The Dantzig Selector:Sparse Signals Recovery via l_(1-q)Minimization Model

We propose the Dantzig selector based on the l_(1-q)(1<q≤2)minimization model for the sparse signal recovery.First,we discuss some properties of l_(1-q)minimization model and give some useful inequalities.Then,we give a sufficient condition based on the restricted isometry property for the stable recovery of signals.The l_(1-2)minimization model of Yin-Lou-He is extended to the l_(1-q)minimization model.

基于Markov模型的胰高血糖素样肽1受体激动剂联合二甲双胍治疗2型糖尿病药物经济学评价

目的 对胰高血糖素样肽1受体激动剂(GLP-1RA)联合二甲双胍治疗2型糖尿病(T2DM)进行经济学评价。方法 从我国卫生体系角度出发,基于7项GLP-1RA联合二甲双胍治疗T2DM的随机对照试验(RCT),构建二甲双胍单药或联合GLP-1RA治疗T2DM的Markov模型,模拟治疗期间T2DM无并发症、T2DM伴并发症以及死亡3种状态的动态变化。模型以质量调整生命年(QALYs)为健康产出指标、以3倍我国2023年人均国内生产总值(GDP)为意愿支付(WTP)阈值。模型循环周期设定为1年,共计模拟20年,采用Markov模型进行队列模拟,以增量成本-效用比(ICUR)为评价指标,从而获得每种治疗策略的长期成本、效用及其经济性。通过对成本、效用及贴现的敏感性分析,检验研究结果的稳定性。结果 与二甲双胍单药治疗相比,5种GLP-1RA类药物(利拉鲁肽、度拉糖肽、艾塞那肽、聚乙二醇洛塞那肽、司美格鲁肽)联合二甲双胍治疗方案的ICUR均小于3倍我国2023年人均GDP,增加的成本可接受。敏感性分析中各参数在设定的范围内变化,或将模拟时间延长至30年或50年,对研究结论无显著影响;概率敏感性分析结果表明,WTP阈值为3倍我国2023年人均GDP值(268 074元)时,二甲双胍联合司美格鲁肽0.5 mg方案具有成本-效用优势的概率最高,约为99.7%。结论 对于T2DM患者,相比于二甲双胍单药治疗,利拉鲁肽、度拉糖肽、艾塞那肽、聚乙二醇洛塞那肽、司美格鲁肽以说明书推荐剂量联合二甲双胍治疗方案均属于优势方案,具有经济性。

基于马尔可夫链的COVID-19流行病患病区域变化趋势预测

在COVID-19疫情环境下,为精准预测不同症状状态的人群人数和区域级别的发展趋势,以SIR传染病模型为基础,基于马尔可夫链预测时空相关的特性,设计了感染人群状态与管控措施相关的K-SIRD传染病预测模型.根据COVID-19疫情人群之间传染性的特征及受距离影响造成有效传染数R e的变化而导致不同的症状状态改变的转移规律,实时精准预测了不同状态的人员人数变化,并根据同一区域人群症状状态分布情况不同,针对不同级别的区域采用不同等级的管控措施.

基于GM(1,1)-Markov模型的商品销售额预测

文章研究安踏公司的商品销售额的预测问题,依据商品销售额的灰色特征及随机性特点,将GM(1,1)模型与Markov链模型相结合,建立GM(1,1)-Markov耦合模型,应用该模型对安踏以半年为期的商品销售额进行实例预测。对GM(1,1)模型进行了精度检验,检验结果表明GM(1,1)有较高的预测精度。在此基础上引入Markov链进行预测值修正,其中,GM(1,1)模型预测的平均相对误差为7.67%,而通过Markov链模型进行优化后,GM(1,1)模型平均相对误差为3.71%。说明,经Markov链优化的GM(1,1)模型的预测精度优于GM(1,1)模型,更具实用性。

Modelling Land Use/Land Cover Change of River Rwizi Catchment, South-Western Uganda Using GIS and Markov Chain Model

Analysis of catchment Land use/Land cover (LULC) change is a vital tool in ensuring sustainable catchment management. The study analyzed land use/land cover changes in the Rwizi catchment, south western Uganda from 1989-2019 and projected the trend by 2040. Landsat images, field observations, key informant interviews and focus group discussions were used to collect data. Changes in cropland, forestland, built up area, grazing land, wetland and open water bodies were analyzed in ArcGIS version 10.2.2 and ERDAS IMAGINE 14 software and a Markov chain model. All the LULC classes increased in area except grazing land. Forest land and builtup area between 2009-2019 increased by 370.03% and 229.53% respectively. Projections revealed an increase in forest land and builtup area by 2030 and only built up area by 2040. LULCC in the catchment results from population pressure, reduced soil fertility and high value of agricultural products.

