Miracle-dz/UniFAR-FTUs huggingface dataset:
WhatsApp with BlackBerry; can Messengers (BBM) be MXit?

In an Information Technology course, learners are confronted with learning about technology; programming, other technical subjects and some mathematics and statistics. Although learners can struggle with the logical thinking necessary for becoming good programmers, the ones who are grasping programming easily, struggle with the different type of rigour necessary in a subject like Systems Analysis and Design (SAD). Learners are confronted with theoretical material overwhelming in quantity and repeating in different contexts – confusing them. Also, a substantial part of the subject should be the practical implementation of a real-life system. Addressing the overwhelming material, the use of Social Networking Sites (SNS) to deliver definitions and explanations to learners, ensuring easy access to material, have been introduced. During the first semester the material is new and the focus is on becoming comfortable with it, while during the second semester the focus moves to applying the material in a practical environment. Since the learners in SAD are mostly from disadvantaged communities, it was important to introduce tools that are readily available to the learners, assistants and the teacher. Three SNSs were introduced to accomplish this; WhatsApp, MXit and BlackBerry Messenger (BBM). The messaging systems were used in conjunction with two new functions in the learning managing system already used by the learners, namely forums and the glossary. This article will look into the complexities arising from the use of messaging systems. Can it be managed by having access to the website? Are some of the systems easier to use from the perspective of the party who needs to set up the environment and send out the messages (the teacher and assistants)? Also, what knowledge can be gained from the interaction between learners within each of the three groups – is there one or more applications that allow easy knowledge transfer and interaction between participants and why? Also, is the intervention helping learners to learn – from their own point of view? Recommendations forthcoming from this project will be used to guide the second SAD semester, as well as future first semesters.
Alternation of Singlet and Triplet States in Carbon‐Based Chain Molecules and Its Astrochemical Implications: Results of an Extensive Theoretical Study

A variety of homologous carbon chains (HCnH, HCnN, CnS, CnO, and OCnO) are found to exhibit an appealing even–odd effect. Chains containing a number of carbon atoms of a certain parity possess singlet ground states, while members of opposite parity have triplet ground states. From a general perspective, it is important that this even–odd effect confounds straightforward chemical intuition. Whether the most stable form is a triplet or a singlet is neither simply related to the fact that the species in question is a “normal” (closed‐shell, nonradical) molecule nor a (di)radical or to the (e.g., cumulene‐type) C─C bond succession across the chain. From a computational perspective, the present results are important also because they demonstrate that electron correlations in carbon‐based chains are extremely strong. Whether the “gold‐standard” CCSD(T) (coupled‐cluster expansions with single and double excitations and triple excitations corrections) framework suffices to describe such strongly correlated systems remains an open question that calls for further clarification. Most importantly for astrochemistry, the present results may explain why certain members are not astronomically observed although larger members of the same homologous series are detected; the missing species are exactly those for which the present calculations predict triplet ground states.
Toxicity of Bioactive Compound from Endophytic Fungi Isolated from Red Ginger (Zingiber officinale var. rubrum) Utilizing Brine Shrimp Lethality Assay

Red ginger (Zingiber officinale var. rubrum) has been proven to show anticancer activity. Direct use bioactive compound from red ginger has many obstacles such as large amount of red ginger’s rhizome needed, limitation of planting area, and very long time of harvesting. Utilization of endophytic fungi from red ginger’s rhizome could be an alternative to the problems. The aims of this study were to determine bioactive compound produced by endophytic fungi and toxicity activity based on LC50. Endophytic fungi were isolated from red ginger and were identified macroscopically and microscopically. The bioactive compounds were extracted using ethanol 96%. Flavonoid test was done qualitatively, bioactive compounds were analyzed by Thin Layer Chromatography (TLC), and the toxicity test was done using Brine Shrimp Lethality Assay (BSLA). The present research found two endophytic fungi isolated from red ginger rhizome. Isolate 1 was similar to Mucor sp. and isolate 2 was similar to Trichoderma sp. Phytochemical test revealed bioactive compound extracted from the isolates were contained flavonoid. TLC analysis did not detect quercetin from the bioactive compound extracted from the isolates. LC50 values of the bioactive compound from the isolates were 2.300 and 1.747 µg/ml, respectively. The toxicological results suggest that both isolates produce non-toxic compound to Artemia salina.
Toolbox of Post‐Synthetic Mordenite Modification Strategies: Impact on Textural, Acidic and Catalytic Properties

