bluuebunny/crossref_metadata_2025 huggingface dataset:
High‐dimensional optimal design of dual‐rotor synchronous reluctance machines based on data‐driven torque decomposition
Authors: Farshbaf‐Roomi Farnam , Shoaei Aran , Zhu Jianguo , Wang Qingsong
Published: January 2025

The multi‐objective optimal design of double‐sided stator dual‐rotor synchronous reluctance machines (DSS‐DRSynRMs) is a challenging high‐dimensional problem. The objective of this paper is to present a new optimal design method based on data‐driven models and the principle of torque decomposition addressing the aforementioned issue. For this purpose, a 26‐parameter optimisation problem is solved by employing the proposed method consisting of three sequential phases. Through the proposed method, the combination of artificial neural network (ANN) and recently introduced waveform targeting surrogate model (WTSM) strategy is investigated to mitigate the computational complexity of the optimisation process. Furthermore, the electromagnetic performance of the final optimal design has been comprehensively analysed showing a significant reduction in torque ripple rate and improved torque density. Moreover, the computational efficiency of the proposed method has been compared to the popular multi‐level multi‐objective optimisation method. From the discussion, it can be found that the proposed method provides a reduced computation time and wider search space.
Microbiome analysis reveals the potential mechanism of herbal sitz bath complementary therapy in accelerating postoperative recovery from perianal abscesses
Authors: Chen Xinghua , Guo Xiutian
Published: January 2025

The herbal sitz bath formula, as a complementary therapy, effectively alleviates postoperative wound pain and accelerates healing time in patients with perianal abscesses. To investigate its mechanism of action, this study conducted 16S rRNA gene sequencing and bioinformatics analysis on wound exudate samples from patients after perianal abscess surgery. Patients were randomly divided into two groups: one receiving the herbal sitz bath as an adjunctive therapy and the other without this adjunctive therapy. Samples were collected on the first and eighth days after surgery to compare the differences in microbial community composition between the two groups on the eighth day and between the first and eighth days within each group. The study revealed that the herbal sitz bath significantly altered the structure of the microbial community, increasing its diversity and abundance. By reducing Enterococcus and increasing Bifidobacterium, Faecalibacterium, and Ruminococcus, the therapy enhanced the wound's anti‐infective capacity and accelerated healing. This study explored the potential mechanism of the herbal sitz bath formula as an adjunctive therapy in promoting postoperative recovery from perianal abscesses, providing valuable data for further research on the role of microorganisms in wound care. These findings contribute to optimising postoperative treatment regimens and facilitating patient recovery.
Comparative characteristics of power delivery and temperature between conventional and hybrid energy storage systems in UAVs
Authors: Phumma Prasophchok , Saikong Waiard
Published: January 2025

This research endeavors to present an analysis the characteristics of the power delivery and temperature between conventional energy storage systems (CESS) and hybrid energy storage systems (HESS) to aid in the development of advanced energy storage solutions for Unmanned Aerial Vehicles (UAVs). The results demonstrate that the HESS allows batteries to achieve one additional discharge cycle compared to CESS. Additionally, HESS reduces the initial current response and power delivery by 30W, enabling greater energy consumption efficiency. The average surface temperature differential between the two systems is 1.2°C and the average peak differential is 1.6°C, as the HESS exhibits a slower temperature rise during discharge two cycle to end, due to the initial assistance provided by the supercapacitor and maintain smooth surface peak temperature. The findings highlight the potential for HESS to enhance UAV performance, indicating the need for longer experiment durations, stable room temperatures, and consideration of the mission profile for more accurate data.
Chemical test and cellular bioactivities and flavor enhancer properties of mung bean protein via glycation reaction
Authors: Srisukpachin Nuttakarn , Tabtimmai Lueacha , Sedtananun Saranya , Laohakunjit Natta , Sonklin Chanikan
Published: January 2025

Mung bean meal is by-product of the vermicelli industry and contains a high protein content of 70 %. Therefore, this research study and development of flavor-enhancers derived from the mung bean meal protein isolated (MPI) reacted with reducing sugars as glucose (Glu) for generating Maillard reaction products (MRPs) under thermal process. Sensory characteristic, antioxidant activities and cellular bioactivities invested under different ratios of MPI:Glu and different reaction times. The optimal production process of MRPs was determined by response surface methodology (RSM) under the desirability criteria. The optimal ratio of MPI:Glu and time for generating high teriyaki odor, savory, DPPH inhibition, ABTS inhibition with low nut odor and bitterness were identified. Results showed that the MPI:Glu ratio of 1.5:1 and reaction time for 150 min (MRP) were the optimum conditions that contributed to the highest teriyaki and savory scores of 2.54 and 2.25, respectively. MRP explored bioactivities, both chemical test and cellular bioactivity. MRP demonstrated both low cytotoxicity and significant anti-inflammatory effects. MRP exhibited non-toxic in Vero cells at 10 mg/mL and it significantly suppressed nitric oxide (NO) release to 18.62 µM with non-toxicity in the murine macrophage; RAW 264.7 at a concentration of 1 mg/mL.
Soluble oil adsorption onto modified chitosan beads
Authors: Jarupanavet Pornpimon , Piyamongkala Kowit
Published: January 2025

