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.

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.

Second-Order MaxEnt Predictive Modelling Methodology. II: Probabilistically Incorporated Computational Model (2nd-BERRU-PMP)

This work presents a comprehensive second-order predictive modeling (PM) methodology based on the maximum entropy (MaxEnt) principle for obtaining best-estimate mean values and correlations for model responses and parameters. This methodology is designated by the acronym 2nd-BERRU-PMP, where the attribute “2nd” indicates that this methodology incorporates second- order uncertainties (means and covariances) and second (and higher) order sensitivities of computed model responses to model parameters. The acronym BERRU stands for “Best-Estimate Results with Reduced Uncertainties” and the last letter (“P”) in the acronym indicates “probabilistic,” referring to the MaxEnt probabilistic inclusion of the computational model responses. This is in contradistinction to the 2nd-BERRU-PMD methodology, which deterministically combines the computed model responses with the experimental information, as presented in the accompanying work (Part I). Although both the 2nd-BERRU-PMP and the 2nd-BERRU-PMD methodologies yield expressions that include second (and higher) order sensitivities of responses to model parameters, the respective expressions for the predicted responses, for the calibrated predicted parameters and for their predicted uncertainties (covariances), are not identical to each other. Nevertheless, the results predicted by both the 2nd-BERRU-PMP and the 2nd-BERRU-PMD methodologies encompass, as particular cases, the results produced by the extant data assimilation and data adjustment procedures, which rely on the minimization, in a least-square sense, of a user-defined functional meant to represent the discrepancies between measured and computed model responses.

Second-Order MaxEnt Predictive Modelling Methodology. I: Deterministically Incorporated Computational Model (2nd-BERRU-PMD)

This work presents a comprehensive second-order predictive modeling (PM) methodology designated by the acronym 2nd-BERRU-PMD. The attribute “2nd” indicates that this methodology incorporates second-order uncertainties (means and covariances) and second-order sensitivities of computed model responses to model parameters. The acronym BERRU stands for “Best- Estimate Results with Reduced Uncertainties” and the last letter (“D”) in the acronym indicates “deterministic,” referring to the deterministic inclusion of the computational model responses. The 2nd-BERRU-PMD methodology is fundamentally based on the maximum entropy (MaxEnt) principle. This principle is in contradistinction to the fundamental principle that underlies the extant data assimilation and/or adjustment procedures which minimize in a least-square sense a subjective user-defined functional which is meant to represent the discrepancies between measured and computed model responses. It is shown that the 2nd-BERRU-PMD methodology generalizes and extends current data assimilation and/or data adjustment procedures while overcoming the fundamental limitations of these procedures. In the accompanying work (Part II), the alternative framework for developing the “second- order MaxEnt predictive modelling methodology” is presented by incorporating probabilistically (as opposed to “deterministically”) the computed model responses.

Second-Order MaxEnt Predictive Modelling Methodology. III: Illustrative Application to a Reactor Physics Benchmark

This work illustrates the innovative results obtained by applying the recently developed the 2nd-order predictive modeling methodology called “2nd- BERRU-PM”, where the acronym BERRU denotes “best-estimate results with reduced uncertainties” and “PM” denotes “predictive modeling.” The physical system selected for this illustrative application is a polyethylene-reflected plutonium (acronym: PERP) OECD/NEA reactor physics benchmark. This benchmark is modeled using the neutron transport Boltzmann equation (involving 21,976 uncertain parameters), the solution of which is representative of “large-scale computations.” The results obtained in this work confirm the fact that the 2nd-BERRU-PM methodology predicts best-estimate results that fall in between the corresponding computed and measured values, while reducing the predicted standard deviations of the predicted results to values smaller than either the experimentally measured or the computed values of the respective standard deviations. The obtained results also indicate that 2nd-order response sensitivities must always be included to quantify the need for including (or not) the 3rd- and/or 4th-order sensitivities. When the parameters are known with high precision, the contributions of the higher-order sensitivities diminish with increasing order, so that the inclusion of the 1st- and 2nd-order sensitivities may suffice for obtaining accurate predicted best- estimate response values and best-estimate standard deviations. On the other hand, when the parameters’ standard deviations are sufficiently large to approach (or be outside of) the radius of convergence of the multivariate Taylor-series which represents the response in the phase-space of model parameters, the contributions stemming from the 3rd- and even 4th-order sensitivities are necessary to ensure consistency between the computed and measured response. In such cases, the use of only the 1st-order sensitivities erroneously indicates that the computed results are inconsistent with the respective measured response. Ongoing research aims at extending the 2nd-BERRU-PM methodology to fourth-order, thus enabling the computation of third-order response correlations (skewness) and fourth-order response correlations (kurtosis).

