Miracle-dz/UniFAR-FTUs huggingface dataset:
Communication of Genetic Modification Science: Consumers’ Communication of Genetic Modification Science: Consumers’ Critical Thinking Style, Perceived Transparency of Information, Critical Thinking Style, Perceived Transparency of Information, and Attitude and Attitude

Abstract Consumers’ attitude toward GM science is not only an important factor to determine the industry’s development, it is also a crucial topic across various countries. How people express their judgment of GM science, involves a highly complicated process. Because of this complexity, influence on attitude toward GM science has become a popular research topic and has been examined through various variables. This study sought to examine the effects of critical thinking styles and perceived transparency of GM science on attitude toward GM science. To fulfill the purpose of the research, an online survey was conducted. A total of 1,047 adults across the United States were recruited using non-probability sampling techniques. The results of the study found critical thinking style had a significant impact on the perceived transparency of GM science information and attitude toward GM science. However, consumers with higher perceived transparency of GM science information reported lower attitudes toward GM science than those who had lower perceived transparency. An interaction effect of critical thinking styles and perceived transparency of GM science on attitude toward GM science was found. Further research examining perceived transparency was recommended. In addition, it was recommended that extension agents or agricultural communicators could develop various communication strategies based on various attribution of consumers.
Strategies for cellular deconvolution in human brain RNA sequencing data [version 1; peer review: awaiting peer review]

Background: Statistical deconvolution strategies have emerged over the past decade to estimate the proportion of various cell populations in homogenate tissue sources like brain using gene expression data. However, no study has been undertaken to assess the extent to which expression-based and DNAm-based cell type composition estimates agree. Results: Using estimated neuronal fractions from DNAm data, from the same brain region (i.e., matched) as our bulk RNA-Seq dataset, as proxies for the true unobserved cell-type fractions (i.e., as the gold standard), we assessed the accuracy (RMSE) and concordance (R2) of four reference-based deconvolution algorithms: Houseman, CIBERSORT, non-negative least squares (NNLS)/MIND, and MuSiC. We did this for two cell-type populations neurons and non-neurons/glia using matched single nuclei RNA-Seq and mismatched single cell RNASeq reference datasets. With the mismatched single cell RNA-Seq reference dataset, Houseman, MuSiC, and NNLS produced concordant (high correlation; Houseman R2 = 0.51, 95% CI [0.39, 0.65]; MuSiC R2 = 0.56, 95% CI [0.43, 0.69]; NNLS R2 = 0.54, 95% CI [0.32, 0.68]) but biased (high RMSE, >0.35) neuronal fraction estimates. CIBERSORT produced more discordant (moderate correlation; R2 = 0.25, 95% CI [0.15, 0.38]) neuronal fraction estimates, but with less bias (low RSME, Open Peer Review Reviewer Status AWAITING PEER REVIEW Any reports and responses or comments on the article can be found at the end of the article. Page 1 of 15 F1000Research 2021, 10:750 Last updated: 03 SEP 2021
UvA-DARE (Digital Academic Repository) More grazing, more damage? Assessed yield loss on agricultural grassland relates nonlinearly to goose grazing pressure

1. In recent decades, conflict between geese and agriculture has increased. Management practices to limit this conflict include concentrating geese in protected areas, derogation shooting or population reduction. To justify such management, we need to understand their effects on goose-related damages, which requires an understanding of how yield loss is influenced by goose abundance and species interactions. 2. We combined data from monthly goose counts and GPS-tracked geese to estimate grazing pressures by barnacle, white-fronted and greylag geese on agricultural grassland in Fryslân, the Netherlands. Using linear mixed models, we related this to damages assessed by professional inspectors. 3. Our results show a positive nonlinear relationship between yield loss and bar-nacle goose grazing pressure, where assessed damage increases with a decelerating rate as grazing pressure increases. For white-fronted geese, we find a negative relationship, while for greylag geese both positive and negative relationships occur. For each species, the relationship is influenced by the abundance of the other two. 4. For barnacle geese, the relationship can be explained by selection of fields offering the best balance between food intake and energy expenditure, and by grass regrowth, with highest grazing pressures occurring over a longer time period. The results for the other species are likely due to spatial and temporal differences in foraging preferences compared to barnacle geese, where larger species avoid areas with highest damages. 5. Synthesis and applications . Our results suggest that decreasing herbivore abundance may not translate directly to decreased yield loss, and management tools such as population reduction or derogation shooting should be used with care.
The Domestication of Netflix in the Gulf

The introduction of subscription video-on-demand (SVoD) services has transformed the media landscape in the television market of Arab Gulf countries. This article adopts Silverstone’s domestication theory as a conceptual framework to analyze how users, as individuals and households, incorporate Netflix and engage with its content, how they create their own symbolic meanings and social practices
BTS’s Pandemic Hits and Their Implications: Discursive Re-vitalization of Neoliberal Hegemony in K-pop Industry

