pHEMA: An Overview for Biomedical Applications

Poly(2-hydroxyethyl methacrylate) (pHEMA) as a biomaterial with excellent biocompatibility and cytocompatibility elicits a minimal immunological response from host tissue making it desirable for different biomedical applications. This article seeks to provide an in-depth overview of the properties and biomedical applications of pHEMA for bone tissue regeneration, wound healing, cancer therapy (stimuli and non-stimuli responsive systems), and ophthalmic applications (contact lenses and ocular drug delivery). As this polymer has been widely applied in ophthalmic applications, a specific consideration has been devoted to this field. Pure pHEMA does not possess antimicrobial properties and the site where the biomedical device is employed may be susceptible to microbial infections. Therefore, antimicrobial strategies such as the use of silver nanoparticles, antibiotics, and antimicrobial agents can be utilized to protect against infections. Therefore, the antimicrobial strategies besides the drug delivery applications of pHEMA were covered. With continuous research and advancement in science and technology, the outlook of pHEMA is promising as it will most certainly be utilized in more biomedical applications in the near future. The aim of this review was to bring together state-of-the-art research on pHEMA and their applications.     

Improving the Pressurized Flushing Efficiency in Reservoirs: an Experimental Study

Sediment flushing in many reservoirs of the world is accomplished with low efficiency. In this study, a new configuration was proposed for reservoir bottom outlet to increase the pressurized flushing efficiency. In the new configuration, a projecting semi-circular structure was connected to the upstream edge of bottom outlet. It was observed that by employing the projecting bottom outlet, the sediment removal efficiency increased significantly compared to the flushing via typical bottom outlet. In the case of new-configuration bottom outlet with L _sc /D _outlet  = 5.26 and D _sc /D _outlet  = 1.32, the dimensionless length, width and depth of flushing cone increased 280%, 45% and 14%, respectively, compared to the reference test. The proposed structure can ensure the sustainable use of reservoirs.     

An open-source multi-DOF articulated robotic educational platform for autonomous object manipulation

This research presents an autonomous robotic framework for academic, vocational and training purpose. The platform is centred on a 6 Degree Of Freedom (DOF) serial robotic arm. The kinematic and dynamic models of the robot have been derived to facilitate controller design. An on-board camera to scan the arm workspace permits autonomous applications development. The sensory system consists of position feedback from each joint of the robot and a force sensor mounted at the arm gripper. External devices can be interfaced with the platform through digital and analog I/O ports of the robot controller. To enhance the learning outcome for beginners, higher level commands have been provided. Advanced users can tailor the platform by exploiting the open-source custom-developed hardware and software architectures. The efficacy of the proposed platform has been demonstrated by implementing two experiments; autonomous sorting of objects and controller design. The proposed platform finds its potential to teach technical courses (like Robotics, Control, Electronics, Image-processing and Computer vision) and to implement and validate advanced algorithms for object manipulation and grasping, trajectory generation, path planning, etc. It can also be employed in an industrial environment to test various strategies prior to their execution on actual manipulators.     

Temperature-dependent diffusion coefficient of soluble substances during ethanol extraction of clove

The temperature dependence of the diffusion coefficient of ethanol-soluble substances from ground cloves (particle size 250 μm) during extraction was estimated by fitting batch extraction data at several temperatures (27.8, 40, 50, and 60°C) to a previously developed mass transfer model. The model was based on spherical geometry of particles. Nonlinear regression analysis was used to develop an equation that describes the diffusivity as a function of temperature. The temperature dependence ofD _A was of the Arrhenius type.     

Prediction of human breast and colon cancers from imbalanced data using nearest neighbor and support vector machines

This study proposes a novel prediction approach for human breast and colon cancers using different feature spaces. The proposed scheme consists of two stages: the preprocessor and the predictor. In the preprocessor stage, the mega-trend diffusion (MTD) technique is employed to increase the samples of the minority class, thereby balancing the dataset. In the predictor stage, machine-learning approaches of K-nearest neighbor (KNN) and support vector machines (SVM) are used to develop hybrid MTD-SVM and MTD-KNN prediction models. MTD-SVM model has provided the best values of accuracy, G-mean and Matthew's correlation coefficient of 96.71%, 96.70% and 71.98% for cancer/non-cancer dataset, breast/non-breast cancer dataset and colon/non-colon cancer dataset, respectively. We found that hybrid MTD-SVM is the best with respect to prediction performance and computational cost. MTD-KNN model has achieved moderately better prediction as compared to hybrid MTD-NB (Naïve Bayes) but at the expense of higher computing cost. MTD-KNN model is faster than MTD-RF (random forest) but its prediction is not better than MTD-RF. To the best of our knowledge, the reported results are the best results, so far, for these datasets. The proposed scheme indicates that the developed models can be used as a tool for the prediction of cancer. This scheme may be useful for study of any sequential information such as protein sequence or any nucleic acid sequence.     

