bluuebunny/crossref_metadata_2025_split huggingface dataset:
Physics-informed neural network simulation of thermal cavity flow
Authors: Fowler Eric , McDevitt Christopher J. , Roy Subrata 
Published: July 2024

AbstractPhysics-informed neural networks (PINNs) are an emerging technology that can be used both in place of and in conjunction with conventional simulation methods. In this paper, we used PINNs to perform a forward simulation without leveraging known data. Our simulation was of a 2D natural convection-driven cavity using the vorticity-stream function formulation of the Navier-Stokes equations. We used both 2D simulations across thexandzdomains at constant Rayleigh (Ra) numbers and 3D simulations across thex,zandRadomains. The 3D simulation was tested for a PINN’s ability to learn solutions in a higher-dimensional space than standard simulations. The results were validated against published solutions atRavalues of 10$$^{3}$$3, 10$$^{4}$$4, 10$$^{5}$$5, and 10$$^{6}$$6. Both the 2D simulations and 3D simulations successfully matched the expected results. For the 2D cases, more training iterations were needed for the model to converge at higherRavalues (10$$^5$$5and 10$$^6$$6) than at lowerRa(10$$^3$$3and 10$$^4$$4) indicating increased nonlinear fluid-thermal coupling. The 3D case was also able to converge but, but it required more training than any of the 2D cases due to the curse of dimensionality. These results showed the validity of standard simulations via PINNs and the feasibility of higher-order parameter space solutions that are not possible using conventional methods. They also showcased the additional computational demand associated with increasing the dimensionality of the learned parameter space.
RETRACTED ARTICLE: Confidence level based complex polytopic fuzzy Einstein aggregation operators and their application to decision-making process
Authors: Rahman Khaista , Khishe Mohammad 
Published: July 2024

Abstract
A complex Polytopic fuzzy set (CPoFS) extends a Polytopic fuzzy set (PoFS) by handling vagueness with degrees that range from real numbers to complex numbers within the unit disc. This extension allows for a more nuanced representation of uncertainty. In this research, we develop Complex Polytopic Fuzzy Sets (CPoFS) and establish basic operational laws of CPoFS. Leveraging these laws, we introduce new operators under a confidence level, including the confidence complex Polytopic fuzzy Einstein weighted geometric aggregation (CCPoFEWGA) operator, the confidence complex Polytopic fuzzy Einstein ordered weighted geometric aggregation (CCPoFEOWGA) operator, the confidence complex Polytopic fuzzy Einstein hybrid geometric aggregation (CCPoFEHGA) operator, the induced confidence complex Polytopic fuzzy Einstein ordered weighted geometric aggregation (I-CCPoFEOWGA) operator and the induced confidence complex Polytopic fuzzy Einstein hybrid geometric aggregation (I-CCPoFEHGA) operator, enhancing decision-making precision in uncertain environments. We also investigate key properties of these operators, including monotonicity, boundedness, and idempotency. With these operators, we create an algorithm designed to solve multiattribute decision-making problems in a Polytopic fuzzy environment. To demonstrate the effectiveness of our proposed method, we apply it to a numerical example and compare its flexibility with existing methods. This comparison will underscore the advantages and enhancements of our approach, showing its efficiency in managing complex decision-making scenarios. Through this, we aim to demonstrate how our method provides superior performance and adaptability across different situations.
Temporally extended successor feature neural episodic control
Authors: Zhu Xianchao 
Published: July 2024

AbstractOne of the long-term goals of reinforcement learning is to build intelligent agents capable of rapidly learning and flexibly transferring skills, similar to humans and animals. In this paper, we introduce an episodic control framework based on the temporal expansion of subsequent features to achieve these goals, which we refer to as Temporally Extended Successor Feature Neural Episodic Control (TESFNEC). This method has shown impressive results in significantly improving sample efficiency and elegantly reusing previously learned strategies. Crucially, this model enhances agent training by incorporating episodic memory, significantly reducing the number of iterations required to learn the optimal policy. Furthermore, we adopt the temporal expansion of successor features a technique to capture the expected state transition dynamics of actions. This form of temporal abstraction does not entail learning a top-down hierarchy of task structures but focuses on the bottom-up combination of actions and action repetitions. Thus, our approach directly considers the temporal scope of sequences of temporally extended actions without requiring predefined or domain-specific options. Experimental results in the two-dimensional object collection environment demonstrate that the method proposed in this paper optimizes learning policies faster than baseline reinforcement learning approaches, leading to higher average returns.
Identification of key biomarkers for early warning of diabetic retinopathy using BP neural network algorithm and hierarchical clustering analysis
Authors: Li Peiyu , Wang Hui , Tian Guo , Fan Zhihui 
Published: July 2024

