Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Plant Growth Regulators (BA, KIN and NAA) on In vitro Propagation of Papaya (Carica papaya)

The present research was carried out in Biotechnology Laboratory of the Department of Biotechnology, Sher-e-Bangla Agricultural University, Sher-e-Bangla Nagar, Dhaka-1207 from the period of September 2017 to June 2018. This research aims to study the effect of Benzyladenine (BA), Kinetin (KIN) and Naphthalene acetic acid (NAA) either in combination or alone on In vitro propagation of papaya (Carica papaya). The shoot tips of young shoots were used as explant, which was sterilized using freshly prepared 0.1% HgCl2 mixing with few drops of Tween-20, were inoculated in MS media supplemented with 0.1% activated charcoal. The minimum days to shoot induction (10.25) were recorded on MS medium containing 0.5 mg/L BA. The highest shoots (4.5) and length of shoot (5.75 cm) observed in 1.0 mg/L BA. The combined treatment 1.0 mg/L BA+0.75 mg/LKIN gave the highest number of shoots (5.25) and length of shoot (5.78 cm). The minimum days (8.5) to root induction was reported in 2.0 mg/L NAA along with maximum 8.25 roots per plantlet. The highest length of root (6.92 cm) was observed in 2.0 mg/L NAA. In regenerated plantlets, 80% survival rates were observed in growth chamber conditions and 75% in the open Original Research Article Hasan et al.; IJPSS, 32(5): 15-23, 2020; Article no.IJPSS. 55471 16 atmosphere were achieved. Finally, the in vitro regeneration protocol described herein can potentially be used as a tool in molecular breeding programs for the improvement of different cultivars and genotypes of papaya.
To walk the talk of go-along methods: navigating the unknown terrains of being-along

ABSTRACT In the dawn of the Anthropocene, it is imperative to identify methods to challenge philosophies of researching upon others and nature and instead identify means of researching with. This paper explores the use of go-along methods in complex and dynamic nature-based tourism settings. Specifically, it re-orients attention towards the collaborative and participatory processes involved when “walking the talking” of go-along methods. Based on 35 highly diverse go-alongs from the westernmost and easternmost parts of Denmark, we illuminate challenging dimensions of go-along methods. Inherent shifts towards participant led ways of engaging with nature force researchers to navigate with others as led along by others through literal and figurative unknown terrains of nature, sociality, (dis)empowerment and embodiment. Go-alongs as a co-navigating and co-learning endeavor is more than the sum of walking and talking as we continuously relate to self, others and nature. Accordingly, we coin being-along as the social and bodily navigation of unknown terrains with others. Findings suggest tourism researchers are still to seize the opportunities of go-along methods and propose a cultivation of more caring, emphatic and attentive ways of engaging with others on which go-along methods thrive.
Arsenic immobilization as crystalline scorodite by gas-diffusion electrocrystallization

The safe immobilization of arsenic present in liquids is a key environmental challenge due to the inherent toxicity of arsenic. This immobilization is mostly restricted by the application of chemicals and several stages of oxidation and precipitation. Although the formation of bioscorodite is a greener alternative, it is intensive in the use of energy for aeration, and it is costly due to nutrient addition. The electrochemically-driven crystallization of arsenic into scorodite is proposed here to overcome these limitations. We disclose gas-diffusion electrocrystallization (GDEx) for the immobilization of arsenic into highly crystalline scorodite (FeAsO4·2H2O) by the in situ production of oxidizing substances (i.e., H2O2) on gas-diffusion electrodes. GDEx yielded an exceptional arsenic immobilization efficiency of up to 70% without the use of any primary minerals or seed crystals. At 70 °C and using As3+ as the precursor, polydisperse micrometric scorodite particles were obtained (from fine particles of <1 μm to large particles of ∼5 μm). In contrast, fine micrometric particles of <1 μm were achieved using As5+ as the precursor. Using one-pot and one-step GDEx enabled the synthesis of scorodite that was 14 times less soluble than required for stable scorodite disposal. Current chemical oxidation–precipitation processes use two separate reactors, including the oxidation of As3+ to As5+, and then the precipitation of the As5+ with Fe3+ to generate scorodite at a temperature higher than 90 °C. In contrast, the new GDEx approach combines both reactors into one to produce crystalline scorodite at 50 °C, hence reducing energy requirements and chemical footprint.
Corporate Hiring under COVID-19