This study critically examines whether BTS poses counterhegemony to the dominant Western popular culture as the literature claims. Investigating its recent hit music videos during the COVID-19 pandemic, I claim that BTS resuscitates the neoliberal hegemony in its seemingly empowering, positive messages and helps revive narratives of neoliberal biopolitics. Reviewing how hegemony becomes naturalized by cultural and psychological measures in broader political economic contexts, I reveal how BTS’s positive messages in bright audio-visuality relegitimize neoliberal mantras that help manage the crisis.
Repository ISTITUZIONALE

—Recently introduced 3D Time-of-Flight (ToF) cameras have shown a huge potential for mobile robotic applications, proposing a smart and fast technology that outputs 3D point clouds, lacking however in measurement precision and robustness. With the development of this low-cost sensing hardware, 3D perception gathers more and more importance in robotics as well as in many other fields, and object registration continues to gain momentum. Registration is a transformation estimation problem between a source and a target point clouds, seeking to find the transformation that best aligns them. This work aims at building a full pipeline, from data acquisition to transformation identification, to robustly detect known objects observed by a ToF camera within a short range, estimating their 6 degrees of freedom position. We focus this work to demonstrating the capability of detecting a part of a satellite floating in space, to support in-orbit servicing missions (e.g. for space debris removal). Experiments reveal that deep learning techniques can obtain higher accuracy and robustness w.r. t. classical methods, handling significant amount of noise while still keeping real-time performance and low complexity of the models themselves.
Accelerating Secure Collaborative Machine Learning with Protocol-Aware RDMA

Secure Collaborative Machine Learning (SCML) suffers from high communication cost caused by secure computation protocols. While modern datacenters offer high-bandwidth and low-latency networks with Remote Direct Memory Access (RDMA) capability, existing SCML implementation remains to use TCP sockets, leading to inefﬁciency. We present CORA 1 to implement SCML over RDMA. By us-ing a protocol-aware design, CORA identiﬁes the protocol used by the SCML program and sends messages directly to the remote party’s protocol buffer, improving the efﬁciency of message exchange. CORA exploits the chance that the SCML task is determined before execution and the pattern is largely input-irrelevant, so that CORA can plan message destinations on remote hosts at compile time. CORA can be readily deployed with existing SCML frameworks such as Piranha with its socket-like interface. We evaluate CORA in SCML training tasks, and our results show that CORA can reduce communication cost by up to 11 ⇥ and achieve 1 . 2 ⇥� 4 . 2 ⇥ end-to-end speedup over TCP in SCML training.
Relationship Among Undergraduates’ Motivation, Self-Efficacy And Use Of Information And Communication Technology For Learning In Kwara State

Information and communication technology (ICT) facilitates quick and easy access to a wide range of information and information resources worldwide for students and teachers. Due to the importance of ICT in learning, it is necessary to identify factors that might influence its integration into the educational environment. This study examined the relationship between motivation, self-efficacy and the use of information and communication technology for learning among college students in Kwara State. This study also examined the influence of gender on motivation, self-efficacy and the use of ICT for learning This research is a descriptive design using the cross sectional survey method. The study samples were taken from one federal, one state and one private university (three universities) in Kwara state. The respondents were 296 students (170 boys and 126 girls and 54 arts, 104 social studies and 138 students. As a result it can be recommended that adequate support should be provided to students to obtain ICT resources and applications for the improvement of their studies, also teachers should integrate the use of ICT in their lessons regularly to encourage students to use it.
Automated Medical Image Captioning with Soft Attention-Based LSTM Model Utilizing YOLOv4 Algorithm

: The medical image captioning field is one of the prominent fields nowadays. The interpretation and captioning of medical images can be a time-consuming and costly process, often requiring expert support. The growing volume of medical images makes it challenging for radiologists to handle their workload alone. However, addressing the issues of high cost and time can be achieved by automating the process of medical image captioning while assisting radiologists in improving the reliability and accuracy of the generated captions. It also provides an opportunity for new radiologists with less experience to benefit from automated support. Despite previous efforts in automating medical image captioning, there are still some unresolved issues, including generating overly detailed captions, difficulty in identifying abnormal regions in complex images, and low accuracy and reliability of some generated captions. To tackle these challenges, we suggest the new deep learning model specifically tailored for captioning medical images. Our model aims to extract features from images and generate meaningful sentences related to the identified defects with high accuracy. The approach we present utilizes a multi-model neural network that closely mimics the human visual system and automatically learns to describe the content of images. Our proposed method consists of two stages. In the first stage, known as the information extraction phase, we employ the YOLOv4
Convolution Neural Networks of Dynamically Sized Filters with Modified Stochastic Gradient Descent Optimizer for Sound Classification

