Miracle-dz/UniFAR-FTUs huggingface dataset:
Beneficial Health Potential of Algerian Polysaccharides Extracted from Plantago ciliata Desf. (Septentrional Sahara) Leaves and Seeds

Today, an ethnobotanical approach makes sense for identifying new active bioactive chemicals from uses of indigenous plants. Two water-soluble enriched polysaccharide fractions (L-PSPN and S-PSPN) were extracted by hot water extraction from the leaves and seeds of Plantago ciliata Desf. (1798), a Mzab indigenous herb currently used in Algeria by traditional healers. Primary investigation was performed for describing the main structural features of these polysaccharides (pectin- and heteroxylan-like compositions) by using colorimetric assays, FTIR spectroscopy, HPAEC/PAD and GC/MS-EI analyses. Some biological activities were also monitored, such as anticomplement, anti-inflammatory (phagocytic ability, NOX2 and MPO inhibitions) and anti-diabetic (α-amylase and α-glucosidase inhibitions). L-PSPC seems able to moderately modulate innate immune system (IC50 around 100 µg/mL) and contribute to wound-healing processes (IC50 close to 217 vs. 443 µg/mL for sodium heparin). S-PSPC shows some potential as an anti-hyperglycemic (IC50 around 4.7 mg/mL) and anti-inflammatory (IC50 ranging from 111 to 203 µg/mL) agent, as well as other (fiber) psyllium-like polysaccharides extracted from Plantago species.
The convolutional neural network approach from electroencephalogram signals in emotional detection

Although brain‐computer interfaces (BCI) progress rapidly, the desired success has not been achieved yet. One of these BCI is to detect emotional states in humans. An emotional state is a brain activity consisting of hormonal and mental reasons in the face of events. Emotions can be detected by electroencephalogram (EEG) signals due to these activities. Being able to detect the emotional state from EEG signals is important in terms of both time and cost. In this study, a method is proposed for the detection of the emotional state by using EEG signals. In the proposed method, we aim to classify EEG signals without any transform (Fourier transform, wavelet transform, etc.) or feature extraction method as a pre‐processing. For this purpose, convolutional neural networks (CNNs) are used as classifiers, together with SEED EEG dataset containing three different emotional (positive, negative, and neutral) states. The records used in the study were taken from 15 participants in three sessions. In the proposed method, raw channel‐time EEG recordings are converted into 28 × 28 size pattern segments without pre‐processing. The obtained patterns are then classified in the CNN. As a result of the classification, three emotion performance averages of all participants are found to be 88.84%. Based on the participants, the highest classification performance is 93.91%, while the lowest classification performance is 77.70%. Also, the average f‐score is found to be 0.88 for positive emotion, 0.87 for negative emotion, and 0.89 for neutral emotion. Likewise, the average kappa value is 0.82 for positive emotion, 0.81 for negative emotion, and 0.83 for neutral emotion. The results of the method proposed in the study are compared with the results of similar studies in the literature. We conclude that the proposed method has an acceptable level of performance.
Stock Price Predictions with LSTM Neural Networks and Twitter Sentiment

Predicting the trend of stock prices is a central topic in financial engineering. Given the complexity and nonlinearity of the underlying processes we consider the use of neural networks in general and sentiment analysis in particular for the analysis of financial time series. As one of the biggest social media platforms with a user base across the world, Twitter offers a huge potential for such sentiment analysis. In fact, stocks themselves are a popular topic in Twitter discussions. Due to the real-time nature of the collective information quasi contemporaneous information can be harvested for the prediction of financial trends. In this study, we give an introduction in financial feature engineering as well as in the architecture of a Long Short-Term Memory (LSTM) to tackle the highly nonlinear problem of forecasting stock prices. This paper presents a guide for collecting past tweets, processing for sentiment analysis and combining them with technical financial indicatorsto forecast the stock prices of Apple 30m and 60m ahead. A LSTM with lagged close price is used as a baseline model. We are able to show that a combination of financial and Twitter features can outperform the baseline in all settings. The code to fully replicate our forecasting approach is available in the Appendix.
Antibiotic Resistance of Bacteria of the Bacillus cereus Group Isolated from Attiéké, A Local Dish Sold in the Streets of the Town of Daloa (Côte d’Ivoire)