Big data on job vacancy postings reveal multiple facets of the impact of Covid-19 on the U.S. job market. Firms have disproportionately cut on hiring for high-skill jobs (downskilling), with small firms nearly halting their hiring altogether. New-hiring cuts and downskilling are most pronounced in labor markets where employment is concentrated within a few firms, in low-income areas, and in areas with greater income inequality. Access to finance modulates corporate hiring, with credit-constrained firms curtailing their high-skill postings the most. Applying machine learning techniques to job ads, we show that firms have skewed their new hiring towards operationally core positions. New positions also display greater flexibility regarding schedules, tasks, and requirements. A "time-to-fill" measure reveals that high-skill job openings remain active for longer in the pandemic, while posting duration drops for low-skill jobs. Our study shows how the 2020 global pandemic shaped the dynamics of hiring, identifying the firms, jobs, places, industries, and labor markets most affected by it.
Deep learning algorithm for breast masses classification in mammograms

A mammogram is an image of a breast used to detect and diagnose breast cancer. This paper emphases a Computer-Aided Detection system based on convolutional neural network (CNN) that uses the concept of deep learning to classify the mammogram images into benign, malignant and normal. The proposed CNN model consists of eight convolutional, four max-pooling and two fully connected layers and achieved better results compared to the pre-trained nets, AlexNet and VGG16. The proposed model demonstrates the feasibility of using CNNs on medical image processing techniques for the classification of breast masses. The results are also compared with the state-of-the-art machine learning algorithm like kNN classifier. Experimentation is done with three datasets. Among them, two are publicly available, Mammographic Image Analysis Society (MIAS), digital database for screening mammography (DDSM) and an internally collected dataset. The proposed model achieved accuracies of 92.54, 96.47 and 95 and the Area under the ROC curve (AUC) score of 0.85, 0.96 and 0.94 for MIAS, DDSM and the internally collected dataset respectively. Furthermore, the images of the three datasets are merged to build one large set and used to fine tune the proposed CNN model and produced accuracy of 98.32 and AUC of 0.98.
Impact of Different Geriatric Conditions on Choice of Therapy and ﻿In-Hospital Outcomes in Elderly Patients with Acute Coronary Syndrome

Introduction The clinical benefit of invasive therapy in elderly patients with acute coronary syndrome (ACS) remains unclear. Furthermore, the decision-making process to treat this growing patient group is also debatable. The purpose of this study was to assess the association between elderly ACS patients, the treatment choice and their in-hospital outcomes after ﻿non-ST-﻿elevation (NSTE)-ACS in a consecutive series of patients >75 years of age. Methods and Results Consecutive patients >75 years presenting with NSTE-ACS in our hospital between July 2017 and July 2018 were included during the first 2 days of hospital admission. Demographic data, prior medical history and present medical condition were documented. During day 0 and day 2, geriatric assessments (﻿Clinical Frailty Scale [CFS], Barthel ﻿index, Charlson comorbidity index, “timed up and go” ﻿test [TUG], Mini-Mental Status Test [MMS], Geriatric D﻿epression Scale [GDS], SF-36 for quality of life, instrumental activities of daily living [IADL], Killip-﻿score, Grace-﻿score and Euro-﻿score) were conducted. After 6 months, patients were re-evaluated. In 106 patients (mean age 81.9±5.3 years, 57% male gender), 68 patients (64%) were treated interventionally, and 38 patients had conservative treatment (36%). Patients treated with intervention were significantly younger (80.9±4.7 years vs 83.5±6.0 years, p=0.015), had a lower rate of prior cerebral events (17.6% vs 26.3%; p=ns) and suffered more often from chronic obstructive pulmonary disease (17.6% vs 34.2%; p=0.050). All other demographic variables were comparable between both groups. The composite clinical endpoint (death, re-infarction, bleeding) was reached in 7 patients (10.3%) of the invasive and in 2 patients (5.3%) of the conservative group. They were not significantly different between both groups. A frailty index, consisting of commonly used parameters of functional impairment in elderly patients, namely, MMS ≤2 at baseline, IADL ≤7, CFS ≥7 and age ≥85 years, significantly predicted conservative treatment. Conclusion Effective revascularization techniques are still underused in patients of older age in the case of ACS. For decision-making, geriatric tests alone may not predict treatment in those patients, but the combination of different tests may better predict treatment and perhaps the clinical outcomes in those patients. Furthermore, frail patients are at higher risk for not receiving guideline recommended therapy.
Phylogeny of millipedes ― a cladistic analysis