Establishment of a humanized ST6GAL1 mouse model for influenza research

Background:This study aimed to construct and characterize a humanized influenza mouse model expressing hST6GAL1.Methods:Humanized fragments,consisting of the endothelial cell-specific K18 promoter,human ST6GAL1-encoding gene,and luciferase gene,were microinjected into the fertilized eggs of mice.The manipulated embryos were transferred into the oviducts of pseudopregnant female mice.The offspring were identified using PCR.Mice exhibiting elevated expression of the hST6GAL1 gene were selectively bred for propagation,and in vivo analysis was performed for screening.Expression of the humanized gene was tested by performing immunohistochemical(IHC)analysis.Hematologic and biochemical analyses using the whole blood and serum of humanized hST6GAL1 mice were performed.Results:Successful integration of the human ST6GAL1 gene into the mouse genome led to the overexpression of human SiaT ST6GAL1.Seven mice were identified as carrying copies of the humanized gene,and the in vivo analysis indicated that hST6GAL1gene expression in positive mice mirrored influenza virus infection characteristics.The IHC results revealed that hST6GAL1 was expressed in the lungs of humanized mice.Moreover,the hematologic and biochemical parameters of the positive mice were within the normal range.Conclusion:A humanized influenza mouse model expressing the hST6GAL1 gene was successfully established and characterized.

Estimation of evapotranspiration from artificial forest in mountainous areas of western Loess Plateau based on HYDRUS-1D model

Evapotranspiration is the most important expenditure item in the water balance of terrestrial ecosystems,and accurate evapotranspiration modeling is of great significance for hydrological,ecological,agricultural,and water resource management.Artificial forests are an important means of vegetation restoration in the western Loess Plateau,and accurate estimates of their evapotranspiration are essential to the management and development of water use strategies for artificial forests.This study estimated the soil moisture and evapotranspiration based on the HYDRUS-1D model for the artificial Platycladus orientalis(L.)Franco forest in western mountains of Loess Plateau,China from 20 April to 31 October,2023.Moreover,the influence factors were identified by combining the correlation coefficient method and the principal component analysis(PCA)method.The results showed that HYDRUS-1D model had strong applicability in portraying hydrological processes in this area and revealed soil water surplus from 20 April to 31 October,2023.The soil water accumulation was 49.64 mm;the potential evapotranspiration(ET_(p))was 809.67 mm,which was divided into potential evaporation(E_(p);95.07 mm)and potential transpiration(T_(p);714.60 mm);and the actual evapotranspiration(ET_(a))was 580.27 mm,which was divided into actual evaporation(E_(a);68.27 mm)and actual transpiration(T_(a);512.00 mm).From April to October 2023,the ET_(p),E_(p),T_(p),ET_(a),E_(a),and T_(a) first increased and then decreased on both monthly and daily scales,exhibiting a single-peak type trend.The average ratio of T_(a)/ET_(a) was 0.88,signifying that evapotranspiration mainly stemmed from transpiration in this area.The ratio of ET_(a)/ET_(p) was 0.72,indicating that this artificial forest suffered from obvious drought stress.The ET_(p) was significantly positively correlated with ET_(a),and the R^(2) values on the monthly and daily scales were 0.9696 and 0.9635(P<0.05),respectively.Furthermore,ET_(a) was significantly positively correlated with temperature,solar radiation,and wind speed,and negatively correlated with relative humidity and precipitation(P<0.05);and temperature exhibited the highest correlation with ET_(a).Thus,ET_(p) and temperature were the decisive contributors to ET_(a) in this area.The findings provide an effective method for simulating regional evapotranspiration and theoretical reference for water management of artificial forests,and deepen understanding of effects of each influence factors on ET_(a) in arid areas.

