Improving the performance of the needleman-wunsch algorithm using parallelization and vectorization techniques

The Needleman-Wunsch (NW) is a dynamic programming algorithm used in the pairwise global alignment of two biological sequences. In this paper, three sets of parallel implementations of the NW algorithm are presented using a mixture of specialized software and hardware solutions: POSIX Threads-based, SIMD Extensions-based and a GPU-based implementations. The three implementations aim at improving the performance of the NW algorithm on large scale input without affecting its accuracy. Our experiments show that the GPU-based implementation is the best implementation as it achieves performance 72.5X faster than the sequential implementation, whereas the best performance achieved by the POSIX threads and the SIMD techniques are 2X and 18.2X faster than the sequential implementation, respectively.

     

11K Hands: Gender recognition and biometric identification using a large dataset of hand images

Human hand not only possesses distinctive feature for gender information, it is also considered one of the primary biometric traits used to identify a person. Unlike face images, which are usually unconstrained, an advantage of hand images is they are usually captured under a controlled position. Most state-of-the-art methods, that rely on hand images for gender recognition or biometric identification, employ handcrafted features to train an off-the-shelf classifier or be used by a similarity metric for biometric identification. In this work, we propose a deep learning-based method to tackle the gender recognition and biometric identification problems. Specifically, we design a two-stream convolutional neural network (CNN) which accepts hand images as input and predicts gender information from these hand images. This trained model is then used as a feature extractor to feed a set of support vector machine classifiers for biometric identification. As part of this effort, we propose a large dataset of human hand images, 11K Hands, which contains dorsal and palmar sides of human hand images with detailed ground-truth information for different problems including gender recognition and biometric identification. By leveraging thousands of hand images, we could effectively train our CNN-based model achieving promising results. One of our findings is that the dorsal side of human hands is found to have effective distinctive features similar to, if not better than, those available in the palmar side of human hand images. To facilitate access to our 11K Hands dataset, the dataset, the trained CNN models, and our Matlab source code are available at (https://goo.gl/rQJndd).

     

Electron transport in dye-sanitized solar cell with tin-doped titanium dioxide as photoanode materials

Journal of Materials Science: Materials in Electronics - Pure and tin-incorporated TiO2 (Sn-TiO2) nanoparticles were prepared utilizing photolysis method. Field emission-scanning electron...     

Development of Social Media Analytics System for Emergency Event Detection and Crisis Management

Social media platforms have proven to be effective for information gathering during emergency events caused by natural or human-made disasters. Emergency response authorities, law enforcement agencies, and the public can use this information to gain situational awareness and improve disaster response. In case of emergencies, rapid responses are needed to address victims’ requests for help. The research community has developed many social media platforms and used them effectively for emergency response and coordination in the past. However, most of the present deployments of platforms in crisis management are not automated, and their operational success largely depends on experts who analyze the information manually and coordinate with relevant humanitarian agencies or law enforcement authorities to initiate emergency response operations. The seamless integration of automatically identifying types of urgent needs from millions of posts and delivery of relevant information to the appropriate agency for timely response has become essential. This research project aims to develop a generalized Information Technology (IT) solution for emergency response and disaster management by integrating social media data as its core component. In this paper, we focused on text analysis techniques which can help the emergency response authorities to filter through the sheer amount of information gathered automatically for supporting their relief efforts. More specifically, we applied state-of-the-art Natural Language Processing (NLP), Machine Learning (ML), and Deep Learning (DL) techniques ranging from unsupervised to supervised learning for an in-depth analysis of social media data for the purpose of extracting real-time information on a critical event to facilitate emergency response in a crisis. As a proof of concept, a case study on the COVID-19 pandemic on the data collected from Twitter is presented, providing evidence that the scientific and operational goals have been achieved.     

