bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improving the Communicative Competence Through the Tutorial Learning Model for Bipa Learners
Authors: Martutik Martutik , Suyitno Imam , Fawzi Ary , Arista Heni Dwi 
Published: December 2023

Abstract
BIPA learning is one of the Indonesian language learning programs whose students are foreign speakers. In BIPA learning, teachers apply the tutorial learning model by involving the learners directly in the context of natural communication. This learning model was widely used to improve student's communication skills in foreign language learning. The study used a qualitative approach to describe the implementation of the tutorial model in BIPA learning and its impact on students' language skills. The study involved three instructors, seven tutors, and fourteen BIPA learners. Collecting data using interviews, questionnaires, and document studies. Data analysis followed a qualitative procedure with the following steps: data reduction, presentation, interpretation, and conclusion. The study found that the BIPA learning tutorial used simple designs, flexible to change, and was oriented to learners' characteristics and learning needs. In the implementation of learning, tutors must be creative in developing various techniques to optimize learners’ opportunities to use the target language. Optimizing the opportunity to use the target language and practicing communication in the natural context improves learners’ language skills and adds value to learners' good perceptual, impacting their success in learning the language. These findings suggested that the diversity of student characteristics needs to be early because it can become an obstacle in developing the design and implementation of tutorial learning. These findings contributed to the development of second language learning and as a reference for further research.
Is a definite genetic diagnosis all we need to manage patients with Inborn Error of Immunity prenatally and during childbearing? Running title: Prenatal and Childbearing Considerations
Authors: Salemi Negin , Bakhshesh Shima , Zamanifar Aryana , Bahreini Amir , Salehi Rasoul , Sherkat Roya 
Published: December 2023

Abstract
Background: Patients with inborn errors of immunity (IEI) who want to have children face concerns about the impact of their condition on fertility, pregnancy, and disease inheritance. In tribal cultures with consanguineous marriages, prenatal genetic counseling is critical. Case presentation: 10 families with genetically confirmed IEI were reported: An architect with an autosomal dominant STAT-1 gain of function who had planned preimplantation genetic diagnosis (PGD) to prevent disease transmission. However, she unexpectedly became pregnant and underwent prenatal diagnosis (PND) at 12 weeks. Despite the diagnosis, her husband refused to allow her to abort the affected child.
Another case involved a consanguineous family who lost their first child to leukocyte adhesion deficiency type 1 (LAD1). The second child was also affected. Despite the availability of PND during the second pregnancy, the father refused the procedure.
In another instance, a first cousin couple had two children with Bruton disease. Despite being informed about the disease, the mother refused to abort her second child. Likewise, a consanguineous couple with two children affected by Ataxia Telangiectasia opted for a donated oocyte for their third child, resulting in a healthy child.
Recurrent pregnancy loss (RPL) was observed in a mother who was later diagnosed with ZAP70 deficiency. In another case, a mother with a child affected by Wiskott Aldrich Syndrome (WAS) underwent in vitro fertilization (IVF) without sex selection, resulting in a healthy boy after PND was performed during pregnancy.
In a family with multiple cases of WAS, another child was mistakenly diagnosed with anaplastic anemia. A case of LAD1 resulted in the parents' divorce, with the father denying the child's condition and impeding necessary bone marrow transplantation. In a non-consanguineous couple, the father was diagnosed with TACHI deficiency and Hypogammaglobulinemia. PND revealed that the mother and child had the same heterozygote gene, leading the mother to decide against continuing the pregnancy.
Conclusion: Genetic diagnosis alone is not enough for optimal prenatal care in immune dysregulation disorders. Factors like patient awareness, social beliefs, ethics, and economic considerations impact pregnancy decisions. Clinical immunologists must consider these factors and provide guidance for better outcomes.
Simulation study of magnetic microcalorimeters for rare event search experiments
Authors: Lee D.Y. , Chung J.S. , Jeon J.A. , Kim H.B. , Kim H.J. , Kim Y.H. , Kim Y.M. , Kwon D.H. , Lee Y.C. , Lim H.S. , Park H.K. , Woo K.R. 
Published: December 2023