: Deep Neural Networks (DNNs), specifically Convolution Neural Networks (CNNs) are found well suited to address the problem of sound classification due to their ability to capture the pattern of time and frequency domain. Mostly the convolutional neural networks are trained and tested with time-frequency patches of sound samples in the form of 2D pattern vectors. Generally, existing pre-trained convolutional neural network models use static-sized filters in all the convolution layers. In this present work, we consider the three different types of convolutional neural network architectures with different variable-size filters. The training set pattern vectors of time and frequency dimensions are constructed with the input samples of the spectrogram. In our proposed architectures, the size of kernels and the number of kernels are considered with a scale of variable length instead of fixed-size filters and static channels. The paper further presents the reformulation of a minibatch stochastic gradient descent optimizer with adaptive learning rate parameters according to the proposed architectures. The experimental results are obtained on the existing dataset of sound samples. The simulated results show the better performance of the proposed convolutional neural network architectures over existing pre-trained networks on the same dataset.
Stochastic Workﬂow Modeling in a Surgical Ward: Towards Simulating and Predicting Patient Flow

Intelligent systems play an increasingly central role in healthcare systems worldwide. Nonetheless, operational friction represents an obstacle to full utilization of scarce resources and improvement of service standards. In this paper we address the challenge of developing data-driven models of complex workﬂow systems - a prerequisite for harnessing intelligent technologies for workﬂow improvement. We present a proof-of-concept model parametrized using real-world data and constructed based on domain knowledge from the Royal Inﬁrmary of Edinburgh, demonstrating how off-the-shelf process mining, machine learning and stochastic process modeling tools can be combined to build predictive models that capture complex control ﬂow, constraints, policies and guidelines.
ScholarWorks ScholarWorks

We report on the magnetoresistance (MR) and electron transport measurements observed on a single crystal magnetite nanowire prepared using a hydrothermal synthesis method. High-resolution electron microscopy revealed the single crystal magnetite nanowires with 80–120 nm thickness and up to 8 l m in length. Magnetic measurements showed the typical Verwey transition around 120 K with a 100 Oe room temperature coercivity and 45 emu/g saturation magnetization, which are comparable to bulk magnetite. Electrical resistance measurements in 5–300 K temperature range were performed by scanning gate voltage and varying applied magnetic ﬁeld. Electrical resistivity of the nanowire was found to be around 5 (cid:2) 10 (cid:3) 4 X m, slightly higher than the bulk and has activation energy of 0.07 eV. A negative MR of about 0.7% is observed for as-synthesized nanowires at 0.3 T applied ﬁeld. MR scaled with increasing applied magnetic ﬁeld representing the ﬁeld-induced alignment of magnetic domain. These results are attributed to the spin-polarized electron transport across the antiphase boundaries, which implicate promising applications for nano-wires in magnetoelectronics. V C 2015 AIP Publishing LLC . [http://dx.doi. org/10.1063/1.4914535] Being part of the Half-metal family, magnetite (Fe 3 O 4 )
The Impact of Financial Technology Development on Money Laundering Risks

The rapid advancement of financial technology has introduced increased complexity into the global landscape of anti-money laundering. This study seeks to examine the influence of a country's financial technology development on money laundering risks, and whether such impact varies between developing and developed countries. To support our research, we collected data from a sample of 20 economies spanning the time frame of 2012 to 2022, and employed principal component analysis and lagged variables within a multiple linear regression framework. The results indicate that the development of financial technology initially increases money laundering risks in the short term but eventually leads to a reduction in these risks in the long term. Furthermore, this effect is more pronounced in developing countries compared to developed countries. These findings contribute novel insights to the research exploring the role of financial technology in the realm of anti-money laundering.
Review of Power Sharing Control Strategies for Islanding Operation of AC Microgrids

Microgrid is a new concept for future energy distribution system that enables renewable energy integration. It generally consists of multiple distributed generators that are usually interfaced to the grid through power inverters. For the islanding operation of ac microgrids, two important tasks are to share the load demand among multiple parallel connected inverters proportionately, and maintain the voltage and frequency stabilities. This paper reviews and categorizes various approaches of power sharing control principles. Simultaneously, the control schemes are graphically illustrated. Moreover, various control approaches are compared in terms of their respective advantages and disadvantages. Finally, this paper presents the future trends.
Harnessing the Power of Ensemble Machine Learning for the Heart Stroke Classification