AbstractDiabetic retinopathy is one of the most common microangiopathy in diabetes, essentially caused by abnormal blood glucose metabolism resulting from insufficient insulin secretion or reduced insulin activity. Epidemiological survey results show that about one third of diabetes patients have signs of diabetic retinopathy, and another third may suffer from serious retinopathy that threatens vision. However, the pathogenesis of diabetic retinopathy is still unclear, and there is no systematic method to detect the onset of the disease and effectively predict its occurrence. In this study, we used medical detection data from diabetic retinopathy patients to determine key biomarkers that induce disease onset through back propagation neural network algorithm and hierarchical clustering analysis, ultimately obtaining early warning signals of the disease. The key markers that induce diabetic retinopathy have been detected, which can also be used to explore the induction mechanism of disease occurrence and deliver strong warning signal before disease occurrence. We found that multiple clinical indicators that form key markers, such as glycated hemoglobin, serum uric acid, alanine aminotransferase are closely related to the occurrence of the disease. They respectively induced disease from the aspects of the individual lipid metabolism, cell oxidation reduction, bone metabolism and bone resorption and cell function of blood coagulation. The key markers that induce diabetic retinopathy complications do not act independently, but form a complete module to coordinate and work together before the onset of the disease, and transmit a strong warning signal. The key markers detected by this algorithm are more sensitive and effective in the early warning of disease. Hence, a new method related to key markers is proposed for the study of diabetic microvascular lesions. In clinical prediction and diagnosis, doctors can use key markers to give early warning of individual diseases and make early intervention.
A retrospective cross-sectional study of risk factors for communicable disease diagnoses among refugees in mainland Greek camps, 2016–2017
Authors: Scales Sarah Elizabeth , Park Jee Won , Nixon Rebecca , Guha-Sapir Debarati , Horney Jennifer A. 
Published: July 2024

AbstractCommunicable disease risk is high in refugee camps and reception centers. To better understand the risks for communicable disease diagnoses among refugees and asylum seekers, this study assesses individual- and camp-level risk factors among individuals utilizing Médecins du Monde clinics in four large refugee camps—Elliniko, Malakasa, Koutsochero, and Raidestos—on mainland Greece between July 2016 and May 2017. Descriptive statistics are reported for the demographic characteristics of the study population and for communicable disease burdens within the four camps—Elliniko, Malakasa, Raidestos, and Koutsochero. A hierarchical generalized linear model was used to assess risk factors for communicable disease diagnoses while accounting for individual-level clustering. This study shows marginal patterns in risk factors for communicable disease. Males had marginally higher risk of communicable disease diagnosis than females (OR = 1.12; 95% CI 0.97—1.29), and increased age was more protective against communicable disease for females (OR = 0.957; 95% CI 0.953—0.961) than for males (OR = 0.963; 95% CI 0.959—0.967). Communicable disease risk was significantly different between camps, with Elliniko (OR = 1.58; 95% CI 1.40–1.79) and Malakasa (OR = 1.43; 95% CI 1.25–1.63) having higher odds of communicable disease than Raidestos. The demographic and epidemiologic profiles of displaced populations differ across settings, and epidemiologic baselines for displaced populations are fundamental to evidence-informed provision of humanitarian aid. Further, while influences and risks for negative health outcomes in complex emergencies are broadly, the causal mechanisms that underpin these relationships are not as well understood. Both practitioners and researchers should engage with further research to elucidate the mechanisms through which these risks operate among displaced populations, including multilevel analyses.
SIGNIFICANCE deep learning based platform to fight illicit trafficking of Cultural Heritage goods
Authors: Malinverni Eva Savina , Abate Dante , Agapiou Antonia , Stefano Francesco Di , Felicetti Andrea , Paolanti Marina , Pierdicca Roberto , Zingaretti Primo 
Published: July 2024

