基于CRIME理论的康美药业财务造假分析及防范研究

随着经济的快速发展,资本市场中财务造假事件频繁发生,损害了中小股东的利益,给债权人、广大投资者乃至社会带来了巨大的损失。利用传统财务指标对舞弊手段进行识别的作用有限,提出的解决措施治标不治本。因此,需要采用系统的理论对上市公司财务舞弊进行深入分析,才能提出针对性的治理对策。本文运用CRIME舞弊行为理论,从舞弊者、舞弊手段、舞弊动机、监管机制、舞弊后果五个角度,对康美药业舞弊现象进行剖析,针对产生的问题提出了相应的预防措施,以期增强投资者的风险辨识能力。

基于CRIME理论的^(*)ST宜生财务舞弊案例研究

资本市场不断完善对我国经济发展起到重要的推动作用,但同时财务舞弊事件也层出不穷。上市公司财务舞弊行为严重侵害了投资者权益,扰乱了我国资本市场的正常运行,不利于社会经济健康发展。运用CRIME理论,分别从造假行为人、造假手段、造假动因、监督机制、造假结果5个方面对^(*)ST宜生财务舞弊事件进行分析,厘清了财务舞弊的来龙去脉,并针对性地从公司、会计师事务所和监管机制3个角度提供启示,希望对相关主体具有借鉴意义,减少财务舞弊行为的发生,肃清资本市场不良风气。

从CRIME理论看公司财务造假

随着经济的飞速发展,我国上市公司数量逐年增加,但并不是所有的上市公司都能够保持持续盈利或者在行业中占据主导地位,财务造假严重影响着正常的经济运作规律,给社会带来巨大的危害。本文从CRIME理论五个因素出发,以中信国安公司财务造假为案例,对该公司造假者、造假手段、造假动机、监管机制、最终后果进行探究,从中寻找启示。

A Survey of Crime Scene Investigation Image Retrieval Using Deep Learning

Crime scene investigation(CSI)image is key evidence carrier during criminal investiga-tion,in which CSI image retrieval can assist the public police to obtain criminal clues.Moreover,with the rapid development of deep learning,data-driven paradigm has become the mainstreammethod of CSI image feature extraction and representation,and in this process,datasets provideeffective support for CSI retrieval performance.However,there is a lack of systematic research onCSI image retrieval methods and datasets.Therefore,we present an overview of the existing worksabout one-class and multi-class CSI image retrieval based on deep learning.According to theresearch,based on their technical functionalities and implementation methods,CSI image retrievalis roughly classified into five categories:feature representation,metric learning,generative adversar-ial networks,autoencoder networks and attention networks.Furthermore,We analyzed the remain-ing challenges and discussed future work directions in this field.

The Windy City’s Dark Side: A Statistical Exploration of Crime in the City of Chicago

This paper presents a detailed statistical exploration of crime trends in Chicago from 2001 to 2023, employing data from the Chicago Police Department’s publicly available crime database. The study aims to elucidate the patterns, distribution, and variations in crime across different types and locations, providing a comprehensive picture of the city’s crime landscape through advanced data analytics and visualization techniques. Using exploratory data analysis (EDA), we identified significant insights into crime trends, including the prevalence of theft and battery, the impact of seasonal changes on crime rates, and spatial concentrations of criminal activities. The research leveraged a Power BI dashboard to visually represent crime data, facilitating an intuitive understanding of complex patterns and enabling dynamic interaction with the dataset. Key findings highlight notable disparities in crime occurrences by type, location, and time, offering a granular view of crime hotspots and temporal trends. Additionally, the study examines clearance rates, revealing variations in the resolution of cases across different crime categories. This analysis not only sheds light on the current state of urban safety but also serves as a critical tool for policymakers and law enforcement agencies to develop targeted interventions. The paper concludes with recommendations for enhancing public safety strategies and suggests directions for future research, emphasizing the need for continuous data-driven approaches to effectively address and mitigate urban crime. This study contributes to the broader discourse on urban safety, crime prevention, and the role of data analytics in public policy and community well-being.

