Highlights in Science, Engineering and Technology
Highlights in Science, Engineering and Technology is an open access, peer-reviewed publication series published by Darcy & Roy Press. It aims to publish conference proceedings dealing with all areas of science, engineering and technology.
Research of Frequency Multiplexing in 5G Networks
Frequency multiplexing techniques for 5G communications include large-scale antenna techniques and frequency division multiplexing techniques such as OFDM and GFDM. Reasonable use and development of these techniques can significantly reduce the cost of 5G technology deployment and improve the spectral efficiency. This paper firstly introduces the current status of multi-input multi-output large-scale antenna technology. This paper then describe the main challenges faced by large-scale antenna technology. Following this, this paper discuss related development and innovation directions, such as the adoption of compact and lightweight radiating devices and high-precision amplitude-phase calibration networks, which are crucial for improving the practicality and efficiency of large-scale antenna systems. Secondly, for OFDM technology, the performance of its improved f-OFDM and CP-OFDM in different situations such as BER is demonstrated. Finally, due to the limitations of OFDM, the SER performance of GFDM is investigated in this paper. To complement these discussions, this paper concludes by exploring the potential synergies between massive MIMO and advanced waveform technologies like f-OFDM and GFDM, highlighting how their integration could further enhance the performance and flexibility of 5G and future wireless communication systems.
Copyright (c) 2024 Highlights in Science, Engineering and Technology https://creativecommons.org/licenses/by-nc/4.0
Design of Explosion-Proof Switch Safety Assessment System
Explosion-proof switches are essential electrical devices in explosive environments and serve as a critical guarantee for safe production in such locations. Based on an analysis of the status of safety assessment technologies both domestically and internationally, this paper proposes a safety assessment technology for explosion-proof switches, grounded in the inverse relationship between contact lifespan and interruption current. Additionally, it designs the overall structure of the safety assessment system for explosion-proof switches and establishes methods for result evaluation, providing a reference for the assessment technology of explosion-proof switches.
Copyright (c) 2025 Highlights in Science, Engineering and Technology https://creativecommons.org/licenses/by-nc/4.0
Comparison of CNN-Based Models in Facial Micro-Expression Classification
With the rapid development of deep learning, especially the application of Convolutional Neural Networks (CNNs), significant progress has been made in the identification and recognition of facial micro-expressions. This paper explores and compares three widely used models in this field: VGG, DenseNet, and ResNet, across seven expression labels—Anger, Disgust, Fear, Happiness, Neutral, Sadness, and Surprise. The comparison is based on several key evaluation metrics commonly used in deep learning classification tasks. To ensure consistency, critical training parameters such as epochs, learning rate, and data preprocessing steps are kept the same across all models. Additionally, a dropout layer is incorporated to address issues such as overfitting and improve generalization in each model. These works indicate that VGG has the highest performance in RAFDB dataset because it as an F1-score of 0.76, an AUC value of 0.95, and a 75% accuracy rate. Additionally, VGG uses only 1243.31MB of memory, making it the most efficient model compared to the other two models. This shows that VGG excels not only in performance but also in memory efficiency.
Integrating Multimodal Data for Deep Learning-Based Facial Emotion Recognition
With the rapid development of neural networks, emotion recognition has become a research area of great concern. It has important applications not only in marketing and human-computer interaction but also holds significant importance for improving emotional computing and user experience. This paper studies various methods for emotion recognition in images and videos, utilizing convolutional neural networks (CNN), multi-layer perceptron (MLP), and fusion models. The Facial Expression Recognition 2013 (FER2013) image dataset and the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) audio and video dataset serve as the basis for this study. The experimental results indicate that ResNet18 outperforms others in image emotion recognition, attributed to its residual block design and the incorporation of regularization techniques. In the realm of video emotion recognition, the audio model based on MLP demonstrates a superior ability to identify emotional information. Although the fusion of image and audio models theoretically could enhance accuracy, the randomness of video frames prevents the fusion model from achieving the desired effect. Future research might further explore the application of time series models in video emotion recognition to capture continuous emotional changes within videos.