AbstractThe illicit traffic of cultural goods remains a persistent global challenge, despite the proliferation of comprehensive legislative frameworks developed to address and prevent cultural property crimes. Online platforms, especially social media and e-commerce, have facilitated illegal trade and pose significant challenges for law enforcement agencies. To address this issue, the European project SIGNIFICANCE was born, with the aim of combating illicit traffic of Cultural Heritage (CH) goods. This paper presents the outcomes of the project, introducing a user-friendly platform that employs Artificial Intelligence (AI) and Deep learning (DL) to prevent and combat illicit activities. The platform enables authorities to identify, track, and block illegal activities in the online domain, thereby aiding successful prosecutions of criminal networks. Moreover, it incorporates an ontology-based approach, providing comprehensive information on the cultural significance, provenance, and legal status of identified artefacts. This enables users to access valuable contextual information during the scraping and classification phases, facilitating informed decision-making and targeted actions. To accomplish these objectives, computationally intensive tasks are executed on the HPC CyClone infrastructure, optimizing computing resources, time, and cost efficiency. Notably, the infrastructure supports algorithm modelling and training, as well as web, dark web and social media scraping and data classification. Preliminary results indicate a 10–15% increase in the identification of illicit artifacts, demonstrating the platform’s effectiveness in enhancing law enforcement capabilities.
The predictive value of GLIM criteria on clinical outcomes and responses to nutritional support in patients with neurocritical illnesses
Authors: Liu Peiqi , Tian Huimin , Gao Lan , Zhong Tangsheng , Wang Yujiao , Chen Li 
Published: July 2024

AbstractNeurocritically ill patients frequently exhibit coma, gastroparesis, and intense catabolism, leading to an increased risk of malnutrition. The Global Leadership Initiative on Malnutrition (GLIM) criteria for the diagnosis of malnutrition was created to achieve a consistent malnutrition diagnosis across diverse populations. This study aimed to validate the concurrent and predictive validity of GLIM criteria in patients with neurocritical illnesses. A total of 135 participants were followed from admission to the neurocritical unit (NCU) until discharge. Comparing GLIM criteria to the Subjective Global Assessment (SGA), sensitivity was 0.95 and specificity was 0.69. Predictive validity of GLIM criteria was assessed using a composite adverse clinical outcome, comprising mortality and various major complications. Adjusted hazard ratios for moderate and severe malnutrition were 2.86 (95% CI 1.45–5.67) and 3.88 (95% CI 1.51–9.94), respectively. Changes in indicators of nutritional status, including skeletal muscle mass and abdominal fat mass, within 7 days of admission were obtained for 61 participants to validate the predictive capability of the GLIM criteria for the patients’ response of standardized nutritional support. The GLIM criteria have a statistically significant predictive validity on changes in rectus femoris muscle thickness and midarm muscle circumference. In conclusion, the GLIM criteria demonstrate high sensitivity for diagnosing malnutrition in neurocritically ill patients and exhibit good predictive validity.
Nickel ferrite decorated noble metal containing nitrogen-doped carbon nanotubes as potential magnetic separable catalyst for dinitrotoluene hydrogenation
Authors: Hajdu Viktória , Sikora Emőke , Muránszky Gábor , Kristály Ferenc , Kaleta Zoltán , Nagy Miklós , Viskolcz Béla , Fiser Béla , Vanyorek László 
Published: July 2024

