Miracle-dz/UniFAR-FTUs huggingface dataset:
Phenolic contents and antioxidant activities in vitro of some selected Algerian plants

In order to find new sources of natural antioxidants, the methanolic extracts of eleven Algerian medicinal plants from eight botanical families were investigated for their in vitro antioxidant activity using total antioxidant capacity (TAC), reducing power, 2,2-diphenyl-1-picrylhydrazyl (DPPH), hydroxyl radical scavenging and β-carotene–linoleate bleaching. The total phenolic, flavonoid and proanthocyanidin contents were also measured. Most of these plants were analyzed for the first time for their antioxidant activities. Results showed that the plants Pistacia atlantica, Thymelaea microphylla and Marubium deserti exhibited higher phenolic content (133.74 to 285.95 mg gallic acid equivalents/g dry matter). The flavonoid and proanthocyanidin contents varied from 0.56 to 12.44 mg catechin equivalents/g dry matter and 1.42 to 25.02 mg catechin equivalents/g dry matter, respectively. The extracts were found to have different levels of antioxidant properties in the test models used. The medicinal plants with the highest antioxidant activities were P. atlantica, Helianthemum lippii (stem, leaf, and fruit), Inula montana, Anabasis articulata and Sedum villosum. A positive correlation, R = 0.906, was observed between total flavonoid contents and TAC values, reflecting a high involvement of flavonoids in antioxidant activity, but no correlation was established between the five tests and the total phenolic and proanthocyanidin contents.
Hues of Satisfaction: Many-valued Institutions for Constraint Speciﬁcation

Service-Oriented Computing ( soc ) is a recent paradigm focusing on the development of software applications based on dynamically changing networks of systems. In short, soc builds upon a need-fulﬁlment mechanism through which software applications (requesters) connect to external suppliers (providers) every time a need for services appears. The requester ﬁrst needs to discover the system components that guarantee, through an interface, the fulﬁlment of its conditions, and then selects and binds to a provider. The interfaces express properties that are independent of the actual implementation of the services (such as functional properties of their input-output behaviour) and are usually deﬁned using algebraic speciﬁcations of abstract data types or temporal speciﬁcations. Besides functional properties, one could also specify constraints that express preferences meant to be used for the selection of a best provider in terms of the maximisation of their satisfaction degrees (see [6]). In this context, constraint systems have already been successfully employed in dealing with such non-functional requirements of orchestrations of service-oriented architectures [9] or in supporting the negotiation of service-level agreements [3]. The two main approaches to soft constraint satisfaction problems, scsp [2] and vcsp [10], generalise the classical crisp variant of csp by evaluating constraints over c-semirings and valuation structures, respectively. Aiming to employ soft constraint speciﬁcation for the selection of a supplier in the context of service discovery and binding, we advance a general technique for enriching logical systems formalised as institutions [8] with soft constraints, generalising the preliminary results from [7] on the extension of algebraic speciﬁcations of interfaces. We obtain soft constraint speciﬁcations by adding constraints (regarded
Complex periodic oscillations and Farey arithmetic in the Belousov–Zhabotinskii reaction

Our experiments on the manganese‐catalyzed Belousov–Zhabotinskii reaction in a stirred flow reactor reveal many sequences of distinct multipeaked periodic states. In the parameter ranges studied the waveform for each periodic state consists of an admixture of small and large amplitude oscillations. No chaos is discernible, and in many cases the transitions from one periodic state to another occur without any observable hysteresis. Two types of sequences were studied in detail, one with waveforms consisting of concatenations of two basic patterns and another with waveforms consisting of concatenations of three basic patterns. The sequences of states with two patterns are described well by Farey arithmetic, which provides rational approximations of irrational numbers. These states can be characterized by a firing number, the ratio of the number of small amplitude oscillations to the total number of oscillations per period. For our data this ratio is a monotone stepwise‐increasing function of flow rate, and the steps have a fractal dimension. The relationship between the observed sequence and the Farey arithmetic and the observation of a fractal dimension for the steps in the firing number suggest that the states formed by concatenating two patterns can be interpreted in terms of frequency locking on a 2 torus in phase space. The sequences of states with three basic patterns are described by a generalized Farey arithmetic that provides rational approximations for pairs of irrational numbers that are mutually irrational; this suggests that these states can be interpreted in terms of frequency locking on a 3 torus. A piecewise‐linear two‐dimensional map is shown to yield a phase diagram in qualitative accord with the measured phase diagram for these sequences.
Plasma‐Based Indoor Air Cleaning Technologies: The State of the Art‐Review

