Miracle-dz/UniFAR-FTUs huggingface dataset:
AGRICULTURAL FINANCE AND ECONOMIC GROWTH: EVIDENCE FROM NIGERIA

The study performed an in-depth examination of the impact of guaranteed agricultural finance to oil palm, cocoa, groundnuts, fishery, poultry, cattle, roots, and tubers on the real gross domestic product of the country. Time series data was sourced from the Central Bank of Nigeria statistical bulletin of various issues. The data sets covered thirty-seven (37) years spanning from 1981 to 2017. The study used Autoregressive Distributed Lag (ARDL) model for its analysis. However, prior estimation and due to several exogenous variables, Phillip Perron stationarity test was used to determine the order of integration because of its robustness to serial correlation and heteroskedasticity. The study also specified the lag criterion based on LR, FPE, AIC, SC, and HQ using Newey-West covariance matrix estimator. Findings from both short-run and long-run models as confirmed by the Wald test, which shows that none of the guaranteed agricultural finance is statistically significant to real gross domestic product. The study, therefore, recommends increased funding and deliberate efforts at determining which of the nominated agricultural spending has the most contributory impact on growth.
A Comparative Study of Overweight and Obesity among Basic School Pupils from Selected Schools in the Asante Akim Central Municipality.

xii CHAPTER ONE . 1 1.0 INTRODUCTION . 1 1.1 Statement of the Problem.4 1.2 Aim of the Study . 5 1.3 Objectives of the Study . 5 1.4 Hypotheses . 5 1.5 Significance of the Study . 6
The effect of preharvest 28.6% chitosan composite film sprays for controlling the soft rot on kiwifruit

This study evaluated the effects of preharvest plant 28.6% chitosan composite film (CTS-Fh) sprays on the postharvest quality and diseases in kiwifruit (Actinidia deliciosa cv. ‘Guichang’), it was screened and prepared by mixing chitosan, calcium, dextrin, ferulic acid and auxiliaries. 28.6% CTS-Fh solutions at different concentrations were sprayed three times during the fruit growing season. The obtained results show that 28.6% CTS-Fh sprays remarkably promoted the improvement of the yield and quality of the kiwifruit, significantly (P < 0.05) increased the Ca content and firmness, delayed the fruit ripening and softening, and enhanced the storability. Moreover, the kiwifruit soft rot was effectively controlled; the control efficiency was 61.68–88.79%. Additionally, the 28.6% CTS-Fh sprays significantly (P < 0.05) increased the content and activity of some defence-related secondary metabolites and enzymes, and could also increase the cell wall compactness in the kiwifruit. These results suggest that 28.6% CTS-Fh might trigger several defence responses in the kiwifruit against pathogenic infections. The doses of 28.6% CTS-Fh 200–400 dilution times were recommended for the practical application with regards to the production of kiwifruit.
Predicting Potential Co-Authorship Using Random Forest: Case of Scientific Publications in Indonesian Institute of Sciences

Research collaboration is one of the strength in research management due to its advantages in quantity and quality of the research. Co-authorship network is one of the proxies to evaluate the emerging research collaborations. Co-authorship that happens for the first time among a pair of author plays an important role as the key of success for their co-authorship in the future. Therefore, the research aims to build a model predicting new co-authorship as potential co-authorship. This research used scientific articles in Indonesian biodiversity research published in Scopus during 2006-2015. New co-authorship of between 4,628 pair of authors were analyzed in terms of their similarity in co-authorship network, research interest, and community to predict whether a pair of author will have a new co-authorship in future. Random forest classifier was used to build the model after applying 10-fold cross validation in various parameter and random undersampling technique as preprocessing procedures. The result shows that the similarity in network, community network, and research interest and becomes good features to predict the potential co-authorship among a pair of author. Furthermore, paired authors that predicted to be co-authored and involving authors from Indonesian Institute of Sciences are identified as the potential patners recommended for development of research teams.
Incidence of Shiga-Toxin-Producing Escherichia coli (STEC) in Diarrheic Calvesand its Profile of Sensitivity to Antimicrobials and to the Extract of Eugeniauniflora L.

