bluuebunny/crossref_metadata_2025 huggingface dataset:
A variable bidentate traceless directing group for nickel-catalyzed regioselective B–H poly-chalcogenation of o-carboranes
Authors: Li Ping , Qiu Zihuan , Lu Jian , Lu Ju-You
Published: January 2025

A nickel-catalyzed direct cage B–H poly-chalcogenation of icosahedral carborane clusters, enabled by a variable bidentate 8-aminoquinoline traceless directing group, has been developed under oxidant-free and weakly basic conditions.
Structural and luminescent properties of a Cr3+/Sm3+ doped GdAlO3 orthorhombic perovskite for solid-state lighting applications
Authors: Elhamdi I. , Souissi H. , Kammoun S. , Dhahri E. , Pina J. , Costa B. F. O. , Brito A. L. B. , Fausto R. , López-Lago E.
Published: January 2025

The Cr3+ and Sm3+ doped GdAlO3 perovskite with formula Gd0.995Sm0.005Al0.995Cr0.005O3, was synthesized via a solid-state reaction method, and its structure, morphology, and photoluminescence properties were thoroughly investigated.
Correction: Three-dimensional TiNb2O7 anchored on carbon nanofiber core–shell arrays as an anode for high-rate lithium ion storage
Authors: Qi Meili , Chao Dongliang , Sun Weifeng , Yin Jinghua , Chen Minghua
Published: January 2025

Correction for ‘Three-dimensional TiNb2O7 anchored on carbon nanofiber core–shell arrays as an anode for high-rate lithium ion storage’ by Meili Qi et al., RSC Adv., 2020, 10, 6342–6350, https://doi.org/10.1039/C9RA10485B.
Connecting the complexity of stereoselective synthesis to the evolution of predictive tools
Authors: Li Jiajing , Reid Jolene P.
Published: January 2025

This review provides an overview of predictive tools in asymmetric synthesis. The evolution of methods from simple qualitative pictures to complicated quantitative approaches is connected with the increased complexity of stereoselective synthesis.
Chiral perovskites with a unique 1D chain structure: impact of chiral ligand geometry on local inversion asymmetry and chiral-induced spin selectivity
Authors: Li Hongxu , Cao Rui , Tao Min , Jiang Jiawei , Xiao Yin
Published: January 2025

The chiral molecular structure and local inversion asymmetry of inorganic octahedrons, beyond global chirality, significantly influence the spin-related properties of chiral perovskites.
Triazine derivatives as metal-free electrocatalysts: do three nitrogen atoms mimic a metal?
Authors: Lebedeva Olga , Kultin Dmitry , Zakharov Valery , Kuznetsova Irina , Aslanov Leonid , Kustov Leonid
Published: January 2025

The use of triazines is effective in the electrocatalytic reactions of water splitting, CO2 reduction (CO2RR), ammonia production (NO3RR and NRR), etc.
Stable and efficient n = 2 Ruddlesden–Popper La/Pr nickelates as oxygen electrodes for SOC applications
Authors: Frugier Romuald , Gamon Jacinthe , Fourcade Sébastien , Buffière Sonia , Bassat Jean-Marc
Published: January 2025

The substitution of La by Pr in the oxygen electrode La3Ni2O7±δ leads to SOEC performance improvement of 25% for Pr amount higher than 25%. The oxides can be prepared under air or oxygen depending on the Pr amount.
Rational hierarchical micellar gel electrolytes with synergistic hydrophobic–hydrophilic integration for dendrite-free zinc-ion batteries
Authors: Chen Zheming , Lin Yushuang , Shi Dehuan , Song Kangwei , Luo Jing , Qiu Yanbin , Liu Zheyuan , Yu Yan , Yang Chengkai
Published: January 2025

The LA gel electrolyte, with hydrophilic AM and hydrophobic LMA domains, forms ion channels, stabilizes hydrogen bonds, and reduces parasitic reactions. CTAB regulates Zn2+ migration and domain interactions.
Constructing well-dispersed active phase via spontaneous redox for electrochemical nitrate reduction to ammonia
Authors: Cao Huiqun , Liang Bin , Chen Wenda , Ye Shenghua , Liu Jianhong , Zhang Qianling
Published: January 2025

A core–shell structure of Co nanoparticles inserted into N-doped carbon dodecahedron@Co(OH)2 was synthesized via a spontaneous redox between Co and NO3−, ultimately materializing the fine dispersion of the active sites for nitrate reduction.
Polarity-sensitive pyrene fluorescent probes for multi-organelle imaging in living cells
Authors: Yang Linshuai , Cao Ying , Bu Xiaoyu , Peng Fei , Ai Qi , Gao Baoxiang
Published: January 2025

Under multi-modal fluorescence imaging conditions, this polarity-sensitive pyrene fluorescent probe revealed the multi-organelle dynamics and micropolarity characteristics in living cells.
Phytantriol and Monoolein in Aqueous Deep Eutectic Solvents and Protic Ionic Liquid Solutions
Authors: Edler Karen J , Warr Gregory , Djerdjev Alexander , Lam Minh , Hawley Adrian M , Mudie Stephen
Published: January 2025

