattern recognition utilizing a deep convolutional neural network (DCNN) and an encoder–decoder module for semantic segmentation and classification tasks, thereby lightening the inspectors’ workload. To achieve this, a series of contrast experiments have been implemented. The results show that the proposed deep-learning network has competitive semantic segmentation accuracy (91.62%) and over-performs compared with other crack detection studies. This proposed advanced DCNN is split into multiple modules, including atrous convolution (AS), atrous spatial pyramid pooling (ASPP), a modified encoder–decoder module, and depthwise separable convolution (DSC). The advancement is that those modules are well-selected for this task and modified based on their characteristics and functions, exploiting their superiority to achieve robust and accurate detection globally. This application improved the overall performance of detection and can be implemented in industrial practices

A Deep Long-Term Joint Temporal–Spectral Network for Spectrum Prediction

Spectrum prediction is a promising technique to release spectrum resources and plays an essential role in cognitive radio networks and spectrum situation generating. Traditional algorithms normally focus on one-dimensional or predict spectrum values in a slot-by-slot manner and thus cannot fully perceive the spectrum states in complex environments and lack timeliness. In this paper, a deep learning-based prediction method with a simple structure is developed for temporal–spectral and multi-slot spectrum prediction simultaneously. Specifically, we first analyze and construct spectrum data suitable for the model to simultaneously achieve long-term and multi-dimensional spectrum prediction. Then, a hierarchical spectrum prediction system is developed that takes advantage of the advanced Bi-ConvLSTM and the seq2seq framework. The Bi-ConvLSTM captures time–frequency characteristics of spectrum data, and the seq2seq framework is used for long-term spectrum prediction. Furthermore, the attention mechanism is used to address the limitations of the seq2seq framework that compresses all inputs into fixed-length vectors, resulting in information loss. Finally, the experimental results have shown that the proposed model has a significant advantage over the benchmark schemes. Especially, the proposed spectrum prediction model achieves 6.15%, 0.7749, 1.0978, and 0.9628 in MAPE, MAE, RMSE, and R2, respectively, which is better than all the baseline deep learning models

A Cognitive Electronic Jamming Decision-Making Method Based on <i>Q-Learning</i> and Ant Colony Fusion Algorithm

In order to improve the efficiency and adaptability of cognitive radar jamming decision-making, a fusion algorithm (Ant-QL) based on ant colony and Q-Learning is proposed in this paper. The algorithm does not rely on a priori information and enhances adaptability through real-time interactions between the jammer and the target radar. At the same time, it can be applied to single jammer and multiple jammer countermeasure scenarios with high jamming effects. First, traditional Q-Learning and DQN algorithms are discussed, and a radar jamming decision-making model is built for the simulation verification of each algorithm. Then, an improved Q-Learning algorithm is proposed to address the shortcomings of both algorithms. By introducing the pheromone mechanism of ant colony algorithms in Q-Learning and using the ε-greedy algorithm to balance the contradictory relationship between exploration and exploitation, the algorithm greatly avoids falling into a local optimum, thus accelerating the convergence speed of the algorithm with good stability and robustness in the convergence process. In order to better adapt to the cluster countermeasure environment in future battlefields, the algorithm and model are extended to cluster cooperative jamming decision-making. We map each jammer in the cluster to an intelligent ant searching for the optimal path, and multiple jammers interact with each other to obtain information. During the process of confrontation, the method greatly improves the convergence speed and stability and reduces the need for hardware and power resources of the jammer. Assuming that the number of jammers is three, the experimental simulation results of the convergence speed of the Ant-QL algorithm improve by 85.4%, 80.56% and 72% compared with the Q-Learning, DQN and improved Q-Learning algorithms, respectively. During the convergence process, the Ant-QL algorithm is very stable and efficient, and the algorithm complexity is low. After the algorithms converge, the average response times of the four algorithms are 6.99 × 10−4 s, 2.234 × 10−3 s, 2.21 × 10−4 s and 1.7 × 10−4 s, respectively. The results show that the improved Q-Learning algorithm and Ant-QL algorithm also have more advantages in terms of average response time after convergence

Depletion of G9A attenuates imiquimod-induced psoriatic dermatitis via targeting EDAR-NF-κB signaling in keratinocyte