Bacillus cereus often causes problems in the food industry. It deteriorates the marketable quality of food. Bacillus cereus spores can withstand high temperatures and persist in finished or semifinished products. The objective of this work was to assess the level of resistance to antibiotics in strains of Bacillus cereus isolated from attiéké, a local delicacy sold in the streets of Daloa. To do this, around sixty (60) suspected strains of Bacillus cereus isolated from attiéké were identified using standard biochemical tests and thirty-five (35) were confirmed by the API 20E gallery. These thirtyOriginal Research Article Athanase et al.; JAMB, 21(4): 76-82, 2021; Article no.JAMB. 66990 77 five confirmed strains were subjected to an antibiogram according to the recommendations of the Antibiotic Committee of the French Society of Microbiology (CASFM). The results obtained after analysis show that the strains of Bacillus cereus are subdivided into four profiles. No resistant strain has been demonstrated for imipenem, gentamycin and vancomycin. No strain produced broad spectrum beta-lactamases. The least active molecule was trimethoprim-sulfametoxazole with a level of 40%. This study showed the existence of multi-resistant strains of Bacillus cereus, which would require monitoring of resistance in street foods, particularly attiéké
PROCESS OF ORLISTAT-LOADED CHITOSAN NANOPARTICLES USING BOX–BEHNKEN DESIGN – AN EVALUATION STUDY

Objective: High lipophilicity and extensive hepatic metabolism limit oral application of orlistat in obesity treatment. Orlistat-loaded chitosan nanoparticles (CONPs) were optimized by 3-factor 3-level Box–Behnken design (BBD) and surfaced engineered to address limitations. Methods: CONPs were prepared by ionic gelation method. Amounts of chitosan (X1), sodium tripoly phosphate (X2), and orlistat (X3) were selected as independent factors, whereas % entrapment efficiency (Y1) and % drug release (Y2) were employed as responses in BBD. Three-dimensional response surface plots were run to understand the main interaction and quadratic effects of independent variables. Further optimized formulation was surface engineered by Eudragit L-100 (ECONPs) and characterized by FTIR, DSC, XRD, particle size, zeta potential, and SEM. Entrapment efficiency, release kinetics, stability, and in vitro cell line studies were carried out. Results: ECONPs were produced with an average size of 534.6 nm, zeta potential of +5.7 mV, EE of 78.62%, and DR of 80.86%. Eudragit coated CONPs anchored the release of orlistat at pH 6.8 desirable for duodenal targeting. Orlistat was released with low, burst, and sustained release manner over 24 h period followed first-order kinetics with Higuchi model with drug content of 84.87% and 78.44% of release. ECONPs possessed lipase inhibition with IC50 value of 8.0 μg/ml and viability against selected cell lines with CTC50 values (26.32–32.21 μg/ml). Conclusion: BBD was a promising tool in elucidating the insights of formulation variables of CONPs. ECONPs fulfilled the rationale of orlistat release, lipase inhibition, and viability against selected cell lines.
Development of a Simpler but Accurate Free Gas Deviation Factor for Fractured (Dual-Porosity) Volumetric Gas Reservoir Fluid