Lyotropic liquid crystal gels of phytantriol and monoolein are well known examples of self-assembled systems in water, which have multiple applications across biomedical and materials science. However aqueous systems can...
Bicyclic nucleoside analogues: synthesis of thiazolopyrimidine-based nucleosides via a copper-catalysed tandem reaction of 5-iodocytidine with isothiocyanates
Authors: Jyothilekshmi Anjana , Lukose Reshma Elizabeth , Johny Angel , Kaloor Uthara , Nellyadan Megha , Babu Sheba Ann , John Jubi
Published: January 2025

This study presents a Cu-catalyzed synthesis of amino-substituted thiazolopyrimidine ribonucleosides, a new class of bicyclic nucleoside analogues.
Correction: Novel pyrimidine linked acyl thiourea derivatives as potent α-amylase and proteinase K inhibitors: design, synthesis, molecular docking and ADME studies
Authors: Zaman Hina , Saeed Aamer , Ismail Hammad , Anwaar Sadaf , Latif Muhammad , Hashmi Muhammad Zaffar , El-Seedi Hesham R.
Published: January 2025

Correction for ‘Novel pyrimidine linked acyl thiourea derivatives as potent α-amylase and proteinase K inhibitors: design, synthesis, molecular docking and ADME studies’ by Hina Zaman et al., RSC Adv., 2024, 14, 33235–33246, https://doi.org/10.1039/D4RA05799F.
Expedient discovery of fluorogenic amino acid-based probes via one-pot palladium-catalysed arylation of tyrosine
Authors: Marshall Olivia , McGrory Rochelle , Songsri Sineenard , Thomson Andrew R. , Sutherland Andrew
Published: January 2025

A one-pot palladium-catalysed arylation process has been used to expedite the synthesis of biaryl α-amino acids, leading to the discovery of an environment-sensitive fluorescent probe that was used to monitor the activity of a serine protease.
Fault diagnosis of high‐voltage circuit breaker based on open‐set theory fusion model
Authors: Zhou Jinglong , Zhao Hongshan , Lin Shiyu , Si Haoming , Li Bohan
Published: January 2025

Fault diagnosis of high voltage circuit breaker is an important aspect of electrical equipment intelligence. To effectively identify unknown faults, this paper proposes a high‐voltage circuit breaker fault diagnosis method based on open set fusion model (OSFM). Firstly, the current data and vibration data are processed using sequential variational mode decomposition and Fourier transform, respectively, to extract data features, thereby constructing the original feature set of the current‐vibration signal, which is then input into the Transformer model for further feature extraction. Secondly, the open‐set discriminant model based on the extreme value theory is proposed, and the data output by transformer is input into classifier to realise open‐set fault diagnosis. Finally, the tree‐structured parzen estimator is used to optimise the selection of transformer model parameters and discriminator acceptance probability. The efficacy of the OSFM was evaluated through experimentation on experimental platform. The results demonstrated that the OSFM method can effectively recognise previously unidentified class faults while maintaining accurate recognition of known classes. Compared with other open‐set classification techniques, OSFM can improve the recognition accuracy by up to 38.36%.
Numerical and analytical investigations of the water immersion cooling strategy for a permanent magnet synchronous motor
Authors: Sharifi Tohid
Published: January 2025

Permanent magnet synchronous motors (PMSMs) experience considerable performance degradation due to the rise in temperature and the resulting partial demagnetisation in the PMs, as well as the shortenings in the insulations' lifetime. To mitigate the temperature of motor components, it is crucial to investigate and continually improve the design of efficient cooling systems. This study implements the water immersion cooling (WIC) concept on a surface‐mounted PMSM (SMPMSM), where through comparing its cooling performance with the forced ventilation cooling (FVC), it is indicated that, even at high inlet velocities for the latter, it cannot maintain the temperature below the specified thresholds and the required input electric power to run the ventilation fan will be increased exponentially to compensate for its ineffectiveness. While in the WIC configuration, the winding and PM temperature values remain well below the margins when the heat transfer coefficient of this method is 40 higher than the FVC. By incorporating the effect of the cooling process through the heat transfer coefficient, the lumped‐parameter thermal network (LPTN) is utilised to study the operation mode of the motor under the mentioned cooling configurations. Besides achieving higher cooling efficiency, the WIC strategy can quickly reduce temperature, which is reflected in the thermal time constant of the cooling method extracted from the LPTN. Consequently, it is demonstrated that up to 35 higher than the nominal generated heat, the SMPMSM under the WIC can operate continuously, while for the FVC, the frequent start‐stop driving scenario should be employed.
Model predictive control with adjustable switching frequency for induction motor drives based on three‐dimensional satisfaction space optimisation
Authors: Yang Bo , Wu Xiang , Tian Yu , Guo Zhikang , Zhang Xu , Tan Guojun
Published: January 2025

This paper proposes a model predictive control strategy for induction motors driven by three‐level inverters, enabling effective switching frequency adjustment. First, a three‐dimensional satisfaction space optimisation strategy is proposed, eliminating the need for weight coefficient adjustments through self‐constraints and mutual constraints of the optimisation variables. The optimal switching state is selected by comparing the maximum average dwell time within the satisfaction space, thus reducing the inverter switching frequency. Second, switching frequency is treated as an auxiliary optimisation variable, and a dynamic sliding window method is designed to efficiently track switching frequency in variable‐speed systems. The boundaries of the three‐dimensional satisfaction space are adjusted to regulate the switching frequency. Finally, experimental results demonstrate that the proposed strategy maintains the switching frequency of 300 Hz across the entire speed range, achieving excellent dynamic and steady‐state performance at this low switching frequency.
Emergent robust oscillatory dynamics in the interlocked feedback‐feedforward loops
Authors: Harika Guturu L. , Sriram Krishnamachari
Published: January 2025