The negative impact of the presence of volatile organic compounds (VOCs) on indoor air quality has motivated researchers to develop different air treatment technologies. Although, building mechanical ventilation systems can provide a comfortable thermal indoor environment, they are not capable of removing the VOCs effectively. Thus, other components must be integrated with them to be able to carry out this function. Plasma-based air treatment techniques are a series of processes in which a high voltage discharge is used for elimination of VOCs. Development of plasma-based methods, and their capabilities for chemical gas decomposition, has motivated designers to employ these methods for indoor air purification. This paper addresses the outcomes of a critical literature review on plasma-based air cleaner technologies, thermal to non-thermal plasma and plasma catalyst, and their application for indoor environment VOCs removal. The reaction mechanism, effect of different parameters on the performance of the method, and abilities and limitations of these methods are discussed. Different types of reactors and the most common used catalysts are classified. The role of the presence of the catalyst in improving the non-thermal plasma efficiency is reviewed. Finally, the scope of the future work to enhance the performance of this method for application in sustainable buildings is discussed.
CS224W Project: Graph-based node feature prediction system using structural features and incomplete node metadata

A myriad of recent machine learning endeavours are devoted towards missing node feature prediction using large-scale graphical datasets. In this paper, we experiment with the GNN, GraphSage, and GCN models using using node2vec embeddings and incomplete node and edge features from the Flickr dataset. We use ablation studies to explore the effectiveness of both portions of the input by running a series of experiments on both input forms on its own as well as combined. We also implement, train and tune each of the three models and compare their performance on image missing tag prediction. We ﬁnd that GCN is best able to tackle the acute class imbalance deep-seated in the dataset and demonstrates most efﬁcacious graphical structure learning in our research
The Interaction of Fuel-Bound Nitrogen and Fuel-Bound Chlorine During Air-Staged Incineration

The simultaneous air-staged combustion of methyl amine (CH3NH2 and methyl chloride (CH3Cl) was studied in a two zone turbulent flow reactor at atmospheric pressure. The temperature in the first stage was held constant at 1750 K. The feed consisted of ethylene (C2H4), CH3NH2, CH3CI, air, and N2 (for first stage temperature control). Fuel-lean and fuel-rich mixtures were fed into the first stage. During the fuel-rich reed runs, secondary air was injected into the second stage to make the overall system fuel-lean. Measurements of species including NO, CO, CO2 and O2 were made in both stages. Three major observations were made: 1) The additon or CH3Cl during air staged combustion of CH3NH2 does not alter the attainment of an NO minimum emission from the second stage; 2) The optimal first stage equivalence ratio corresponding to the minimum NO in the second stage outlet is approximately 1.3 in both cases; and 3) The minimum NO emission level from air staged combustion significantly decreases when CH3Cl is adde.
Study and Improvement on BiDAF and BERT Model Framework