The adsorption soluble oil emulsion was studied in the laboratory scale. The effect of initial concentration of soluble oil to adsorb onto the surface of modified chitosan beads with 0.25 M hydrochloric acid solution was investigated. It was found that the maximum adsorption capacity soluble oil onto modified chitosan beads was 10,000 mg/g. The proton from hydrochloric acid which using as modified showed the role to destabilize soluble oil. The adsorption isotherm corresponded to the Langmuir equation.
Occurrence of 1,4-dioxane in domestic wastewater and processed water: A case study of Thailand’s central plain
Authors: Rattanit Mantiya , Kusanthia Ladawan , Sethbhakdi Pavin , Pornwongthong Peerapong
Published: January 2025

1,4-Dioxane is an emerging water contaminant and probable human carcinogen. Its use in various applications and improper waste management can lead to water contamination. This study surveyed 1,4-dioxane contamination in domestic wastewater and water from Thailand’s central plain. Samples of influent, effluent, and activated sludge from seven domestic wastewater treatment plants, as well as tap water from 28 public water systems, were collected and analyzed for 1,4-dioxane. The results showed 1,4-dioxane presence in all wastewater samples, with concentrations ranging from 4.34 to 1285.44 μg/L and effluent loads between 0.54 and 254.95 kg/day. Additionally, 1,4-dioxane was detected in five public water systems (17.86% of the sites) at concentrations between 44.54 and 81.24 μg/L. These findings indicate that some municipal water and wastewater treatment plants in Thailand’s central plain are not effectively removing 1,4-dioxane, highlighting the need for monitoring and improved treatment technologies to manage contamination in the area.
Traffic Flow Prediction Based on Large Language Models and Future Development Directions
Authors: Zhang Muhua , Zhao Wenzheng
Published: January 2025

As the application of deep learning in intelligent transportation systems becomes increasingly widespread, the accuracy and reliability of traffic flow prediction have become crucial. However, existing deep learning methods are often complex in model design and lack intuitiveness, making it challenging to provide responsible explanations for traffic predictions. This paper references a responsible and reliable traffic flow prediction model (R2T-LLM) based on large language models (LLMs). This model captures complex spatiotemporal patterns and external factors by converting multimodal traffic data into natural language descriptions. By leveraging LLMs’ advanced understanding capabilities, R2T-LLM provides more transparent and interpretable predictions. It bridges the gap between technical performance and real-world application needs. While maintaining accuracy comparable to deep learning baselines, R2T-LLM offers intuitive and reliable prediction explanations. This paper also explores the spatiotemporal and input dependencies of conditional future traffic predictions and compares the model with other different approaches and types. Furthermore, it evaluates the potential of R2T-LLM in addressing challenges in large-scale urban traffic systems, highlighting its developmental and application prospects.
The Application of Reinforcement Learning in Traffic Flow Prediction: Advantages, Problems, and Prospects
Authors: Li Minghui , Zhou Decheng , Zhang Shiqi
Published: January 2025

Traffic flow prediction (TFP) is an important topic in the fields of operation research and traffic engineering. It is dedicated to predicting the flow of people and vehicles in the transportation network within a specific time frame in the future. Accurate TFP has great significance for traffic management, urban planning, road design, and the development of intelligent transportation systems (ITS). This article summarizes three traditional methods of TFP: parameter-based prediction, shallow machine learning-based prediction, and deep learning (DL)-based prediction. However, traditional TFP methods only focus on predicting time series in traffic data, and it is difficult for these methods to capture the interdependent relationship between the spatial distribution of traffic across a network and the temporal evolution of traffic conditions at each location. sequences. How to fully extract the spatiotemporal correlation of traffic flow (SCTF) is an urgent problem that needs to be solved based on DL prediction models. Concurrently, as science and technology advance, a growing variety of academics are attempting to incorporate reinforcement learning (RL) into TFP. Experimental results show that it can reduce vehicle queuing time and average delay to a greater extent, and alleviate air pollution. The article summarizes the models of DL and RL in TFP, comprehensively compares the benefits and drawbacks of various approaches, and proposes a vision for existing problems and future development.
Research on Personal Loan Default Risk Assessment Based on Machine Learning
Authors: Liu Guangsen
Published: January 2025

In the present era of rapid development of the Internet and big data, the scale of personal loans and the complexity of personal credit data are growing rapidly. Accurately assessing personal credit rating and personal loan default risk has become an important topic in the financial field. This paper analyzes the current research status of other scholars on machine learning in personal loan default risk assessment in recent years, and selects Logistic Regression, Support Vector Machine, Naïve Bayes and Deep Neural Networks as research model. Meanwhile, this paper selects the Kaggle website data of a bank and credit information bureau in India, preprocesses the dataset and applies it to the training and testing of the models, and finally derives the performance results of the four models. The results of the study show that the machine learning models have better accuracy and higher efficiency in analyzing personal credit data and assessing the risk of personal loan default. Among them, the Deep Neural Network has the best overall performance compared to the other three machine learning models. The research in this paper has certain research significance for the research of machine learning in personal loan default risk assessment.
Research on Spam Filters Based on NB Algorithm
Authors: Su Shengyue
Published: January 2025

