08 33956 Critical Thinking 2024

Java Python Assignment/Coursework Remit

Programme Title

BSc Accounting & Finance

Module Title

Critical Thinking

Module Code

08 33956

Assignment Title

The SDGs - The role of the accounting profession

Level

LC

Weighting

80%

Lecturers

Tim Mason, Helen Brain

Hand Out Date

08/07/2024

Due Date & Time

08/08/2024

08/08/2024

Feedback Post Date

TBC

Assignment Format

Essay

Assignment Length

1,500 words

Submission Format

Online

Individual

Assignment:

In 2015 the United Nations set out 17 Sustainable Development Goals (SDGs) as a plan of action to ensure that all people enjoy peace and prosperity and to put the planet on a more sustainable path by 2030.

You are required to:

Choose and explain four SDGs. Critically evaluate how, and to what extent, the accounting profession can help achieve your chosen SDGs.

You should use the evidence and arguments from the three publications listed below as a starting point:

1.   IFAC (2016) The 2030 agenda for sustainable development. A snapshot of the accountancy profession’s contribution. Available at:

https://www.ifac.org/knowledge-gateway/developing-accountancy- profession/publications/2030-agenda-sustainable-development

2.    CGMA (2018) Creating asustainable future. The role of the accountant in implementing the Sustainable Development Goals. Available at:

https://www.aicpa-cima.com/resources/download/sustainable-development-goals- and-the-role-of-the-accountant

A free account is required to download this document and it is also available on Canvas.

3.   Laine, M., Tregidga, H., and  Unerman, I. (2022) Sustainability Accounting and Accountability. 3rd  Edn. London: Routledge.

Parts of chapters 1 and 2 in particular maybe useful 08 33956 Critical Thinking 2024

Additional resources: You may also use and cite:

•   The content of, and resources used during, lectures and seminars.

Relevant additional academic and/or professional literature

Marks will be deducted for long reference lists that include poor quality and/or irrelevant articles/reports and for the inaccurate use of the content of articles/reports.

Module Learning Outcomes:

In this assessment the following learning outcomes will be covered :

•     Identify and explain the key building blocks of academic arguments. Demonstrate engagement with own personal, academic and professional development activities;

•     Demonstrate the ability to formulate and deliver logical arguments;

•     Define critical thinking and practice critique of the academic work of others and selves.

Please note that these learning outcomes will be covered by addressing the requirement.

You should not attempt to address them directly.

Grading Criteria:

Essays will be graded using the rubric at the end of this remit. This rubric can be used as a guide, but you should ensure that your essay is focused on addressing the requirement rather than becoming too focused on each individual item in the rubric.

Feedback to Students:

Both Summative and Formative feedback is given to encourage students to reflect on their learning that feed forward into following assessment tasks. The preparation for all assessment tasks will be supported by formative feedback within the tutorials/seminars. Written feedback is provided as appropriate. Please be aware to use the browser and not the Canvas App as you may not be able to view all comments.

Plagiarism:

It is your responsibility to ensure that you understand correct referencing practices. You are expected to use appropriate references and keep carefully detailed notes of all your information sources, including any material downloaded from the Internet. It is your responsibility to ensure that you are not vulnerable to any alleged breaches of the assessment regulations. More information is available at

https://intranet.birmingham.ac.uk/as/registry/policy/conduct/plagiarism/index.aspx .

Use of Generative AI:

Unless explicitly stated otherwise, students should assume that the use of generative AI within an assessment or assignment is not permitted. Any assessment submitted that is not a student’s own work, including that written by generative AI tools, are in breach of the University’s Code of Practice on Academic Integrity

(https://intranet.birmingham.ac.uk/as/registry/policy/conduct/plagiarism/index.aspx

Please also see the additional notes below.

Wellbeing Extenuating Circumstances:

The process for Extenuating Circumstances is to support students who have experienced unforeseen issues that have impacted their ability to engage with their studies and/or complete assessments. Students should notify Wellbeing of any extenuating circumstances as soon as possible via the online form, following the guidance provided.

https://intranet.birmingham.ac.uk/social-sciences/college-services/wellbeing/index.aspx

Additional notes

Self-plagiarism (auto-plagiarism)

If you are completing this assignment as a resit then you should be particularly careful to avoid self-plagiarism,i.e. submitting work that includes sections that you have previously submitted for another assignment.

Word limit

1,500 words is the absolute maximum for this essay, and therefore marks will be deducted for exceeding it. The word limit includes in-text citations but not the reference list or appendix.

Use of Generative AI

For this assignment, generative AI may be used to help you understand sections of the three listed resources. You should not rely solely on any summaries produced by generative AI, as they can be incomplete and/or inaccurate. Instead generative AI can be used to supplement your reading of the articles.