Question Answering has become a central task in Natural Language Processing. In this work, we aim to propose improvements on some state-of-the-art Question Answering models and test their performance on the Stanford Question Answering Dataset 2.0. Two model families are discussed in this paper, i.e. BiDAF and BERT. For BiDAF model, we veriﬁed that character embedding layer contributes to the model prediction accuracy and outperforms the model without character embedding layer by 2-3%. We proposed a BiDAF model with ensembling techniques to split the prediction into two tasks. One is classiﬁcation and the other is text span prediction. The experiment result shows the improvement of the prediction accuracy by about 1% compared with BiDAF model. For the BERT model family, we based our research on top of the bert-base-uncased model. After 5000 training steps, the ﬁne-tuned BERT has already outperformed our best BiDAF model. Further we experimented with two sets of modiﬁcations on top of the baseline BERT model. The ﬁrst set of models focuses on modifying the FC SQuAD output layer baseline BERT uses. The three models proposed in this category have steeper learning curves than the baseline BERT; however, in the end, the improvement is marginal (1%). The second set of models focuses on using pre-trained (no further optimization) BERT as word encoders. One model is proposed in this category. The experiment result shows that further work needs to be done, and the current model underperforms the ﬁne-tuned baseline BERT.
The effect of regulatory changes on relationship between earnings management and financial reporting timeliness: The case of COVID-19 pandemic

The purpose of this paper was to examine the effect of regulatory changes regarding financial reporting deadlines introduced because of COVID-19 pandemic on relationship between earnings management and financial reporting timeliness Using sample of companies listed on stock exchange in Republic of Croatia for the period from 2015 to 2019, pooled OLS regression model was estimated with panel data Financial reporting timeliness was measured with financial reporting delay, while level of earnings management was proxied by discretionary accruals Empirical results supported the hypothesis that the aforementioned changes of regulatory framework during the extraordinary pandemic circumstances had a statistically significant positive effect on relationship between earnings management and financial reporting delay, indicating that financial reporting delays after regulatory changes during pandemic could be attributed to earnings management activities Furthermore, after separating income-increasing and income-decreasing accruals, moderating effect of regulatory changes was significant and negative only in case of income decreasing accruals, suggesting that companies were adjusting their financial information in accordance with pessimistic economic forecasts to mitigate probable profitability deterioration in future periods © 2020, University of Rijeka All rights reserved
BiDETS: Binary Differential Evolutionary based Text Summarization

In extraction-based automatic text summarization (ATS) applications, feature scoring is the cornerstone of the summarization process since it is used for selecting the candidate summary sentences. Handling all features equally leads to generating disqualified summaries. Feature Weighting (FW) is an important approach used to weight the scores of the features based on their presence importance in the current context. Therefore, some of the ATS researchers have proposed evolutionary-based machine learning methods, such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), to extract superior weights to their assigned features. Then the extracted weights are used to tune the scored-features in order to generate a high qualified summary. In this paper, the Differential Evolution (DE) algorithm was proposed to act as a feature weighting machine learning method for extraction-based ATS problems. In addition to enabling the DE to represent and control the assigned features in binary dimension space, it was modulated into a binary coded format. Simple mathematical calculation features have been selected from various literature and employed in this study. The sentences in the documents are first clustered according to a multi-objective clustering concept. DE approach simultaneously optimizes two objective functions, which are compactness measuring and separating the sentence clusters based on these objectives. In order to automatically detect a number of sentence clusters contained in a document, representative sentences from various clusters are chosen with certain sentence scoring features to produce the summary. The method was tested and trained using DUC2002 dataset to learn the weight of each feature. To create comparative and competitive findings, the proposed DE method was compared with evolutionary methods: PSO and GA. The DE was also compared against the best and worst systems benchmark in DUC 2002. The performance of the BiDETS model is scored with 49% similar to human performance (52%) in ROUGE-1; 26% which is over the human performance (23%) using ROUGE-2; and lastly 45% similar to human performance (48%) using ROUGE-L. These results showed that the proposed method outperformed all other methods in terms of F-measure using the ROUGE evaluation tool.
Impact of microbial activity on the performance of planted and unplanted wetland at laboratory scale