This comprehensive collection of articles explores the evolution and strategic implementation of MIMO technologies, focusing on LS-MIMO and mMIMO systems. These systems play pivotal roles in advancing 5G and prospective 6G networks. The articles span a broad spectrum, from theoretical foundations to practical deployment strategies. They explore significant technological innovations such as spectral and energy efficiencies, addressing both current and emerging challenges in the wireless communication sector. The discussion begins with the transition from traditional small-scale MIMO systems to more sophisticated LS-MIMO configurations, highlighting key innovations like "clean-slate" approaches and advanced beamforming techniques tailored for heterogeneous networks. Subsequent articles build on this foundation, offering in-depth analyses of practical application scenarios, system performance enhancements, and the integration of cutting-edge technologies such as terahertz communication and machine learning within Massive MIMO frameworks. The articles highlight the importance of addressing real-world challenges, including pilot contamination, channel estimation issues, and the optimization of CSI acquisition. The synthesis of research demonstrates the significant potential of MIMO technologies to enhance network capacity and system reliability. Additionally, it outlines the necessary adaptations for their successful integration into future telecommunications infrastructure. The collective insights from these studies provide a robust framework for ongoing technological advancement, emphasizing the need for continuous innovation and strategic deployment of MIMO technologies. This body of work guides future research and implementations, ensuring MIMO technology remains at the forefront of telecommunications evolution and drives the capabilities of next-generation wireless networks.
The Role of Machine Learning in Healthcare: Predictive Models, Digital Interventions, and Randomized Clinical Trials
Machine learning (ML) is transforming healthcare by providing advanced predictive models that help diagnose disease, optimize treatment, and monitor patients. These technologies enable clinicians to make more accurate, data-driven decisions, improve early detection of diseases, optimize personalized treatment plans, and continuously monitor patient health. This review explores the application of various ML models, including Random Forest, Support Vector Machines (SVM), and neural networks, which have shown considerable potential in analyzing large and complex medical data sets to predict patient outcomes and assist in clinical decision making. In addition, the review examines the current landscape of randomized clinical trials (RCTS) that assess the effectiveness of ML interventions in real care Settings. Despite the increasing implementation of ML in healthcare, there remains a significant need for rigorous RCTS to validate the clinical utility and safety of these models. In addition, the review discusses the integration of ML in digital health interventions, such as portable devices and mobile health applications, which are widely used for remote monitoring and chronic disease management. The study presents the potential of ML in healthcare while addressing ethical considerations, including transparency, algorithm accountability and data privacy. The importance of human loop systems, where health professionals work alongside ML models, is also highlighted as a key factor in improving the accuracy, reliability and adoption of these technologies in clinical practice.
Personality Prediction Using RNN Leveraging the Big Five Model and Its Application in Personality-Adaptive Chatbots
This study proposes a method to predict users' personality traits, using Recurrent Neural Networks (RNNs) based on the widely used Big Five personality model, and its application to generative AI-based personality-adaptive chatbots. Text data from essays are used as dataset for the RNN model to predict these personality traits, showcasing the innovativeness of using RNN model to capture certain personality patterns from long, coherent texts instead of short posts on social media. The results show that the model can predict some personality traits effectively, but it has certain difficulties with others, which could be a potential area that may need further improvement. The study also apply the predicted personality traits into generative AI models to examine personalized chatbot responses. The findings demonstrate that after considering the predicted traits from the prediction model, generative AI models can generate more targeted responses according to the given traits. This research underscores the importance of adjusting chatbot responses based on users' personality traits, contributing to more empathetic and user-centered digital interactions.
The Application of AI Technology in Self-Driving Cars
With the rapid development and widespread application of autonomous driving technology and artificial intelligence technology, more and more people are participating in the discussion and learning of these emerging technologies. However, some individuals are concerned about the actual impact of these technologies on their own and pedestrian safety. This article aims to help eliminate public concerns and encourage them to form independent opinions by clarifying the basic principles of these technologies and the problems they can solve. The research content mainly focuses on the in-depth analysis of the principles and applications of artificial intelligence technology in autonomous vehicles, focusing on the public's concerns about the safety of this technology. This analysis will be based on the principles and definitions of the relevant technologies to provide a theoretical basis for understanding the potential impact of these technologies.
