Miracle-dz/UniFAR-FTUs huggingface dataset:
Physiological and Biochemical Dynamics of Pinus massoniana Lamb. Seedlings under Extreme Drought Stress and during Recovery

In recent years, global forests have been facing an increase in tree mortality owing to increasing droughts. However, the capacity for plants to adjust their physiology and biochemistry during extreme drought and subsequent recovery is still unclear. Here, we used 1.5-year-old Pinus massoniana Lamb. seedlings and simulated drought conditions to achieve three target stress levels (50%, 85%, and 100% loss of stem hydraulic conductivity (PLC)), followed by rehydration. Needle water status, gas exchange, and biochemical parameters were assessed during drought and recovery. The results showed that drought had significantly negative impacts on needle water status and gas exchange parameters, with gas exchange declining to 0 after PLC85 was achieved. Soluble protein concentration (SPC), soluble sugar concentration (SSC), malondialdehyde (MDA) content, superoxide dismutase (SOD) activity, and needle water-use efficiency showed fluctuations. The activity of antioxidant enzymes and the values of osmotic regulators were then gradually decreased as the physiological and biochemical functions of seedlings were disturbed. Seedlings showed a stronger ability to recover from PLC50 than PLC85 and PLC100. We conclude that the physiological and biochemical recovery of P. massoniana seedlings is more likely to be inhibited when plants experience increasing drought stress that induces 85% and greater loss of hydraulic conductance.
A Novel Bidirectional Five-Level Multimode CLLC Resonant Converter

In order to widen voltage gain of bidirectional on-board charger to meet requirements of electric vehicle with different voltage levels, a novel bidirectional five-level multimode CLLC resonant converter is proposed by combining the CLLC resonant tank module (RTM) with the five-level cascaded neutral-point clamp active bridge which is composed of two three-level neutral-point clamp (3L-NPC) bridge arms. Furthermore, the converter can, respectively, establish three voltage gain modes of high, medium, and low to effectively widen voltage gain adjustment capabilities through the fixed-frequency plus phase-shift hybrid modulation independently adopted in each 3L-NPC bridge arm, while soft-switching is realized by phase control between resonant current and input voltage of RTM. Meanwhile, the key parameters of RTM would be symmetrically designed to make bidirectional power transmission characteristics, which become consistent based on soft-switching and monotonic voltage gain control. Finally, according to the simulation platform and experimental prototype, it can be shown that the practical voltage gain is close to the theoretical design gain and converter has good dynamic performances after realizing soft-switching in each mode.
Whole-Genome Sequencing Revealed a Late-Maturing Isogenic Rice Koshihikari Integrated with Hd16 Gene Derived from an Ise Shrine Mutant

We identified the key genes controlling the late maturation of the Japonica cultivar Isehikari, which was found at Ise Jingu Shrine and matures 6 days later than Koshihikari. We conducted a genetics-based approach through this study. First, the latest mature plants, which flowered later than Isehikari, were segregated in the F2 and F3 generations of Koshihikari×Isehikari. Next, the linkage relationship of a single late-maturing gene with the SSR markers on the long arm of chromosome 3 was inferred by using late-maturing homozygous F2 segregants. Moreover, genetic analyses of late maturity were conducted through the process of six times of continuous backcross with Koshihikari as a recurrent parent by using the late-maturing homozygous F3 line as a nonrecurrent parent, thus developing a late-maturing isogenic Koshihikari (BC6F2). As a result, we elucidated a single late-maturing gene with incomplete dominance that caused the 14-day maturation delay of Koshihikari. The whole-genome sequencing was conducted on both of Koshihikari and the late-maturing isogenic Koshihikari. Then, the SNP call was conducted as the reference genome of Koshihikari. Finally, a single SNP was identified in the key gene Hd16 of the late-maturing isogenic Koshihikari.
Acceptance of Digital Sports: A Study Showing the Rising Acceptance of Digital Health Activities Due to the SARS-CoV-19 Pandemic