Which post‐synthetic modification strategy is the best suited for the generation of particular textural or chemical features in mordenite for a desired catalytic application? The aim of this contribution is to shed light on the effective quality of zeolite hierarchization through the rational study of structure/catalytic property dependency. Our study focused on mordenite, as this zeolite is of particular importance due to its strong acidity and monodimensional channel system, which explains why mordenite is industrially extensively employed. A toolbox of post‐synthetic hierarchization strategies on aluminum rich and poor mordenites has been developed based on basic treatments assisted by CTAB and pyridine at various temperatures (in conventional and microwave heating) and acid HF etching in aqueous and organic medium. A systematic study of over 30 post‐synthetically modified mordenites was conducted, which allowed us to assess the impact of each hierarchization strategy on textural, morphological, chemical and catalytic properties. Major correlations between structural features and catalytic activity, selectivity and stability in both the monomolecular n‐hexane cracking and the bimolecular toluene disproportionation were deduced.
Cognitive and neural mechanisms of episodic memory reactivation: from consolidation to retrieval

Introduction: Episodic memory is the result of highly dynamic processing. After encoding, the memory trace undergoes a set of transformations that range from the unconscious replay during waking rest periods to the full-blown reactivation that gives rise to our experience of recollection. The literature review reveals that, despite our knowledge of the neocorticalhippocampal circuits involved, their brain oscillatory mechanisms remain unclear. Objective: The main goal of this doctoral thesis is to track episodic memory reactivation during consolidation and retrieval by means of its oscillatory signatures. Explicitly, we aim to unveil how their underlying neural mechanisms unfold over time and space to coordinate behaviour.
Review of research on mobile-assisted language learning in familiar, authentic environments

Familiarity with learning contexts is important in the field of mobile-assisted language learning (MALL). Several review studies on MALL have been published to date. However, scholars have not covered certain aspects of familiar contexts in their reviews, such as which learning/instructional methodologies support learning in familiar contexts or what the affordances of familiar contexts are for language learning. To address this gap, the authors in this study reviewed research articles related to MALL in familiar, authentic environments published in the past 10 years. The reviewed articles were from journals published in the Social Science Citation Index between 2009 and 2018. The main aim of this study was to review the published articles to understand (1) pedagogical approaches, (2) data collection, (3) locations, (4) affordances of authentic environments and (5) issues in MALL research. The results revealed that the most frequent items in reviewed articles were task-based learning and communicative language teaching (pedagogical approaches); questionnaires, pretest and posttests and interviews (data collection); the local community and campus (locations); daily encounters, language learning and cognitive load (affordances of authentic environments); and small sample sizes and short-term interventions (issues in MALL research). Based on the results, the authors in this study made several suggestions and provided implications for educators and researchers in the field about MALL in familiar, authentic environments.
The impact of different commercial probiotic cultures with starters on technological, physicochemical and sensorial properties of a
traditional yogurt-based appetizer “Cacık”

The selection of proper probiotic culture is essential for maintaining adequate numbers of viable cells until consumption since potential adverse interaction between selected strain and starter culture. This study examined the suitability of Cacık as a potential probiotic carrier including Lactobacillus acidophilus 74-2, Lactobacillus rhamnosus Howaru HN001TM and Bifidobacterium longum BB536 in different combinations. The presence of three probiotic strains caused higher post acidification and shorter fermentation time associated to higher counts of L. bulgaricus. Except for certain combinations containing B. longum BB536, the counts of individual probiotic bacteria were higher than 6 log g-1 for all probiotic supplemented Cacık samples. Results showed that bacterial interaction was decisive for survival over 21 days of storage. Probiotic products containing only B. longum BB536 or B. longum BB536 with L. acidophilus 74-2 did not accomplish in terms of the technological point, since the viable cell counts decerased for 2.45 log and 1.95 log per g, respecitevly, throughout the storage period. In three of totally four combinations included L. rhamnosus Howaru HN001TM alone or combined with other probiotic bacteria, the viable cell counts of L. rhamnosus Howaru HN001TM remained at the inoculated level, while the counts significantly increased in co-culture with L. acidophilus 74-2. Considering the overall sensorial attributes and survival of probiotics, Cacık supplemented with L. rhamnosus Howaru HN001TM alone, combinations of B. longum BB536 and L. rhamnosus Howaru HN001TM or L. acidophilus 74-2 and L. rhamnosus Howaru HN001TM or by all of the three tested probiotics, were suggested as suitable for further production.
Heart Rate assessment by means of a novel approach applied to signals of different nature