A Comprehensive Study on the Application of Data Visualization in Feature Selection
Feature selection is a critical step in machine learning and data analysis, where reducing the dimensionality of data helps to enhance model performance and interpretability. This paper provides a comprehensive review of the application of various data visualization techniques in feature selection, such as scatter plots, heatmaps, parallel coordinates, etc. These visual methods not only help in identifying key patterns but also aid in assessing the relevance and interactions between features. Visualization provides a clearer understanding of the data, facilitating more informed decisions during the feature selection process. Despite its importance, gaps remain in the existing literature, particularly regarding the scalability of visualization tools for large datasets. This review highlights these challenges and suggests potential research directions, including the development of advanced visualization methods tailored to complex datasets. Overall, the findings of this paper offer valuable insights for improving feature selection practices, leading to more efficient and accurate machine learning models to do better data analysis.
A Multi-Method Integrated Study on Parameter Constraint Analysis and Optimization for Emergency Maneuver Launch Missions
With the intensifying competition in space capabilities, emergency maneuver launches have emerged as a critical research area due to their unique military value and application prospects. Efficient and safe responses to emergency launch requirements are paramount, especially for the co-ordination of multiple tasks within the same launch window. This study integrates various data sources, including rocket depot numbers and satellite depot numbers, to analyze constraints such as launch windows, satellite types, and road traffic. Utilizing unsupervised cluster analysis, the relationships between different constraints and their classification rules are identified. A neural network algorithm establishes connections among various launch mission indicators, and a dynamic comprehensive planning and evaluation model is developed by adjusting S and H parameters in conjunction with a dynamic planning algorithm (DDP). This model optimizes planning schemes across different launch tasks and multiple windows. Additionally, Pearson’s correlation coefficient is used to assess the dynamic impact of constraint changes on mission planning. The proposed optimal planning scheme, based on data analysis and dynamic optimization, offers theoretical support and practical guidance for the efficient execution of space emergency maneuver launches.
The Assembly and Manipulation of the Intelligent Transport Vehicle Equipped Robotic Arm
With the advancement of artificial intelligence technology, robots are increasingly replacing human labor and playing an increasingly important role in production. The research focused on goods transportation, which plays a great role in society. However, the traditional method for goods transportation was only to use the labor force, which was very inefficient and expensive if applied on a large scale and could contain life risks under dangerous working conditions. Therefore, the research attempts to explore a new method by using robotics and automation in the transportation system, where robots are the ones to carry the heavy goods, whereas human is the controller remotely. This can substantially increase productivity, and accuracy and lower the costs for transportation, leading to enormous economic benefits, as well as safety. At the end of the research, we successfully created a small remote-controlled transport cart assembled with an artificial arm, with the cart that can be moved omnidirectionally. The research carries out potential benefits for future skills development, applies theory to reality, and builds up a correlation between them.
Research on Leakage Protection Test Technology of Coal Mine Explosion-Proof Electric Appliances
Leakage protection is an important project for the type testing and factory inspection of explosion-proof electrical equipment in coal mines, serving as a crucial indicator for assessing product safety. Based on an analysis of the current state of leakage protection testing technology for coal mine explosion-proof electrical equipment, a leakage protection testing system has been designed. This system details the processes for leakage lock testing and leakage unlock testing, providing a reference for the development of intelligent testing technology for coal mine explosion-proof electrical equipment.