The present work had the goal of evaluating the presence of the Shiga-Toxin-Producing bacterium Escherichia coli (STEC) in diarrheic calves, its resistance profile against the antimicrobials usually employed in clinical practice, and evaluating the antimicrobial activity of the plant extract of the leaves of Eugenia uniflora L. against the isolates. The samples were obtained from animals belonging to dairy farms in the South of Minas Gerais and submitted to microbial isolation and identification. Molecular assays were performed, by the search for genes stx1 and stx2, for STEC identification. The isolates were subjected to tests of antimicrobial sensitivity to the drugs usually employed in clinical treatment, using the disk diffusion method. The Minimum Inhibitory Concentration (MIC) of the antimicrobials gentamicin and sulfamethoxazole+trimethoprim was performed by the Etest technique. The sensitivity of the plant extract was investigated by the methods of diffusion in agar and broth microdilution, and the MIC and the Minimal Bactericidal Concentration (MBC) were determined. From the isolates analyzed, 17% presented the searched genes isolated (10%stx1 and 1%stx2) and in combination (6%stx1+stx2). These isolates also presented multiresistance to the antimicrobials tested. The hydroalcoholic extract of the E. uniflora leaves presented antimicrobial activity against all isolates analyzed, with a MIC of 12.5 mg/mL and bacteriostatic activity. The data obtained contribute to the knowledge of STEC incidence associated to bovine diarrhea and point to the need of monitoring and controlling these multiresistant enteropathogens. Although Eugenia uniflora L. shows a potential antimicrobial action in vitro, other preclinical and/or clinical studies must be developed in order to ensure its therapeutic application.
Employee Performance Prediction using Naïve Bayes

Employee performance drives company success to achieve its goals. Predicting employee performance in the future is a necessity for companies to success. This paper presented employee performance prediction in a company using machine learning. The machine learning process follows Cross-industry standard process for data mining (CRIPS-DM). The Naive Bayes classification method is employed to create the prediction model. The result shows that Naïve Bayes successfully correctly classified instances as high as 95.48 percent.
Can abusive supervision motivate customer-oriented service sabotage? A multilevel research

ABSTRACT This study explores the influence of abusive supervision on customer-oriented service sabotage (COSS). Based on social identity theory, it investigates the mediating role of organizational identification. Meanwhile, it examines the moderating role of sensitivity to interpersonal mistreatment (SIM) in strengthening the effects of abusive supervision on COSS and organizational identification. To reduce the concerns of common method bias (CMB), we administrated three-wave and two-resource data collection from thirteen hotels of three cities in China. Hierarchical linear modeling (HLM) and bootstrapping are utilized to examine hypotheses. The results showed that abusive supervision positively affects service employees’ COSS through lowering their organizational identification. Furthermore, it was found that SIM can magnify abusive supervision’s impacts on COSS and organizational identification such that these influences were stronger for those with higher SIM. Finally, contributions and limitations are discussed.
Variation in Maize Chlorophyll Biosynthesis Alters Plant Architecture12[OPEN]

Chlorophyll reduction in maize suppresses branching in classical tillering mutants and high-tillering natural variants, reduces stem girth, and shortens plants, but the effect on height is nonlinear. Chlorophyll is a tetrapyrrole metabolite essential for photosynthesis in plants. The first committed step of chlorophyll biosynthesis is catalyzed by a multimeric enzyme, magnesium chelatase, the subunit I of which is encoded by the oil yellow1 (oy1) gene in maize (Zea mays). A range of chlorophyll contents and net CO2 assimilation rates can be achieved in maize by combining a semidominant mutant allele of oy1 (Oy1-N1989) and a cis-regulatory modifier named very oil yellow1 (vey1) that varies between different inbred lines. We previously demonstrated that these allelic interactions can delay reproductive maturity. In this study, we demonstrate that multiple gross morphological traits respond to a reduction in chlorophyll. We found that stalk width, number of lateral branches (tillers), and branching of the inflorescence decline with a decrease in chlorophyll level. Chlorophyll deficit suppressed tillering in multiple maize mutants, including teosinte branched1, Tillering1, and grassy tillers1. In contrast to these traits, plant height showed a nonlinear response to chlorophyll levels. Weak suppression of Oy1-N1989 by vey1B73 resulted in a significant increase in mutant plant height. By contrast, enhancement of the severity of the Oy1-N1989 phenotype by the vey1Mo17 allele resulted in reduced plant height. We demonstrate that the effects of reduced chlorophyll contents on plant growth and development are complex and depend on the trait being measured. We propose that the lack of chlorophyll exerts growth control via energy balance sensing, which is upstream of the known genetic networks for branching and architecture.
Shoulder Functional Outcome after Latissimus Dorsi Muscle Transfer and Subscapularis Muscle Release in Children with Obstetrical Brachial Plexus Palsy