Abstract

We present advancements in a finite element method for computing the physical properties of magnetic microcalorimeters (MMCs). Utilizing the COMSOL package, we conducted 3D simulations of a meander-shaped Nb coil with a realistic geometry. The resulting magnetic field distribution showed good agreement with a previous 2D simulation and revealed non-negligible differences at the side edge of the sensor material. Employing the simulation results, we calculated the MMC properties and compared them with previous measurements. Our calculated values closely align with the measured values for the sensor magnetization, the pulse heights from alpha detection, and the coil inductance.
Robots, Exports and Top Income Inequality: Evidence for the U.S.
Authors: César Andres Manuel , Falcone Guillermo , Garriga Pablo 
Published: December 2023

Abstract
The last decades have witnessed a revolution in manufacturing production characterized by increasing technology adoption and a strong expansion of international trade. Simultaneously, the income distribution has exhibited both polarization and concentration among the richest. Combining datasets from the U.S. Census Bureau, the U.S. Internal Revenue Service, the International Federation of Robotics, EU KLEMS, and COMTRADE, we study the causal effect of industrial automation on income inequality in the U.S. during 2010--2015. We exploit spatial and time variations in exposure to robots arising from past differences in industry specialization across U.S. metropolitan areas and the evolution of robot adoption across industries. We document a robust positive impact of robotics on income for only the top 1 percent of taxpayers, which is largest for top income fractiles. Therefore, industrial automation fuels income inequality and, particularly, top income inequality. According to our estimates, one more robot per thousand workers results in relative increments of the total taxable income accruing to fractiles P99 to P99.9, P99.9 to P99.99 and P99.99 to P100, of 2.1 percent, 3.5 percent and 5.9 percent, respectively. We also find that robotization leads to increased exports to high-income and upper-middle-income economies, and that this is one of the key mechanisms behind the surge in top income inequality.
JEL Classification: J23, J24, J31, O14, O33.
CMA-YOLO: A Wine Grape Detection Model Based on YOLOv5x Combining Mixed Attention Mechanism
Authors: Lu Hu , Zhang Xiao , Sun Jun , Wan Shaohua 
Published: December 2023

Abstract
The progress of object detection technology can promote the automation and modernization of agricultural production. In order to improve the feasibility and efficiency of automated fruit harvesting, this study proposes an improved object detection model based on YOLOv5x. Firstly, considering the lack of training samples, we improved the data loading method by adding an input branch for grayscale processing. Combined with the mosaic data augmentation method, the diversity of training samples has been dramatically improved, enriching the training set and significantly improving training effectiveness. Secondly, we add a global self-attention mechanism to the middle layer of the backbone network to highlight essential features and suppress irrelevant features, making the distinction between targets and the background more obvious. Finally, we recombined channel and spatial attention in serial and parallel methods. We integrated them into the C3 module of the neck section of the model to obtain a new CMA-C3 module, effectively improving the model's attention to features. Compared with the baseline network, our model only adds minimal complexity, but the detection precision, F1-score, mAP@0.5, and mAP@[0.5:0.95] have improved by 1.7%, 3.2%, 8.7%, and 10.2% respectively. In addition, we have verified the effectiveness and generalization of the improved modules and schemes through multiple ablation experiments and confirmed that the proposed improved model could achieve accurate object detection of wine grapes, providing fast and accurate guidance for the automatic harvesting of grapefruits and promoting the automation and intelligence of orchard agricultural production.
Estimation of Heterosis in Cotton Genotypes for Yield Performance at Wuhan Location PR China
Authors: Wahid Muhammad Atif , Mahmood Zahid 
Published: December 2023