A Comparative Analysis of Printed Circuit Boards Defect Detection Leveraging Deep Learning Approaches
Printed circuit boards (PCBs) are the foundations of modern industry. Their prevalence across electronic industries is explicit, ranging from computers and smartphones to televisions, digital cameras, and other devices. However, during the production process of PCBs, defects are inevitable, leading to substandard products, hence the detection of defects is a crucial task. Deep learning offers significant advantages over conventional machine learning approaches. These advantages include but not limited to the capacity to automatically extract complex embeddings from input samples, leading to improved accuracy and performance. Fueled deep learning, algorithms designed for PCB defect detection are anticipated to deliver superior precision and efficiency, attributed to their capability to recognize novel defect patterns. This article provides a comprehensive analysis of these algorithms, separated into three major categories, including Transformer-based solutions, one-stage, and two-stage approaches. Improvement methods and advantages of these algorithms are detailed. Finally, prospective future research is pinpointed to tackle existing challenges within the algorithms.
A survey on the use of artificial intelligence in autonomous driving
Autonomous driving and artificial intelligence are the most popular research projects in the field of technology today. As the high technology, autonomous driving relies on perception, decision-making, and control systems, and the performance of these systems largely depends on the application of artificial intelligence nowadays. Fortunately, there are plenty of applications of artificial intelligence in several aspects of autonomous driving. This paper aims to introduce the relationship between autonomous driving and artificial intelligence by reviewing several literatures and analyzing the applications of deep learning (DL), reinforcement learning (RL), and graph neural networks (GNN) in autonomous driving.
Applications of Reinforcement Learning on Humanoid Robot Controlling
Amidst the swift advancement of artificial intelligence and machine learning technologies, humanoid robots are increasingly demonstrating their potential in mimicking human motions, adapting to intricate environments, and executing a wide array of tasks. Reinforcement Learning (RL), as an advanced learning paradigm that optimizes decision-making processes through environmental interaction, has emerged as a pivotal tool in enhancing the capabilities of humanoid robots. In this paper, the researcher delves into a variety of RL techniques and their implementations, spotlighting key accomplishments and addressing the prevailing challenges alongside envisaging future trajectories. Through an in-depth examination of these applications, our aim is to elucidate the transformative potential of RL in the domain of humanoid robotics, paving the way for more adaptive, intelligent, and autonomous systems. This paper posits that, with the deepening of research and technological progress, RL will catalyze breakthroughs in the humanoid robot sector, propelling smart robots towards greater integration within human society.
A Comparative Analysis Between CNNs and ViTs for MRI-based Brain Tumor Classification
Brain tumor classification from Magnetic Resonance Imaging (MRI) images is an important task in medical imaging for the determination of appropriate treatment strategies and improvement in patient outcomes. Brain tumors, including gliomas, meningiomas, and glioblastomas, are of the most lethal forms of cancer. This research explored the potential of replacing convolutional neural networks (CNNs) by Vision Transformers (ViTs) on classifying brain tumors by MRI images. The paper focused on pretrained model Vit-B16, comparing it with traditional CNNs, including VGG16, ResNet-50, and EfficientNet-B0. ViT-B16, pretrained on ImageNet-21k, achieves improved accuracy, precision, recall, and F1-Score after being fine-tuned on the brain tumor dataset when applying data augmentation. The self-attention mechanism helps ViTs capture long-range dependencies and global context from the images, significantly improving the performance. As shown in the results, ViTs can efficiently handle complex dataset and become a useful tool for the area of medical imaging classification. This paper emphasizes the potential of Vision transformers in improving classification accuracy in the diagnosis of brain tumors. Future work can be focused on exploring better ViT architectures and data augmentation techniques.
Integrating Multi-Sensor Fusion, AI, and 5G Communication for Advancing Autonomous Driving and Overcoming Key Challenges
Autonomous driving technology has evolved significantly, transitioning from early experimental stages to a central pillar of future transportation systems. Initially driven by advancements in computer vision and robotics, autonomous vehicles (AVs) are now on the brink of revolutionizing urban mobility, enhancing road safety, and improving traffic efficiency through the integration of sophisticated AI, multi-sensor fusion, and advanced communication networks. This paper explores the development and key components of autonomous driving, including multi-sensor fusion, intelligent power systems, vehicle control mechanisms, and system integration. It also highlights the transformative potential of 5G-enabled Vehicle-to-Everything (V2X) communication and AI in advancing AV performance and reliability. However, the widespread adoption of AVs faces significant challenges, including regulatory barriers, cybersecurity risks, and ethical considerations, all of which must be addressed to realize the full potential of autonomous vehicles. This paper provides a comprehensive overview of these advancements and challenges, outlining the critical factors that will influence the future trajectory of autonomous driving technology.
