Qingdao Crime Research

The existence of crime,do harm to people's personal and property safety,Interfere with the social order,stability and economic development。 During this period,the of there are a variety of types of crime coexistent and new crime emergent in Tsingdao; constantly improve the legal system; the judiciary has not yet completely independent,foreign forces privileged.

Machine learning applications in healthcare clinical practice and research

Machine learning(ML)is a type of artificial intelligence that assists computers in the acquisition of knowledge through data analysis,thus creating machines that can complete tasks otherwise requiring human intelligence.Among its various applications,it has proven groundbreaking in healthcare as well,both in clinical practice and research.In this editorial,we succinctly introduce ML applications and present a study,featured in the latest issue of the World Journal of Clinical Cases.The authors of this study conducted an analysis using both multiple linear regression(MLR)and ML methods to investigate the significant factors that may impact the estimated glomerular filtration rate in healthy women with and without non-alcoholic fatty liver disease(NAFLD).Their results implicated age as the most important determining factor in both groups,followed by lactic dehydrogenase,uric acid,forced expiratory volume in one second,and albumin.In addition,for the NAFLD-group,the 5th and 6th most important impact factors were thyroid-stimulating hormone and systolic blood pressure,as compared to plasma calcium and body fat for the NAFLD+group.However,the study's distinctive contribution lies in its adoption of ML methodologies,showcasing their superiority over traditional statistical approaches(herein MLR),thereby highlighting the potential of ML to represent an invaluable advanced adjunct tool in clinical practice and research.

Synthetic data as an investigative tool in hypertension and renal diseases research

There is a growing body of clinical research on the utility of synthetic data derivatives,an emerging research tool in medicine.In nephrology,clinicians can use machine learning and artificial intelligence as powerful aids in their clinical decision-making while also preserving patient privacy.This is especially important given the epidemiology of chronic kidney disease,renal oncology,and hypertension worldwide.However,there remains a need to create a framework for guidance regarding how to better utilize synthetic data as a practical application in this research.

Harnessing artificial intelligence for identifying conflicts of interest in research

This editorial explores the transformative potential of artificial intelligence(AI)in identifying conflicts of interest(COIs)within academic and scientific research.By harnessing advanced data analysis,pattern recognition,and natural language processing techniques,AI offers innovative solutions for enhancing transparency and integrity in research.This editorial discusses how AI can automatically detect COIs,integrate data from various sources,and streamline reporting processes,thereby maintaining the credibility of scientific findings.

Patients Involved in Funding Research.A Look Back at the Unique Experience of the ECLAIR Working Group of Canceropole CLARA

This article aims to share an innovative experience of organizing and funding research involving those most directly affected:patients.The“ECLAIR”working group of the Canceropole Lyon Auvergne-Rhone-Alpes(CLARA)was created at the end of 2020 with the goal of contributing to the development of a call for projects on the patient experience in oncology,which was launched in January 2021.Initially composed of 8 members,including 7 patients,coordinated by a project manager from CLARA,the ECLAIR working group actively participated in drafting the specifications of the call for projects,developing the eligibility criteria for applications,revising the evaluation and selection criteria for projects,and monitoring the selected projects.This experience was repeated twice.With significant freedom of action,the working group made two decisions that strongly demonstrate the commitment to supporting research partnerships and the active involvement of those affected:firstly,by making partnership a mandatory requirement for the eligibility of applications,and secondly,by conducting the selection of projects themselves,after an independent scientific evaluation phase.Seeking to shed light on the“black box”of partnership,the article also presents the concrete modalities of interaction among the working group members,the adjustments made between different editions of the call for projects,and the relationships maintained with CLARA.

Embracing the complexity of lived experiences in psychiatry research:Reflexivity,cultural sensitivity,and emergent design

This article examines the critical integration of reflexivity,cultural sensitivity,and emergent design in qualitative psychiatry research focused on lived experiences.While quantitative methods offer essential clinical insights,qualitative approaches provide a deeper understanding of the emotional,psychological,and social dimensions of mental health.Reflexivity enables researchers to remain aware of how their personal biases and professional backgrounds shape data interpretation.Cultural sensitivity ensures that mental health conditions are understood within their broader cultural contexts,helping avoid misrepresentation and promoting authentic participant expression.Emergent design offers flexibility in adapting the research process to evolving themes,particularly in the dynamic and multifaceted realm of psychiatric conditions.Together,these principles promote ethically sound,participant-centered research that captures the full complexity of lived experiences.The article also highlighted the practical implications of these principles for enhancing both academic knowledge and clinical practice in psychiatry.

