Miracle-dz/UniFAR-FTUs huggingface dataset:
Transformational Leadership Educational Bundle for Nursing Supervisors and Nurses in-Charges and its Impact on Clinical Performances of the Nurses in the Labour Room

Background: In the process of transformational leadership, leaders and followers have mutual understandingand help each other to promote a higher levels of morale and motivation among them. Employees in theorganization take ownership of their roles and responsibilities and perform the work activities out ofexpectation of the leaders, leaders describe only the goals and objectives of the organization.Methodology: In this study quantitative research approach was used and one group pretest posttest quasiexperimental research design was used. The study conducted in the labour rooms of selected maternityhospitals. The samples were nursing supervisors, nursing in-charges and nursing officers of the maternitywards and participants were chosen by adopting convenient sampling technique. Structured questionnairewas used, one tool consisted of 20 questions regarding knowledge on transformational leadership andanother tool comprised of clinical performance evolution check list. Tools were validated by the expertsand reliability values includes Cronbach’s alphas were r= 0.88, & 0.96 for knowledge transformationalleadership and nurses’ job performance evaluation respectively.Results: Majority of the nursing officers were falling under the category of age between 26 to 30 years.Around 62 % of the nursing officers have an experience of 6 to 10 years. Majority of the nursing officershave completed GNM qualification. About 40 % of the nursing supervisors and nursing in charges havecompleted BSc Nursing qualification. In pretest, majority of supervisors and in charges have poor knowledgeregarding transformational leadership while in post test77 % of subjects gained the good knowledge. Inposttest majority of the nursing officers were competent in establishing the expected behavior of leaders,maintaining nursing process model, providing quality of the care and adequate information and education tothe patients and relatives as per the requirement.Conclusion: It is concluded that, as transformational leadership is successful in many domains of healthcare delivery system, and this study results showed that, using transformational is effectives labour roomand other maternity wards for successful maternal and newborn outcome.
Extraction and characterisation of phytochemicals from white seringa (kirkia acuminata) bark extracts

The intention of this study was to extract and characterise phytochemicals with analgesic effect from Kirkia Acuminata bark extracts. Soxhlet extraction and Steam distillation were used for the extraction of compounds. Methanol,dichloromethane and hexane were used as solvents. Classes of phytochemicals were identified by qualitative tests and Thin Layer Chromatography using UV light.The qualitative tests of the phytochemical screening indicated the presence of alkaloids, anthraquinones, glycosides, flavonoids, phenols, tannins to name a few. Alkaloids, flavonoids, phenols and tannins were also observed on Thin Layer Chromatography. Menthol, catechol, 1,2 benzenediol 4 methyl, nitro phenyl salicylate, phenol dimethoxy,tau-cadinol,Isopropenyl,8 dimethyl, menthone and levomenthone were identified using Gas Chromatography-Mass Spectrometer. The hexane fraction which is highly a non-polar solvent showed that very few phytochemicals were taken up in it.The polar solvents showed compatibility with the various chemical classes. The presence of these compounds gives Kirkia acuminataits characteristic property of being an analgesic. It thus finds application in the field of medicine.
Reconstructing Spectra from RGB Images by Relative Error Least-Squares Regression

Spectral reconstruction (SR) algorithms attempt to map RGB- to hyperspectral-images. Classically, simple pixel-based regression is used to solve for this SR mapping and more recently patch-based Deep Neural Networks (DNN) are considered (with a modest performance increment). For either method, the 'training' process typically minimizes a Mean-Squared-Error (MSE) loss. Curiously, in recent research, SR algorithms are evaluated and ranked based on a relative percentage error, so-called MeanRelative-Absolute Error (MRAE), which behaves very differently from the MSE loss function. The most recent DNN approaches - perhaps unsurprisingly - directly optimize for this new MRAE error in training so as to match this new evaluation criteria. In this paper, we show how we can also reformulate pixelbased regression methods so that they too optimize a relative spectral error. Our Relative Error Least-Squares (RELS) approach minimizes an error that is similar to MRAE. Experiments demonstrate that regression models based on RELS deliver better spectral recovery, with up to a 10% increment in mean performance and a 20% improvement in worst-case performance depending on the method.
Biological control of insect pests of strawberry (Fragariax ananassa Duch.) in Irapuato, Guanajuato, Mexico