Spam filtering is a crucial part of network security. As spam becomes more complex, traditional rule-based methods struggle to meet the needs of modern email systems. The SpamAssassin dataset is used in this study to explore the use of the Naive Bayes (NB) algorithm for spam detection. The algorithm demonstrated high accuracy and efficiency in classifying large-scale text data, achieving an accuracy of 97.74%, a recall rate of 96.60%, and a precision rate of 96.8%, with an F1 score of 0.97. Through confusion matrix and Receiver Operating Characteristic (ROC) curve analyses, the model’s effectiveness in spam filtering was demonstrated by its high True Positive Rate (TPR) and low False Positive Rate (FPR). However, limitations arise from the NB algorithm’s independence assumption, which may affect performance in more complex spam scenarios. Future work may focus on improving the model’s accuracy and robustness by integrating it with other machine learning models, like Support Vector Machines (SVMs) and deep learning techniques, to enhance spam classification capabilities.
Identifying the Origin of Cyber Attacks Using Machine Learning and Network Traffic Analysis
Authors: Du Tianqing
Published: January 2025

In this paper, PCAP refers to Packet Capture, Network Intrusion Detection Systems refers to NIDS, Artificial Intelligence refers to AI, machine learning refers to ML, Computer Vision refers to CV, and Natural Language Processing refers to NLP. While the development of the internet promotes global progress, it also brings various cyber-attacks, such as phishing, junk emails, and keylogging. To ensure a clean internet environment, it is essential to identify the origin of cyber-attacks for effective defense and mitigation. This paper introduces an effective method of internet protection—machine learning. A common technique in the modern world, machine learning offers significant insights into locating the IP address and data origin. The focus of this paper is on how supervised machine learning is used to determine the data origin. The Random Forest Classifier is the key model analyzing network traffic data to predict the origin of cyber-attacks. By converting IP addresses, packet lengths, and protocol types into numerical features from PCAP files, this study applies machine learning techniques to classify attack behaviors. Additionally, an experiment testing the model’s effectiveness is designed to prove its efficiency and ensure the model’s precision.
Federated Learning-Based Credit Card Fraud Detection: A Comparative Analysis of Advanced Machine Learning Models
Authors: Zheng Han
Published: January 2025

Because of the privacy concerns about the transaction data, it is essential not to leak it when training prediction models for credit card fraud analysis. Challenges for credit card fraud monitoring include highly imbalanced datasets and the need for advanced models to detect fraud patterns. This paper introduced federated learning and discussed a few federated learning algorithms applied to the problem—these methods include Federated Graph Attention Network with Dilated Convolution Neural Network (FedGAT-DCNN), FedAvg with Convolutional Neural Network (CNN), and Federated Averaging with Distance-based Weighted Aggregation (FedAvg-DWA) with Random Forest (RF). Federated Averaging (FedAvg) aggregates data from local clients and then creates a global model; fedavg-dwa provides dynamic weight averaging, which enhances each client’s performance based on their data quality. The FedGAT-DCNN model improves accuracy by integrating GAT with Dilated Convolutions to catch spatial and temporal patterns in transaction data. FedGAT—DCNN performs best on highly imbalanced datasets with a high Area under the Receiver Operating Characteristic Curve (ROC-AUC) score. FedAVG-DWA provides the best performance in different clients’ systems. However, system heterogeneity, communication costs, and data imbalance remain critical. Oversampling techniques, model optimization, and reduced communication rounds were used to mitigate the issues. Therefore, federated learning’s ability can enhance credit card fraud sensing issues without privacy concerns. The paper highlights both the benefits and challenges of using federated learning in the domain.
Reducing Judicial Inconsistency through AI: A Review of Legal Judgement Prediction Models
Authors: Wu Yifan
Published: January 2025

Ensuring equitable sentencing is a fundamental objective of the judicial system. However, disparities in law enforcement standards, policies, and personnel competence across regions can lead to divergent sentencing outcomes for similar cases. This inconsistency undermines the integrity of justice and diminishes public confidence. With the development of AI technology, especially in the field of NLP, more and more researchers are focusing on the role that AI can play in legal judgements, and the LJP model has been developed. The LJP model is widely expected to help reduce the judicial inconsistency that currently exists, and better help to maintain the fairness and justice of the law. This paper summarizes the latest developments in the field of LJP, introduces and compares some of the current representative works, including the advantages and disadvantages of current technology. After that, it discusses possible future research directions and considers the significance of the development of this field.
Few-shot Learning: Methods and Applications
Authors: Li Jiaxiang , Li Mingyang
Published: January 2025

