bluuebunny/crossref_metadata_2025 huggingface dataset:
Efficient harvesting of triplet excitons via multiple fast TTA up-conversion and high-lying reverse intersystem crossing channels for efficient blue fluorescent organic light-emitting diodes
Authors: Qin Jianwen , Qiao Xianfeng , Xiao Shu , Yang Dezhi , Dai Yanfeng , Chen Jiangshan , Sun Qian , Ma Dongge
Published: January 2025

A high efficiency blue OLED with fast TTA up-conversion and highly-lying reverse intersystem crossing channels was successfully fabricated. The maximum EQE reached 11.4% with CIE coordinates of (0.13, 0.11).
Chemical upcycling of polybutadiene into size controlled α,ω-dienes and diesters via sequential hydrogenation and cross-metathesis
Authors: Vos Christophe , Beckers Igor , O'Rourke Galahad , De Vos Dirk
Published: January 2025

Tuning of the hydrogenation degree and isomerization in the consecutive hydrogenation-metathesis of polybutadiene rubber allows excellent control over the upcycled α,ω-dienes and diesters.
Palladium-catalyzed intramolecular aerobic oxidative cross-coupling of BH/CH between o-carborane and arenes
Authors: Wang Zhen , Yu Jiahui , Zhang Jie , Zhang Dengsong , Qiu Zaozao , Xie Zuowei
Published: January 2025

Efficient Pd-catalyzed regioselective intramolecular aerobic oxidative coupling of BH/CH has been achieved with the construction of a series of five-, six- and seven-membered rings, facilitating potential applications in carborane-based luminogens.
Investigating the redshift evolution of lensing galaxy density slopes via model-independent distance ratios
Authors: Geng S. , Grespan M. , Thuruthipilly H. , Harikumar S. , Pollo A. , Biesiada M.
Published: January 2025

Context. Strong-lensing systems are expected to be discovered in great numbers by next-generation surveys. They provide a powerful tool for studying cosmology and the galaxy evolution. The coupling of the galaxy structure and cosmology through distance ratios means that it is essential for advancing both fields to examine the evolution of the lensing galaxy mass density profiles.

Aims. We introduce a novel method that is independent of the dark energy assumed in the model to investigate the mass density slopes of lensing galaxies and their redshift evolution using an extended power-law (EPL) model.

Methods. We adopted a nonparametric approach based on artificial neural networks trained on type Ia supernovae data to reconstruct the distance ratios of strong-lensing systems. These reconstructed ratios were compared with theoretical predictions to estimate the evolution of EPL model parameters.

Results. A negative evolutionary trend of the mass density power-law exponent with increasing redshift is observed across different analysis levels. Assuming a triangular prior for the anisotropy of lensing galaxies, we find evidence for a redshift evolution of the mass density slope, quantified as ∂γ/∂z = −0.20 ± 0.12.

Conclusions. This study confirms that the redshift evolution of the matter density slopes in lensing galaxies can be determined independent of dark energy models at the population level. The Legacy Survey of Space and Time (LSST) Rubin Observatory forecasts are expected to identify 100 000 strongly lensed galaxies. We show based on simulations with data from the LSST that spectroscopic follow up of just 10% of these systems can reduce the uncertainty in the redshift evolution coefficient of the total mass density slope (Δ∂γ/∂z) to 0.021. This precision would be able to distinguish between evolving and nonevolving scenarios for lensing galaxies.
Enhanced electrochemical performance of activated carbon derived from Acacia catechu bark by KOH activation for supercapacitor application
Authors: Mishra Pawan Kumar , Aryal Sabin , Bhakta Oli Hari , Shrestha Timila , Sapkota Thakur , Shrestha Ram Lal Swagat , Prasad Bhattarai Deval
Published: January 2025

The compelling necessity for sustainable energy storage solutions has propelled the advancement of high-performance energy storage strategies. Conventional carbon materials derived from fossils sources cause the limitations of being high rate, processing challenges, and potential harm. Conversely, activated carbon sourced from biomass has emerged as a promising substitute due to its abundant presence in nature, high carbon content, and ease of processing with minimal toxicity. This study outlines the production of activated carbon from the matured bark of Acacia catechu through a sequence of processes, including pre-carbonization, carbonization, and activation at varying temperatures utilizing potassium hydroxide (KOH) as an activating agent. The prepared specimen underwent analysis using diverse methods such as iodine and methylene blue number techniques, XRD, FTIR-test, Raman test, TEM, and its potential storage capacity was appraised via CV, GCD, EIS, and CV stability tests paying a three-electrode arrangement. The activated carbon synthesized at 800 °C (KBAC-8) demonstrated a specific capacitance of 264.51 F/g at 1 A/g, with outstanding retention of 94.64% and superior coulombic efficiency equated to KBAC-6 and KBAC-4. Overall results suggest the relevance of KBAC-8 for supercapacitor applications due to its subordinate resistance.
Characteristic and flavor profiles of specialty Arabica coffee derived from fermentation with protease, at different conditions
Authors: Wongyai Intugon , Suttisintong Khomson , Thumthanaruk Benjawan , Foophow Tita , Yasutomo Tamaki , Rungsardthong Vilai
Published: January 2025