Strawberry is one of the main crops in the state of Guanajuato, Mexico. The main insect pests are: Tetranychus urticae (Koch.) , Trialeurodes vaporariorum (Westwood) and, Chaetosiphon fragaefolii (Cockerell) . In order to evaluate the impact of different natural enemies on these pests, two treatments were compared. The first treatment (T 1 ) consisted in pesticide application, the second treatment (T 2 ) was composed of the liberation of two natural enemies of these insect pests: Chrysoperla carnea (Stephens) and Hippodamia convergens (Guérin-Méneville). Coriander ( Coriandrumsativum L.) was planted on the side of the fields where the natural enemies were released. The adults of T. urticae were more abundant in T 1 than T 2 , by contrast,there were lower number of nymphs and adults of Ch. fragaefolii in T 1 ; nymphs and pupae of T. vaporariorum there were not significantly different between treatments. The populations of T. urticae and Ch. fragaefolii followed a similar pattern, appearing since the end of November and reaching a peak in April, whereas the population of T. vaporariorum showed three population peaks (December, February and June). The presence of C. carnea and H. covergens was similar between T. urticae and T. vaporariorum , but significant and different with respect of Ch. fragaefolii . The average number of T. urticae ’s eggs per leaf in the treatment with the release of natural enemies was of 130, compared with 1000 eggs/leaf in the conventional treatment. The population of whiteflies was lower in the biological control treatment. There was an effective pest control with the biocontrol treatment.
The lncRNA MIR31HG regulates the senescence associated secretory phenotype

Senescent cells secrete cytokines, chemokines and growth factors collectively known as the senescence-associated secretory phenotype (SASP) which can reinforce senescence and activate the immune response. However, it can also negatively impact neighbouring tissues facilitating tumor progression. We have previously shown that in proliferating cells the nuclear long non-coding RNA (lncRNA) MIR31HG inhibits p16/CDKN2A expression through interaction with polycomb repressor complexes (PRC1/2) and that during BRAF-induced senescence MIR31HG is overexpressed and translocates to the cytoplasm. Here, we show that MIR31HG regulates the expression of a subset of SASP components during BRAF-induced senescence. The SASP secreted from senescent cells depleted for MIR31HG fails to induce paracrine invasion without affecting the growth inhibitory effect. Mechanistically, MIR31HG interacts with YBX1 facilitating its phosphorylation at serine 102 (p-YBX1S102) by the kinase RSK. p-YBX1S102 induces IL1A translation which acts as upstream regulator inducing the transcription of the other SASP mRNAs. Our results suggest a dual role for MIR31HG in senescence depending on its cellular localization and points to the lncRNA as a potential therapeutic target in the treatment of senescence-related pathologies.
A Social Science Perspective on Artificial Intelligence: Building Blocks for a Research Agenda

In this article, we discuss and outline a research agenda for social science research on artificial intelligence. We present four overlapping building blocks that we see as keys for developing a perspective on AI able to unpack the rich complexities of sociotechnical settings. First, the interaction between humans and machines must be studied in its broader societal context. Second, technological and human actors must be seen as social actors on equal terms. Third, we must consider the broader discursive settings in which AI is socially constructed as a phenomenon with related hopes and fears. Fourth, we argue that constant and critical reflection is needed over how AI, algorithms and datafication affect social science research objects and methods. This article serves as the introduction to this JDSR special issue about social science perspectives on AI.
A Narrative Investigation into the Meaning and Experience of Career Destabilization in Italian NEET

