Miracle-dz/UniFAR-FTUs huggingface dataset:
Utilizing Dual Coding Theory and Animated Images to Enhance ESL Students' Vocabulary Learning

This study utilized Paivio’s Dual Coding Theory and multimedia glosses to investigate the effects of animated images, when shown simultaneously with verbal information, on ESL students’ vocabulary learning. Two groups of low-intermediate adult ESL learners took the target word pretest prior to the experiment. During the experiment, both groups read the same electronic text but the target words were glossed differently, with the Malay translations and animated images for the treatment group, and the Malay translations plus static images for the comparison group. Immediately after the experiment, both groups retook the same test. One week later, the groups took the delayed posttest. Two 2x2 mixed-factorial ANOVAs were run, one using the scores of the immediate postttest, the other using the scores of the delayed posttest, to determine if the scores of both groups were statistically significant. The results showed that the group with access to animated images retained more target words. Further pedagogical implications are discussed.
BIOREMEDIATION METHODS ASSISTED WITH HUMIC ACID FOR THE TREATMENT OF OIL-CONTAMINATED DRILL CUTTINGS

In this study it is reported the treatment of oil-based drill cuttings (OBDC) by the analysis of performance 8 biopiles placed within reactors. Biopiles were prepared with a mixture of drill cutting, bulking agents, microbial consortium, inorganic nutrients and humic acid (HA) solutions at 500, 1,000, 1,500, 2,000, 2,500 and 3,000 mg / L. Experimental follow-up was done registering temperature, CO 2 generation and pH. After 8 weeks, oil removal was evaluated using total petroleum hydrocarbons (TPH) soxhlet extraction, the highest value was 72% removal of TPH obtained with the biopile of 1,500 mg / L of HA. Also, this biopile showed higher temperatures, higher CO 2 generation and the lowest pH, indicating a more active biological system compared with other concentrations. The obtained results show that the addition of HA signiﬁcantly improves the performance of biopiles, indicating that the combination of technologies employed with addition of HA are e ﬀ ective for the treatment of oil-based drill cuttings. estudio se informa el tratamiento de recortes de perforación base aceite (OBDC por sus siglas en inglés) mediante el análisis del desempeño de 8 biopilas dentro de reactores. Las biopilas se prepararon con una mezcla de recorte de perforación, agentes volumétricos, consorcio microbiano, nutrientes inorgánicos y soluciones de ácido húmico (HA) a 500, 1,000, 1,500, 2,000, 2,500 y 3,000 mg / L. El seguimiento experimental se realizó registrando la temperatura, la generación de CO 2 y el pH. Después de 8 semanas, la extracción de aceite se evaluó utilizando extracción soxhlet de los hidrocarburos totales del petróleo (TPH por sus siglas en inglés). La mayor remoción de TPH fue del 72% obtenida con la biopila de 1,500 mg / L de HA. Además, esta biopila mostró temperaturas más altas, mayor generación de CO 2 y el pH más bajo, lo que indica un sistema biológico más activo en comparación con otras concentraciones. Los resultados obtenidos muestran que la adición de HA mejora signiﬁcativamente el rendimiento de las biopilas, lo que indica que la combinación de tecnologías empleadas con la adición de HA es eﬁcaz para el tratamiento de los recortes de perforación base aceite.
Morphopathological and Biochemical Changes Induced by Cisplatin as Anticancer Drug and The Protective Role of Moringa Leaf Ethanolic Extract and L-Carnitine in Rat