Extracting lexical and phrasal paraphrases: a review of the literature

Recent advances in natural language processing have increased the popularity of paraphrase extraction. Most of the attention, however, has been focused on the extraction methods only without taking the resource factor into the consideration. Unknowingly, there is a strong relationship between them and the resource factor also plays an equally important role in paraphrase extraction. In addition, almost all of the previous studies have been focused on corpus-based methods that extract paraphrases from corpora based solely on syntactic similarity. Despite the popularity of corpus-based methods, a considerable amount of research has consistently shown that these methods are vulnerable to several types of erroneous paraphrases. For these reasons, it is necessary to evaluate whether the trend is moving in a positive direction. This paper reviews the major research on paraphrase extraction methods in detail. It begins by exploring the definition of paraphrase from different perspectives to provide a better understanding of the concept of paraphrase extraction. It then studies the characteristics and potential uses of different types of paraphrase resources. After that, it divides paraphrase extraction methods into four main categories: heuristic-based, knowledge-based, corpus-based and hybrid-based and summarizes their strengths and weaknesses. This paper concludes with some potential open research issues for future directions.     

Feature extraction and ML techniques for static gesture recognition

The main objective of this study is to explore the utility of a neural network-based approach in hand gesture recognition. The proposed system presents two recognition algorithms to recognize a set of six specific static hand gestures, namely open, close, cut, paste, maximize, and minimize. The hand gesture image is passed through three stages: preprocessing, feature extraction, and classification. In the first method, the hand contour is used as a feature that treats scaling and translation of problems (in some cases). However, the complex moment algorithm is used to describe the hand gesture and to treat the rotation problem in addition to scaling and translation. The back-propagation learning algorithm is employed in the multilayer neural network classifier. The second method proposed in this article achieves better recognition rate than the first method.     

Chlorophyll and plankton of the Gulf coastal waters of Saudi Arabia bordering a desalination plant

As on land, plants are the real producers in the sea, and on them depend all marine living resources and the basic sustainability of ecosystems. Primary production is performed by chlorophyll-bearing plants ranging from the tiny phytoplankton to the giant kelps through the process ofphotosynthesis. Zooplankton play an important role as secondary producers, and together with phytoplankton they support the vast assemblages of marine food chain with all their diversity and complexity. Data on chlorophyll pigments, phytoplankton and zooplankton are regarded as a sound basis for environmental appraisal of ecosystems. This paper presents a set of data collected from the Saudi Arabian coastal waters near the desalination plants in AI-Jubail. Materials were collected from six different sites covering the intake and discharge zones during cruises carried out in 1997-1998. Analyses of chlorophyll pigments were made using the spectrophotometric method. Plankton samples were collected using a Nansen plankton net with a mesh size of 75 μ and analyzed following standard procedures. Chlorophyll a, b, c and phaeophytin are the most commonly occurring pigments in seawater. Their concentrations showed wide fluctuation. The phytoplankton community was composed of 35 genera representing the Diatoms, Dinoflagellates and blue- green algae. Zooplankton were composed ofProtozoa, Coelenterata, Ctenophora, Aschelminthes, Annelida, Mollusca, Arthropoda, Echinodermata and Chordata. Arthropoda, represented by Cladocera, Copepoda and Crustacean larvae, formed the largest group followed by Chordata. The distribution of phyto- and zooplankton was examined and discussed on a seasonal, annual and inter-annual basis. In terms of species, overall species composition was not affected by plant discharge. The study brings out a greater understanding of the changes experienced by biotic communities as a result of impingement, entrainment and entrapment consequent to water passage through the plant structures. The study reflects the ecological relationships that the phytoplankton and the zooplankton of the region possess with respect to intake and discharge. Further, the study has brought to light a very redeeming feature of the ecosystem to sustain its productivity and planktonic abundance. It was observed that seawater temperature, conductivity and total suspended solids did not act as limiting factors. Besides throwing much light on the little known biological aspects of desalination sites, the data provided constitute a significant addition to the knowledge base of marine living resources in an industrial zone of Gulf coastal waters.     

Wear of graphite anodes during electrolysis of add sulphate solutions

Graphite electrodes were prepared by mixing calcined coke and coal tar pitch. They were pressed under 250 kg cm^–2 and heat treated up to 2800° C. Rectangles measuring 70 mm x 40 mm x 8 mm were anodically polarized under galvanostatic and potentiostatic conditions. Electrolyses were conducted at 10–50 mA cm^–2 for periods ranging from 10–120 hours in Na₂SO₄ solutions acidified with sulphuric acid to various pH values. The wear of graphite anodes increased with decreasing bath temperature, increasing acid concentration, decreasing pH of the electrolyte and increasing current density. A model is suggested which assumes that corrosion takes place via the formation of a lamellar crystal compound with the formula (C ₈ ⁰ O)(OH)₃HSO ₄ ^– ·2H₂SO₄.The compound is unstable at higher temperatures when corrosion is effected by oxidation of graphite by atomic oxygen. The formation of the carbon ions was found to be a necessary precondition for the formation of the complex.