Objective: To evaluate the shoulder functional outcome in children with obstetrical brachial plexus palsy after performing latissimus dorsi muscle transfer and subscapularis muscle release. Methods: this is a retrospective analysis to fifty patients with 52 shoulders underwent latissimus dorsi muscle transfer and subscapularis muscle release for obstetrical brachial plexus palsy, between June 2008 and April 2012. Reviewing patient’s files and pre-operative videos. Mallet scoring system was used to evaluate the shoulder functional changes. Results: Fifty children with fifty two shoulders with a ranging age between 18 months up to 13 years were involved in this study, 1:1 male to female ratio, the average birth weight was 3.89KG, the brachial plexus extent of injury were ranging from upper injury 80% to total palsy 20%. Regarding the affected limb the right side involvement was 60% the left side 36% and bilateral involvement was 4%. there is significant difference in the global upper limb function between younger age group(below five years) when compare to older age group(above five years of age). Five active ranges of motion around the shoulder joint involved in the injury were analyzed with the following functional outcome improvement: Global Abduction 46.6%, Global External Rotation 62.9%, Hand to neck 49%, Hand to spine 53.3%, Hand to mouth 57.2% Conclusion: we found that this type of surgery improves the global function of the upper limb in patients with obstetrical brachial plexus palsy, this improvement become less remarkable in older children(>5years of age). And the majority of patients and their families were satisfied by the achieved results.
Travel Mode Identification With GPS Trajectories Using Wavelet Transform and Deep Learning

Accurate identification in public travel modes is an essential task in intelligent transportation systems. In recent years, GPS-based identification is gradually replacing the conventional survey-based information-gathering process due to the more detailed and precise data on individual’s travel patterns. Nonetheless, existing research suffers from deficient feature selection, high data dimensionality, and data under-utilization issues. In this work, we propose a novel travel mode identification mechanism based on discrete wavelet transform and recent developments of deep learning techniques. The proposed mechanism aims to take GPS trajectories of arbitrary lengths to develop accurate travel mode results in both global and online identification scenarios. In this mechanism, raw GPS data is first pre-processed to compute preliminary motion and displacement attributes, which are input into a tailor-made deep neural network. Discrete wavelet transform is also adopted to further extract time-frequency domain characteristics of the trajectories to assist the neural network in the classification task. To evaluate the performance of the proposed mechanism, a series of comprehensive case studies are conducted. The results indicate that the mechanism can notably outperform existing travel mode identifications on a same data set with minuscule computation time. Furthermore, an architecture test is performed to determine the best-performing structure for the proposed mechanism. Lastly, we demonstrate the capability of the mechanism in handling online identifications, and the performance sensitivity of the selected attributes is evaluated.
Potassium fertilization for enhancing yield attributes, yield and economics of wheat (Triticum aestivum)

A 2-year field experiment was conducted during the winter (rabi) seasons of 201516 and 201617 at New Delhi, to evaluate the effect of rate, method, time and source of potassium (K) fertilization on yield attributes, yield and economics of wheat (Triticum aestivum L.). Potassium supply enhanced the yield attributes (spike weight, spike length, grains/spike, fertility %) and yield (grain and straw) significantly. Application of 60 kg K O/ha in 2 2 splits, half basal and remaining half at spike-initiation stage or 75% basal and remaining 25% at spike-initiation stage increased the grain yield (5.5 t/ha), net returns ( 78,000) and benefit: cost ratio (2.2) by 8.8, 9.5 and 10%, respectively over applying the entire dose as basal. A strong positive and significant correlation was observed be- tween yield attributes (r2 = 0.800.96) (total tillers/m2, grains/spike, spike weight, spike length and fertility %) and grain yield. Two foliar sprays of 2.5% potassium nitrate increased fertility percentage by 5 and grain yield of wheat by 6% over the control. However, foliar spray of potassium nitrate was not beneficial if combined with split applica- tion of recommended dose of potassium (60 kg K O/ha), whereas foliar spray proved good substitute for top- 2 dressing of potassium. Basal application of 53.8 kg K O/ha followed by 2 foliar sprays of potassium nitrate, was 2 equally beneficial to 2 split application of 60 kg K O/ha. Similarly, 2 split application of 60 kg K O/ha at 50 : 50 or 2 2 75 : 25 ratio increased the benefit: cost ratio by 10% (2.2) over application of entire dose as basal.
License plate segmentation and recognition system using deep learning and OpenVINO

As Intelligent Transportation Systems applications increase in prevalence, Automatic License Plate Recognition solutions must be made continually faster and more accurate. The authors propose an embedded system for fast and accurate license plate segmentation and recognition using a modified single shot detector (SSD) with a feature extractor based on depthwise separable convolutions and linear bottlenecks. The feature extractor requires less parameters than the original SSD + VGG implementation, enabling fast inference. Tested on the Caltech Cars dataset, the proposed model achieves 96.46% segmentation and 96.23% recognition accuracy. Tested on the UCSD-Stills dataset, the proposed model achieves 99.79% segmentation and 99.79% recognition accuracy. The authors achieve a per-plate (resized to 300 × 300 px) processing time of 59 ms on an Intel Xeon CPU with 12 cores (2.60 GHz per core), 14 ms using the same CPU and OpenVINO (a neural network acceleration platform), and 66 ms using the proposed low-cost Raspberry Pi 3 and Intel Neural Compute Stick 2 with OpenVINO embedded system.
The breast cancer oncogene IKKε coordinates mitochondrial function and serine metabolism

