Comparative analysis of empirical and deep learning models for ionospheric sporadic E layer prediction

Sporadic E(Es)layers in the ionosphere are characterized by intense plasma irregularities in the E region at altitudes of 90-130 km.Because they can significantly influence radio communications and navigation systems,accurate forecasting of Es layers is crucial for ensuring the precision and dependability of navigation satellite systems.In this study,we present Es predictions made by an empirical model and by a deep learning model,and analyze their differences comprehensively by comparing the model predictions to satellite RO measurements and ground-based ionosonde observations.The deep learning model exhibited significantly better performance,as indicated by its high coefficient of correlation(r=0.87)with RO observations and predictions,than did the empirical model(r=0.53).This study highlights the importance of integrating artificial intelligence technology into ionosphere modelling generally,and into predicting Es layer occurrences and characteristics,in particular.

Dynamics of a Stochastic Epidemic Model with Age-group

A stochastic epidemic model with two age groups is established in this study,in which the susceptible(S),the exposed(E),the infected(I),the hospitalized(H)and the recovered(R)are involved within the total population,the aging rates between two age groups are set to be constant.The existence-and-uniqueness of global positive solution is firstly showed.Then,by constructing several appropriate Lyapunov functions and using the high-dimensional Itô’s formula,the sufficient conditions for the stochastic extinction and stochastic persistence of the exposed individuals and the infected individuals are obtained.The stochastic extinction indicator and the stochastic persistence indicator are less-valued expressions compared with the basic reproduction number.Meanwhile,the main results of this study are modified into multi-age groups.Furthermore,by using the surveillance data for Fujian Provincial Center for Disease Control and Prevention,Fuzhou COVID-19 epidemic is chosen to carry out the numerical simulations,which show that the age group of the population plays the vital role when studying infectious diseases.

Aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorders:progress of experimental models based on disease pathogenesis

Neuromyelitis optica spectrum disorders are neuroinflammatory demyelinating disorders that lead to permanent visual loss and motor dysfunction.To date,no effective treatment exists as the exact causative mechanism remains unknown.Therefore,experimental models of neuromyelitis optica spectrum disorders are essential for exploring its pathogenesis and in screening for therapeutic targets.Since most patients with neuromyelitis optica spectrum disorders are seropositive for IgG autoantibodies against aquaporin-4,which is highly expressed on the membrane of astrocyte endfeet,most current experimental models are based on aquaporin-4-IgG that initially targets astrocytes.These experimental models have successfully simulated many pathological features of neuromyelitis optica spectrum disorders,such as aquaporin-4 loss,astrocytopathy,granulocyte and macrophage infiltration,complement activation,demyelination,and neuronal loss;however,they do not fully capture the pathological process of human neuromyelitis optica spectrum disorders.In this review,we summarize the currently known pathogenic mechanisms and the development of associated experimental models in vitro,ex vivo,and in vivo for neuromyelitis optica spectrum disorders,suggest potential pathogenic mechanisms for further investigation,and provide guidance on experimental model choices.In addition,this review summarizes the latest information on pathologies and therapies for neuromyelitis optica spectrum disorders based on experimental models of aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorders,offering further therapeutic targets and a theoretical basis for clinical trials.

Solar flare forecasting based on a Fusion Model

Solar flare prediction is an important subject in the field of space weather.Deep learning technology has greatly promoted the development of this subject.In this study,we propose a novel solar flare forecasting model integrating Deep Residual Network(ResNet)and Support Vector Machine(SVM)for both≥C-class(C,M,and X classes)and≥M-class(M and X classes)flares.We collected samples of magnetograms from May 1,2010 to September 13,2018 from Space-weather Helioseismic and Magnetic Imager(HMI)Active Region Patches and then used a cross-validation method to obtain seven independent data sets.We then utilized five metrics to evaluate our fusion model,based on intermediate-output extracted by ResNet and SVM using the Gaussian kernel function.Our results show that the primary metric true skill statistics(TSS)achieves a value of 0.708±0.027 for≥C-class prediction,and of 0.758±0.042 for≥M-class prediction;these values indicate that our approach performs significantly better than those of previous studies.The metrics of our fusion model’s performance on the seven datasets indicate that the model is quite stable and robust,suggesting that fusion models that integrate an excellent baseline network with SVM can achieve improved performance in solar flare prediction.Besides,we also discuss the performance impact of architectural innovation in our fusion model.