Gas compressibility factor, also known as gas deviation factor or Z-factor, is a thermodynamic correction factor which describes the deviation of a real gas from ideal gas behaviour. The, free gas Z-factor in the Material Balance Equation (MBE) of single-porosity gas reservoirs with insignificant rock (matrix) compaction (after pressure depletion) does not reflect cases in lowpermeability gas reservoirs having remarkable rock compaction. Through gas MBE modifications, previous researchers developed Z-factors for dual-porosity (fractured) low permeability gas reservoirs by incorporating gas desorption; however, their approaches create complexity for routine calculations. Therefore this study was designed with the purpose of deriving a free gas Z-factor for single-porosity low-permeability gas reservoirs and further modifying it for more simplicity and accuracy in a dual-porosity scenario. The free gas Z-factor derived for single-porosity lowpermeability gas reservoirs is expressed as: ∗ = ∙ 1 − ∆ where , , , , and ∆ are single-porosity Z-factor without rock compaction at pressure , water Original Research Article Alawode and Falode; JERR, 20(6): 88-103, 2021; Article no.JERR. 67405 89 compressibility, initial water saturation, matrix compressibility, initial gas saturation and pressure depletion, respectively. However, the developed dual porosity free gas Z-factor model incorporates ratio of dual porosity to initial matrix porosity, and it is expressed as: ∗∗ = ∙ 1 − 1 − + ∙ ∆ where and are initial matrix porosity and fracture porosity, respectively. The Z-factor model was graphically and statistically correlated with an existing free gas Z-factor model for dual porosity reservoirs. For all the hydraulically fractured shale gas formations considered, the correlations yield R values of 1.000.
Scaling of Organisations of Persons with Disabilities: A Case Study in the People’s Democratic Republic of Lao

Purpose: The ability to have impact at scale is an important concern for Organisations of Persons with Disabilities (OPDs), but little is known about how scaling occurs in practice and the capacity of OPDs to undertake scaling. The aim of this study was to assess perceptions and experiences of scaling and broader organisational practices among a sample of people working for OPDs in Lao PDR. The study also aimed to deductively analyse the qualitative data in relation to a pre-defined socially inclusive scaling framework. Methods: In-depth interviews were conducted with a sample of people working with 10 OPDs operating in Lao PDR (n = 12; 6 female and 6 male). Participants included directors, administration personnel, and advisers of OPDs. Grounded theory was used to analyse the qualitative data. A deductive approach, comprising a multiple cycling coding process, was used to analyse the data in relation to the IPILA socially inclusive scaling framework. Results: Participants reported their perceptions of scaling and broader organisational practices in relation to eight themes: OPDs’ relationship with government; collaboration among OPDs; legal registration; operational challenges; good practices for success; different strategies across OPDs; next steps to improve the work of OPDs; and implementation of policies. Findings from the deductive analyses indicated that OPDs use different strategies to include their members and/or service-users. The OPDs scaled in different directions, despite facing operational challenges such as funding and technical expertise. While OPDs identified scaling practices, they react to rather than plan scaling opportunities. Conclusion: This study addressed the ways in which scaling is understood by OPDs in the context of Lao PDR. The findings show that OPDs recognise their good practices and the steps needed to scale, but lack planning and monitoring processes for scaling.
Strategy for Accelerating Poverty Reduction in Klungkung Regency, Bali

Poverty is an illustration to determine a household’s ability to fulfill fundamental rights or the necessities of a decent and dignified life as measured by the poverty line. Klungkung Regency, in the last five years, has made efforts to accelerate poverty reduction through the macro side. It maintains economic growth, reduces inflation, increases investment, creates jobs, and the micro side, namely increasing income and reducing the burden on public spending. Through pro-poor, pro-employment, and pro-regional growth programs. This research aimed to determine the poverty conditions and the reduction in the percentage of poor people in the Klungkung. The research method used quantitative and qualitative approaches, using government data sources and narrated qualitatively. This research uses the theory of poverty culture, various innovative development programs, and poverty to reduce the percentage and number of poor people from year to year. In 2016 the percentage of poverty was 6.35%, or the number of poor people was 11,210. In 2017 the percentage of poor people was 6, 29%, or the number of poor people by 11,150. In 2018 the percentage of poor people was 5.86%, or the number of poor people was 10,430. In 2019, the percentage of poor people was 5.4%, or the number of poor people was 9,960 people. In 2020 the percentage of poor people was 4.87%, or the number of poor people is 8,760. It is expected that by the end of 2023, the percentage of poverty in Klungkung Regency will be 3%, according to the target of the National Medium‐Term Plan (RPJMN.
Exploring Organizational Leadership for English Learner Equity