基于CRIME理论视角下东方金钰财务舞弊分析

近年来,随着资本市场的不断完善,我国经济得到了高速发展,但与此同时伴随着上市公司财务舞弊行为的频繁发生,这不仅极大损害了投资者的利益,还严重影响了资本市场的诚信机制。因此,对上市公司财务舞弊行为进行有效识别与采取防范手段极为重要。本文以东方金钰财务造假案件为例,运用CRIME理论,分别从舞弊行为人、舞弊手段、舞弊动因、监管机制、舞弊结果五个方面对东方金钰财务舞弊事件进行分析,并针对这五个方面提出有关的建议。希望对相关研究具有借鉴意义,以减少财务舞弊行为的发生。

基于CRIME理论的财务舞弊问题研究——以Z公司为例

随着数字经济和互联网的兴起,上市公司财务舞弊现象层出不穷。本文基于CRIME理论,从财务的舞弊者、舞弊手段、舞弊动机、舞弊监管机制、舞弊结果五个方面系统分析了Z公司的财务舞弊案例,从内部防范、人员素质、外部监管三个方面提出了建议,希望对资本市场的完善有所启示。

An Experimental Study of Network-based Language Learning in Less-developed Areas

The advent of the Age of Information brings about bright prospects to Network-based Language Learning(NBLL).The thesis adopts the Engagement Theory as guided principles.The purpose is to use the novel NBLL model effectively with the help of modern technology especially in less-developed areas.This thesis focuses on network-based experimental study.The research shows that the students under NBLL environment have cultivated the capabilities in information collection,computer operation,and information evaluation,as well as the abilities in problem solving,reasoning with criticism,and cooperating with others.

A Network-Based VPN Architecture Using Virtual Routing

A network-based Virtual Private Network (VPN) architecture by using fundamental routing mechanism is proposed. This network is a virtual overlay network based on the relay of IP-in-IP tunneling of virtual routing modules. The packet format employs the encapsulation of IPSec ESP(Encapsulating Security Payload), an impact path code and an extended DS(Differentiated Services) code to support multi-path routing and QoS. Comparing with other models of VPN, this network system can be deployed in the current network with little investment, and it is easy to implement. The simulation result shows its performance is better than the traditional VPN system of black box mode.

A precise tidal prediction mechanism based on the combination of harmonic analysis and adaptive network-based fuzzy inference system model

An efficient and accurate prediction of a precise tidal level in estuaries and coastal areas is indispensable for the management and decision-making of human activity in the field wok of marine engineering. The variation of the tidal level is a time-varying process. The time-varying factors including interference from the external environment that cause the change of tides are fairly complicated. Furthermore, tidal variations are affected not only by periodic movement of celestial bodies but also by time-varying interference from the external environment. Consequently, for the efficient and precise tidal level prediction, a neuro-fuzzy hybrid technology based on the combination of harmonic analysis and adaptive network-based fuzzy inference system（ANFIS）model is utilized to construct a precise tidal level prediction system, which takes both advantages of the harmonic analysis method and the ANFIS network. The proposed prediction model is composed of two modules： the astronomical tide module caused by celestial bodies’ movement and the non-astronomical tide module caused by various meteorological and other environmental factors. To generate a fuzzy inference system（FIS） structure,three approaches which include grid partition（GP）, fuzzy c-means（FCM） and sub-clustering（SC） are used in the ANFIS network constructing process. Furthermore, to obtain the optimal ANFIS based prediction model, large numbers of simulation experiments are implemented for each FIS generating approach. In this tidal prediction study, the optimal ANFIS model is used to predict the non-astronomical tide module, while the conventional harmonic analysis model is used to predict the astronomical tide module. The final prediction result is performed by combining the estimation outputs of the harmonious analysis model and the optimal ANFIS model. To demonstrate the applicability and capability of the proposed novel prediction model, measured tidal level samples of Fort Pulaski tidal station are selected as the testing database. Simulation and experimental results confirm that the proposed prediction approach can achieve precise predictions for the tidal level with high accuracy, satisfactory convergence and stability.

