基于可视化技术的农产品防伪溯源监控系统设计

随着中国农产品物流规模不断壮大,农产品物流系统无法从源头保证产品采摘、包装、物流中的可信性。为了解决这个问题,提出一种现场作业可视化系统,即将可视化技术应用于农产品防伪溯源监控系统中,从而满足远程管理的需求。对相关可视化技术的一般方法进行了分析,选择COFDM作为主要解决方案。农产品防伪溯源监控系统有效克服了复杂环境下的各类干扰,将物流中的实时监控信息传送到中继通信和监控设备上,并依托公用通信网络或专用通信网络进一步实现广域范围内的实时高质量监控图像传输、存储和浏览。通过实时现场视频监控,消费者可以保证买到真实可信的高质量农产品;生产者可以提高农产品的生产、运输效率,还可以提升相应产品的信誉,促进高质量农产品的销售。     

国内外反贫困研究的可视化分析

反贫困问题一直是世界各国持续关注的一个难题。借助信息可视化分析软件CiteSpace，以中国知网和WOS数据库中近21年收录的4 763篇有关反贫困的研究文献为分析对象，从时间分布、高产出研究学者和学术机构、研究热点、研究趋势等方面对进行深入的分析和对比。研究发现，国内反贫困研究文献较国外少，并且国内学者之间的认可度较低、研究机构学术合作网络较为松散；在研究热点方面，中文研究者更多地采用实证研究法，以某一贫困地区为调查研究对象，针对性探讨其反贫困举措，较少探索普适性理论，外文研究者更加偏重以经济增长的角度来研究反贫困措施，并强调人与自然的和谐发展;在研究趋势方面，中文文献多以政策为导向，外文文献更偏向理论产出为导向。     

BIM+Unity3D引擎在构建海塘工程三维场景中的应用

通过在建的台州市循环经济产业集聚区海塘提升工程对BIM+Unity3D技术在水利海塘工程三维仿真场景构建中的实现问题进行了研究。采用此技术可提升人机交互自由,拓宽BIM模型维度,解决建设过程中大体量BIM模型高质可视化展示与深度使用技术难题,为模型深化、多源数据在建设管理集成化应用提供了支撑,为同类型长距离复杂线性工程探索了一条高效可行的技术线路。     

Goode Food Trucks Inc.*

This fictional case centers around a young Dutch couple who emigrated to Canmore, Alberta. Upon arriving, Saul and Rens opened two successful food trucks and are now looking to promote each truck's main chef to truck manager. They plan to offer a performance bonus based on the net income of each truck and have requested a local accounting student to advise them on the new bonus structure. Goode Food Trucks Inc. presents an opportunity for students to demonstrate technical competence in managerial accounting (rate methods, period cost allocation, and data visualization); strategy and governance (employee performance incentives); and financial reporting (reporting needs) while incorporating enabling competencies (communicating and adding value). The case and teaching notes were adapted from principles used to train and evaluate Chartered Professional Accountant (CPA) candidates and tailored to an appropriate level for undergraduate learners. MBA instructors may also use this case to apply multiple concepts in a defined context, thus enhancing a course's “real-life” applicability.     

Unravelling the JPMorgan spoofing case using particle physics visualization methods

On 29 September 2020, JPMorgan was ordered to pay a settlement of $920.2 million for spoofing the metals and Treasury futures markets from 2008 to 2016. We examine these cases using a visualization method developed in particle physics (CERN) and the messages that the exchange receives about market activity rather than time-based snapshots. This approach allows to examine multiple indicators related to market manipulation and complement existing research methods, thereby enhancing the identification and understanding of, as well as the motivation for, market manipulation. In the JPMorgan cases, we offer an alternative motivation for spoofing than moving the price.