Social Media Tools as a Learning Resource

Social media tools have become ubiquitous. You can see our students use them all the time. Among them most popular tools are Facebook, Wiki, YouTube, bulletin board, LinkedIn, blogging, and twittering. The advancement of modern technologies tries its best to accommodate the needs from people, especially the younger generation. As educators, how can we take advantage of this momentum? This paper will share a research study that was conducted in fall of 2009 at the central campus of the University of Houston. The study investigated student’s use of different social media tools, their perceptions and attitudes towards these tools, and their preference of social networking groups. The results show that the three top-used social media tools are Facebook, Wikipedia and YouTube; the top four reasons for using social media tools are for social engagement, direction communication, speed of feedback, and relationship building. Regarding social networking group, they preferred a group of civically engaged and no membership required as well as a group based on contemporary topic that may not last long. Based on their input, the author suggested some educational implications of some of these tools as a valuable resource for teaching and learning

Quantized passive filtering for switched delayed neural networks

The issue of quantized passive filtering for switched delayed neural networks with noise interference is studied in this paper. Both arbitrary and semi-Markov switching rules are taken into account. By choosing Lyapunov functionals and applying several inequality techniques, sufficient conditions are proposed to ensure the filter error system to be not only exponentially stable, but also exponentially passive from the noise interference to the output error. The gain matrix for the proposed quantized passive filter is able to be determined through the feasible solution of linear matrix inequalities, which are computationally tractable with the help of some popular convex optimization tools. Finally, two numerical examples are given to illustrate the usefulness of the quantized passive filter design methods

Purification and characterization of an antimicrobial protein from Gastrodia elata Blume tubers

Purpose: To purify and characterize a novel antimicrobial protein from the Gastrodia elata Blume (Bl.) plant, which has long been used in herbal medicine.Methods: The procedure for isolation and purification of Gastrodia elata protein (GEP) involved phosphate buffer extraction, ammonium sulfate precipitation, ion-exchange chromatography, and gelfiltration chromatography. Sodium dodecyl sulfate - polyacrylamide gel electrophoresis was employed to detect the apparent molecular mass and determine homogeneity, while paper disc diffusion was used to measure the antibacterial activity of GEP. A hemolytic assay was performed on rabbit red blood cells. The effect of pH, salt concentration, and temperature on the antibacterial activity of GEP was evaluated by minimum inhibitory concentration assay.Results: GEP was a 14-kDa monomer and displayed antimicrobial activity against Staphylococcus aureus and Candida albicans, with 8.0-mm and 9.4-mm zones of inhibition, respectively, but no antibacterial activity was observed against Escherichia coli. GEP had little hemolytic activity on red blood cells even at a concentrations of up to 200 mg/ml. GEP was thermally stable at temperatures below 70 °C for 30 min, and displayed higher antibacterial activity in the pH range 5.0 to 7.0.Conclusion: GEP protein is relatively thermostable and possesses antimicrobial activity. The results suggest that GEP protein has potential agricultural and industrial applications, such as in transgenic plants.Keywords: Antimicrobial protein, Gastrodia elata, Protein characterizatio

AcoMYB4, an Ananas comosus L. MYB transcription factor, functions in osmotic stress through negative regulation of ABA signaling

Drought and salt stress are the main environmental cues affecting the survival, development, distribution, and yield of crops worldwide. MYB transcription factors play a crucial role in plants’ biological processes, but the function of pineapple MYB genes is still obscure. In this study, one of the pineapple MYB transcription factors, AcoMYB4, was isolated and characterized. The results showed that AcoMYB4 is localized in the cell nucleus, and its expression is induced by low temperature, drought, salt stress, and hormonal stimulation, especially by abscisic acid (ABA). Overexpression of AcoMYB4 in rice and Arabidopsis enhanced plant sensitivity to osmotic stress; it led to an increase in the number stomata on leaf surfaces and lower germination rate under salt and drought stress. Furthermore, in AcoMYB4 OE lines, the membrane oxidation index, free proline, and soluble sugar contents were decreased. In contrast, electrolyte leakage and malondialdehyde (MDA) content increased significantly due to membrane injury, indicating higher sensitivity to drought and salinity stresses. Besides the above, both the expression level and activities of several antioxidant enzymes were decreased, indicating lower antioxidant activity in AcoMYB4 transgenic plants. Moreover, under osmotic stress, overexpression of AcoMYB4 inhibited ABA biosynthesis through a decrease in the transcription of genes responsible for ABA synthesis (ABA1 and ABA2) and ABA signal transduction factor ABI5. These results suggest that AcoMYB4 negatively regulates osmotic stress by attenuating cellular ABA biosynthesis and signal transduction pathways