Research on Temperature Rise Testing Technology of Explosion-Proof Low-Voltage Feeder Switches in Coal Mines
Based on the analysis of the operating environment and protective functions of explosion-proof low-voltage feeder switches in coal mines, this paper proposes a temperature rise test data processing method for these switches. A temperature rise testing system for coal mine explosion-proof low-voltage feeder switches is designed to accurately measure the temperature rise at different points of the switches. This provides a reference for the development of intelligent inspection technologies for these switches in coal mines.
Research on the Protection Performance Testing Technology of Explosion-Proof Electrical Appliances in Mines
Based on an analysis of the current situation of safety production in mines and the testing of protection characteristics of explosion-proof electrical appliances, a testing system and method for the protection performance of explosion-proof electrical appliances in mines are proposed. The system performs tests such as overload tests, short-circuit tests, phase-loss tests, leakage lock and unlock tests, and action characteristic tests, providing a reference for the development of intelligent testing technology for explosion-proof electrical appliances in mines.
Research on Synchronous Acquisition and Processing Technology of Arc During Breaking Tests of Low-Voltage Electrical Devices
Based on the analysis of the arc generation mechanism in low-voltage electrical devices, a synchronous acquisition system for arc during breaking tests of low-voltage electrical equipment has been designed. A wavelet analysis-based method for enhancing arc images has been proposed to improve the accuracy of arc image acquisition and recognition, providing a reference for the development of breaking test technology for low-voltage electrical devices.
Design of a Selective Leakage Protection System for Coal Mine Load Center
Leakage protection is one of the three major protections in coal mines and is a crucial safeguard for the safety of underground workers. Based on an analysis of the current state of selective leakage protection systems in coal mine load centers, this paper designs a leakage fault protection system for coal mine load centers using a combination of an additional DC power supply method and zero-crossing triggering method, thereby improving the reliability of leakage protection in coal mine load centers.
The Effects of Lifestyle on Thyroid Nodules in Women
From the perspective of the influence of lifestyle on thyroid nodules in women, this study analyzed the causes of multiple thyroid nodules in women. Based on the fact that female thyroid cancer has become the eighth most common tumor in women, this study first conducted an extensive search and collation of relevant theories, and then used literature research methods, case study methods, survey methods, empirical methods, and other research methods to study the problems of lifestyle and the disease, intraoperative, and prognosis of thyroid nodules in women. Large changes in estrogen levels, large mood swings, and easy malnutrition are the three major factors that women are prone to disease. Lifestyle changes do not directly cure thyroid nodules, but can help improve overall health and may help slow the development of nodules or the effects of associated symptoms. A healthy diet, psychological adjustment, and routine can help patients maintain or restore a normal quality of life after surgery. We need to work together at different levels to reduce the prevalence of thyroid nodules in women and improve the treatment outcomes.
Research on the establishment of Net-Zero Energy Buildings (NZEB) in China and the EU
This paper offers a thorough analysis of nearly zero energy buildings (nZEB) in many Chinese areas as well as in nations that are members of the European Union (EU). It delves into the various standards and energy-saving technologies employed in these two distinct regions, highlighting both their techniques' commonalities and contrasts in achieving energy efficiency. By comparing the regulatory frameworks and building practices, the research aims to identify best practices that can be adopted across different contexts. Furthermore, the study predicts and analyzes the potential for achieving NZEB goals in the future, considering factors such as policy developments, technological advancements, and market trends. It emphasizes the importance of local conditions in shaping energy-saving measures, suggesting that tailored strategies are essential for effectively meeting the requirements of net-zero energy consumption. The paper ultimately seeks to provide valuable references for policymakers, architects, and builders in both the EU and China, encouraging a collaborative approach to energy efficiency. By fostering a deeper understanding of the prospects and difficulties in implementing NZEB, the study aims to the global conversation about energy-efficient building techniques and the shift to a more sustainable future.