In the last few years, changes in the work market have produced effects on the transition school-to-work and, more specifically, on the ability to plan for the future, in particular in the Italian context. This study aims to explore the experiences of young people who have encountered difficulties in transition-to-work, and how unsuccessful experience are re-elaborated on the level of autobiographical narration. Using an Italian blog, the authors obtained narrative data from 461 NEET, young people not engaged in education, employment, or training. Textual Analysis was carried out to identify the thematic clusters and obtain the network pattern of lemmas to understand the process of meaning construction of NEET. The analysis shows three clusters: “Ideals: from training to professional life”, “Necessity: the job’s function”, and “Reality: the doing”. The potential of the narrative and the possible interventions are discussed.
Molecular Docking Studies on Various Food Grade Dyes as a Potential Inhibitor of COVID-19

In December 2019, the Coronavirus disease-2019 (COVID-19) virus emerged in Wuhan, China. The first resolved COVID-19 crystal structure (main protease) has been developed and various repurposing activities are in process. In this study, a knowledge gap in relation to COVID-19, with the previously known fatal Coronavirus (CoV) epidemics, Severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) CoVs, is covered by the investigation of sequence statistics, molecular modelling, virtual screening, docking, and sequence comparison statistics of the COVID-19 main protease. COVID-19 main protease Mpro formed a sequence similarity group with SARS CoV that was distant from MERS CoV. The identity % was 96 and 51 for COVID-19/SARS and COVID-19/MERS CoV sequence comparisons, respectively. We used molecular docking and a molecular interaction approach to identify small-molecules that bind to the isolated Viral S-protein at its host receptor region. These molecules have good solubility and pharmacodynamics properties. They also obey Lipinski’s rule, which makes them promising compounds to pursue further biochemical and cell-based assays to explore their potential for use against COVID-19. We hypothesize that the top score identified molecules that may be used to limit viral recognition of host cells and/or disrupt host-virus interactions. A ranked list of selected compounds is given that can be tested experimentally.
Novel biodegradable polyanhydrides based on betulin disuccinate and sebacic acid for medical purpose

: The paper describes synthesis and the characterization of novel biodegradable betulin-based polyanhydrides, exhibiting anti-cancer activity. Polyanhydrides were obtained by a melt polycondensation of a disuccinate betulin (3,28-di-O-succinyl betulin) and sebacic acid with the use of acetic anhydride. Mentioned polyanhydrides were then thoroughly characterized by 1 H NMR and 13 C NMR, size exclusion chromatography, differential scanning calorimetry and FT-IR spectroscopy. The content of sebacic acid in obtained copolymers was from 20 to 80 wt%. The use of sebacic acid as a comonomer increases the crystallinity of polymers. Under physiological conditions copolymers undergo hydrolytic degradation to betulin disuccinate, whose biological activity is known and confirmed and to sebacic acid approved by the US Food and Drug Administration (FDA) for use in drug delivery systems. Polyanhydrides were also tested for cytostatic activity against a wide range of cancer cell lines (HeLa, A-549, U-87MG, KB and HepG2), proving its efficiency in inhibiting the growth of selected cell lines. Obtained polymers can be used as carriers in drug delivery system, in form of microspheres. Microspheres with diameter within the range of D n = 15–30 µm were prepared by using emulsion (O/W) solvent evaporation method.
Covalently bonded double-charged ionic liquid on magnetic graphene oxide as a novel, efficient, magnetically separable and reusable sorbent for extraction of heavy metals from medicine capsules

