bluuebunny/crossref_metadata_2025_split huggingface dataset:
CRRGM:A Contextualized Real-time RGAT and GraphTransformer Method for multimodal emotion recognition in reinforcement learning
Authors: Chen Guoshun , Cao Xiaopeng , Liu Shuai 
Published: September 2024

Abstract

The task of emotion recognition in dialogues is crucial for constructing empathetic machines. Current research primarily focuses on learning emotion-related common features in multimodal data. However, it does not adequately address various dependency information of emotional features in dialogues. This oversight may lead to lower accuracy in multimodal emotion recognition and inability to recognize emotion in real time. To address this problem, we propose a contextualized approach using enhanced Relational Graph Attention Network and GraphTransformer for multimodal emotion recognition. This model employs Transformer to capture the global information between modalities. It then constructs a heterogeneous graph using the extracted global features and employs enhanced RGAT and GraphTransformer to model the complex dependencies in a conversation. Finally, a reinforcement learning algorithm is used to implement a real-time emotion recognition model. Extensive experiments on two benchmark datasets indicate that CRRGM achieves state-of-the-art performance.
A radial basis neural network using the optimization of Bayesian regularization of the malaria disease model
Authors: sabir zulqurnain , Ismail Tala , Sleem Hussein 
Published: September 2024

Abstract

The current research investigations are used to provide a stochastic computational radial basis function neural network (RBFNN), which is one of the kinds of the artificial neural network that applies radial basis as an activation function. The optimization is performed through the Bayesian regularization (BR) and the proposed solver is named as RBFNN-BR in order to solve the malaria disease model (MDM). The mathematical form of the MDM is categorized into host and vector populations that are based on pesticides and medication. A dataset is constructed based on the explicit Runge-Kutta scheme, which is used to reduce the mean square error (MSE) by selecting the data for testing 0.15, validation 0.12 and training 0.72 for the numerical solutions of the mathematical MDM. The solution of the MDM is presented by taking twenty numbers of neurons, RBF as an activation function in the hidden layers, RB for the optimization, and data selection based different values. The correctness of the RBFNN solver is observed by using the comparison with the published literature results accurateness and the reference solutions for solving the MDM. Moreover, the negligible absolute error performances also approve the precision of the scheme. The competency of the proposed solver is authenticated by using different performances in the sense of MSE, regression and error histogram.
An optimal solution for classifying Arabic fake news via efficient deep learning algorithms
Authors: alhussayni khaldoon H. , Dawood Amina Atiya , Hanon Wial 
Published: September 2024

Abstract

Misinformation becomes publicly available on social media and websites. Some journalists write fake news in different languages to target different societies. This study aims to develop an optimized algorithm for detecting and classifying fake news in the Arabic language. The proposed work investigates different deep learning techniques to identify effective and efficient algorithms that can handle fake news problems. The utilized techniques involve individual algorithms such as convolutional neural networks (CNNs), gate recurrent neural networks (GRUs), and bidirectional long short-term memory (BiLSTM). This work also considers hybrid algorithms such as CNNs with BiLSTM (CNN-BiLSTM) and CNNs with BiGRU (CNN-BiGRU). This research focused on Arabic news, motivated by the limited works interested in the Arabic language because of its linguistic difficulty. English has been widely investigated due to its simplicity. The proposed model used the Arabic Fake News Dataset (AFND) as an input channel for training and testing purposes. The model was trained to discriminate fake from real news. It achieved premium results with accuracies of 88.63%, 89.7%, 91.73%, and 92% for BiLSTM, BiGRU, CNN-BiLSTM, and CNN-BiGRU, respectively. The significant value achieved by CNN-BiGRU algorithms refers to the power of CNNs in feature extraction, the efficiency of the GRU structure and its simplicity in mitigating the vanishing gradient problem. This research aims to enhance model accuracy for unseen data in Arabic fake news detection.
The Integrated Stress Response Engages a Cell-Autonomous, Ligand-independent, DR5-driven Apoptosis Switch
Authors: Acosta-Alvear Diego , Zappa Francesca , Muniozguren Nerea Lopez , Conrad Julia 
Published: September 2024

