Miracle-dz/UniFAR-FTUs huggingface dataset:
MOLECULAR DETECTION OF ANTIBIOTICS PROFILE OF GARDENERELLA VAGINALIS WHICH ISOLATED FROM PRETERM LABOR

In this study, one hundred fifty samples were collected from patients with preterm labor (PTL), who have been attending to Babylon Maternity and pediatric hospital and Al-Hilla Teaching Hospital, at the period from February to October 2016. Two swabs were collected one for culturing and the other for direct extraction for isolation Gardnerella vaginalis. Out of the 150 samples only 6(4%) on culture and 30 (20%) on molecular level isolated from preterm labor caused by Bacterial vaginosis, Urinary tract infection and aborted women. The results shown that only 6 isolates belong to G. vaginalis confirmed by using Vitek 2 system and molecular detection by specific primers. Preterm birth is one of the most common causes of neonatal morbidity and mortality. Associated with sub sequent preterm labor in up to 40% of cases as shown in our results. Three Antibiotics were used at a molecular level (Tet b) were varied between resistance and sensitive to this gene (10) sample are resist,(RdxA) all sample are sensitive to its gene at percentage (100%), whereas (erna) gene were resist in all samples (100%).
Removal of basic dye Auramine-O by ZnS:Cu nanoparticles loaded on activated carbon: optimization of parameters using response surface methodology with central composite design

This research is focused on the ultrasound-assisted removal of Auramine-O (AO) dye from aqueous solutions using ZnS:Cu nanoparticles loaded on activated carbon (ZnS:Cu-NP-AC) as an adsorbent. ZnS:Cu nanoparticles were synthesized and characterized using FESEM (Field-Emission Scanning Electron Microscopy) and XRD (X-Ray Diffraction) analysis. The experiments were designed by response surface methodology. A quadratic model was used to predict the variables. Analysis of variance was used for investigation of variables and interaction between them. High F-value (48.91), very low P-value (<0.00001), non-significant lack of fit, and the determination coefficient (R2 = 0.977) demonstrate good correlation between experimental and predicted values of the response. The highest removal percent attained was 99.76%, and the optimum parameters achieved are: adsorbent amount (0.02 g), initial dye concentration (20 mg L−1), sonication time (3 min) and pH = 7. Adsorption processes of AO by ZnS:Cu-NP-AC could be well described by a Langmuir isotherm and a pseudo-second-order kinetic model. The maximum adsorption capacity of AO by ZnS:Cu-NP-AC was determined as 183.15 mg g−1, suggesting a highly promising potential for ZnS:Cu-NP-AC to be used as a new adsorbent.
Online reputation management by cancer hospitals: A systematic literature review in the USA and Spain

Cancer hospitals manage social media platforms in a professional way to improve their relationships with internal and external stakeholders and reinforce their corporate brand. To do so, they need their health professionals to be involved: these professionals become brand ambassadors able to influence society. Nevertheless, they face different challenges: legal issues, new patients’ demands, privacy-related matters, or the difficulty of disseminating scientific content. This literature review paper analyzes how cancer hospitals manage their social media platforms to improve their reputation. To do this, we carry out a systematic literature review focused on papers published in the USA and Spain, based on the Salsa framework proposed by Grant and Booth (2009). We then define an online corporate communication model allowing cancer hospitals to improve their reputation through Facebook, Twitter, and YouTube (MedPac Model for Building Cancer Hospital Brands). The paper concludes that this model is useful for cancer hospitals because it prioritizes persons (brand ambassadors) rather than companies, focuses on scientific and emotional content rather than business information, and is based on human values.
Bioprospecting of Callistemon citrinus explored for present chemical species using FTIR

Despite a great breakthrough of scientific advancements and knowledge about diversity, biochemical composition of a vast array of plant species still remains unestablished or unquantified. FTIR spectroscopy was used in this study to evaluate the phytochemical potential of Callistemon citrinus. Leaves, stems and bark tissues of the mentioned species were sampled, and the ATR-FTIR spectra recorded in the spectral range 485 - 4000 cm-1 at a resolution of 4 cm-1. The data indicated presence of the following significant functional groups; β-carophyllene, tristerpenes betulin and α-pinene in the leaf samples, phemol, Linalool, β-Pinene (bicyclic monoterpenes), phloroglucinol derivatives, alkaloids and terpenen-4-ol in the bark samples, pectin,1-8 cineol, alkynes and γ-terpineole in the stem samples. These results demonstrates a great potential of application of C. citrinus plant species in pharmaceutical industry recommends further scrutiny on quantities of each of the compound present.
NERPy: A Framework for Named Entity Recognition Experiments