Three horizontal subsurface flow constructed wetland prototypes were set up to identify and understand the role of microflora in nutrient removal under diverse operating conditions. Out of three setups, one setup served as a control (without plants), and the rest were planted with Typha domingensis. The setups were operated at two different hydraulic loading rates (5 cm/day and 16 cm/day) for two months each. Among 27 bacteria species isolated, 80% of nitrate-reducing bacteria were observed in control, and 50–77% of nitrate-reducing bacteria were observed in the plant setups. Presence of diverse denitrifying bacteria and soil organic carbon contributed to high Nitrate-N removal in the control at both HLRs. Similar Ammonium-N (29%) and Ortho-P removal (30%) efficiency was observed at both HLRs in the control setup. Processes such as chemical sorption and adsorption dominated the Ammonium-N and Ortho-P removal in the control setup. High average Ammonium-N removal efficiency of 89% and 52% was observed in plant setups at 5 cm/day and 16 cm/day HLR. At low HLR, Ammonium-N removal in plant setups was dominated by nutrient uptake. In the plant setups, 35% and 15% Ortho-P removal efficiency was observed at low HLR (5 cm/day) and high HLR (16 cm/day) respectively. Hydraulic Retention Time (HRT) limited the uptake of ortho-P, thereby allowing mineralised phosphorus to escape the system without being absorbed by the plants.
Family Motivation, Ethical Leadership and Family-Supportive Supervisor Behaviours: A Review of Integrated Literature

The researchers wanted to identify if there is any link between ethics in leadership and family motivation as well as family-supportive supervisor behaviours and family motivation. The specific objectives of the study were (1) to examine effects of family motivation on job performance, (2) to analyse effects of ethical leadership on family motivation and (3) to analyse effects of family-supportive supervisor behaviour on family motivation. A narrative approach was used to answer the research objectives in the study. Research findings reflected that family motivation is linked to the performance of workers. Employees who have families that they are looking after are more motivated to expand their effort at work than employees who do not have families to look after. Findings also reflected that ethical leadership is an important concept in ensuring a family motivated worker. As such, ethical leadership have a direct impact on family motivation. Finally, the study revealed that family-supportive supervisor behavior has an effect on family motivation. Family-supportive supervisor behaviour ensures effectiveness and success of an organisation. Based on these findings, the researchers concluded that employers must take advantage of the family motivation model to ensure the success of their corporations. Employers need to recognize the importance of employees’ families, and should channel resources that motivates their workers of fulfilling their dreams of looking after their families. Employers are encouraged to be ethically motivated to ensure they fulfill the family motivation. Finally, leaders, by nature are encouraged to adopt the familysupportive supervisor mode in their day-to-day duties. This will help to balance the work-family challenges.
INDONESIAN CHEMISRY AND APPLICATION JOURNAL (ICAJ) ISSN : 2549-2314; Volume : (...); Number (...)

This research was conducted to optimize the casting thickness in the preparation of Polyetherimide (PEI) membrane. PEI was performed using an immersion-precipitation-induced phase inversion method with a thickness range from 0.5 to 0.8 mm. The results of morphological analysis and cross-sections performed by using Scanning Electron Microscope (SEM) showed decreased pore size along with decreased casting thickness. The results of pure water permeability analysis using "dead end" membrane reactor showed membrane flux at 55,290.16 67,244.79 L/m.h and Young’s modulus of the prepared membranes were in the range of 1,443.25 1,687.59 N/m. Thus, the decreased casting thickness has resulted in decreased mechanical strength and increased pure water permeability.
Deep Learning Based 6D Pose Estimation for Bin-Picking Using 3D Point Clouds

 Abstract — Estimating the 6D pose of objects is a core step for robot bin-picking tasks. The problem is that various objects are usually randomly stacked with heavy occlusion in real applications. In this work, we propose a method to regress 6D poses by predicting three points for each object in the 3D point cloud through deep learning. To solve the ambiguity of symmetric pose, we propose a labeling method to help the network converge better. Based on the predicted pose, an iterative method is employed for pose optimization. In real-world experiments, our method outperforms the classical approach in both precision and recall.
IMPACT OF ETHANE & PROPANE VARIATION IN NATURAL GAS ON THE PERFORMANCE OF A MODEL GAS TURBINE COMBUSTOR