ABSTRACT In this study, we explore leadership practices in a dual-language elementary school led by three leaders of color committed to the ideals of cultural responsiveness. We employ an organizational leadership lens informed by aspects of culturally responsive school leadership (CRSL) and teaching (CRT) to interpret interview and observational data collected during the implementation of an equity-oriented engineering program for English learner (EL) students. In the midst of attempting to implement this school-research partnership, preexisting tensions between the school’s leadership and instructional culture rose to the forefront, offering the opportunity to analyze the data with this particular intersectional lens (organizational leadership and CRSL). Thus, subsequent data analysis focused not on program implementation but rather the existing challenges present in the school. Insights from our data suggest that both school leaders and teachers faced considerable challenges that appeared to stem from disparate understandings of how to achieve equity for their EL students. Ultimately, these challenges prevented leaders’ successful enactment of CRSL within the existing organizational infrastructure. We suggest that the lack of explicit processes of critical consciousness defined the school culture and that accountability practices limited leaders’ ability to implement CRSL.
Learner-Centred Learning Tasks in Higher Education: A Study on Perception among Students

Universities face the challenge of constantly improving the quality of higher education and changing the learning behaviour of students, from passive reactive learning to active self-regulated learning. Learner-centred, constructively designed learning tasks offer a great opportunity here. This paper investigates to what extent the learning process is challenged by these learning tasks, and how these tasks are perceived by the students, using a before and after survey of students studying at bachelor level in business courses at a German university. The paper starts with a short description of constructivism in the context of task design and the main characteristics of learner-centred, constructivist-orientated learning tasks: openness to problems, situation orientation, openness to solution paths, and degree of difficulty. Then the research method used is outlined before the findings are presented. The before and after survey shows that despite an increased complexity and workload, the motivation to deal with topics on the subject remained stable.
A Snapshot of Microfluidics in Point‐of‐Care Diagnostics: Multifaceted Integrity with Materials and Sensors

Over four decades, point‐of‐care (POC) technologies and their pivotal applications in the biomedical arena have increased irrepressibly and allowed to realize the potential of portable and accurate diagnostic strategies. Today, in the light of these advances, POC systems dominate the medical inventions and bring the diagnostics to the bedside settings, potentially minimizing the workload in the centralized laboratories, as well as remarkably reducing the associated‐cost and time. In contrast to the conventional technologies, microfluidics paves the way to create more efficient and applicable POC diagnostic devices through their inherent fashions such as minute volume of samples, easy manipulations, shorter assay time, and low‐cost production. In this review, the current status and advancements of microfluidic systems along with the current limitations in the aspect of POC diagnostic strategies are elaborated. Further, the integration of novel materials and innovative sensing platforms to the microfluidic systems are comprehensively evaluated to address the real‐world challenges for diagnosing various maladies at the POC settings.
What is recovery? Views of psychologists in private practice

ABSTRACT Objective Personal recovery is a concept that emphasises an individual’s unique process of achieving a meaningful and fulfilling life, even if mental illness symptoms are ongoing. This is in contrast to clinical perspectives that focus on reducing or eliminating symptoms. The aim of this study was to examine private practice psychologists’ perceptions of recovery in the context of mental illness. Method Semi-structured interviews were completed with eight psychologists who worked in private practice in Victoria, Australia. Analysis was conducted with Interpretative Phenomenological Analysis. Results Six of the eight participants were not familiar with frameworks of recovery. Instead, their views had largely been shaped by interactions with clients. There was a view that clients undertake a unique journey towards greater functioning and reflective awareness. Symptom reduction was also considered part of this journey. The therapeutic relationship with clients was viewed as crucial to recovery. Discussion Efforts are needed to educate mental health practitioners working in private practice about recovery. Mechanisms to achieve this could be the inclusion of recovery frameworks in pre-registration courses, directing recovery-focused training and resources towards the private practice sector and establishing explicit links between recovery and commonly employed therapeutic interventions.
Investigation of microbial community changes in petroleum polluted sediments during hydrocarbons degradation

