Proactive task migration with a self-adjusting migration threshold for dynamic thermal management of multi-core processors

Request for more computation power steadily forces designers to provide more powerful processors using more number of cores on a single chip. The increasing complexity of processors leads to higher integration density, power density, and temperature. For avoiding thermal emergencies, various dynamic thermal management techniques have been presented. In this paper, we present a novel online self-adjusting temperature threshold schema for dynamic thermal management to minimize both average and peak temperature with very low performance overhead. Our proposed algorithm adjusts migration threshold according to workload and hardware platforms. The experimental results indicate that our technique can significantly decrease the average and peak temperature compared to Linux standard scheduler, and two well-known thermal management techniques: PDTM and TAS.     

Predictive model for shear strength estimation in reinforced concrete beams with recycled aggregates using Gaussian process regression

Neural Computing and Applications - In order to attain sustainable development, recycled concrete aggregates (RCAs) are increasingly utilized in civil engineering projects. Therefore, it is vital...     

A new set of sufficient conditions based on coupling parameters for synchronization of Hopfield like Chaotic Neural Networks

In this paper, we consider a Hopfield like Chaotic Neural Networks which have both self-coupling and non-invertible activation functions. We show that the interactions between neurons can be used as a means of chaos generation or suppression to neuron’s outputs when more adaptability or stability is required. Furthermore, a new set of sufficient conditions based on coupling weights is proposed so that the synchronization of all neuron’s outputs with each other is guaranteed, when all neuron’s have identical activation functions. Finally, the effectiveness of the proposed approach is evaluated by performing simulations on three illustrative examples.     

Numerical solution of fuzzy relational equations based on smooth fuzzy norms

In this paper, we study and formulate a BP learning algorithm for fuzzy relational neural networks based on smooth fuzzy norms for functions approximation. To elaborate the model behavior more, we have used different fuzzy norms led to a new pair of fuzzy norms. An important practical case in fuzzy relational equations (FREs) is the identification problem which is studied in this work. In this work we employ a neuro-based approach to numerically solve the set of FREs and focus on generalized neurons that use smooth s-norms and t-norms as fuzzy compositional operators.     

A Wavelet-ANFIS Hybrid Model for Groundwater Level Forecasting for Different Prediction Periods

Artificial neural network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) have an extensive range of applications in water resources management. Wavelet transformation as a preprocessing approach can improve the ability of a forecasting model by capturing useful information on various resolution levels. The objective of this research is to compare several data-driven models for forecasting groundwater level for different prediction periods. In this study, a number of model structures for Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Wavelet-ANN and Wavelet-ANFIS models have been compared to evaluate their performances to forecast groundwater level with 1, 2, 3 and 4 months ahead under two case studies in two sub-basins. It was demonstrated that wavelet transform can improve accuracy of groundwater level forecasting. It has been also shown that the forecasts made by Wavelet-ANFIS models are more accurate than those by ANN, ANFIS and Wavelet-ANN models. This study confirms that the optimum number of neurons in the hidden layer cannot be always determined by using a specific formula but trial-and-error method. The decomposition level in wavelet transform should be determined according to the periodicity and seasonality of data series. The prediction of these models is more accurate for 1 and 2 months ahead (for example RMSE?=?0.12, E?=?0.93 and R ²?=?0.99 for wavelet-ANFIS model for 1 month ahead) than for 3 and 4 months ahead (for example RMSE?=?2.07, E?=?0.63 and R ²?=?0.91 for wavelet-ANFIS model for 4 months ahead).     

Detection of forest reserve incursion in south-western Nigeria from a combination of multi-date aerial photographs and high resolution satellite imagery

Forest reserves were established in Nigeria to serve as repositories of the primary habitats of the forest ecosystems.However, with increasing population pressure and the need to feed the population, the peasant farmers are already making incursion into these reserves. This phenomenon has not attracted the serious attention of relevant government establishments, perhaps because they do not know the extent of the incursion in order to appreciate the magnitude of the danger it poses to the environment at large. This paper is therefore an attempt to quantify the extent of biotic degradation consequent upon the incursion. The study makes use of aerial photos and SPOT XS imagery and demonstrates the importance of combination of the two data sources to environmental monitoring in a country like Nigeria, with a poor geo-information/data bank.     

利用不稳定压力数据建立碳酸盐岩储集层模型

地质模型通常是根据地质信息,利用地球物理和岩石物理资料来建立.为了建立准确的碳酸盐岩油藏(伊朗海上油田)模型,不仅利用了地震和测井数据,也结合了该油田现有99井次的试井数据.在试井解释过程中观察到了许多特征,如封闭断层、水层、裂缝和分层系统,其中某些特征只能利用压力瞬态数据来识别,如地震数据中观察不到的一条封闭断层在试井曲线上则可识别.除了识别构造特征外,试井解释得到的渗透率数据也可用于绘制渗透率等值线图,而不再采用岩心分析得到的渗透率数据.试井得到的渗透率是泄油区域的有效渗透率,而岩心分析得到的渗透率并不能代表非均质油藏的渗透性.利用非稳态压力分析结果建立油藏模型,可以提高碳酸盐岩油藏建模的准确性.图6表1参8     

Review: the latest advances in biomedical applications of chitosan hydrogel as a powerful natural structure with eye-catching biological properties

Journal of Materials Science - Chitosan is one of the natural cationic polymers with unique properties such as non-toxicity, biodegradability, biocompatibility, environmentally friendly that has...     

Fermentation of table cream by Lactobacillus plantarum strains: effect on fungal growth,aflatoxin M1 and ochratoxin A

Aflatoxin M1 (AFM₁) and ochratoxin A (OTA) are two main mycotoxins in milk and dairy products. In the present work, the ability of four Lactobacillus strains (L. plantarum PTCC 1058, L. plantarum LP3, L. plantarum AF1 and L. plantarum LU5) to remove AFM1 and OTA in fermented cream was studied during 24 h fermentation. The antifungal activity of the mentioned lactobacilli against the defined fungi (Aspergillus flavus PTCC 5004, Aspergillus parasiticus PTCC 5018, Aspergillus nidulans PTCC 5014, Aspergillus ochraceus PTCC 5060) was also evaluated. The results showed that the cell counts of all strains were increased by 64–70% during fermentation. All Lactobacillus strains decreased the amount of AFM₁ significantly (P ≤ 0.05) in the range of 26–52%, which the highest AFM₁-reducing effect was related to L. plantarum LU5 (from 0.5 to 0.24 μg kg⁻¹). The mean OTA removal by Lactobacillus strains in fermented cream also ranged from 32 to 58%. Amongst Lactobacillus strains, the cell-free culture supernatants of L. plantarum LU5 showed the highest (inhibition zone of 26.7 ± 1.2 mm) and L. plantarum LP3 and L. plantarum PTCC 1058 the lowest antifungal activities. The fermented creams contained Lactobacillus strains exhibited the highest and lowest antifungal activities against A. ochraceus and A. parasiticus, respectively. L. plantarum LU5, with the inhibition zone of 27.6 ± 0.9 mm, was the most effective fungal inhibitor, while L. plantarum PTCC 1058 had the lowest antifungal activity.