Abstract

The integrated stress response (ISR) is a central signaling network that leverages the cell’s biosynthetic capacity against different stresses to restore homeostasis. However, when homeostasis is unattainable, the ISR switches to drive cell death and eliminate irreparably damaged cells. During persistent endoplasmic reticulum (ER) stress, prolonged activity of the ISR kinase PERK induces apoptosis downstream of death receptor 5 (DR5). ER stress provides activating signals that engage the ectodomain (ED) of DR5 to drive its unconventional activation in the Golgi apparatus. Here, using chemical genetics to uncouple stress sensing from ISR activation, we found that DR5 signaling from the Golgi apparatus is integral to the ISR and not specific to ER stress. Furthermore, we show that DR5 activation can be driven solely by increased expression and does not require its ED. These findings indicate that a general ISR kill switch eliminates irreversibly injured cells.
Risks Associated with the Dissemination of Misleading and Deceptive Information on Social Networking Sites and the Requirement for Prevention Strategies
Authors: Kasi BASKAR , Govindaraju Sakthi , Kamalakkannan Kasi , Ramalingam saravanan 
Published: September 2024

Abstract

In today's digital era, the majority of social networking site users prefer communication through oral or video means. Unfortunately, this trend has led to the spread of inaccurate information for two primary reasons. Firstly, individuals knowingly or unknowingly make false claims and influence others to do the same, creating an illusion of accuracy and leading to poor decision-making. Secondly, information related to minors under the age of 13 is being disseminated through websites and social networking platforms. This poses a challenge for young individuals to make informed choices as many people tend to believe and act upon such information. While social networking sites offer various benefits, they also come with inherent risks. To address these issues, we propose a preventive measure using a pre-translated framework system called Deep Support Vector Machine (DSVM). This innovative approach combines a Support Vector Machine (SVM) with a Deep Neural Network (DNN) to effectively reduce these hazards. We emphasize the importance of promoting positive and accurate information about kids, while strictly disallowing the spread of any false or misleading content about minors. In our paper, we thoroughly contrast and evaluate the effectiveness of our suggested DSVM method with the well-established preventive techniques of SVM and LSTM. By doing so, we aim to devise a more robust and reliable system to ensure a safer and more responsible use of social networking sites, particularly when it comes to sharing information about minors.
WITHDRAWN: Microaneurysm Detection in fundus images using Convolutional with Lite Transformer Network
Authors: A Jayachandran 
Published: September 2024

Abstract

Diabetic Retinopathy (DR) is a complication of diabetes that can cause vision impairment and may lead to permanent blindness in working-age people. The motivation behind this is the significant increase in the number of diabetic patients compared to the limited number of ophthalmologists available. Hence, there is a crucial need for an automated screening tool to promptly detect and diagnose DR in patients. Microaneurysms (MAs) are the initial stage of DR and are crucial for computerized diagnosis. However, automatically detecting microaneurysms in fundus images is challenging due to complex backgrounds and varying illumination conditions. In recent years, convolutional neural networks (CNNs) have experienced significant advancements within the domain of medical imaging. Notably, their efficacy has been pronounced through the application of the u-shaped architecture, which has exhibited exceptional effectiveness in diverse image segmentation tasks. However, these advantages notwithstanding, convolutional operations reveal limitations in capturing extensive global and long-range semantic interactions. Therefore, this manuscript introduces the CRUnet, a converter resembling the architecture of Unet, specifically designed for the task of MAs detection in retinal images. To reduce the computational cost, a lightweight transformer block is designed as the bottleneck layer, this model processes the feature maps derived from the CNN as input sequences, facilitating the extraction of the global context. Simultaneously, the global context information is integrated with the CNN feature map to attain precise localization of features, and a refined block is designed to adjust the deep features. Our assertion is that the Transformer architecture can serve as a robust encoder for medical image segmentation tasks. Its integration with convolutional networks holds the potential to enhance segmentation performance, particularly by facilitating the restoration of local spatial information. The proposed model achieving an average accuracy of 99.13%. In this case, the respective accuracy for the three different data sets, DRIVE, ROC, and e-ophtha, is 99.67%, 98.16%, and 99.76%.
Ex Vivo Machine Perfusion as a Platform for Lentiviral Gene Delivery in Rat Livers
Authors: Uygun Korkut , von Reiterdank Irina Filz , Mojoudi Mohammadreza , Bento Raphaela , Taggart McLean , Dinicu Antonia , Wojtkiewicz Gregory , Coert J. , Molen Aebele Mink van der , Weissleder Ralph , Parekkadan Biju 
Published: September 2024