Antimicrobial resistance or AMR is a phenomenon that is characterized by a resistance towards antibiotics found in bacteria. It has become especially prevalent in the last 5 decades due along with development of antibiotics which are often misused. Bacteria obtain these resistant characteristics from mutation and all other mechanisms of acquiring new genes. A variety of resistant mechanisms are present in bacteria, each of which are better suited for certain antibiotics which include: Inactivation of the drug, alteration of target sites, alteration of cell permeability, and efflux pumps. There are two main ways which drugs are inactivated. Firstly, the drug is completely degraded and second, the drug is inactivated by binding to a chemical group. Both are done by enzymes within the bacteria. Alteration of drug sites involves the mutation of potential targets within the cell for antibiotics. This often takes the form of changing its chemical structure, inhibiting the target, or increasing in potential target sites. Alteration of permeability are often mutations in the cell membrane so that antibiotics are unable to enter the cell. This often involves the alteration of porins which are channels that allow foreign substance into the cell. The mechanism of efflux pumps involves the action of removing antibitics that are already in the cell. Efflux pumps are believed to be an innate factor of bacteria originally being used to remove potentially harmful but natural substances.
Micronutrient Supplementation as an Alternative Therapy for Managing ADHD Symptoms
The neurodevelopmental disorder known as Attention-Deficit/Hyperactivity Disorder (ADHD) is a long-term condition marked by impulsivity, hyperactivity, and inattention, affecting both children and adults. Although amphetamines and methylphenidate are prominent stimulant drugs used to treat similar symptoms, there are concerns about adverse effects, including potential reliance, appetite suppression, and disturbed sleep—have driven interest in alternative or complementary therapies. One emerging area of research focuses on micronutrient supplementation, which involves providing vitamins and minerals at higher doses than those typically consumed in a regular diet. Although the precise causal relationship between these deficiencies and ADHD symptoms remains unclear, it is hypothesized that correcting these imbalances might alleviate some of the condition's cognitive and behavioral challenges. Early findings suggest that micronutrient supplementation could be a valuable adjunct to traditional treatments, potentially offering a more holistic approach to managing ADHD symptoms. However, further research is needed to fully understand the therapeutic potential and establish clear guidelines for its use in clinical practice. Mendelian randomization studies have found no significant links between most micronutrients and ADHD, with some suggesting a potential connection to copper levels. Furthermore, the possibility of using omega-3 polyunsaturated fatty acids (PUFAs) to reduce the main symptoms of ADHD has been investigated. Ongoing studies indicate that consistent micronutrient therapy could yield results comparable to those of stimulants, often enhancing mood and reducing anxiety. Future research should prioritize personalized treatment approaches that integrate genetic, environmental, and dietary factors to optimize ADHD management.
Research Progress on Stem Cell Regulation of Macrophage Polarization in Promoting Spinal Cord Injury Recovery in Stem Cell Transplantation
With the advancement of spinal cord injury (SCI) research, stem cell therapy has garnered increasing attention. However, the field encompasses numerous research topics and branches, necessitating further organization and focus. This review aims to provide a coherent summary of the mechanisms and roles of stem cells in SCI treatment, addressing three key aspects: the role of macrophage polarization in SCI, the regulatory mechanisms by which stem cells, specifically mesenchymal stem cells (MSCs) and neural stem cells (NSCs), influence macrophage polarization, and the signaling pathways involved in stem cell-regulated macrophage polarization. Correct macrophage polarization is crucial for effective SCI treatment, with MSCs and NSCs contributing through chemical mediators, modulation of macrophage polarization, and interactions with signaling pathways. Ultimately, the review will explore how these signaling pathways enhance SCI treatment through diverse forms of signal transduction and regulation. In conclusion, this review will summarize current insights into stem cell therapy for SCI, integrating the three interconnected aspects discussed.