Generative AI must not be used to help write any element of your essay. This must be solely your own work.

Appendix

All essays must include an appendix detailing how generative AI has been employed, including the software used and the prompts entered.

The use of generative AI is not compulsory and if you do not use it then the appendix needs to state this and give clear, logical reasons for not using it         

内容概要:本文介绍了一种基于蒙特卡洛模拟和拉格朗日优化方法的电动汽车充电站有序充电调度策略,重点针对分时电价机制下的分散式优化问题。通过Matlab代码实现,构建了考虑用户充电需求、电网负荷平衡及电价波动的数学模【电动汽车充电站有序充电调度的分散式优化】基于蒙特卡诺和拉格朗日的电动汽车优化调度(分时电价调度)(Matlab代码实现)型,采用拉格朗日乘子法处理约束条件,结合蒙特卡洛方法模拟大量电动汽车的随机充电行为,实现对充电功率和时间的优化分配,旨在降低用户充电成本、平抑电网峰谷差并提升充电站运营效率。该方法体现了智能优化算法在电力系统调度中的实际应用价值。; 适合人群:具备一定电力系统基础知识和Matlab编程能力的研究生、科研人员及从事新能源汽车、智能电网相关领域的工程技术人员。; 使用场景及目标:①研究电动汽车有序充电调度策略的设计与仿真;②学习蒙特卡洛模拟与拉格朗日优化在能源系统中的联合应用;③掌握基于分时电价的需求响应优化建模方法;④为微电网、充电站运营管理提供技术支持和决策参考。; 阅读建议:建议读者结合Matlab代码深入理解算法实现细节,重点关注目标函数构建、约束条件处理及优化求解过程,可尝试调整参数设置以观察不同场景下的调度效果,进一步拓展至多目标优化或多类型负荷协调调度的研究。
为清晰解释决策树(Decision Tree)模型的定义和基本原理,可从以下方面着手: ### 定义层面 - **形象比喻**:将决策树类比为问路的决策过程,它如同一个树形结构,通过对数据特征的不断划分来做出决策,这样的比喻使模型更易于理解[^3][^4]。 - **应用场景说明**:在法律领域,决策树模型可作为决策支持工具,帮助法律职业人士做出更理性的决策,这展示了决策树在实际中的用途[^1]。 ### 基本原理层面 - **构建过程阐述**:决策树从根节点开始,选择一个最优特征对数据集进行划分,生成子节点,再对子节点继续选择最优特征划分,直至满足停止条件,如所有样本属于同一类别或无特征可用于划分,这清晰地说明了模型的构建逻辑。 - **特征划分标准公式**: - **信息熵**:$H(D)=-\sum_{i=1}^{n}p_i\log_2p_i$,用于衡量数据的不确定性,其中$D$表示数据集,$p_i$表示第$i$类样本在数据集中所占比例,$n$表示类别数。 - **信息增益**:$Gain(D,A)=H(D)-H(D|A)$,衡量特征$A$对数据集$D$划分前后信息熵的减少量,$H(D|A)$是在特征$A$给定条件下数据集$D$的条件熵。 - **信息增益率**:$Gain_ratio(D,A)=\frac{Gain(D,A)}{IV(A)}$,其中$IV(A)=-\sum_{v=1}^{V}\frac{|D^v|}{|D|}\log_2\frac{|D^v|}{|D|}$,可解决信息增益偏向选择取值较多特征的问题,$V$是特征$A$取值个数,$|D^v|$是特征$A$取值为$v$时的样本数,$|D|$是数据集$D$的样本总数。 - **GINI系数**:$Gini(D)=1-\sum_{i=1}^{n}p_i^2$,用于衡量数据纯度,决策树中选择使GINI系数最小的特征进行划分。 ### 批判性思维融入 在解释过程中,可提及不同特征划分标准的优缺点。例如,信息增益偏向取值多的特征,而信息增益率虽解决了此问题,但计算相对复杂;GINI系数计算简单,但在某些情况下可能不如信息增益和信息增益率有效。同时,可说明决策树模型在处理高维数据、噪声数据时可能存在的问题,以及其对数据分布的敏感性。 以下是使用Python和Scikit - learn库实现决策树分类的示例代码: ```python from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import accuracy_score # 加载数据集 iris = load_iris() X = iris.data y = iris.target # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) # 创建决策树分类器 clf = DecisionTreeClassifier() # 训练模型 clf.fit(X_train, y_train) # 预测 y_pred = clf.predict(X_test) # 计算准确率 accuracy = accuracy_score(y_test, y_pred) print("Accuracy:", accuracy) ```
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