One of the challenges that beset modelling complex biological networks is to relate networks to function to dynamics. A further challenge is deciphering the cellular function and dynamics that can change drastically when the network edge is tinkered with by adding or removing it. To illustrate this, the authors took a well‐studied three‐variable Goodwin oscillatory motif with only a negative feedback loop. To this motif, an edge was added that results in an emergent structure consisting of new feedforward and feedback loops while retaining Goodwin's original negative feedback loop. To relate emergent structure to oscillatory dynamics, the authors took all the combinations of edge signs in the interlocked motif. Bifurcation analysis reveals that all the structural combinations can be grouped into two categories based on their unique dynamics. These two groups also exhibit unique amplitude‐frequency (amp‐freq) plots. These two categories are attributed to the emergence of interlocked motifs with specific edge signs. To support the ideas, a well‐studied plant circadian model of Arabidopsis thaliana was taken to illustrate the importance of interlocked motifs in fine‐tuning amplitude and frequency in circadian oscillators. The authors briefly discuss its implications for central oscillators' adaptation to different environmental cues.
Dynamics of energetic electrons scattered in the solar wind
Authors: Houeibib A. , Pantellini F. , Griton L.
Published: January 2025

We model the transport of solar energetic particles (SEPs) in the solar wind. We propagated relativistic test particles in the field of a steady three-dimensional magnetohydrodynamic simulation of the solar wind. We used the code MPI-AMRVAC for the wind simulations and integrated the relativistic guiding center equations using a new third-order-accurate predictor-corrector time-integration scheme. Turbulence-induced scattering of the particle trajectories in velocity space was taken into account through the inclusion of a constant field-aligned scattering mean free path λ∥. We considered mid-range SEP electrons of 81 keV injected into the solar wind at a heliocentric distance of 0.28 AU and a magnetic latitude of 24°. For λ∥ = 0.5 AU, the simulated velocity pitch angle distributions agree qualitatively well with in situ measurements at 1 AU. More generally, for λ∥ in the range 0.1–1 AU, an energy-loss rate associated with the velocity drift of about 10% per day is observed. The energy loss is attributable to the magnetic curvature and gradient-induced poleward drifts of the electrons against the dominant component of the electric field. In our case study, which is representative of the average solar wind conditions, the observed drift-induced energy-loss rate is fastest near a heliocentric distance of 1.2 AU. We emphasize that adiabatic cooling is the dominant mechanism during the first 1.5 hours of propagation. Only at later times does the drift-associated loss rate become dominant.
Hot Jupiter engulfment by an early red giant in 3D hydrodynamics
Authors: Lau Mike Y. M. , Cantiello Matteo , Jermyn Adam S. , MacLeod Morgan , Mandel Ilya , Price Daniel J.
Published: January 2025

Hot Jupiters are gas giant planets with orbital periods of a few days and are found in 0.1–1% of Sun-like stars. They are expected to be engulfed during their host star’s radial expansion on the red giant branch, which may account for observed rapidly rotating and chemically enriched giant stars. We performed 3D hydrodynamical simulations of hot Jupiter engulfment by a 1 M⊙, 4 R⊙ early red giant. Our ‘global’ simulations simultaneously resolve the stellar envelope and planetary structure, modelling the hot Jupiter as a polytropic gas sphere. The hot Jupiter spirals in due to ram-pressure drag. A substantial fraction of its mass is continuously ablated in this process, although the mass-loss rate is resolution dependent. We estimate that this could enhance the surface lithium abundance by up to ≈0.1 dex. The hot Jupiter is disrupted by a combination of ram pressure and tidal forces near the base of the convective envelope, with the deepest material penetrating to the radiative zone. The star experiences modest spin-up (∼1 km s−1), and engulfing a more massive companion may be required to produce a rapidly rotating giant. Drag heating near the surface and hydrogen recombination in the small amount of unbound ejecta recorded in the simulation could power an optical transient, although this needs to be confirmed by a calculation that has adequate resolution at the stellar surface.
Three warm Jupiters orbiting TOI-6628, TOI-3837, and TOI-5027 and one sub-Saturn orbiting TOI-2328
Authors: Tala Pinto Marcelo , Jordán Andrés , Acuña Lorena , Jones Matías , Brahm Rafael , Reinarz Yared , Eberhardt Jan , Espinoza Néstor , Henning Thomas , Hobson Melissa , Rojas Felipe , Schlecker Martin , Trifonov Trifon , Bakos Gaspar , Boyle Gavin , Csubry Zoltan , Hartmann Joel , Knepper Benjamin , Kreidberg Laura , Suc Vincent , Teske Johanna , Butler Robert Paul , Crane Jeffrey , Schectman Steve , Thompson Ian , Osip Dave , Ricker George , Collins Karen A. , Watkins Cristilyn N. , Bieryla Allyson , Stockdale Chris , Wang Gavin , Zambelli Roberto , Seager Sara , Winn Joshua , Rose Mark E. , Rice Malena , Essack Zahra
Published: January 2025