Abstract
The present study was conducted with the aim to find out the best general and specific combiner, type of their gene action, hetterotic effects, correlation and path coefficient among morphological, yield and yield contributing and fiber traits through line x tester mating design. In this research work 6 line parents and 6 tester parents along with their 36 F1 generation were studied. The traits i.e. days to first flower, flower shedding (%), boll formation, boll opening, 1st monopodial node number plant-1, 1st sympodial nodes number plant-1, number of monopodial branches plant-1, boll shedding (% age), number of leaves plant-1, plant height, total number of fruiting points plant-1, number of bolls harvested plant-1, boll weight, seed index, harvest index, biological weight plant-1, GOT %, fibre length (mm), fibre strength, micronaire value, fibre mean length, upper half means length and fibre uniformity index (%age) were studied. Results of gene action revealed that flower shedding, number of sympodial branches plant-1 and number of leaves indicated greater value than 1 revealing over dominance type of gene action at both locations. Total number of fruiting point, number of harvest bolls, flower shedding, number of sympodial branches, boll opening, GOT% and fiber strength showed a greater GCA at both Wuhan; Hubei and location. Maximum SCA effects were observed in traits i.e. boll formation, boll opening, number of monopodial branches, number of leaves, plant height, fruiting points, boll weight, seed cotton yield, biological weight and staple length. Heterosis results showed that days to first flower, flower shedding, boll formation, sympodial node number, plant height, fruiting points, number of bolls, seed cotton yield, GOT% and staple length had maximum significant mid heterosis at both locations. The traits including flower shedding, boll formation, sympodial node numbers, number of sympodial branches, boll shedding, plant height, fruiting points, number of bolls harvested, boll weight, seed cotton yield and upper half mean length indicated maximum standard heterosis at both locations. Correlation results indicated that boll shedding, boll formation, boll opening, sympodial node number, number of bolls harvested, boll weight, plant height, GOT%, fiber strength, micronaire value, seed index and seed cotton yield had positive correlation at both locations. Path analysis results indicated that flower shedding, boll formation, boll opening, sympodial branches, fruiting point and boll weight had positive and direct effect on seed cotton yield at both locations. GOT%, staple length and micronaire value showed negative and direct effect at both locations.
Sunflower seed pulp ash as an efficient and eco-friendly adsorbent for Congo red uptake: characteristics, kinetics, and optimization
Authors: Mohadesi Majid , Gouran Ashkan , Darabi Farzaneh , Samimi Mohsen 
Published: December 2023

Abstract

This investigation focused on the Congo red uptake, as the azo dye, from an aqueous environment. Herein, ash derived from sunflower seed pulp waste (SSPA) was used as an eco-friendly low-cost adsorbent. Based on the BET results, the specific active surface area of SSPA was approximately 102 m2/g. The effect of the initial analyte concentration (10–50 mg/L), the concentration of the adsorbent (1–5 g/L), and the processing time (10–240 min) on the rate of Congo red uptake was also evaluated and optimized. According to the results, the maximum dye removal from synthetic solution (91.89%) was achieved at a dye concentration of 50 mg/L, an adsorbent concentration of 3 g/L, and a processing time of 180 min. The maximum SSPA capacity for Congo red uptake from aquatic solution was 15.34 mg/g, achieving under optimized operational conditions. The adsorption process of SSPA also follows a pseudo-second-order kinetic model (qe = 15.85 mg/g; R2 &gt; 0.99), considering the results of BET and FTIR, suggesting that the rate-controlling step in analyte removal is the chemical interaction between functional groups in SSPA and used dye. Finally, the SSPA washing with different solvents showed that the adsorbent treated with 1 M sodium hydroxide still performed well after his five reuses.
Educational Intervention on Cleaning and Sanitation of Surfaces in a Pediatric Hospitalization Unit
Authors: de Oliveira Bernardes Lucas , dos Santos Fernando Ribeiro , Luchesi Bruna Moretti , Ferreira Adriano Menis , Carneiro Liliane Moretti , Furlan Mara Cristina Ribeiro , da Silva Barcelos Larissa , de Andrade Denise , Santos Thalita Grazielly , dos Santos Junior Aires Garcia 
Published: December 2023

Introduction:
Environmental surfaces may serve as a reservoir for various microorganisms and consequently, they represent a potential risk for the spread of healthcare-associated infections.