ABSTRACT Bioremediation is among the most basic and reliable way to eliminate pollutants by transforming the toxic petroleum components into less toxic metabolites. In order to compare the change of bacterial community during oil-biodegradation process, two oil-polluted sediments sampled from Tunisia were analyzed using 16S rDNA clone libraries. During the experimental period, measurements of microbial abundance along with qualitative and quantitative analysis of pollutants were carried out. Fingerprinting analysis of natural samples showed the presence of different classes of bacteria with the dominance of Gammaproteobacteria (>60%). The bacterial diversity decreased in enrichment cultures with crude oil with the appearance and predominance of Alcanivorax spp. Our data also showed that several Obligate Marine Hydrocarbonoclastic Bacteria (OMHB: Alcanivorax, Cycloclasticus and Marinobacter) were stimulated by the addition of petroleum. The shift of diversity and the dominance of OMHB were associated with high degradation rate of crude oil (the total degradation of petroleum TERHCs was estimated at 90% and 70% in TST1E and TST2E) and a decrease in sediment toxicity to Corophium orientale. Since several factors can influence the clean-up in-field, the effectiveness of biostimulation treatment and its optimization at lab scale under controlled conditions would facilitate and allow the application in-situ. Moreover, the study of the community diversity allows the detection and the identification of the dominant hydrocarbonoclastic species such as Alcanivorax which it would be beneficial in future experiments to facilitate the selection of the autochthonous strains (adapted to support the contamination) for the reconstruction of efficient consortia useful in the cleanup of hydrocarbon contaminated sites.
Relaxor Ferroelectric Oxides: Concept to Applications

Ferroelectric ceramic is one of the most important functional materials, which has great importance in modern technologies. A ferroelectric ceramic simultaneously exhibits dielectric, piezoelectric, ferroelectric, and pyroelectric properties. The inherent ferroelectric properties are directly related to long-range electric dipoles arrangement in the ferroelectric domains and its response to external stimuli. However, the interruption of the long-range ordering of dipoles leads to the formation of a special class of material is known as relaxor ferroelectric. It shows quite different physical properties as compared to ferroelectric (normal ferroelectric). The origin and design of relaxor ferroelectric are quite interesting for fundamental perspective along with device applications. Therefore, the origin of relaxor ferroelectric along with its fundamental understanding for possible future applications, have been explained briefly in the present chapter.
QVigourMap: A GIS Open Source Application for the Creation of Canopy Vigour Maps

In a precision agriculture context, the amount of geospatial data available can be difficult to interpret in order to understand the crop variability within a given terrain parcel, raising the need for specific tools for data processing and analysis. This is the case for data acquired from Unmanned Aerial Vehicles (UAV), in which the high spatial resolution along with data from several spectral wavelengths makes data interpretation a complex process regarding vegetation monitoring. Vegetation Indices (VIs) are usually computed, helping in the vegetation monitoring process. However, a crop plot is generally composed of several non-crop elements, which can bias the data analysis and interpretation. By discarding non-crop data, it is possible to compute the vigour distribution for a specific crop within the area under analysis. This article presents QVigourMaps, a new open source application developed to generate useful outputs for precision agriculture purposes. The application was developed in the form of a QGIS plugin, allowing the creation of vigour maps, vegetation distribution maps and prescription maps based on the combination of different VIs and height information. Multi-temporal data from a vineyard plot and a maize field were used as case studies in order to demonstrate the potential and effectiveness of the QVigourMaps tool. The presented application can contribute to making the right management decisions by providing indicators of crop variability, and the outcomes can be used in the field to apply site-specific treatments according to the levels of vigour.