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。

基于CRIME理论的同济堂财务舞弊案例研究

随着社会经济的迅速发展,资本市场中财务舞弊事件频发,财务舞弊对投资者权益、公司运营及市场发展等都带来了损害。如何有效识别并防范财务舞弊发生,成为中国市场发展中亟待解决的问题。选取同济堂财务舞弊案例,基于CRIME理论对其财务舞弊成因进行识别与分析,认为公司实际控制人利用股权结构实施的违规操作是造成舞弊事件的直接原因,并面向公司和社会层面提出治理措施及建议。希望对医药行业上市公司乃至整个资本市场优化持股结构、完善治理体系、加强监督管理有一定借鉴作用。

Recent Ventures in Interdisciplinary Arctic Research:The ARCPATH Project

This paper celebrates Professor Yongqi GAO's significant achievement in the field of interdisciplinary studies within the context of his final research project Arctic Climate Predictions: Pathways to Resilient Sustainable Societies-ARCPATH(https://www.svs.is/en/projects/finished-projects/arcpath). The disciplines represented in the project are related to climatology, anthropology, marine biology, economics, and the broad spectrum of social-ecological studies. Team members were drawn from the Nordic countries, Russia, China, the United States, and Canada. The project was transdisciplinary as well as interdisciplinary as it included collaboration with local knowledge holders. ARCPATH made significant contributions to Arctic research through an improved understanding of the mechanisms that drive climate variability in the Arctic. In tandem with this research, a combination of historical investigations and social, economic, and marine biological fieldwork was carried out for the project study areas of Iceland, Greenland, Norway, and the surrounding seas, with a focus on the joint use of ocean and sea-ice data as well as social-ecological drivers. ARCPATH was able to provide an improved framework for predicting the near-term variation of Arctic climate on spatial scales relevant to society, as well as evaluating possible related changes in socioeconomic realms. In summary, through the integration of information from several different disciplines and research approaches, ARCPATH served to create new and valuable knowledge on crucial issues, thus providing new pathways to action for Arctic communities.

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。

Global trends in diabetic eye disease research from 2012 to 2021

Diabetic eye disease refers to a group of eye complications that occur in diabetic patients and include diabetic retinopathy, diabetic macular edema, diabetic cataracts, and diabetic glaucoma. However, the global epidemiology of these conditions has not been well characterized. In this study, we collected information on diabetic eye disease-related research grants from seven representative countries––the United States, China, Japan, the United Kingdom, Spain, Germany, and France––by searching for all global diabetic eye disease journal articles in the Web of Science and Pub Med databases, all global registered clinical trials in the Clinical Trials database, and new drugs approved by the United States, China, Japan, and EU agencies from 2012 to 2021. During this time period, diabetic retinopathy accounted for the vast majority(89.53%) of the 2288 government research grants that were funded to investigate diabetic eye disease, followed by diabetic macular edema(9.27%). The United States granted the most research funding for diabetic eye disease out of the seven countries assessed. The research objectives of grants focusing on diabetic retinopathy and diabetic macular edema differed by country. Additionally, the United States was dominant in terms of research output, publishing 17.53% of global papers about diabetic eye disease and receiving 22.58% of total citations. The United States and the United Kingdom led international collaborations in research into diabetic eye disease. Of the 415 clinical trials that we identified, diabetic macular edema was the major disease that was targeted for drug development(58.19%). Approximately half of the trials(49.13%) pertained to angiogenesis. However, few drugs were approved for ophthalmic(40 out of 1830;2.19%) and diabetic eye disease(3 out of 1830;0.02%) applications. Our findings show that basic and translational research related to diabetic eye disease in the past decade has not been highly active, and has yielded few new treatment methods and newly approved drugs.

Brain Research on Mental Disorders: A Criticism

Objective: To expose the problems and inherent limitations of neuroscience-based brain research on mental disorders. Method: Discussion of the theory underlying brain research on mental disorders, followed by a systematic evaluation of typical studies. Results: The fundamental problem is that brain researchers fail to differentiate between biological mental disorders in which brain processes cause the disorder (notably schizophrenia, bipolar disorder, and melancholic depression) and learned mental disorders in which brain processes mediate but do not cause the disorder (which is the case with reactive depression, reactive anxiety, OCD, and PTSD). Researchers have been unsuccessful in identifying mechanisms in the brain that cause biological mental disorders, and will never be able to locate the innumerable specific neural connections that mediate learned mental disorders. Moreover, the author’s review of typical studies in this field shows that they have serious problems with theory, measurement, and data analysis, and that their findings cannot be trusted. Conclusions: Neuroscience-based brain research on mental disorders, unlike other neurological research, has been an expensive failure and it is not worth continuing.