Objective:
This study aimed to assess the cleaning and sanitation of surfaces (CSS) before and after implementing a Standardization Program for Cleaning and Sanitation of Surfaces (SPCSS).

Methods:
An analytical, comparative, and intervention study was conducted from 2020 to 2021 in a pediatric hospitalization unit in Midwest Brazil. Four frequently touched surfaces were monitored before and after the cleaning and sanitation process using the following methods: Adenosine Triphosphate (ATP) quantification, Colony-Forming Unit (CFU) count, and visual inspection. The study consisted of three stages: stage I (situational diagnosis of the CSS process), stage II (implementation of the SPCSS), and stage III (assessment 60 days after implementing the program). A total of 576 assessments were performed in all three study stages.

Results:
The CSS process was effective in all three study stages by using the ATP and CFU methods. In stage I, statistically significant results were obtained for four surfaces using the ATP method, and two by the CFU count. In stages II and III, all surfaces presented lower ATP and CFU results (p<0.05). In the visual inspection, only the bathroom door handle (stage I: p=0.041; stage III: p=0.007) and toilet flush handle (stage I: p=0.026; stage III: p=0.007) passed the test.

Implications for Nursing:
This study presents subsidies to evaluate the cleaning and disinfection process carried out by the nursing and hygiene team.

Conclusion:
The SPCSS exerted a positive impact on the CSS process.
Emotion Recognition in Reddit Comments Using Recurrent Neural
Networks
Authors: Rezapour Mahdi 
Published: December 2023

Background:
Reddit comments are a valuable source of natural language data
where emotion plays a key role in human communication. However, emotion recognition is a
difficult task that requires understanding the context and sentiment of the texts. In this paper,
we aim to compare the effectiveness of four recurrent neural network (RNN) models for classifying the emotions of Reddit comments.

Methods:
We use a small dataset of 4,922 comments labeled with four emotions: approval,
disapproval, love, and annoyance. We also use pre-trained Glove.840B.300d embeddings as
the input representation for all models. The models we compare are SimpleRNN, Long ShortTerm Memory (LSTM), bidirectional LSTM, and Gated Recurrent Unit (GRU). We experiment with different text preprocessing steps, such as removing stopwords and applying stemming, removing negation from stopwords, and the effect of setting the embedding layer as
trainable on the models.

Results:
We find that GRU outperforms all other models, achieving an accuracy of 74%. Bidirectional LSTM and LSTM are close behind, while SimpleRNN performs the worst. We observe that the low accuracy is likely due to the presence of sarcasm, irony, and complexity in
the texts. We also notice that setting the embedding layer as trainable improves the performance of LSTM but increases the computational cost and training time significantly. We analyze some examples of misclassified texts by GRU and identify the challenges and limitations
of the dataset and the models

Conclusion:
In our study GRU was found to be the best model for emotion classification of
Reddit comments among the four RNN models we compared. We also discuss some future directions for research to improve the emotion recognition task on Reddit comments. Furthermore, we provide an extensive discussion of the applications and methods behind each technique in the context of the paper.
An IoMT-based Federated Learning Survey in Smart Transportation
Authors: Karnam Geetha Vani , Maddikunta Praveen Kumar Reddy 
Published: December 2023

