• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础
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After the identification of a potential failure we have to analyze the impacts on other elements. Since we already know the theoretical causal relation of a failure we do not have to analyze our system top-down. Therefore, we have to apply a bottom-up approach to monitor effects on elements which are logical dependent on the faulty element. Components which are highly negative affected by the failure have to be adapted by CMs. Architecture analyses are a possibility to identify these elements. As described above, EAM already uses these analyses successfully what yields us to conduct a concept transfer of a FIA approach by [6]. However, a few important aspects have to be changed to make the approach suitable for safety-critical systems. The most important change is the type of leading question for analysis as [6] analyzing their system top-down. Therefore, we need to shift metrics, variables, tools and use other BBN formulas.

Our FIA approach is divided in $5(\mathbf{A}-\mathbf{E})$ steps to clarify the execution of the analysis:
(A) A graphical representation of the whole system or of a specific process/service is required. To model our system we adapted on ArchiMate version 3.0.1 since it is an updated version with elements for Internet of Things (IoT) systems, which are kinds of safety-critical systems. Therefore, we distinguish between active/passive structure elements and behavioral elements for the representation of nodes. In addition, there are 11 relation types categorized in 4 classes which address diverse connections concerning structure, dependency and other aspects. Depending on the use case different layered approaches can be used. Exemplary, a layered architecture approach for IoT is presented in Fig. 3 . This approach differs strongly from EAM layers as IoT systems have other features like openness, flexibility, connection of autonomous devices with each other to measure and send data, etc. The presented layered approach is based on [13] and $[7]$ and consists of 8 layers.
(B) Before mapping the model into a BBN model an analysis attribute, e.g., availability, has to be chosen. Depending on this attribute the BBN relations respectively dependencies can be determined.

Assuming that experts have realized CMs in the last step, a CIA is needed to analyze which kind of effects necessary CMs will have. As a CM can require new nodes or the elimination of nodes, i.e., the SGH requires an update. Only an updated SGH can be used to evaluate the new safety-as-is status of an model in the future. First, it must be clarified which node types of the SGH are affected by the CIA and in which direction (top-down $\downarrow$ or bottom-up $\uparrow$ ) the impacts will be propagandized:

1. Goal $\rightarrow$ Goal: $\downarrow \uparrow$
2. Goal $\rightarrow \mathrm{POV}: \downarrow$
3. $\mathrm{POV} \rightarrow$ Goal: $\uparrow$
4. $\mathrm{POV} \rightarrow$ Alternative Solution: $\downarrow$.
This means, that an amendment of a goal can have both impacts on goals with higher abstraction level and goals with lower abstraction level. Moreover, POVs are involved as well. Modifications regarding POVs will affect goals on the next overlying layer. Furthermore, amendments of the POVs directly influence the alternative solutions. To perform the CIA step by step we need impact rules [10] with the following syntax:
$$A . X \rightarrow \text { B. } Y$$
In general, this statement expresses if source element A has the characteristic $X$, it follows that target element B has the characteristic Y. Concretely, this implies $\mathrm{A} \in{$ Goal, $P O V}$ and $\mathrm{B} \in \mathrm{A} \cup{$ AlternativeSolution $}$. The operations or effects, which are represented by $X$ and $Y$ are defined as follows: $\mathrm{X}, \mathrm{Y} \in{$ noEffect, extend, modify, delete $}$. Extending a $\mathrm{SGH}$ element means to refine an element, e.g., by adding new elements. If an element is modified, the necessary information will be updated. Deleting a SGH element implies to remove it from the SGH. For instance, if the impact rule G1.modify $\rightarrow$ G2. extend is applied, it means that $G 1$ will be modified and, in this regard $G 2$ must be extended as an impact. In this paper, we distinguish between Best-Case (BC) and Worst-Case (WC) CIA. The first one requires a minimum number of change impacts or lightweight change impacts and vice versa for the $\mathrm{WC}$ analysis. In the following, we define the change impact rules for the SGH, split into BC and WC (cf. Table 2). Hereinafter, the WC rules are explained in more detail. In case of deleting, modifying or extending a goal, the underlying goal or POV must deleted, modified or extended as well. Furthermore, if a goal or POV is deleted, modified or extended the overlying goal must be modified in each case since information of the child nodes must be transmitted onto the corresponding parent node. The consequence of amendments on POVs is modifying all concerned alternative solutions. This can be justified by the fact that solutions directly and only depend on the POVs.