Coffee is one of the most popular beverages in the world. Development of coffee beans with specific flavor profiles can enhance the quality and value of the product. In this study, the flavor profiles of Arabica coffee (Coffea arabica L.) beans treated with protease, at different enzyme loading, time, and pH, were investigated using gas chromatography tandem mass spectrometry (GC-MS/MS). The coffee beverages were evaluated for their sensory characteristics by Q-Grader (professional coffee taster). The coffee beans obtained from protease hydrolysis exhibited specific and unique volatile compounds or flavor profiles than the control (coffee beans without enzyme loading). Specific volatile profiles in the enzymetreated coffee beans, such as isopropenyl acetate, 2-acetyl-5-methylfuran, and delta-dodecalactone were identified. Coffee beverages obtained from roasted coffee beans from protease fermentation with 0.84% protease, pH 6.2 at 41 h (R-FP5), and 1.00% protease, pH 5.0 at 30 h (R-FP8) were evaluated as specialty coffees (a score of 79.75 points), showing their specific aroma profiles as fruity, spice, mango, dry fruit, ripe fruit, nutty and tamarind notes. The present work revealed that enzymatic fermentation of the green Arabica coffee beans is a promising method for the modulating and improving of specialty coffee with specific acceptable flavors and aromas.
Effect of silica fertilizer on accumulation of phytolith in rice plant and soil
Authors: Phakeenuya Vanarat , Jose Diana , Kunmanee Punyanuch , Sanyapeung Marsuton , Pongprayoon Wasinee , Katam Keerthi , Sriariyanun Malinee
Published: January 2025

Phytoliths, silica structures formed in plant tissues, significantly contribute to carbon capture by stabilizing organic carbon within their structures and aiding long-term carbon sequestration in soils. Phytoliths play a crucial role in enhancing plant resistance to biotic and abiotic stresses and improving soil properties. This study investigates the impact of two types of silica fertilizers—silicic acid and sodium metasilicate —on the accumulation of phytoliths in rice plants and soil. The experiment was conducted under controlled conditions, with rice plants, RD43 cultivar, receiving applications of either silicic acid or sodium metasilicate at the ratio of 0.33 g/kg-soil. Results indicated a significant increase in phytolith content in both the rice plant tissues (3.76 and 2.79 fold) after applications of silicic acid and sodium metasilicate, respectively. Similarly, in the surrounding soil, accumulations of phytolith contents increased 3.50 and 1.90 fold compared to control soil, without addition of silica fertilizers. Silicic acid was found to be more effective in enhancing phytolith accumulation in rice tissues, while sodium metasilicate showed a greater impact on plant growth and yield. These findings suggest that the type of silica fertilizer used can influence the distribution and accumulation of phytoliths in plants and soils. The study underscores the importance of selecting appropriate silica fertilizers for sustainable rice cultivation and soil management practices.
Effects of operating parameters on dielectric barrier discharge non-thermal plasma ozone generator performance and NO Conversion
Authors: Chonpan Phurin , Theinnoi Kampanart , Iamcheerangkoon Teerapong , Promhuad Punya , Sittichompoo Sak
Published: January 2025

This research investigates how gas flow rate, oxygen concentration, discharge voltage, and frequency influence the production of ozone (O3) and nitrogen dioxide (NO2). Findings reveal that increased discharge voltage and frequency enhance O3 and NO2 formation by raising free charge and energy, facilitating oxygen molecule dissociation and three-body collisions. Lower gas flow rates lead to higher specific energy density (SED) and thus greater O3 and NO2 concentrations due to longer oxygen molecule residence times. Higher oxygen concentrations in the feed gas result in a linear increase in O3 and NO2 levels, driven by more oxygen molecules available for ozone production which enhances NO oxidation. Additionally, increased input ozone concentration improves NO oxidation to NO2 and further increasing O3 input resulted in NO2 reduction via N2O5 formation. However, the presence of N2O5 was not quantified due to limitation in measurement. Understanding these factors is crucial for optimizing ozone generation for controlling NO2 emissions, with important implications for air purification and pollutant control.
The Applications and Prospects of Large Language Models in Traffic Flow Prediction
Authors: Liu Yuxuan
Published: January 2025