We report the discovery and characterization of three new transiting giant planets orbiting TOI-6628, TOI-3837, and TOI-5027 and one new warm sub-Saturn orbiting TOI-2328, whose transits events were detected in the light curves of the Transiting Exoplanet Survey Satellite (TESS) space mission. By combining TESS light curves with ground-based photometric and spectroscopic followup observations, we confirm the planetary nature of the observed transits and radial velocity variations. TOI-6628 b has a mass of 0.74±0.06 MJ and a radius of 0.98−0.05+0.06 RJ and orbits a metal-rich star with a period of 18.18424 ± 0.00001 days and an eccentricity of 0.670−0.016+0.015, making it one of the most eccentric orbits of all known warm giants. TOI-3837 b has a mass of 0.59±0.05 MJ and a radius of 0.97−0.06+0.05 RJ and orbits its host star every 11.88865 ± 0.00003 days, with a moderate eccentricity of 0.221−0.046+0.042. With a mass of 2.02±0.13 MJ and a radius of 0.96−0.06+0.05 RJ, TOI-5027 b orbits its host star in an eccentric orbit with e = 0.385−0.026+0.025 every 10.24368±0.00001 days. TOI-2328 b is a Saturn-like planet with a mass of 0.16±0.02 MJ and a radius of 0.89−0.05+0.04 RJ; it orbits its host star in a nearly circular orbit with e = 0.057−0.029+0.046 at a period of 17.10197±0.00001 days. All four planets have orbital periods above ten days, and our planet’s interior structure models are consistent with a rocky-icy core with an H/He envelope, providing evidence supporting the core-accretion model of planet formation for this kind of planet.
Unveiling the nature of the donor stars of the ultraluminous X-ray sources in NGC 1559
Authors: Allak Sinan , Akyuz Aysun
Published: January 2025

Context. X-ray data provide insights into accretion processes and the compact objects of ultraluminous X-ray sources (ULXs), while optical and IR observations help identify the donor star and surrounding environment. Together, these approaches shed light on the origins of the high X-ray luminosities observed in ULXs

Aims. This study examines the optical and IR properties of eight ULXs in NGC 1559 using archival data from the Hubble Space Telescope and the James Webb Space Telescope. To constrain the nature of the donor stars of the ULXs, photometric results were obtained from the temporal analysis, spectral energy distributions (SEDs), and color-magnitude diagrams (CMDs). Furthermore, the long-term and spectral characteristics of one ULX, X-1, were investigated.

Methods. ULX counterparts were determined from astrometric calculations. The long-term light curves and SEDs were constructed to interpret the origin of the optical and IR emissions. The masses and ages of donor star candidates were determined using CMDs. To constrain the mechanism of X-ray emission, the time-averaged spectrum and long-term light curve of X-1 were obtained.

Results. Unique optical and IR counterparts for ULXs X-14 and X-24 were determined, while only optical counterparts were detected for X-1 and X-18. Our findings indicate that the optical emission originates from the donor stars of X-14 and X-24, whereas for X-1 and X-18 it is dominated by the accretion disk. In addition, X-1 exhibits long-term X-ray variability spanning orders of magnitude.
Enhancing Medical Diagnostics with Machine Learning: A Study on Ensemble Methods and Transfer Learning
Authors: Zhang Jiaming
Published: January 2025

This paper explores the use of machine learning (ML) in medicine, emphasizing how important it is to enhance patient outcomes and diagnostic precision. As medical data grows in complexity and volume, advanced ML techniques are increasingly necessary. The research focuses on leveraging Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Ensemble Methods, and Transfer Learning to enhance medical diagnostics. Specifically, these techniques are applied to large-scale datasets, to address tasks like disease detection, patient outcome prediction, and managing uncertainty in medical data. According to the study, CNNs performs substantially better when handling uncertainty when using the U-Multiclass technique, as seen by the largest Area Under the Curve (AUC) for Cardiomegaly detection. When it comes to diabetes prediction, Ensemble Methods outperform other approaches, and Transfer Learning works well for modifying trained models for use in novel medical applications. The research holds practical value since it can improve patient care and productivity within the healthcare industry. By integrating these ML techniques, the study contributes valuable insights into improving diagnostic processes and optimizing patient outcomes.
Text vectorization in sentiment analysis: A comparative study of TF-IDF and Word2Vec from Amazon Fine Food Reviews
Authors: Lu Jiaxin
Published: January 2025

Sentiment analysis is a practical tool for marketing and branding teams. Companies can collect and analyze opinions or reviews from social media platforms, blog posts, and other numerous forums. It may help them acquire positive feedback to reinforce strengths or identify negative emotions to make improvements. The research is to compare two text vectorization methods in opinion mining: Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec, using Amazon Fine Food Reviews dataset. This study will use these two methods to vectorize preprocessed text data and also input the vectorized data to the emotion classification model, analyzing the performance of two methods in the emotion classification task. The consequence indicates that the former outperforms the latter in handling large datasets, particularly in distinguishing between different sentiment categories, but latter is superior in capturing the semantic relationship of words. Therefore, it is suggested that the advantages of the two methods be combined in practical applications to improve the accuracy and efficiency.
Data Visualization and Prediction Model Analysis
Authors: Hua Ziyi
Published: January 2025