After presenting the steps of our approach we conduct the evaluation with aid of a medical use case. As mentioned before IoT is a kind of safety-critical system if devices with safety goals are included, like IoT of medical devices or medical smart homes. Therefore, we use a system for Ambient Assisted Living (AAL) to evaluate our approach. Figure 4 depicts an exemplary AAL system with 4 medical or wellbeing devices delivering health support. As it exceeds the scope of our evaluation, just a small cutout of the system is shown and only 5 layers of the presented layered architecture in Sect. $4.3$ are visible. The devices include sensors, actuators or RFID tags to measure and to trigger actions. For instance, the insulin pump measures data which are sent to the IoT-Hub which reviews the data and to trigger the SOS call if necessary.

in consideration of the following context: For the success of an AAL system correctly functioning of the AAL sensors is a prerequisite, e.g., insulin pump sensors. Furthermore, AAL actuators must work correctly, e.g., activating pillbox. Moreover, the software running on an AAL system must work correctly. To ensure this, results of calculations must be correct and be performed in time without any delay. In addition, software must be acceptably secure against third party hacking attacks. The fourth aspect, which has been taken into account is the reliability of the AAL communication. For this purpose, the system must be secured against data theft and manipulation. Moreover, messages must be correctly transferred in time. As mentioned in Sect. $4.1$ all nodes within the SGH must be annotated with an attribute. These attributes will be described in detail in step 4. The $\mathrm{SGH}$ of the AAL use case is extended by two alternative solutions.

## 商业建模代考

(A) 需要整个系统或特定流程/服务的图形表示。为了对我们的系统进行建模，我们在 ArchiMate 版本 3.0.1 上进行了调整，因为它是一个更新版本，其中包含物联网 (IoT) 系统的元素，这是一种安全关键系统。因此，我们区分了主动/被动结构元素和行为元素来表示节点。此外，还有 11 种关系类型，分为 4 类，涉及结构、依赖和其他方面的不同连接。根据用例，可以使用不同的分层方法。示例性的，物联网的分层架构方法如图 3 所示。这种方法与 EAM 层有很大不同，因为物联网系统具有其他特性，如开放性、灵活性、[7]由8层组成。
(B) 在将模型映射到 BBN 模型之前，必须选择分析属性，例如可用性。根据这个属性，可以确定 BBN 关系和依赖关系。

1. 目标→目标：↓↑
2. 目标→观点:↓
3. 观点→目标：↑
4. 观点→替代解决方案：↓.
这意味着，目标的修改可以同时对抽象级别较高的目标和抽象级别较低的目标产生影响。此外，还涉及 POV。有关 POV 的修改将影响下一个覆盖层的目标。此外，POV 的修订直接影响替代解决方案。要逐步执行 CIA，我们需要使用以下语法的影响规则 [10]：
一个.X→ B. 是
一般而言，该语句表示源元素 A 是否具有特征X，因此目标元素 B 具有特征 Y。具体而言，这意味着一个∈$G○一个l,$磷○在和乙∈一个∪$一个l吨和rn一个吨一世在和小号○l在吨一世○n$. 操作或效果，由X和是定义如下：X,是∈$n○和FF和C吨,和X吨和nd,米○d一世F是,d和l和吨和$. 扩展一个新加坡总医院element 意味着细化一个元素，例如，通过添加新元素。如果元素被修改，必要的信息将被更新。删除 SGH 元素意味着将其从 SGH 中删除。例如，如果影响规则 G1.modify→G2。应用了extend，这意味着G1将被修改，在这方面G2必须扩大影响。在本文中，我们区分了最佳情况 (BC) 和最坏情况 (WC) CIA。第一个要求最少数量的变更影响或轻量级变更影响，反之亦然厕所分析。在下文中，我们定义了 SGH 的变更影响规则，分为 BC 和 WC（参见表 2）。在下文中，将更详细地解释WC规则。在删除、修改或扩展目标的情况下，基础目标或 POV 也必须删除、修改或扩展。此外，如果目标或 POV 被删除、修改或扩展，则必须在每种情况下修改上层目标，因为必须将子节点的信息传输到相应的父节点。修改 POV 的结果是修改所有相关的替代解决方案。这可以通过解决方案直接且仅依赖于 POV 的事实来证明。

## 有限元方法代写

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## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。