Summary Application of the principles of cladistics, or phylogenetic systematics, to the class Diplopoda indicates monophyly (in the strict sense) of the taxa Penicillata, Pentazonia, Colobognatha (minus Siphoniulus? ), Nemathophora, Merocheta, and Juliformia. The result agrees with traditional classifications but stands in contrast to the classification by Hoffman (R. L. Hoffman, 1979: Classification of the Diplopoda. 237 pp. Museum d'Histoire naturelle, Geneve). At higher levels, the analysis indicates strict monophyly of the taxa Diplopoda, Chilognatha, Helminthomorpha, and Eugnatha. The following sister-group relationships are proposed: Penicillata – Chilognatha Pentazonia – Helminthomorpha Colobognatha (minus Siphoniulus? ) – Eugnatha The interrelationships of the eugnathan groups: Nematophora, Merocheta and Juliformia, remain unsettled, as does the position of Siphoniulus. A formal classification expressing the results is presented. Zusammenfassung Phylogenie der Tausendfuser- eine kladistische Analyse Die Anwendung von den Prinzipien der kladistischen – oder phylogenetischen – Systematik an der Klasse Diplopoda zeigt die Monophylie (sensu strictu) der Taxa Penicillata, Pentazonia, Colobognatha (ohne Siphoniulida? ), Nematophora, Merocheta and Juliformia. Diese Ergebnisse stimmen mit traditionellen Klassifikationen uberein, stehen aber in Widerspruch zur Klassifikation von R. L. Hoffman N (1979). Auf hoheren taxonomischen Niveaus zeigt die Analyse Monophylie (sensu strictu) fur die Taxa Diplopoda, Chilognatha, Helminthomorpha and Eugnatha. Die folgenden Schwester-Gruppen-Verhaltnisse werden vorgeschlagen: Penicillata – Chilognatha Pentazonia – Hehninthomorpha Colobognatha (ohne Siphoniulida? ) – Eugnatha. Die verwandschaftlichen Beziehungen der eugnathen Gruppen: Nematophora, Merocheta and Juliformia sind nosh unsicher, ebenso die Stellung von Siphoniulida. Die Ergebnisse werden in foreteller Klassifikation dargelegt.
Risk analysis, analysis of variance: getting more from our data.

Analysis of variance (ANOVA) and regression are common statistical techniques used to analyze agronomic experimental data and determine significant differences among yields due to treatments or other experimental factors. Risk analysis provides an alternate and complimentary examination of the same data by determining yield probabilities for each treatment or factor. We generated and analyzed a synthetic data set to illustrate that data with similar means, as determined by ANOVA, can have markedly different probability distributions due to differences in standard deviations. We then applied the techniques to data from a five-year yield trial of twelve Bermudagrass cultivars. ANOVA detected significant year-by-cultivar interactions while risk analysis illustrated differences among the cultivars in yield stability and in the probabilities of achieving specific yield goals. Together, ANOVA and risk analysis provide a more complete view of the data that facilitates technical transfer of experimental results to producers and other end-users.
Virtual manipulatives as assistive technology to support students with disabilities with mathematics

Abstract Assistive technology supports students with disabilities in a variety of ways, including serving as a means of instruction. Although virtual manipulatives can serve as an assistive technology tool, limited research examines virtual manipulatives in-and-of-themselves to support students with disabilities. This study explored app-based virtual manipulatives as mathematics-based assistive technology to support middle school students to acquire and maintain solving division with remainder problems. Through a single-case multiple probe across participants design, researchers found the three students acquired and maintained their accuracy in solving division with remainder problems when provided with the app-based manipulative. The study corroborates the use of virtual manipulatives by students with disabilities when learning mathematics.
Regulation of Thyroid Function in Mice with Extracts of Emblica officinalis L. and Bauhinia purpurea L.