The Few-shot learning (FSL) approach distills meaningful features from a constrained sample set, allowing models to swiftly adjust to novel tasks and decreasing the dependency on extensive datasets. This approach leverages methods involving meta-learning, transfer learning, and data augmentation to boost the model's ability to recognize new categories. In many areas of artificial intelligence, obtaining large annotated datasets is often high in financial demand and extensively time-consuming, particularly in specialized fields with limited data availability. Therefore, the study of FSL is particularly critical. This paper first reviews the relevant methods of FSL, primarily categorizing them into model fine-tuning based FSL, data augmentation-based FSL, and transfer learning-based FSL. Model fine-tuning based FSL involves making slight adjustments to pre-trained models, allowing them to adapt to new tasks. Data augmentation-based FSL enhances the model's generalization ability by generating or expanding existing data. Transfer learning-based FSL transfers knowledge acquired by models from large datasets to smaller ones, enhancing the learning outcomes. Subsequently, this paper reviews the application areas of FSL and explores its impact in these fields. This paper aims to present the current state and prospects of this technique, providing valuable insights for researchers in related fields.
Advancements in Image Classification: From Machine Learning to Deep Learning
Authors: Cheng Haoran
Published: January 2025

Image classification, as an essential task within the realm of computer vision, has evolved from traditional machine learning methods to deep learning techniques. This paper systematically reviews the growth of image classification technology, beginning with the introduction of commonly used datasets such as CIFAR-10, ImageNet, and MNIST, and exploring their impact on algorithm development. Subsequently, the paper provides an in-depth analysis of image classification methods based on machine learning, including traditional algorithms such as Support Vector Machine (SVM), Random Forest, and Decision Tree. These methods achieve image classification through two stages: feature extraction and classification, but they encounter limitations when confronted with large-scale datasets and complicated tasks. Convolutional Neural Networks (CNNs) have gradually replaced traditional methods in image classification due to the rise of deep learning, resulting in improved accuracy and robustness. The paper also focuses on discussing classic deep learning models such as AlexNet, VGGNet, ResNet and ViT, analyzing their strengths and weaknesses. By comparing the performance of different methods, this paper aims to provide references for researchers in the realm of image classification, promoting further development in this area.
Diabetes Risk Assessment: A Comparative Study of Decision Trees and Ensemble Learning Models
Authors: Lei Tianxing
Published: January 2025

Diabetes poses a significant threat to global health, making accurate prediction and effective treatment of the disease critical. This study explores the application of machine learning algorithms in assessing diabetes risk, with a particular focus on Decision Trees (DT) and Ensemble Learning techniques. DT methodically evaluate various indicators that impact classification outcomes, using sequential decisions to classify each indicator based on the results of previous classifications. This process ensures that all possible combinations of indicators are mapped to a single classification result. Ensemble Learning, on the other hand, leverages multiple classifiers with assigned weights to form a robust ensemble. Each classifier provides its prediction, and the final classification result is derived from a weighted voting mechanism based on the performance of each learner. The study’s experimental results demonstrate that applying Principal Component Analysis (PCA) to preprocess the data, followed by training a Random Forest (RF) model with 80% of the dataset, achieves an impressive accuracy of 89.86%. This high accuracy highlights the effectiveness of machine learning algorithms in predicting diabetes risk. The findings underscore the potential of these methods in enhancing diabetes management and offer a valuable contribution to the field of medical predictive analytics.
Advancements of Deep Learning Model-Based Rehabilitation Training System
Authors: Xu Chiyu
Published: January 2025

Traditional therapies for rehabilitation training in modern society are difficult to track patients dynamically, so this paper introduces a rehabilitation training evaluation system under deep learning modeling to help assess the effectiveness of rehabilitation training. In this paper, one of the studies proposed the concept of posture-guided matching based on paired Siamese Convolutional Neural Networks (SCNN), abbreviated as ST-AMCNN, on a dataset of the traditional Chinese rehabilitation training Baduanjin. Another study classified the output layers of shoulder pain rehabilitation using IMU sensors with multiple training programs for different patients wearing IMUs. IMU sensors for rehabilitation training that requires some time to analyze data and feedback data, there are more efficient studies that promote finger movement by giving patients robotic gloves to wear and propose a hand rehabilitation system thus helping stroke survivors with active rehabilitation. In addition, it was suggested to use a Smart Movement and Rehabilitation Monitoring System (SMRMS) to focus more on the participants’ training precision and recuperation. The experimental results show that there is still room for the development of rehabilitation training assessment systems in terms of privacy, interpretation ability, and application scenarios, and that researchers can address the above issues by using federated learning, developing an expert system, and using transfer learning domain adaptation, respectively.
Exploring CycleGAN Technique for Improved Plant Disease Detection and Analysis
Authors: Ouyang Luyi
Published: January 2025