基于InVEST与CA-Markov模型的宝鸡市碳储量时空演变与预测

土地利用类型的变化是影响陆地生态系统碳储量变化的重要因素,分析区域未来土地利用格局对碳储量的影响、对维持碳平衡具有重要意义.基于宝鸡市2000—2020年土地利用数据,运用InVEST模型和CA-Markov模型,对2000—2020年宝鸡市不同土地利用类型碳储量的变化特征进行分析,并对2030年不同情景下宝鸡市土地利用变化及碳储量变化情况进行了预测.研究表明:①2000—2020年宝鸡市建设用地面积不断增加,耕地、林地和草地为主要土地利用类型.②2000—2020年,宝鸡市碳储量共损失0.42×10^(6)t,耕地和林地面积减少是其碳储量减少的主要原因.③2020—2030年自然发展情景下宝鸡市碳储量损失最多,耕地保护情景和生态保护情景下宝鸡市碳储量相差不大,控制城市扩张情景下宝鸡市碳储量损失最少、固碳能力最强.因此,未来进行国土空间规划时,可统筹考虑控制城市扩张情景、生态保护情景和耕地保护情景,以确保在粮食供给和生态安全的基础上,提高研究区的碳储量.

D-S理论和Markov链组合的桥梁性能退化预测研究

为准确预测桥梁性能退化,考虑到数据随机性和微小扰动发生状态跳跃,提出了一种D-S(Dempster-Shafer)证据理论和Markov链组合的桥梁性能退化组合预测模型和性能退化率的概念.该模型基于指数平滑(exponential smoothing,ES)方法获得新的预测数据序列,并利用Markov链和D-S理论不断进行优化,从而实现桥梁性能退化的组合预测.实际工程的应用结果表明:性能退化率可以直观地表征在梁性能退化的速度.其次,该模型的平均相对误差为1.54%,较于回归、灰色和模糊加权Markov链模型,精度分别提高了1.11%,0.88%和2.8%,而后验差比值为0.242,小于0.35;模型的标准差为9.021,相比其他模型分别减小了3.978,3.405和7.500,而变异系数为0.109,均小于其他模型,验证了组合预测模型在精度和稳定性方面的优越性,可为在役桥梁结构性能退化预测与维护提供理论基础.

基于动态稳定性验证和CA-Markov预测的耕地时空演变特征--以赣榆区为例

为阐明耕地变化动态稳定性验证的重要性,预测“十四五”目标年(2025年)与远景目标年(2035年)的耕地变化,本研究运用耕地面积变异系数、地理集中指数、不平衡指数及趋势分析法,从耕地数量、空间分布与空间发展趋势三个方面,对江苏省连云港市赣榆区耕地变化的动态稳定性进行验证,并运用CA-Markov模型对未来不同政策情境下的耕地变化情况进行模拟。结果表明,2009-2016年赣榆区耕地总量持续下降,未存在较大反复与波动,耕地空间分布较为均衡,耕地占用与补充的空间发展趋势较为连续,总体上具有较高的动态稳定性,符合运用CA-Markov模型的前提条件。预测在各种政策情境下,2025年之前赣榆区将经历快速的耕地非农化过程,至2035年耕地减少的速率有所放缓,具有区位优势的赣榆北部地区将是未来耕地非农化的重点区域,耕地将逐渐向南部内陆地区转移。研究表明,对耕地变化的动态稳定性进行验证可以有效保证CA-Markov模型的模拟精度,赣榆区未来的耕地保护形势不容乐观,应合理协调经济发展与耕地保护之间的关系,保障社会、生态与粮食安全。