Predicting traffic flow is crucial for the functionality of intelligent transportation systems. It is of critical importance to relieve traffic pressure, reduce accident rates, and alleviate environmental pollution. It is an important part of the construction of modern intelligent road networks. With advancements in deep learning (DL), DL models have made notable strides in prediction. However, due to the complexity and non-transparency of DL models themselves, there are still problems of low accuracy and interpretability in traffic flow prediction (TFP). Leveraging large language models (LLM) helps to improve the negative conditions caused by other DL models in prediction. This paper first briefly summarizes the basic characteristics of LLM and their advantages in TFP; then conducts relevant research and analysis in the order of experimental design steps comparison and results and conclusions comparison; then analyzes and discusses the current problems and challenges faced by LLM; finally, it looks forward to future research directions and development trends, and summarizes this paper.
The Application Prospects of Event Data in Traffic Flow Prediction
Authors: Chen Haochi , Guo Zixin , Jiang Zicong
Published: January 2025

Conventional methods of predicting traffic flow can often be based on weekdays, certain holidays, road conditions, or signal light circumstances. Nevertheless, because there isn’t always an impact study done for some exceptional occurrences, these forecasting techniques or models might be ineffective. Recent studies have pointed out that social events such as concerts and large-scale events have a huge impact on Traffic flow. Considering these social events, to more accurately predict Traffic flow, this article refers to previous relevant literature, comprehensively describes the significant improvement of event data in traffic flow prediction (TFP) and how to use it, and generally discusses the relevant models that may apply event data to TFP. In detail, the current research results on STG-NCDE and Bi-LSTM models are presented, and the correlation, advantages, and disadvantages of the two are compared. In addition, the problems and challenges faced by the current application of event data in TFP are innovatively analyzed and discussed. Finally, the further achievements and related technology development trends that may be made in TFP based on this research direction in the prospect, and the article is summarized.
Federated Learning Applications in Fingerprint and Finger Vein Recognition
Authors: Wang Yongchao
Published: January 2025

Fingerprints and finger veins are widely used in security identification in many fields due to their uniqueness and identifiability. However, their privacy issues are often criticized. This article summarizes several approaches that combine federated learning with fingerprint and finger vein recognition to solve privacy issues. One of the frameworks for fingerprint recognition, Federated Learning-Fingerprint Recognition, uses sparse representation techniques such as the Discrete Cosine Transform for data preprocessing. The framework also references the ResNet18 model and reservoir sampling so that each client can participate in training fairly. As for finger vein recognition, the Federated Learning-based Finger Vein authentication framework allows clients to share model weights to solve the data island problem and divide client data into shared and personalized parts to ensure privacy. This paper also points out its challenges, such as poor interpretability and applicability, and provides optimization solutions. For example, the interpretability issue can be solved by implementing an expert system. The expert system uses its robust knowledge base and inference engine to track model behavior and derive reasonable explanations. Transfer learning can also eliminate the applicability issue. It transfers the knowledge gained from training clients with concentrated data to clients with sparse data. In summary, this article comprehensively reviews the methods of federated learning in fingerprint and finger vein, respectively, and discusses the shortcomings and prospects.
Relieve Adversarial Attacks Based on Multimodal Training
Authors: Lai Hongjie
Published: January 2025

This paper explores the role of multimodal training in mitigating the problems caused by adversarial attacks, building on the foundations of deep learning. Deep learning models have reached great success in many areas such as image recognition and natural language processing. But their robustness has always been a concern. However, the emergence of adversarial attacks has exposed shortages of neural networks, forcing people to confront their limitations and further increasing concerns about the security of deep learning models. Adversarial training is an effective defense mechanism that incorporates adversarial samples into the training data, enabling models to better detect and resist attacks. This paper first introduces the principles and types of adversarial attacks, as well as basic concepts and related methods, including Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), DeepFool, and Jacobian Saliency Map Attack (JSMA). The paper then focuses on analyzing the robustness of the multimodal model CLIP based on contrastive learning. Finally, the paper proposes whether audio data can be added to the training samples of the CLIP model to further improve its robustness, and raises related issues and bottlenecks.
Research on Analyzing the Emotional Polarity of Malicious Swipe Comments on E-commerce Platforms Based on NPL
Authors: Ren Chaoyi
Published: January 2025

In the era of rapid advancements in natural language processing (NLP) models, these technologies have immense potential to detect and address societal issues, enhancing the functioning of the digital society. Online shopping platforms rely heavily on user reviews to influence buyer decisions, yet malicious reviews can significantly degrade user experience. This study focuses on analyzing the emotional polarity of malicious brushorder (falsely generated) reviews in e-commerce product comments, utilizing the Jingdong product review dataset. The methodology involves utilizing the Word2Vec model to vectorize the text data, followed by principal component analysis (PCA) for outlier detection to identify potential malicious reviews based on their unique characteristics. The PCA results are further leveraged for dimensionality reduction, simplifying the dataset. Subsequently, the BERT model is employed to perform semantic similarity analysis, allowing for the screening and expansion of the experimental dataset with similar malicious comments. This enriched dataset is then subjected to sentiment polarity analysis, enabling a deeper tinderstanding of the nature and impact of these malicious reviews. By facilitating buyers in making informed decisions based on genuine reviews, this research underscores the practical value of NLP hi addressing real-world challenges in e-commerce.
Research Progress of Causal Inference in Bias Elimination
Authors: Chen Limanxi
Published: January 2025