Abstract:
Internet of Medical Things (IoMT) is a technology that encompasses medical devices, wearable sensors, and applications connected to the Internet. In road accidents, it plays a
crucial role in enhancing emergency response and reducing the impact of accidents on victims.
Smart Transportation uses this technology to improve the efficiency and safety of transportation systems. The current Artificial Intelligence applications lack transparency and interpretability which is of utmost importance in critical transportation scenarios, such as autonomous
vehicles, air traffic control systems, and traffic management systems. Explainable Artificial Intelligence (XAI) provides a clear, transparent explanation and actions. Traditional Machine
Learning techniques have enabled Intelligent Transportation systems by performing centralized
vehicular data training at the server where data sharing is needed, thus introducing privacy issues. To reduce transmission overhead and achieve privacy, a collaborative and distributed
machine learning approach called Federated Learning (FL) is used. Here only model updates
are transmitted instead of the entire dataset. This paper provides a comprehensive survey on the
prediction of traffic using Machine Learning, Deep Learning, and FL. Among these, FL can
predict traffic accurately without compromising privacy. We first present the overview of XAI
and FL in the introduction. Then, we discuss the basic concepts of FL and its related work, the
FL-IoMT framework, and motivations for using FL in transportation. Subsequently, we discuss
the applications of using FL in transportation and open-source projects. Finally, we highlight
several research challenges and their possible directions in FL
Robotic Pills as Innovative Personalized Medicine Tools: A Mini Review
Authors: Rane Komal , Kukreja Garima , Deshmukh Siddhi , Kakad Urmisha , Jadhav Pranali , Patole Vinita 
Published: December 2023

Abstract:
The most common route for drug administration is the oral route due to the various advantages
offered by this route, such as ease of administration, controlled and sustained drug delivery,
convenience, and non-invasiveness. In spite of this, oral drug absorption faces challenges due
to various issues related to its stability, permeability and solubility in the GI tract. Biologic drugs
generally face problems when administered by oral route as they are readily degradable and thus required
to be injected. To overcome these issues in oral absorption, different approaches like novel
drug delivery systems and newer pharmaceutical technologies have been adopted. With a combined
knowledge of drug delivery and pharmaceutical technology, robotic pills can be designed and used
successfully to enhance the adhesion and permeation of drugs through the mucus membrane of the
GI tract to achieve drug delivery at the target site. The potential application of robotic pills in diagnosis
and drug dispensing is also discussed. The review highlights recent developments in robotic
pill drug-device technology and discusses its potential applications to solve the problems and challenges
in oral drug delivery.
Patient and Public Willingness to Share Personal Health Data for Third-Party or Secondary Uses: Systematic Review
Authors: Baines Rebecca , Stevens Sebastian , Austin Daniela , Anil Krithika , Bradwell Hannah , Cooper Leonie , Maramba Inocencio Daniel , Chatterjee Arunangsu , Leigh Simon 
Published: December 2023

Background
International advances in information communication, eHealth, and other digital health technologies have led to significant expansions in the collection and analysis of personal health data. However, following a series of high-profile data sharing scandals and the emergence of COVID-19, critical exploration of public willingness to share personal health data remains limited, particularly for third-party or secondary uses.

Objective
This systematic review aims to explore factors that affect public willingness to share personal health data for third-party or secondary uses.

Methods
A systematic search of 6 databases (MEDLINE, Embase, PsycINFO, CINAHL, Scopus, and SocINDEX) was conducted with review findings analyzed using inductive-thematic analysis and synthesized using a narrative approach.

Results
Of the 13,949 papers identified, 135 were included. Factors most commonly identified as a barrier to data sharing from a public perspective included data privacy, security, and management concerns. Other factors found to influence willingness to share personal health data included the type of data being collected (ie, perceived sensitivity); the type of user requesting their data to be shared, including their perceived motivation, profit prioritization, and ability to directly impact patient care; trust in the data user, as well as in associated processes, often established through individual choice and control over what data are shared with whom, when, and for how long, supported by appropriate models of dynamic consent; the presence of a feedback loop; and clearly articulated benefits or issue relevance including valued incentivization and compensation at both an individual and collective or societal level.

Conclusions
There is general, yet conditional public support for sharing personal health data for third-party or secondary use. Clarity, transparency, and individual control over who has access to what data, when, and for how long are widely regarded as essential prerequisites for public data sharing support. Individual levels of control and choice need to operate within the auspices of assured data privacy and security processes, underpinned by dynamic and responsive models of consent that prioritize individual or collective benefits over and above commercial gain. Failure to understand, design, and refine data sharing approaches in response to changeable patient preferences will only jeopardize the tangible benefits of data sharing practices being fully realized.