Data visualization plays an important role in the process of data analysis. It can transform complex data into intuitive charts, which helps us understand data more effectively. The purpose of this paper is to study the application of data visualization and compare different prediction models. In this work, I present three different prediction models and apply them on the US airline industry datasets by using Python as the programming language. Then these data sets are made into a bar graph and analy1zed through these three prediction models. Finally, this paper provides some suggestions on how to choose appropriate visualization tools based on data characteristics. This study not only enriches the application examples of data visualization, but also provides valuable reference for decision-making and predicting in data analysis.
Comprehensive Analysis of Face Recognition Technologies
Authors: Liu Bowei
Published: January 2025

This article provides a comprehensive review of face recognition research, focusing on advancements made over the past century. It presents a detailed examination of the core concepts, principles, steps, and classifications of face recognition technology. The review highlights the practical applications of face recognition in contemporary contexts and summarizes key datasets and preprocessing methods used in the field. The paper categorizes face recognition methods into three main types and places particular emphasis on hybrid methods. It explores the principles and research processes associated with these methods, offering an in-depth analysis of their results. Among the various techniques reviewed, deep learning methods emerge as the most promising for face recognition due to their superior performance. This review serves as a valuable resource for students and novice researchers by providing a clear overview of current research methodologies and tools. Additionally, it outlines potential research directions and contributes to the advancement of the field of computer vision.
Comparative Analysis of YOLO Variants Based on Performance Evaluation for Object Detection
Authors: Chen Aoxiang
Published: January 2025

This study focuses on analysing and exploring the You Only Look Once (YOLO) algorithm. Specifically, this article analyses the evolution and performance of three versions (YOLOv1, YOLOv5, and YOLOv8) in object detection. The research begins by detailing the fundamental concepts of object detection and the datasets commonly used in this field. It then delves into the specific architectures and experimental outcomes associated with each YOLO version. The analysis reveals that while YOLOv8 introduces advanced features and improvements, earlier versions like YOLOv5 may offer superior stability and performance under certain conditions, particularly in specific tasks such as car detection. The discussion emphasizes the significant impact of factors such as batch size on model performance, suggesting that fine-tuning these parameters can optimize the algorithm for particular applications. The study concludes that the future of YOLO development lies in exploring and refining different variants, particularly those of YOLOv8, to better meet diverse requirements. By focusing on five distinct YOLOv8 variants, the research aims to enhance the adaptability and effectiveness of the YOLO framework across a wide range of object detection challenges, thereby contributing valuable insights into the ongoing advancement of this technology.
Analysis of The Role of Deep Learning Models in Image Classification Applications
Authors: Li Xiang
Published: January 2025

Image classification is a fundamental task in computer science, underpinning various applications such as object detection, face recognition, and object interaction analysis. The concept holds significant value due to its wide-ranging applications across multiple fields. Traditional methods for image classification, however, have been limited by their slow processing speed, rigidity, and high costs. The integration of deep learning models, particularly Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs), has revolutionized this process, enabling the development of automated, fast, and practical systems. These advanced models are now employed in diverse areas, including biomedical science, remote sensing, and business management, thanks to their ability to achieve high accuracy across a broad spectrum of scenarios. Training these models involves the use of well-known datasets like Canadian Institute for Advanced Research (CIFAR) and Modified National Institute of Standards and Technology (MNIST), which provide the necessary data for optimization and validation. The paper examines the structure, functionality, advantages, and limitations of CNNs and SVMs in the context of image classification, demonstrating that deep learning-driven classification is now a mainstream research focus. This study highlights the transformative impact of these models and provides insights into their future potential.
Enhancing Brain Tumor Detection: A Comparative Study of CNN Architectures Using MRI Data
Authors: Zhu Zhimeng
Published: January 2025

Deep learning models have become essential for automated medical image analysis in brain tumor detection. Existing Convolutional Neural Network (CNN) models like Visual Geometry Group 19 (VGG19), Residual Network 18 (ResNet18), and Residual Network 34 (ResNet34), despite their success in image classification and recognition, face challenges such as unclear boundary detection, limited generalization, and lower computational efficiency in detecting brain tumors. To address these issues, this study introduces VGG19-Brain’s MRI for Tumor (BMT), an enhanced version of the classic VGG19 model. VGG19-BMT incorporates targeted optimizations, including adjustments to convolutional layers and improved feature extraction modules. A systematic comparative analysis of VGG19-BMT and traditional CNN models was conducted using the Kaggle dataset “Brain MRI Images for Brain Tumor Detection.” The results demonstrate that VGG19-BMT outperforms conventional models’ boundary recognition accuracy, generalization, and computational efficiency, providing a more effective solution for automated brain tumor detection. This advancement not only enhances diagnostic capabilities but also sets the stage for future model improvements and clinical applications in medical imaging.
LSTM-Based Time Series Prediction Model: A Case Study with YFinance Stock Data
Authors: Yu Yimiao
Published: January 2025

The goal of this study is to anticipate the time series of stock data that YFinance provides using a Long Short-Term Memory (LSTM) model, with a particular emphasis on the closing prices and daily returns of Apple Inc. (AAPL). The historical closing price data from January 1, 2010, to September 20, 2021, was used as the training set, while the data from September 21, 2021, to August 22, 2024, was employed as the validation set to test the model’s predictive capability. The experimental results demonstrate that the LSTM architecture performs excellently in handling data with long-term dependencies and trends, attaining a root mean square error (RMSE) of 5.2129 and a coefficient of determination (R2) of 0.94, thus accurately forecasting the stock price movements of Apple Inc. However, the model exhibits poor performance in predicting high-frequency fluctuations, with an R2 of only -0.1, indicating a weak ability to capture high-frequency volatility.
Enhancing Handwritten Digit Recognition using Auxiliary Classifier Generative Adversarial Networks and Self-attention Mechanism
Authors: Hu Tingkai
Published: January 2025