CHART:Intelligent Crime Hotspot Detection and Real-Time Tracking Using Machine Learning

Crime hotspot detection is essential for law enforcement agencies to allocate resources effectively,predict potential criminal activities,and ensure public safety.Traditional methods of crime analysis often rely on manual,time-consuming processes that may overlook intricate patterns and correlations within the data.While some existing machine learning models have improved the efficiency and accuracy of crime prediction,they often face limitations such as overfitting,imbalanced datasets,and inadequate handling of spatiotemporal dynamics.This research proposes an advanced machine learning framework,CHART(Crime Hotspot Analysis and Real-time Tracking),designed to overcome these challenges.The proposed methodology begins with comprehensive data collection from the police database.The dataset includes detailed attributes such as crime type,location,time and demographic information.The key steps in the proposed framework include:Data Preprocessing,Feature Engineering that leveraging domain-specific knowledge to extract and transform relevant features.Heat Map Generation that employs Kernel Density Estimation(KDE)to create visual representations of crime density,highlighting hotspots through smooth data point distributions and Hotspot Detection based on Random Forest-based to predict crime likelihood in various areas.The Experimental evaluation demonstrated that CHART shows superior performance over benchmark methods,significantly improving crime detection accuracy by getting 95.24%for crime detection-I(CD-I),96.12%for crime detection-II(CD-II)and 94.68%for crime detection-III(CD-III),respectively.By designing the application with integrating sophisticated preprocessing techniques,balanced data representation,and advanced feature engineering,the proposed model provides a reliable and practical tool for real-world crime analysis.Visualization of crime hotspots enables law enforcement agencies to strategize effectively,focusing resources on high-risk areas and thereby enhancing overall crime prevention and response efforts.

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.

Navigating interdisciplinary research:Historical progression and contemporary challenges

Interdisciplinary research plays a crucial role in addressing complex problems by integrating knowledge from multiple disciplines.This integration fosters innovative solutions and enhances understanding across various fields.This study explores the historical and sociological development of interdisciplinary research and maps its evolution through three distinct phases:pre-disciplinary,disciplinary,and post-disciplinary.It identifies key internal dynamics,such as disciplinary diversification,reorganization,and innovation,as primary drivers of this evolution.Additionally,this study highlights how external factors,particularly the urgency of World War II and the subsequent political and economic changes,have accelerated its advancement.The rise of interdisciplinary research has significantly reshaped traditional educational paradigms,promoting its integration across different educational levels.However,the inherent contradictions within interdisciplinary research present cognitive,emotional,and institutional challenges for researchers.Meanwhile,finding a balance between the breadth and depth of knowledge remains a critical challenge in interdisciplinary education.

Applications and advancements in animal models for antiviral research on mosquito-borne arboviruses

Vector-borne diseases caused by arthropod-borne viruses(arboviruses) are a considerable challenge to public health globally. Mosquito-borne arboviruses, such as Chikungunya, Dengue, and Zika viruses, cause a range of human illnesses and may be fatal. Currently, efforts to control these diseases still face challenges due to growing vector resistance towards insecticides, urbanization, and limited effective antiviral treatments and vaccines. Animal models are crucial in antiviral research on mosquito-borne arboviruses, playing a role in understanding disease mechanisms,vaccine development, and toxicity testing, but the application of animal models still faces the challenges of ethical considerations and animal-to-human translational success. Genetically engineered mouse models, hamster models and non-human primate(NHP) are currently used in arbovirus research, but new models such as tree shrews and novel humanized mice are emerging. In the context of Malaysian research, the use of long-tailed macaques as potential NHP models for arbovirus research is possible;however, it faces the ethical dilemma of using an endangered species for scientific purposes. Overall, animal models play a crucial role in advancing infectious disease research, but a balance between medical research and species conservation must be upheld.

Biobanks and biomarkers:Their current and future role in biomedical research

The importance and utility of biobanks has increased exponentially since their inception and creation.Initially used as part of translational research,they now contribute over 40%of data for all cancer research papers in the United States of America and play a crucial role in all aspects of healthcare.Multiple classification systems exist but a simplified approach is to either classify as population-based or disease-oriented entities.Whilst historically publicly funded institutions,there has been a significant increase in industry funded entities across the world which has changed the dynamic of biobanks offering new possibilities but also new challenges.Biobanks face legal questions over data sharing and intellectual property as well as ethical and sustainability questions particularly as the world attempts to move to a low-carbon economy.International collaboration is required to address some of these challenges but this in itself is fraught with complexity and difficulty.This review will examine the current utility of biobanks in the modern healthcare setting as well as the current and future challenges these vital institutions face.

Visual Analysis of Domestic and Foreign Building Brick Skin Research Based on Citespace

With the progress of the times and the leap of science and technology,the application of brick materials and the research on the brick skin in modern architectural design have shown a dual-track development trend of returning to tradition and innovation.Based on the core collection database resources of Web of Science and the CiteSpace visual analysis tool,this paper constructed and analyzed the spatio-temporal map of keyword co-occurrence network,cluster structure,mutation phenomenon,time course and regional distribution map of building brick skin research.The study revealed that in recent years,the research on brick materials has spanned the study of single material properties and extensively involved in the broad world of construction,especially in the integration of green energy-saving technology,the innovation of fine construction technology of brick skin,and the frontier exploration of digital technology in brick masonry,which has shown particularly significant research vitality and development potential.