Electrocardiographic (ECG) signal presents many clinically relevant features (e.g. QT-interval, that is the duration of the ventricular depolarization). A novel processing technique has been demonstrated to be capable to measure some important characteristics according to the morphology of the waveform. Basing on that, the aim of this work is to propose an improved algorithm and to prove its efficacy in the assessment of the subject’s Heart Rate (HR) in comparison to standard algorithms (i.e. Pan & Tompkins). Results obtained in experimentally collected ECG signals for the identification of the main feature (R-peak) are comparable to those obtained with the traditional approach (sensitivity of 99.55% and 99.95%, respectively). Moreover, the use of this algorithm has been broaden to signals coming from different biomedical sensors (based on optical, acoustical and mechanical principles), all related to blood flow, for the computation of HR. In particular, it has been employed to PCG (Phonocardiography), PPG (Photoplethysmography) and VCG (Vibrocardiography), where standard algorithms could not be widely applied. HR results from a measurement campaign on 8 healthy subjects have shown, with respect to ECG, deviations (calculated as 2σ) of ±3.3 bpm, ±2.3 bpm and ±1.5 bpm for PCG, PPG and VCG, respectively. In conclusion, it is possible to state that the adopted algorithm is able to measure HR accurately from different biosignals. Future work will involve the extraction of additional morphological features in order to characterise the waveforms more deeply and to better describe the subject’s health status.
Employing cueing systems to decode text and negotiate text-meaning in a second language

Many educators state that teaching English-Language Learners (ELLs) is just a matter of good teaching. While effective teaching is something that all children deserve, there are some differences between the approach a teacher may have to take with ELLs and with other students. One aspect of this difference is the fact that, while children go through the same stages of language development, ELLs may be at stages typically not found in other students. Based on a case study of an EL and his teacher, this article explains cueing systems and their role in literacy for ELs.
Cadmium adsorption using novel MnFe2O4-TiO2-UIO-66 magnetic nanoparticles and condition optimization using a response surface methodology

In this study, magnetic nanocomposites (UIO-66-MnFe2O4-TiO2) were synthesized based on the metal–organic framework. To investigate the synthesized adsorbent structure, XRD, SEM, FT-IR, BET, and VSM techniques were used, and also an EDX test was applied after adsorption of Cd(ii). The synthesized nanocomposite was used for Cd(ii) adsorption. The effects of four parameters such as the amount of adsorbent (0.05 to 0.25 g), pH (1 to 9), adsorption time (24 to 120 minutes), initial amount of metal ion (100 to 900 mg), at five levels (−2 to +2) were evaluated during the experiment based on the Response Surface Methodology (RSM) using Central Composite Design (CCD) and then, the optimal levels were determined. The F-value and P-value of the fitted second order model were obtained as 6039.62 and 0.0001, respectively. Optimization of the values was also performed for variables and, initial concentration of metal, pH, adsorption time and amount of adsorbent were obtained as 5.2 mg l−1, 5, 63 minutes, and 0.18 g, respectively as optimum conditions. For optimal conditions, the maximum adsorbance was equal to 98%. Investigation of kinetic and isotherm adsorption showed that a second-order kinetic model and a Langmuir isotherm cover the Cd(ii) adsorption data well. It was also revealed that the adsorbent was removed from the environment by an external magnetic field.
Rule-Based Automatic Question Generation Using Semantic Role Labeling

This paper proposes a new rule-based approach to automatic question generation. The proposed approach focuses on analysis of both syntactic and semantic structure of a sentence. Although the primary objective of the designed system is question generation from sentences, automatic evaluation results shows that, it also achieves great performance on reading comprehension datasets, which focus on question generation from paragraphs. Especially, with respect to METEOR metric, the designed system significantly outperforms all other systems in automatic evaluation. As for human evaluation, the designed system exhibits similar performance by generating the most natural (human-like) questions. key words: question generation, rule-based, semantic role labeling, METEOR
Brine Shrimp Toxicity of Essential Oils from Musa spp.

Essential oils of seven Musa spp. ’s fruits namely, Musa acuminata colla ‘gros michel’ (PE), Musa acuminata colla ‘lakatan’ (PB), Musa acuminata colla ‘sucrier’ (PM), Musa acuminata × balbisiana ‘horn plantain’ (PT), Musa acuminata × balbisiana colla ‘saba’ (PN), Musa acuminata colla 'inarnibal' (PO) and Musa acuminata colla ‘red’ (PJ) were extracted by hydrodistillation method using Clevenger apparatus. The essential oils were analysed using GC-FID and identified using Kovat Indeces compared with published information. PE gave the highest yields of oils compared to the other species with yield of 0.43% and 0.28% for its peels and flesh, respectively. The most abundant groups present in most of the essential oils are ester and alcohol groups. The brine shrimp toxicity of PT, PM, PN and PO essential oils were tested and showed toxicity against brine shrimp in dose dependent manner. It can be concluded that the essential oils showed some biological activities which may be a potent medicine in curing tumor. This study implies that the presence of ester and alcohol groups are a good marker for the biological activities of plants. Further studies should focus on the potential of the essential oils of Musa spp. as an antitumor medicine.
An Adaptive Fuzzy-Sliding Mode Controller for The Bridge Crane