ABSTRACT The role of Emblica officinalis L. and Bauhinia purpurea L. extracts in regulating thyroid functions was studied in male mice. Oral administration of Emblica officinalis L. fruit extract at 30 mg/kg body weight (b.wt.) each day for 20 days decreased serum T3 and T4 concentrations and hepatic O2 consumption. In contrast, daily administration of Bauhinia purpurea at 2.5 mg/kg b. wt. each day for 20 days increased serum T4 concentration and O2 consumption. Both the plant extracts exhibited hepatoprotective effects as evidenced by decreased lipid per-oxidation. In animals treated with Emblica officinalis, activities of super-oxide dismutase and catalase remained unaffected, but a significant increase in both of these antioxidative enzymes was observed in Bauhinia purpurea treated animals, indicating that extract of Emblica officinalis, but not the extract of Bauhinia purpurea, may have direct free radical scavenging role.
Phylogenetic relationships between Arabidopsis and sugarcane bZIP transcriptional regulatory factors

We built a complete and non-redundant database of bZIP transcriptional regulatory factors from the Arabidopsis reference genome. These Arabidopsis bZIP factors were ordered into thirteen families of evolutionary related proteins and this classification was used to identify and organize sugarcane cDNAs encoding bZIP proteins. We also show how this classification should help in defining putative clusters of orthologous groups of higher plant bZIP regulators and briefly discuss the expected benefits of this procedure to efficiently characterize sugarcane bZIP transcriptional regulators.
Regression Tree Model for Predicting Game Scores for the Golden State Warriors in the National Basketball Association

Data mining is becoming increasingly used in sports. Sport data analyses help fans to understand games and teams’ results. Information provided by such analyses is useful for game lovers. Specifically, the information can help fans to predict which team will win a game. Many scholars have devoted attention to predicting the results of various sporting events. In addition to predicting wins and losses, scholars have explored team scores. Most studies on score prediction have used linear regression models to predict the scores of ball games; nevertheless, studies have yet to use regression tree models to predict basketball scores. Therefore, the present study analyzed game data of the Golden State Warriors and their opponents in the 2017–2018 season of the National Basketball Association (NBA). Strong and weak symmetry requirements were identified for each team. We developed a regression tree model for score prediction. After predicting the scores of each player on two teams, we summed and compared the predicted total scores to obtain the predicted results (lose or win) of the team of interest. The results of this study revealed that the regression tree model can effectively predict the score of each player and the total score of the team. The model achieved a predictive accuracy of 87.5%.
Do Citizens Support Cohesion Policy? Measuring European support for redistribution within the EU and its correlates

espanolA medida que la Union Europea ingresa en la proxima decada, sus lideres aparentemente luchan por mas integracion futura en lugar de menos, a pesar del reciente reves del Brexit y el surgimiento de los partidos populistas anti-UE. En su estado de la Union en 2018, Jean Claude Junker hizo hincapie en la "solidaridad europea". Una politica clave de "expresion de solidaridad" seria la Politica de Cohesion y los Fondos Estructurales, que son "el unico mecanismo redistributivo real y significativo en la UE. " (Fratesi 2017). A pesar de las opiniones de la elite, sorprendentemente sabemos poco sobre lo que piensan los ciudadanos de la UE sobre la logica detras de la Politica de Cohesion, de la redistribucion economica dentro de la UE. Como parte del proyecto PERCEIVE Horizon2020, llevamos a cabo una encuesta unica para investigar como se sienten los ciudadanos acerca de la integracion economica dentro de la Union, entrevistando a 17.200 ciudadanos. En este documento, mostramos como medimos el apoyo a la politica, los resultados y una serie de elementos relacionados. Nuestro analisis muestra la variacion en el apoyo de los ciudadanos a la politica de cohesion de la UE entre paises, como varia el apoyo entre los grupos demograficos, asi como la medida en la que el apoyo se correlaciona con factores utilitarios e ideologicos. Se discuten las implicaciones para futuros desarrollos de esta politica. EnglishAs the European Union enters into the next decade, its leaders seemingly strive towards more future integration rather than less, despite the recent setback of Brexit and the rise of anti-EU populist parties. In his state of the Union in 2018, Jean Claude Junker emphasized s ‘European solidarity’. One key policy ‘expression of solidary’ would be Cohesion Policy and the Structural Funds, which are “the only real, significant redistributive mechanism in the EU…” (Fratesi 2017). Despite elite commentary, we know surprisingly little about what EU citizens think of the rationale behind the policy of Cohesion – e.g. economic redistribution within the EU. As part of the PERCEIVE Horizon2020 project, we launched a unique survey to investigate how citizens feel about economic integration within the Union, where 17,200 citizens were interviewed. In this paper, we show how we measure support for the policy, the results as well as a host of correlates. Our analysis shows the variation in citizens’ support for EU Cohesion policy between countries, how support varies between demographic groups, as well as the extent to which support is correlated with utilitarian and ideational factors as well as cue taking. Implications for future developments of this policy are discussed.
Analytical approach for the design of flexural elements made of reinforced ultra‐high performance concrete