Engine Misfire Fault Detection Based on the Channel Attention Convolutional Model

To accurately diagnosemisfire faults in automotive engines,we propose a Channel Attention Convolutional Model,specifically the Squeeze-and-Excitation Networks(SENET),for classifying engine vibration signals and precisely pinpointing misfire faults.In the experiment,we established a total of 11 distinct states,encompassing the engine’s normal state,single-cylinder misfire faults,and dual-cylinder misfire faults for different cylinders.Data collection was facilitated by a highly sensitive acceleration signal collector with a high sampling rate of 20,840Hz.The collected data were methodically divided into training and testing sets based on different experimental groups to ensure generalization and prevent overlap between the two sets.The results revealed that,with a vibration acceleration sequence of 1000 time steps(approximately 50 ms)as input,the SENET model achieved a misfire fault detection accuracy of 99.8%.For comparison,we also trained and tested several commonly used models,including Long Short-Term Memory(LSTM),Transformer,and Multi-Scale Residual Networks(MSRESNET),yielding accuracy rates of 84%,79%,and 95%,respectively.This underscores the superior accuracy of the SENET model in detecting engine misfire faults compared to other models.Furthermore,the F1 scores for each type of recognition in the SENET model surpassed 0.98,outperforming the baseline models.Our analysis indicated that the misclassified samples in the LSTM and Transformer models’predictions were primarily due to intra-class misidentifications between single-cylinder and dual-cylinder misfire scenarios.To delve deeper,we conducted a visual analysis of the features extracted by the LSTM and SENET models using T-distributed Stochastic Neighbor Embedding(T-SNE)technology.The findings revealed that,in the LSTMmodel,data points of the same type tended to cluster together with significant overlap.Conversely,in the SENET model,data points of various types were more widely and evenly dispersed,demonstrating its effectiveness in distinguishing between different fault types.

Optimizing Fine-Tuning in Quantized Language Models:An In-Depth Analysis of Key Variables

Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in specific tasks with reduced training costs,the substantial memory requirements during fine-tuning present a barrier to broader deployment.Parameter-Efficient Fine-Tuning(PEFT)techniques,such as Low-Rank Adaptation(LoRA),and parameter quantization methods have emerged as solutions to address these challenges by optimizing memory usage and computational efficiency.Among these,QLoRA,which combines PEFT and quantization,has demonstrated notable success in reducing memory footprints during fine-tuning,prompting the development of various QLoRA variants.Despite these advancements,the quantitative impact of key variables on the fine-tuning performance of quantized LLMs remains underexplored.This study presents a comprehensive analysis of these key variables,focusing on their influence across different layer types and depths within LLM architectures.Our investigation uncovers several critical findings:(1)Larger layers,such as MLP layers,can maintain performance despite reductions in adapter rank,while smaller layers,like self-attention layers,aremore sensitive to such changes;(2)The effectiveness of balancing factors depends more on specific values rather than layer type or depth;(3)In quantization-aware fine-tuning,larger layers can effectively utilize smaller adapters,whereas smaller layers struggle to do so.These insights suggest that layer type is a more significant determinant of fine-tuning success than layer depth when optimizing quantized LLMs.Moreover,for the same discount of trainable parameters,reducing the trainable parameters in a larger layer is more effective in preserving fine-tuning accuracy than in a smaller one.This study provides valuable guidance for more efficient fine-tuning strategies and opens avenues for further research into optimizing LLM fine-tuning in resource-constrained environments.