A heart stroke, also known as a myocardial infarction or heart attack, is a critical medical condition that arises when there is an obstruction in the coronary arteries that provide blood to the heart muscles. This blockage results in a diminished flow of blood and oxygen to a specific area of the heart. This abrupt interruption initiates a gradual sequence of heart muscle damage, which can lead to varying degrees of functional impairment. The severity of these impairments is primarily determined by the precise location of the heart muscle affected. Therefore, it is of utmost importance to identify the warning signs and symptoms of a stroke as soon as possible. This is the objective of this paper is to early recognition and prompt action can significantly improve the chances of a healthy and fulfilling life following a stroke. In this research work, the Stroke dataset is pre-processed and on pre-processed dataset machine learning and ensemble machine learning techniques were employed to develop and assess several models aimed at creating a stable framework for predicting the enduring stroke risk. And various matrices like accuracy, F1 score, ROC, precision, and recall are calculated. Among all models, AdaBoost model demonstrated exceptional performance validated through multiple metrics, including Precision, AUC, recall, accuracy, and F1-measure. The results underscored superiority of the AdaBoost classification method, achieving an impressive Accuracy of 99%. AdaBoost model may serve as a stable framework for predicting enduring stroke risk, emphasizing its potential utility in clinical settings for identifying individuals at higher risk of experiencing a stroke.
Genetic Diversity and Its Relationship of Dendrobium (Orchidaceae) Based on Bioactive Compounds and Their Biological Activities: A Meta-Analysis

Information on genetic diversity and its relationship is fundamental for the preservation and improvement of orchid germplasm. For Dendrobium, such information, particularly by a meta-analysis, was limited. The study aimed to assess the genetic diversity and relationships of Dendrobium germplasm based on bioactive compounds, their biological activities, and plant organs by a meta-analysis approach. A total of 51 species of Dendrobium have been collected and identified as producing bioactive compounds, including their biological activities and plant organs (parts). In this case, the highest genetic diversity was shown by polyphenols (H' index = 0.90) as substances, neuroprotective (H' = 0.80) for activity, and the leaf organ with an H' index of 0.89. The UPGMA analysis showed that Dendrobium grouped into seven clusters, where the furthest relationship was presented by D. moschatum and D. catenatum. However, the closest relation was by D. scabrilingue with D. delacourii, including D. snowflake and D. ovatum. Following these parameters, Dendrobium shows unique genetic diversity and relationships. Thus, it is valuable for future preservation and improvement programs of Dendrobium.
Adolescents’ Self-Determined Motivation to Learn About Safer Sex Online

Self-determined motivation fosters better learning and sustained health behaviour adoption than controlled motivation. Self-determined motivation can be strengthened by satisfying the need for autonomy. Therefore, this study investigated if adolescents’ (1) intrinsic motivation to learn about safer sex and (2) self-determined motivation to perform safer sex would be higher when an online message is presented in autonomy-supportive language compared to controlling language. Furthermore, this study also investigated if adolescents’ (1) intrinsic motivation to learn about safer sex and (2) self-determined motivation to perform safer sex changes depending on whether the online message is written by someone that is similar to them. To this end, a 2 (autonomy-supportive language vs. controlling language) x 2 (identification vs. no identification) experimental study (N = 214) was conducted using a website page about safe sex from the Dutch sexual health website sense.info. Even though we found some indications of the expected effects, a two-way analysis of covariance (ANCOVA) showed no significant difference for either autonomy-supportive language, identification, or an interaction of both strategies. However, we found some preliminary evidence that this could be due to a ceiling effect. We therefore recommend further exploring these strategies, considering some lessons learned.
Optimized design for time-of-flight total neutron scattering instrumentation using Monte Carlo simulations with application for the characterization of disordered macromolecules

Abstract A time-of-flight neutron total scattering instrument for the China Spallation Neutron Source (CSNS) was designed. The instrument is designed for disordered macromolecular systems with characteristic lengths below 10 nm. The coupled hydrogen moderator of CSNS was selected to ensure the favorable statistics at the smallest scattering vector of 0.01 Å−1. The optics and geometry of the instrument were optimized to cover a wide range of scattering vectors from 0.01 to 70 Å−1. The performance of the instrument was examined using a series of Monte Carlo simulations, which demonstrate that the flux on the sample, the resolution, and the range of the scattering vectors are comparable to those of other advanced neutron total instruments. Finally, a virtual experiment was performed with silica powder. The resulting scattering pattern is consistent with both the numeric calculation and the estimated resolution of the instrument. The designed total scattering instrument at CSNS allows the simultaneous observation of both atomic and nanometer scale structures, having the potential to become a powerful tool for studying the structure of disordered materials in the fields of polymers, biology, and condensed matter physics.
Application and Technical Challenges in Design, Cloning, and Transfer of Large DNA

In the field of synthetic biology, rapid advancements in DNA assembly and editing have made it possible to manipulate large DNA, even entire genomes. These advancements have facilitated the introduction of long metabolic pathways, the creation of large-scale disease models, and the design and assembly of synthetic mega-chromosomes. Generally, the introduction of large DNA in host cells encompasses three critical steps: design-cloning-transfer. This review provides a comprehensive overview of the three key steps involved in large DNA transfer to advance the field of synthetic genomics and large DNA engineering.