AbstractThe 2,4-toluenediamine (TDA) is one of the most important chemicals in the polyurethane industry, produced by the catalytic hydrogenation of 2,4-dinitrotoluene (DNT). The development of novel catalysts that can be easily recovered from the reaction mixture is of paramount importance. In our work, a NiFe2O4/N-BCNT supported magnetic catalyst was prepared by a modified coprecipitation method. The catalyst support alone also showed activity in the synthesis of TDA. Platinum nanoparticles were deposited on the catalyst support surface by a fast, relatively simple, and efficient sonochemical method, resulting in a readily applicable catalytically active system. The prepared catalyst exhibited high activity in hydrogenation tests, which was proved by the exceptionally high DNT conversion (100% for 120 min at 333 K) and TDA yield (99%). Furthermore, the magnetic catalyst can be easily recovered from the reaction medium by the action of an external magnetic field, which can greatly reduce catalyst loss during separation.
Employing a magnetic chitosan/molybdenum disulfide nanocomposite for efficiently removing polycyclic aromatic hydrocarbons from milk samples
Authors: Rezagholizade-shirvan Alieh , Mohammadi Mansoureh , Mazaheri Yeganeh , Fallahizadeh Saeid , Ghorbani Haniyeh , Shokri Samira , Shariatifar Nabi , Darroudi Majid , Shamloo Ehsan 
Published: July 2024

AbstractThis study aimed to develop a highly efficient nanocomposite composed of magnetic chitosan/molybdenum disulfide (CS/MoS2/Fe3O4) for the removal of three polycyclic aromatic hydrocarbons (PAHs)—pyrene, anthracene, and phenanthrene. Novelty was introduced through the innovative synthesis procedure and the utilization of magnetic properties for enhanced adsorption capabilities. Additionally, the greenness of chitosan as a sorbent component was emphasized, highlighting its biodegradability and low environmental impact compared to traditional sorbents. Factors influencing PAH adsorption, such as nanocomposite dosage, initial PAH concentration, pH, and contact time, were systematically investigated and optimized. The results revealed that optimal removal efficiencies were attained at an initial PAH concentration of 150 mg/L, a sorbent dose of 0.045 g, pH 6.0, and a contact time of 150 min. The pseudo-second-order kinetic model exhibited superior fitting to the experimental data, indicating an equilibrium time of approximately 150 min. Moreover, the equilibrium adsorption process followed the Freundlich isotherm model, with kf and n values exceeding 7.91 mg/g and 1.20, respectively. Remarkably, the maximum absorption capacities for phenanthrene, anthracene, and pyrene on the sorbent were determined as 217 mg/g, 204 mg/g, and 222 mg/g, respectively. These findings underscore the significant potential of the CS/MoS2/Fe3O4 nanocomposite for efficiently removing PAHs from milk and other dairy products, thereby contributing to improved food safety and public health.
Power spectral density-based resting-state EEG classification of first-episode psychosis
Authors: Redwan Sadi Md. , Uddin Md Palash , Ulhaq Anwaar , Sharif Muhammad Imran , Krishnamoorthy Govind 
Published: July 2024

AbstractHistorically, the analysis of stimulus-dependent time–frequency patterns has been the cornerstone of most electroencephalography (EEG) studies. The abnormal oscillations in high-frequency waves associated with psychotic disorders during sensory and cognitive tasks have been studied many times. However, any significant dissimilarity in the resting-state low-frequency bands is yet to be established. Spectral analysis of the alpha and delta band waves shows the effectiveness of stimulus-independent EEG in identifying the abnormal activity patterns of pathological brains. A generalized model incorporating multiple frequency bands should be more efficient in associating potential EEG biomarkers with first-episode psychosis (FEP), leading to an accurate diagnosis. We explore multiple machine-learning methods, including random-forest, support vector machine, and Gaussian process classifier (GPC), to demonstrate the practicality of resting-state power spectral density (PSD) to distinguish patients of FEP from healthy controls. A comprehensive discussion of our preprocessing methods for PSD analysis and a detailed comparison of different models are included in this paper. The GPC model outperforms the other models with a specificity of 95.78% to show that PSD can be used as an effective feature extraction technique for analyzing and classifying resting-state EEG signals of psychiatric disorders.
Evaluation of the effects of temperature and centrifugation time on elimination of uncured resin from 3D-printed dental aligners
Authors: Kim Ji-Eun , Mangal Utkarsh , Yu Jae-Hun , Kim Gi-Tae , Kim Hoon , Seo Ji-Young , Cha Jung-Yul , Lee Kee-Joon , Kwon Jae-Sung , Choi Sung-Hwan 
Published: July 2024