改进CA-Markov模型的城市土地空间结构演变预测

城市土地空间结构演变预测需要大量的数据支持,现阶段预测手段存在预测精度不佳的问题,为此提出GIS技术下城市土地空间结构演变预测方法。通过GIS技术获取城市土地空间结构数据,并对其进行结构演变分析;通过人工神经网络模型、轮盘竞争机制和惯性系数对CA模型实行优化处理;将改进CA模型与Markov结构结合,建立改进CA-Markov模型,根据城市土地空间结构演变分析结果实现城市土地空间结构演变预测。实验结果表明,所提方法的城市土地空间结构演变预测精度更高,整体应用效果更好。

基于Markov模型的三种方案治疗胰腺癌的药物经济学分析

目的通过建立Markov模型,对三种治疗胰腺癌的方案进行药物经济学评价,为临床用药提供循证依据。方法参考药品价格、治疗成本、健康效用值、状态转移概率建立马尔可夫(Markov)模型,对兰州大学第一医院2017年6月~2022年5月三种方案的有效性和经济性进行评价,并进行敏感性分析。结果Markov模型模拟回乘分析结果显示60个周期(5年)后,吉西他滨联合白蛋白结合型紫杉醇方案组(AG方案)的成本-效果比为437903.43元/11.80质量调整生命月(QALMs);吉西他滨联合卡培他滨方案组(GX方案)的成本-效果比为162234.52元/6.58 QALMs;吉西他滨联合替吉奥方案组(GS方案)的成本-效果比为429029.92元/15.64 QALMs。与GX方案相比较,AG方案的成本高出275668.91元,效果高出5.22 QALMs,ICER却为负值,处于绝对劣势;GS方案比GX方案效果高出9.06 QALMs,ICER为正值。结论从成本-效果的角度来看,方案AG的成本最高,治疗效果也相对较差,处于绝对劣势;GS方案比GX方案效果高,ICER也为正值,但成本较高,GX方案为优选方案。从药物经济学的角度来看,GS方案和GX方案相对于AG方案具有更高的性价比,患者在接受治疗后能够获得更长的生存期和更好的生活质量,从而提高整体的经济效果。在实际应用中,需要结合患者的具体情况和医生的专业建议来制定最佳治疗方案。

基于Markov链的上海市枫林社区骨质疏松全人群防治工作的成本效果分析

背景随着社区卫生服务综合改革的逐步深化,社区对全人群的单病种防治工作也在不断优化,但目前仍缺乏合适的方法来评价社区防治的成效。目的通过分析2016—2022年度上海市徐汇区枫林街道社区卫生服务中心现有骨质疏松全人群防治措施的成本效果,探究社区单病种全人群防治的初步成效及可能存在的问题。方法本研究以4293例枫林社区2016—2022年度接受骨质疏松防治的社区居民为研究对象进行成本-效果分析。根据骨密度值将研究对象划分为三种状态:健康、低骨量、骨质疏松,以此构建Markov模型,分析不同状态转移的影响因素,并依据Markov模型预测值结合质量调整生命年(QALY)计算QALY增量,统计2016—2022年防治工作投入的所有成本,以成本/QALY增量比来评价防治效果,通过文献检索确定的健康效用值和期望寿命计算求得QALY。结果枫林社区骨质疏松防治总成本为33814102.15元。第一次诊断时社区骨质疏松防治人群的总QALY为77098.2889,人均均值为17.959,标准差为9.34;第二次诊断时防治人群的总QALY为79616.9361,人均均值为18.546,标准差为9.342,两次诊断QALY的差值为2518.6472。增量成本效果比(ICER)为2132.9070元/QALY,ICER值小于1倍(66965.10元)人均GDP,表明干预方案完全具有成本效果。结论本研究结合Markov模型及QALY对枫林社区持续接受骨质疏松随访管理的人群的防治效果进行了初步评价,结果显示枫林社区的骨质疏松防治干预方案是值得投入的,为后续骨质疏松的精准防治决策提供了一定的依据。