Creating a high-performing sequence named entity recognition (NER) system requires a series of interconnected design decisions, including the choice of entity encoding, and for some models, the design and selection of features. In this paper, we introduce NERPy, an MIT-licensed NER framework designed to support ﬂexible experiments in named entity recognition. We demonstrate NERPy by performing a sample experiment using the CoNLL 2003 English NER data that explores the performance of different entity encoding schemes across a wide range of training data sizes.
INFLUENCE OF ANTIBIOTICS AND STICK SWEET CHERRY (PRUNUS AVIUM) ON PATHOGENIC BACTERIA AND EVALUATION OF TISSUES BIOAVAILABILITY, BIOACTIVE PHYTOCHEMICAL COMPOUND AND FUNCTIONAL PROPERTIES

Infectious diseases pose one of the challenges greatest health in the medical world. Though many antimicrobial drugs are available commercially, they often lack recently effectiveness against developed multidrug resistant microorganisms. Recent study carried for the first time, the scope of the experiments to investigate the effect of three types of antibiotic sensitivity Erythromycin, Amoxicillin, Trimethoprim, for inhibition of microorganism which include 8 species of pathogenic bacteria and to estimate the effect of antimicrobial activity. Strains of bacteria are: Klebsiella pneumoniae, Salmonella typhi, , Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, Citrobacter freundii, Lactobacillus, Proteus mirabilis. There is no report on bioactive phytochemical compound and functional properties of stick cherry extract, and on the effect of the extract on animal tissues. Also no data on inhibition of pathogenic bacteria growth by the extract. Extract used for inhibition of some types of pathogenic bacteria, process was done by using four different solvents; chloroform, methanol, ethanol, and hot water at concentration of 75 mg\ml to investigate growth inhibition of pathogenic bacteria which involves: Proteus mirabilis, Escherichia coli, Pseudomonas aeruginosa, Lactobacillus, Candida albicans. Antibacterial activity for stick cherry extract was illustrated by using diffusion agar-well assay. Extract of stick cherry were used for determination of the minimum inhibitory concentration of some types pathogenic bacteria. Analysis of the bioactive phytochemical compound and functional properties were illustrated by using, Fourier transform infra-red spectra (FTIR spectra) and gas chromatography (GC-Mass techniecs). Result of antibiotic sensitivity test illustrate that erythromycin and Amoxicillin has no influence on pathogenic bacteria only trimethoprim affected on the different strains of microorganism except Staphylococcus aureus and Pseudomonas aeruginosa. There was difference in the inhibition zone for pathogenic bacteria growth by the extract. FTIR spectral analysis for the extract investigate the appearance of different phytochemical bioactive compound, such as aromatic carbonyl of aldehyde, hydroxyl functional groups, aliphatic amines, polyphenols, indicate characteristic of carbonyls group. Analysis by GC-Mass chromatography illustrated the presence of 28 different phytochemical compounds of polyphenol compound and 13 sugars, organic acid. Also the study revealed histological analysis of liver and kidney tissues by using laboratory animals at three different concentration. The result shows no difference in morphological shape in tissues of liver and kidney in comparison with control group of
Ethnobotanical study of traditional medicinal plants used by tribe of Dindori district (M.P.)