The threat of plant disease is a significant problem affecting the world, when untreated these diseases can affect food production. Diagnosis of these diseases in an un-delayed manner is very important, however, methods described in current use that only involve the use of sight are inefficient and are also subject to errors. This paper tackles the problem by using Cycle-Consistent General Adversarial Networks (CycleGAN) to create artificial images of diseased plant leaves. The advantage of this approach is that augmenting the training data with images that do not exist in the real world helps improve the performance of disease classifications. The research takes into consideration the apple leaves diseased images, is of various pathogens, and CycleGAN creates images to even it. The results indicate that CycleGAN is indeed able to generate artificial images for the less complicated sicknesses associated with a mere shift in color, with an achieved micro-average Area Under the Curve (AUC) of .98 and macro-average AUC of 0.94. On the contrary, this model has problems in striking a balance while dealing with more complex diseases that have problems that are underlying structural deformation. However, adding such images in training datasets increases the classification accuracy in total. Future work should involve making the model more robust to complex and rich visual details as well as employing more sophisticated models for better applicability in real farming settings.
Advanced Network Traffic Prediction Using Deep Learning Techniques: A Comparative Study of SVR, LSTM, GRU, and Bidirectional LSTM Models
Authors: Wang Yuxin
Published: January 2025

Accurate prediction of network traffic patterns is essential for optimizing network resource allocation, managing congestion, and strengthening cybersecurity. This study examines the effectiveness of four machine learning models—Support Vector Regression (SVR), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Bidirectional Long Short-Term Memory (Bi-LSTM)—in forecasting traffic patterns using both web-based and real-world datasets. The models are evaluated based on their generalization accuracy, as measured by Mean Absolute Percentage Error (MAPE), computational efficiency, and their ability to capture underlying traffic dynamics. Results indicate that GRU surpasses SVR and LSTM in terms of prediction accuracy and computational speed, while Bidirectional LSTM demonstrates superiority in capturing long-term dependencies across extended periods. These findings underscore the significant potential of deep learning models, particularly GRU and Bidirectional LSTM, in improving the precision and reliability of network traffic predictions. The study offers insights into the strengths and limitations of each model, contributing to the ongoing development of more robust and efficient network traffic forecasting methods.
Dual Convolution Neural Networks of Ensemble Learning with Attention Mechanism for Rice Classification
Authors: Cheng Linfeng
Published: January 2025

Machine vision has been widely applied across fields. Image classification is one of the most classic fields. The aim of this project is to develop a dual convolutional neural network for ensemble learning based on the initial model and the res network model, and apply the ensemble model to the rice classification problem. The ensemble model in this article combines two deep models, InceptionNet and ResNet, and incorporates self-attention block method to construct an attention mechanism that uses multi head attention layers to capture relationships in the input. Attention output is added back to input. At the same time, 10 evaluation indicators were introduced as the results of testing and evaluation. In the result analysis, it can be concluded that the ensemble model has demonstrated excellent training efficiency in these indicators, and the learning rate hyperparameter has been replaced to improve the stability of the model. At the same time, for a more comprehensive comparison, the ensemble model studied in this article was also compared and analyzed reasonably with three pre trained models: VGG-16, ResNet50, and MobileNet. In the future, it is necessary to continuously optimize the structure of integrated models and adjust their hyperparameters to achieve better stability.
The Impact of GPT Models on Education: Enhancing Learning Outcomes and Addressing Challenges
Authors: Yang Nuoya
Published: January 2025

This paper investigates the impact of Generative Pre-Trained Transformer (GPT) models in the field of education, a rapidly evolving area as artificial intelligence (AI) technologies become more integrated into learning environments. The primary' objective of the study is to evaluate both the benefits and challenges of implementing GPT models to enhance educational outcomes. The research delves into how GPT can drive personalized learning experiences, automate routine administrative tasks, and provide immediate, adaptive feedback to students. It also critically examines potential issues, including data privacy concerns and the need for teachers to adapt to these new technologies. The study utilizes a comprehensive methodology’ that involves a detailed analysis of GPT model architecture and its application across various educational contexts. Key areas of focus include the deployment of GPT in adaptive learning platforms, its role in automating grading processes, and its capacity to generate interactive and engaging educational content. This balanced approach aims to provide a nuanced understanding of the transformative potential of GPT in education, highlighting both its ability to enhance learning outcomes and the challenges it presents for broader implementation.
Enhancing Stock Price Forecasting Accuracy Using LSTM and Bi-LSTM Models
Authors: Wang Hao
Published: January 2025

Accurately predicting stock price trends is of critical importance in the financial sector, enabling both individuals and enterprises to make informed and profitable decisions. In recent years, researchers have employed a variety’ of techniques to forecast stock market trends, yet the challenge of improving accuracy remains. This research introduces an innovative approach to predicting stock prices, employing two sophisticated models: Long Short-Tenn Memory (LSTM) and Bidirectional Long Short-Tenn Memory (Bi-LSTM) networks. Through rigorous analysis, the research demonstrates that, with proper hypeiparameter tuning. LSTM models are capable of making highly accurate predictions of future stock trends, a capability’ that is also exhibited by Bi-LSTM models. The study’ evaluates the models by’ measuring the Root Mean Square Error (RMSE) while varying key factors. Publicly available stock market information. such as the highest and lowest prices, and opening and closing prices, is utilized for evaluating model effectiveness. The results indicate that the Bi-LSTM model is superior to the LSTM model in terms of RMSE. making it a more effective methodology for stock price forecasting and aiding in strategic decision-making.
Evolution of machine learning in financial risk management: A survey
Authors: Lu Kuan-I
Published: January 2025