In pandemic times, the possibilities for conventional sports activities are severely limited; many sports facilities are closed or can only be used with restrictions. To counteract this lack of health activities and social exchange, people are increasingly adopting new digital sports solutions—a behavior change that had already started with the trend towards fitness apps and activity trackers. Existing research suggests that digital solutions increase the motivation to move and stay active. This work further investigates the potentials of digital sports incorporating the dimensions gender and preference for team sports versus individual sports. The study focuses on potential users, who were mostly younger professionals and academics. The results show that the SARS-CoV-19 pandemic had a significant negative impact on sports activity, particularly on persons preferring team sports. To compensate, most participants use more digital sports than before, and there is a positive correlation between the time spent physically active during the pandemic and the increase in motivation through digital sports. Nevertheless, there is still considerable skepticism regarding the potential of digital sports solutions to increase the motivation to do sports, increase performance, or raise a sense of team spirit when done in groups.
Tech savvy or tech oriented? Information-seeking behaviour and the Net Generation

This thesis is an in-depth investigation into the information-seeking behaviour of the so-called Net Generation, Generation Y or Millennials using a target population of young people at the upper age limits of this group (students aged 18 to 22 at the time of this study). Very early in the history of the Internet, young people were observed to be using new technologies associated with the Internet and the World Wide Web with ease, and terms such as ‘digital native’ and digital immigrant’ became very popular in social commentary and the popular press. As a result, a growing body of educational practitioner and academic literature, as well as social commentary in the popular media emerged, and for the first time, attributed a set of technical and information-seeking skills to young people born after a particular date. This idea of a Net Generation that is tech-savvy, and by default, information-savvy, has continued to have resonance with educators, politicians and the general public. It has affected political and systemic educational thinking and decision-making despite a growing body of academic research which has produced evidence to the contrary. This research investigates the Net Generation’s personal perceptions of how they use a range of technologies and their emotional response (confidence levels) to using these technologies according to the Net Generation attributes as outlined in the practitioner literature and the popular press. It then examines closely, how a group of young people were actually using these technologies to find information using the Internet and electronic resources, to determine whether the Net Generation attributes accurately reflect what young people are doing in practice. Any assumptions being made about the information-seeking behaviour of today’s students need to be closely examined to ensure that tomorrow’s citizens are not disenfranchised or disempowered as users in a world in which government, education and economic information; and the provision of essential services is provided wholly online. Hence the overarching premise of this study centres on the belief that a greater understanding of how young adults seek and interact with information and the online environment, is an important first step in developing strategies to prepare them for tertiary education, the workplace and a future that will be characterised by an increasingly complex and constantly evolving information landscape.
Brain Tumor Classification using Machine Learning

This paper presents a technical analysis of tumor data with Machine Learning and Classification Approach. Feature parameters which are dependent for classification of tumor are used for analyzing and classifying the class of tumor. In the classification of tumor, KNN-Classifier is implemented with cross validating accuracy score and tuning hyper parameters. Experimental simulation for best average score for K makes it to the cross validation. Approaching the prediction with the best accuracy score, hyper parameters of KNN Classifier states the best score. Using Principal Component Analysis on the data, miss-classification of tumor class in data is visualized. Aims: To declare and analyse tumor data from the source of MRI, CT scan, etc. for medication of tumor. To utilize smart predictions for the upcoming tumor patients using Machine Learning. Study Design: Tumor classification using K Nearest Neighbor algorithm and analysis of the miss-classification. Methodology: We included 11 different studies and research papers which were relevant with tumor classification. Research papers include classification of tumors with different supervised learning approaches. Our proposed analysis and classification give visualization of two classes of tumor. Results: The Project results in classification of tumor data using Machine Learning and analyzing the miss-classification of tumor. In implementation of KNN Algorithm, the accuracy score after cross validation and tuning K values is 0.97. The confusion matrix shows 4 false positives and 1 false negative value in testing. Conclusion: Less miss-classification of tumor results best accuracy score and more efficient working on testing data. Visualizing the classification with 3-dimensional scatter plots made the analysis accurate.
EFFECTIVENESS OF GLOBALIZATION AND HUMAN CAPITAL ON MARKET & NET INCOME INEQUALITY IN BRICS COUNTRIES: A PANEL DATA ANALYSIS