Tidal modeling based on satellite altimetry observations of TOPEX/ Poseidon, Jason1, Jason2, and Jason3 with high prediction capability: A case study of the Baltic Sea

This research aims to optimize the utilization of long-term sea level data from the TOPEX/Poseidon,Jason1,Jason2,and Jason3 altimetry missions for tidal modeling.We generate a time series of along-track observations and apply a developed method to produce tidal models with specific tidal constituents for each location.Our tidal modeling methodology follows an iterative process:partitioning sea surface height(SSH)observations into analysis/training and prediction/validation parts and ultimately identi-fying the set of tidal constituents that provide the best predictions at each time series location.The study focuses on developing 1256 time series along the altimetry tracks over the Baltic Sea,each with its own set of tidal constituents.Verification of the developed tidal models against the sSH observations within the prediction/validation part reveals mean absolute error(MAE)values ranging from 0.0334 m to 0.1349 m,with an average MAE of 0.089 m.The same validation process is conducted on the FES2014 and EOT20 global tidal models,demonstrating that our tidal model,referred to as BT23(short for Baltic Tide 2023),outperforms both models with an average MAE improvement of 0.0417 m and 0.0346 m,respectively.In addition to providing details on the development of the time series and the tidal modeling procedure,we offer the 1256 along-track time series and their associated tidal models as supplementary materials.We encourage the satellite altimetry community to utilize these resources for further research and applications.

Application of Cellular Automata and Markov Chain model for urban green infrastructure in Kuala Lumpur,Malaysia

Kuala Lumpur of Malaysia,as a tropical city,has experienced a notable decline in its critical urban green infrastructure(UGI)due to rapid urbanization and haphazard development.The decrease of UGI,especially natural forest and artificial forest,may reduce the diversity of ecosystem services and the ability of Kuala Lumpur to build resilience in the future.This study analyzed land use and land cover(LULC)and UGI changes in Kuala Lumpur based on Landsat satellite images in 1990,2005,and 2021and employed the overall accuracy and Kappa coefficient to assess classification accuracy.LULC was categorized into six main types:natural forest,artificial forest,grassland,water body,bare ground,and built-up area.Satellite images in 1990,2005,and 2021 showed the remarkable overall accuracy values of 91.06%,96.67%,and 98.28%,respectively,along with the significant Kappa coefficient values of 0.8997,0.9626,and 0.9512,respectively.Then,this study utilized Cellular Automata and Markov Chain model to analyze the transition of different LULC types during 1990-2005 and 1990-2021 and predict LULC types in 2050.The results showed that natural forest decreased from 15.22%to 8.20%and artificial forest reduced from 18.51%to 15.16%during 1990-2021.Reductions in natural forest and artificial forest led to alterations in urban surface water dynamics,increasing the risk of urban floods.However,grassland showed a significant increase from 7.80%to 24.30%during 1990-2021.Meanwhile,bare ground increased from 27.16%to 31.56%and built-up area increased from 30.45%to 39.90%during 1990-2005.In 2021,built-up area decreased to 35.10%and bare ground decreased to 13.08%,indicating a consistent dominance of built-up area in the central parts of Kuala Lumpur.This study highlights the importance of integrating past,current,and future LULC changes to improve urban ecosystem services in the city.

Emoti-Shing: Detecting Vishing Attacks by Learning Emotion Dynamics through Hidden Markov Models

This study examines vishing, a form of social engineering scam using voice communication to deceive individuals into revealing sensitive information or losing money. With the rise of smartphone usage, people are more susceptible to vishing attacks. The proposed Emoti-Shing model analyzes potential victims’ emotions using Hidden Markov Models to track vishing scams by examining the emotional content of phone call audio conversations. This approach aims to detect vishing scams using biological features of humans, specifically emotions, which cannot be easily masked or spoofed. Experimental results on 30 generated emotions indicate the potential for increased vishing scam detection through this approach.

An approach to estimate tree height using PolInSAR data constructed by the Sentinel-1 dual-pol SAR data and RVoG model

We estimate tree heights using polarimetric interferometric synthetic aperture radar(PolInSAR)data constructed by the dual-polarization(dual-pol)SAR data and random volume over the ground(RVoG)model.Considering the Sentinel-1 SAR dual-pol(SVV,vertically transmitted and vertically received and SVH,vertically transmitted and horizontally received)configuration,one notes that S_(HH),the horizontally transmitted and horizontally received scattering element,is unavailable.The S_(HH)data were constructed using the SVH data,and polarimetric SAR(PolSAR)data were obtained.The proposed approach was first verified in simulation with satisfactory results.It was next applied to construct PolInSAR data by a pair of dual-pol Sentinel-1A data at Duke Forest,North Carolina,USA.According to local observations and forest descriptions,the range of estimated tree heights was overall reasonable.Comparing the heights with the ICESat-2 tree heights at 23 sampling locations,relative errors of 5 points were within±30%.Errors of 8 points ranged from 30%to 40%,but errors of the remaining 10 points were>40%.The results should be encouraged as error reduction is possible.For instance,the construction of PolSAR data should not be limited to using SVH,and a combination of SVH and SVV should be explored.Also,an ensemble of tree heights derived from multiple PolInSAR data can be considered since tree heights do not vary much with time frame in months or one season.