This research study investigates the integration of the self-attention mechanism and the Auxiliary Classifier Generative Adversarial Networks (ACGAN)to improve handwritten digital recognition using the MNIST data set. Although progression has been made in the generative Adversarial Networks (GANs) used for image synthesis, it is still tested to attain top-notch, context-accurate photo generation, specifically under various information problems. This study fixes these spaces by incorporating self-attention with ACGANs, improving the integrity and labelling accuracy of the produced images. This approach entails changing the ACGAN framework to incorporate the self-focus module, so far better identify the context dependencies in the photo. This assimilation advertises much more detailed and accurate numerical depiction, which is especially helpful when converting data sets with dark histories right into more clear, segmented white background pictures, which enhances the differentiation between data classes. The outcomes reveal that the recognition accuracy and processing speed have been significantly boosted, validating the adaptability and usefulness of the design in various procedure circumstances. The research results show that this method can establish brand-new standards for producing high-grade electronic photos in numerous applications and show considerable progress in the field of image recognition technology.
Sentiment Analysis of Mobile Phone Reviews Using XGBoost and Word Vectors
Authors: Wang Zekai
Published: January 2025

Consumer reviews are an important source of data used to judge and examine consumer sentiment, and data mining for reviews of electronic products is an important way to help improve the design of electronic products. The research is based on the consumer reviews of online cell phone e-commerce, The paper constructs a sentiment dictionary in this field based on the Sentiment Oriented Point Mutual Information (SO-PMI) algorithm, and the sentiment weight of the review word vectors. An extreme Gradient Boosting Tree (XGBoost) is used to integrate word vectors and a Large Language Model (LLM) to construct a sentiment recognition model, and finally, a review sentiment index is derived, which unfolds from multiple dimensions to analyze the sentiment tendency in consumer reviews. The empirical analysis shows that the accuracy, recall, area under the curve (AUC), and other validation indexes of the constructed sentiment recognition model are further improved compared with the LLM model, which has a certain application value. When applying the weighted word vector method, the model has been significantly improved compared with the LLM model, the accuracy is increased by 5%, the accuracy is increased by 10%, and the comprehensive accuracy is increased by 2% after the comprehensive application of the two.
A Hybrid Machine Learning Framework for Soccer Match Outcome Prediction: Incorporating Bivariate Poisson Distribution
Authors: Chen Zhong An
Published: January 2025

The 2022 FIFA World Cup final attracted 1.5 billion viewers, while billions of dollars are wagered on soccer matches every year. The increasing demand for accurate predictions, both for academic research and betting purposes, has driven the development of advanced forecasting models. This study explores the application of mathematical and machine learning models to predict results of soccer matches, with the dual aim of academic advancement and profitable betting. The author utilizes a comprehensive dataset from top European leagues (2014-2022) and employ models including Bivariate Poisson Distribution, Naive Bayes, Neural Networks, Support Vector Machines, Random Forests, and Gradient Boosting. The paper’s feature engineering combines historical match statistics, FIFA ratings, and betting odds. While Random Forests achieved the highest accuracy (56.25%), predicting draws remains challenging. The study highlights the potential for improved prediction systems and suggests future research in advanced draw prediction techniques and profitability analysis, the paper provides research directions for researchers in related fields.
Comparison of Fully Convolutional Networks and U-Net for Optic Disc and Optic Cup Segmentation
Authors: Jin Zixiao
Published: January 2025

Glaucoma, the leading cause of irreversible blindness, must be diagnosed early and thus treated in time. However, it has no noticeable symptoms in its early stages and may not be detected easily. This paper aims to compare two well-known convolutional neural network (CNN) structures, namely Fully Convolutional Networks (FCNs) and U-Net for the segmentation of the optic disc (OD) and optic cup (OC) from retinal fundus images which play an important role in glaucoma diagnosis. The performance of both models is assessed using qualitative parameters such as the Dice coefficient, Jaccard index, and cup-to-disc ratio (CDR) error. In our experiment, the U-Net model yields more accurate segmentation results with 0.9601 average pixel accuracy and 0.9255 dice score for OD segmentation, outperforming the FCNs model with 0.9560 average pixel accuracy and 0.9132 dice score for OD segmentation. However, FCNs have a shorter inference time of 0. 0043 seconds against U-net’s 0. 0062 seconds making FCNs more suitable for real-time applications. The restrictions related to this study include biases from using only one dataset acquired from particular imaging devices, dependency on mask-based cropping techniques, and comparison being restricted to two fundamental architectures. This work presents the contribution of the deep learning models in improving glaucoma screening and therefore helping in avoiding blindness.
Performance and Analysis of FCN, U-Net, and SegNet in Remote Sensing Image Segmentation Based on the LoveDA Dataset
Authors: Yang Shuhao
Published: January 2025