Cisplatin is a potent, highly effective, broad spectrum chemotherapeutic agent, its clinical application is limited due to its adverse effects on normal tissues Since the pathogenesis of cisplatin-induced toxicities mainly involve free radical and lipid peroxidation, this study was designed to evaluate the protective efficacy of Moringa leaf ethanolic extract and L-carnitine against cisplatin-nephrotoxicity and hepatotoxicity in rats. 72 male albino rats were divided into 6 groups GI- control received no treatment, GII- cisplatin (7mg/kg for 4 days), GIII- Prophylaxis by Moringa (500mg/kg for 10 days) then cisplatin as in G2, GIV- Prophylaxis by L-Carnitine (200 mg/kg for 10 days) then cisplatin as in G2, GV- treatment by Moringa received cisplatin as group II plus Moringa extract for 14 days. VI- treatment by L-Carnitine received cisplatin as group II plus L-Carnitine for 14 days. The results indicate that cisplatin induced significant increase in serum ALT, AST, Urea, Creatinine and kidney MDA level, also significant decrease in kidney GSH and CAT levels and histopathological alteration in kidney tissues as cloudy swelling, necrobiotic changes of tubular epithelium, congestion of glomerular tuft, also hepatic lesions as congestion of the central and portal vein with necrosis of its lining intimal cells, formation of newly formed bile duct with lymphocytic cell infiltrations in portal area and coagulative necrosis of some hepatocytes and another hepatocytes showed hydropic degeneration. Both moringa leaf ethanolic extract and L-carnitine ameliorated these toxicities as evidenced by restoring normal serum ALT, AST, Urea, Creatinine and histopathological architecture of kidney and liver.
Bi-level Hybrid Algorithm for Greener Environment via Vehicular Networks in a Single Intersection

In this paper, we propose a bi-level optimization model (BLOM) with improved hybrid metaheuristics. The hybrid GA and PSO algorithm is applied in both upper-level and lower-level model. This improved hybrid algorithm differs from the general hybrid algorithm which GA or PSO is applied in the single upper-level or lower-level model. BLOM is intended to schedule the phases of each isolated traffic signal and eco-driving environmentally. The upper-level optimization model (ULOM) considers the real-time traffic characteristics of the traffic flows near the signalized road intersection. At the same time, vehicles in the lower-level optimization model (LLOM) retrieve the real-time traffic signals using vehicular networks. Then, the traffic signals update the schedule and the vehicles are optimized motion states for greener environment factor respectively. We evaluate the performance of BLOM in a single road intersection using OMNET++ and SUMO. From the simulation results, we conclude that the BLOM with improved hybrid algorithm reduce fuel consumption and CO2 emissions compared with Maximize Throughput Model (MaxTM). Moreover, compared with the ordinary single algorithm, the proposed improved hybrid algorithm decreases the average operation cycle.
Camshift Ship Tracking Algorithm Based on Multi-feature Adaptive Fusion

Multi-target tracking based on vessel image sequences monitored on waterway is the premise of carrying out the vessel active warning and improving bridge-area ship navigation safety. Accurate tracking results are unable to be obtained by traditional Camshift tracking only based on color histogram under complex weather conditions. For this reason, a Camshift ship tracking algorithm based on color, shape and texture multi-feature adaptive fusion is proposed. In this method, the target model is realized by multi-feature adaptive fusion, which increases the reliability of observation and improves the robustness of observation model. When tracking targets, the target model based on fusion is included in the Camshift tracking algorithm. The experimental results show that the proposed tracking algorithm can provide higher correctness and stability than traditional Camshift algorithm.
A News Event Detection and Tracking Algorithm Based on Dynamic Evolution Model

Via analyzing news data on the Internet,an algorithm is presented for news event detection and tracking based on a dynamic evolution model,which borrows the idea of single-pass clustering and combines the specialties of news. The dynamic model is given based on the living characteristics of news event,including similarity computing model based on time distance between news story and news event, event model evolution algorithm,and dynamic threshold idea. This algorithm can automatically organize news data into news special topics,and furthermore provide personalized service for users. Finally,experimental results are used to indicate the validity of the algorithm.
Power-Aware Data Management Based on Hybrid RAM-NVM Memory for Smart Bracelet