This study scrutinizes the impact of defacto and dejure GLOB (KOF GLOB index 2018) on Income inequality on economically emerging countries; BRICS countries. The defacto GLOB indicates the estimate of GLOB including variables representing activities and flows; de jure estimate includes variables which show policies representing enable flows and activities. Our analysis separates the impact of globalization on net and market income inequalities. Pretax/transfer and the post-tax/transfer GINI indices were employed as the measures of income inequality. This analysis used balanced panel for BRICS countries for the period 1990-2015. Economic globalization both defacto and dejure showed positive sign that depicts a significant relationship with dependent variable. It explains that defacto political has positive sign and dejure political globalization decreases inequality while economic globalizations in both divisions have positive sign and significant impact on inequality. Interestingly, defacto social globalization has positive sign but dejure social has positive sign. Moreover, the purchasing power parity and age dependency both have negative sign and significant influence on inequality. These conclusions point out that social and political globalization may be a hindering factor for governance in these countries.
Bistable compliant mechanism using magneto active elastomer actuation

One of the challenges in the emerging field of origami engineering is achieving large deformations to enable significant shape transformations. Bistable compliant mechanisms provide a means to achieve this, and the goal of this research is to investigate the feasibility and design of a compliant bistable mechanism that is actuated by magneto active elastomer material. When exposed to an external field, magneto active elastomer material deforms to align embedded magnetic particles with the field. We investigate a case study using magneto active elastomer actuation through the development of finite element analysis models to predict the magnetic field required to snap the device from its first stable position to its second for various geometries and field strengths. The finite element analysis model also predicts the displacement of the mechanism as it moves from one position to the other to determine whether the device is in fact bistable. These results can be used to understand the relationship between the substrate properties and the bistability of the device. The experimental results validate the finite element analysis models and demonstrate the functionality of active magneto active elastomer materials to be used as actuators for such devices and applications of origami engineering.
Insight into the preparation of biopolymeric nanoparticles with lithium and their cellular uptake studies using PC 12 cells

ABSTRACT Biopolymeric nanoparticles like chitosan, gelatin, alginate, and chitosan-alginate were prepared using different methods to deliver drugs to the brain . The hydrodynamic size of the synthesised nanoparticles ranged from 49 nm to 383 nm for the concentration of nanoparticles studied. The zeta potential measurements ranged from −1.71 mV to +26 mV for the synthesised nanoparticles. Structural characterisation by XRD and FT IR indicated encapsulation of lithium. The porous structure of chitosan nanoparticles with lithium was observed by HR SEM. The nanoformulations were evaluated for in vitro proliferation study using MTT assay using the PC 12 cell lines, and the cellular uptake of the nanoformulations was assessed using ICP-OES. Cellular uptake of lithium carbonate in chitosan nanoparticles showed the maximum cellular uptake by the PC 12 cells with 70.63% compared to other groups. Chitosan nanoparticles in the present study seem to be an ideal carrier of lithium.
Automatic Classification of Fatty Liver Disease Based on Supervised Learning and Genetic Algorithm