Remote sensing image segmentation is a vital method in image analysis that significantly contributes to the extraction of surface information and aids in land use planning. This study utilizes the LoveDA dataset to investigate the segmentation performance of three classic deep learning models: Fully Convolutional Networks(FCN), U-Net, and SegNet, in both urban and rural scenarios. By partitioning the Urban-Rural dataset of LoveDA for training and testing, it was determined that SegNet excels in detail restoration and boundary handling, while U-Net demonstrates robust adaptability across various scenarios. In contrast, FCN, with its simpler architecture, shows lower segmentation accuracy in certain contexts. This paper offers a comprehensive comparison of the strengths and weaknesses of different models in remote sensing image segmentation and proposes enhancements in model structure and data preprocessing optimization. The findings provide valuable insights for improving the performance of semantic segmentation models and are of significant importance for the precise analysis and practical applications of remote sensing images.
Standardized Reporting of HCC with LI-RADS and mRECIST: Update on the Situation in Germany
Authors: Nelles Christian , Ristow Inka , Juchems Markus S , Wessling Johannes , Ringe Kristina Imeen , Fehrmann-Efferoth Ana , Lennartz Simon , Afat Saif , Othman Ahmed , Eisenblätter Michel , Paul Roman , Gerwing Mirjam , Persigehl Thorsten
Published: January 2025

Online survey about the current status of CT protocols in hepatocellular carcinoma (HCC) in the year 2023/2024. Moreover, the usage of structured reporting using LI-RADS and mRECIST was surveyed and the results were compared with a survey from 2020.

Radiologists working in outpatient or inpatient care in Germany were invited. The survey was conducted between 10/2022 and 06/2023 and between 06/2024 and 08/2024. HCC-related questions were asked regarding the commonly used imaging modalities, body coverage, and contrast phases in CT, as well as the usage of structured assessment and treatment response using mRECIST and LI-RADS.

More than half of the participants stated that they “frequently” perform imaging of HCC. In the CT protocol, acquisition of a pre-contrast phase was widespread. While a late arterial and a portal venous contrast phase was acquired in most cases, a delayed phase was used much less frequently (at small and medium-sized hospitals only in 26.5%). For staging, LI-RADS was used in structured reports in only 13%; for response monitoring mRECIST was used at university hospitals in only 26.5% and LI-RADS in 14.7%, whereas these have been almost never used in routine practice at all other sites. The main reasons given for the lack of application were the expenditure of time, the lack of reporting templates, problems with integration into the IT infrastructure and a lack of reimbursement.

The recommendation of a three phase CT examination in late arterial, portal venous, and delayed phase for HCC diagnostics according to LI-RADS is only partially implemented in Germany. Structured reporting for staging and response monitoring using LI-RADS and mRECIST was at a similarly low level in Germany in 2023 compared to 2020. Possible solutions include the development and distribution of online educational resources, structured reporting templates, and inexpensive IT solutions.
Women with Gynecological Cancers Receiving Specialized Ambulatory Palliative Care (SAPV)
Authors: Just Johannes Maximilian , Schmitz Marie-Therese , Nitz Ulrike , Ladermann Katharina , Grabenhorst Ulrich
Published: January 2025

A dignified final phase of life spent at home is what many people with terminal cancer wish for. There are currently no studies with large datasets on the palliative care of patients with gynecological tumors in the home environment.

Data from 5487 patients with gynecological-oncological disease who received specialized outpatient palliative care (SAPV) between 2017 and 2021 were included in the analysis.

The analysis showed that 88.8% of the patients died in a home environment and that their symptom burden remained stable or even decreased slightly during treatment. The median duration of active SAPV was 23 days. Factors such as age, comorbidities, and symptom burden at the start of treatment influenced the survival time of patients. The SAPV team and relatives who provided care in the home environment were mostly very satisfied with the care provided.

The study provides important insights into the palliative care of patients with gynecological tumors and underscores the importance of SAPV for dignified end-of-life care.
Reassessing the Role of Morning Cortisol in Adrenal Insufficiency
Diagnosis: Insights from a Multicentric Cohort
Authors: Guia Lopes Maria Leonor , Regala Catarina , Limbert Clotilde , Silva Tiago Nunes , Sequeira Duarte João , Leite Valeriano , Prazeres Susana
Published: January 2025

The diagnosis of adrenal insufficiency (AI) is challenging due to nonspecific
symptoms. Measuring 8 AM serum cortisol levels is a common screening test, but
its accuracy in predicting AI remains uncertain. This study aimed to evaluate
the predictive diagnostic value of basal morning cortisol levels in suspected AI
cases and compare them with levels in healthy individuals. We have conducted a
retrospective multicentric cohort study. The study included an epidemiological
cohort (patients with AI suspicion) and a control cohort (healthy volunteers).
In the epidemiological cohort an ACTH-stimulation test was performed to confirm
(AI group) or exclude (non-AI group) the disease. We have included 273
individuals (168 suspected AI cases and 105 controls). Basal cortisol levels
were higher in the control group compared to the AI-suspected group (9.9±3.2
μg/dl vs. 7.7±3.3 μg/dl, p<0.001), though both were below the 15 μg/dl
recommended threshold for excluding AI. Within the epidemiological cohort, even
after a propensity score matching, taking into consideration sex, age and AI
symptoms complaints, no significant difference in basal cortisol levels was
found between patients with and without AI [6.10 (4.43; 8.45) vs. 7.14 (4.68;
12.15), p=0.128]. Logistic regression and ROC curve analyses showed a low
predictive value for basal cortisol, with a positive predictive value of 18.9%
and low specificity. AI patients more frequently experienced hypotension and
nausea, compared with non-AI patients. Morning cortisol levels alone are
unreliable for diagnosing AI. Revising cortisol cutoffs and incorporating
symptom-based criteria may improve diagnostic accuracy.
Visible-Light-Driven Thiolate-Catalyzed Carboxylation of C(sp2)-H Bonds in Azines with CO2
Authors: Jiang Yuan-Xu , Liao Li-Li , Ye Jian-Heng , Yu Da-Gang
Published: January 2025