The IκB kinase ε (IKKε) is a key molecule at the crossroads of inflammation and cancer. Known for its role as an activator of NFκB and IRF3 signalling leading to cytokine secretion, the kinase is also a breast cancer oncogene, overexpressed in a variety of tumours. However, to what extent IKKε remodels cellular metabolism is currently unknown. Here we used a combination of metabolomics and phosphoproteomics to show that IKKε orchestrates a complex metabolic reprogramming that affects mitochondrial metabolism and serine biosynthesis. Acting independently of its canonical signalling role, IKKε upregulates the serine biosynthesis pathway (SBP) mainly by limiting glucose and pyruvate derived anaplerosis of the TCA cycle. In turn, this elicits activation of the transcription factor ATF4 and upregulation of the SBP genes. Importantly, pharmacological inhibition of the IKKε-induced metabolic phenotype reduces proliferation of breast cancer cells. Finally, we show that in a set of basal ER negative and highly proliferative human breast cancer tumours, IKKε and PSAT1 expression levels are positively correlated corroborating the link between IKKε and the SBP in the clinical context.
Machine learning–based optimization for hydrogen purification performance of layered bed pressure swing adsorption

An adsorption, heat and mass transfer model for the five‐component gas from coal gas (H2/CO2/CH4/CO/N2 = 38/50/1/1/10 vol%) in a layered bed packed with activated carbon and zeolite was established by Aspen Adsorption software. Compared with published experimental results, the hydrogen purification performance by pressure swing adsorption (PSA) in a layered bed was numerically studied. The results show that there is a contradiction between the hydrogen purity and recovery, so the multi‐objective optimization algorithms are needed to optimize the PSA process. Machine learning methods can be used for data analysis and prediction; the polynomial regression (PNR) and artificial neural network (ANN) were used to predict the purification performance of two‐bed six‐step process. Finally, two ANN models combined with sequence quadratic program (SQP) algorithm were used to achieve multi‐objective optimization of hydrogen purification performance. According to the analysis of the optimization results, the ANN models are more suitable for optimizing the purification performance of hydrogen than the PNR model.
The contribution of cell wall remodeling and signaling to lateral organs formation

Lateral organs are formed in plants by post embryonic developmental programs. Leaves, and flowers differentiate from the shoot apical meristem and lateral roots from the primary root pericycle meristem. Adventitious roots are roots formed from non-root lateral meristematic tissues, mostly the cambium, in many cases in response to stress signals. The ability of plants to regenerate adventitious roots is fundamental for selection and breading programs which are based on vegetative propagation of elite clones. Thus, recalcitrant plants, losing their rooting capability, may form a genuine commercial barrier in agricultural and forestry improvement programs. Some cellular mechanisms underlying adventitious root formation have been revealed, but much is yet to be clarified. The plant primary cell wall is a dynamic organ that can change its form, and perceive and relay molecular signals inward and outward during certain stages of development in particular cells. Therefore, before the secondary cell wall is deposited and plants become the wood from which walls and furniture are built, and the fibers from which cloths are woven, primary cell walls actively participate in plant cell differentiation and developmental programs. While auxin is a major regulator, cell walls are important in regulating coherent formative cell division and synchronized polar elongation of cell lineages that are necessary for lateral organ induction and formation, and collaborative cell functioning. Nevertheless, little is known of how cell wall changes are molecularly sensed and translated to intracellular signals during differentiation of adventitious roots. Here we summarize recent data linking, directly or indirectly, cell wall events to auxin signaling and to lateral or adventitious root induction and formation.
The Affordances of Augmented Reality in Delivering the Science Curriculum to Elementary Grades

Th e current science books for Palestinian elementary schools essentially consist of picture-based activities. Each lesson features images which students are asked to reﬂ ect on, and provide oral and written answers to questions linked to each image. Th is rich curriculum is being delivered in traditional methods, and few teachers have to date ventured into utilizing modern technologies that can be easily accessible. Th e present research eﬀ ort measures the aﬀ ordances of augmented reality in delivering the science curriculum to elementary grades in the West Bank. An interaction analysis study was used, in which activities from the third grade science textbook were demonstrated using AR technologies to identify their role in enhancing learners’ interaction with pictures in the science school books. A sample of 50 third grade female students, from a West Bank basic school for girls, was drawn from the study population (all third grade students in the West Bank). Th e 50 students were divided evenly into homogeneous control and intervention groups. Seven activities were delivered conventionally for the former and with augmented reality technologies for the latter. Th e ﬁ ndings suggest that students taught with augmented reality-en-hanced procedures were particularly engaged and eﬀ ectively responsive, both orally and in written tasks.