Fatty liver disease is considered a critical illness that should be diagnosed and detected at an early stage. In advanced stages, liver cancer or cirrhosis arise, and to identify this disease, radiologists commonly use ultrasound images. However, because of their low quality, radiologists found it challenging to recognize this disease using ultrasonic images. To avoid this problem, a Computer-Aided Diagnosis technique is developed in the current study, using Machine Learning Algorithms and a voting-based classifier to categorize liver tissues as being fatty or normal, based on extracting ultrasound image features and a voting-based classifier. Four main contributions are provided by our developed method: firstly, the classification of liver images is achieved as normal or fatty without a segmentation phase. Secondly, compared to our proposed work, the dataset in previous works was insufficient. A combination of 26 features is the third contribution. Based on the proposed methods, the extracted features are Gray-Level Co-Occurrence Matrix (GLCM) and First-Order Statistics (FOS). The fourth contribution is the voting classifier used to determine the liver tissue type. Several trials have been performed by examining the voting-based classifier and J48 algorithm on a dataset. The obtained TP, TN, FP, and FN were 94.28%, 97.14%, 5.71%, and 2.85%, respectively. The achieved precision, sensitivity, specificity, and F1-score were 94.28%, 97.05%, 94.44%, and 95.64%, respectively. The achieved classification accuracy using a voting-based classifier was 95.71% and in the case of using the J48 algorithm was 93.12%. The proposed work achieved a high performance compared with the research works.
Towards an Internal Process Model for Haptic Interactions within Virtual Environments*

Interactive human-machine systems (HMS), such as compute-based virtual environments (VEs), have been increasingly relied upon for decision-making. Building trust between human users and machines is crucial to enable a cooperative relationship. One aspect of building trust requires modeling sensory feedback from virtual objects in VEs to the users for appropriate understanding and utilization. In current VEs, of interest is modelling the integration of vibrotactile and force cues for providing sensory feedback to stimulate the haptic modality of the users. Behavioral models, such as maximum likelihood estimation, have failed to interpret the integration. Underlying this failure might be subtle internal processes of the human brain. Hence, we conducted an experiment to investigate the feasibility of modeling the integration using a drift-diffusion model (DDM), which is known to bridge observed behavioral outcomes and internal processes. In the experiment, human participants undertook a navigation and detection task within a 3D VE. Their task execution was aided by vibrotactile or/and force cues. Analyses on task accuracy and response time to the cues confirmed that DDM was feasible to interpret behavioral outcomes of the participants. The interpretation implies a link between the outcomes and the internal processes, paving a potential way to use DDM for elucidating the integration of vibrotactile and force cues.
Document Retrieval and Ranking using Similarity Graph Mean Hitting Times.

We present a novel approach to information retrieval and document analysis based on graph analytic methods. Traditional information retrieval methods use a set of terms to define a query that is applied against a document corpus to identify the documents most related to those terms. In contrast, we define a query as a set of documents of interest and apply the query by computing mean hitting times between this set and all other documents on a document similarity graph abstraction of the semantic relationships between all pairs of documents. We present the steps of our approach along with a simple example application illustrating how this approach can be used to find documents related to two or more documents or topics of interest.
New Insights to Understand the CoMFA Analysis within the Density Functional Theory Framework

The Three-Dimensional Quantitative Structure-Activity Relationship (3D QSAR) models now have a wide range of applications; however, new methodologies are required due to the complexity in understanding their results. This research presents a generalized version of quantum similarity field and chemical reactivity descriptors within the density functional theory framework. By taking reference compounds, this generalized methodology can be used to understand the biological activity of a molecular set. In this sense, this methodology allows to study of the CoMFA in quantum similarity and chemical reactivity. It is feasible to investigate steric and electrostatic effects on local substitutions using this method. They were considering that how these methodologies could be used when the receptor is known or unknown.
5-Hydroxymethylcytosine Signatures in Circulating Cell-Free DNA as Early Warning Biomarkers for COVID-19 Progression and Myocardial Injury