东北地区土地覆被格局变化模拟:基于CLUE-S和Markov-CA模型的对比分析

研究以遥感解译的东北地区2000年、2010年、2015年的土地覆被变化为基础,充分考虑自然和社会因素对土地覆被变化的影响,分别通过CLUE-S模型和Markov-CA模型对东北地区2015年和2030年土地覆被格局进行模拟,研究结果表明:通过与遥感解译的2015年实际土地覆被类型数据对比验证,CLUE-S模型和Markov-CA模型模拟结果的总体Kappa指数分别为0.9700和0.9649,结果表明2种模型的模拟结果较为理想,CLUE-S模型的模拟精度较Markov-CA模型更高。2015—2030年东北地区草地、耕地、湿地、其他用地和水体面积呈现下降趋势,林地、人工表面面积呈现增加趋势,人地关系越发紧张。东北地区作为生态环境相对脆弱的区域需警惕不可持续的土地覆被变化,需权衡生态保护、粮食增加与基础设施建设的用地需求和协调发展。

NUMERICAL SIMULATION OF METHANE-AIR TURBULENT JET FLAME USING A NEW SECOND-ORDER MOMENT MODEL

A new second-order moment model for turbulent combustion is applied in the simulation of methane-air turbulent jet flame. The predicted results are compared with the experimental results and with those predicted using the well-known EBU-Arrhenius model and the original second-order moment model. The comparison shows the advantage of the new model that it requires almost the same computational storage and time as that of the original second-order moment model, but its modeling results are in better agreement with experiments than those using other models. Hence, the new second-order moment model is promising in modeling turbulent combustion with NOx formation with finite reaction rate for engineering application.

Comparison of a Full Second-Order Moment Model and an Algebraic Stress Two-Phase Turbulence Model for Simulating Bubble-Liquid Flows in a Bubble Column

A full second-order moment (FSM) model and an algebraic stress (ASM) two-phase turbulence modelare proposed and applied to predict turbulent bubble-liquid flows in a 2D rectangular bubble column. Predictiongives the bubble and liquid velocities, bubble volume fraction, bubble and liquid Reynolds stresses and bubble-liquidvelocity correlation. For predicted two-phase velocities and bubble volume fraction there is only slight differencebetween these two models, and the simulation results using both two models are in good agreement with the particleimage velocimetry (PIV) measurements. Although the predicted two-phase Reynolds stresses using the FSM are insomewhat better agreement with the PIV measurements than those predicted using the ASM, the Reynolds stressespredicted using both two models are in general agreement with the experiments. Therefore, it is suggested to usethe ASM two-phase turbulence model in engineering application for saving the computation time.

Closed-form steady-state solutions for forced vibration of second-order axially moving systems

Second-order axially moving systems are common models in the field of dynamics, such as axially moving strings, cables, and belts. In the traditional research work, it is difficult to obtain closed-form solutions for the forced vibration when the damping effect and the coupling effect of multiple second-order models are considered.In this paper, Green's function method based on the Laplace transform is used to obtain closed-form solutions for the forced vibration of second-order axially moving systems. By taking the axially moving damping string system and multi-string system connected by springs as examples, the detailed solution methods and the analytical Green's functions of these second-order systems are given. The mode functions and frequency equations are also obtained by the obtained Green's functions. The reliability and convenience of the results are verified by several examples. This paper provides a systematic analytical method for the dynamic analysis of second-order axially moving systems, and the obtained Green's functions are applicable to different second-order systems rather than just string systems. In addition, the work of this paper also has positive significance for the study on the forced vibration of high-order systems.