The natural language processing (NLP) models have recently gained widespread attention and are increasingly being applied to real-world tasks. However, due to the presence of bias, the application of NLP models in specialized fields has led to various issues. This paper introduces various types of biases and. through a comparative analysis of multiple existing methods, explains why previous approaches cannot fundamentally solve the bias problem. Additionally, this paper proposes that using causal inference to eliminate bias is an advanced method, and research teams that reduce bias by building causal relationship models have already studied this approach. This paper conducts a detailed analysis of several cutting-edge studies, exploring the practical application of causal inference in bias elimination and the challenges involved. Experimental results indicate that although causal inference methods have eliminated bias to some extent, further research and optimization are still needed. Finally, this paper summarizes the previous sections and provides an outlook on future research directions.
The Use of Natural Language Processing Model in Literary Style Analysis of Chinese Text
Authors: Ye Jinze
Published: January 2025

In recent years, research on Natural Language Processing (NLP) has made consistent progress and has become a popular topic. As a promising branch of Machine Learning, NLP focuses on the understanding, generating and analysing of human languages. The applications of NLP include chatbots and language translation. This paper represents a HanLP based NLP model. The model is capable of analysing the literary style of given Chinese text by quantifying the literary style of the text on the basis of five fundamental elements, namely literary grace, sentiments, momentum, climate and lingering charm. This paper presents the input and output data of the research and conducts analyses on these data. Moreover, this paper draws a conclusion on the deviation rate and robustness of the model. It is reckoned that this model initially possesses the function of literary style analysis of Chinese text. The research, per se, along with its data, is capable of being reference for research in NLP and related fields.
Comparative Analysis of TF-IDF and Word2Vec in Sentiment Analysis: A Case of Food Reviews
Authors: Zhan Zerui
Published: January 2025

Sentiment analysis is an important area of natural language processing that supports applications such as market analysis, customer feedback, and social media monitoring by identifying and classifying opinions in text. Text representation is the basis of sentiment analysis, and TF-IDF and Word2Vec are two commonly used methods to carry out text vectorization by counting word frequency and capturing semantic relations respectively. This paper compares the performance of TF-IDF and Word2Vec in sentiment analysis of food reviews to provide a more effective basis for enterprises and researchers to choose text representation techniques. Based on 560,000 food review data, this paper focuses on comparing the accuracy and generalization ability of the two methods under different dataset sizes. The results showed that TF-IDF showed high accuracy in training data (99.16%), but showed obvious overfitting problems in test data (73.9%). In contrast, Word2Vec was more balanced on training and testing data (68.4% vs. 68.65%), showing better generalization. This finding has guiding implications for choosing text representation methods, especially in sentiment analysis tasks on large data sets.
Federated Learning for Brain Tumor Diagnosis: Methods, Challenges and Future Prospects
Authors: Ma Yuhan
Published: January 2025

With the widespread application of Artificial Intelligence (AI) and Machine Learning (ML) in medical field, early diagnosis of brain tumors has become increasingly significant. However, traditional methods face challenges such as data privacy, model interpretability, and data heterogeneity. This paper presents a detailed literature review of the application of Federated Learning (FL) in brain tumor classification, focusing on the necessity of privacy-preserving ML using Magnetic Resonance Imaging (MRI) technology. This paper analyzes various Convolutional Neural Network (CNN) models, including VGG16, ResNet50, DenseNet121, and EfficientNet, exploring their integration within the FL framework to enhance diagnostic accuracy while preserving patient data privacy. Through the discussion, core issues in this field emerge, including model interpretability, non-independent and identically distributed (Non-IID) data distributions, and computational challenges in FL architecture. Although these factors limit the widespread application of FL in medical settings, this paper also proposes potential solutions, such as improving algorithm interpretability through interpretable tools, and utilizing transfer learning and domain adaptation methods to enhance model effectiveness across different datasets. Techniques like knowledge distillation, model quantization, and pruning are proposed to enhance computational efficiency and minimize communication costs. Future research should focus on the application of these methods to enhance model reliability and efficiency.
Hierarchical Learning: A Hybrid of Federated Learning and Personalization Fine-Tuning
Authors: Li Shuyi , Zhang Bairong
Published: January 2025

