统计代写|决策与风险作业代写decision and risk代考|Application for Mining Activities

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• 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

统计代写|决策与风险作业代写decision and risk代考|Ulas Cinar, Omer Faruk Ugurlu, and Selcuk Cebi

The current novel coronavirus (COVID-19) is a global pandemic that has caused infections and deaths all over the globe. People with weakened immune systems and over 40 are more vulnerable. The risk of serious illness increases with age and chronic diseases such as diabetes, heart, and lung diseases (WHO 2020a). The places where the virus has transmitted most are the workplaces. Therefore, personal hygiene and social distancing are the two key parameters to avoid COVID-19 transmission, particularly in the workplace (WHO $2020 \mathrm{~b}$ ).

This unpredicted and unprecedented outbreak has not only affected human lives, but has also wrecked the global economy (Ahamed and Samad 2004). The economy of many developed and developing countries directly depends on the activities in the mining sector. Therefore, mining activities must inevitably continue to keep the supply chain intact in the industry. However, the outbreak has a profound impact on the mining activities which are essential services. According to Fernandes (2020a), the mining sector has fallen by more than $30 \%$. The demand for metals and minerals has decreased immensely. The reduction has caused extensive falls in the mineral prices and the production rate in the short term. These falls have been most dramatic for aluminum and copper (Laing 2019). The medium and long-term effects are highly uncertain (Baker et al. 2020); therefore, the risk assessment of virus transmission is vital to ensure that the mining sector can continue the operations.

The risk of transmission of the COVID-19 virus and its effects have only just begun to be understood, and the virus is still unknown. There have been a lot of studies conducted to explore the transmission characteristic of the virus (Hassen et al. 2020). COVID-19 often spreads by the droplets of infected fluids of someone who has coughed or even exhaled (Chen 2020 ). Meteorological conditions such as temperature, humidity, and ventilation speed have a crucial impact on the effect of the virus (Rosario et al. 2020). Touching contaminated surfaces and objects is one of the main reasons for the transmission of the virus (WHO 2019). Another reason is standing within a meter with an infected person (WHO 2020a). Mines are one of the environments with a high risk of COVID-19 transmission because mining activities often require large numbers of workers working, eating, sleeping, and bathing together in confined spaces. Social distancing is difficult and nearly impossible to practice in those conditions, contributing to increased risks of transmission. There is nothing more important than the safety and health of the workforce. Therefore, companies must adhere to strict preventive measures. While different companies have different measures and guidelines in place for businesses to operate through the pandemic such as reducing the production and workforce, social distancing measures, workplace hygiene policy, and temperature checks at the operations must be implemented (WHO 2020a).

统计代写|决策与风险作业代写decision and risk代考|Literature Review

COVID-19 is a new phenomenon around the globe. There is a lot of research that has been carried out and most of them have been going on. It is expected to have accurate results in the near future. In this section, some researches related to the risk analysis and the fuzzy inference system are examined to show the eligibility of the method in order to measure the risk of COVID-19 transmission.

Rezaee et al. (2020) presented a hybrid approach based on the Linguistic FMEA, Fuzzy Inference System (FIS), and Fuzzy Data Envelopment Analysis (DEA) model to calculate a novel score for covering shortcomings and the prioritization of health, safety, and environmental risk factors in the chemical industry. The task of the fuzzy inference mechanism in this model is to remove the ambiguity in linguistic expressions and to transform complex data into meaningful outputs. Jamshidi et al. (2013) developed an application to assess pipeline risk using the Mamdani Fuzzy Inference System in engineering problems. The researchers aimed to integrate Relative Risk Score (RRS) methodology depending on the Mamdani algorithm with experts’ knowledge. When compared with the evaluations made with classical methods, it has been observed that the proposed method gives more accurate and precise results.
Kim et al. (2016) conducted a study to provide valuable information regarding worker safety represented by a numerical accident analysis in dynamic environments such as construction sites. Firstly, computer vision was used to monitor a construction site and extract spatial information for each entity (workers and equipment). Then, a fuzzy inference system was used to assess the proper safety levels of each entity using spatial information. It was aimed to represent a safety level that shows the potential hazard or the integrating danger in the working environment.