A R T I C L E I N F O A B S T R A C T Article history: Received:27 April, 2019 Accepted:27 May, 2019 Online: 10 June, 2019 This article presents a bridge crane nonlinear dynamic model in 2-dimensional space, and then given a novel adaptive fuzzy-sliding mode controller based on combining sliding mode control with fuzzy logic and Lyapunov function. Firstly, the article proposes an intermediate variable to link signal between two slide surfaces, related to trolley movement and payload swing. Then the fuzzy controller, compensative controller and parameter adaptive update law for the bridge crane are defined. The asymptotic stability of the proposed bridge crane control system is proven based on Lyapunov stability theory. Simulation results show that the adaptive fuzzy-sliding mode controller ensures the trolley follows the input reference with the short settling time, eliminating steady error, and antipayload swing, anti-disturbance.
Convolutional neural network architecture study for aerial visual localization

In unmanned aerial navigation the ability to determine the aircrafts’s location is essential for safe flight. The Global Positioning System (GPS) is the default modern application used for geospatial location determination. GPS is extremely robust, very accurate, and has essentially solved aerial localization. Unfortunately, the signals from all Global Navigation Satellite Systems (GNSS) to include GPS can be jammed or spoofed. To this response it is essential to develop alternative systems that could be used to supplement navigation systems, in the event of a lost GNSS signal. Public and governmental satellites have provided large amounts of high-resolution satellite imagery. These could be exploited through machine learning to aid onboard navigation equipment to provide a geospatial location solution. Deep learning and Convolutional Neural Networks (CNNs) have provided significant advances in specific image processing algorithms. This thesis will discuss the performance of CNN architectures with various hyperparameters and industry leading model designs to address visual aerial localization. The localization algorithm is trained and tested through satellite imagery of a localized area of 150 square kilometers. Three hyper-parameters of focus are: initializations, optimizers, and finishing layers. The five model architectures are: MobileNet V2, Inception V3, ResNet 50, Xception, and DenseNet 201. The hyper-parameter analysis demonstrates that specific initializations, optimizations and finishing layers can have significant effects on the training of a CNN architecture for this specific task. The lessons learned from the hyper-parameter analysis were implemented into the CNN comparison study. After all the models were trained for 150 epochs they were evaluated on the test set. The Xception model with pretrained 1 initialization outperformed all other models with a Root Mean Squared (RMS) error of only 85 meters.
Developing efficient and effective intrusion detection system using evolutionary computation

The internet and computer networks have become an essential tool in distributed computing organisations especially because they enable the collaboration between components of heterogeneous systems. The efficiency and flexibility of online services have attracted many applications, but as they have grown in popularity so have the numbers of attacks on them. Thus, security teams must deal with numerous threats where the threat landscape is continuously evolving. The traditional security solutions are by no means enough to create a secure environment, intrusion detection systems (IDSs), which observe system works and detect intrusions, are usually utilised to complement other defence techniques. However, threats are becoming more sophisticated, with attackers using new attack methods or modifying existing ones. Furthermore, building an effective and efficient IDS is a challenging research problem due to the environment resource restrictions and its constant evolution. To mitigate these problems, we propose to use machine learning techniques to assist with the IDS building effort. In this thesis, Evolutionary Computation (EC) algorithms are empirically investigated for synthesising intrusion detection programs. EC can construct programs for raising intrusion alerts automatically. One novel proposed approach, i.e. Cartesian Genetic Programming, has proved particularly effective. We also used an ensemble-learning paradigm, in which EC algorithms were used as a meta-learning method to produce detectors. The latter is more fully worked out than the former and has proved a significant success. An efficient IDS should always take into account the resource restrictions of the deployed systems. Memory usage and processing speed are critical requirements. We apply a multi-objective approach to find trade-offs among intrusion detection capability and resource consumption of programs and optimise these objectives simultaneously. High complexity and the large size of detectors are identified as general issues with the current approaches. The multi-objective approach is used to evolve Pareto fronts for detectors that aim to maintain the simplicity of the generated patterns. We also investigate the potential application of these algorithms to detect unknown attacks.
Cycle Sketch GAN: Unpaired Sketch to Sketch Translation Based on Cycle GAN Algorithm

Unlike pixel image generation, sketch drawing generative model outputs a sequence of pen stroke information. This paper proposes Cycle Sketch GAN: the ﬁrst model that learns to translate a sketch drawing from source domain to target domain in the absence of paired dataset. Based on Cycle GAN algorithm, this model uses Transformer Encoder architecture in generators. Transformer Encoder feeds the input stroke information in source domain and generates the parameters for output distribution, from which the stroke information in target domain is sampled by reparameterization trick. The negative log likelihood of the distribution is used as cycle consistency loss. This model is trained and evaluated by some QuickDraw datasets. Qualitative evaluation shows that this model can practically translate sketch drawings from source domain to target domain. Quantitative evaluation by user study showed that 42 % of the translated sketches is recognizable compared to 71 % of the human sketches.