Abstract

Developing new strategies for local monitoring and delivery of immunosuppression is critical to making allografts safer and more accessible. Ex vivo genetic modification of grafts using machine perfusion presents a promising approach to improve graft function and modulate immune responses while minimizing risks of off-target effects and systemic immunogenicity in vivo. This proof-of-concept study demonstrates the feasibility of using normothermic machine perfusion (NMP) to mimic in vitro conditions for effective gene delivery. In this study, lentiviral vectors carrying biosensor constructs with Gaussia Luciferase (GLuc) were introduced to rodent livers during a 72-hour perfusion period, with a targeted delivery of 3 x 107 infection units (IU). Following the initial 24-hour exposure required for viral transduction, an additional 48 hours was necessary to observe gene expression, analogous to in vitro benchmarks. The perfused livers displayed significantly increased luminescence compared to controls, illustrating successful genetic modification. These findings validate the ex vivo use of lentiviral particles in a rodent liver model and lay the groundwork for a broad range of applications through genetic manipulation of organ systems. Future studies will focus on refining this technology to enhance precision in gene expression and explore its implications for clinical transplantation.
Fine-mapping of the brittle rachis locus Qbr.sau-2DL in Tibetan semi-wild wheat (Triticum aestivum ssp. tibetanum Shao)
Authors: Chen Qing , Zhao Kan , Gao Ting , Li Yang , Li Qingcheng , Zhou Linlin , Zhang Ziyi , Peng Shijing , Tang Jie , Zhang Mi , Zhu Jing , Liu Caihong , Lei Lu , Guo Zhenru , Kong Li , Jiang Yunfeng , Chen Guoyue , Ning Shunzong , Jiang Qiantao , Zhang Yazhou , Xu Qiang , Wei Yuming , Ma Jian , Zheng Youliang , (Qi) 鹏飞 (Peng-Fei) 祁 
Published: September 2024

Abstract

The brittle rachis is the most important trait in wheat domestication and de-domestication. Tibetan semi-wild wheat (Triticum aestivum ssp. tibetanum Shao) is a unique common wheat subspecies that originated through de-domestication. Qbr.sau-2DL on chromosome 2DL is one of the three main brittle rachis loci in the Tibetan semi-wild wheat accession “Q1028.” However, the gene responsible for the brittle rachis trait at the Qbr.sau-2DL locus remains unknown. In this study, “QZ110,” a monogenic line for Qbr.sau-2DL isolated from a recombinant inbred line population derived from hybridization between “Q1028” and the common wheat cultivar “ZM9023,” was crossed with the common wheat cultivar “SM482” to construct F2, F2:3, and F3 populations. Genetic analyses indicated that Qbr.sau-2DL was recessive. Using Bulked Segregant Exome Capture Sequencing data and Kompetitive Allele Specific PCR markers, Qbr.sau-2DL was located within an 836.4-kb interval containing 12 high-confidence predicted genes. One of them, TreasCS2D02G502900, was a homolog of OsLG1, which affects rice shattering. The transcript level of TreasCS2D02G502900 in the disarticulation layer was significantly higher in “QZ110” than in “SM482,” consistent with the transcript profile of OsLG1 in rice. Therefore, TreasCS2D02G502900 was identified as the candidate gene for Qbr.sau-2DL. Microscopic observations indicated that Qbr.sau-2DL reduces cell wall thickness and the lignin content in the disarticulation layer. These results provide a basis for clarifying the function and underlying molecular mechanisms of Qbr.sau-2DL in the brittle rachis trait.
Green Synthesis, Characterization, and Antimicrobial Activity of Silver Nanoparticles from Water-Soluble Fractions of Brazilian Kefir
Authors: Bernardes Lucas Matos Martins , Malta Serena Mares , Santos Ana Carolina Costa , Silva Rafael Alves da , Rodrigues Tamiris Sabrina , Silva Murillo Néia Thomaz da , Mendes-Silva Ana Paula , Ueira-Vieira Carlos 
Published: September 2024