The increasing demands of sustainable design and construction with economical sections, reduced cover, and more efficient time schedule require more flexibility in the design methodologies. The development of ultra‐high performance concrete (UHPC) have gained increasing interests as an attractive option for structural members with lightweight and superior performances. Concrete members reinforced with steel bars and fibers, generally known as hybrid reinforced concrete (HRC), offer a feasible solution in terms of reducing reinforcing materials and achieving desired structural performance. This paper proposes an analytical model to predict the flexural behavior of hybrid reinforced UHPC with steel reinforcements. Moment–curvature solutions are derived for reinforced sections based on parameterized tension‐compression constitutive models. The approach is applicable to customized cross section and derivation of T‐section is demonstrated. The moment–curvature response is further simplified as a tri‐linear model, which is used for the development of full‐range displacement solutions in analytical form. The proposed model is validated with the experimental data from literature covering a range of materials and member sizes. The full‐range solutions may provide insights into the serviceability design approach based on the criterion of maximum crack width or allowable deflection.
Invasive aphids of the tribe Siphini: a model of potentially suitable ecological niches

Sipha (Rungsia) maydis, Sipha (Rungsia) elegans, Sipha (Sipha) glyceriae and especially Sipha (Sipha) flava are considered to be virus vectors and serious pests of crops and pasture grasses. Ecological niche modelling, a useful tool for assessing potential geographical distributions of species, was used to predict the risk of invasion of these four species of the Siphini (Hemiptera, Aphididae) on a global scale. The maximum entropy model based on associations between unique occurrence localities and a set of environmental variables was used. Obtained models of potentially suitable habitats, based only on climatic variables, suggest that favourable conditions for each species may be present on every continent. However, S. (S.) flava appears to be potentially the most widespread species. Moreover, the resulting maps provide important information on the corridors by which invasive species are able to penetrate into new areas. A mean of the area under the receiver operating characteristic curve at the levels of 0.937, 0.947, 0.968, 0.937 for S. (R.) maydis, S. (R.) elegans, S. (S.) glyceriae and S. (S.) flava, respectively, indicated a high level of discriminatory power of the maximum entropy model. A jackknife test indicated that the precipitation of the coldest quarter with the highest gain value was the most important environmental variable restricting the expansion of the studied species.
Robot Position/Force Control in Unknown Environment Using Hybrid Reinforcement Learning

Abstract Robot position/force control provides an interaction scheme between the robot and the environment. When the environment is unknown, learning algorithms are needed. But, the learning space and learning time are big. To balance the learning accuracy and the learning time, we propose a hybrid reinforcement learning method, which can be in both discrete and continuous domains. The discrete-time learning has poor learning accuracy and less learning time. The continuous-time learning is slow but has better learning precision. This hybrid reinforcement learning learns the optimal contact force, meanwhile it minimizes the position error in the unknown environment. Convergence of the proposed learning algorithm is proven. Real-time experiments are carried out using the pan and tilt robot and the force/torque sensor.