AbstractThe study investigated the effects of temperature and centrifugation time on the efficacy of removing uncured resin from 3D-printed clear aligners. Using a photo-polymerizable polyurethane resin (Tera Harz TC-85, Graphy Inc., Seoul, Korea), aligners were printed and subjected to cleaning processes using isopropyl alcohol (IPA) or centrifugation (g-force 27.95g) at room temperature (RT, 23 °C) and high temperature (HT, 55 °C) for 2, 4, and 6 min. The control group received no treatment (NT). Cleaning efficiency was assessed through rheological analysis, weight measurement, transparency evaluation, SEM imaging, 3D geometry evaluation, stress relaxation, and cell viability tests. Results showed increased temperature and longer centrifugation times significantly reduced aligner viscosity, weight (P < 0.05), and transmittance. IPA-cleaned aligners exhibited significantly lower transparency and rougher surfaces in SEM images. All groups met ISO biocompatibility standards in cytotoxicity tests. The NT group had higher root mean square (RMS) values, indicating greater deviation from the original design. Stress relaxation tests revealed over 95% recovery in all groups after 60 min. The findings suggest that a 2-min HT centrifugation process effectively removes uncured resin without significantly impacting the aligners’ physical and optical properties, making it a clinically viable option.
Effect of true triaxial principal stress unloading rate on strain energy density of sandstone
Authors: Liu Zhixi , Zhao Guangming , Meng Xiangrui , Gu Qingheng 
Published: July 2024

AbstractDeep rock are often in a true triaxial stress state. Studying the impacts of varying unloading speeds on their strain energy (SE) density is highly significant for predicting rock stability. Through true triaxial unloading principal stress experiments and true triaxial stress equilibrium unloading experiments on sandstone, this paper proposes a method to compute the SE density in a true triaxial compressive unloading principal stress test. This method aims to analyze the SE variation in rocks under the action of true triaxial unloading principal stresses. Acoustic emission is used to verify the correctness of the SE density calculation method in this paper. This study found that: (1) Unloading in one principal stress direction causes the SE density to rise in the other principal stress directions. This rise in SE, depending on its reversibility, can be categorized into elastic and dissipated SE. (2)When unloading principal stresses, the released elastic SE density in the unloading direction is influence by the stress path and rate. (3) The higher the unloading speed will leads to greater increases in the input SE density, elastic SE density, and dissipative SE density in the other principal stress directions. (4) The dissipated SE generated under true triaxial compression by unloading the principal stress is positively correlated with the damage to the rock; with an increase in unloading rate, there is a corresponding increase in the formation of cracks after unloading. (5) Utilizing the stress balance unloading test, we propose a calculation method for SE density in true triaxial unloading principal stress tests.
Fluorescence lifetime multiplexing with fluorogen activating protein FAST variants
Authors: Bogdanova Yulia A. , Solovyev Ilya D. , Baleeva Nadezhda S. , Myasnyanko Ivan N. , Gorshkova Anastasia A. , Gorbachev Dmitriy A. , Gilvanov Aidar R. , Goncharuk Sergey A. , Goncharuk Marina V. , Mineev Konstantin S. , Arseniev Alexander S. , Bogdanov Alexey M. , Savitsky Alexander P. , Baranov Mikhail S. 
Published: July 2024

AbstractIn this paper, we propose a fluorescence-lifetime imaging microscopy (FLIM) multiplexing system based on the fluorogen-activating protein FAST. This genetically encoded fluorescent labeling platform employs FAST mutants that activate the same fluorogen but provide different fluorescence lifetimes for each specific protein-dye pair. All the proposed probes with varying lifetimes possess nearly identical and the smallest-in-class size, along with quite similar steady-state optical properties. In live mammalian cells, we target these chemogenetic tags to two intracellular structures simultaneously, where their fluorescence signals are clearly distinguished by FLIM. Due to the unique structure of certain fluorogens under study, their complexes with FAST mutants display a monophasic fluorescence decay, which may facilitate enhanced multiplexing efficiency by reducing signal cross-talks and providing optimal prerequisites for signal separation upon co-localized and/or spatially overlapped labeling.