A new, efficient and reusable double-charged ionic liquid modified magnetic graphene oxide (DIL-MGO) was used as a special sorbent in the solid-phase extraction process for simultaneous separation and preconcentration of Pb(II), Cd(II), Ni(II), Cu(II) and Cr(III). The ionic liquid was chemically attached to magnetic graphene oxide nano sheets and then characterized by field emission scanning electron microscopy (FE-SEM), thermo gravimetric analysis (TGA), X-ray diffraction (XRD), vibrating sample magnetometry (VSM) and Fourier transform infra-red spectroscopy (FT-IR). This sorbent employs a hybrid combination of an ionic liquid and graphene oxide extraction properties, which contributed to the increased extraction efficiency via improving mass transfer yield. An ultrasound-assisted double charged ionic liquid-linked magnetic micro solid phase extraction method (US-assisted DIL-MμSPE) coupled with micro sampling atomic absorption spectrometry was utilized for the determination of heavy metals. Effecting parameters, including sample pH, amount of DIL-MGO, extraction time, type and concentration of desorption solvent, were investigated and optimized. Good recoveries and satisfactory relative standard deviations (RSDs) under the optimal conditions were achieved. The limits of detection were from 0.2 to 1.8 μg L−1. Method performance was investigated by the determination of the mentioned heavy metals in complex medicine capsule matrixes with good recoveries. Finally, the sorbent was reused for eight runs without significant loss of its recovery.
FAMILY ADAPTATION TO DEVELOPMENTAL NEEDS OF THEIR CHILDREN WITH MULTIPLE DISABILITIES: A COMPARATIVE ASSESSMENT STUDY OF SOME SWEDISH AND CAMEROONIAN FAMILIES

This study assesses, in a comparatively perspective, how some families adapt to daily routines of the developmental needs of their children with multiple disabilities in order to enhance their wellbeing and learning. The study sampled (N=20) parents from 10 families in Cameroon and Sweden using the mixed method approach to data collection and analysis. Findings reveal commonalities in family adaptation to their children’s health condition through: sacrificing extra time; rescheduling working hours; adjusting income, expenditure and plans; exploiting the available social support resources to meet the children’s special developmental needs. In addition, since parents perceive multiple disabilities as a critical health condition, they maximize the scope of support activities to enhance child development. In distinction, Cameroonian nucleus families are structurally larger in size than Swedish. Functionally, the former is dependent upon and interacts with extended family members while the latter makes use of outsiders ranging from professionals to pets for support services. In child functioning, children in the standardized monolingual society of Sweden change key communication partners indiscriminately whereas those in the multilingual society of Cameroon permanently cling to one or few partner(s) influential in enhancing their development. In conclusion, the study recommended that parents should be given more professional help and voice in policy making regarding the support of their children with multiple disabilities. Article visualizations:
Effect of pretreatment of wool fabric with keratin on its dyeability with acid and reactive dyes

Keratin has been extracted from wool using ammonium thioglycolate under nitrogen atmosphere and then used for treating wool fabric in the presence of epichlorohydrin as a cross-linking agent. The treated fabrics are then dyed with acid and reactive dyes at various temperatures. Complete exhaustion of these dyes on the treated fibres takes place at room temperature within 20 min. Higher dyeing temperatures result in complete exhaustion of the dyebath within a short time. The dyeing kinetics of the treated fibres are also evaluated. The half dyeing time of the treated samples decreases to <12 s against the 300 s for the untreated ones. The wash and rub fastness of the dyed fabrics have also been assessed. It is found that urea-bisulphite solubility values assure that epichlrohydrin is able to form permanent crosslinks between keratin and wool. Scanning electron micrographs of the treated fabrics disclose formation of very thin keratin layer onto wool surface.
Anti-discrimination Laws, AI, and Gender Bias: A Case Study in Non-mortgage Fintech Lending