Determination of trap density-of-states distribution of nitrogen-doped ultrananocrystalline diamond/hydrogenated amorphous carbon composite films

Thin films comprising nitrogen-doped ultrananocrystalline diamond/hydrogenated amorphous-carbon(UNCD/a-C∶H)composite films were experimentally investigated.The prepared films were grown on Si substrates by the coaxial arc plasma de-position method.They were characterized by temperature-dependent capacitance-frequency measurements in the temperat-ure and frequency ranges of 300-400 K and 50 kHz-2 MHz,respectively.The energy distribution of trap density of states in the films was extracted using a simple technique utilizing the measured capacitance-frequency characteristics.In the measured tem-perature range,the energy-distributed traps exhibited Gaussian-distributed states with peak values lie in the range:2.84×1016-2.73×1017 eV-1 cm-3 and centered at energies of 120-233 meV below the conduction band.These states are generated due to a large amount of sp2-C and π-bond states,localized in GBs of the UNCD/a-C∶H film.The attained defect parameters are accommodating to understand basic electrical properties of UNCD/a-C∶H composite and can be adopted to suppress defects in the UNCD-based materials.     

Electrocoalescence of binary water droplets falling in oil: Experimental study

The approaching movement and consequent coalescence of binary water droplets falling in stagnant oil and exposed to an external electric field are investigated using a high speed camera. Different situation of the droplets and electric field intensities are applied in the experiments. The qualitative results of the experimental observations are exhibited through the scaled images of the binary droplets snapshots in milliseconds. Furthermore, different approaching trends of the droplets are presented as quantitative plots and discussed based on the theoretical electrostatic and hydrodynamic models. The effect of the applied voltage amplitude, initial distance of the drop pair, and skew angle of the electric field are investigated. The experimental results prove the electrostatic theories; as acceleration in electrocoalescence demonstrated using a stronger electric field as well as closer distance between the droplets. It was also revealed that the skew angle of the electric field decelerates the electrocoalescence until alignment of the droplets.     

Mass transfer from nanofluid drops in a pulsed liquid–liquid extraction column

Mass transfer in gas–liquid systems has been significantly enhanced by recent developments in nanotechnology. However, the influence of nanoparticles in liquid–liquid systems has received much less attention. In the present study, both experimental and theoretical works were performed to investigate the influence of nanoparticles on the mass transfer behaviour of drops inside a pulsed liquid–liquid extraction column (PLLEC). The chemical system of kerosene–acetic acid–water was used, and the drops were organic nanofluids containing hydrophobic SiO₂ nanoparticles at concentrations of 0.01, 0.05, and 0.1 vol%. The experimental results indicate that the addition of 0.1 vol% nanoparticles to the base fluid improves the mass transfer performance by up to 60%. The increase in mass transfer with increased nanoparticle content was more apparent for lower pulsation intensities (0.3–1.3 cm/s). At high pulsation intensities, the Sauter mean diameter (d₃₂) decreased to smaller sizes (1.1–2.2 mm), leading to decreased Brownian motion in the nanoparticles. Using an analogy for heat and mass transfer, an approach for determining the mass diffusion coefficient was suggested. A new predictive correlation was proposed to calculate the effective diffusivity and mass transfer coefficient in terms of the nanoparticle volume fraction, Reynolds number, and Schmidt number. Finally, model predictions were directly compared with the experimental results for different nanofluids. The absolute average relative error (%AARE) of the proposed correlation for the mass transfer coefficient and effective diffusivity were 5.3% and 5.4%, respectively.     

Collaboration networks of arab biomedical researchers

Social networks (SN) consist of a set of actors and connections between them. A collaboration network (ColNet) is a special type of SN, in which the actors represent researchers and the link between them indicate that they have co-authored at least one paper. ColNet analysis reveals how researchers interact and behave. A wide range of applications can be based on such studies. The current works on ColNet usually focus on a specific domain/discipline, country/geographical region or time interval. In our study, we focus on one of the understudied regions (the Arab world), and present a novel study on the ColNet of researchers in this region. The domain of interest in our study is biomedicine. We construct, analyze, and study ColNet of biomedical researchers in the Arab world. We divide the region of interest (the Arab world) into four geographical regions and look into the evolution of ColNet of each region separately over time. Our analysis reveals that there is an increase in the number of both authors and publications over time, and that authors tend to work in increasingly larger groups rather than working individually, which is consistent with what is assumed about the nature of research in this field. Our analysis also reveals that a researcher’s productivity is correlated with the amount of change in his/her circle of collaborators over time. For example, researchers working in stable or fixed groups and researchers who have completely different research group every few years are not necessarily the most productive ones.