Ethno-botanical study on traditional medicinal plants was conducted between 2018-2019 in Dindori district of Madhya Pradesh, India and documented different types of traditional medicinal plants used by the indigenous peoples. The study was focused on identifying medicinal plants, disease treated, part of the plant used, methods of preparation, route of administration, ingredients added etc. The data was collected using interview and questionnaires by selecting 16 healers using purposive sampling method. A total of 32 medicinal plant species were collected and identified from the study area for treating various human aliments. The paper enumerates these medicinal plant species belonging to 26 genera and 18 families.
Lecture Notes on Parametricity

As discussed in the previous lecture, parametric polymorphism is the idea that a function of type ∀α. τ will “behave the same” on all types σ that might be used for α. This has far-reaching consequences, in particular for modularity and data abstraction. As we will see in the next lecture, if a client to a library that hides an implementation type is parametric in this type, then the library implementer or maintainer has the opportunity to replace the implementation with a different one without risk of breaking the client code. The informal idea that a function behaves parametrically in a type variable α is surprisingly difficult to capture technically. Reynolds [Rey83] realized that it must be done relationally. For example, a function f : ∀α. α→ α is parametric if for any two types τ and σ, and any relation between values of type τ and σ, if we pass f related arguments it will return related results. As an example, let’s consider some (unknown) function
Rethinking Portfolio Analysis Living Standard Risk and Reward Over the Life Cycle

Each of us must decide how to build and invest our savings. People need to ensure that they have enough income and assets to maintain their living standard at a desired level through time. Traditional financial planning and portfolio management tools tend to focus on the tradeoff between expected returns and risk at a point in time. While these traditional tools provide important investing insights, we demonstrate that a more holistic life-cycle financial planning framework can help form better spending, saving and investment decisions and reduce living-standard risk through time. Investing and spending are not independent decisions. Spending too aggressively can be as risky, if not far more risky as investing aggressively in determining one’s future living standard. Our focus on living standard risk helps to highlight what we believe should be the household’s ultimate concern – namely, their ability to spend at their desired level in retirement. In this context, the paper also shows the significant advantages of diversifying one’s investment within a life-cycle risk-reward framework based on the case of Canadian investors who must decide whether or not to invest globally and, importantly, whether or not to hedge the associated exchange rate risk. We found that maintaining an exposure to U.S. dollars may help to provide both insurance against unanticipated inflation and reduce portfolio risk – a finding that may provide guidance to investors currently grappling with how best to manage their foreign exchange rate risk. Rethinking Portfolio Analysis Living Standard Risk and Reward Over the Life Cycle
Role of Islamic banking in economic growth: Case analysis of Malaysia

In the Islamic paradigm, the banking sector aims at mobilizing financial resources and investing them in an attempt to achieve predetermined social and financial objectives, by following a system of reasonable profit and return from investments. Interest being the cogwheel of the modern banking, is strictly prohibited in the Islam form of banking. This paper is framed with an intention to outline in brief the conceptual framework of Islamic Banking, while aiming at conducting a deep dive analysis of its effectiveness. It seeks to examine the relationship between economic growth and development of Islamic Finance in Malaysia, using econometric analysis. The paper adopts a mathematical approach to examining practically the efficacy of Islamic finance in the perspective of an economy's GDP. The exploratory study on the subject matter was carried out by empirically analysing the relationship between Islamic Banks ‘growth, Gross Domestic Product Growth and Gross Fixed Capital Formation Growth.
Systematization of Knowledge: Quantifying Cyber Risk

—This paper introduces a causal model inspired by structural equation modeling that explains cyber risk outcomes in terms of latent factors measured using reﬂexive indicators. First, we use the model to classify empirical cyber harm studies. We discover cyber harms are not exceptional in terms of typical or extreme losses. The increasing frequency of data breaches is contested and stock market reactions to cyber incidents are becoming less negative over time. Focusing on harms alone breeds fatalism; the causal model is most useful in evaluating the effectiveness of security interventions. We show how simple statistical relationships lead to spurious results in which more security spending or applying updates are associated with greater rates of compromise. When accounting for threat and exposure, indicators of security are shown to be important factors in explaining the variance in rates of compromise, especially when the studies use multiple indicators of the security level.
GROUND ADDITION EFFECT OF SODIUM CHLORIDE SALT ON GROWTH OF ZEA MAYS L