基于FLUS-Markov模型的玉溪市生态系统服务价值时空演变与预测

基于玉溪市2010年、2020年2期生态景观类型数据和粮食产量经济价值修正生态系统服务价值系数,分析玉溪市生态景观类型及生态系统服务价值分布特征,采用FLUS-Markov模型预测玉溪市2030年生态系统服务价值及其分布。结果表明,该模型预测结果的Kappa系数提高至0.8969,整体精度为0.9393,精度较高;2010—2020年玉溪市林地、草地的面积呈减少趋势,以林地、草地、水域为主的生态系统仍然面临威胁;2030年玉溪市生态系统服务价值为535.0471亿元,变化率为-0.1297%,玉溪市应加强对林地、草地的保护,加强退耕还林、退耕还草政策的实施,合理控制草地、林地向耕地的转化;2030年玉溪市生态系统服务价值依然表现为西部>中部>东部;2030年玉溪市各县市区生态系统服务价值贡献率由高到低依次为新平县、元江县、峨山县、易门县、华宁县、红塔区、江川区、澄江市、通海县。

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。

Customer Segment Prediction on Retail Transactional Data Using K-Means and Markov Model

Retailing is a dynamic business domain where commodities and goods are sold in small quantities directly to the customers.It deals with the end user customers of a supply-chain network and therefore has to accommodate the needs and desires of a large group of customers over varied utilities.The volume and volatility of the business makes it one of the prospectivefields for analytical study and data modeling.This is also why customer segmentation drives a key role in multiple retail business decisions such as marketing budgeting,customer targeting,customized offers,value proposition etc.The segmentation could be on various aspects such as demographics,historic behavior or preferences based on the use cases.In this paper,historic retail transactional data is used to segment the custo-mers using K-Means clustering and the results are utilized to arrive at a transition matrix which is used to predict the cluster movements over the time period using Markov Model algorithm.This helps in calculating the futuristic value a segment or a customer brings to the business.Strategic marketing designs and budgeting can be implemented using these results.The study is specifically useful for large scale marketing in domains such as e-commerce,insurance or retailers to segment,profile and measure the customer lifecycle value over a short period of time.

Modeling urban land use dynamics using Markov-chain and cellular automata in Gondar City,Northwest Ethiopia

Modeling urban land-use dynamics is critical for urban experts’and infrastructure managers’planning.This study attempts to explore the land-use/land-cover(LULC)dynamics of Gondar using satellite images from 1984 to 2020.Markov-Chain and Cellular Automata(MC-CA)models have been recognized as performing well in predicting urban land-use change.However,only a few models work in Ethiopia in general,and no study in Gondar has applied this approach to study urban land-use patterns.Therefore,Gondar land-use/land cover changes of Gondar were predicted using the MC-CA model in IDRISI.The built-up area in Gondar city covered 1413 ha(3%of the total area)in 1984 and increased to 2380 ha(5%)in 1994;21153 ha(45.5%)in 2004;22622 ha(48.7%)in 2014;and 23427 ha(50.5%)in 2020.The area has been predicted to reach 57.5%in the 2050s,showing a faster increase that will cause a very vast loss of farmland.This will increase urban sprawl challenges as well as overall environmental disequilibrium in the preceding decade.Thus,innovative and careful structures and systems in urban planning are required to secure a sustainable urban future and to make our cities livable and competitive in the paradigm of sustainable cities.

Noncooperative Model Predictive Game With Markov Jump Graph

In this paper,the distributed stochastic model predictive control(MPC)is proposed for the noncooperative game problem of the discrete-time multi-player systems(MPSs)with the undirected Markov jump graph.To reflect the reality,the state and input constraints have been considered along with the external disturbances.An iterative algorithm is designed such that model predictive noncooperative game could converge to the socalledε-Nash equilibrium in a distributed manner.Sufficient conditions are established to guarantee the convergence of the proposed algorithm.In addition,a set of easy-to-check conditions are provided to ensure the mean-square uniform bounded stability of the underlying MPSs.Finally,a numerical example on a group of spacecrafts is studied to verify the effectiveness of the proposed method.