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。

Standardization of meibomian gland dysfunction in an Egyptian population sample using a non-contact meibography technique

AIM:To develop normative data for meibomian gland dysfunction(MGD)parameters,using non-contact meibography technique of Sirius Costruzione Strumenti Oftalmici(CSO)machine,in an Egyptian population sample.METHODS:Observational,cross-sectional,analytic study,in which 104 Egyptian volunteers were included.Both upper lids were examined,using“Sirius CSO”machine.Each eyelid was given a degree of meibomian gland loss(MGL),which was calculated by the software of the machine.RESULTS:Mean percentage MGL in right upper lid was of 30.9%±12.6%,and that of left upper lid was 32.6%±11.8%.Thirty-four volunteers(32.7%)had first-degree MGL in their right upper lid,and 67.3%had second-degree loss.One volunteer(1%)had zero-degree MGL in left upper lid,28(26.9%)had first-degree loss,and 75(72.1%)had second-degree loss.Degree of MGL in right upper eyelid was not related to age,but degree of MGL in left upper eyelid increased with age.There was statistically significant difference between both genders for degree of MGL in right eye(P=0.036)and in left eye(P=0.027).CONCLUSION:Noncontact meibography is a useful non-invasive tool for diagnosing MGL.MGL is diagnosed in 100%of apparently normal individuals;26.9%-32.7%of which have first-degree MGL,and 67.3%-72.1%have second-degree MGL.

Non-contact wide-field viewing system-assisted scleral buckling surgery for retinal detachment in silicone oilfilled eyes

AIM:To evaluate scleral buckling(SB)surgery using a noncontact wide-field viewing system and 23-gauge intraocular illumination for the treatment of rhegmatogenous retinal detachment in silicone oil(SO)-filled eyes.METHODS:Totally 9 patients(9 eyes)with retinal detachment in SO-filled eyes were retrospectively analyzed.All patients underwent non-contact wide-field viewing system-assisted buckling surgery with 23-gauge intraocular illumination.SO was removed at an appropriate time based on recovery.The patients were followed up for at least 3mo after SO removal.Retinal reattachment,complications,visual acuity and intraocular pressure(IOP)before and after surgery were observed.RESULTS:Patients were followed up for a mean of 8.22mo(3-22mo)after SO removal.All patients had retinal reattachment.At the final follow-up,visual acuity showed improvement for 8 patients,and no change for 1 patient.The IOP was high in 3 patients before surgery,but it stabilized after treatment;it was not affected in the other patients.None of the patients had infections,hemorrhage,anterior ischemia,or any other complication.CONCLUSION:This new non-contact wide-field viewing system-assisted SB surgery with 23-gauge intraocular illumination is effective and safe for retinal detachment in SO-filled eyes.

Video-Based Deception Detection with Non-Contact Heart Rate Monitoring and Multi-Modal Feature Selection

Deception detection plays a crucial role in criminal investigation.Videos contain a wealth of information regarding apparent and physiological changes in individuals,and thus can serve as an effective means of deception detection.In this paper,we investigate video-based deception detection considering both apparent visual features such as eye gaze,head pose and facial action unit(AU),and non-contact heart rate detected by remote photoplethysmography(rPPG)technique.Multiple wrapper-based feature selection methods combined with the K-nearest neighbor(KNN)and support vector machine(SVM)classifiers are employed to screen the most effective features for deception detection.We evaluate the performance of the proposed method on both a self-collected physiological-assisted visual deception detection(PV3D)dataset and a public bag-oflies(BOL)dataset.Experimental results demonstrate that the SVM classifier with symbiotic organisms search(SOS)feature selection yields the best overall performance,with an area under the curve(AUC)of 83.27%and accuracy(ACC)of 83.33%for PV3D,and an AUC of 71.18%and ACC of 70.33%for BOL.This demonstrates the stability and effectiveness of the proposed method in video-based deception detection tasks.

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。

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。

Benchmarking dynamic properties of structures using non-contact sensing

Non-contact sensing can be a rapid and convenient alternative for determining structure response compared to conventional instrumentation.Computer vision has been broadly implemented to enable accurate non-contact dynamic response measurements for structures.This study has analyzed the effect of non-contact sensors,including type,frame rate,and data collection platform,on the performance of a novel motion detection technique.Video recordings of a cantilever column were collected using a high-speed camera mounted on a tripod and an unmanned aerial system(UAS)equipped with visual and thermal sensors.The test specimen was subjected to an initial deformation and released.Specimen acceleration data were collected using an accelerometer installed on the cantilever end.The displacement from each non-contact sensor and the acceleration from the contact sensor were analyzed to measure the specimen′s natural frequency and damping ratio.The specimen′s first fundamental frequency and damping ratio results were validated by analyzing acceleration data from the top of the specimen and a finite element model.