Financial risk management plays a crucial role in daily financial decision-making, aiming to mitigate risk and maximize profit. Given its reliance on data, financial risk management can greatly benefit from the application of machine learning tools. Over the years, we've observed a clear trend in the evolution of these applications, marked by increasing model complexity and a broader range of manageable tasks. This paper contributes to the field in three key dimensions: First, we provide a clear taxonomy of risks and an introduction to relevant machine learning methods to establish a foundation and identify the targeted issues. Next, we explore real-world data applications, discussing the pros and cons of three methods, from the earliest to the most recent. Finally, based on the observed results, we highlight current challenges and limitations and propose potential directions for improvement.
Stroke Prediction Based on Machine Learning
Authors: Zhang Yuhan
Published: January 2025

Stroke has become an important cause of death and disability worldwide, which highlights the need for early detection and intervention. Machine learning technology can analyze patients’ historical health data and biometrics to identify high-risk individuals in a timely manner, thereby effectively predicting stroke.This paper evaluates the predictive performance Random Forest and Support Vector Machine (SVM). Data preprocessing encompasses managing missing data, processing categorical variables, and tackling issues related to class imbalance. Analysis of the quantitative results indicates that the Random Forest model reaches an accuracy of 95% and a precision of 93%, providing a slight edge over the SVM, which records an accuracy of 92% and a precision of 90%.. However, both models exhibit high false-negative rates, with Random Forest showing a false-negative rate of 12% and SVM at 15%, which significantly impacts their clinical utility. To improve performance, further model optimization, such as adjusting class weights or employing ensemble methods, is necessary to reduce these false-negative rates and enhance diagnostic accuracy. This study highlights the potential and limitations of machine learning in stroke prediction, showing that people need further optimization to enhance diagnostic performance.
Clinical outcomes and long-term efficacy of high tibial osteotomy in treating knee instability: An updated systematic review
Authors: Mustamsir Edi , Aji Aulia Pandu , Adiwangsa Ahmad Abdilla , Akmalizzan Azfar Ahnaf
Published: January 2025

Introduction: Knee joint stability is influenced by force distribution and ligament structures. High Tibial Osteotomy (HTO) treats knee deformities and redistributes load, reducing further invasive procedures. High Tibial Osteotomy (HTO) is a well-established procedure for addressing knee instability, particularly in cases involving ligament deficiencies such as ACL and PCL insufficiencies. This systematic review aims to evaluate the clinical outcomes and long-term efficacy of HTO in improving knee stability and function. Methods: A systematic literature search was conducted using Cochrane Central, PubMed, MEDLINE, and ProQuest databases for studies published between 2000 and June 2024. Eligible studies included human subjects with at least six months of follow-up and focused on HTO for knee instability. Exclusion criteria included animal studies, non-knee joint studies, and reviews. Data on patient demographics, follow-up duration, subjective and objective outcomes, and complications were extracted. Results: Out of 536 studies identified, 11 met the inclusion criteria, encompassing 303 patients. Combining HTO with ACL or PCL reconstruction significantly improved both subjective instability and objective measures, including Lachman and Pivot Shift test grades. Patient satisfaction was high, and functional scores such as Lysholm and Tegner improved markedly. The incidence of complications was low, with minor issues such as infections and delayed union, and no reported graft failures. Conclusion: HTO, particularly when combined with ligament reconstruction, effectively treats knee instability due to ACL or PCL deficiency. The procedure demonstrates strong mid- to long-term outcomes, high patient satisfaction, and a low rate of complications. It remains a viable option for patients with knee instability.
Comment on “Surgical Morbidity following Planned Hysterectomy versus Conservative Management for Placenta Accreta Spectrum: A Systematic Review and Meta-analysis”
Authors: Thangavelu Lakshmi
Published: January 2025

Not applicable
Design, Synthesis, and Biological Evaluation of Thioether Steroids as Glucocorticoid Receptor Modulators
Authors: Seaton Wesley B. , West Clayton , Burke Susan J. , Schilletter William A. , Tade Opeyemi O. , Collier J. Jason , Campagna Shawn R.
Published: January 2025