Persistence of a Stochastic HPV Epidemic Model with Markov Switching

In order to study the influence of stochastic disturbance and environment switching on the HPV infection and provide a theoretical basis for the development of effective HPV disease prevention measures,in this paper we establish a kind of two-sex stochastic HPV epidemic model with white noise and Markov switching.We show that the model has a unique local positive solution and a unique global positive solution.Then we identify the threshold conditions for the persistence of the HPV epidemic,and verify the persistence of the disease using the Lyapunov method and the Ito^formula.At last,the numerical simulation is carried out to illustrate the rationality of the theoretical results.

Numerical Models and Methods of Atmospheric Parameters Originating in the Formation of the Earth’s Climatic Cycle

Atmospheric models are physical equations based on the ideal gas law. Applied to the atmosphere, this law yields equations for water, vapor (gas), ice, air, humidity, dryness, fire, and heat, thus defining the model of key atmospheric parameters. The distribution of these parameters across the entire planet Earth is the origin of the formation of the climatic cycle, which is a normal climatic variation. To do this, the Earth is divided into eight (8) parts according to the number of key parameters to be defined in a physical representation of the model. Following this distribution, numerical models calculate the constants for the formation of water, vapor, ice, dryness, thermal energy (fire), heat, air, and humidity. These models vary in complexity depending on the indirect trigonometric direction and simplicity in the sum of neighboring models. Note that the constants obtained from the equations yield 275.156˚K (2.006˚C) for water, 273.1596˚K (0.00963˚C) for vapor, 273.1633˚K (0.0133˚C) for ice, 0.00365 in/s for atmospheric dryness, 1.996 in2/s for humidity, 2.993 in2/s for air, 1 J for thermal energy of fire, and 0.9963 J for heat. In summary, this study aims to define the main parameters and natural phenomena contributing to the modification of planetary climate. .

基于PLSR-FLUS-MarKov模型的生态系统服务价值多情景预测--以漓江流域为例

以漓江流域为研究单元,运用修正后的PLSR-FLUS-MarKov模型和当量因子法,对流域自然发展、生态优先、旅游优先情景下2030年生态系统服务价值做出预测,探究多情景土地利用变化对生态系统服务价值的影响。结果表明,PLSR-FLUS-MarKov模型能更准确刻画流域土地利用动态变化,模拟精度高达83.57%。流域土地利用变化集中区位于桂林市辖区、兴安县和阳朔县,多表现为耕地、林地向建设用地的转入以及耕地和林地间的相互转化。2030年,漓江流域生态系统服务价值均呈现下降趋势,自然发展情景、生态优先情景、旅游优先情景分别为218.07亿、225.61亿、222.91亿元,较2020年下降了9.02亿、1.48亿和4.18亿元,并呈现“东北高,西南低”的格局特征,实施生态优先策略后其下降速度放缓。林地是漓江流域生态系统服务价值的关键来源,占比超过80%,耕地和水域也贡献了一定比例的服务价值。生态优先情景下的生态系统服务价值最高,旅游优先情景次之,自然发展情景最低。研究结果可为桂林世界级旅游城市建设及区域生态系统健康管理提供科学指导。