Bottleneck Prediction Method Based on Improved Adaptive Network-based Fuzzy Inference System (ANFIS) in Semiconductor Manufacturing System

Semiconductor manufacturing (SM) system is one of the most complicated hybrid processes involved continuously variable dynamical systems and discrete event dynamical systems. The optimization and scheduling of semiconductor fabrication has long been a hot research direction in automation. Bottleneck is the key factor to a SM system, which seriously influences the throughput rate, cycle time, time-delivery rate, etc. Efficient prediction for the bottleneck of a SM system provides the best support for the consequent scheduling. Because categorical data (product types, releasing strategies) and numerical data (work in process, processing time, utilization rate, buffer length, etc.) have significant effect on bottleneck, an improved adaptive network-based fuzzy inference system (ANFIS) was adopted in this study to predict bottleneck since conventional neural network-based methods accommodate only numerical inputs. In this improved ANFIS, the contribution of categorical inputs to firing strength is reflected through a transformation matrix. In order to tackle high-dimensional inputs, reduce the number of fuzzy rules and obtain high prediction accuracy, a fuzzy c-means method combining binary tree linear division method was applied to identify the initial structure of fuzzy inference system. According to the experimental results, the main-bottleneck and sub-bottleneck of SM system can be predicted accurately with the proposed method.

Predictive Tracking Control of Network-Based Agents With Communication Delays

This paper investigates the remote tracking control problem of Network-based Agents with communication delays existing in both forward and feedback communication channels.A networked predictive tracking controller is proposed to compensate the negative effects caused by bilateral time-delays in a wireless network. Furthermore, the problem of consecutive data loss in the feedback channel is solved using aforementioned controller, where lateral movement perturbations are introduced.Simulations and experiments are provided for several cases,which verify the realizability and effectiveness of the proposed controller.

Parallel Neural Network-Based Motion Controller for Autonomous Underwater Vehicles

A parallel neural network-based controller （PNNC） is presented for the motion control of underwater vehicles in this paper. It consists of a real-time part, a self-learning part and a desired-state programmer, and it is different from normal adaptive neural network controller in structure. Owing to the introduction of the self-learning part, on-line learning can be performed without sample data in several sample periods, resulting in high learning speed of the controller and good control performance. The desired-state programmer is utilized to obtain better learning samples of the neural network to keep the stability of the controller. The developed controller is applied to the 4-degree of freedom control of the AUV “IUV- IV” and is successful on the simulation platform. The control performance is also compared with that of neural network controller with different structures such as normal adaptive neural network and different learning methods. Current effects and surge velocity control are also included to demonstrate the controller＇ s performance. It is shown that the PNNC has a great possibility to solve the problems in the control system design of underwater vehicles.

Probe on Network-based Collaborative Maintenance Model for After-sales Equipment

In order to make equipment run safely, economically and continuously, some new maintenance models were put forward to improve the equipment after-sale maintenance service, such as E-maintenance, third-party maintenance, etc. To certain extent, the models solved the problem of the distance between the manufacturer and customer and the dispersion of the maintenance technologies, however, those resources are still widely distributed and do not collaborate efficiently. In this paper, a network-based collaborative maintenance service model was proposed for after-sales equipment to solve the problem of maintenance resources integration. Concretely, equipment designers, maintainers, spare parts suppliers and maintenance experts were grouped together to establish dynamic alliance. The leader of the alliance is the manufacturer under guaranty period or equipment user exceeding the guaranty period. The process of maintenance service was divided into three stages which are fault diagnosis, maintenance decision and maintenance implementation. The sub-alliances were established to carry out maintenance work at each stage. In addition, the business process of network-based collaborative maintenance was analyzed and collaborative business system for equipment＇s after-sales collaborative maintenance service was designed. In the end, an informational economics model of network-based collaborative maintenance was established to demonstrate the effectiveness of this maintenance model.