Exploiting fly models to investigate rare human neurological disorders

Rare neurological diseases,while individually are rare,collectively impact millions globally,leading to diverse and often severe neurological symptoms.Often attributed to genetic mutations that disrupt protein function or structure,understanding their genetic basis is crucial for accurate diagnosis and targeted therapies.To investigate the underlying pathogenesis of these conditions,researchers often use non-mammalian model organisms,such as Drosophila(fruit flies),which is valued for their genetic manipulability,cost-efficiency,and preservation of genes and biological functions across evolutionary time.Genetic tools available in Drosophila,including CRISPR-Cas9,offer a means to manipulate gene expression,allowing for a deep exploration of the genetic underpinnings of rare neurological diseases.Drosophila boasts a versatile genetic toolkit,rapid generation turnover,and ease of large-scale experimentation,making it an invaluable resource for identifying potential drug candidates.Researchers can expose flies carrying disease-associated mutations to various compounds,rapidly pinpointing promising therapeutic agents for further investigation in mammalian models and,ultimately,clinical trials.In this comprehensive review,we explore rare neurological diseases where fly research has significantly contributed to our understanding of their genetic basis,pathophysiology,and potential therapeutic implications.We discuss rare diseases associated with both neuron-expressed and glial-expressed genes.Specific cases include mutations in CDK19 resulting in epilepsy and developmental delay,mutations in TIAM1 leading to a neurodevelopmental disorder with seizures and language delay,and mutations in IRF2BPL causing seizures,a neurodevelopmental disorder with regression,loss of speech,and abnormal movements.And we explore mutations in EMC1 related to cerebellar atrophy,visual impairment,psychomotor retardation,and gain-of-function mutations in ACOX1 causing Mitchell syndrome.Loss-of-function mutations in ACOX1 result in ACOX1 deficiency,characterized by very-long-chain fatty acid accumulation and glial degeneration.Notably,this review highlights how modeling these diseases in Drosophila has provided valuable insights into their pathophysiology,offering a platform for the rapid identification of potential therapeutic interventions.Rare neurological diseases involve a wide range of expression systems,and sometimes common phenotypes can be found among different genes that cause abnormalities in neurons or glia.Furthermore,mutations within the same gene may result in varying functional outcomes,such as complete loss of function,partial loss of function,or gain-of-function mutations.The phenotypes observed in patients can differ significantly,underscoring the complexity of these conditions.In conclusion,Drosophila represents an indispensable and cost-effective tool for investigating rare neurological diseases.By facilitating the modeling of these conditions,Drosophila contributes to a deeper understanding of their genetic basis,pathophysiology,and potential therapies.This approach accelerates the discovery of promising drug candidates,ultimately benefiting patients affected by these complex and understudied diseases.

A promising approach for quantifying focal stroke modeling and assessing stroke progression:optical resolution photoacoustic microscopy photothrombosis

To investigate the mechanisms underlying the onset and progression of ischemic stroke,some methods have been proposed that can simultaneously monitor and create embolisms in the animal cerebral cortex.However,these methods often require complex systems and the effect of age on cerebral embolism has not been adequately studied,although ischemic stroke is strongly age-related.In this study,we propose an optical-resolution photoacoustic microscopy-based visualized photothrombosis methodology to create and monitor ischemic stroke in mice simultaneously using a 532 nm pulsed laser.We observed the molding process in mice of different ages and presented age-dependent vascular embolism differentiation.Moreover,we integrated optical coherence tomography angiography to investigate age-associated trends in cerebrovascular variability following a stroke.Our imaging data and quantitative analyses underscore the differential cerebrovascular responses to stroke in mice of different ages,thereby highlighting the technique's potential for evaluating cerebrovascular health and unraveling age-related mechanisms involved in ischemic strokes.