Problem definition: We study the impact of the existing anti-discrimination laws in different countries on gender bias in the non-mortgage consumer fintech lending setting. Academic/Practical Relevance: Building on the study of discrimination in operations, financial economics, and computer science, our paper investigates the impact and drivers of discrimination in machine learning models, trained on alternative data, and provides technically and legally permissible approaches for firms to reduce discrimination, whilst managing profitability. Methodology: We train statistical and machine learning (ML) models on a large and realistically rich publicly available dataset and measure the impact on model discrimination, predictive quality, and firm profitability. We use ML explainability techniques to understand the drivers of ML discrimination. Results: We find that laws which prohibit the use of gender (e.g., those in the US) substantially increase discrimination, and slightly decrease firm profitability. We observe ML models are less discriminatory, of better predictive quality, and more profitable compared to traditional statistical models like logistic regression. Unlike omitted variable bias which drives discrimination in statistical models, ML discrimination is driven by changes in feature engineering and feature selection when gender is excluded. We observe that down-sampling the training data to rebalance gender, gender-aware hyperparameter tuning, and up-sampling the training data to rebalance gender, all reduce discrimination, with varying trade-offs in predictive quality and firm profitability. Probabilistic gender proxy modeling (imputing the gender of applicants) further reduces discrimination, with negligible impact to predictive quality or firm profitability. Managerial Implications: Consequently, a rethink is required of the anti-discrimination laws, specifically with respect to the collection and use of protected attributes for machine learning models. Firms should be able to collect protected attributes to, at minimum, measure discrimination, and ideally, take steps to reduce it. However, increased data access should come with greater accountability and responsibility for firms.
RUMICAM: A New Device for Cattle Rumination Analysis

Rumination may reveal important behavioral aspects of livestock animals and has been increasingly studied using new sensors technologies. In this work a new device was developed to collect close-up videos from the animal mouth during the rumination period. Using shallow and deep machine learning techniques, a software that classifies the basic mouth movements from these images has also been developed. A baseline performance for this equipment has been established using the Fscore metric. SVM achieved the highest F-score of 79.3% for the shallow learning approach. The best F-score using deep learning was 75% using VGG16.
FEMALES IN GOVERNANCE AND CORPORATE TAX AVOIDANCE: THE MODERATING EFFECT OF ACCOUNTING CONSERVATISM

High cash outflow in the form of corporate taxes reduces the corporate firms’ liquidity, available funds for re-investment and growth opportunities. Corporate firms’ attention is therefore geared towards ensuring minimum tax liability. The purpose of this study is to examine the effect of females’ presence in the governance on corporate tax avoidance, moderating for the role of accounting conservatism. The study is based on the companies listed on the Nigerian Stock Exchange and utilised the ex-post factor research design. The panel corrected standard regressions was employed to test the hypothesis. Female CEO, percentage of female directors and presence of female in the audit committee have significant positive effects on tax avoidance. The moderating for accounting conservatism, the percentage of female directors on the board and female director presence in the audit committee remains significant. The findings may be of interest to the academic researchers, investors and regulators. For academic researchers, it is interested in discovering whether females’ presence in the governance affect tax avoidance and the moderating role accounting conservatism. For investors, it shows that women in the boardroom can improve the bottom line financial performance through tax reduction strategies.This study extends the existing literature by examining the mediating role of accounting conservatism on the relationship between females in governance and tax avoidance in the Nigerian context.
Morphological, phylogenetic, and pathogenic analyses of Fusarium andiyazi associated with sugar beet root diseases

Abstract Fusarium andiyazi isolates, EGF1 and EGF2, were recovered from sugar beets exhibiting wilting, chlorosis, and root rot. Morphological characteristics and sequencing of translation elongation factor-1α (TEF-1α), the ITS region of rDNA, and β-tubulin (TUB) wear combined to corroborate the identity of these isolates. Phylogenetic analysis of the partial TEF gene revealed genetic variation among F. andiyazi isolates in three genetic clades. Both isolates infected seedlings of all sugar beet cultivars and caused varying degrees of wilting symptoms. 38-day-old sugar beet cultivars differed in their response to the isolates, being either susceptible (Kawemira) or resistant (Pleno). Both isolates induced symptoms of wilting and root rot in sorghum cultivars and caused bakanae in rice cultivars. Cultivars of sorghum (Dorado) and rice (Sakha 101) were susceptible to F. andiyazi infection. This is the first report of F. andiyazi, isolated from sugar beets, causing typical symptoms of seedling wilt and root rot.