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。

基于CRIME理论的紫晶存储财务舞弊案例分析

在以信息披露为“生命线”的注册制下,紫晶存储开了一个恶劣的顶风欺诈上市先例,引起广泛关注。这种行为破坏了市场规律,使众多投资者对市场失去信心。紫晶存储以及相关中介机构负有重大责任。因此,对于注册制上市公司财务舞弊行为的识别与治理对策的研究具有重要意义。文章基于CRIME理论,从舞弊行为人(cooks)、舞弊手段(recipes)、舞弊动因(incentives)、监管机制(monitoring)、结果(end result)五个方面系统全面地分析了紫晶存储财务舞弊案,并针对这五个方面提出有关的建议。

基于CRIME五因素论的浪奇财务舞弊案例研究

近年来,我国上市公司财务舞弊事件频发,这不仅损害了上市公司的声誉与形象,还使我国资本市场无法健康运行。此次通过CRIME五因素论模型,对浪奇的舞弊主体、舞弊手段、舞弊动机、舞弊监管机制和舞弊造成的后果进行全方位的分析,从上市公司层面、审计机构层面和政府层面提出预防上市公司舞弊的具体措施,促进我国市场经济健康可持续发展。

Detection and Mapping of Violent Crime Hotspots in Southwestern Nigeria

The increasing rate of insecurity in Nigeria, especially the southwest requires a paradigm shift from popular approach to crime hotspots detection. This study employed geospatial technologies to integrate spatio-temporal crime, social media and field observation data from the communities in all the six states in the southwest to develop crime hotspots that can serve as preliminary information to assist in allocating resources for crime control and prevention. Historical crime data from January 1972 to April, 2021 were compiled and updated with rigorous field survey in September, 2021. The field data were encoded, input to the SPSS 17 and analyzed using descriptive statistics and multivariate analysis. A total 936 crime locations data were geolocated and exported to ArcGIS 10.5 for spatial mapping using point map operation and further imported to e-Spatial web-based and QGIS for the generation of hotspot map using heatmap tool. The results revealed that armed robbery, assassination and cultism were more pronounced in Lagos and Ogun States. Similarly, high incidences of farmers/herdsmen conflicts are observed in Oyo and Osun States. Increasing incidences of kidnapping are common in all the south-western states but very prominent in Ondo, Lagos and Oyo States. Most of the violent crime incidents took place along the highways, with forests being their hideouts. Violent crimes are dominantly caused by high rate of unemployment while farmer/herdsmen conflicts were majorly triggered by the scarcity of grazing fields and destruction of arable crops. The conflicts have resulted in the increasing cases of rape and disruption of social group, intake of hard drugs, cult-related activities, low income and revenue generation, and displacement of farmers and infrastructural damages. The study advocates regular retraining and equipping of security agents, establishment of cattle ranch, and installation of sophisticated IP Camera at the crime hotspots to assist in real-time crime monitoring and management.

Ontology-Based Crime News Semantic Retrieval System

Every day,the media reports tons of crimes that are considered by a large number of users and accumulate on a regular basis.Crime news exists on the Internet in unstructured formats such as books,websites,documents,and journals.From such homogeneous data,it is very challenging to extract relevant information which is a time-consuming and critical task for the public and law enforcement agencies.Keyword-based Information Retrieval(IR)systems rely on statistics to retrieve results,making it difficult to obtain relevant results.They are unable to understandthe user’s query and thus facewordmismatchesdue to context changes andthe inevitable semanticsof a given word.Therefore,such datasets need to be organized in a structured configuration,with the goal of efficiently manipulating the data while respecting the semantics of the data.An ontological semantic IR systemis needed that can find the right investigative information and find important clues to solve criminal cases.The semantic system retrieves information in view of the similarity of the semantics among indexed data and user queries.In this paper,we develop anontology-based semantic IRsystemthat leverages the latest semantic technologies including resource description framework(RDF),semantic protocol and RDF query language(SPARQL),semantic web rule language(SWRL),and web ontology language(OWL).We have conducted two experiments.In the first experiment,we implemented a keyword-based textual IR systemusing Apache Lucene.In the second experiment,we implemented a semantic systemthat uses ontology to store the data and retrieve precise results with high accuracy using SPARQL queries.The keyword-based system has filtered results with 51%accuracy,while the semantic system has filtered results with 95%accuracy,leading to significant improvements in the field and opening up new horizons for researchers.

