Trajectories of knowledge economy in SSA and MENA countries

In the first critical assessment of knowledge economy dynamic paths in Africa and the Middle East, but for a few exceptions, we find overwhelming support for diminishing cross-country disparities in knowledge-based economy dimensions. The paper employs all the four components of the World Bank's Knowledge Economy Index (KEI): economic incentives, innovation, education, and information infrastructure. The main finding suggests that sub-Saharan African (SSA) and the Middle East and North African (MENA) countries with low levels of KE dynamics and catching-up their counterparts of higher KE levels. We provide the speeds of integration and time necessary to achieve full (100%) integration. Policy implications are also discussed.     

Angiotensin I‐converting enzyme inhibitory and Ca‐binding activities of peptides prepared from tuna cooking juice and spleen proteases

Tuna cooking juice is a by‐product from the tuna canning industry. In this study, tuna cooking juice was hydrolysed by proteases extracted from the spleen. Tuna cooking juice showed the highest ACE inhibitory and Ca‐binding activities after hydrolysis for 270 and 180 min, respectively. The hydrolysate was further fractionated by ultrafiltration. The permeate exhibited highest ACE inhibitory and Ca‐binding activities when passed through 1 and 5 kDa cut‐off membranes, respectively. Gel filtration chromatography was used to determine the MW of bioactive peptides that exhibited highest ACE inhibitory and Ca‐binding activities. Those peptides that exhibited highest ACE inhibitory and Ca‐binding activities were the MW range of 238–829 Da and 1355–1880 Da, respectively. These results suggest that the tuna cooking juice and the spleen protease extract are a potential source of bioactive peptides that can be utilised as bioactive ingredients in functional food and nutraceuticals.     

Application of higher order spectral features and support vector machines for bearing faults classification

Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals.     

Characterization,in vitro bioactivity and biological studies of sol-gel-derived TiO2 substituted 58S bioactive glass

Bioactive glasses (BGs) have been used for bone formation and bone repair processes in recent years. This study investigated the titanium substitution effect on 58S BGs (Ti-BGs) 60SiO₂-(36 − X)CaO-4P₂O₅-XTiO₂ (X = 0, 3, and 5 mol.%) prepared by the sol-gel technique, and the main goal was to find the optimum amount of titanium in Ti-BGs. Synthesized BGs, which were investigated after immersion in simulated body fluid (SBF), were tested by X-ray diffraction (XRD) analysis, Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy. Moreover alkaline phosphate (ALP) activity, 3-(4,5dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, and antibacterial studies were employed to investigate the biological properties of Ti-BGs. According to the FTIR and XRD test results, hydroxyapatite (HA) formation on Ti-BGs surfaces was confirmed. Meanwhile, the presence of 5 mol.% compared to 3 mol.% increased the HA grain distribution and their size on the Ti-BGs surface. Additionally, MTT and ALP results confirmed that the optimal amount of titanium substitution in BG was 5 mol.%. Since 5 mol.% Ti incorporated BG (BG-5) had the highest biocompatibility level, antibacterial properties, maximum cell proliferation, and ALP activity among the synthesized Ti-BGs, it is presented as the best candidate for further in vivo investigations.     

Operational time-series data modeling via LSTM network integrating principal component analysis based on human experience

Today’s information technologies involve increasingly intelligent systems, which come at the cost of increasingly complex equipment. Modern monitoring systems collect multi-measuring-point and long-term data which make equipment health prediction a “big data” problem. It is difficult to extract information from such condition monitoring data to accurately estimate or predict health statuses. Deep learning is a powerful tool for big data processing that is widely utilized in image and speech recognition applications, and can also provide effective predictions in industrial processes. This paper proposes the Long Short-term Memory Integrating Principal Component Analysis based on Human Experience (HEPCA-LSTM), which uses operational time-series data for equipment health prognostics. Principal component analysis based on human experience is first conducted to extract condition parameters from the condition monitoring system. The long short-term memory (LSTM) framework is then constructed to predict the target status. Finally, a dynamic update of the prediction model with incoming data is performed at a certain interval to prevent any model misalignment caused by the drifting of relevant variables. The proposed model is validated on a practical case and found to outperform other prediction methods. It utilizes a powerful deep learning analysis method, the LSTM, to fully process big condition monitoring series data; it effectively extracts the features involved with human experience and takes dynamic updates into consideration.     

PMU based voltage security assessment of power systems exploiting principal component analysis and decision trees

The need for feature selection and dimension reduction is felt as a fundamental step in security assessment of large power systems in which the number of features representing the state of power grids dramatically increases. These large amounts of attributes are not proper to be used for computational intelligence (CI) techniques as inputs, because it may lead to a time consuming procedure with insufficient results and they are not suitable for on-line purposes and updates.This paper proposes a combined method for an online voltage security assessment in which the dimension of the token data from phasor measurement units (PMUs) is reduced by principal component analysis (PCA). Then, the features with different stability indices are put into several categories and feature selection is done by correlation analysis in each category. These selected features are then given to decision trees (DTs) for classification and security assessment of power systems.The method is applied to 39-bus test system and a part of Iran power grid. It is seen from the results that the DTs with reduced data have simpler splitting rules, better performance in saving time, reasonable DT error and they are more suitable for constant updates.