A genetic programming model to generate risk-adjusted technical trading rules in stock markets

Technical trading rules can be generated from historical data for decision making in stock markets. Genetic programming (GP) as an artificial intelligence technique is a valuable method to automatically generate such technical trading rules. In this paper, GP has been applied for generating risk-adjusted trading rules on individual stocks. Among many risk measures in the literature, conditional Sharpe ratio has been selected for this study because it uses conditional value at risk (CVaR) as an optimal coherent risk measure. In our proposed GP model, binary trading rules have been also extended to more realistic rules which are called trinary rules using three signals of buy, sell and no trade. Additionally we have included transaction costs, dividend and splits in our GP model for calculating more accurate returns in the generated rules. Our proposed model has been applied for 10 Iranian companies listed in Tehran Stock Exchange (TSE). The numerical results showed that our extended GP model could generate profitable trading rules in comparison with buy and hold strategy especially in the case of risk adjusted basis.     

Analysis and forecasting of IPO underpricing

Probability of withdrawal is a feature of initial public offering (IPOs), which can be an important parameter in decisions of investors and issuers. Considering the probability of offering withdrawal facilitates more precise estimation of underpricing. In this paper, the effective factors on probability of IPO withdrawal and underpricing in Tehran Stock Exchange have been characterized using regression, and then neural network is applied to estimate the probability of IPO withdrawal and underpricing. To evaluate the performance of our applied method, fuzzy regression is employed and compared with neural network. According to the obtained empirical results, neural network demonstrates better accuracy than fuzzy regression. The results indicate that there is a meaningful relationship between underpricing and probability of withdrawal, and the probability of IPO withdrawal plays an important role in precise evaluation of underpricing.     

Using hash tables to manage the time-storage complexity in a point location problem: Application to explicit model predictive control

The online computational burden of linear model predictive control (MPC) can be moved offline by using multi-parametric programming, so-called explicit MPC. The solution to the explicit MPC problem is a piecewise affine (PWA) state feedback function defined over a polyhedral subdivision of the set of feasible states. The online evaluation of such a control law needs to determine the polyhedral region in which the current state lies. This procedure is called point location; its computational complexity is challenging, and determines the minimum possible sampling time of the system. A new flexible algorithm is proposed which enables the designer to trade off between time and storage complexities. Utilizing the concept of hash tables and the associated hash functions, the proposed method solves an aggregated point location problem that overcomes prohibitive complexity growth with the number of polyhedral regions, while the storage–processing trade-off can be optimized via scaling parameters. The flexibility and power of this approach is supported by several numerical examples.     

Continuous microwave assisted flow synthesis and characterization of calcium deficient hydroxyapatite nanorods

Synthetic calcium deficient hydroxyapatite (CDHA) nanorods (<100?nm) were rapidly prepared with the help of a new continuous microwave assisted flow synthesis (CMFS) reactor in 5?min only from aqueous solution of calcium hydroxide and orthophosphoric acid at pH 8.5. The effect of various reaction parameters like, pH, concentration, temperature, residence time, degree of crystallinity and particle surface area were studied in detail. The phase purity, particle size and morphology of the powder samples were characterised by techniques such as X-ray diffraction analysis, transmission electron microscopy, scanning electron microscopy and FTIR and Raman spectroscopy. With the help of X-ray photoelectron spectroscopy, the chemical analysis was completed. Measurements were taken into account to estimate the particle size following the dynamic light scattering. The results showed that the employed synthesis procedure offered an efficient and economical route to achieve high quality nano-sized products with suitable size and low level of impurities.     

基于电话语料的维吾尔连续音素识别

结合维吾尔语的语音特征和语义信息,在大量电话语音语料库的基础上,以建立维吾尔语连续音素识别平台为目标,通过构建隐马尔科夫模型工具HTK(Hidden Markov Model Toolkit)工具实现了维吾尔语连续音素识别算法:首先根据具体技术指标完成了较大规模电话语音语料库的录制和标注工作;确定音素为基元,通过训练获得了每个音素的HMM(Hidden Markov Model)声学模型,随后对输入的语音进行识别,声学模型在不同的高斯混合数目下,得出了识别结果;统计了32个音素的识别率并对它进行分析,为了进一步提高识别率奠定了基础。     