Abstract

Background: Nanotechnology offers innovative approaches to combat drug-resistant diseases. Silver nanoparticles (AgNPs) have emerged as potent antimicrobial agents in vitro and in vivo. Green synthesis methods, which leverage the bioactive components of the water-soluble fractions of Brazilian kefir (whole water-soluble fraction and smaller than 10 kDa fraction), provide sustainable alternatives to conventional nanoparticles production. However, despite the documented therapeutic benefits of kefir, its potential in nanomedicine remains underexplored.
Results: The successful synthesis of silver nanoparticles using water-soluble fractions of kefir was confirmed by UV-Visible spectroscopy and Fourier-transform infrared analyses. The hydrodynamic radius of nanoparticles derived from the entire water-soluble fraction was 1300 nm, while those from the smaller than 10 kDa fraction displayed a radius of 400 nm. All synthesized AgNPs exhibited a zeta potential of -30 mV. The disk diffusion method demonstrated the antimicrobial efficacy of our AgNPs against a range of multidrug-resistant bacteria and Candida fungi (p<0.0001), with no observed toxicity on Drosophila melanogasteron a long-term treatment.
Conclusion:This study highlights the potential of these AgNPs as effective antimicrobial agents, particularly against drug-resistant pathogens. Future research is needed to evaluate the minimum inhibitory concentrations of our AgNPs and enhance specificity through conjugation with other compounds. Additionally, further investigations into electron microscopy analysis and various applications, such as disinfectant solutions, wound healing, and antibiotic production, will advance the utilization of kefir-derived AgNPs in healthcare.
Accelerating Charge Estimation in Molecular Dynamics Simulations Using Physics-Informed Neural Networks: Corrosion Applications
Authors: Zapiain David Montes de Oca , Venkatraman Aditya , Wilson Mark 
Published: September 2024

Abstract

Molecular Dynamics (MD) simulations can be utilized to develop a fundamental understanding of the effect that corrosion has on metallic materials in the presence of a salt brine. The use of reactive force fields in classical MD offers the capability to accurately model the formation and breakage of bonds within the aqueous medium and at the interface between the metal and the electrolyte. Additionally, MD protocols can facilitate dynamic partial charge equilibration. Nevertheless, despite their capabilities, MD simulations are unsuitable for modeling the long time scales characteristic of corrosive phenomena given their computationally intensive nature. Hence, there is a critical need for a protocol to decrease the computational expense associated with variable-charge molecular dynamics simulations. This work addresses this concern by developing reduced-order machine learning models to provide accurate and computationally efficient predictions of the charge density in corrosive environments. Specifically, Long Short Term Memory (LSTM) networks are used to assess and forecast the charge density evolution as a function of the atomic environment, represented using Smooth Overlap of Atomic Positions (SOAP) descriptors. Furthermore, this work utilizes a physics-informed loss function to enforce charge neutrality and the electronegativity equivalence. Lastly, even though the protocols are developed in the context of corrosion, they have been formulated in a phenomena agnostic manner, facilitating their utilization for a wide-class of variable-charge interatomic potentials and related applications.
Global analysis of countries’ exposure to climate driven simultaneous staple crop failures
Authors: Murgatroyd Anna , Verschuur Jasper , Davis Kyle , Tuninetti Marta , Hall Jim 
Published: September 2024