Glucocorticoids (GCs) are an important class of therapeutic steroids commonly prescribed to treat inflammation and autoimmune disorders. However, long term GC use can lead to serious metabolic complications including alterations in insulin sensitivity, resulting in increased risk for diabetes. The anti-inflammatory activity stems from GCs binding to the glucocorticoid receptor (GR) and functioning to oppose pro-inflammatory outcomes, while their undesired side effects arise through a variety of incompletely understood mechanisms. Previously, a set of thiobenzothiazole modified GCs were shown to elicit modest anti-inflammatory activity. In this study, a series of structurally diverse GC scaffolds were derivatized with thio-heteroaryl moieties and biologically and computationally examined for their capacity to effectively engage the GR. Of the compounds studied, a C-21 thiobenzoxazole substituted prednisolone analogue demonstrated a 56% reduction in 3x-GRE promoter reporter response and no loss in anti-inflammatory potential.
Nonsyndromic Hypodontia and Associated Developmental Dental Anomalies in Siblings: A Case Series and a Review
Authors: Al-Abdallah Mariam , Al-Sadi Nabeel , Tayeh Hadeel , Althagafi Nebras , Eshky Rawah , Dar-Odeh Najla
Published: January 2025

A dental anomaly is any deviation from normal tooth development, affecting the number, size, form, position, or structure of one or more teeth. Examining affected family members can enhance our understanding of the genetic factors involved in both normal and abnormal dental development. Hypodontia is a common dental anomaly characterized by the congenital absence of one or more teeth. It may exhibit a familial pattern associated with certain dental anomalies in siblings. The two main treatment modalities for hypodontia are space opening for prosthetic replacement of the missing tooth or space closure, which underscores the need for an interdisciplinary approach of management. This case series aims to present the clinical dental features and treatment modalities of 10 siblings from 5 families who exhibited hypodontia and other nonsyndromic dental anomalies with malocclusion. A thorough literature review on familial and genetic basis of hypodontia and associated dental anomalies in siblings is also presented.
Management of Malignant Colon Polyps
Authors: Charboneau Alex , Law Joanna K. , Kaplan Jennifer A.
Published: January 2025

Surgically resected stage I colon cancer results in the best possible prognosis for patients diagnosed with colorectal cancer. However, there remains some debate about whether formal oncologic resection is necessary for the earliest of tumors—those detected in a polyp. To best understand whether an oncologic resection is “avoidable” in select cases of patients with a malignant polyp, it is necessary to review the contemporary evidence on endoscopic techniques, risks of lymph node metastasis, and nuances of the decision-making between oncologic resection and surveillance.
Home Respiratory Polygraphy to Detect Obstructive Sleep Apnea Syndrome after Supracricoid Partial Laryngectomy
Authors: Hernández-Sandemetrio Rosa , Oishi Natsuki , Chavero Tomás , Navarro Rafael , López Isabel , Zapater Enrique
Published: January 2025

Introduction Supracricoid partial laryngectomy is a surgical treatment for advanced laryngeal cancer which is implemented to preserve organ function, but it may cause obstructive sleep apnea syndrome (OSAS) due to anatomical changes after surgery that may be neglected by clinicians.

Although the gold standard for the diagnosis of OSAS is polysomnography, respiratory polygraphy is an alternative valid method with a high level of diagnostic sensitivity and specificity; since the equipment is portable, it can be used at home, with no need for hospitalization.

Objective To describe the polygraphy result of patients submitted to supracricoid partial laryngectomy.

Methods The present study included 13 patients, and we collected data on age, date of the surgery, body mass index, cardiovascular risk factors, Epworth score, and apnea-hypopnea index (AHI).

Results The 13 patients were all male, with a mean age of 62 years. As for the AHI, one patient was classified as severe, six, as moderate, and three patients, as mild; moreover, 3 patients were simple snorers. While 77% of the sample presented OSAS, only 23% presented symptoms of drowsiness.

Conclusion The study group, who underwent supracricoid partial laryngectomy, did not present self-reported symptoms of OSAS. Nevertheless, polygraphy was a useful tool in this group, and we recommend its systematic use after decannulation to avoid leaving OSAS undiagnosed.

Level of evidence 4; case series study
Inflammation and Coagulation in Neurologic and Psychiatric Disorders
Authors: Khoury Rabee , Chapman Joab
Published: January 2025

Coagulation factors are intrinsically expressed in various brain cells, including astrocytes and microglia. Their interaction with the inflammatory system is important for the well-being of the brain, but they are also crucial in the development of many diseases in the brain such as stroke and traumatic brain injury. The cellular effects of coagulation are mediated mainly by protease-activated receptors. In this review, we sum up the role of the coagulation cascade in the development of different diseases including psychiatric disorders. In inflammatory diseases such as multiple sclerosis, fibrinogen activates microglia and suppresses the differentiation of oligodendrocytes, leading to axonal damage and suppression of remyelination. In ischemic stroke, thrombin activity is associated with the size of infarction, and the inhibition of either thrombin- or protease-activated receptor 1 promotes neuronal survival and reduces the size of infarction. Patients suffering from Alzheimer's disease express higher levels of thrombin, which in turn damages the endothelium, increases blood–brain barrier permeability, and induces cell apoptosis. In major depressive disorder, a positive correlation is present between prothrombotic states and suicidality. Moreover, both protein S deficiency and antiphospholipid antibodies are associated with schizophrenia and there is an effect of warfarin on psychosis-free intervals. Studying the coagulation in the brain could open a new door in understanding and treating neurological and psychiatric disorders, and extensive research should be conducted in this field.
Chiral correlated-plasmons enhanced Raman optical activity from spin-polarized, correlated s band in highly oriented single-crystalline gold quantum-dots
Authors: Ho Shermine , Ong Bin Leong , Naradipa Muhammad Avicenna , Fauzi Angga Dito , B. M. Ali M. Saifudin , Tok Eng Soon , Rusydi Andrivo
Published: January 2025