Bounded real lemmas for positive descriptor systems

A well known result in the theory of linear positive systems is the existence of positive definite diagonal matrix (PDDM) solutions to some well known linear matrix inequalities (LMIs). In this paper, based on the positivity characterization, a novel bounded real lemma for continuous positive descriptor systems in terms of strict LMI is first established by the separating hyperplane theorem. The result developed here provides a necessary and sufficient condition for systems to possess H?H? norm less than ? and shows the existence of PDDM solution. Moreover, under certain condition, a simple model reduction method is introduced, which can preserve positivity, stability and H?H? norm of the original systems. An advantage of such method is that systems? matrices of the reduced order systems do not involve solving of LMIs conditions. Then, the obtained results are extended to discrete case. Finally, a numerical example is given to illustrate the effectiveness of the obtained results

Investigation on strengthening and toughening mechanisms of Nb-Ti-ZrB2 metal matrix ceramic composites reinforced with in situ niobium and titanium boride

Nb-Ti-ZrB2 metal matrix ceramic composites with a fixed atomic ratio Nb/Ti = 2/1 and ZrB2 volume fraction changing from 0, 11 vol%, 23 vol% to 36 vol% were hot pressed at 1600 °C under 30 MPa. The influence of ZrB2 content and Ti addition on the phase constitution, microstructure evolution, toughening mechanisms and strengthening mechanisms were investigated. It was shown that the formation of in situ Nb-rich (Ti,Nb)B and Ti-rich (Nb,Ti)B was attributed to a high mutual solubility of monoborides and the amount of niobium and titanium borides increased with increasing ZrB2 content. The needle-shaped (Ti,Nb)B phase weakened the damage to fracture toughness caused by ZrB2 particle fracture due to crack bridging, crack defection and the pull-out toughening mechanisms. The highest fracture toughness of the Nb-Ti-ZrB2 composites was 12.0 MPa·m1/2. The stiff (Nb,Ti)B phase acted as a strong obstacle to the dislocation motion, leading to dislocation pile-up and enhancing the strength of the Nb-Ti-ZrB2 composites during compression tests. However, stress concentration around the needle-shaped (Ti,Nb)B phase easily leads to crack initiation and extension, resulting in decreased strength. The yield strength of Nb-Ti-ZrB2 composites ranged from 657.3 MPa to 1783.0 MPa owing to the combined influence of the strenghening mechanism caused by (Nb,Ti)B and the weakening mechanism caused by (Ti,Nb)B. The compressive deformation and failure process were also discussed in detail in this study

Modelling and control design for an electro-pneumatic braking system in trains with multiple locomotives

This paper focuses on the modelling and control design for an electro-pneumatic braking system used in multi-locomotives in order to achieve accurately and steadily braking control of heavy haul trains. To deal with various time delays, a T-S fuzzy model based on satisfaction degree is proposed to simplify the cylinder model construction and a fuzzy clustering algorithm with forgetting factors is deployed to achieve parameter self-learning in order to improve the fuzzy control accuracy. Then a fuzzy genetic algorithm is adopted as the rolling optimisation method to reduce the effect of coupling noise, system disturbance and communication random interference such that the system robustness and controller response capability are improved. The effectiveness of the proposed method is verified by simulation and practical implementations. Copyright © 2012 Inderscience Enterprises Ltd

Research on the Application of Artificial Intelligence in Public Health Management: Leveraging Artificial Intelligence to Improve COVID-19 CT Image Diagnosis

Since the start of 2020, the outbreak of the Coronavirus disease (COVID-19) has been a global public health emergency, and it has caused unprecedented economic and social disaster. In order to improve the diagnosis efficiency of COVID-19 patients, a number of researchers have conducted extensive studies on applying artificial intelligence techniques to the analysis of COVID-19-related medical images. The automatic segmentation of lesions from computed tomography (CT) images using deep learning provides an important basis for the quantification and diagnosis of COVID-19 cases. For a deep learning-based CT diagnostic method, a few of accurate pixel-level labels are essential for the training process of a model. However, the translucent ground-glass area of the lesion usually leads to mislabeling while performing the manual labeling operation, which weakens the accuracy of the model. In this work, we propose a method for correcting rough labels; that is, to hierarchize these rough labels into precise ones by performing an analysis on the pixel distribution of the infected and normal areas in the lung. The proposed method corrects the incorrectly labeled pixels and enables the deep learning model to learn the infected degree of each infected pixel, with which an aiding system (named DLShelper) for COVID-19 CT image diagnosis using the hierarchical labels is also proposed. The DLShelper targets lesion segmentation from CT images, as well as the severity grading. The DLShelper assists medical staff in efficient diagnosis by providing rich auxiliary diagnostic information (including the severity grade, the proportions of the lesion and the visualization of the lesion area). A comprehensive experiment based on a public COVID-19 CT image dataset is also conducted, and the experimental results show that the DLShelper significantly improves the accuracy of segmentation for the lesion areas and also achieves a promising accuracy for the severity grading task