Catalytic carboxylation of C–H bonds with CO2 represents an efficient strategy to synthesize highly valuable carboxylic acids from simple substrates, featuring high atom, step, and redox economy. Recently, our group was successful in developing a visible-light-driven thiolate-catalyzed carboxylation of C(sp2)–H bonds in azines with CO2. This strategy provides facile access to a range of valuable N-heteroarene carboxylic acids from readily available azines. Furthermore, the synthetic potential of this approach is demonstrated through the synthesis of bioactive molecules and the modification of commonly used nitrogen ligands. Mechanistic studies suggest that the EDA complex formed between thiolate and azines is a crucial factor in the activation of azines.
Synthesis of Branched Sugars and Chiral Aromatic Building Blocks Derived from Glycals
Authors: Maheswari Mittali , Hussain Nazar
Published: January 2025

The transformation of sugar enolic ethers into branched sugars through C-H activation or cross-coupling reaction represents a compelling strategy in carbohydrate chemistry. These branched sugars serve as versatile intermediates for the synthesis of annulated sugars, which are key components in numerous biologically active molecules. This review highlights various methodologies for the development of branched sugars, focusing on approaches utilizing glycals and alkenes via C-H activation and cross coupling reactions. Additionally, it explores literature reports on the synthesis of chiral fused ring systems derived from functionalized glycals, offering insights into their significance in synthetic chemistry.
Is There an Association between Bell Palsy in Pediatric Patients and COVID-19?
Authors: El-Deeb Mohamed E. , Elzayat Saad , Salamah Abeer , Gamal Ali , Elgamal Shimaa , El-Sobki Ahmed
Published: January 2025

Introduction Bell palsy (BP) is an acquired, idiopathic facial palsy linked to lower motor neuron malfunction of the seventh cranial nerve. Several studies have identified BP as one of the many neuropathies that coronavirus disease 2019 (COVID-19) patients have developed, while other studies disagree.

Objective To study if there is an association between BP in pediatric patients and COVID-19, and to examine the pattern of recovery in all pediatric cases of BP during the COVID-19 pandemic.

Methods We performed a prospective cohort study on pediatric patients with acute onset unilateral facial weakness of unknown etiology (BP) during the pandemic period. All included patients were submitted to a reverse transcription-polymerase chain reaction (RT-PCR) test through nasopharyngeal and oropharyngeal swabs for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at the time of the BP diagnosis.

Results No significant differences were found regarding COVID-19 infection and recovery from BP at the first, third, or sixth months of follow-up. According to the results, it seems that there is no association between COVID-19 infection and facial palsy; however, the patients infected with COVID-19 in the sample experienced a rapid, early recovery from BP. The mean incidence of BP in 5 years (2017–2021) was of 1.73/100 thousand individuals, with a statistically insignificant change throughout the years.

Conclusion We were not able to show any association between BP and COVID-19. The patients underwent follow-up for up to 6 months, and we studied their patterns of recovery from BP, which were like those observed before the pandemic.
Pitfalls and Challenges in the Interpretation of Rectal Cancer Magnetic Resonance Imaging
Authors: Wani Abdul Haseeb , Choh Naseer A. , Syed Humairah M. , Mir Waris A. , Wani Rauf A. , Robbani Irfan , Shera Tahleel A.
Published: January 2025

Magnetic resonance imaging (MRI) has taken a center stage in the imaging armamentarium of rectal cancer evaluation. Rectal cancer staging has undergone a paradigm shift from a surgico-pathological approach to a chemo-radiological one, helping effectively stratify patients for appropriate management. Primary lesion characterization, its morphology and internal characteristics, proximity of tumor to the mesorectal fascia, presence of extramural venous invasion, presence of extra mesorectal pelvic lymph nodes, and involvement of peritoneum and distant metastases are critical findings that impact patient management for which MRI is the preoperative gold standard. However, there are pitfalls, challenges, and misinterpretations related to technique, image quality, and knowledge gaps among the radiologists. These have major implications for patient management and their outcomes. In this article, we highlight the pitfalls and challenges in rectal cancer MRI and present practical solutions to circumvent these.
Rational autocrats? Drivers of corruption patterns in competitive authoritarian regimes: towards an explanatory framework with empirical applications from Hungary
Authors: Hajnal Áron
Published: January 2025

Competitive authoritarianism, a specific type of hybrid regime, is gaining ground globally. Corruption is particularly prevalent in these contexts as regimes utilise it to consolidate their power. However, some competitive authoritarian regimes also take significant measures to curb certain types of corruption. The present article posits that competitive authoritarian regimes, acting as rational utility-maximising actors, curb or enhance corruption types based on the net costs and benefits—that is, net gains—they yield for the regime. To elucidate the factors influencing net gains, an explanatory framework is presented. Its three constituent elements are accountability costs, transaction costs, and political benefits. The applicability of the framework is demonstrated with case studies of two corruption types in Hungary, namely, informal payments in healthcare and clientelism in awarding tobacco retail concessions.