基于CRIME理论的财务舞弊案例分析——以康美药业为例

康美药业财务舞弊案作为A股史上最大的财务舞弊案,一直备受市场关注。文章基于CRIME理论,从舞弊行为人、舞弊手段、舞弊激励、监管机制、结果五个方面系统全面地分析了康美药业财务舞弊案,并从内部监管与外部监管两方面提出建议。

Design and implementation of self-protection agent for network-based intrusion detection system

Static secure techniques, such as firewall, hierarchy filtering, distributed disposing,layer management, autonomy agent, secure communication, were introduced in distributed intrusion detection. The self-protection agents were designed, which have the distributed architecture,cooperate with the agents in intrusion detection in a loose-coupled manner, protect the security of intrusion detection system, and respond to the intrusion actively. A prototype self-protection agent was implemented by using the packet filter in operation system kernel. The results show that all the hosts with the part of network-based intrusion detection system and the whole intrusion detection system are invisible from the outside and network scanning, and cannot apperceive the existence of network-based intrusion detection system. The communication between every part is secure. In the low layer, the packet streams are controlled to avoid the buffer leaks exist ing in some system service process and back-door programs, so as to prevent users from misusing and vicious attack like Trojan Horse effectively.

Where Have Network-based Self-learning Classes Gone?——Reflections & Expectations on the Employment of Network-based Self-learning Classes

To respond to the further development of college English reforms,many universities employed network-based selflearning classes to aid the traditional classroom teaching,especially in teaching listening,but as time went by,some universities gradually gave them up.The paper intends to reflect on the employment of network-based self-learning listening classes,analyz ing the learning with and without its aid,and meanwhile introduce the need to re-employ it,and discuss how we can improve the network-based self-learning classes to help with students' listening.

State feedback control of network-based systems with packet disordering

A new method that stabilizes network-based systems with both bounded delay and packet disordering is discussed under the state feedback controller. A novel model, fully describing the dynamic characteristic of network-based systems with packet disordering, is constructed. Different from the existing models of network-based systems, the number of delay items is time-varying in the model proposed. Further, this model is converted into a parameter-uncertain discrete-time system with time-varying delay item numbers in terms of matrix theory. Moreover, the less conservative stability condition is obtained by avoiding utilisation of Moon et al.’ inequality and bounding inequalities for quadratic functional terms. By solving a minization problem based on linear matrix inequalities, the state feedback controller is presented. A numerical example is given to illustrate the effectiveness of the proposed method.

Error code analysis and selection principle of M-ary modulation in network-based control systems

Error codes induced by M-ary modulation and modulation selection in network-based control systems are studied.It is the first time the issue of error codes induced by M-ary modulation is addressed in control field.In network-based control systems,error codes induced by noisy channel can significantly decrease the quality of control.To solve this problem,the network-based control system with delay and noisy channel is firstly modeled as an asynchronous dynamic system(ADS).Secondly,conditions of packet with error codes(PEC)loss rate by using M-ary modulation are obtained based on dynamic output feedback scheme.Thirdly,more importantly,the selection principle of M-ary modulation is proposed according to the measured signal-to-noise ratio(SNR)and conditions of PEC loss rate.Finally,system stability is analyzed and controller is designed through Lyapunov function and linear matrix inequality(LMI)scheme,and numerical simulations are made to demonstrate the effectiveness of the proposed scheme.

基于CRIME理论的瑞幸咖啡财务造假案例分析

伴随着我国资本市场的高速发展,财务造假事件频发,造假手法也越来越复杂,造假事件恶劣程度愈演愈烈。目前,识别造假行为难度越来越大。造假者对市场和投资者毫无敬畏之心,伤害投资者合法利益,扰乱市场秩序,甚至影响了我国经济的健康运行。瑞幸咖啡从闪电上市到跌落神坛,其财务造假涉案金额高达22亿元。本文以瑞幸咖啡为案例,首先对其财务造假事件进行回顾,使用CRIME五因素理论分析其财务造假动因,然后对其造假手段进行分析,最后针对瑞幸咖啡现状提出相应的治理措施。