Crime Prediction Methods Based on Machine Learning:A Survey

The objective of crime prediction,one of the most important technologies in social computing,is to extract useful information from many existing criminal records to predict the next process-related crime.It can aid the police in obtaining criminal information and warn the public to be vigilant in certain areas.With the rapid growth of big data,the Internet of Things,and other technologies,as well as the increasing use of artificial intelligence in forecasting models,crime prediction models based on deep learning techniques are accelerating.Therefore,it is necessary to classify the existing crime prediction algorithms and compare in depth the attributes and conditions that play an essential role in the analysis of crime prediction algorithms.Existing crime prediction methods can be roughly divided into two categories:those based on conventional machine learning and those based on contemporary deep learning.This survey analyses the fundamental theories and procedures.The most frequently used data sets are then enumerated,and the fundamental procedures of various algorithms are also analyzed in this paper.In light of the insufficient scale of existing data in this field,the ambiguity of data types used to predict crimes,and the absence of public data sets that have a significant impact on the research of algorithm models,this survey proposes the construction of a machine learning-based big data research model to address these issues.Future researchers who will enter this field are provided with a guide to the direction of future research development.

Spatial Pattern and Distribution of Crime in Suleja Lga, Niger State, Nigeria

The study examines the Spatial Pattern and Distribution of Crime in Suleja LGA, Niger State, Nigeria. The study used GIS and statistical methods to analyse the pattern and distribution of crime incidence in the study area. The records of each crime incidence were geocoded. Microsoft Excel was used to collate and organise the crime entries before they were imported into the ArcGIS Pro 2.0 environment. A geodatabase was created where the spatial and aspatial data were encoded and geospatial analysis was performed. The study reveals that the crime distribution pattern is generally clustered with a Global Moran’s I index of 0.097, a Z-score of 1.87, and a P-value < 0.06. Furthermore, the study reveals that armed robbery (61), kidnapping (40), car theft (33), culpable homicide (31), rape (29), and robbery (13) cases rank the highest in crime rate. Equally, findings of the study show that Chaza, Kwamba, Madalla, Suleja central, and Gaboda are the major crime hotspot zones at 90% confidence, as analysed using the Getis-Ord Gi* (Hot spot analysis) spatial statistics tool in ArcGIS Pro 2.0. The research therefore recommends that more effort be put into fighting crime, especially in areas where there are low-security formations, as they mostly have the highest record of crimes committed. Also, the patrol units should be equipped with GPS for better surveillance and real-time tracking of criminal activities.

Agreement of intraocular pressure measurement with Corvis ST,non-contact tonometer,and Goldmann applanation tonometer in children with ocular hypertension and related factors

AIM:To access the agreement of intraocular pressure(IOP)values obtained from biomechanically corrected tonometer[Corvis ST(CST)],non-contact tonometer(NCT),and Goldmann applanation tonometer(GAT)in children with NCT measured-IOP(NCT-IOP)values of 22 mm Hg or more,and related factors.METHODS:A total of 51 eyes with NCT-IOP≥22 mm Hg in children aged 7 to 14y were examined and IOP was measured by CST,NCT,and GAT.Based on GAT measured IOP(GAT-IOP),ocular hypertension(OHT)group(≥22 mm Hg,24 eyes)and the non-OHT group(<22 mm Hg,27 eyes)were defined.We compared the agreement of the three measurements,i.e.,CST measured IOP(CST-IOP),GAT-IOP,and NCT-IOP,and further analyzed the correlation between the differences in tonometry readings,central corneal thickness(CCT),axial length(AL),optic disc rim volume,and age.RESULTS:Compared with the OHT group,thicker CCT,larger rim volume,and higher differences between NCTIOP and GAT-IOP,were found in the non-OHT group.The differences between CST-IOP and GAT-IOP were lower than the differences between NCT-IOP and GAT-IOP in both groups.The mean differences in CST-IOP and GAT-IOP were 1.26 mm Hg(95%limit of agreement ranged from 0.1 to 2.41 mm Hg,OHT group)and 1.20 mm Hg(95%limit of agreement ranged from-0.5 to 3.00 mm Hg,non-OHT group),and the mean differences in NCT and GAT were 3.90 mm Hg(95%limit of agreement ranged from-0.19 to 9.70 mm Hg,OHT group)and 6.00 mm Hg(95%limit of agreement ranged from 1.50 to 10.50 mm Hg,non-OHT group).The differences between CST-IOP and GAT-IOP were not related to CCT,age,and AL in both groups;while the differences between NCT-IOP and GAT-IOP were related to CCT in the OHT group(r=0.93,P<0.001)and to CCT and AL in the non-OHT group(r=0.66,P<0.001,r=-0.81,P<0.001).CONCLUSION:The accuracy of NCT in the diagnosis of pediatric OHT is low.The agreement of CST-IOP and GATIOP was significantly higher in children with and without OHT than in those with NCT-IOP and GAT-IOP.Therefore,CST can be used as a good alternative for IOP measurement in children.The impacts of CCT and AL on NCT measurement need to be fully considered when managing childhood IOP.