Wearable devices become popular because they can help people observe health condition. The battery life is the critical problem for wearable devices. The non-volatile memory (NVM) attracts attention in recent years because of its fast reading and writing speed, high density, persistence, and especially low idle power. With its low idle power consumption, NVM can be applied in wearable devices to prolong the battery lifetime such as smart bracelet. However, NVM has higher write power consumption than dynamic random access memory (DRAM). In this paper, we assume to use hybrid random access memory (RAM) and NVM architecture for the smart bracelet system. This paper presents a data management algorithm named bracelet power-aware data management (BPADM) based on the architecture. The BPADM can estimate the power consumption according to the memory access, such as sampling rate of data, and then determine the data should be stored in NVM or DRAM in order to satisfy low power. The experimental results show BPADM can reduce power consumption effectively for bracelet in normal and sleeping modes.
Multichannel WPAN Protocol for Coexistence Under Densely Deployed Bluetooth LE in ISM

With the dramatic growth of Bluetooth low energy (BLE), the indoor radio environment suffers from complex spectrum interference in the 2.4-GHz Industrial Scientific and Medical equipment (ISM) band, because BLE unconditionally transmits data without any carrier sensing or energy detection. With some recent trends reintroducing traditional wireless personal area networks standard for supporting the deployment of Internet of Things (e.g., Thread), IEEE 802.15. 4 is spotlighted again, requiring mitigation of the mutual interference problem in the ISM band. For suitable coexistence under densely deployed BLE, this paper considers key impact phenomena of BLE characteristics, including studying a channelization and frequency-hopping mechanism. It finds a solution that can efficiently prevent problems by designing a multiple spectrum access scheme employing the same channelization that comes with BLE. To investigate the performance, this paper constructs a 4D Markov chain model describing the behavior of one communication node, and adopts an analytic matrix method to solve the model. The numerical results and experimental output are presented for further evaluation.
Numerical study of inspiratory and expiratory flow in a human nasal cavity

The complicated architecture associated with the nasal anatomy makes it difficult for visualization and measurement of flow parameters inside the nasal cavity. Objective measurement devices like rhinomanometry or acoustic rhinometry fail to assist the understanding of the physiology at every location within the nasal cavity. Therefore, in order to visualize the flow features inside the nasal cavity and to compare the inspiratory phase and expiratory phase in terms of parameters like velocity, resistance, wall shear stress, vortex formation and turbulence intensity, a computational fluid dynamics study was carried out. This study presents, the usefulness of a technique based on functional imaging and computational fluid dynamics (CFD) modeling in generating useful data that can be used to determine and diagnose upper-airway conditions. Variations in flow patterns and flow features such as pressure drop, velocity and the left and right cavity were observed. Resistance to flow was greater during inspiratory phase when compared to the expiratory phase. Turbulence intensity was more predominant during expiratory phase, whereas vortex formation could be observed only during the inspiration mechanism.
Comparison of Wearable E-Textile Split Ring Resonator and Slotted Patch RFID Reader Antennas Embedded in Work Gloves

We present a glove-integrated slotted patch and split ring resonator (SRR) antennas for a wearable UHF RFID reader operating at 866 MHz. The prototype antennas are made of adhesive electro-textile on low-permittivity foam material. To characterize the antennas, we tested them wirelessly in communication with a common dipole type RFID tag to estimate its reflection coefficient, realized gain, maximum power transmission according to specific absorption rate (SAR) limitations, and maximum read range. We also analyze the effects of variable separation between the human hand and the antenna to confirm stable operation required by the application. The results showed that both—slotted patch and SRR—antennas are feasible for the work glove applications providing the read range up to 300 cm and 180 cm respectively.
Modular omnidirectional mobile robot with four driving and steering wheels

In this paper the authors present a modular omnidirectional mobile robot with four driving and steering robot. The paper describes the experimental prototype, the kinematic analysis in two configurations (four driving and steering wheels configuration and synchro-drive configuration), the electronic components, the testing process of the robot and future research directions.
Enhanced Electrochemical and Physical Properties of Ag/AgCl Planar Reference Electrodesin Potentiometric Sensors by Graphite Oxide Layer