Hierarchical Federated Learning (FL) presents a novel approach that combines global model training with localized personalization fine-tuning to enhance the predictive accuracy of decentralized machine learning systems. Traditional FL methods, which allow multiple clients to collaboratively train a global model without sharing raw data, are hindered by issues such as non-independent and identically distributed (non-IID) data, communication overhead, and limited generalization across diverse client datasets. This study proposes a hierarchical model that mitigates these challenges by incorporating a global model, trained using the Federated Averaging (FedAvg) algorithm, and applying client-specific fine-tuning to improve local model performance. The experiment conducted on a movie recommendation system demonstrates that this hierarchical approach significantly reduces the global model’s error while offering personalized improvements on client-specific datasets. Results show an average Root Mean Squared Error (RMSE) reduction of 0.0460 following local personalization. This hybrid approach not only enhances model accuracy but also preserves data privacy and increases scalability, making it a promising solution for decentralized recommendation systems.
Application Analysis of the Language Model
Authors: Cai Bochao
Published: January 2025

Language models(LM) like Claude3, ChatGPT and Llamas have prominent development recent years. However, with the rapid development of theses technology, how to better utilize them and avoid potential risks become an important research topic. Therefore, this paper aims to investigate how these LMs can be better utilized to serve humans and avoid potential risks. This paper mainly analyzes the use of LM in four fields, namely finance, healthcare, entertainment, and customer service to illustrate the usefulness of LMs. It is an emerging technology with considerable potential in various fields to help humans do their jobs better and faster. But at the same time, they may have problems about privacy, decision affectivity, ethics, and unemployment. This essay will hopefully provide a suggestion for the use and specification of LMs that will come later. These tools need to be used correctly, with reasonable avoidance of the risks it poses, to more fully utilize the strengths of LMs.
Real-Time Football Match Prediction Platform
Authors: An Zhongqi
Published: January 2025

The integration of real-time data into sports analytics has significantly enhanced the accuracy of football match predictions, which is vital for team management, tactical planning, and commercial applications *such as sports betting. This paper presents a Python-based platform for predicting football match outcomes by collecting and processing real-time data from the SofaScore website. The platform employs machine learning models, including Random Forest, Support Vector Machines (SVM), and Neural Networks, combined with feature engineering techniques, to generate accurate predictions. A user-friendly interface is also developed to facilitate easy access and analysis of this data. The platform’s real-time data updating mechanism ensures prediction accuracy, while the integration of multiple models through a Stacking method further enhances reliability. The platform’s innovative design addresses key challenges in sports analytics by providing a robust tool for data-driven decision-making. Future work will focus on enhancing model algorithms and incorporating more complex data sources, such as social media sentiment analysis, to further improve prediction accuracy.
Temperature and Humidity Prediction Based on Machine Learning
Authors: Xiong Yanqi
Published: January 2025

The growing impact of global climate change, emphasizing the critical importance of accurately predicting weather conditions, particularly temperature and humidity. These predictions are crucial for key sectors such as agriculture, energy management, and public safety. This paper employs various machine learning models, including Linear Regression(LR). Support Vector Machine(SVM), Neural Network(NN), and Random Forest(RF). to analyze their accuracy in predicting temperature and humidity. The results indicate that the NN model outperforms the others, showing excellent performance in the dataset. Li addition to the outstanding performance of the neural NN. the RF and SVM also demonstrated strong performance, particularly hi handling specific features within the dataset, the model's performance can be further optimized by adjusting the NN's hyperparameters or introducing more feature engineering, which could lead to even better results hi future data analyses. This research highlights the significant potential of machine learning techniques hi enhancing meteorological forecasting, providing valuable insights and tools for improving decision-making in industries heavily influenced by weather conditions.
Explore Machine Learning's Prediction of Football Games
Authors: Pan Bomao
Published: January 2025

The aim of this study is to predict the outcome and score of football matches. To achieve this goal, this paper employs a variety of machine learning models, including Random Forest, support vector classifiers (SVC), and Logistic Regression, and conducts in-depth analysis of the data. The results show that home teams have a significantly higher win rate than away teams. In addition, the score changes show a high degree of randomness, reflecting that the game is affected by a variety of factors. The prediction performance of these models is different, and the prediction accuracy of the random forest model is better than the other two models. Through the prediction of the winning rate, this paper aims to provide more scientific reference for the majority of fans and deepen the understanding of the strength of each team and the influence of external factors on the result of the game. This study not only helps to improve the analysis ability of football matches, but also provides a theoretical basis for the optimization of game strategies.
Advances in Image Generation Technology: Exploring GANs and MirrorGANs
Authors: Shi Lewuqiong
Published: January 2025

This paper is an in-depth study by delving into the latest in image generation technology, where thesis is focusing on the Generative Adversarial Networks (GANs) and MirrorGANs possibilities. Image Generation is the backbone of visual computing, mostly utilized in intelligent designs. It is for this reason that this research aims at unravelling the theoretical basis and consolidated practices of GANs when it conies to generating both high-quality and semantically consistent imagery. The study will investigate the whole of the image generation process, starting from data preprocessing to the use of GANs to generate images from textual descriptions. The work discussed the relevance as well as the limitations of these technologies from the artistic point of view, medical imaging, and virtual reality. Tire article concludes that the paper sketches the data and experiments that show that the realism and richness hi picture quality are accentuated when GANs and MirrorGANs are incorporated. This suggests the scope of image-generation technology to enhance human-machine collaboration and allow for innovating hi smart tech. Further studies will be geared to enhancing these methods and consequently drawing humanity and machines closer, which hi nun will fuel the ongoing progress in this fast-paced sphere.
Effectiveness Evaluation of Random Forest, Naive Bayes, and Support Vector Machine Models for KDDCUP99 Anomaly Detection Based on K-means Clustering
Authors: Zhang Majun
Published: January 2025