Background: The symptoms of coronavirus disease 2019 (COVID-19) range from moderate to critical conditions, leading to death in some patients, and the early warning indicators of the COVID-19 progression and the occurrence of its serious complications such as myocardial injury are limited. Methods: We carried out a multi-center, prospective cohort study in three hospitals in Wuhan. Genome-wide 5-hydroxymethylcytosine (5hmC) profiles in plasma cell-free DNA (cfDNA) was used to identify risk factors for COVID-19 pneumonia and develop a machine learning model using samples from 53 healthy volunteers, 66 patients with moderate COVID-19, 99 patients with severe COVID-19, and 38 patients with critical COVID-19. Results: Our warning model demonstrated that an area under the curve (AUC) for 5hmC warning moderate patients developed into severe status was 0.81 (95% CI 0.77–0.85) and for severe patients developed into critical status was 0.92 (95% CI 0.89–0.96). We further built a warning model on patients with and without myocardial injury with the AUC of 0.89 (95% CI 0.84–0.95). Conclusion: This is the first study showing the utility of 5hmC as an accurate early warning marker for disease progression and myocardial injury in patients with COVID-19. Our results show that phosphodiesterase 4D and ten-eleven translocation 2 may be important markers in the progression of COVID-19 disease.
Understanding Safety Management Practices and Safety Performance amid Coronavirus (Covid-19) Pandemic among Nurses in Public Hospitals

ABSTRACT The study aims to examine the role of safety management practices and safety performance among nurses working in public hospitals amid the Coronavirus (Covid-19) pandemic in Nigeria. Self-administered questionnaires were distributed for data collection and a total of 229 nursing staff from the public health facilities in Abuja, Nigeria participated in the study. The results demonstrated that the level of safety performance was moderately high. These findings support the influence of management commitment on safety, safety training, and employees’ involvement on safety compliance. It was also found that management commitment to safety, safety training, and safety promotion policies posed significant effects on safety participation. This study provides empirical support for the assertion that safety management practices are of paramount importance in improving safety performance among nurses in public hospitals, especially during the Covid-19 Pandemic.
Impact of Pandemic Communication on Brand-specific Outcomes: Testing the Moderating Role of Brand Attitude and Product Category

The unprecedented crisis due to the COVID-19 pandemic has resulted in a shift in consumers’ attitude, behaviour and purchasing habits across the globe. While brands make all the efforts to cope up, they also need to devise strategies to survive in the post-crisis situation. It is in the light of this disturbed equilibrium that the present study is undertaken. Using structural equation modeling (SEM)-based analysis of 240 consumer responses, the article analyses the direct influence of pandemic communication and the indirect impact of brand attitude and product category on three specific brand outcomes, viz. image, trust and loyalty. The findings reveal a positive and significant impact of communication during pandemic on all three brand outcomes under investigation. Further, though the results do not divulge the moderating role of brand attitude, they establish the impact of pandemic communication on brand loyalty for non-essential product category. On the basis of the findings, the study yields useful suggestions that can be implemented by brands to hold themselves more strongly in the post-pandemic future.
A P1 bubble/P1 finite element velocity-pressure solution of a pseudo homogeneous flow model simulating a two phase flow: application to lake eutrophication remedial by air injection

The climatological variations, mainly and specifically thermal variations, generate in lakes an unsteady dynamic process making water quality decrease progressively; when the dissolved oxygen concentration reaches a low level, the lake is considered in eutrophication. The dynamic aeration process is one of the best remedial techniques against eutrophication. It consists in injecting air in the bottom of the lake. A good understanding of the eutrophication treatment through aeration requires a modeling based on a two phase ow (water-air bubbles) leading to many difficulties. In this paper, we describe the implementation of the aeration effect correction in a modified one-phase model to restitute as much as possible an equivalent behavior of the two-phase model. Preliminary studies of a mixed velocity-pressure formulation for 2D ows with variable density are presented in [1]. Using the characteristics method to approximate the total derivative convection term, the generic system is of Quasi-Stokes type. For the space discretization, we use the P1 bubble- P1 finite element method. Numerical experiments are then performed for scenario analysis.