The most common reference electrode (RE) which is used in electrochemical measurements is the Ag/AgCl electrode. In this study, we present a novel solid-state Ag/AgCl planar electrode that was coated with a thin layer of Graphite Oxide (GO) as a protective layer. The Ag/AgCl planar electrode was fabricated by using the photolithography and lift-off method combined with the Ag[NH3]2Cl complex. The GO was produced by Hummer’s method and was deposited on top of the Ag/AgCl layer by drop-casting method. The layers of the fabricated reference electrode were characterized by micro Raman spectroscopy, Scanning Electron Microscopy (SEM) and Energy-dispersive X-ray spectroscopy (EDX). The responding time of the planar electrode was short and the signal was more stable in comparison to the plainAg/AgCl electrode. The Open Circuit Potential (OCP) measurement with the fabricated electrode as the RE and the platinum electrode as the working electrode in electrolyte solutions which had various pH values was performed with a linear response in pH ranges from pH 5 to pH 8 and the linear correlation coefficient (R = 0.9899). Moreover, the coating of the GO layers also enhanced the durability of the modified electrode. The results showed that the modified Ag/AgCl electrode with a thin layer GO as the protective layer could be used as plana REs for the potentiometric sensors.
Radicalisation Influence in Social Media

Identifying signs of online extremism is one of the top priorities for counter-extremist agencies. Social media platforms have become prime locations for radicalisation content and behaviour, and therefore much research and practice nowadays are focused on detecting radicalisation material, and accounts that publish such material, on these platforms. However, there is currently a limited understanding of how people on social media platforms are influenced by such content and behaviour, and what are the dynamics of this influence. In this paper, we propose a computational approach for detecting and predicting the radicalisation influence that a user is subjected to. Our approach is grounded on the notion of ‘roots of radicalisation’ from social science theories. We use our approach to analyse and compare the radicalisation influence of 112 pro-ISIS and 112 “general” Twitter users. Our results show the effectiveness of our proposed algorithms in detecting and predicting radicalisation influence, obtaining up to 0.9 F-1 measure for detection and between 0.7 and 0.8 precision for prediction. We have also conducted an in-depth analysis of the social influence received by the 112 pro-ISIS accounts, and reported on the origin, frequency and topical diversity of this influence. While this is an initial attempt towards the effective combination of social and computational perspectives, more work is needed to bridge these disciplines, and to build on their strengths to target the problem of online radicalisation.
Halal Cosmetics: A Review on Ingredients, Production, and Testing Methods

The demand for halal cosmetic products among the 2.4 billion Muslim consumers worldwide is increasing. However, the demand for halal cosmetics remains unmet because cosmetics production is dominated by non-halal cosmetic manufacturers, whose production methods may not conform with the requirements of halal science. The development of halal cosmetics and the assessment of their product performance is still in its infancy. The integration of halal science in the manufacture of most cosmetic products remains inadequate. Moreover, there is a global dearth of guiding documents on the development and assessment techniques in the production of comprehensively halal cosmetics. This paper aims to abridge existing literature and knowledge of halal and cosmetic science in order to provide essential technical guidance in the manufacture of halal cosmetics. In addition, the adoption of these methods addresses the unique ethical issues associated with conformance of cosmetics’ product performance to religious practices and halal science. It highlights the applicability of established methods in skin science in the assessment of halal cosmetics.
Robust and discriminative zero-watermark scheme based on invariant feature and similarity-based retrieval for protecting large-scale DIBR 3D videos

Digital rights management (DRM) of depth-image-based rendering (DIBR) 3D video is an emerging area of research. Existing schemes for DIBR 3D video cause video distortions, are vulnerable to severe signal and geometric attacks, cannot protect 2D frame and depth map independently or can hardly deal with large-scale videos. To address these issues, a novel zero-watermark scheme based on invariant feature and similarity-based retrieval for protecting DIBR 3D video (RZW-SR3D) is proposed in this study. In RZW-SR3D, invariant features are extracted to generate master and ownership shares for providing distortion-free, robust and discriminative copyright identification under various attacks. Different from traditional zero-watermark schemes, features and ownership shares are stored correlatively, and a similarity-based retrieval phase is designed to provide effective solutions for large-scale videos. In addition, flexible mechanisms based on attention-based fusion are designed to protect 2D frame and depth map independently and simultaneously. Experimental results demonstrate that RZW-SR3D have superior DRM performances than existing schemes. First, RZW-SR3D can extracted the ownership shares relevant to a particular 3D video precisely and reliably for effective copyright identification of large-scale videos. Second, RZW-SR3D ensures lossless, precise, reliable and flexible copyright identification for 2D frame and depth map of 3D videos.
Characterization of low molecular weight carbohydrates in dietary foods by chromatographic techniques coupled to mass spectrometry