In the area of stationary power generation, there exists a growing interest in understanding the role that gaseous fuel composition plays on the performance of natural gas-fired gas turbine systems. In this study, an atmospherically fired model gas turbine combustor with a fuel flexible fuel/air premixer is employed to investigate the impact of significant amounts of ethane and propane addition into a baseline natural gas fuel supply. The impacts of these various fuel compositions, in terms of the emissions of NO X and CO, and the coupled impact of the degree of fuel/air mixing, are captured explicitly for the present system by means of a statistically oriented testing methodology. These explicit expressions are also compared to emissions maps that encompass and expand beyond the statistically based test matrix to verify the validity of the employed statistical approach.
Adapting Behaviour via Intrinsic Reward

Learning about many things can provide numerous benefits to a reinforcement learning system. How to behave to best learn a collection of predictions in parallel in the reinforcement learning setting remains an open problem. The research presented in this work explores the behavior, and effect, that different intrinsic reward mechanisms drive in this multi-prediction, parallel learning, setting. The setup of this thesis is a staple thesis in which the core of the work is an accepted journal paper, with a new introduction and conclusion written by the author. The additional introduction and conclusion are original works. They serve to demonstrate the author’s knowledge of the subject matter and journal paper in question, the author’s individual perspective on topic area, and the future directions for research that the author would like to pursue. Adapting Behavior via Intrinsic Reward: A Survey and Empirical Study, has been accepted into the Journal of Artificial Intelligence Research (JAIR). The paper is co-authored by Cam Linke (the author of this thesis), Nadia M. Ady, Martha White, Thomas Degris and Adam White. The paper was a collaborative work that began as a proposed conference paper, was accepted as an extended abstract into the Reinforcement Learning and Decision Making (RLDM) conference, and finally was accepted into JAIR. The author’s specific contributions were the open-sourced code and notebook for replicating the empirical work; writing sections of the intro, experiments and conclusion; and contributing to the large survey of previous literature performed in the paper.
SYNTHESIS, CHARACTERIZATION AND ANTI-TUMOUR ACTIVITY OF IRON(III) SCHIFF BASE COMPLEXES WITH UNSYMMETRIC TETRADENTATE LIGANDS

The synthesis and characterization of two new iron(III) complexes, [Fe(pythsalI)]Cl 2 and [Fe(pythsalBr)]Cl 2 with the NSNO-donor tetradentate Schiff base ligands pythsalHX [(5–X-N-(2pyridylethylsulfanylethyl) salicylideneimine] (X = I, Br)) obtained from the inserted condensation of 1-(2-pyridyl)-3-thia-5-aminopentane with the respective derivative salicylaldehyde in a 1:1 molar ratio is reported. The iron(III) complexes were characterized by several techniques using elemental analysis (C, H, N), FT-IR, electronic spectra and molar conductance measurements. The elemental analysis data suggest the stoichiometry to be 1:2 [M:L] ratio formation. The molar conductance measurements reveal the presence of 1:2 electrolytic nature complexes. Infrared spectral data agreed with the coordination to the central metal ion through deprotonated phenolic oxygen, imine and pyridine type nitrogens and the thioether sulfur atoms. From ligand field spectral data an octahedral geometry is assigned to the iron(III) ion in all these complexes. These new compounds have showed anti-tumour activity against two kinds of cancer cells that are K562 (human chronic myeloid leukemia) and Jurkat (human T lymphocyte carcinoma). KEY WORDS : Iron(III) complexes, Tetradentate ligands, Unsymmetric Schiff base, NSNO-donor, Anti-tumour, K562, Jurkat Bull. Chem. Soc. Ethiop. 2010, 24(2), 193-199.
16. “TRIPLET APPROACH”- COMBINATION

A 13 year old female patient came to our department with chief complain of excessive gum show. On Clinical examination patient presented with convex facial profile, obtuse nasolabial angle, mesocephalic, mesoprosopic, incompetent lips and increased interlabial gap. Intraorally she had class II canine and molar relation bilaterally with crowding irt 31, 32, 33, 41, 42, 43. Patient was really concerned about her facial profile. So we decided to manage the same with triplet approach i.e the combination of orthopedic, myofunctional and fixed mechanotherapy. The total treatment time was 28 months which include 13 month phase I therapy and 15 months phase II therapy and the results were exuberant.