Security in the World Wide Web has recently seen an enormous upgrade in almost every aspect. Identifying malicious activities hi a network such as network attacks and malicious users plays a significant role hi these upgraded security directions. This research utilizes the KDDCUP99 dataset to incorporate K-means clustering with three classifiers: Random Forest (RF). Naïve Bayes (NB). and Support Vector Machine (SVM) with the goal to boost the accuracy of predicting network intrusions. In tins paper. K-means clustering technique is applied as a preprocessing step to enhance the overall quality of network intrusion detection and maximize the accuracy of the network security measures. The goal is to identify anomalies with high accuracy. Experimental results hidicate that the optimal combination is K-means + RF. which outperformed the others hi precision, recall, and Fl-score. Although K-means + NB demonstrated superior recall for certahi smaller anomalies, it underperformed compared to the RF model. The paper concludes by highlighting the value of ensemble approaches, in particular Random Forest, for tackling anomaly detection and network security issues, particularly hi light of the expanding significance of social networks and the internet.
Sql injection detection using Naïve Bayes classifier: A probabilistic approach for web application security
Authors: Lu Zhexi
Published: January 2025

A pervasive security issue in web applications is database injection, enabling attackers to alter SQL queries in order to get unauthorized access to confidential information. Using the Naive Bayes classifier, a probabilistic model specifically developed for text classification tasks, this work introduces a novel method for detecting SQL injection vulnerabilities.The process begins by collecting and organizing a comprehensive dataset, which includes both harmful and non-malicious SQL queries. Feature extraction is later employed to identify patterns and characteristics commonly associated with SQL injection, such as certain SQL clauses and logical operators. This collection of attributes is employed to generate a feature vector that serves as the input for the Naive Bayes classification algorithms. The classifier is trained using a labeled dataset and then learns to distinguish between benign and malicious requests by assessing their computed probabilities. Conventional measures such as accuracy, precision, recall, and F1-score are employed to assess the model’s ability in correctly identifying SQL while reducing false positive classifications.The present study demonstrates the potential of Naive Bayes in enhancing online application security by providing a methodical and scalable strategy for identifying SQL injection attacks.
DragGAN: Interactive Point-Based Image Manipulation on Generative Adversarial Networks
Authors: Wu Muran
Published: January 2025

Users have increasingly demanded greater control over generated images, including flexibility, precision, and versatility as a result of the development of Generative Adversarial Networks (GANs). This post introduces DragGAN, a picture-enhancing method that uses engaging dragging to obtain exact control over philosophical photos. DragGAN enables users to move, and adjust the position, style, location, and size of specific areas of a picture, and do so by integrating feature-operated motion supervision and point-tracking techniques. DragGAN demonstrates its ability to move key points more specifically to the precise positions in the framework of combined image restoration, for instance. Experimental results demonstrate that DragGAN outperforms conventional methods in terms of the generated graphics ‘realism and objective-level accuracy. This method significantly enhances the flexibility and efficiency of picture editing, lowers the technical barrier, and enables quasi-expert users to easily accomplish higher-quality image editing, marking a significant advancement in the field of image synthesis. Future research will focus on curbing reliance on write-up-trained GAN patterns and increasing the person’s steadiness and accuracy in complex scenes. This indicates that DragGAN’s engineering is still developing, and future additions and changes may be made to improve the user experience and control results.
Sentiment Analysis of Product Reviews Using Fine-Tuned LLaMa-3 Model: Evaluation with Comprehensive Benchmark Metrics
Authors: Wang Yili
Published: January 2025

Sentiment analysis, a crucial subfield of natural language processing, enables businesses and policymakers to understand public emotions and opinions, essential for crafting effective strategies across industries like marketing and customer service. As the volume of online reviews grows, automated sentiment classification models have become vital for efficiently processing this data. This study explores fine-tuning the LLaMA-8B large language model based on the Amazon Product Reviews dataset from Kaggle, aiming to improve sentiment classification accuracy. Using the LoRA fine-tuning approach combined with the Variant Greedy Search Technique (VGST) and TextBlob for polarity handling, the research addresses dataset size challenges. The model’s fine-tuning process includes one-shot learning and chain-of-thought prompting to better capture nuanced sentiment expressions. Evaluated using comprehensive metrics, LLaMA-8B demonstrates superior precision compared to Qwen2-7B and achieves near LLaVA performance with enhanced speed. Additionally, it outperforms models like Decision Tree, SVM, Multinomial NB, and XLNet in accuracy. This work underscores the potential of large language models for sentiment analysis and sets the stage for future extensions to multimodal input scenarios.
Time Series Analysis: Application of LSTM model in predicting PM 2.5 concentration in Beijing
Authors: Yang Rui
Published: January 2025