Vertical gradients of neutral winds observed by ICON and estimated by the Horizontal Wind Model during the geomagnetic storm on August 26−28,2021

The Michelson Interferometer for Global High-resolution Thermospheric Imaging(MIGHTI)onboard the Ionospheric Connection Explorer(ICON)satellite offers the opportunity to investigate the altitude profile of thermospheric winds.In this study,we used the red-line measurements of MIGHTI to compare with the results estimated by Horizontal Wind Model 14(HWM14).The data selected included both the geomagnetic quiet period(December 2019 to August 2022)and the geomagnetic storm on August 26-28,2021.During the geomagnetic quiet period,the estimations of neutral winds from HWM14 showed relatively good agreement with the observations from ICON.According to the ICON observations,near the equator,zonal winds reverse from westward to eastward at around 06:00 local time(LT)at higher altitudes,and the stronger westward winds appear at later LTs at lower altitudes.At around 16:00 LT,eastward winds at 300 km reverse to westward,and vertical gradients of zonal winds similar to those at sunrise hours can be observed.In the middle latitudes,zonal winds reverse about 2-4 h earlier.Meridional winds vary more significantly than zonal winds with seasonal and latitudinal variations.According to the ICON observations,in the northern low latitudes,vertical reversals of meridional winds are found at 08:00-13:00 LT from 300 to 160 km and at around 18:00 LT from 300 to 200 km during the June solstice.Similar reversals of meridional winds are found at 04:00-07:00 LT from 300 to 160 km and at 22:00-02:00 LT from 270 to 200 km during the December solstice.In the southern low latitudes,meridional wind reversals occur at 08:00-11:00 LT from 200 to 160 km and at 21:00-02:00 LT from 300 to 200 km during the June solstice.During the December solstice,reversals of the meridional wind appear at 20:00-01:00 LT below 200 km and at 06:00-11:00 LT from 300 to 160 km.In the northern middle latitudes,the northward winds are dominant at 08:00-14:00 LT at 230 km during the June solstice.Northward winds persist until 16:00 LT at 160 and 300 km.During the December solstice,the northward winds are dominant from 06:00 to 21:00 LT.The vertical variations in neutral winds during the geomagnetic storm on August 26-28 were analyzed in detail.Both meridional and zonal winds during the active geomagnetic period observed by ICON show distinguishable vertical shear structures at different stages of the storm.On the dayside,during the main phase,the peak velocities of westward winds extend from a higher altitude to a lower altitude,whereas during the recovery phase,the peak velocities of the westward winds extend from lower altitudes to higher altitudes.The velocities of the southward winds are stronger at lower altitudes during the storm.These vertical structures of horizontal winds during the storm could not be reproduced by the HWM14 wind estimations,and the overall response to the storm of the horizontal winds in the low and middle latitudes is underestimated by HWM14.The ICON observations provide a good dataset for improving the HWM wind estimations in the middle and upper atmosphere,especially the vertical variations.

Reduced mesencephalic astrocyte-derived neurotrophic factor expression by mutant androgen receptor contributes to neurodegeneration in a model of spinal and bulbar muscular atrophy pathology

Spinal and bulbar muscular atrophy is a neurodegenerative disease caused by extended CAG trinucleotide repeats in the androgen receptor gene,which encodes a ligand-dependent transcription facto r.The mutant androgen receptor protein,characterized by polyglutamine expansion,is prone to misfolding and forms aggregates in both the nucleus and cytoplasm in the brain in spinal and bulbar muscular atrophy patients.These aggregates alter protein-protein interactions and compromise transcriptional activity.In this study,we reported that in both cultured N2a cells and mouse brain,mutant androgen receptor with polyglutamine expansion causes reduced expression of mesencephalic astrocyte-de rived neurotrophic factor.Overexpressio n of mesencephalic astrocyte-derived neurotrophic factor amelio rated the neurotoxicity of mutant androgen receptor through the inhibition of mutant androgen receptor aggregation.Conversely.knocking down endogenous mesencephalic astrocyte-derived neurotrophic factor in the mouse brain exacerbated neuronal damage and mutant androgen receptor aggregation.Our findings suggest that inhibition of mesencephalic astrocyte-derived neurotrophic factor expression by mutant androgen receptor is a potential mechanism underlying neurodegeneration in spinal and bulbar muscular atrophy.