Abstract

The extent to which import-reliant countries may be vulnerable to co-occurring – and geographically distal – climate disruptions in crop production remains poorly understood. By analysing weather variability and covariation in staple food crop production across 181 countries, we identify the countries most exposed to simultaneous crop failures across their import partners and from their domestic production. Results indicate that over half of countries importing maize, wheat or rice are identified as at risk of their top three suppliers experiencing simultaneous crop failures.
Performance-based Seismic Assessments of Steel Fibre Reinforced Concrete Columns
Authors: Han Daguang , Ying Chunli , Chen Liyuan , Wu Xuguang , Lin Huizhong 
Published: September 2024

Abstract

This study presents a comprehensive investigation into the seismic performance of steel fibre reinforced concrete (SFRC) columns, aiming to assess their ductility and energy dissipation capacity. A large-scale database consisting of 116 individual tests of flexure-dominated SFRC and plain concrete (PC) columns subjected to cyclic lateral loading is utilized. The research begins by conducting parametric sensitivity analyses and empirical calibration of hysteretic parameters, crucial for accurately modelling the seismic response of reinforced concrete columns. These analyses are performed in accordance with a nonlinear static procedure. Additionally, fragility analyses of both reinforced PC and SFRC columns are conducted, based on their respective lateral drift ratios, with a specific drift ratio defined as the damage limit state. The findings of this work reveal intriguing insights. It is found that the axial load ratio has a comparable effect on the plastic rotations at the yield and ultimate points of PC and SFRC columns. However, it exhibits an entirely opposite effect on the rotation corresponding to the peak lateral load, especially for those with over 0.75% of fibres by volume. This discrepancy suggests that the current seismic design standards for PC columns are not directly applicable to SFRC columns. Furthermore, it is observed that the benefits of steel fibres in SFRC columns diminish at high lateral drift ratios or when flexural cracks are larger. Under these conditions, the effectiveness of fibres reduces, resulting in similar failure probabilities for both PC and SFRC columns.
Preparation and characterization of CaO-Al2O3-SiO2 glass-ceramic microspheres from coal-based solid waste
Authors: Liu Runze , Guo Yi , Li Haiqing , Chang Zhu , Lu Hao , Wang shuming 
Published: September 2024

Abstract

To alleviate the environmental pollution caused by the stockpile of coal-based solid waste, glass-ceramic microspheres prepared by a simple and low-cost wet process with coal-based solid waste, and the effect of different solid waste content on the network structure and properties were investigated by X-ray diffraction(XRD), Fourier infrared spectroscopy(FTIR), Scanning electron microscopy(SEM) and Universal electronic testing machine. The results show that, with the diversification of coal-based solid waste content, the amount of anorthite crystalline, the glass network structure, and the main structure of glass Q3 changed also. Accordingly, significant changes have occurred in the properties of the crushing strength. Optimizing the raw material ratio, a coal-based solid waste glass-ceramic microsphere with unique network structure is well developed and the crushing strength reaches the maximum value of 221.23N. The findings contribute significantly to improving the properties of matrix materials and the utilization of coal-based solid waste resources.
Beam steering for single antenna element using different optical lens
Authors: Mohamed Aya W. , Mahmoud Korany R. , Montaser Ahmed M. 
Published: September 2024

Abstract

This paper presents a beam steering technique for a single antenna element with optical lenses. The nanoantenna operates at a wavelength of 1550 nm. The plasmonic nanoantenna consists of silicon, silicon dioxide, and silver. It is fed with light from below. The light inside the plasmonic nanoantenna radiates vertically from bottom to top, and we obtain a gain of 8.46 dBi. When we put the lens on the antenna, the efficiency of the antenna increases according to the design of the antenna, the lens, and the material the lens is made of. In this article, we will choose two types of lenses, the hemispherical dielectric lens and the flat lens, which consists of 17 layers of different materials. The lenses are moved above the nanoantenna to obtain the best gain and efficiency. The lenses move right and left in the X direction, and thus we get the beam steering at different angles. In general, using any lens improves the efficiency of a nanoantenna, and different lenses provide different levels of gain and efficiency.