成纤维细胞生长因子受体1 抑制剂对胶原诱导关节炎模型大鼠骨破坏的影响

背景:课题组前期的研究表明靶向成纤维细胞生长因子受体1(fibroblast growth factor receptor 1,FGFR1)可能是治疗类风湿性关节炎的有效靶点。目的:探讨FGFR1抑制剂(PD173074)对胶原诱导关节炎模型大鼠骨破坏的影响。方法:将25只雌性SD大鼠随机分为5组,正常对照组、模型组、甲氨蝶呤组、PD173074低剂量组、PD173074高剂量组。除正常对照组外,其余各组大鼠建立Ⅱ型胶原诱导关节炎模型。造模成功后正常组及模型组大鼠腹腔注射无菌PBS,甲氨蝶呤组药物注射剂量为1.04 mg/kg,PD173074低剂量组和高剂量组药物注射剂量分别为5,20 mg/kg,1次/周。给药4周后取材,观察大鼠临床症状以及关节肿胀情况,踝关节Micro-CT三维重建及分析,观察踝关节病理变化,检测关节周围血管生成情况及核因子κB受体活化因子配体的表达,检测关节滑膜中p-FGFR1、血管内皮生长因子A、抗酒石酸酸性磷酸酶的表达,观察肝、脾、肾病理变化并计算肝、脾、肾指数。结果与结论:①PD173074能够减轻模型大鼠踝关节临床症状及关节肿胀,延缓骨质丢失,改善骨结构,减轻关节滑膜侵袭以及软骨骨侵蚀,降低关节周围破骨细胞数量,抑制关节滑膜组织中的血管生成,降低核因子κB受体活化因子配体的表达,抑制FGFR1磷酸化蛋白、抗酒石酸酸性磷酸酶和血管内皮生长因子A的蛋白表达。②大鼠肝、脾、肾病理观察表明经过PD173074治疗后无明显的毒副作用。③研究证明了FGFR1抑制剂能够延缓Ⅱ型胶原诱导关节炎模型大鼠关节炎症及骨破坏的进展,并抑制血管的生成。初步验证了PD173074在Ⅱ型胶原诱导关节炎模型中的治疗作用,其可能是通过抑制FGFR1磷酸化发挥作用,为寻找类风湿性关节炎新的治疗靶点提供了方向。

Application of Modelica Based Multi- Domain Modeling and Simulation for Gravity-1

In the R&D phase of Gravity-1(YL-1), a multi-domain modeling and simulation technology based on Modelica language was introduced, which was a recent attempt in the practice of modeling and simulation method for launch vehicles in China. It realizes a complex coupling model within a unified model for different domains, so that technologists can work on one model. It ensured the success of YL-1 first launch mission, supports rapid iteration, full validation, and tight design collaboration.

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.

重复观测的Poisson-Lindley INAR(1)模型

对过离散的重复观测时间序列数据,考虑一种具有Poisson-Lindley边际分布的INAR(1)(PLINAR(1))过程的独立重复观测模型.先通过条件最小二乘估计、Yule-W alker估计、拟似然估计和条件极大似然估计方法估计模型的参数,讨论估计量的渐近性质,并给出模型的预测,再通过数值模拟比较不同估计方法的性能以及重复观测带来的影响,最后对一组重复观测的每周太阳黑子群数量的数据进行拟合,拟合结果验证了模型的有效性.

Investigating the impact of intestinal glucagon-like peptide-1 on hypoglycemia in type 1 diabetes

Recent advances in understanding type 1 diabetes(T1D)highlight the complexity of managing hypoglycemia,a frequent and perilous complication of diabetes therapy.This letter delves into a novel study by Jin et al,which elucidates the role of intestinal glucagon-like peptide-1(GLP-1)in the counterregulatory response to hypoglycemia in T1D models.The study employed immunofluorescence,Western blotting,and enzyme-linked immunosorbent assay to track changes in GLP-1 and its receptor expression in diabetic mice subjected to recurrent hypoglycemic episodes.Findings indicate a significant increase in intestinal GLP-1 and GLP-1 receptor expression,correlating with diminished adrenal and glucagon responses,crucial for glucose stabilization during hypoglycemic events.This letter aims to explore the implications of these findings for future therapeutic strategies and the broader understanding of T1D management.

Markov残差修正的灰色GM(1,N)模型在粮食产量预测中的应用

将灰色理论和Markov预测方法用于中国粮食产量的预测。运用灰色关联分析,从众多影响粮食产量的因素中确定了影响粮食产量的主要因素为:乡村从业人员、农作物有效灌溉面积、粮食作物播种面积、受灾面积。以粮食产量作为特征变量,以影响粮食产量的主要因素作为选定变量构建了粮食产量的灰色GM(1,5)预测模型,并对我国粮食产量进行拟合和预测。为了捕获粮食产量时间序列的随机波动性,使用马尔可夫模型对GM(1,5)模型的残差进行修正,以便改善预测精度。实例结果:事后检验阶段GM(1,5)模型的平均绝对百分比误差为15.45%,灰色Markov模型的平均绝对百分比误差为5.625%。灰色Markov模型能够提高预测的精度,是一种有效的、具有鲁棒性的预测方法。