Assessing the possibility of using large language models in ocular surface diseases

AIM:To assess the possibility of using different large language models(LLMs)in ocular surface diseases by selecting five different LLMS to test their accuracy in answering specialized questions related to ocular surface diseases:ChatGPT-4,ChatGPT-3.5,Claude 2,PaLM2,and SenseNova.METHODS:A group of experienced ophthalmology professors were asked to develop a 100-question singlechoice question on ocular surface diseases designed to assess the performance of LLMs and human participants in answering ophthalmology specialty exam questions.The exam includes questions on the following topics:keratitis disease(20 questions),keratoconus,keratomalaciac,corneal dystrophy,corneal degeneration,erosive corneal ulcers,and corneal lesions associated with systemic diseases(20 questions),conjunctivitis disease(20 questions),trachoma,pterygoid and conjunctival tumor diseases(20 questions),and dry eye disease(20 questions).Then the total score of each LLMs and compared their mean score,mean correlation,variance,and confidence were calculated.RESULTS:GPT-4 exhibited the highest performance in terms of LLMs.Comparing the average scores of the LLMs group with the four human groups,chief physician,attending physician,regular trainee,and graduate student,it was found that except for ChatGPT-4,the total score of the rest of the LLMs is lower than that of the graduate student group,which had the lowest score in the human group.Both ChatGPT-4 and PaLM2 were more likely to give exact and correct answers,giving very little chance of an incorrect answer.ChatGPT-4 showed higher credibility when answering questions,with a success rate of 59%,but gave the wrong answer to the question 28% of the time.CONCLUSION:GPT-4 model exhibits excellent performance in both answer relevance and confidence.PaLM2 shows a positive correlation(up to 0.8)in terms of answer accuracy during the exam.In terms of answer confidence,PaLM2 is second only to GPT4 and surpasses Claude 2,SenseNova,and GPT-3.5.Despite the fact that ocular surface disease is a highly specialized discipline,GPT-4 still exhibits superior performance,suggesting that its potential and ability to be applied in this field is enormous,perhaps with the potential to be a valuable resource for medical students and clinicians in the future.

基于Panzar-Rosse模型的中国银行业市场结构与竞争的实证检验

文章基于Panzar-Rosse模型研究了2000年到2007年之间中国金融业中银行市场结构和竞争状态,利用H统计值法,测定H值在0.147 491~0.895 820之内,研究表明中国的银行业仍处于垄断竞争状态,并且从垄断过渡到适度竞争阶段,银行业需要更多的竞争政策,以确保在中国金融市场的适度竞争并促进其发展。

金融市场发展与中国银行业竞争度——基于Panzar-Rosse模型的实证研究

文章从竞争效应和融合效应两个方面系统分析金融市场发展对我国银行业竞争度的影响机制,并采用Panzar-Rosse模型度量1998～2008年我国银行业的竞争度,在此基础上实证检验了金融市场发展对我国银行业竞争度的影响。结果表明,我国银行业市场基本处于垄断竞争格局,现阶段金融市场发展的竞争效应对银行业竞争的影响更显著,股票市场资产规模的扩大和流动性的增强都会对我国银行业的竞争起到显著的促进作用。

互联网金融发展对我国银行业竞争度的影响——基于Panzar-Rosse模型的实证研究

随着互联网金融的崛起,其对银行业的影响逐步深入,从一定程度上抢占了银行的资产业务、负债业务和中间业务,本文在建立Panzar-Rosse模型测定2005-2014年我国银行业市场竞争度的基础上,构建参数方程探究互联网金融对我国银行业市场竞争度的影响。研究表明,近十年来我国银行业处于垄断竞争状态且竞争度不断加大,互联网金融在萌芽时期对银行业竞争度影响不大,但是,当互联网金融发展到一定水平时,会与我国银行业的竞争度呈现显著正相关关系。未来,应当积极推进银行业与互联网金融的合作,实现社会经济资源配置的最优化。