A hybrid method including Fuzzy Inference System, Fuzzy AHP, and Fine Kinney methods was proposed by Ilbahar et al. (2018). Occupational health and safety risks were evaluated using the hybrid method. An application has been implemented in the construction industry using the Fuzzy Inference System to transform linguistic expressions into analytical data. It was aimed to provide a more accurate risk assessment in dynamic environments such as construction sites. The hybrid method and other methods were compared and the results showed that the hybrid method produced reliable and informative outcomes to represent better vagueness of the decision-making process. Similarly, Debnath et al. (2016) formulated a model to consider the risk factors and controlling factors for accidental injuries in construction sites. The Takagi-Sugeno Fuzzy Inference System was applied to the occupational health and safety risk assessment study recommended for the construction industry. In the model formulation process, the risk factors and controlling factors for accidental injuries were considered as input parameters. The applicability of the model was tested in the selected construction sites to validate the approach. Another study was conducted about the risk assessment of a construction project by using fuzzy systems (Ebrat and Ghodsi 2014). The authors designed to evaluate the risk of construction projects using the neuro-fuzzy inference system. The results of the study show that the model gives satisfactory information to practitioners.

统计代写|决策与风险作业代写decision and risk代考|Methods

In the proposed method, the parameters affecting COVID-19 transmission risk in mining activities are determined as the number of employees, co-working time, co-working distance, and working environment for the production techniques. The literature studies about the COVID-19 were taken into consideration in determining the parameters and establishing the rule base for the mining activities (Liu et al. 2020 ). Each mining activity is weighted using the parameters by the Mamdani fuzzy inference system. The model characterizes a rule-based system, and the general

structure of the system used in the model is given in Eq. (5.1) (Mamdani and Assilian 1999; Mamdani 1977).
if $x_{1}=Z_{i 1}$ and $x_{2}=Z_{i 2}$ and $x_{3}=Z_{i 3}$ and $\ldots x_{n}=Z_{\text {in }}$ then $y=P_{i} . i=1,2,3, \ldots, k$
where $x_{n}\left(n=1,2,3, \ldots m\right.$ ) represents the input dataset, $Z_{i}$ and $P_{i}$ are linguistic expressions of membership function, $y$ is the output value, and $k$ is the number of rules in the rule base. If multiple discrete rules existing in the system are activated simultaneously, the result is usually obtained by using the max-min operator which is given in Eq. (5.2) (Mamdani and Assilian 1999; Mamdani 1977).
$$\mu_{P k}(y)=\operatorname{maks}\left[\min \left[\mu_{Z 1 k}\left(x_{1}\right), \mu_{Z 2 k}\left(x_{2}\right)\right]\right], \quad k=1,2,3, \ldots, n$$
The $\mu_{p k}, \mu_{Z l k}$, and $\mu_{Z 2 k}$ given in the equation are the membership degrees of the $y$, $x_{1}$, and $x_{2}$, respectively. If there are more than one evaluator, the output value which is obtained as a fuzzy value from the model should be clarified. The centroid of area (also called center of gravity) method is used for the clarifying process which is given in Eqs. (5.3) and (5.4) (Mamdani and Assilian 1999; Mamdani 1977).
$$\begin{gathered} Z_{\mathrm{COZ}}^{}=\frac{\int_{Z}^{x} \mu_{X}(x) x d x}{\int_{Z}^{x} \mu_{Z}(x) d x} \ Z_{C O Z}^{}=\frac{\sum_{i}^{q} \mu_{Z}\left(x_{i}\right) x_{i}}{\sum_{i}^{q} \mu_{A}\left(x_{i}\right)} i=1,2,3, \ldots, q \end{gathered}$$
where $Z_{C O z}^{*}$ is the exact value obtained from the system. More information about the Mamdani fuzzy inference system can be found in Ilbahar et al. (2018), Cinar and Cebi (2019), and Karasan et al. (2018).