The experiment was carried out in a private field in the province of Diyala / Al-Muqdadiya district / Abi Saida area, 60 km north-east of the city of Baquba (center of the governorate) during the spring season of 2019. The plant was grown in a soil with a sandy clay texture in order to understand the ground addition effect of sodium chloride salt in some morphological characteristics of Zea mays L. Moreover, the experiment included the cultivation of yellow maize (5018 class) in pots, with four levels of sodium chloride addition (0, 50, 100, 150 and 200 mmol) including control treatment of a factorial experiment with five treatments and four replicates and equal to 20 experimental units conducted according to Compete Randomized Design (C.R.D). The results showed that the treatments) control, 50 and 100 mmol/L) was superior in the average plant height, stem diameter, leaf area and chlorophyll guide in the leaf, where the highest average were 154.25, 168.00, 152.50 cm, 1.47, 1.65 and 1.39 cm and 3. 76, 4.03 and 3.06 dsm2/plant and 22.02, 20.79 and 19.55 SPAD, respectively, compared with the treatments of 150 and 200 mmol/L. These concentrations gave the lowest average reached 139.62, 139.75 cm, 1.25, 1.27 cm, 2.69, 2.59 dsm2/plant and 17.58, 15.60 SPAD, respectively. The control treatment 0 and treatment of 150 mmol/L was superior in the average number of leaves and gave the highest average of 13.50 and 13.00 leaf/plant, compared to the treatments of 100, 50 and 200 mmol/L, which gave the lowest average of 12.00, 12.62 and 12.75 leaf/plant, respectively.
EVALUATION OF FOLIAR APPLICATION WITH NANO FERTILIZER (SUPER MICRO PLUS) IN DIFFERENT TIMES ON AVAILABILITY AND UPTAKE OF SOME MICRONUTRIENTS AND SOME QUALITY PROPERTIES OF RICE (ORIZA SATIVA L.)

A field experiment was conducted in Al – Najaf Al Ashraf Governorate during summer season 2017 to study the effect of foliar application with Nano fertilizer with three levels (0, 1 and 2) gm L-1 (super micro plus that contains (5% N, 3% P, 3% K, 4.5% Fe, 8% Zn, 6% Ca, 6% Mg, 0.7% Mn, 0.65 % Cu, 0.65% B and 0.1% Mo) and three stages of application at tillering stage, booting stage and flowering stage in Fe, Zn, Cu and Mn availability, uptake in straw, protein and white grains present of rice (Oriza sativa L.) Amber 33 variety. A experiment was conducted in accordance with to RCBD with three replicates. The following results were obtained Superior the level 2g L-1 Nano fertilizer in availability of Iron, cooper & manganese in soil (4.1 & 1.70 & 3.22) mg kg-1 soil respectively. uptake of Iron, zinc, cooper & manganese in straw (0.504 & 0.107 & 0.080 & 0.289) kg h-1 and protein, white grains percent (8.20 & 67.97) % respectively. Superior stage of adding Nano fertilizer in flowering stage on manganese availability in soil (2.90) mg kg-1 soil, and cooper uptake and manganese in straw (0.055 & 0.252) kg h-1 respectively. Superior the interaction between levels of nano fertilizer application and stage of foliar application in level 2gm L-1 in flowering stage in availability of manganese (3.44) mg kg-1 soil. Iron uptake, cooper uptake and manganese (0.529 & 0.082 &0.298) kg h-1 respectively, and protein present (8.41).
Benchmarking Hierarchical Task Sampling for Few-Shot Multi-Label Domain Adaptation

Land-cover classiﬁcation has applications in human settlement mapping [Qiu et al., 2020], agricultural yield prediction [Bargiel and Herrmann, 2011], and vegetation monitoring [Tucker et al., 1985]. While previous work focuses on single-label land cover classiﬁcation, in the real world, land cover is a multi-label problem, as satellite images often contain multiple land types within a single frame. However, multi-label meta-learning is relatively underexplored. Meta-learning is particularly well-suited for multi-label problems with large and sparse label-permutation spaces: while traditional supervised methods require signiﬁcant amounts of data in each class to learn, meta-learning-based domain adaptation can adeptly handle such sparse label spaces using few-shot learning techniques. Current few-shot meta-learning approaches are not well-deﬁned for few-shot multi-label classiﬁcation, since for the standard k -shot n -way setup, k and n are no longer trivially well-deﬁned. To combat this, we propose a novel task-deﬁnition framework for few-shot multi-label classiﬁcation based on hierarchical sampling, that ﬁrst randomly samples a subset of n labels, and subsequently samples training examples to cover those n labels, thus enabling well-deﬁned few-shot multi-label meta-learning. We benchmark on the BigEarthNet [Sumbul et al., 2019] land cover classiﬁcation task. We evaluate thoroughly combinations of sampling approaches, multi-label schemes, and meta-learning models in order to evaluate this framework for ﬁtting meta-learning models to the multi-label space. Our task sampling methods are speciﬁcally developed to address the multi-label problem and the inherent class imbalances that arise in large multi-label sets. We evaluate two support set sampling methods: greedy sampling, which greedily adds training examples to the support set, and uniform permutation sampling, which deterministically adds examples with ﬁxed multi-label permutations
INNOVATION IN AGRICULTURE WITH REFERENCE TO WOMEN IN RURAL HARYANA, INDIA