Characterization of a Cohesive Soil Bed using a Cone Pressuremeter

A new cone pressuremeter has been developed at the University of Engineering and Technology, Lahore-Pakistan. The new device is called the Akbar Pressuremeter (APMT). This paper is based on the APMT testing of a cohesive soil deposit comprised of low plastic lean clay (CL) to sandy silty clay (CL-ML). The APMT testing, using a full-displacement technique, standard penetration tests and undisturbed samples, was carried out at two locations. The soil strength and type were determined using the undisturbed samples. The applied pressure-cavity strain curves of the APMT tests performed at different levels were analysed to determine soil strength and stiffness. The undrained shear strength of the undisturbed samples was determined in the laboratory by unconfined compression tests. This paper provides a comparison of the parameters interpreted from the pressuremeter and those determined from other field and laboratory methods.     

Practical evaluation of relationship between concrete resistivity,water penetration,rapid chloride penetration and compressive strength

There are several factors and test methods for evaluating the durability of concrete. In recent years a great deal of attention has been paid to research and development of relationships of these parameters for production of sustainable concretes: water penetration and Rapid Chloride Penetration Test (RCPT) methods which are most commonly used to evaluate the permeability of concrete are two of the most famous methods specified by BS EN-12390-8:2000 and ASTM C1202 respectively. Concrete surface resistivity (SR) test is also a suitable indicator for concrete penetration and chloride ion permeability. It is a non-destructive, simple, rapid and economical method that can also be used on site.The present study is an exploratory research concerned with the relationship of these methods. Based on the correlation of concrete resistivity with water penetration and Rapid Chloride Penetration Test (RCPT) results, two new models for relating these parameters are presented.     

Optimal visual sensor placement for coverage based on target location profile

The proper placement of visual sensors across a sensor field for covering targets with arbitrary location and orientation is a mission-critical decision in surveillance applications. The specifics of sensor deployment in these applications not only determine the maximum achievable coverage, but it also affects the quality of the target’s appearance in cameras for subsequent use in vision tasks. However, the inaccuracies inherent in localization techniques and the lack of knowledge regarding the target orientation render existing proposals insufficient for real-life scenarios. In this paper, we address both challenges. First, we extend the conventional point representation of targets with a circular model to account for full-angle coverage of targets with known location yet with unknown orientation from all directions. We then assume, in the absence of precise location information, a trajectory profile of targets could instead be generated through the importance sampling of the environment, indicating spots where the target is most likely located. This profile-based abstraction enables us to capture the uncertainty in target’s location by encircling every agglomeration of proximal samples within one cluster. Each cluster can then be viewed as a virtual macroscopic circular target for which we formulate the coverage problem in terms of a Binary Integer Programming (BIP) model. We have also taken into account the presence of obstruction in between multiple targets by calculating the angles of view of the sensors in an occlusion-dependant manner, effectively determining optimal placement for maximal instead of full-angle coverage. Evaluation results derived from our simulation experiments reveal that the proposed mechanism can effectively achieve high coverage accuracy with minimum number of deployed sensors.     

Foliar Application of Silicon Enhances Growth,Flower Yield,Quality and Postharvest Life of Tuberose (Polianthes tuberosa L.) under Saline Conditions by Improving Antioxidant Defense Mechanism

Silicon - Tuberose is native to Mexico and then reached Europe and spread other parts of the world. In Pakistan, tuberose stalks come in the market during late summer and autumn when only few...     

Pipe failure rate prediction in water distribution networks using multivariate adaptive regression splines and random forest techniques

ABSTRACT

This paper presents the results of a comparison between multivariate adaptive regression splines (MARS) and random forest (RF) techniques in pipe failure prediction in two water distribution networks. In this regard, pipe diameter, pipe length, pipe installation depth, pipe age and average hydraulic pressure are considered as input variables. Results show that the RF outperforms the MARS which is found as an accurate pipe failure rate predictor. The proposed models are further evaluated through dividing the data into three parts of lower, medium and higher pipe failure rate values. According to the equations produced by MARS technique, three variables of pipe diameter, pipe age and average hydraulic pressure are distinguished as the most effective variables in predicting pipe failure rate in the first case study. Four variables of pipe diameter, pipe length, pipe age and average hydraulic pressure are determined as the most effective variables in the second case study.