我国银行业市场竞争度研究——基于Panzar-Rosse模型的实证分析

本文选取2005—2014年做为我国银行业竞争度的研究样本期,国内16家已上市的股份制银行做为研究样本,从两个层次上分别建立Panzar-Rosse模型对我国银行业近十年的竞争度进行实证分析,其一,从整体上衡量我国银行业竞争度的变化情况,其二,将我国银行业分为五家大型商业银行和另外11家股份制银行两个市场,比较这两个局部市场间竞争水平的差异。得出结论:近十年我国银行业处于垄断竞争状态并且竞争度不断增大,五大行间的竞争度较股份制银行来说更高。同时,在实证分析的基础上,针对银行业未来发展形势,提出优化我国银行业竞争环境、提高银行业综合竞争力、增强金融服务多样化等政策建议。

中国银行业市场竞争结构检验——基于修正的Panzar-Rosse模型设定

运用Panzar-Rosse方法对我国29家商业银行2007—2011年的市场结构进行检验,结果表明,银行业整体形成了不均衡的垄断竞争结构;同时考虑到不同性质银行机构间的竞争度的差异,分别以4家大型商业银行、12家股份制商业银行、13家城市商业银行作为样本检验了竞争度的变化。结果表明,除了四家大型商业银行形成了完全竞争的市场结构,其他子市场均为垄断竞争状态,竞争程度由高到低分别为:大型商业银行、城市商业银行、股份制商业银行。

信用卡发展与我国商业银行市场结构变迁研究——基于Panzar-Rosse模型的实证研究

首先采用Panzar-Rosse模型定量研究近几年来我国商业银行业市场结构的变迁,在此基础上进一步研究信用卡的发展与商业银行市场结构变迁的关系。对我国商业银行近十年数据的实证研究表明,由于近些年我国对于银行业的政策不断放开,市场竞争总体来说越来越激烈。但是,信用卡的发展却减弱了商业银行业市场的竞争强度。

采用STAMP-24Model的多组织事故分析

安全生产事故往往由多组织交互、多因素耦合造成,事故原因涉及多个组织。为预防和遏制多组织生产安全事故的发生,基于系统理论事故建模与过程模型(Systems-Theory Accident Modeling and Process,STAMP)、24Model,构建一种用于多组织事故分析的方法,并以青岛石油爆炸事故为例进行事故原因分析。结果显示:STAMP-24Model可以分组织,分层次且有效、全面、详细地分析涉及多个组织的事故原因,探究多组织之间的交互关系;对事故进行动态演化分析,可得到各组织不安全动作耦合关系与形成的事故失效链及管控失效路径,进而为预防多组织事故提供思路和参考。

基于改进24Model-ISM-SNA建筑工人不安全行为关联路径研究

建筑施工现场环境复杂,为有效控制不安全行为发生,基于行为安全“2-4”模型对360份具有代表性的建筑安全事故调查报告进行分析,提取出22个不安全行为的主要影响因素。利用灰色关联分析方法(GRA)改进的集成ISM-SNA模型,将不安全行为风险因素划分为表层、过渡层与深层,然后对风险因素进行可视化分析、中心度分析及凝聚子群分析,揭示了各致因因素间的关联关系和传导路径。结果表明,建筑工人不安全行为影响因素可划分成7级3阶的多级递阶结构,安全意识、现场监管、外部环境是建筑工人不安全行为的关键影响因素,同时现场监管和隐患排查到位能有效降低不安全行为的发生。

外资银行进入对我国银行业竞争度的影响——基于Panzar-Rosse模型的实证研究

1997~2011年这15年间我国银行业处于垄断竞争状态，市场竞争度呈现先减后增的U型变化趋势，而2008年国际金融危机后我国银行业市场竞争度下降。外资银行资产份额与市场竞争程度存在负相关关系，而机构数量与竞争度不相关，外资银行进入未能促进我国银行业的竞争，中国银行业市场逐步发展完善是市场竞争度变化的根本原因。