In this research article, the researcher discusses the issues and importance of innovation in agriculture in the context of women. Researcher argues that for social inclusion, women empowerment and to close the gender gap; innovation in agriculture can play a significant role. This study was based on the 15 semi-structured interviews from women, eight from the Josar and seven from Kirmach village of the Kurukshetra District of Haryana. The study was qualitative in nature. The major characteristics of the interviewed women were that still, they were residing under the patriarchal norms. Results indicate that innovation in agriculture has a significant role to change their life but many societal, political, cultural and economic barriers are making the harder for them. The results provide an ample commendation to the policymakers that the innovation in agriculture should be promoted and regarding issues should be tackled on a high priority basis so that the multidimensional benefits of innovation might be achieved.
Determination of Rb, Sr, Cs, Ba, and Pb in K‐feldspars in small sample amounts by total reflection X‐ray fluorescence

The results of the determination of Rb, Sr, Cs, Ba, and Pb in K-feldspars by total reflection X-ray fluorescence analysis using 0.05 g sample in mass are presented. Prepared suspensions of the rock Certified Reference Materials and the K-feldspar samples were chosen as the sample preparation method. Studying the particle sizes of the K-feldspar sample powders was carried out. The concentrations of the elements were evaluated using the internal standard method. As internal standard, either a Se solution was added to the sample or the concentration of Rb was preliminary determined or known, have been used. The accuracy of the element determinations was estimated by comparison between obtained and certified values of the elements determined in the Certified Reference Materials of the K-feldspars, such as JF-1 and JF-2. Copyright © 2013 John Wiley & Sons, Ltd.
Unsupervised Face Recognition via Meta-Learning

We build an unsupervised face recognition system without using any labeled data in the training process. We use synthetic human faces generated by StyleGAN, which is also trained without labeled data, to train a prototypical network that can identify real human faces. To generate K face images with similar facial features for each of the N classes, we apply rejection sampling to sample N anchor vectors in latent space and K vectors near the anchors in the w -space of StyleGAN. For these near vectors in w -space, we concatenate the portion that impacts the facial feature with other random vectors. The synthesizer then used these concatenated vectors to generate images with similar facial features. During meta-validation and meta-testing, we give K test real human face images for each of the N people to construct an N -way- K -shot task. We experiment with two pre-trained StyleGAN models trained on CelebAMask-HQ and FFHQ dataset and test our method with the CelebA dataset. The FFHQ dataset, though with a different style from CelebA, contains a greater variety of faces. Both models outperform other unsupervised meta-learning methods such as CACTUs, UMTRA[5], and LASIUM[7] even with less K test and achieves comparable accuracy to the supervised ProtoNets with the same set of hyper-parameters. With this setting, our CelebAMask-HQ model achieves a peak accuracy of 72% in the 5-way-5-shot task, 70% in the 5-way-4-shot task, 62% in the 5-way-2-shot task, and 48% in the 5-way-1-shot task. Our FFHQ model achieves a peak accuracy of 86% in the 5-way-5-shot task, 76% in the 5-way-4-shot task, 74% in the 5-way-2-shot task, and 63% in the 5-way-1-shot task. Therefore, we demonstrate that it is possible to achieve a reasonable accuracy in face recognition task without using any labeled data during meta-training. Meanwhile, we study how the difference in distribution between synthetic and real data can cause overﬁtting. Our experiment with different datasets also shows that the variety of tasks can have more impact on the performance than the similarity tasks between meta-training and meta-testing. 1