Air pollution forecasting for public health and policy-making has a critical importance, this paper employs a Long Short-Term Memory (LSTM) model to perform in-depth prediction of PM2.5 concentrations measured at the U.S. Embassy in Beijing, outperforming regular forecasting approaches. In the LSTM model, the research examines a very detailed hourly dataset and beats regular forecasting approaches. A key finding is the model’s ability to effectively generalize from historical data to predict future air quality trends, with its adeptness at handling time-dependent relationships. This research emphasizes the importance of LSTM in air pollution prediction and management in environmental science as it provides an effective means for planning and making decisions on air quality management. This research is of great importance in providing a groundwork for further enhancement of prediction modeling. By offering a more reliable and sophisticated picture of air quality variations, this study addresses the current problem about how urban air pollution control could be improved in the city.
Research and Application of Heart Disease Prediction Model Based on Machine Learning
Authors: Bao Yongli
Published: January 2025

As heart disease has become the leading cause of death worldwide, early and accurate prediction is crucial to help doctors make initial judgments about patients and improve their survival rates. This study aims to improve the accuracy and efficiency of heart disease prediction through Machine learning (ML) methods to help medical diagnosis. A heart disease dataset was used in the study, and multiple ML models were used to analyze multiple key health features, and the model performance was verified through a test set. This paper concludes that Logistic regression and random forests perform well in this task and have high practical value. Future research can stack models and optimize data sources to improve the practical performance of the model. This study provides a basic framework for building an intelligent medical auxiliary diagnosis system, which helps to achieve early prevention and timely judgment of heart disease, thereby improving the overall efficiency of medical services.
Multiple Machine Learning Algorithms-based NBA Team Playoffs Prediction
Authors: Yeung Manho
Published: January 2025

With the rapid development of data analytics in sports, it is vital to use machine learning methods to make decisions and predictions. This study focuses on predicting NBA playoff qualifications using machine learning techniques. By utilizing team-level statistics from 1947 to 2024, the paper implemented models such as Logistic Regression, K-Nearest Neighbors, Random Forest, and Elastic Net Regression. The data was preprocessed by scaling, centering, and handling missing values, followed by rigorous 5-fold cross-validation to ensure robust evaluation. Among the models, Random Forest outperformed the others, achieving the highest ROC-AUC score of 0.841. Its ensemble approach allowed for the effective capture of complex feature interactions, making it the most accurate model for predicting whether a team would qualify for the playoffs based on team performance. The research demonstrates the power of machine learning in improving prediction accuracy, providing insights for future sports analytics, and offering a foundation for integrating more complex data like player metrics or strategic factors. This work contributes to advancing predictive modeling in sports.
Prediction of earthquake by machine learning models and neural network
Authors: Xu Bohong
Published: January 2025

The occurrence of earthquakes has been examined from many aspects. Obviously, earthquakes occur without any warning and can damage entire cities in just a moment, causing enormous casualties and large amount of economic loss. In the last few years, people have given great effort to examine the earthquake from many aspects with various methods. The use of machine learning has solved seemingly intractable problems, inspiring the prospect of finding suitable solutions to dilemmas that are now considered unsolvable. Earthquake prediction, in this article, is executed with a neural network and machine learning models. After reading many articles, the author discovered that not many articles use a fully connected neural network to predict earthquake magnitude. In this article, the author first process the dataset sought from the internet, then predict the magnitude and the alert of earthquakes, with four machine learning models and a neural network. After that, the result of the accuracy are shown at the end of the article.
The Relationship Between Consumption Power and GDP Growth in China and Machine Learning Prediction Analysis
Authors: Hu Runsong
Published: January 2025

With the rapid development of China’s economy, consumption has become a key driving force to boost domestic demand and promote economic structural transformation. By analyzing the current situation of China's economic development, this paper focuses on the relationship between residents' consumption capacity and Gross Domestic Product (GDP) growth. The paper examines the use of machine learning for predicting consumption capacity by integrating various economic models, including Support Vector Regression (SVR) and Long Short-Term Memory Network (LSTM). Moreover, by analyzing data on China's GDP and consumption capacity from 1991 to 2020, the study demonstrates a strong positive correlation between the two. This finding further highlights the significant impact of consumption capacity on economic growth. The article also applies univariate and bivariate analyses to calculate the concentration trend and degree of dispersion of GDP and consumption capacity, and it uses regression models to explore the relationship between the two further. This article can provide policymakers with a deeper understanding of regional consumption differences and economic development trends, thus providing a decision-making basis for optimizing economic policies.
Single-Session Surgical Intervention with Resection of a Primary Cranial Osteosarcoma and Cranioplasty using a 3D-Printed Craniotomy Template and Cranioplasty Molds
Authors: Duda Sven , Ivanyi Philipp , Omar Mohamed , Hartmann Christian , Musienko Eugen , Wessling Heinrich
Published: January 2025

Although osteosarcomas are the most frequent primary malignant bone tumors, the primary cranial manifestation of this condition is very rare with only a limited number of cases presented in the literature.