Interactions of chiral light with chiral matter, such as Raman optical activity (ROA) and, independently, spin-polarized materials have attracted a lot of interest for both fundamental science and applications. The ROA, on the one hand, provides information on chiral phonons of molecules. However, the short-lifetime ROA signal in general is extremely weak and requires long exposure times, making it not accessible for many important systems with short lifetime. Materials exhibiting high spin polarization in d or f band, on the other hand, remain very limited even at very low temperature. There has been no report on materials exhibiting spin polarization in s band. Herewith, we report a room temperature, full spin polarization in unconventional, correlated s band of highly oriented single-crystalline gold quantum-dots (HOSG-QDs). Intriguingly, the HOSG-QDs produce a chiral correlated-plasmons enhanced Raman optical activity (CP-ROA) with anomalous ROA enhancement and strong spin-dependent chiral coupling. We then address a fundamental problem in crystal violet. Using spin-polarized HOSG-QDs chips, we observe strong CP-ROA signal, revealing chiral properties. The chiral correlated-plasmons of HOSG-QDs interact with the spin, electronic, and lattice structures of crystal violet, revealing chiral phonons and chiral electronic Raman excitations of crystal violet. Such a strong CP-ROA spectrum is obtained within a minute of measurement and a simple preparation without patterning. Our result shows that the CP-ROA based on a spin-polarized HOSG-QDs is extremely sensitive to the chiral property of phonon and spin and electronic structures and a fast, label-free chiral spectroscopic-based detection.
High-crystallinity and enhanced mobility in In2O3 thin-film transistors via metal-induced method
Authors: Chen Zhipeng , Fu Zhaoxing , Jin Tingting , Jing Liang , Ren Junyan , Lei Minghang , Sun Jingting , Chen Hongyu , Liang Lingyan , Cao Hongtao
Published: January 2025

Meeting the advanced demands of display technology, developing oxide semiconductor thin-film transistors (TFTs) with high mobility remains a significant challenge in current research. This paper reports the fabrication of high-crystallinity In2O3 thin films and high-mobility TFT devices through low-temperature annealing using aluminum (Al) and tantalum (Ta) for induced crystallization. In the control film, partial crystallization occurs only in the central region, with grain lateral dimensions around 50 nm, resulting in a reasonable field-effect mobility of 23.9 cm2/V s for the corresponding TFTs. In contrast, metal-induced films form In2O3 grains with lateral dimensions exceeding 100 nm, along with numerous spherical crystalline particles at the metal/In2O3 interface. The well-defined front-channel structure allows the Al- and Ta-induced In2O3 TFTs to achieve high field-effect mobilities of 65.2 and 101.0 cm2/V s, respectively. Additionally, Al induction improves the subthreshold swing and threshold voltage (Vth), enhancing overall electrical performance. This study investigates the crystallization behavior of induced technology in the In2O3 system, elucidates the mechanism of metal-induced crystallization, and demonstrates that Al-induced crystallization significantly enhances the performance of metal oxide TFTs under processing temperature constraints.
Alkylamine-tuned MoOx with synergistic manipulation of interlayer spacing and oxygen vacancies toward advanced Li–S batteries
Authors: Niu Fengjun , Xu Guobao , Yang Hengyu , Liang Yongle , Xu Zhan , Li Huihong , Wei Xiaolin , Yang Liwen
Published: January 2025

Lithium–sulfur (Li–S) batteries have been considered a promising next-generation energy storage device. However, the serious polysulfide shuttle effect and slow reaction kinetics hampered their development. Herein, alkylamine-tuned MoOx with synergistic manipulation of interlayer spacing and oxygen vacancies as a bifunctional mediator for separator modification (refer to as MOC/PP) in Li–S batteries is proposed. The increased interlayer spacing provides a rapid and stable pathway for Li+ diffusion, facilitating uniform Li+ deposition on lithium anode. Rich oxygen vacancies serve as active sites for efficient chemisorption and catalysis with polysulfide. As demonstrated by theoretical calculations and experimental results successively, MOC/PP efficiently captures and accelerates the redox reaction of polysulfide. Therefore, LiǁLi symmetric cells with MOC/PP exhibit stable cycling over 1000 h at a current density of 1 mA cm−2. The full cells deliver a notable discharge-specific capacity of 602 mAh g−1 at 5 C (1 C = 1675 mA g−1) and maintain stable cycling for 800 cycles at 1 C, with 0.07% capacity decay per cycle. Even under conditions of lean electrolyte (E/S = 7 μL mgs−1) and high sulfur mass loading (4.3 mg cm−2), the initial capacity exceeds 1200 mAh g−1.