Research on Computer Model of Seismic Performance and Shear Bearing Capacity of Prefabricated Building Wall Structure Based on BIM
The Markov model limited maximum likelihood algorithm is used to analyze the image. Then, the original image block and the image block affected by noise are estimated respectively. Local adaptive regression kernel and principal component analysis were used to extract feature and cluster the image block structure. The original image block's mean value and covariance matrix were computed. Subsequently, by applying weighted averaging techniques to these matrices, the noise reduction estimates for each individual image block could be achieved. The maximum likelihood method is used to make the completely confocal non-correlation, and the Markov random field model is used to limit it. This method can recover the high frequency component of the image well. The seismic response characteristics of fabricated building steel frames are studied. The results show that each group of tests follows the structural system of "block-rib-frame column triple seismic defense system", and its failure mode is one of overall shear failure. The combination of SRC frame and inclined ribbed composite shear wall can not only significantly improve its shear performance, but also obtain better damping effect.
Combating false information in Emergencies: Leveraging LLMs for Targeted False Information and Clarification Generation
The rapid dissemination of false information during emergencies has become a significant challenge in managing online public opinion. While large language models (LLMs) have enhanced the speed and efficiency of information generation, they have also exacerbated the complexity of public sentiment by facilitating the spread of both false and clarifying information. This paper addresses the critical need for targeted clarification information to counteract false narratives during emergencies. We propose a novel approach by fine-tuning open-source LLMs to generate both false information and corresponding clarification texts, tailored to specific emergency scenarios and public opinion dynamics. By constructing a high-quality dataset of 1,715 paired false and clarification information samples from authoritative platforms, we employ a task-separated fine-tuning strategy using LoRA (Low-Rank Adaptation) to optimize model performance. Our evaluation metrics, including text fluency (BLEU), novelty (NOV), and diversity (DIV), demonstrate that fine-tuned models, particularly LLaMA3.1, excel in generating coherent and relevant texts. The results highlight the potential of LLMs in both generating and debunking false information, offering a robust framework for improving public opinion management during emergencies. This research contributes to the growing body of work on false information mitigation and provides practical insights for leveraging LLMs in crisis communication.
Impacts of Dietary Patterns on Cardiovascular Disease
Cardiovascular disease (CVD), one of the world's leading causes of mortality, has diet as a significant modifiable risk factor. Studies show that following healthy eating guidelines including the Mediterranean Diet (MD), Healthy Eating Index (HEI-2015), Healthy Plant-based Diet Index (HPDI), and Alternative Healthy Eating Index (AHEI) significantly lowers the risk of CVD. These benefits are brought about by some mechanisms such as better lipid profiles, reduced inflammation, improved glucose metabolism, and gut microbiota modification. Thus, there is currently study in a number of areas, including cross-cultural applicability, long-term survivability, and several customized intervention strategies. In order to improve the human CVD prevention, this review can address some effects and mechanisms of healthy eating on CVD and also highlight the need for more research on precision nutrition, cross-cultural adaptation, and technology-based individualized dietary treatments.
Relationship between capsule formation and drug resistance of Cryptococcus
The polysaccharide capsule of Cryptococcus plays a pivotal role in both its pathogenicity and resistance to antifungal therapies. This capsule acts as a physical barrier, impeding the penetration of antifungal drugs and thereby reducing their efficacy in killing fungal cells. Moreover, chemical modifications of the capsule, including acetylation and xylosylation, further enhance its resistance to drugs, particularly azole antifungals. Beyond its role in drug resistance, the capsule also facilitates immune evasion, enabling Cryptococcus to survive and persist within the host by interfering with the host's immune defenses. Recent studies have highlighted the importance of glucuronoxylomannan (GXM) and its modified sugar derivatives in the structure and function of the capsule. However, the precise mechanisms governing the transport and interactions involved in the assembly of the capsule’s fibrillar network remain poorly understood. Furthermore, alterations in capsule composition, density, and modification significantly influence the pathogen's resistance to antibiotics. Future research is needed to elucidate the synthesis, modification, and assembly processes of the capsule, as well as its specific contributions to drug resistance and immune evasion. Such insights could provide new therapeutic targets and strategies for combating Cryptococcus infections.