We present the case of a 20-year-old male patient who underwent single-session surgical intervention for resection of right frontal osteosarcoma with a tailor-made craniotomy and cranioplasty using virtually designed 3D-printed templates and molds. Subsequently, the patient was treated according to the EURAMOS protocol and received adjuvant systemic chemotherapy.

At 18-month follow-up, the patient was clinically asymptomatic, and both the magnetic resonance imaging scan of the head and the staging computed tomography showed no signs of tumor recurrence or metastases. The case presented shows that the use of 3D-printed molds facilitate a safe preoperative planning of the resection area and a single-session surgery including a custom-made cranioplasty responding to the highest esthetical standards.
Endoscopic Orbital Clearance/Debridement: A Potential Substitute for Orbital Exenteration in Rhino-orbital Mucormycosis
Authors: Sweed Ahmed Hassan , Anany Ahmed Mohammad , Hussein Atef , Nada Waleed , Eesa Mohamed , Elnashar Ismail , Mobashir Mohamed , Ibrahim Enas Moustafa , Elmaghawry Mohammed Elsayed
Published: January 2025

Introduction Mucormycosis is an aggressive, lethal fungal infection affecting the nasal and paranasal territory in immunocompromised patients. Orbital involvement is not uncommon and may require orbital exenteration.

Objectives The management of orbital involvement in invasive fungal sinusitis is challenging, ranging from conservative retrobulbar amphotericin B injection in the early stages to orbital exenteration in late stages. Endoscopic endonasal debridement is a minimally invasive technique used to manage orbital fungal involvement in the late stages.

Methods Endoscopic endonasal orbital clearance was performed to manage late-stage orbital invasive fungal infection (≥ stage 3c) or after failure of retrobulbar amphotericin B injection with no light perception. Removal of the lamina papyracea (LP) and incision of the periorbita were done to expose all the necrotic intraorbital content in the extra and intraconal spaces. A microdebrider was utilized to debride necrotic fungal infected tissue until a healthy vascularized plane was reached. Gelfoam (Pfizer Inc., New York, NY, United States) soaked in amphotericin B was applied as an adjunctive step to deliver antifungal medication to the orbital content.

Results Fourteen patients were included in the study, 9 of whom were male and 5 female, with a mean age of 58.5 years. Eleven patients showed no evidence of disease progression (complete recovery and cessation of medical treatment). Two patients died 15 days after the surgery. The last patient developed frontal lobe abscess but has been treated with double antifungal medication.

Conclusion Endoscopic endonasal orbital debridement could be an effective method to treat late-stage orbital fungal infection without jeopardizing the patient's life.

Level of Evidence: 4.
A Retrospective Study on the Imaging Spectrum of Functional Single Ventricle and Its Associations
Authors: Niva B. , Rajeshkannan Ramiah
Published: January 2025

Objective The aim of the study was to identify the various spectrum of single ventricle (SV) physiology and to correlate the anatomical variants.

Design and Methods All congenital heart disease cases with computed tomography/magnetic resonance imaging during the period from 2008 to 2023 were retrospectively assessed. Among these, we identified those corresponding to the definition of SV (n = 160). In each case, we described the situs, looping, atrioventricular (AV)/ventriculoarterial (VA) connections, and associated anomalies. Descriptive statistics of each parameter were obtained and compared with similar parameters of other published studies.

Results Among the 160 cases, there were 93 males (58.1%) and 67 females (41.9%). The mean age was 8.41 ± 8.84 years. The most common defects were tricuspid atresia (TA) at 28%, double inlet left ventricle (DILV) at 23%, and AV canal defect (AVCD) at 22%. Other conditions include double outlet right ventricle, large ventricular septal defect, dextro-transposition of great arteries, levo-transposition of great arteries, mitral atresia, pulmonary atresia, hypoplastic left heart syndrome, and crisscross heart. There were 123 situs solitus, 28 right isomerism, 4 left isomerism, and 5 situs inversus cases. Among the patients with right isomerism, the most common defect was AVCD (86%). Sixty-five percent of DILV had discordant VA connections, while only 14% showed concordance. Sixty-three percent of TA cases showed VA concordance. Knowing the underlying anatomy in detail helps the physician to anticipate the wide array of problems unique to SV circulation and treat them accordingly.

Conclusion The conditions coming under the SV spectrum can be classified based on AV and VA connections. The incidence of each condition and observed associations were described.