We present a solution to the problem posed by Zhang et al. [1] regarding Call Option price C T under linear investment hedging for the stochastic interest rate modeled by a CIR Process. A closed form representation for C T by expected value of the path-integral along a square functional of n-dimensional Ornstein-Uhlenbeck process is derived. The method is suitable for Monte-Carlo simulation and illustrated by an example.
We derive a Put Option price associated with selling strategy of the underlying security in a random interest rate environment. This extends Put Option pricing under linear investment strategy from the Black-Scholes setting to Hull-White stochastic interest rate model. As an application, Call Option price for the linear investment strategy in the Hull-White model is established. Our results address recent emergence of developing dynamic investment strategies for the purpose of reducing the investor risk exposure associated with European-type options.
Objective: Rheumatoid arthritis (RA) is the most prevalent autoimmune arthritis. Berberine is an alkaloid isolated from Berberis vulgaris and its anti-inflammatory effect has been identified. Method: Twenty newly diagnosed RA patients and 20 healthy controls participated. Peripheral mononuclear cells were prepared and stimulated with bacterial lipopolysachharide (LPS,1 µg/ml), exposed to different concentrations of berberine (10 and 50µM) and dexamethasone (10-7 M) as a reference. Toxicity of compounds was evaluated by WST-1 assay. Expression of TNF-α and IL-1β were determined by quantitative real-time PCR. Protein level of secreted TNF-α and IL1β were measured by using ELISA. Result: Berberine did not have any toxic effect on cells, whereas Lipopolysachharide (LPS) stimulation caused a noticeable rise in TNF-α and IL-1β production. Berberine markedly downregulated the expression of both TNF-α and IL1β and inhibits TNF-α and IL-1β secretion from LPS-stimulated PBMCs. Discussion: This study provided molecular basis for anti-inflammatory effect of berberine on human mononuclear cells through the suppression of TNF-a and IL-1secretion. Our findings highlighted the significant inhibitory effect of berberine on proinflammatory responses of mononuclear cells from rheumatoid arthritis individuals, which may be responsible for antiinflammatory property of Barberry. We observed that berberine at high concentration exhibited anti-inflammatory effect in PBMCs of both healthy and patient groups by suppression of TNF-a and IL-1cytokines at both mRNA and protein levels. Conclusions: Berberine may inhibit the gene expression and production of pro-inflammatory cytokines by mononuclear cells in rheumatoid arthritis and healthy individuals without affecting cells viability. Future studies with larger sample size is needed to prove the idea.
The detection of brain tumors using magnetic resonance imaging is currently one of the biggest challenges in artificial intelligence and medical engineering. It is important to identify these brain tumors as early as possible, as they can grow to death. Brain tumors can be classified as benign or malignant. Creating an intelligent medical diagnosis system for the diagnosis of brain tumors from MRI imaging is an integral part of medical engineering as it helps doctors detect brain tumors early and oversee treatment throughout recovery. In this study, a comprehensive approach to diagnosing benign and malignant brain tumors is proposed. The proposed method consists of four parts: image enhancement to reduce noise and unify image size, contrast, and brightness, image segmentation based on morphological operators, feature extraction operations including size reduction and selection of features based on the fractal model, and eventually, feature improvement according to segmentation and selection of optimal class with a fuzzy deep convolutional neural network. The BraTS data set is used as magnetic resonance imaging data in experimental results. A series of evaluation criteria is also compared with previous methods, where the accuracy of the proposed method is 98.68%, which has significant results.
Mobile network operators store an enormous amount of information like log files that describe various events and users’ activities. Analysis of these logs might be used in many critical applications such as detecting cyber attacks, finding behavioral patterns of users, security incident response, and network forensics. In a cellular network, call detail records (CDRs) is one type of such logs containing metadata of calls and usually includes valuable information about contacts such as the phone numbers of originating and receiving subscribers, call duration, the area of activity, type of call (SMS or voice call), and a timestamp. With anomaly detection, it is possible to determine abnormal reduction or increment of network traffic in an area or for a particular person. This paper’s primary goal is to study subscribers’ behavior in a cellular network, mainly predicting the number of calls in a region and detecting anomalies in the network traffic. In this paper, a new hybrid method is proposed based on various anomaly detection methods such as GARCH, K-means, and neural network to determine the anomalous data. Moreover, we have discussed the possible causes of such anomalies.
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