Abstract Psoriasis is a common and recurrent inflammatory skin disease characterized by inflammatory cells infiltration of the dermis and excessive proliferation, reduced apoptosis, and abnormal keratosis of the epidermis. In this study, we found that G9A, an important methyltransferase that mainly mediates the mono-methylation (me1) and di-methylation (me2) of histone 3 lysine 9 (H3K9), is highly expressed in lesions of patients with psoriasis and imiquimod (IMQ)-induced psoriasis-like mouse model. Previous studies have shown that G9A is involved in the pathogenesis of various tumors by regulating apoptosis, proliferation, differentiation, and invasion. However, the role of G9A in skin inflammatory diseases such as psoriasis remains unclear. Our data so far suggest that topical administration of G9A inhibitor BIX01294 as well as keratinocyte-specific deletion of G9A greatly alleviated IMQ-induced psoriatic alterations in mice for the first time. Mechanistically, the loss function of G9A causes the downregulation of Ectodysplasin A receptor (EDAR), consequently inhibiting the activation of NF-κB pathway, resulting in impaired proliferation and increased apoptosis of keratinocytes, therefore ameliorating the psoriatic dermatitis induced by IMQ. In total, we show that inhibition of G9A improves psoriatic-like dermatitis mainly by regulating cell proliferation and apoptosis rather than inflammatory processes, and that this molecule may be considered as a potential therapeutic target for keratinocyte hyperproliferative diseases such as psoriasis

Integrating tourism supply-demand and environmental sensitivity into the tourism network identification of ecological functional zone

One of the challenges facing ecological functional zones (EFZs) is achieving a balance between economic growth and environmental protection (management). Tourism presents an important avenue to tackle this challenge. However, research inadequately addresses the identification of tourism networks. Combining geo-referenced social media data analysis, the three-step floating catchment area method, and the minimum cumulative resistance model, this paper developed a multi-tiered mechanism for identifying tourism networks using scenic spots as nodes. This approach involved indicators like tourism potential (supply), tourists’ emotional appeal (demand), and ecological sensitivity. We employed the Taihang Mountains (THM), a representative EFZ, as an application case. Results indicate spatial heterogeneity in THM’s tourism potential, with higher tourism potential and relatively greater ecological sensitivity in the South and East THM. Furthermore, a substantial spatial mismatch in tourism demand and supply is evident, with South THM leading with a match of 0.29, while East THM recording the lowest match at 0.16. Based on this, this study identified a multi-level tourism development network having 34 tourism sources (9 primary sources, 13 secondary sources and 12 tertiary sources) and 51 corridors (11 primary corridors, 21 secondary corridors, and 19 tertiary corridors) consisted of a total length of 5,263 km, with an average length of 67 km. Our tourism networks have been tested to not only protect ecologically sensitive areas but also connect areas with economic advantages in tourism (i.e., South and East THM), which is conducive to achieving mutual benefits between tourism development and environmental protection. Our findings are conducive to improving the efficiency of tourism planning and management and provide a new path for coordinating EFZs’ conservation and development

Probing the Weak Interaction of Proteins with Neutral and Zwitterionic Antifouling Polymers

Protein-polymer interactions are of great interest in a wide range of scientific and technological applications. Neutral poly(ethylene glycol) (PEG) and zwitterionic poly(sulfobetaine methacrylate)(pSBMA) are two well-known nonfouling materials that exhibit strong surface resistance to proteins. However, it still remains unclear or unexplored how PEG and pSBMA interact with proteins in solution. In this work, we examine the interactions between two model proteins (bovine serum albumin and lysozyme) and two typical antifouling polymers of PEG and pSBMA in the aqueous solution using fluorescence spectroscopy, atomic force microscopy, and NMR. The effect of mass ratios of protein:polymer on the interactions is also examined. Collective data clearly demonstrate the existence of weak hydrophobic interactions between PEG and proteins, while no detectable interactions between pSBMA and proteins. The elimination of protein interaction with pSBMA could be due to an enhanced surface hydration of zwitterionic groups in pSBMA. New evidence is given to demonstrate the interactions between PEG and proteins, which are often neglected in literature because the PEG-protein interactions are weak and reversible, as well as the structural change caused by hydrophobic interaction. This work provides a better fundamental understanding of the intrinsic structure-activity relationship of polymers underlying polymer-protein interactions, which are important for designing new biomaterials for biosensor, medical diagnostics, and drug delivery applications