统计代写|决策与风险作业代写decision and risk代考|Ulas Cinar, Omer Faruk Ugurlu, and Selcuk Cebi

COVID-19 病毒的传播风险及其影响才刚刚开始被了解，该病毒仍然未知。已经进行了大量研究来探索病毒的传播特征（Hassen et al. 2020）。COVID-19 通常通过咳嗽甚至呼出的人的感染液体飞沫传播（Chen 2020）。温度、湿度和通风速度等气象条件对病毒的影响具有至关重要的影响（Rosario et al. 2020）。接触受污染的表面和物体是病毒传播的主要原因之一（WHO 2019）。另一个原因是与感染者站在一米以内（WHO 2020a）。矿山是 COVID-19 传播风险高的环境之一，因为采矿活动通常需要大量工人在密闭空间中一起工作、吃饭、睡觉和洗澡。在这种情况下，保持社交距离是困难的，而且几乎是不可能的，从而增加了传播的风险。没有什么比劳动力的安全和健康更重要的了。因此，企业必须坚持严格的预防措施。尽管不同的公司为企业在大流行期间运营制定了不同的措施和指导方针，例如减少生产和劳动力，但必须实施社会疏离措施、工作场所卫生政策和运营中的温度检查（WHO 2020a）。在这种情况下，保持社交距离是困难的，而且几乎是不可能的，从而增加了传播的风险。没有什么比劳动力的安全和健康更重要的了。因此，企业必须坚持严格的预防措施。尽管不同的公司为企业在大流行期间运营制定了不同的措施和指导方针，例如减少生产和劳动力，但必须实施社会疏离措施、工作场所卫生政策和运营中的温度检查（WHO 2020a）。在这种情况下，保持社交距离是困难的，而且几乎是不可能的，从而增加了传播的风险。没有什么比劳动力的安全和健康更重要的了。因此，企业必须坚持严格的预防措施。尽管不同的公司为企业在大流行期间运营制定了不同的措施和指导方针，例如减少生产和劳动力，但必须实施社会疏离措施、工作场所卫生政策和运营中的温度检查（WHO 2020a）。

统计代写|决策与风险作业代写decision and risk代考|Literature Review

COVID-19 是全球范围内的一种新现象。已经进行了很多研究，其中大多数一直在进行。预计在不久的将来会有准确的结果。在本节中，检查了一些与风险分析和模糊推理系统相关的研究，以显示该方法的适用性，以衡量 COVID-19 传播的风险。

Ilbahar 等人提出了一种混合方法，包括模糊推理系统、模糊层次分析法和精细 Kinney 方法。（2018 年）。使用混合方法评估职业健康和安全风险。使用模糊推理系统将语言表达转换为分析数据的应用程序已在建筑行业实施。它旨在在建筑工地等动态环境中提供更准确的风险评估。将混合方法与其他方法进行了比较，结果表明混合方法产生了可靠且信息丰富的结果，以更好地代表决策过程的模糊性。同样，Debnath 等人。（2016）建立了一个模型来考虑建筑工地意外伤害的风险因素和控制因素。Takagi-Sugeno 模糊推理系统应用于推荐给建筑业的职业健康和安全风险评估研究。在模型制定过程中，将意外伤害的危险因素和控制因素作为输入参数。该模型的适用性在选定的建筑工地进行了测试，以验证该方法。另一项关于使用模糊系统对建设项目进行风险评估的研究（Ebrat 和 Ghodsi 2014）。作者旨在使用神经模糊推理系统评估建设项目的风险。研究结果表明，该模型为从业者提供了令人满意的信息。在模型制定过程中，将意外伤害的危险因素和控制因素作为输入参数。该模型的适用性在选定的建筑工地进行了测试，以验证该方法。另一项关于使用模糊系统对建设项目进行风险评估的研究（Ebrat 和 Ghodsi 2014）。作者旨在使用神经模糊推理系统评估建设项目的风险。研究结果表明，该模型为从业者提供了令人满意的信息。在模型制定过程中，将意外伤害的危险因素和控制因素作为输入参数。该模型的适用性在选定的建筑工地进行了测试，以验证该方法。另一项关于使用模糊系统对建设项目进行风险评估的研究（Ebrat 和 Ghodsi 2014）。作者旨在使用神经模糊推理系统评估建设项目的风险。研究结果表明，该模型为从业者提供了令人满意的信息。另一项关于使用模糊系统对建设项目进行风险评估的研究（Ebrat 和 Ghodsi 2014）。作者旨在使用神经模糊推理系统评估建设项目的风险。研究结果表明，该模型为从业者提供了令人满意的信息。另一项关于使用模糊系统对建设项目进行风险评估的研究（Ebrat 和 Ghodsi 2014）。作者旨在使用神经模糊推理系统评估建设项目的风险。研究结果表明，该模型为从业者提供了令人满意的信息。

统计代写|决策与风险作业代写decision and risk代考|Methods

μ磷到(是)=最大限度⁡[分钟[μ从1到(X1),μ从2到(X2)]],到=1,2,3,…,n

广义线性模型代考

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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。