The present thesis reports the development and use of chromatographic techniques coupled to mass spectrometry for the characterization of low molecular weight carbohydrates (LMWC) in dietary foods of economic relevance or identified as “functional food”. The interest for the analysis of LMWC in different dietary foods is based on the involvement of these compounds in biological reactions in plants and the bioactive properties reported for some LMWC. A general overview of the carbohydrates, the analytical methodologies to perform their analysis and a brief description of the different dietary foods selected for this study (cocoa beans, commercial green tea and kale) are reported in the different chapters (chapter 1-3) of the introduction. The study of LMWC in cocoa beans includes three chapters (chapter 4-6). The content of chapters 4 and 5 is a comprehensive characterization of the LMWC profile in cocoa beans using HILIC-ESI-TOF MS, HILIC-ESI-MSn and GC-MS, the quantification of the main and minor LMWC from different origins and a proposal of different indicators of fermentation. Chapter 6 covers a detailed chemometric and kinetic approach to monitor the LMWC changes during the spontaneous fermentation of cocoa beans. The study of commercial green tea (chapter 7) evaluates the LMWC together with other physical and chemical established quality indicators (soluble solids, color and antioxidant capacity) to characterize CGT. This approach has resulted to be useful for the characterization of the samples according to the type of processing employed during the manufacturing. Chapter 8 reports the identification for the first time of different LMWC in kale. This chapter also includes the analysis of the LMWC content in three types of kale during the development of the plant, as well as the monitoring of the changes produced as a consequence of cold temperature during farming.
Social Media Text Classification by Enhancing Well-Formed Text Trained Model

Social media are a powerful communication tool in our era of digital information. The large amount of user-generated data is a useful novel source of data, even though it is not easy to extract the treasures from this vast and noisy trove. Since classification is an important part of text mining, many techniques have been proposed to classify this kind of information. We developed an effective technique of social media text classification by semi-supervised learning utilizing an online news source consisting of well-formed text. The computer first automatically extracts news categories, well-categorized by publishers, as classes for topic classification. A bag of words taken from news articles provides the initial keywords related to their category in the form of word vectors. The principal task is to retrieve a set of new productive keywords. Term Frequency-Inverse Document Frequency weighting (TF-IDF) and Word Article Matrix (WAM) are used as main methods. A modification of WAM is recomputed until it becomes the most effective model for social media text classification. The key success factor was enhancing our model with effective keywords from social media. A promising result of 99.50% accuracy was achieved, with more than 98.5% of Precision, Recall, and F-measure after updating the model three times.
Linearizing Convolutional Neural Network improves P300 detection

Author(s): Ravindran, Sriram | Advisor(s): Sa, Virginia de | Abstract: P300 is an event-related potential evoked as a response to external stimuli. The P300-speller is a widely used BCI that has proven to be a reliable method in enabling people who cannot communicate via normal methods. Improving single-trial P300 classification helps increase communication bandwidth as it reduces the averaging process necessary to reduce noise. Deep learning approaches in this area have become more popular recently [2][3]. Certain architectural choices result in a better classifier. In this paper, we performed a study of EEGNet [2] as it performed well in our experiments. We report some improvements to the model. We also confirm that these suggestions improve other CNN based models by showing improved results with CNN-1 proposed in [4] on BCI Competition III Dataset II. We perform knowledge distillation of the resultant linear model into a logistic regression model and show that the model learns information similar but superior to LDA. We show that the logistic regression model obtained this way outperforms more complex models and is easy to interpret.